👤 Huafei Wang

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Also published as: A Wang, Ai-Ling Wang, Ai-Ting Wang, Aihua Wang, Aijun Wang, Aili Wang, Aimin Wang, Aiting Wang, Aixian Wang, Aiyun Wang, Aizhong Wang, Alexander Wang, Alice Wang, Allen Wang, Anlai Wang, Anli Wang, Annette Wang, Anni Wang, Anqi Wang, Anthony Z Wang, Anxiang Wang, Anxin Wang, Ao Wang, Aoli Wang, B R Wang, B Wang, Baihan Wang, Baisong Wang, Baitao Wang, Bangchen Wang, Banghui Wang, Bangmao Wang, Bangshing Wang, Bao Wang, Bao-Long Wang, Baocheng Wang, Baofeng Wang, Baogui Wang, Baojun Wang, Baoli Wang, Baolong Wang, Baoming Wang, Baosen Wang, Baowei Wang, Baoying Wang, Baoyun Wang, Bei Bei Wang, Bei Wang, Beibei Wang, Beilan Wang, Beilei Wang, Ben Wang, Benjamin H Wang, Benzhong Wang, Bi Wang, Bi-Dar Wang, Biao Wang, Bicheng Wang, Bijue Wang, Bin Wang, Bin-Xue Wang, Binbin Wang, Bing Qing Wang, Bing Wang, Binghai Wang, Binghan Wang, Bingjie Wang, Binglong Wang, Bingnan Wang, Bingyan Wang, Bingyu Wang, Binquan Wang, Biqi Wang, Bo Wang, Bochu Wang, Boyu Wang, Bruce Wang, C Wang, C Z Wang, Cai Ren Wang, Cai-Hong Wang, Cai-Yun Wang, Cailian Wang, Caiqin Wang, Caixia Wang, Caiyan Wang, Can Wang, Cangyu Wang, Carol A Wang, Catherine Ruiyi Wang, Cenxuan Wang, Chan Wang, Chang Wang, Chang-Yun Wang, Changduo Wang, Changjing Wang, Changliang Wang, Changlong Wang, Changqian Wang, Changtu Wang, Changwei Wang, Changying Wang, Changyu Wang, Changyuan Wang, Changzhen Wang, Chao Wang, Chao-Jun Wang, Chao-Yung Wang, Chaodong Wang, Chaofan Wang, Chaohan Wang, Chaohui Wang, Chaojie Wang, Chaokui Wang, Chaomeng Wang, Chaoqun Wang, Chaoxian Wang, Chaoyi Wang, Chaoyu Wang, Chaozhan Wang, Charles C N Wang, Chau-Jong Wang, Chen Wang, Chen-Cen Wang, Chen-Ma Wang, Chen-Yu Wang, Chenchen Wang, Chenfei Wang, Cheng An Wang, Cheng Wang, Cheng-Cheng Wang, Cheng-Jie Wang, Cheng-zhang Wang, Chengbin Wang, Chengcheng Wang, Chenggang Wang, Chenghao Wang, Chenghua Wang, Chengjian Wang, Chengjun Wang, Chenglin Wang, Chenglong Wang, Chengniu Wang, Chengqiang Wang, Chengshuo Wang, Chenguang Wang, Chengwen Wang, Chengyan Wang, Chengyu Wang, Chengze Wang, Chenji Wang, Chenliang Wang, Chenwei Wang, Chenxi Wang, Chenxin Wang, Chenxuan Wang, Chenyang Wang, Chenyao Wang, Chenyin Wang, Chenyu Wang, Chenzi Wang, Chi Chiu Wang, Chi Wang, Chi-Ping Wang, Chia-Chuan Wang, Chia-Lin Wang, Chien-Hsun Wang, Chien-Wei Wang, Chih-Chun Wang, Chih-Hao Wang, Chih-Hsien Wang, Chih-Liang Wang, Chih-Yang Wang, Chih-Yuan Wang, Chijia Wang, Ching C Wang, Ching-Jen Wang, Chiou-Miin Wang, Chong Wang, Chongjian Wang, Chonglong Wang, Chongmin Wang, Chongze Wang, Christina Wang, Christine Wang, Chu Wang, Chuan Wang, Chuan-Chao Wang, Chuan-Hui Wang, Chuan-Jiang Wang, Chuan-Wen Wang, Chuang Wang, Chuanhai Wang, Chuansen Wang, Chuansheng Wang, Chuanxin Wang, Chuanyue Wang, Chuduan Wang, Chun Wang, Chun-Chieh Wang, Chun-Juan Wang, Chun-Li Wang, Chun-Lin Wang, Chun-Ting Wang, Chun-Xia Wang, Chung-Hsi Wang, Chung-Hsing Wang, Chung-Teng Wang, Chunguo Wang, Chunhong Wang, Chuning Wang, Chunjiong Wang, Chunjuan Wang, Chunle Wang, Chunli Wang, Chunlong Wang, Chunmei Wang, Chunsheng Wang, Chunting Wang, Chunxia Wang, Chunxue Wang, Chunyan Wang, Chunyang Wang, Chunyi Wang, Chunyu Wang, Chuyao Wang, Cindy Wang, Ciyang Wang, Cong Wang, Congcong Wang, Congrong Wang, Congrui Wang, Cui Wang, Cui-Fang Wang, Cui-Shan Wang, Cuili Wang, Cuiling Wang, Cuizhe Wang, Cun-Yu Wang, Cunchuan Wang, Cunyi Wang, D Wang, Da Wang, Da-Cheng Wang, Da-Li Wang, Da-Yan Wang, Da-Zhi Wang, Dadong Wang, Dai Wang, Daijun Wang, Daiwei Wang, Daixi Wang, Dajia Wang, Dake Wang, Dali Wang, Dalong Wang, Dalu Wang, Dan Wang, Dan-Dan Wang, Danan Wang, Dandan Wang, Danfeng Wang, Dang Wang, Dangfeng Wang, Danling Wang, Danqing Wang, Danxin Wang, Danyang Wang, Dao Wen Wang, Dao-Wen Wang, Dao-Xin Wang, Daolong Wang, Daoping Wang, Daozhong Wang, Dapeng Wang, Daping Wang, Daqi Wang, Daqing Wang, David Q H Wang, David Q-H Wang, David Wang, Dawei Wang, Dayan Wang, Dayong Wang, Dazhi Wang, De-He Wang, Dedong Wang, Dehao Wang, Deli Wang, Delin Wang, Delong Wang, Demin Wang, Deming Wang, Dengbin Wang, Dennis Qing Wang, Dennis Wang, Deqi Wang, Deshou Wang, Dezhong Wang, Di Wang, Dinghui Wang, Dingting Wang, Dingxiang Wang, Dong D Wang, Dong Hao Wang, Dong Wang, Dong-Dong Wang, Dong-Jie Wang, Dong-Mei Wang, DongWei Wang, Dongdong Wang, Donggen Wang, Donghao Wang, Donghong Wang, Donghui Wang, Dongliang Wang, Donglin Wang, Dongmei Wang, Dongqin Wang, Dongshi Wang, Dongxia Wang, Dongxu Wang, Dongyan Wang, Dongyang Wang, Dongyi Wang, Dongying Wang, Dongyu Wang, Doudou Wang, Du Wang, Duan Wang, Duanyang Wang, Duo-Ping Wang, E Wang, Edward Wang, En-bo Wang, En-hua Wang, Endi Wang, Enhua Wang, Er-Jin Wang, Erfei Wang, Erika Y Wang, Ermao Wang, Erming Wang, Ertao Wang, Eryao Wang, Eunice S Wang, Exing Wang, F Wang, Fa-Kai Wang, Fan Wang, Fanchang Wang, Fang Wang, Fang-Tao Wang, Fangfang Wang, Fangjie Wang, Fangjun Wang, Fangyan Wang, Fangyong Wang, Fangyu Wang, Fanhua Wang, Fanwen Wang, Fanxiong Wang, Fei Wang, Fei-Fei Wang, Fei-Yan Wang, Feida Wang, Feifei Wang, Feijie Wang, Feimiao Wang, Feixiang Wang, Feiyan Wang, Fen Wang, Feng Wang, Feng-Sheng Wang, Fengchong Wang, Fengge Wang, Fenghua Wang, Fengliang Wang, Fenglin Wang, Fengling Wang, Fengqiang Wang, Fengyang Wang, Fengying Wang, Fengyong Wang, Fengyun Wang, Fengzhen Wang, Fengzhong Wang, Fu Wang, Fu-Sheng Wang, Fu-Yan Wang, Fu-Zhen Wang, Fubao Wang, Fubing Wang, Fudi Wang, Fuhua Wang, Fuqiang Wang, Furong Wang, Fuwen Wang, Fuxin Wang, Fuyan Wang, G Q Wang, G Wang, G-W Wang, Gan Wang, Gang Wang, Ganggang Wang, Ganglin Wang, Gangyang Wang, Ganyu Wang, Gao T Wang, Gao Wang, Gaofu Wang, Gaopin Wang, Gavin Wang, Ge Wang, Geng Wang, Genghao Wang, Gengsheng Wang, Gongming Wang, Guan Wang, Guan-song Wang, Guandi Wang, Guanduo Wang, Guang Wang, Guang-Jie Wang, Guang-Rui Wang, Guangdi Wang, Guanghua Wang, Guanghui Wang, Guangliang Wang, Guangming Wang, Guangsuo Wang, Guangwen Wang, Guangyan Wang, Guangzhi Wang, Guanrou Wang, Guanru Wang, Guansong Wang, Guanyun Wang, Gui-Qi Wang, Guibin Wang, Guihu Wang, Guihua Wang, Guimin Wang, Guiping Wang, Guiqun Wang, Guixin Wang, Guixue Wang, Guiying Wang, Guo-Du Wang, Guo-Hua Wang, Guo-Liang Wang, Guo-Ping Wang, Guo-Quan Wang, Guo-hong Wang, GuoYou Wang, Guobin Wang, Guobing Wang, Guodong Wang, Guohang Wang, Guohao Wang, Guoliang Wang, Guoling Wang, Guoping Wang, Guoqian Wang, Guoqiang Wang, Guoqing Wang, Guorong Wang, Guowen Wang, Guoxiang Wang, Guoxiu Wang, Guoyi Wang, Guoying Wang, Guozheng Wang, H J Wang, H Wang, H X Wang, H Y Wang, H-Y Wang, Hai Bo Wang, Hai Wang, Hai Yang Wang, Hai-Feng Wang, Hai-Jun Wang, Hai-Long Wang, Haibin Wang, Haibing Wang, Haibo Wang, Haichao Wang, Haichuan Wang, Haifei Wang, Haifeng Wang, Haihe Wang, Haihong Wang, Haihua Wang, Haijiao Wang, Haijing Wang, Haijiu Wang, Haikun Wang, Hailei Wang, Hailin Wang, Hailing Wang, Hailong Wang, Haimeng Wang, Haina Wang, Haining Wang, Haiping Wang, Hairong Wang, Haitao Wang, Haiwei Wang, Haixia Wang, Haixin Wang, Haixing Wang, Haiyan Wang, Haiying Wang, Haiyong Wang, Haiyun Wang, Haizhen Wang, Han Wang, Hanbin Wang, Hanbing Wang, Hanchao Wang, Handong Wang, Hang Wang, Hangzhou Wang, Hanmin Wang, Hanping Wang, Hanqi Wang, Hanying Wang, Hanyu Wang, Hanzhi Wang, Hao Wang, Hao-Ching Wang, Hao-Hua Wang, Hao-Tian Wang, Hao-Yu Wang, Haobin Wang, Haochen Wang, Haohao Wang, Haohui Wang, Haojie Wang, Haolong Wang, Haomin Wang, Haoming Wang, Haonan Wang, Haoping Wang, Haoqi Wang, Haoran Wang, Haowei Wang, Haoxin Wang, Haoyang Wang, Haoyu Wang, Haozhou Wang, He Wang, He-Cheng Wang, He-Ling Wang, He-Ping Wang, He-Tong Wang, Hebo Wang, Hechuan Wang, Heling Wang, Hemei Wang, Heming Wang, Heng Wang, Heng-Cai Wang, Hengjiao Wang, Hengjun Wang, Hequn Wang, Hesuiyuan Wang, Heyong Wang, Hezhi Wang, Hong Wang, Hong Yi Wang, Hong-Gang Wang, Hong-Hui Wang, Hong-Kai Wang, Hong-Qin Wang, Hong-Wei Wang, Hong-Xia Wang, Hong-Yan Wang, Hong-Yang Wang, Hong-Ying Wang, Hongbin Wang, Hongbing Wang, Hongbo Wang, Hongcai Wang, Hongda Wang, Hongdan Wang, Hongfang Wang, Hongjia Wang, Hongjian Wang, Hongjie Wang, Hongjuan Wang, Hongkun Wang, Honglei Wang, Hongli Wang, Honglian Wang, Honglun Wang, Hongmei Wang, Hongpin Wang, Hongqian Wang, Hongshan Wang, Hongsheng Wang, Hongtao Wang, Hongwei Wang, Hongxia Wang, Hongxin Wang, Hongyan Wang, Hongyang Wang, Hongyi Wang, Hongyin Wang, Hongying Wang, Hongyu Wang, Hongyuan Wang, Hongyue Wang, Hongyun Wang, Hongze Wang, Hongzhan Wang, Hongzhuang Wang, Horng-Dar Wang, Houchun Wang, Hsei-Wei Wang, Hsueh-Chun Wang, Hu WANG, Hua Wang, Hua-Qin Wang, Hua-Wei Wang, Huabo Wang, Huai-Zhou Wang, Huaibing Wang, Huaili Wang, Huaizhi Wang, Huajin Wang, Huajing Wang, Hualin Wang, Hualing Wang, Huan Wang, Huan-You Wang, Huang Wang, Huanhuan Wang, Huanyu Wang, Huaquan Wang, Huating Wang, Huawei Wang, Huaxiang Wang, Huayang Wang, Huei Wang, Hui Miao Wang, Hui Wang, Hui-Hui Wang, Hui-Li Wang, Hui-Nan Wang, Hui-Yu Wang, HuiYue Wang, Huie Wang, Huiguo Wang, Huihua Wang, Huihui Wang, Huijie Wang, Huijun Wang, Huilun Wang, Huimei Wang, Huimin Wang, Huina Wang, Huiping Wang, Huiquan Wang, Huiqun Wang, Huishan Wang, Huiting Wang, Huiwen Wang, Huixia Wang, Huiyan Wang, Huiyang Wang, Huiyao Wang, Huiying Wang, Huiyu Wang, Huizhen Wang, Huizhi Wang, Huming Wang, I-Ching Wang, Iris X Wang, Isabel Z Wang, J J Wang, J P Wang, J Q Wang, J Wang, J Z Wang, J-Y Wang, Jacob E Wang, James Wang, Jeffrey Wang, Jen-Chun Wang, Jen-Chywan Wang, Jennifer E Wang, Jennifer T Wang, Jennifer X Wang, Jenny Y Wang, Jeremy R Wang, Jeremy Wang, Ji M Wang, Ji Wang, Ji-Nuo Wang, Ji-Yang Wang, Ji-Yao Wang, Ji-zheng Wang, Jia Bei Wang, Jia Bin Wang, Jia Wang, Jia-Liang Wang, Jia-Lin Wang, Jia-Mei Wang, Jia-Peng Wang, Jia-Qi Wang, Jia-Qiang Wang, Jia-Ying Wang, Jia-Yu Wang, Jiabei Wang, Jiabo Wang, Jiafeng Wang, Jiafu Wang, Jiahao Wang, Jiahui Wang, Jiajia Wang, Jiakun Wang, Jiale Wang, Jiali Wang, Jialiang Wang, Jialin Wang, Jialing Wang, Jiamin Wang, Jiaming Wang, Jian Wang, Jian'an Wang, Jian-Bin Wang, Jian-Guo Wang, Jian-Hong Wang, Jian-Long Wang, Jian-Wei Wang, Jian-Xiong Wang, Jian-Yong Wang, Jian-Zhi Wang, Jian-chun Wang, Jianan Wang, Jianbing Wang, Jianbo Wang, Jianding Wang, Jianfang Wang, Jianfei Wang, Jiang Wang, Jiangbin Wang, Jiangbo Wang, Jianghua Wang, Jianghui Wang, Jiangong Wang, Jianguo Wang, Jianhao Wang, Jianhua Wang, Jianhui Wang, Jiani Wang, Jianjiao Wang, Jianjie Wang, Jianjun Wang, Jianle Wang, Jianli Wang, Jianlin Wang, Jianliu Wang, Jianlong Wang, Jianmei Wang, Jianmin Wang, Jianning Wang, Jianping Wang, Jianqin Wang, Jianqing Wang, Jianqun Wang, Jianru Wang, Jianshe Wang, Jianshu Wang, Jiantao Wang, Jianwei Wang, Jianwu Wang, Jianxiang Wang, Jianxin Wang, Jianye Wang, Jianying Wang, Jianyong Wang, Jianyu Wang, Jianzhang Wang, Jianzhi Wang, Jiao Wang, Jiaojiao Wang, Jiapan Wang, Jiaping Wang, Jiaqi Wang, Jiaqian Wang, Jiatao Wang, Jiawei Wang, Jiawen Wang, Jiaxi Wang, Jiaxin Wang, Jiaxing Wang, Jiaxuan Wang, Jiayan Wang, Jiayang Wang, Jiayi Wang, Jiaying Wang, Jiayu Wang, Jiazheng Wang, Jiazhi Wang, Jie Jin Wang, Jie Wang, Jieda Wang, Jieh-Neng Wang, Jiemei Wang, Jieqi Wang, Jieyan Wang, Jieyu Wang, Jifei Wang, Jiheng Wang, Jihong Wang, Jiliang Wang, Jilin Wang, Jin Wang, Jin'e Wang, Jin-Bao Wang, Jin-Cheng Wang, Jin-Da Wang, Jin-E Wang, Jin-Juan Wang, Jin-Liang Wang, Jin-Xia Wang, Jin-Xing Wang, Jincheng Wang, Jindan Wang, Jinfei Wang, Jinfeng Wang, Jinfu Wang, Jing J Wang, Jing Wang, Jing-Hao Wang, Jing-Huan Wang, Jing-Jing Wang, Jing-Long Wang, Jing-Min Wang, Jing-Shi Wang, Jing-Wen Wang, Jing-Xian Wang, Jing-Yi Wang, Jing-Zhai Wang, Jingang Wang, Jingchun Wang, Jingfan Wang, Jingfeng Wang, Jingheng Wang, Jinghong Wang, Jinghua Wang, Jinghuan Wang, Jingjing Wang, Jingkang Wang, Jinglin Wang, Jingmin Wang, Jingnan Wang, Jingqi Wang, Jingru Wang, Jingtong Wang, Jingwei Wang, Jingwen Wang, Jingxiao Wang, Jingyang Wang, Jingyi Wang, Jingying Wang, Jingyu Wang, Jingyue Wang, Jingyun Wang, Jingzhou Wang, Jinhai Wang, Jinhao Wang, Jinhe Wang, Jinhua Wang, Jinhuan Wang, Jinhui Wang, Jinjie Wang, Jinjin Wang, Jinkang Wang, Jinling Wang, Jinlong Wang, Jinmeng Wang, Jinning Wang, Jinping Wang, Jinqiu Wang, Jinrong Wang, Jinru Wang, Jinsong Wang, Jintao Wang, Jinxia Wang, Jinxiang Wang, Jinyang Wang, Jinyu Wang, Jinyue Wang, Jinyun Wang, Jinzhu Wang, Jiou Wang, Jipeng Wang, Jiqing Wang, Jiqiu Wang, Jisheng Wang, Jiu Wang, Jiucun Wang, Jiun-Ling Wang, Jiwen Wang, Jixuan Wang, Jiyan Wang, Jiying Wang, Jiyong Wang, Jizheng Wang, John Wang, Jou-Kou Wang, Joy Wang, Ju Wang, Juan Wang, Jue Wang, Jueqiong Wang, Jufeng Wang, Julie Wang, Juling Wang, Jun Kit Wang, Jun Wang, Jun Yi Wang, Jun-Feng Wang, Jun-Jie Wang, Jun-Jun Wang, Jun-Ling Wang, Jun-Sheng Wang, Jun-Sing Wang, Jun-Zhuo Wang, Jundong Wang, Junfeng Wang, Jung-Pan Wang, Junhong Wang, Junhua Wang, Junhui Wang, Junjiang Wang, Junjie Wang, Junjun Wang, Junkai Wang, Junke Wang, Junli Wang, Junlin Wang, Junling Wang, Junmei Wang, Junmin Wang, Junpeng Wang, Junping Wang, Junqin Wang, Junqing Wang, Junrui Wang, Junsheng Wang, Junshi Wang, Junshuang Wang, Junwen Wang, Junxiao Wang, Junya Wang, Junying Wang, Junyu Wang, Justin Wang, Jutao Wang, Juxiang Wang, K Wang, Kai Wang, Kai-Kun Wang, Kai-Wen Wang, Kaicen Wang, Kaihao Wang, Kaihe Wang, Kaihong Wang, Kaijie Wang, Kaijuan Wang, Kailu Wang, Kaiming Wang, Kaining Wang, Kaiting Wang, Kaixi Wang, Kaixu Wang, Kaiyan Wang, Kaiyuan Wang, Kaiyue Wang, Kan Wang, Kangli Wang, Kangling Wang, Kangmei Wang, Kangning Wang, Ke Wang, Ke-Feng Wang, KeShan Wang, Kehan Wang, Kehao Wang, Kejia Wang, Kejian Wang, Kejun Wang, Keke Wang, Keming Wang, Kenan Wang, Keqing Wang, Kesheng Wang, Kexin Wang, Keyan Wang, Keyi Wang, Keyun Wang, Kongyan Wang, Kuan Hong Wang, Kui Wang, Kun Wang, Kunhua Wang, Kunpeng Wang, Kunzheng Wang, L F Wang, L M Wang, L Wang, L Z Wang, L-S Wang, Laidi Wang, Laijian Wang, Laiyuan Wang, Lan Wang, Lan-Wan Wang, Lan-lan Wang, Lanlan Wang, Larry Wang, Le Wang, Le-Xin Wang, Ledan Wang, Lee-Kai Wang, Lei P Wang, Lei Wang, Lei-Lei Wang, Leiming Wang, Leishen Wang, Leli Wang, Leran Wang, Lexin Wang, Leying Wang, Li Chun Wang, Li Dong Wang, Li Wang, Li-Dong Wang, Li-E Wang, Li-Juan Wang, Li-Li Wang, Li-Na Wang, Li-San Wang, Li-Ting Wang, Li-Xin Wang, Li-Yong Wang, LiLi Wang, Lian Wang, Lianchun Wang, Liang Wang, Liang-Yan Wang, Liangfu Wang, Lianghai Wang, Liangli Wang, Liangliang Wang, Liangxu Wang, Lianshui Wang, Lianyong Wang, Libo Wang, Lichan Wang, Lichao Wang, Liewei Wang, Lifang Wang, Lifei Wang, Lifen Wang, Lifeng Wang, Ligang Wang, Lihong Wang, Lihua Wang, Lihui Wang, Lijia Wang, Lijin Wang, Lijing Wang, Lijuan Wang, Lijun Wang, Liling Wang, Lily Wang, Limeng Wang, Limin Wang, Liming Wang, Lin Wang, Lin-Fa Wang, Lin-Yu Wang, Lina Wang, Linfang Wang, Ling Jie Wang, Ling Wang, Ling-Ling Wang, Lingbing Wang, Lingda Wang, Linghua Wang, Linghuan Wang, Lingli Wang, Lingling Wang, Lingyan Wang, Lingzhi Wang, Linhua Wang, Linhui Wang, Linjie Wang, Linli Wang, Linlin Wang, Linping Wang, Linshu Wang, Linshuang Wang, Lintao Wang, Linxuan Wang, Linying Wang, Linyuan Wang, Liping Wang, Liqing Wang, Liqun Wang, Lirong Wang, Litao Wang, Liting Wang, Liu Wang, Liusong Wang, Liuyang Wang, Liwei Wang, Lixia Wang, Lixian Wang, Lixiang Wang, Lixin Wang, Lixing Wang, Lixiu Wang, Liyan Wang, Liyi Wang, Liying Wang, Liyong Wang, Liyuan Wang, Liyun Wang, Long Wang, Longcai Wang, Longfei Wang, Longsheng Wang, Longxiang Wang, Lou-Pin Wang, Lu Wang, Lu-Lu Wang, Lueli Wang, Lufang Wang, Luhong Wang, Luhui Wang, Lujuan Wang, Lulu Wang, Luofu Wang, Luping Wang, Luting Wang, Luwen Wang, Luxiang Wang, Luya Wang, Luyao Wang, Luyun Wang, Lynn Yuning Wang, M H Wang, M Wang, M Y Wang, M-J Wang, Maiqiu Wang, Man Wang, Mangju Wang, Manli Wang, Mao-Xin Wang, Maochun Wang, Maojie Wang, Maoju Wang, Mark Wang, Mei Wang, Mei-Gui Wang, Mei-Xia Wang, Meiding Wang, Meihui Wang, Meijun Wang, Meiling Wang, Meixia Wang, Melissa T Wang, Meng C Wang, Meng Wang, Meng Yu Wang, Meng-Dan Wang, Meng-Lan Wang, Meng-Meng Wang, Meng-Ru Wang, Meng-Wei Wang, Meng-Ying Wang, Meng-hong Wang, Mengge Wang, Menghan Wang, Menghui Wang, Mengjiao Wang, Mengjing Wang, Mengjun Wang, Menglong Wang, Menglu Wang, Mengmeng Wang, Mengqi Wang, Mengru Wang, Mengshi Wang, Mengwen Wang, Mengxiao Wang, Mengya Wang, Mengyao Wang, Mengying Wang, Mengyuan Wang, Mengyue Wang, Mengyun Wang, Mengze Wang, Mengzhao Wang, Mengzhi Wang, Mian Wang, Miao Wang, Mimi Wang, Min Wang, Min-sheng Wang, Ming Wang, Ming-Chih Wang, Ming-Hsi Wang, Ming-Jie Wang, Ming-Wei Wang, Ming-Yang Wang, Ming-Yuan Wang, Mingchao Wang, Mingda Wang, Minghua Wang, Minghuan Wang, Minghui Wang, Mingji Wang, Mingjin Wang, Minglei Wang, Mingliang Wang, Mingmei Wang, Mingming Wang, Mingqiang Wang, Mingrui Wang, Mingsong Wang, Mingxi Wang, Mingxia Wang, Mingxun Wang, Mingya Wang, Mingyang Wang, Mingyi Wang, Mingyu Wang, Mingzhi Wang, Mingzhu Wang, Minjie Wang, Minjun Wang, Minmin Wang, Minxian Wang, Minxiu Wang, Minzhou Wang, Miranda C Wang, Mo Wang, Mofei Wang, Monica Wang, Mu Wang, Mutian Wang, Muxiao Wang, Muxuan Wang, N Wang, Na Wang, Nan Wang, Nana Wang, Nanbu Wang, Nannan Wang, Nanping Wang, Neng Wang, Ni Wang, Niansong Wang, Ning Wang, Ningjian Wang, Ningli Wang, Ningyuan Wang, Nuan Wang, Oliver Wang, Ouchen Wang, P Jeremy Wang, P L Wang, P N Wang, P Wang, Pai Wang, Pan Wang, Pan-Pan Wang, Panfeng Wang, Panliang Wang, Pei Chang Wang, Pei Wang, Pei-Hua Wang, Pei-Jian Wang, Pei-Juan Wang, Pei-Wen Wang, Pei-Yu Wang, Peichang Wang, Peigeng Wang, Peihe Wang, Peijia Wang, Peijuan Wang, Peijun Wang, Peilin Wang, Peipei Wang, Peirong Wang, Peiwen Wang, Peixi Wang, Peiyao Wang, Peiyin Wang, Peng Wang, Peng-Cheng Wang, Pengbo Wang, Pengchao Wang, Pengfei Wang, Pengjie Wang, Pengju Wang, Penglai Wang, Penglong Wang, Pengpu Wang, Pengtao Wang, Pengxiang Wang, Pengyu Wang, Pin Wang, Ping Wang, Pingchuan Wang, Pingfeng Wang, Pingping Wang, Pintian Wang, Po-Jen Wang, Pu Wang, Q Wang, Q Z Wang, Qi Wang, Qi-Bing Wang, Qi-En Wang, Qi-Jia Wang, Qi-Qi Wang, Qian Wang, Qian-Liang Wang, Qian-Wen Wang, Qian-Zhu Wang, Qian-fei Wang, Qianbao Wang, Qiang Wang, Qiang-Sheng Wang, Qiangcheng Wang, Qianghu Wang, Qiangqiang Wang, Qianjin Wang, Qianliang Wang, Qianqian Wang, Qianrong Wang, Qianru Wang, Qianwen Wang, Qianxu Wang, Qiao Wang, Qiao-Ping Wang, Qiaohong Wang, Qiaoqi Wang, Qiaoqiao Wang, Qifan Wang, Qifei Wang, Qifeng Wang, Qigui Wang, Qihao Wang, Qihua Wang, Qijia Wang, Qiming Wang, Qin Wang, Qing Jun Wang, Qing K Wang, Qing Kenneth Wang, Qing Mei Wang, Qing Wang, Qing-Bin Wang, Qing-Dong Wang, Qing-Jin Wang, Qing-Liang Wang, Qing-Mei Wang, Qing-Yan Wang, Qing-Yuan Wang, Qing-Yun Wang, QingDong Wang, Qingchun Wang, Qingfa Wang, Qingfeng Wang, Qinghang Wang, Qingliang Wang, Qinglin Wang, Qinglu Wang, Qingming Wang, Qingping Wang, Qingqing Wang, Qingshi Wang, Qingshui Wang, Qingsong Wang, Qingtong Wang, Qingyong Wang, Qingyu Wang, Qingyuan Wang, Qingyun Wang, Qingzhong Wang, Qinqin Wang, Qinrong Wang, Qintao Wang, Qinwen Wang, Qinyun Wang, Qiong Wang, Qiqi Wang, Qirui Wang, Qishan Wang, Qiu-Ling Wang, Qiu-Xia Wang, Qiuhong Wang, Qiuli Wang, Qiuling Wang, Qiuning Wang, Qiuping Wang, Qiushi Wang, Qiuting Wang, Qiuyan Wang, Qiuyu Wang, Qiwei Wang, Qixue Wang, Qiyu Wang, Qiyuan Wang, Quan Wang, Quan-Ming Wang, Quanli Wang, Quanren Wang, Quanxi Wang, Qun Wang, Qunxian Wang, Qunzhi Wang, R Wang, Ran Wang, Ranjing Wang, Ranran Wang, Re-Hua Wang, Ren Wang, Rencheng Wang, Renjun Wang, Renqian Wang, Renwei Wang, Renxi Wang, Renxiao Wang, Renyuan Wang, Rihua Wang, Rikang Wang, Rixiang Wang, Robert Yl Wang, Rong Wang, Rong-Chun Wang, Rong-Rong Wang, Rong-Tsorng Wang, RongRong Wang, Rongjia Wang, Rongping Wang, Rongyun Wang, Ru Wang, RuNan Wang, Ruey-Yun Wang, Rufang Wang, Ruhan Wang, Rui Wang, Rui-Hong Wang, Rui-Min Wang, Rui-Ping Wang, Rui-Rui Wang, Ruibin Wang, Ruibing Wang, Ruibo Wang, Ruicheng Wang, Ruifang Wang, Ruijing Wang, Ruimeng Wang, Ruimin Wang, Ruiming Wang, Ruinan Wang, Ruining Wang, Ruiquan Wang, Ruiwen Wang, Ruixian Wang, Ruixin Wang, Ruixuan Wang, Ruixue Wang, Ruiying Wang, Ruizhe Wang, Ruizhi Wang, Rujie Wang, Ruling Wang, Ruming Wang, Runci Wang, Runuo Wang, Runze Wang, Runzhi Wang, Ruo-Nan Wang, Ruo-Ran Wang, Ruonan Wang, Ruosu Wang, Ruoxi Wang, Rurong Wang, Ruting Wang, Ruxin Wang, Ruxuan Wang, Ruyue Wang, S L Wang, S S Wang, S Wang, S X Wang, Sa A Wang, Sa Wang, Saifei Wang, Saili Wang, Sainan Wang, Saisai Wang, Sangui Wang, Sanwang Wang, Sasa Wang, Sen Wang, Seok Mui Wang, Seungwon Wang, Sha Wang, Shan Wang, Shan-Shan Wang, Shang Wang, Shangyu Wang, Shanshan Wang, Shao-Kang Wang, Shaochun Wang, Shaohsu Wang, Shaokun Wang, Shaoli Wang, Shaolian Wang, Shaoshen Wang, Shaowei Wang, Shaoyi Wang, Shaoying Wang, Shaoyu Wang, Shaozheng Wang, Shasha Wang, Shau-Chun Wang, Shawn Wang, Shen Wang, Shen-Nien Wang, Shenao Wang, Sheng Wang, Sheng-Min Wang, Sheng-Nan Wang, Sheng-Ping Wang, Sheng-Quan Wang, Sheng-Yang Wang, Shengdong Wang, Shengjie Wang, Shengli Wang, Shengqi Wang, Shengya Wang, Shengyao Wang, Shengyu Wang, Shengyuan Wang, Shenqi Wang, Sheri Wang, Shi Wang, Shi-Cheng Wang, Shi-Han Wang, Shi-Qi Wang, Shi-Xin Wang, Shi-Yao Wang, Shibin Wang, Shichao Wang, Shicung Wang, Shidong Wang, Shifa Wang, Shifeng Wang, Shih-Wei Wang, Shihan Wang, Shihao Wang, Shihua Wang, Shijie Wang, Shijin Wang, Shijun Wang, Shikang Wang, Shimiao Wang, Shiqi Wang, Shiqiang Wang, Shitao Wang, Shitian Wang, Shiwen Wang, Shixin Wang, Shixuan Wang, Shiyang Wang, Shiyao Wang, Shiyin Wang, Shiyu Wang, Shiyuan Wang, Shiyue Wang, Shizhi Wang, Shouli Wang, Shouling Wang, Shouzhi Wang, Shu Wang, Shu-Huei Wang, Shu-Jin Wang, Shu-Ling Wang, Shu-Na Wang, Shu-Song Wang, Shu-Xia Wang, Shu-qiang Wang, Shuai Wang, Shuaiqin Wang, Shuang Wang, Shuang-Shuang Wang, Shuang-Xi Wang, Shuangyuan Wang, Shubao Wang, Shudan Wang, Shuge Wang, Shuguang Wang, Shuhe Wang, Shuiliang Wang, Shuiyun Wang, Shujin Wang, Shukang Wang, Shukui Wang, Shun Wang, Shuning Wang, Shunjun Wang, Shunran Wang, Shuo Wang, Shuping Wang, Shuqi Wang, Shuqing Wang, Shuren Wang, Shusen Wang, Shusheng Wang, Shushu Wang, Shuu-Jiun Wang, Shuwei Wang, Shuxia Wang, Shuxin Wang, Shuya Wang, Shuye Wang, Shuyue Wang, Shuzhe Wang, Shuzhen Wang, Shuzhong Wang, Shyi-Gang P Wang, Si Wang, Sibo Wang, Sidan Wang, Sihua Wang, Sijia Wang, Silas L Wang, Silu Wang, Simeng Wang, Siqi Wang, Siqing Wang, Siwei Wang, Siyang Wang, Siyi Wang, Siying Wang, Siyu Wang, Siyuan Wang, Siyue Wang, Song Wang, Songjiao Wang, Songlin Wang, Songping Wang, Songsong Wang, Songtao Wang, Sophie H Wang, Stephani Wang, Su'e Wang, Su-Guo Wang, Su-Hua Wang, Sufang Wang, Sugai Wang, Sui Wang, Suiyan Wang, Sujie Wang, Sujuan Wang, Suli Wang, Sun Wang, Supeng Perry Wang, Suxia Wang, Suyun Wang, Suzhen Wang, T Q Wang, T Wang, T Y Wang, Taian Wang, Taicheng Wang, Taishu Wang, Tammy C Wang, Tao Wang, Taoxia Wang, Teng Wang, Tengfei Wang, Theodore Wang, Thomas T Y Wang, Tian Wang, Tian-Li Wang, Tian-Lu Wang, Tian-Tian Wang, Tian-Yi Wang, Tiancheng Wang, Tiange Wang, Tianhao Wang, Tianhu Wang, Tianhui Wang, Tianjing Wang, Tianjun Wang, Tianlin Wang, Tiannan Wang, Tianpeng Wang, Tianqi Wang, Tianqin Wang, Tianqing Wang, Tiansheng Wang, Tiansong Wang, Tiantian Wang, Tianyi Wang, Tianying Wang, Tianyuan Wang, Tielin Wang, Tienju Wang, Tieqiao Wang, Timothy C Wang, Ting Chen Wang, Ting Wang, Ting-Chen Wang, Ting-Hua Wang, Ting-Ting Wang, Tingting Wang, Tingye Wang, Tingyu Wang, Tom J Wang, Tong Wang, Tong-Hong Wang, Tongsong Wang, Tongtong Wang, Tongxia Wang, Tongxin Wang, Tongyao Wang, Tony Wang, Tzung-Dau Wang, Victoria Wang, Vivian Wang, W Wang, Wanbing Wang, Wanchun Wang, Wang Wang, Wangxia Wang, Wanliang Wang, Wanxia Wang, Wanyao Wang, Wanyi Wang, Wanyu Wang, Wayseen Wang, Wei Wang, Wei-En Wang, Wei-Feng Wang, Wei-Lien Wang, Wei-Qi Wang, Wei-Ting Wang, Wei-Wei Wang, Weicheng Wang, Weiding Wang, Weidong Wang, Weifan Wang, Weiguang Wang, Weihao Wang, Weihong Wang, Weihua Wang, Weijian Wang, Weijie Wang, Weijun Wang, Weilin Wang, Weiling Wang, Weilong Wang, Weimin Wang, Weina Wang, Weining Wang, Weipeng Wang, Weiqin Wang, Weiqing Wang, Weirong Wang, Weiwei Wang, Weiwen Wang, Weixiao Wang, Weixue Wang, Weiyan Wang, Weiyu Wang, Weiyuan Wang, Weizhen Wang, Weizhi Wang, Weizhong Wang, Wen Wang, Wen-Chang Wang, Wen-Der Wang, Wen-Fei Wang, Wen-Jie Wang, Wen-Jun Wang, Wen-Qing Wang, Wen-Xuan Wang, Wen-Yan Wang, Wen-Ying Wang, Wen-Yong Wang, Wen-mei Wang, Wenbin Wang, Wenbo Wang, Wence Wang, Wenchao Wang, Wencheng Wang, Wendong Wang, Wenfei Wang, Wengong Wang, Wenhan Wang, Wenhao Wang, Wenhe Wang, Wenhui Wang, Wenjie Wang, Wenjing Wang, Wenju Wang, Wenjuan Wang, Wenjun Wang, Wenkai Wang, Wenkang Wang, Wenke Wang, Wenming Wang, Wenqi Wang, Wenqiang Wang, Wenqing Wang, Wenran Wang, Wenrui Wang, Wentao Wang, Wentian Wang, Wenting Wang, Wenwen Wang, Wenxia Wang, Wenxian Wang, Wenxiang Wang, Wenxiu Wang, Wenxuan Wang, Wenya Wang, Wenyan Wang, Wenyi Wang, Wenying Wang, Wenyu Wang, Wenyuan Wang, Wenzhou Wang, William Wang, Won-Jing Wang, Wu-Wei Wang, Wuji Wang, Wuqing Wang, Wusan Wang, X E Wang, X F Wang, X O Wang, X S Wang, X Wang, X-T Wang, Xi Wang, Xi-Hong Wang, Xi-Rui Wang, Xia Wang, Xian Wang, Xian-e Wang, Xianding Wang, Xianfeng Wang, Xiang Wang, Xiang-Dong Wang, Xiangcheng Wang, Xiangding Wang, Xiangdong Wang, Xiangguo Wang, Xianghua Wang, Xiangkun Wang, Xiangrong Wang, Xiangru Wang, Xiangwei Wang, Xiangyu Wang, Xianna Wang, Xianqiang Wang, Xianrong Wang, Xianshi Wang, Xianshu Wang, Xiansong Wang, Xiantao Wang, Xianwei Wang, Xianxing Wang, Xianze Wang, Xianzhe Wang, Xianzong Wang, Xiao Ling Wang, Xiao Qun Wang, Xiao Wang, Xiao-Ai Wang, Xiao-Fei Wang, Xiao-Hui Wang, Xiao-Jie Wang, Xiao-Juan Wang, Xiao-Lan Wang, Xiao-Li Wang, Xiao-Lin Wang, Xiao-Ming Wang, Xiao-Pei Wang, Xiao-Qian Wang, Xiao-Qun Wang, Xiao-Tong Wang, Xiao-Xia Wang, Xiao-Yi Wang, Xiao-Yun Wang, Xiao-jian WANG, Xiao-liang Wang, Xiaobin Wang, Xiaobo Wang, Xiaochen Wang, Xiaochuan Wang, Xiaochun Wang, Xiaodan Wang, Xiaoding Wang, Xiaodong Wang, Xiaofang Wang, Xiaofei Wang, Xiaofen Wang, Xiaofeng Wang, Xiaogang Wang, Xiaohong Wang, Xiaohu Wang, Xiaohua Wang, Xiaohui Wang, Xiaojia Wang, Xiaojian Wang, Xiaojiao Wang, Xiaojie Wang, Xiaojing Wang, Xiaojuan Wang, Xiaojun Wang, Xiaokun Wang, Xiaole Wang, Xiaoli Wang, Xiaoliang Wang, Xiaolin Wang, Xiaoling Wang, Xiaolong Wang, Xiaolu Wang, Xiaolun Wang, Xiaoman Wang, Xiaomei Wang, Xiaomeng Wang, Xiaomin Wang, Xiaoming Wang, Xiaona Wang, Xiaonan Wang, Xiaoning Wang, Xiaoqi Wang, Xiaoqian Wang, Xiaoqin Wang, Xiaoqing Wang, Xiaoqiu Wang, Xiaoqun Wang, Xiaorong Wang, Xiaorui Wang, Xiaoshan Wang, Xiaosong Wang, Xiaotang Wang, Xiaoting Wang, Xiaotong Wang, Xiaowei Wang, Xiaowen Wang, Xiaowu Wang, Xiaoxia Wang, Xiaoxiao Wang, Xiaoxin Wang, Xiaoxin X Wang, Xiaoxuan Wang, Xiaoya Wang, Xiaoyan Wang, Xiaoyang Wang, Xiaoye Wang, Xiaoying Wang, Xiaoyu Wang, Xiaozhen Wang, Xiaozhi Wang, Xiaozhong Wang, Xiaozhu Wang, Xichun Wang, Xidi Wang, Xietong Wang, Xifeng Wang, Xifu Wang, Xijun Wang, Xike Wang, Xin Wang, Xin Wei Wang, Xin-Hua Wang, Xin-Liang Wang, Xin-Ming Wang, Xin-Peng Wang, Xin-Qun Wang, Xin-Shang Wang, Xin-Xin Wang, Xin-Yang Wang, Xin-Yue Wang, Xinbo Wang, Xinchang Wang, Xinchao Wang, Xinchen Wang, Xincheng Wang, Xinchun Wang, Xindi Wang, Xindong Wang, Xing Wang, Xing-Huan Wang, Xing-Jin Wang, Xing-Jun Wang, Xing-Lei Wang, Xing-Ping Wang, Xing-Quan Wang, Xingbang Wang, Xingchen Wang, Xingde Wang, Xingguo Wang, Xinghao Wang, Xinghui Wang, Xingjie Wang, Xingjin Wang, Xinglei Wang, Xinglong Wang, Xingqin Wang, Xinguo Wang, Xingxin Wang, Xingxing Wang, Xingye Wang, Xingyu Wang, Xingyue Wang, Xingyun Wang, Xinhui Wang, Xinjing Wang, Xinjun Wang, Xinke Wang, Xinkun Wang, Xinli Wang, Xinlin Wang, Xinlong Wang, Xinmei Wang, Xinqi Wang, Xinquan Wang, Xinran Wang, Xinrong Wang, Xinru Wang, Xinrui Wang, Xinshuai Wang, Xintong Wang, Xinwen Wang, Xinxin Wang, Xinyan Wang, Xinyang Wang, Xinye Wang, Xinyi Wang, Xinying Wang, Xinyu Wang, Xinyue Wang, Xinzhou Wang, Xiong Wang, Xiongjun Wang, Xiru Wang, Xitian Wang, Xiu-Lian Wang, Xiu-Ping Wang, Xiufen Wang, Xiujuan Wang, Xiujun Wang, Xiurong Wang, Xiuwen Wang, Xiuyu Wang, Xiuyuan Hugh Wang, Xixi Wang, Xixiang Wang, Xiyan Wang, Xiyue Wang, Xizhi Wang, Xu Wang, Xu-Hong Wang, Xuan Wang, Xuan-Ren Wang, Xuan-Ying Wang, Xuanwen Wang, Xuanyi Wang, Xubo Wang, Xudong Wang, Xue Wang, Xue-Feng Wang, Xue-Hua Wang, Xue-Lei Wang, Xue-Lian Wang, Xue-Rui Wang, Xue-Yao Wang, Xue-Ying Wang, Xuebin Wang, Xueding Wang, Xuedong Wang, Xuefei Wang, Xuefeng Wang, Xueguo Wang, Xuehao Wang, Xuejie Wang, Xuejing Wang, Xueju Wang, Xuejun Wang, Xuekai Wang, Xuelai Wang, Xuelian Wang, Xuelin Wang, Xuemei Wang, Xuemin Wang, Xueping Wang, Xueqian Wang, Xueqin Wang, Xuesong Wang, Xueting Wang, Xuewei Wang, Xuewen Wang, Xuexiang Wang, Xueyan Wang, Xueyi Wang, Xueying Wang, Xueyun Wang, Xuezhen Wang, Xuezheng Wang, Xufei Wang, Xujing Wang, Xuliang Wang, Xumeng Wang, Xun Wang, Xuping Wang, Xuqiao Wang, Xuru Wang, Xusheng Wang, Xv Wang, Y Alan Wang, Y B Wang, Y H Wang, Y L Wang, Y P Wang, Y Wang, Y Y Wang, Y Z Wang, Y-H Wang, Y-S Wang, Ya Qi Wang, Ya Wang, Ya Xing Wang, Ya-Han Wang, Ya-Jie Wang, Ya-Long Wang, Ya-Nan Wang, Ya-Ping Wang, Ya-Qin Wang, Ya-Zhou Wang, Yachen Wang, Yachun Wang, Yadong Wang, Yafang Wang, Yafen Wang, Yahong Wang, Yahui Wang, Yajie Wang, Yajing Wang, Yajun Wang, Yake Wang, Yakun Wang, Yali Wang, Yalin Wang, Yaling Wang, Yalong Wang, Yan Ming Wang, Yan Wang, Yan-Chao Wang, Yan-Chun Wang, Yan-Feng Wang, Yan-Ge Wang, Yan-Jiang Wang, Yan-Jun Wang, Yan-Ming Wang, Yan-Yang Wang, Yan-Yi Wang, Yan-Zi Wang, Yana Wang, Yanan Wang, Yanbin Wang, Yanbing Wang, Yanchun Wang, Yancun Wang, Yanfang Wang, Yanfei Wang, Yanfeng Wang, Yang Wang, Yang-Yang Wang, Yange Wang, Yanggan Wang, Yangpeng Wang, Yangyang Wang, Yangyufan Wang, Yanhai Wang, Yanhong Wang, Yanhua Wang, Yanhui Wang, Yani Wang, Yanjin Wang, Yanjun Wang, Yankun Wang, Yanlei Wang, Yanli Wang, Yanliang Wang, Yanlin Wang, Yanling Wang, Yanmei Wang, Yanming Wang, Yanni Wang, Yanong Wang, Yanping Wang, Yanqing Wang, Yanru Wang, Yanting Wang, Yanwen Wang, Yanxia Wang, Yanxing Wang, Yanyang Wang, Yanyun Wang, Yanzhe Wang, Yanzhu Wang, Yao Wang, Yaobin Wang, Yaochun Wang, Yaodong Wang, Yaohe Wang, Yaokun Wang, Yaoling Wang, Yaolou Wang, Yaoxian Wang, Yaoxing Wang, Yaozhi Wang, Yapeng Wang, Yaping Wang, Yaqi Wang, Yaqian Wang, Yaqiong Wang, Yaru Wang, Yatao Wang, Yating Wang, Yawei Wang, Yaxian Wang, Yaxin Wang, Yaxiong Wang, Yaxuan Wang, Yayu Wang, Yazhou Wang, Ye Wang, Ye-Ran Wang, Yefu Wang, Yeh-Han Wang, Yehan Wang, Yeming Wang, Yen-Feng Wang, Yen-Sheng Wang, Yeou-Lih Wang, Yeqi Wang, Yezhou Wang, Yi Fan Wang, Yi Lei Wang, Yi Wang, Yi-Cheng Wang, Yi-Chuan Wang, Yi-Ming Wang, Yi-Ni Wang, Yi-Ning Wang, Yi-Shan Wang, Yi-Shiuan Wang, Yi-Shu Wang, Yi-Tao Wang, Yi-Ting Wang, Yi-Wen Wang, Yi-Xin Wang, Yi-Xuan Wang, Yi-Yi Wang, Yi-Ying Wang, Yi-Zhen Wang, Yi-sheng Wang, YiLi Wang, Yian Wang, Yibin Wang, Yibing Wang, Yichen Wang, Yicheng Wang, Yichuan Wang, Yifan Wang, Yifei Wang, Yigang Wang, Yige Wang, Yihan Wang, Yihao Wang, Yihe Wang, Yijin Wang, Yijing Wang, Yijun Wang, Yikang Wang, Yike Wang, Yilin Wang, Yilu Wang, Yimeng Wang, Yiming Wang, Yin Wang, Yin-Hu Wang, Yinan Wang, Yinbo Wang, Yindan Wang, Ying Wang, Ying-Piao Wang, Ying-Wei Wang, Ying-Zi Wang, Yingbo Wang, Yingcheng Wang, Yingchun Wang, Yingfei Wang, Yingge Wang, Yinggui Wang, Yinghui Wang, Yingjie Wang, Yingmei Wang, Yingna Wang, Yingping Wang, Yingqiao Wang, Yingtai Wang, Yingte Wang, Yingwei Wang, Yingwen Wang, Yingxiong Wang, Yingxue Wang, Yingyi Wang, Yingying Wang, Yingzi Wang, Yinhuai Wang, Yining E Wang, Yinong Wang, Yinsheng Wang, Yintao Wang, Yinuo Wang, Yinxiong Wang, Yinyin Wang, Yiou Wang, Yipeng Wang, Yiping Wang, Yiqi Wang, Yiqiao Wang, Yiqin Wang, Yiqing Wang, Yiquan Wang, Yirong Wang, Yiru Wang, Yirui Wang, Yishan Wang, Yishu Wang, Yitao Wang, Yiting Wang, Yiwei Wang, Yiwen Wang, Yixi Wang, Yixian Wang, Yixuan Wang, Yiyan Wang, Yiyi Wang, Yiying Wang, Yizhe Wang, Yong Wang, Yong-Bo Wang, Yong-Gang Wang, Yong-Jie Wang, Yong-Jun Wang, Yong-Tang Wang, Yongbin Wang, Yongdi Wang, Yongfei Wang, Yongfeng Wang, Yonggang Wang, Yonghong Wang, Yongjie Wang, Yongjun Wang, Yongkang Wang, Yongkuan Wang, Yongli Wang, Yongliang Wang, Yonglun Wang, Yongmei Wang, Yongming Wang, Yongni Wang, Yongqiang Wang, Yongqing Wang, Yongrui Wang, Yongsheng Wang, Yongxiang Wang, Yongyi Wang, Yongzhong Wang, You Wang, Youhua Wang, Youji Wang, Youjie Wang, Youli Wang, Youzhao Wang, Youzhi Wang, Yu Qin Wang, Yu Tian Wang, Yu Wang, Yu'e Wang, Yu-Chen Wang, Yu-Fan Wang, Yu-Fen Wang, Yu-Hang Wang, Yu-Hui Wang, Yu-Ping Wang, Yu-Ting Wang, Yu-Wei Wang, Yu-Wen Wang, Yu-Ying Wang, Yu-Zhe Wang, Yu-Zhuo Wang, Yuan Wang, Yuan-Hung Wang, Yuanbo Wang, Yuanfan Wang, Yuanjiang Wang, Yuanli Wang, Yuanqiang Wang, Yuanqing Wang, Yuanyong Wang, Yuanyuan Wang, Yuanzhen Wang, Yubing Wang, Yubo Wang, Yuchen Wang, Yucheng Wang, Yuchuan Wang, Yudong Wang, Yue Wang, Yue-Min Wang, Yue-Nan Wang, YueJiao Wang, Yuebing Wang, Yuecong Wang, Yuegang Wang, Yuehan Wang, Yuehong Wang, Yuehu Wang, Yuehua Wang, Yuelong Wang, Yuemiao Wang, Yueshen Wang, Yueting Wang, Yuewei Wang, Yuexiang Wang, Yuexin Wang, Yueying Wang, Yueze Wang, Yufei Wang, Yufeng Wang, Yugang Wang, Yuh-Hwa Wang, Yuhan Wang, Yuhang Wang, Yuhua Wang, Yuhuai Wang, Yuhuan Wang, Yuhui Wang, Yujia Wang, Yujiao Wang, Yujie Wang, Yujiong Wang, Yulai Wang, Yulei Wang, Yuli Wang, Yuliang Wang, Yulin Wang, Yuling Wang, Yulong Wang, Yumei Wang, Yumeng Wang, Yumin Wang, Yuming Wang, Yun Wang, Yun Yong Wang, Yun-Hui Wang, Yun-Jin Wang, Yun-Xing Wang, Yunbing Wang, Yunce Wang, Yunchao Wang, Yuncong Wang, Yunduan Wang, Yunfang Wang, Yunfei Wang, Yunhan Wang, Yunhe Wang, Yunong Wang, Yunpeng Wang, Yunqiong Wang, Yuntai Wang, Yunzhang Wang, Yunzhe Wang, Yunzhi Wang, Yupeng Wang, Yuping Wang, Yuqi Wang, Yuqian Wang, Yuqiang Wang, Yuqin Wang, Yusha Wang, Yushe Wang, Yusheng Wang, Yutao Wang, Yuting Wang, Yuwei Wang, Yuwen Wang, Yuxiang Wang, Yuxing Wang, Yuxuan Wang, Yuxue Wang, Yuyan Wang, Yuyang Wang, Yuyin Wang, Yuying Wang, Yuyong Wang, Yuzhong Wang, Yuzhou Wang, Yuzhuo Wang, Z P Wang, Z Wang, Z-Y Wang, Zai Wang, Zaihua Wang, Ze Wang, Zechen Wang, Zehao Wang, Zehua Wang, Zekun Wang, Zelin Wang, Zeneng Wang, Zengtao Wang, Zeping Wang, Zexin Wang, Zeying Wang, Zeyu Wang, Zeyuan Wang, Zezhou Wang, Zhan Wang, Zhang Wang, Zhanggui Wang, Zhangshun Wang, Zhangying Wang, Zhanju Wang, Zhao Wang, Zhao-Jun Wang, Zhaobo Wang, Zhaofeng Wang, Zhaofu Wang, Zhaohai Wang, Zhaohui Wang, Zhaojing Wang, Zhaojun Wang, Zhaoming Wang, Zhaoqing Wang, Zhaosong Wang, Zhaotong Wang, Zhaoxi Wang, Zhaoxia Wang, Zhaoyu Wang, Zhe Wang, Zhehai Wang, Zhehao Wang, Zhen Wang, ZhenXue Wang, Zhenbin Wang, Zhenchang Wang, Zhenda Wang, Zhendan Wang, Zhendong Wang, Zheng Wang, Zhengbing Wang, Zhengchun Wang, Zhengdong Wang, Zhenghui Wang, Zhengkun Wang, Zhenglong Wang, Zhenguo Wang, Zhengwei Wang, Zhengxuan Wang, Zhengyang Wang, Zhengyi Wang, Zhengyu Wang, Zhenhua Wang, Zhenning Wang, Zhenqian Wang, Zhenshan Wang, Zhentang Wang, Zhenwei Wang, Zhenxi Wang, Zhenyu Wang, Zhenze Wang, Zhenzhen Wang, Zheyi Wang, Zheyue Wang, Zhezhi Wang, Zhi Wang, Zhi Xiao Wang, Zhi-Gang Wang, Zhi-Guo Wang, Zhi-Hao Wang, Zhi-Hong Wang, Zhi-Hua Wang, Zhi-Jian Wang, Zhi-Long Wang, Zhi-Qin Wang, Zhi-Wei Wang, Zhi-Xiao Wang, Zhi-Xin Wang, Zhibo Wang, Zhichao Wang, Zhicheng Wang, Zhicun Wang, Zhidong Wang, Zhifang Wang, Zhifeng Wang, Zhifu Wang, Zhigang Wang, Zhige Wang, Zhiguo Wang, Zhihao Wang, Zhihong Wang, Zhihua Wang, Zhihui Wang, Zhiji Wang, Zhijian Wang, Zhijie Wang, Zhijun Wang, Zhilun Wang, Zhimei Wang, Zhimin Wang, Zhipeng Wang, Zhiping Wang, Zhiqi Wang, Zhiqian Wang, Zhiqiang Wang, Zhiqing Wang, Zhiren Wang, Zhiruo Wang, Zhisheng Wang, Zhitao Wang, Zhiting Wang, Zhiwu Wang, Zhixia Wang, Zhixiang Wang, Zhixiao Wang, Zhixin Wang, Zhixing Wang, Zhixiong Wang, Zhixiu Wang, Zhiying Wang, Zhiyong Wang, Zhiyou Wang, Zhiyu Wang, Zhiyuan Wang, Zhizheng Wang, Zhizhong Wang, Zhong Wang, Zhong-Hao Wang, Zhong-Hui Wang, Zhong-Ping Wang, Zhong-Yu Wang, ZhongXia Wang, Zhongfang Wang, Zhongjing Wang, Zhongli Wang, Zhonglin Wang, Zhongqun Wang, Zhongsu Wang, Zhongwei Wang, Zhongyi Wang, Zhongyu Wang, Zhongyuan Wang, Zhongzhi Wang, Zhou Wang, Zhou-Ping Wang, Zhoufeng Wang, Zhouguang Wang, Zhuangzhuang Wang, Zhugang Wang, Zhulin Wang, Zhulun Wang, Zhuo Wang, Zhuo-Hui Wang, Zhuo-Jue Wang, Zhuo-Xin Wang, Zhuowei Wang, Zhuoying Wang, Zhuozhong Wang, Zhuqing Wang, Zi Wang, Zi Xuan Wang, Zi-Hao Wang, Zi-Qi Wang, Zi-Yi Wang, Zicheng Wang, Zifeng Wang, Zihan Wang, Ziheng Wang, Zihua Wang, Zihuan Wang, Zijian Wang, Zijie Wang, Zijue Wang, Zijun Wang, Zikang Wang, Zikun Wang, Ziliang Wang, Zilin Wang, Ziling Wang, Zilong Wang, Zining Wang, Ziping Wang, Ziqi Wang, Ziqian Wang, Ziqiang Wang, Ziqing Wang, Ziqiu Wang, Zitao Wang, Ziwei Wang, Zixi Wang, Zixia Wang, Zixian Wang, Zixiang Wang, Zixu Wang, Zixuan Wang, Ziyi Wang, Ziying Wang, Ziyu Wang, Ziyun Wang, Zongbao Wang, Zonggui Wang, Zongji Wang, Zongkui Wang, Zongqi Wang, Zongwei Wang, Zou Wang, Zulong Wang, Zumin Wang, Zun Wang, Zunxian Wang, Zuo Wang, Zuoheng Wang, Zuoyan Wang, Zusen Wang
articles
Yutong Jin, Zhengyang Li, Lin Qi +7 more · 2024 · Human & experimental toxicology · SAGE Publications · added 2026-04-24
The objective of this study was to investigate the potential of salidroside (SAL) (a major active compound in The expression of HIF-1 SAL enhanced the expression of HIF-1 SAL promotes osteoclast proli Show more
The objective of this study was to investigate the potential of salidroside (SAL) (a major active compound in The expression of HIF-1 SAL enhanced the expression of HIF-1 SAL promotes osteoclast proliferation, differentiation and bone resorption through HIF-1 Show less
no PDF DOI: 10.1177/09603271241269028
ANGPTL4
Guangming Mao, Wenhao Xu, Lingli Wan +8 more · 2024 · Frontiers in immunology · Frontiers · added 2026-04-24
Type 2 Diabetes Mellitus (T2D) and Osteoarthritis (OA) are both prevalent diseases that significantly impact the health of patients. Increasing evidence suggests that there is a big correlation betwee Show more
Type 2 Diabetes Mellitus (T2D) and Osteoarthritis (OA) are both prevalent diseases that significantly impact the health of patients. Increasing evidence suggests that there is a big correlation between T2D and OA, but the molecular mechanisms remain elusive. The aims of this study are to investigate the shared biomarkers and potential molecular mechanisms in T2D combined with OA. T2D and OA-related differentially expressed genes (DEGs) were identified via bioinformatic analysis on Gene Expression Omnibus (GEO) datasets GSE26168 and GSE114007 respectively. Subsequently, extensive target prediction and network analysis were finished with Gene Ontology (GO), protein-protein interaction (PPI), and pathway enrichment with DEGs. The transcription factors (TFs) and miRNAs coupled in co-expressed DEGs involved in T2D and OA were predicted as well. The key genes expressed both in the clinical tissues of T2D and OA were detected with western blot and qRT-PCR assay. Finally, the most promising candidate compounds were predicted with the Drug-Gene Interaction Database (DGIdb) and molecular docking. In this study, 209 shared DEGs between T2D and OA were identified. Functional analysis disclosed that these DEGs are predominantly related to ossification, regulation of leukocyte migration, extracellular matrix (ECM) structural constituents, PI3K/AKT, and Wnt signaling pathways. Further analysis via Protein-Protein Interaction (PPI) analysis and validation with external datasets emphasized MMP9 and ANGPTL4 as crucial genes in both T2D and OA. Our findings were validated through qRT-PCR and Western blot analyses, which indicated high expression levels of these pivotal genes in T2D, OA, and T2D combined with OA cases. Additionally, the analysis of Transcription Factors (TFs)-miRNA interactions identified 7 TFs and one miRNA that jointly regulate these important genes. The Receiver Operating characteristic (ROC) analysis demonstrated the significant diagnostic potential of MMP9 and ANGPTL4.Moreover, we identified raloxifene, ezetimibe, and S-3304 as promising agents for patients with both T2D and OA. This study uncovers the shared signaling pathways, biomarkers, potential therapeutics, and diagnostic models for individuals suffering from both T2D and OA. These findings not only present novel perspectives on the complex interplay between T2D and OA but also hold significant promise for improving the clinical management and prognosis of patients with this concurrent condition. Show less
📄 PDF DOI: 10.3389/fimmu.2024.1353915
ANGPTL4
Xiaomin Liu, Yiliang Zhang, Bingqian Han +10 more · 2024 · JCI insight · added 2026-04-24
Fuel substrate switching between carbohydrates and fat is essential for maintaining metabolic homeostasis. During aerobic exercise, the predominant energy source gradually shifts from carbohydrates to Show more
Fuel substrate switching between carbohydrates and fat is essential for maintaining metabolic homeostasis. During aerobic exercise, the predominant energy source gradually shifts from carbohydrates to fat. While it is well known that exercise mobilizes fat storage from adipose tissues, it remains largely obscure how circulating lipids are distributed tissue-specifically according to distinct energy requirements. Here, we demonstrate that aerobic exercise is linked to nutrient availability to regulate tissue-specific activities of lipoprotein lipase (LPL), the key enzyme catabolizing circulating triglyceride (TG) for tissue uptake, through the differential actions of angiopoietin-like (ANGPTL) proteins. Exercise reduced the tissue binding of ANGPTL3 protein, increasing LPL activity and TG uptake in the heart and skeletal muscle in the postprandial state specifically. Mechanistically, exercise suppressed insulin secretion, attenuating hepatic Angptl8 transcription through the PI3K/mTOR/CEBPα pathway, which is imperative for the tissue binding of its partner ANGPTL3. Constitutive expression of ANGPTL8 hampered lipid utilization and resulted in cardiac dysfunction in response to exercise. Conversely, exercise promoted the expression of ANGPTL4 in white adipose tissues, overriding the regulatory actions of ANGPTL8/ANGPTL3 in suppressing adipose LPL activity, thereby diverting circulating TG away from storage. Collectively, our findings show an overlooked bifurcated ANGPTL-LPL network that orchestrates fuel switching in response to aerobic exercise. Show less
📄 PDF DOI: 10.1172/jci.insight.181553
ANGPTL4
Rui Xie, Nan You, Wan-Yan Chen +21 more · 2024 · Research (Washington, D.C.) · added 2026-04-24
📄 PDF DOI: 10.34133/research.0409
ANGPTL4
Jian Xiao, Shuqing Cao, Jiawei Wang +8 more · 2024 · Cancer communications (London, England) · Wiley · added 2026-04-24
Lymph node metastasis (LNM) is the primary mode of metastasis in gastric cancer (GC). However, the precise mechanisms underlying this process remain elusive. Tumor cells necessitate lipid metabolic re Show more
Lymph node metastasis (LNM) is the primary mode of metastasis in gastric cancer (GC). However, the precise mechanisms underlying this process remain elusive. Tumor cells necessitate lipid metabolic reprogramming to facilitate metastasis, yet the role of lipoprotein lipase (LPL), a pivotal enzyme involved in exogenous lipid uptake, remains uncertain in tumor metastasis. Therefore, the aim of this study was to investigate the presence of lipid metabolic reprogramming during LNM of GC as well as the role of LPL in this process. Intracellular lipid levels were quantified using oil red O staining, BODIPY 493/503 staining, and flow cytometry. Lipidomics analysis was employed to identify alterations in intracellular lipid composition following LPL knockdown. Protein expression levels were assessed through immunohistochemistry, Western blotting, and enzyme-linked immunosorbent assays. The mouse popliteal LNM model was utilized to investigate differences in LNM. Immunoprecipitation and mass spectrometry were employed to examine protein associations. In vitro phosphorylation assays and Phos-tag sodium dodecyl-sulfate polyacrylamide gel electrophoresis assays were conducted to detect angiopoietin-like protein 4 (ANGPTL4) phosphorylation. We identified that an elevated intracellular lipid level represents a crucial characteristic of node-positive (N+) GC and further demonstrated that a high-fat diet can expedite LNM. LPL was found to be significantly overexpressed in N+ GC tissues and shown to facilitate LNM by mediating dietary lipid uptake within GC cells. Leptin, an obesity-related hormone, intercepted the effect exerted by ANGPTL4/Furin on LPL cleavage. Circulating leptin binding to the leptin receptor could induce the activation of inositol-requiring enzyme-1 (IRE1) kinase, leading to the phosphorylation of ANGPTL4 at the serine 30 residue and subsequently reducing its binding affinity with LPL. Moreover, our research revealed that LPL disrupted lipid homeostasis by elevating intracellular levels of arachidonic acid, which then triggered the cyclooxygenase-2/prostaglandin E2 (PGE2) pathway, thereby promoting tumor lymphangiogenesis. Leptin-induced phosphorylation of ANGPTL4 facilitates LPL-mediated lipid uptake and consequently stimulates the production of PGE2, ultimately facilitating LNM in GC. Show less
📄 PDF DOI: 10.1002/cac2.12583
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Meng-Jie Zhang, Ting-Ting Xue, Xiao-Ya Fei +9 more · 2024 · Clinical and experimental immunology · Oxford University Press · added 2026-04-24
Psoriasis is a chronic immune-mediated recurrent skin disease causing systemic damage. Increased angiogenesis has been reported to participate in the progression of psoriasis. However, angiogenesis-re Show more
Psoriasis is a chronic immune-mediated recurrent skin disease causing systemic damage. Increased angiogenesis has been reported to participate in the progression of psoriasis. However, angiogenesis-related genes (ARGs) in psoriasis have not been systematically elucidated. Therefore, we aim to identify potential biomarkers and subtypes using two algorithmsr. Transcriptome sequencing data of patients with psoriasis were obtained, in which differentially expressed genes were assessed by principal component analysis. A diagnostic model was developed using random forest algorithm and validated by receiver operating characteristic (ROC) curves. Subsequently, we performed consensus clustering to calculate angiogenesis-associated molecular subtypes of psoriasis. Additionally, a correlation analysis was conducted between ARGs and immune cell infiltration. Finally, validation of potential ARG genes was performed by quantitative real-time PCR (qRT-PCR). We identified 29 differentially expressed ARGs, including 13 increased and 16 decreased. Ten ARGs, CXCL8, ANG, EGF, HTATIP2, ANGPTL4, TNFSF12, RHOB, PML, FOXO4, and EMCN were subsequently sifted by the diagnostic model based on a random forest algorithm. Analysis of the ROC curve (area under the curve [AUC] = 1.0) indicated high diagnostic performance in internal validation. The correlation analysis suggested that CXCL8 has a high positive correlation with neutrophil (R =0.8, P < 0.0001) and interleukins pathway (R = 0.79, P < 0.0001). Furthermore, two ARG-mediated subtypes were obtained, indicating potential heterogeneity. Finally, the qRT-PCR demonstrated that the mRNA expression levels of CXCL8 and ANGPTL4 were elevated in psoriasis patients, with a reduced expression of EMCN observed. The current paper indicated potential ARG-related biomarkers of psoriasis, including CXCL8, ANGPTL4, and EMCN, with two molecular subtypes. Show less
no PDF DOI: 10.1093/cei/uxae052
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Jingying Zhao, Xuehai Ge, Tao Li +10 more · 2024 · Poultry science · Elsevier · added 2026-04-24
The quality and flavor of chicken are affected by muscle metabolites and related regulatory genes, and the molecular regulation mechanism of meat quality is different among different breeds of chicken Show more
The quality and flavor of chicken are affected by muscle metabolites and related regulatory genes, and the molecular regulation mechanism of meat quality is different among different breeds of chicken. In this study, 40 one-day-old Daweishan mini chicken (DM) and Cobb broiler (CB) were selected from each group, with 4 replicates and 10 chickens in each replicate. The chickens were reared until 90 d of age under the same management conditions. Then, metabolomics and transcriptomics data of 90-day-old DM (n = 4) and CB (n = 4) were integrated to analyze metabolites affecting breast muscle quality and flavor, and to explore the important genes regulating meat quality and flavor related metabolites. The results showed that a total of 38 significantly different metabolites (SDMs) and 420 differentially expressed genes (DEGs) were detected in the breast muscle of the 2 breeds. Amino acid and lipid metabolism may be the cause of meat quality and flavor difference between DM and CB chickens, involving metabolites such as L-methionine, betaine, N6, N6, N6-Trimethyl-L-lysine, L-anserine, glutathione, glutathione disulfide, L-threonine, N-Acetyl-L-aspartic acid, succinate, choline, DOPC, SOPC, alpha-linolenic acid, L-palmitoylcarnitine, etc. Important regulatory genes with high correlation with flavor amino acids (GATM, GSTO1) and lipids (PPARG, LPL, PLIN1, SCD, ANGPTL4, FABP7, GK, B4GALT6, UGT8, PLPP4) were identified by correlation analysis, and the gene-metabolite interaction network of breast muscle mass and flavor formation in DM chicken was constructed. This study showed that there were significant differences in breast metabolites between DM and CB chickens, mainly in amino acid and lipid metabolites. These 2 kinds of substances may be the main reasons for the difference in breast muscle quality and flavor between the 2 breeds. In general, this study could provide a theoretical basis for further research on the molecular regulatory mechanism of the formation of breast muscle quality and flavor differences between DM and CB chickens, and provide a reference for the development, utilization and genetic breeding of high-quality meat chicken breeds. Show less
📄 PDF DOI: 10.1016/j.psj.2024.103920
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Jeffrey Wang, Maaike Kockx, Magdalena Bolek +4 more · 2024 · Journal of lipid research · Elsevier · added 2026-04-24
Patients with schizophrenia show a disproportionally increased risk of cardiovascular disease. Hypertriglyceridemia is prevalent in this population; however, how this relates to levels of remnant chol Show more
Patients with schizophrenia show a disproportionally increased risk of cardiovascular disease. Hypertriglyceridemia is prevalent in this population; however, how this relates to levels of remnant cholesterol, triglyceride (TG)-rich lipoprotein (TRL) particle size and composition, TG turnover, and apolipoprotein (apo) and angiopoietin-like protein (ANGPTL) concentrations is unknown. Fasting levels of cholesterol (total [TC], LDL-C, HDL-C, non-HDL-C and remnant cholesterol) and TG were determined in 110 patients diagnosed with schizophrenia, and 46 healthy controls. TRL particle size, concentration and composition, and β-hydroxybutyrate (TG turnover marker) were assessed by NMR. Levels of apoCII, apoCIII, apoE, ANGPTL3, ANGPTL4, and ANGPTL8 were measured by ELISA, and apoCII, apoCIII and apoE were further evaluated in HDL and non-HDL fractions. Patients with schizophrenia had significantly elevated TG, TG:apoB ratio, non-HDL-C, remnant cholesterol, non-HDL-apoCII and non-HDL-apoCIII, and HDL-apoE (all P < 0.05), lower HDL-C and apoA-I (all P < 0.001), and comparable apoB, TC, TC:apoB ratio, LDL-C, β-hydroxybutyrate, ANGPTL3, ANGPTL4 and ANGPTL8 to healthy controls. Patients had a 12.0- and 2.5-fold increase in the concentration of large and medium TRL particles respectively, but similar cholesterol:TG ratio within each particle. Plasma TG, remnant cholesterol, and large and medium TRL particle concentrations correlated strongly with apoCII, apoCIII, and apoE in the non-HDL fraction, and with apoCIII and apoE in the HDL fraction in patients with schizophrenia. Differences in TG, HDL-C, TRL particle concentrations, apoCIII, and apoE persisted after adjustment for conventional risk factors. These results are consistent with impaired TRL lipolysis and clearance in patients with schizophrenia which may be responsive to targeting apoCIII. Show less
📄 PDF DOI: 10.1016/j.jlr.2024.100577
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Chun-Heng Kuo, Shu-Huei Wang, Hsien-Chia Juan +5 more · 2024 · BioFactors (Oxford, England) · Wiley · added 2026-04-24
Angiopoietin-like protein 4 (ANGPTL4) is a secretory glycoprotein involved in regulating glucose homeostasis in non-pregnant subjects. However, its role in glucose metabolism during pregnancy and the Show more
Angiopoietin-like protein 4 (ANGPTL4) is a secretory glycoprotein involved in regulating glucose homeostasis in non-pregnant subjects. However, its role in glucose metabolism during pregnancy and the pathophysiology of gestational diabetes mellitus (GDM) remains elusive. Thus, this study aimed to clarify the relationship between ANGPTL4 and GDM and investigate the pathophysiology of placental ANGPTL4 in glucose metabolism. We investigated this issue using blood and placenta samples in 957 pregnant women, the human 3A-sub-E trophoblast cell line, and the L6 skeletal muscle cell line. We found that ANGPTL4 expression in the placenta was higher in obese pregnant women than in lean controls. Palmitic acid significantly induced ANGPTL4 expression in trophoblast cells in a dose-response manner. ANGPTL4 overexpression in trophoblast cells resulted in endoplasmic reticulum (ER) stress, which stimulated the expression and secretion of growth hormone-variant (GH2) but not human placental lactogen. In L6 skeletal muscle cells, soluble ANGPTL4 suppressed insulin-mediated glucose uptake through the epidermal growth factor receptor (EGFR)/extracellular signal-regulated kinases 1/2 (ERK 1/2) pathways. In pregnant women, plasma ANGPTL4 concentrations in the first trimester predicted the incidence of GDM and were positively associated with BMI, plasma triglyceride, and plasma GH2 in the first trimester. However, they were negatively associated with insulin sensitivity index ISI Show less
no PDF DOI: 10.1002/biof.2076
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Peigeng Wang, Zhencong Li, Dongping Ye · 2024 · BMC musculoskeletal disorders · BioMed Central · added 2026-04-24
Increasing studies have shown degeneration of nucleus pulposus cells (NPCs) as an critical part of the progression of intervertebral disc degeneration (IVDD). However, there are relatively few studies Show more
Increasing studies have shown degeneration of nucleus pulposus cells (NPCs) as an critical part of the progression of intervertebral disc degeneration (IVDD). However, there are relatively few studies on single-cell transcriptome contrasts in human degenerated NPCs. Moreover, differences in Wnt/Ca We performed bioinformatics analysis using our previously published findings to construct single cell expression profiles of normal and degenerated nucleus pulposus. Then, in-depth differential analysis was used to characterize the expression of Wnt/Ca The obtained cell data were clustered into five different chondrocytes clusters, which chondrocyte 4 and chondrocyte 5 mainly accounted for a high proportion in degenerated nucleus pulposus tissues, but rarely in normal nucleus pulposus tissues. Genes associated within the Wnt/Ca Single-cell RNA sequencing revealed differential expression of Wnt/Ca Show less
📄 PDF DOI: 10.1186/s12891-024-07368-3
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Chaojun Zhu, Lan Teng, Yihong Lai +14 more · 2024 · Cellular and molecular life sciences : CMLS · Springer · added 2026-04-24
Peritoneal metastasis, the third most common metastasis in colorectal cancer (CRC), has a poor prognosis for the rapid progression and limited therapeutic strategy. However, the molecular characterist Show more
Peritoneal metastasis, the third most common metastasis in colorectal cancer (CRC), has a poor prognosis for the rapid progression and limited therapeutic strategy. However, the molecular characteristics and pathogenesis of CRC peritoneal metastasis are poorly understood. Here, we aimed to elucidate the action and mechanism of adipose-derived stem cells (ADSCs), a prominent component of the peritoneal microenvironment, in CRC peritoneal metastasis formation. Database analysis indicated that ADSCs infiltration was increased in CRC peritoneal metastases, and high expression levels of ADSCs marker genes predicted a poor prognosis. Then we investigated the effect of ADSCs on CRC cells in vitro and in vivo. The results revealed that CRC cells co-cultured with ADSCs exhibited stronger metastatic property and anoikis resistance, and ADSCs boosted the intraperitoneal seeding of CRC cells. Furthermore, RNA sequencing was carried out to identify the key target gene, angiopoietin like 4 (ANGPTL4), which was upregulated in CRC specimens, especially in peritoneal metastases. Mechanistically, TGF-β1 secreted by ADSCs activated SMAD3 in CRC cells, and chromatin immunoprecipitation assay showed that SMAD3 facilitated ANGPTL4 transcription by directly binding to ANGPTL4 promoter. The ANGPTL4 upregulation was essential for ADSCs to promote glycolysis and anoikis resistance in CRC. Importantly, simultaneously targeting TGF-β signaling and ANGPTL4 efficiently reduced intraperitoneal seeding in vivo. In conclusion, this study indicates that tumor-infiltrating ADSCs promote glycolysis and anoikis resistance in CRC cells and ultimately facilitate peritoneal metastasis via the TGF-β1/SMAD3/ANGPTL4 axis. The dual-targeting of TGF-β signaling and ANGPTL4 may be a feasible therapeutic strategy for CRC peritoneal metastasis. Show less
📄 PDF DOI: 10.1007/s00018-024-05215-1
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Xiaojun Wang, Hung-Chen Chang, Xuchao Gu +8 more · 2024 · Mechanisms of ageing and development · Elsevier · added 2026-04-24
Renal tubular epithelial cells are vulnerable to stress-induced damage, including excessive lipid accumulation and aging, with ANGPTL4 potentially playing a crucial bridging role between these factors Show more
Renal tubular epithelial cells are vulnerable to stress-induced damage, including excessive lipid accumulation and aging, with ANGPTL4 potentially playing a crucial bridging role between these factors. In this study, RNA-sequencing was used to identify a marked increase in ANGPTL4 expression in kidneys of diet-induced obese and aging mice. Overexpression and knockout of ANGPTL4 in renal tubular epithelial cells (HK-2) was used to investigate the underlying mechanism. Subsequently, ANGPTL4 expression in plasma and kidney tissues of normal young controls and elderly individuals was analyzed using ELISA and immunohistochemical techniques. RNA sequencing results showed that ANGPTL4 expression was significantly upregulated in the kidney tissue of diet-induced obesity and aging mice. In vitro experiments demonstrated that overexpression of ANGPTL4 in HK-2 cells led to increased lipid deposition and senescence. Conversely, the absence of ANGPTL4 appears to alleviate the impact of free fatty acids (FFA) on aging in HK-2 cells. Additionally, aging HK-2 cells exhibited elevated ANGPTL4 expression, and stress response markers associated with cell cycle arrest. Furthermore, our clinical evidence revealed dysregulation of ANGPTL4 expression in serum and kidney tissue samples obtained from elderly individuals compared to young subjects. Our study findings indicate a potential association between ANGPTL4 and age-related metabolic disorders, as well as injury to renal tubular epithelial cells. This suggests that targeting ANGPTL4 could be a viable strategy for the clinical treatment of renal aging. Show less
no PDF DOI: 10.1016/j.mad.2024.111932
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Yang Yang, Xinyu Yang, Shiqi Ren +3 more · 2024 · Heliyon · Elsevier · added 2026-04-24
Colon adenocarcinoma (COAD) is a highly lethal gastrointestinal malignancy. The five-year survival rate of metastatic colorectal cancer remains low, at 14 percent. Numerous publications have suggested Show more
Colon adenocarcinoma (COAD) is a highly lethal gastrointestinal malignancy. The five-year survival rate of metastatic colorectal cancer remains low, at 14 percent. Numerous publications have suggested a role for peroxisome proliferator-activated receptors (PPARs) in malignancy. Recent studies have shown that PPARs, as nuclear transcription factors, may serve as potential targets for the treatment of metabolic syndrome tumors and their associated complications. However, the molecular mechanism has not been thoroughly investigated. Hence, in order to enhance the prediction of personalized medicine for PPAR-associated modulators in malignancy treatment, a timely review becomes essential. Utilizing TCGA-COAD expression profile data and patient overall survival (OS) information, this study systematically conducted investigations to identify and develop Hub stem cell-related diagnostic and prognostic identification models, aiming to enhance the multi-gene markers for COAD. Utilizing the differential expression profiles of stem cell-related genes, an 11-gene (SLC27A4, CPT1C, CPT1B, CPT2, CYP4A11, FABP3, FABP7, AQP7, MMP1, ACOX1, ANGPTL4) diagnostic and prognostic model was developed. This model demonstrated precise diagnostic and prognostic capabilities and holds the potential to characterize the clinicopathologic features of COAD. Univariate and multivariate Cox proportional hazards regression analyses were conducted to ascertain the independent factors influencing OS outcomes in COAD. The results revealed that CPT1B, SLC27A4, and FABP3 were identified as independent risk prognostic factors for OS in COAD, whereas ACOX1 and CPT2 served as independent protective prognostic factors. The hub genes associated with PPARs were identified through the differential expression of contrast agent COAD and normal tissues. Finally, the investigation of variations in immune infiltration and the analysis of relevant biological pathways validate the prognostic significance of the independent post-factors within this molecular model. This research aims to provide references for comprehending the mechanism of post-transcriptional regulation of COAD and molecular therapy. Show less
📄 PDF DOI: 10.1016/j.heliyon.2024.e27388
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Zhiyu Ma, Nana Wang, Tingting Meng +3 more · 2024 · Journal of biochemical and molecular toxicology · Wiley · added 2026-04-24
Recent studies have shown that epithelial-mesenchymal transition (EMT) plays an important role in paraquat (PQ)-induced tissue fibrosis, which is the main cause of death in patients with PQ poisoning. Show more
Recent studies have shown that epithelial-mesenchymal transition (EMT) plays an important role in paraquat (PQ)-induced tissue fibrosis, which is the main cause of death in patients with PQ poisoning. However, no effective treatment for pulmonary interstitial fibrosis caused by PQ poisoning exists. It is of great significance for us to find new therapeutic targets through bioinformatics in PQ-induced EMT. We conducted transcriptome sequencing to determine the expression profiles of 1210 messenger RNAs (mRNAs), 558 long noncoding RNAs, 28 microRNAs (miRNAs), including 18 known-miRNAs, 10 novel-miRNAs and 154 circular RNAs in the PQ-exposed EMT group mice. Using gene ontology and Kyoto Encyclopaedia of Genes and Genomes analyses, we identified the pathways associated with signal transduction, cancers, endocrine systems and immune systems were involved in PQ-induced EMT. Furthermore, we constructed long noncoding RNA-miRNA-mRNA interrelated networks and found that upregulated genes included Il22ra2, Mdm4, Slc35e2 and Angptl4, and downregulated genes included RGS2, Gabpb2, Acvr1, Prkd3, Sp100, Tlr12, Syt15 and Camk2d. Thirteen new potential competitive endogenous RNA targets were also identified for further treatment of PQ-induced pulmonary tissue fibrosis. Through further study of the pathway and networks, we may identify new molecular targets in PQ-induced pulmonary EMT. Show less
no PDF DOI: 10.1002/jbt.23681
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Sijia Ma, Jia Wang, Zhiwei Cui +4 more · 2024 · Scientific reports · Nature · added 2026-04-24
Hypoxia-mediated chemoresistance plays a crucial role in the development of ovarian cancer (OC). However, the roles of hypoxia-related genes (HRGs) in chemoresistance and prognosis prediction and thei Show more
Hypoxia-mediated chemoresistance plays a crucial role in the development of ovarian cancer (OC). However, the roles of hypoxia-related genes (HRGs) in chemoresistance and prognosis prediction and theirs underlying mechanisms remain to be further elucidated. We intended to identify and validate classifiers of hub HRGs for chemoresistance, diagnosis, prognosis as well as immune microenvironment of OC, and to explore the function of the most crucial HRG in the development of the malignant phenotypes. The RNA expression and clinical data of HRGs were systematically evaluated in OC training group. Univariate and multivariate Cox regression analysis were applied to construct hub HRGs classifiers for prognosis and diagnosis assessment. The relationship between classifiers and chemotherapy response and underlying pathways were detected by GSEA, CellMiner and CIBERSORT algorithm, respectively. OC cells were cultured under hypoxia or transfected with HIF-1α or HIF-2α plasmids, and the transcription levels of TGFBI were assessed by quantitative PCR. TGFBI was knocked down by siRNAs in OC cells, CCK8 and in vitro migration and invasion assays were performed to examine the changes in cell proliferation, motility and metastasis. The difference in TGFBI expression was examined between cisplatin-sensitive and -resistant cells, and the effects of TGFBI interference on cell apoptosis, DNA repair and key signaling molecules of cisplatin-resistant OC cells were explored. A total of 179 candidate HRGs were extracted and enrolled into univariate and multivariate Cox regression analysis. Six hub genes (TGFBI, CDKN1B, AKAP12, GPC1, TGM2 and ANGPTL4) were selected to create a HRGs prognosis classifier and four genes (TGFBI, AKAP12, GPC1 and TGM2) were selected to construct diagnosis classifiers. The HRGs prognosis classifier could precisely distinguish OC patients into high-risk and low-risk groups and estimate their clinical outcomes. Furthermore, the high-risk group had higher percentage of Macrophages M2 and exhibited higher expression of immunecheckpoints such as PD-L2. Additionally, the diagnosis classifiers could accurately distinguish OC from normal samples. TGFBI was further verified as a specific key target and demonstrated that its high expression was closely correlated with poor prognosis and chemoresistance of OC. Hypoxia upregulated the expression level of TGFBI. The hypoxia-induced factor HIF-2α but not HIF-1α could directly bind to the promoter region of TGFBI, and facilitate its transcription level. TGFBI was upregulated in cisplatin-sensitive and resistant ovarian cancer cells in a cisplatin time-dependent manner. TGFBI interference downregulated DNA repair-related markers (p-p95/NBS1, RAD51, p-DNA-PKcs, DNA Ligase IV and Artemis), apoptosis-related marker (BCL2) and PI3K/Akt pathway-related markers (PI3K-p110 and p-Akt) in cisplatin-resistant OC cells. In summary, the HRGs prognosis risk classifier could be served as a predictor for OC prognosis and efficacy evaluation. TGFBI, upregulated by HIF-2α as an HRG, promoted OC chemoresistance through activating PI3K/Akt pathway to reduce apoptosis and enhance DNA damage repair pathway. Show less
📄 PDF DOI: 10.1038/s41598-024-53854-y
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Jie Wu, Yuting Zhang, Guoxing You +8 more · 2024 · Aging · Impact Journals · added 2026-04-24
Lung adenocarcinoma (LUAD) is a malignant tumor of the respiratory system that has a poor 5-year survival rate. Anoikis, a type of programmed cell death, contributes to tumor development and metastasi Show more
Lung adenocarcinoma (LUAD) is a malignant tumor of the respiratory system that has a poor 5-year survival rate. Anoikis, a type of programmed cell death, contributes to tumor development and metastasis. The aim of this study was to develop an anoikis-based stratified model, and a multivariable-based nomogram for guiding clinical therapy for LUAD. Through differentially expressed analysis, univariate Cox, LASSO Cox regression, and random forest algorithm analysis, we established a 4 anoikis-related genes-based stratified model, and a multivariable-based nomogram, which could accurately predict the prognosis of LUAD patients in the TCGA and GEO databases, respectively. The low and high-risk score LUAD patients stratified by the model showed different tumor mutation burden, tumor microenvironment, gemcitabine sensitivity and immune checkpoint expressions. Through immunohistochemical analysis of clinical LUAD samples, we found that the 4 anoikis-related genes (PLK1, SLC2A1, ANGPTL4, CDKN3) were highly expressed in the tumor samples from clinical LUAD patients, and knockdown of these genes in LUAD cells by transfection with small interfering RNAs significantly inhibited LUAD cell proliferation and migration, and promoted anoikis. In conclusion, we developed an anoikis-based stratified model and a multivariable-based nomogram of LUAD, which could predict the survival of LUAD patients and guide clinical treatment. Show less
📄 PDF DOI: 10.18632/aging.205521
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Wei Li, Yongyi Wang, Ritai Huang +4 more · 2024 · BMC cardiovascular disorders · BioMed Central · added 2026-04-24
Coronary artery disease (CAD) is a complex disease that is influenced by environmental and genetic factors. In this study, we aimed to investigate the relationship between coding variants in lipid met Show more
Coronary artery disease (CAD) is a complex disease that is influenced by environmental and genetic factors. In this study, we aimed to investigate the relationship between coding variants in lipid metabolism-related genes and CAD in a Chinese Han population. A total of 252 individuals were recruited for this study, including 120 CAD patients and 132 healthy control individuals. Rare and common coding variants in 12 lipid metabolism-related genes (ANGPTL3, ANGPTL4, APOA1, APOA5, APOC1, APOC3, CETP, LDLR, LIPC, LPL, PCSK9 and SCARB1) were detected via next-generation sequencing (NGS)-based targeted sequencing. Associations between common variants and CAD were evaluated by Fisher's exact test. A gene-based association test of rare variants was performed by the sequence kernel association test-optimal (SKAT-O test). We found 51 rare variants and 17 common variants in this study. One common missense variant, LIPC rs6083, was significantly associated with CAD after Bonferroni correction (OR = 0.47, 95% CI = 0.29-0.76, p = 1.9 × 10 Targeted sequencing is a powerful tool for identifying rare and common variants in CAD. The common missense variant LIPC rs6083 confers protection against CAD. The clinical relevance of rare variants in CAD aetiology needs to be investigated in larger sample sizes in the future. Show less
📄 PDF DOI: 10.1186/s12872-024-03759-5
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Ruicheng Yang, Xinyi Wang, Hulin Liu +4 more · 2024 · Cell communication and signaling : CCS · BioMed Central · added 2026-04-24
Bacterial meningitis remains a leading cause of infection-related mortality worldwide. Although Escherichia coli (E. coli) is the most common etiology of neonatal meningitis, the underlying mechanisms Show more
Bacterial meningitis remains a leading cause of infection-related mortality worldwide. Although Escherichia coli (E. coli) is the most common etiology of neonatal meningitis, the underlying mechanisms governing bacterial blood-brain barrier (BBB) disruption during infection remain elusive. We observed that infection of human brain microvascular endothelial cells with meningitic E. coli triggers the activation of early growth response 1 (Egr-1), a host transcriptional activator. Through integrated chromatin immunoprecipitation sequencing and transcriptome analysis, we identified Egr-1 as a crucial regulator for maintaining BBB integrity. Mechanistically, Egr-1 induced cytoskeletal changes and downregulated tight junction protein expression by directly targeting VEGFA, PDGFB, and ANGPTL4, resulting in increased BBB permeability. Meanwhile, Egr-1 also served as a master regulator in the initiation of neuroinflammatory response during meningitic E. coli infection. Our findings support an Egr-1-dependent mechanism of BBB disruption by meningitic E. coli, highlighting a promising therapeutic target for bacterial meningitis. Show less
📄 PDF DOI: 10.1186/s12964-024-01488-y
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Zhongyi Wang, Fengqi Li, Chunjing Feng +9 more · 2024 · Advanced biology · Wiley · added 2026-04-24
In vitro cell culturing witnessed its applications in scientific research and industrial activities. Attempts to shorten the doubling time of cultured cells have never ceased. In plants, auxin is appl Show more
In vitro cell culturing witnessed its applications in scientific research and industrial activities. Attempts to shorten the doubling time of cultured cells have never ceased. In plants, auxin is applied to promote plant growth, the synthetic derivative 1-Naphthaleneacetic acid (NAA) is a good example. Despite the auxin's naturally occurring receptors are not present in mammalian cells, studies suggested they may affect cell culturing. Yet the effects and mechanisms are still unclear. Here, an up to 2-fold increase in the yield of in vitro cultured human cells is observed. Different types of human cell lines and primary cells are tested and found that NAA is effective in all the cells tested. The PI staining followed by FACS suggested that NAA do not affect the cell cycling. Apoptosis-specific dye staining analysis implicated that NAA rescued cell death. Further bulk RNA sequencing is done and it is identified that the lipid metabolism-engaging and anti-apoptosis gene, ANGPTL4, is enhanced in expression upon NAA treatment. Studies on ANGPTL4 knockout cells indicated that ANGPTL4 is required for NAA-mediated response. Thus, the data identified a beneficial role of NAA in human cell culturing and highlighted its potency in in vitro cell culturing. Show less
no PDF DOI: 10.1002/adbi.202300593
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I-Weng Yen, Shin-Yu Lin, Ming-Wei Lin +12 more · 2024 · Clinica chimica acta; international journal of clinical chemistry · Elsevier · added 2026-04-24
Large-for-gestational-age (LGA) neonates have increased risk of adverse pregnancy outcomes and adult metabolic diseases. We aimed to investigate the relationship between plasma angiopoietin-like prote Show more
Large-for-gestational-age (LGA) neonates have increased risk of adverse pregnancy outcomes and adult metabolic diseases. We aimed to investigate the relationship between plasma angiopoietin-like protein 4 (ANGPTL4), a protein involved in lipid and glucose metabolism during pregnancy, placental function, growth factors, and the risk of LGA. We conducted a prospective cohort study and recruited women with singleton pregnancies at the National Taiwan University Hospital between 2013 and 2018. First trimester maternal plasma ANGPTL4 concentrations were measured. Among 353 pregnant women recruited, the LGA group had higher first trimester plasma ANGPTL4 concentrations than the appropriate-for-gestational-age group. Plasma ANGPTL4 was associated with hemoglobin A1c, post-load plasma glucose, plasma triglyceride, plasma free fatty acid concentrations, plasma growth hormone variant (GH-V), and birth weight, but was not associated with cord blood growth factors. After adjusting for age, body mass index, hemoglobin A1c, and plasma triglyceride concentrations, plasma ANGPTL4 concentrations were significantly associated with LGA risk, and its predictive performance, as measured by the area under the receiver operating characteristic curve, outperformed traditional risk factors for LGA. Plasma ANGPTL4 is associated with glucose and lipid metabolism during pregnancy, plasma GH-V, and birth weight, and is an early biomarker for predicting the risk of LGA. Show less
no PDF DOI: 10.1016/j.cca.2024.117775
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Chao Xue, Liqing Jiang, Bin Zhang +12 more · 2024 · Heliyon · Elsevier · added 2026-04-24
Aortic dissection (AD) is a critical emergency in cardiovascular disease. AD occurs only in specific sites of the aorta, and the variation of shear stress in different aortic segments is a possible ca Show more
Aortic dissection (AD) is a critical emergency in cardiovascular disease. AD occurs only in specific sites of the aorta, and the variation of shear stress in different aortic segments is a possible cause not reported. This study investigated the key molecules involved in shear stress-induced AD through quantitative bioinformatic analysis of a public RNA sequencing database and clinical tissue sample validation. Gene expression data from the GSE153434, GSE147026, and GSE52093 datasets were downloaded from the Gene Expression Omnibus. Next, differently expressed genes (DEGs) in each dataset were identified and integrated to identify common AD DEGs. STRING, Cytoscape, and MCODE were used to identify hub genes and crucial clustering modules, and Connectivity Map (CMap) was used to identify positive and negative agents. The same procedure was performed for the GSE160611 dataset to obtain shear stress-induced human aortic endothelial cell (HAEC) DEGs. After the integration of these two DEGs sets to identify shear stress-associated hub DEGs in AD, Gene Ontology Enrichment Analysis was performed. The common chemokine receptors and ligands in AD were identified by analyzing AD's three RNA sequencing datasets. Their origin was verified by analyzing AD single-cell sequencing data and validated by immunoblotting and immunofluorescence. We identified 100 down-regulated and 50 up-regulated AD common DEGs. Enrichment results showed that common DEGs were closely related to blood vessel morphogenesis, muscle structure development, muscle tissue development, and chemotaxis. Among those DEGs, MYC, CCL2, and SPP1 are the three molecules with the highest degree. A crucial cluster of 15 genes was identified using MCODE, which contained inflammation-related genes with elevated expression and muscle cell-related genes with decreased expression, and CCL2 is central to immune-related genes. CMap confirmed MEK inhibitors and ALK inhibitors as possible therapeutic agents for AD. Moreover, 366 shear stress-associated DEGs in HAEC were identified in the GSE160611 dataset. After taking the intersection, we identified five shear stress-associated hub DEGs in AD (ANGPTL4, SNAI2, CCL2, GADD45B, and PROM1), and the enrichment analysis indicated they were related to the endothelial cell apoptotic process. Chemokine CCL2 was the molecule with a high degree in both DEG sets. Besides CCL2, CXCL5 was the only chemokine ligand differentially expressed in the three datasets. Additionally, immunoblotting confirmed the increased expression of CCL2 and CXCL5 in clinical tissue samples. Further research at the single-cell level revealed that CCL2 has multiple origins, and CXCL5 is macrophage-derived. Through integrative analysis, we identified core common AD DEGs and possible therapeutic agents based on these DEGs. We elucidated that the chemokine CCL2 and CXCL5-mediated "Endothelial-Monocyte-Neutrophil" axis may contribute to the development of shear stress-induced AD. These findings provide possible therapeutic targets for the prevention and treatment of AD. Show less
📄 PDF DOI: 10.1016/j.heliyon.2023.e23312
ANGPTL4
Qi Zhang, Shounan Qi, Jiaxin You +1 more · 2024 · Biochemical and biophysical research communications · Elsevier · added 2026-04-24
Macular edema (ME) has emerged as a leading cause of visual impairment, representing a critical clinical manifestation and complication associated with many eye diseases. In the occurrence and develop Show more
Macular edema (ME) has emerged as a leading cause of visual impairment, representing a critical clinical manifestation and complication associated with many eye diseases. In the occurrence and development of ME, retinal glial cells like Müller cells and microglial cells play vital roles. Moreover, growth factor and cytokines associated with them, such as vascular endothelial growth factor (VEGF), pigment epithelium-derived factor (PEDF), hypoxia-inducible factor-1α (HIF-1α), angiopoietin-like protein 4 (ANGPTL4), interleukin-6(IL-6), interleukin-8 (IL-8), monocyte chemoattractant protein-1 (MCP-1), prostaglandin, etc., also take part in the pathogenesis of ME. Changes in these cytokines can lead to retinal angiogenesis, increased vascular permeability, blood-retinal barrier (BRB) breakdown, and fluid leakage, further causing ME to occur or deteriorate. Research on the role of retinal glial cells and related cytokines in ME will provide new therapeutic directions and effective remedies. This article is a literature review on the role of Müller cells, microglial cells and related factors in ME pathogenesis. Show less
no PDF DOI: 10.1016/j.bbrc.2023.149415
ANGPTL4
Hongling Hu, Sheng Luo, Pinglin Lai +18 more · 2024 · Proceedings of the National Academy of Sciences of the United States of America · National Academy of Sciences · added 2026-04-24
Leptin protein was thought to be unique to leptin receptor (LepR), but the phenotypes of mice with mutation in LepR [
📄 PDF DOI: 10.1073/pnas.2310685120
ANGPTL4
Hung-Chen Chang, Xiaojun Wang, Xuchao Gu +6 more · 2024 · Experimental gerontology · Elsevier · added 2026-04-24
Secretory factors linked to lymphogenesis, such as vascular endothelial growth factor C (VEGF-C), angiopoietin like protein 4 (ANGPTL4), and activin A (ACV-A), have been recognized as potential marker Show more
Secretory factors linked to lymphogenesis, such as vascular endothelial growth factor C (VEGF-C), angiopoietin like protein 4 (ANGPTL4), and activin A (ACV-A), have been recognized as potential markers of chronic inflammatory status and age-related diseases. Furthermore, these factors may also be linked to frailty. The primary objective of this study was to examine the serum VEGF-C, ANGPTL4, and ACV-A levels in young individuals, healthy older individuals, and older individuals with pre-frailty and frailty, and to determine their association with pro-inflammatory factor levels. We conducted an observational study, enrolling a total of 210 older individuals and 20 young healthy volunteers. Participants were divided into four groups based on the Freid frailty phenotype: healthy young group, older patients without frailty group, pre-frail older group, and frail older group. Plasma and peripheral blood mononuclear cells (PBMCs) were collected from all four groups. ELISA was used to measure the serum levels of VEGF-C, ANGPTL4, ACV-A, and pro-inflammatory cytokines, while RT-qPCR was used to measure the transcription level of VEGF-C, ANGPTL4 and ACV-A in PBMCs. In comparison to healthy young individuals and older participants without frailty, older participants with frailty exhibited lower renal function, higher serum levels and transcription levels of VEGF-C, ANGPTL4, ACV-A, and elevated levels of pro-inflammatory cytokines (CRP, IL-1β, and TNF-α). Multiple linear regression analysis revealed that serum levels of VEGF-C, ANGPTL4, and ACV-A were positively correlated with the frailty index, independent of age, eGFR, and comorbidities. Furthermore, the receiver operating characteristic (ROC) curve analysis demonstrated that serum levels of VEGF-C, ANGPTL4, and ACV-A have great accuracy in predicting frailty. Elevated serum levels of VEGF-C, ANGPTL4, and ACV-A are associated with frailty status. Show less
no PDF DOI: 10.1016/j.exger.2023.112345
ANGPTL4
Yiyun Liu, Naima Hamid, Rakia Manzoor +4 more · 2024 · The Science of the total environment · Elsevier · added 2026-04-24
Di-2-ethylhexyl phthalic acid (DEHP) is one of the most widely used plasticizers in the industry, which can improve the flexibility and durability of plastics. It is prone to migrate from various dail Show more
Di-2-ethylhexyl phthalic acid (DEHP) is one of the most widely used plasticizers in the industry, which can improve the flexibility and durability of plastics. It is prone to migrate from various daily plastic products through wear and leaching into the surrounding environment and decompose into the more toxic metabolite mono-2-ethylhexyl phthalic acid (MEHP) after entering the human body. However, the impacts and mechanisms of MEHP on neuroblastoma are unclear. We exposed MYCN-amplified neuroblastoma SK-N-BE(2)C cells to an environmentally related concentration of MEHP and found that MEHP increased the proliferation and migration ability of tumor cells. The peroxisome proliferator-activated receptor (PPAR) β/δ pathway was identified as a pivotal signaling pathway in neuroblastoma, mediating the effects of MEHP through transcriptional sequencing analysis. Because MEHP can bind to the PPARβ/δ protein and initiate the expression of the downstream gene angiopoietin-like 4 (ANGPTL4), the PPARβ/δ-specific agonist GW501516 and antagonist GSK3787, the recombinant human ANGPTL4 protein, and the knockdown of gene expression confirmed the regulation of the PPARβ/δ-ANGPTL4 axis on the malignant phenotype of neuroblastoma. Based on the critical role of PPARβ/δ and ANGPTL4 in the metabolic process, a non-targeted metabolomics analysis revealed that MEHP altered multiple metabolic pathways, particularly lipid metabolites involving fatty acyls, glycerophospholipids, and sterol lipids, which may also be potential factors promoting tumor progression. We have demonstrated for the first time that MEHP can target binding to PPARβ/δ and affect the progression of neuroblastoma by activating the PPARβ/δ-ANGPTL4 axis. This mechanism confirms the health risks of plasticizers as tumor promoters and provides new data support for targeted prevention and treatment of neuroblastoma. Show less
no PDF DOI: 10.1016/j.scitotenv.2023.168949
ANGPTL4
Zehuan Liao, Joseph Jing Heng Lim, Jeannie Xue Ting Lee +12 more · 2024 · Advanced healthcare materials · Wiley · added 2026-04-24
Epithelial-to-mesenchymal transition (EMT) plays a crucial role in metastatic cancer progression, and current research, which relies heavily on 2D monolayer cultures, falls short in recapitulating the Show more
Epithelial-to-mesenchymal transition (EMT) plays a crucial role in metastatic cancer progression, and current research, which relies heavily on 2D monolayer cultures, falls short in recapitulating the complexity of a 3D tumor microenvironment. To address this limitation, a transcriptomic meta-analysis is conducted on diverse cancer types undergoing EMT in 2D and 3D cultures. It is found that mechanotransduction is elevated in 3D cultures and is further intensified during EMT, but not during 2D EMT. This analysis reveals a distinct 3D EMT gene signature, characterized by extracellular matrix remodeling coordinated by angiopoietin-like 4 (Angptl4) along with other canonical EMT regulators. Utilizing hydrogel-based 3D matrices with adjustable mechanical forces, 3D cancer cultures are established at varying physiological stiffness levels. A YAP:EGR-1 mediated up-regulation of Angptl4 expression is observed, accompanied by an upregulation of mesenchymal markers, at higher stiffness during cancer EMT. Suppression of Angptl4 using antisense oligonucleotides or anti-cAngptl4 antibodies leads to a dose-dependent abolishment of EMT-mediated chemoresistance and tumor self-organization in 3D, ultimately resulting in diminished metastatic potential and stunted growth of tumor xenografts. This unique programmable 3D cancer cultures simulate stiffness levels in the tumor microenvironment and unveil Angptl4 as a promising therapeutic target to inhibit EMT and impede cancer progression. Show less
no PDF DOI: 10.1002/adhm.202303481
ANGPTL4
Baoluo Ma, Linghui Qin, Zhou Sun +6 more · 2024 · Environmental toxicology · Wiley · added 2026-04-24
Clear cell renal cell carcinoma (ccRCC) is the most prevalent and aggressive subtype of renal cell carcinoma, originating from renal tubular epithelial cells in the kidney. Hypoxia proves to be a feat Show more
Clear cell renal cell carcinoma (ccRCC) is the most prevalent and aggressive subtype of renal cell carcinoma, originating from renal tubular epithelial cells in the kidney. Hypoxia proves to be a feature commonly observed in solid tumors, leading to increased resistance to treatment and tumor progression. scRNA-seq data were procured from GSE159115 data set. We utilized UMAP and NMF algorithm for clustering and dimensionality reduction. The FindAllMarkers function was used to compare various groups and identify potential hypoxia marker genes. A series of in vitro experiments, including CFA, flow cytometry targeting cell cycle, CCK-8, and EDU, was applied to investigate how ANGPTL4 regulated the ccRCC progression. Two cell lines of ccRCC cells, 786-O and Caki, were used for si-ANGPTL4 transfection. We annotated a total of a total of 6 cell clusters, namely ccRCC malignant cells, T cells, endothelial cells, myeloid cells, smooth muscle cells, and B cells. We observed higher levels of hypoxia-score in the ccRCC malignant cells, while lowest hypoxia-score in T and B cells. We detected multiple hypoxia-related subclusters of TME cells in ccRCC, among which S100A4 CD8+ T cells and nonhypoxia CD8+ T cells were found with a marked elevation of T cell inhibitory gene score. We identified that ANGPTL4+ endothelial cells might function as an integrative role in tumor angiogenesis. Multiple TME subclusters showed high potency in stratification of the prognosis of ccRCC patients. Moreover, by a series of in vitro experiment, we found ANGPTL4 regulated the ccRCC cell proliferation, probably through ERK/P38 pathway. We discerned multiple hypoxia-related subclusters of TME cells in ccRCC, which displayed distinct functional features and great potency in predicting prognosis of ccRCC patients. We identified the role of ANGPTL4 in regulating ccRCC proliferation via ERK/p38 pathway. Show less
no PDF DOI: 10.1002/tox.24009
ANGPTL4
Baisheng Sun, Lina Bai, Qinglin Li +5 more · 2024 · Toxicology in vitro : an international journal published in association with BIBRA · Elsevier · added 2026-04-24
Sepsis-induced acute lung injury (ALI) is a life-threatening disease. Macrophage pyroptosis has been reported to exert function in ALI. We aimed to investigate the mechanisms of ANGPTL4-mediated cell Show more
Sepsis-induced acute lung injury (ALI) is a life-threatening disease. Macrophage pyroptosis has been reported to exert function in ALI. We aimed to investigate the mechanisms of ANGPTL4-mediated cell pyroptosis in sepsis-induced ALI, thus providing new insights into the pathogenesis and prevention and treatment measures of sepsis-induced ALI. In vivo animal models and in vitro cell models were established by cecal ligation and puncture (CLP) method and lipopolysaccharide-induced macrophages RAW264.7. ANGPTL4 was silenced in CLP mice or macrophages, followed by the determination of ANGPTL4 expression in bronchoalveolar lavage fluid (BALF) or macrophages. Lung histopathology was observed by H&E staining, with pathological injury scores evaluated and lung wet and dry weight ratio recorded. M1/M2 macrophage marker levels (iNOS/CD86/Arg1), inflammatory factor (TNF-α/IL-6/IL-1β/iNOS) expression in BALF, cell death and pyroptosis, NLRP3 inflammasome, cell pyroptosis-related protein (NLRP3/Cleaved-caspase-1/caspase-1/GSDMD-N) levels, NF-κB pathway activation were assessed by RT-qPCR/ELISA/flow cytometry/Western blot, respectively. ANGPTL4 was highly expressed in mice with sepsis-induced ALI, and ANGPTL4 silencing ameliorated sepsis-induced ALI in mice. In vivo, ANGPTL4 silencing repressed M1 macrophage polarization and macrophage pyroptosis in mice with sepsis-induced ALI. In vitro, ANGPTL4 knockout impeded LPS-induced activation and pyroptosis of M1 macrophages and hindered LPS-induced activation of the NF-κB pathway in macrophages. Knockdown of ANGPTL4 blocks the NF-κB pathway activation, hinders macrophage M1 polarization and pyroptosis, thereby suppressing sepsis-induced ALI. Show less
no PDF DOI: 10.1016/j.tiv.2023.105709
ANGPTL4
Wei Zhang, Junhui Liu, Xin Ren +7 more · 2024 · Journal of cellular and molecular medicine · Blackwell Publishing · added 2026-04-24
Peroxisome proliferator-activated receptors (PPARs) are essential for cellular physiological processes. However, there is less research on the PPAR-related genes in lung adenocarcinoma (LUAD). Open-ac Show more
Peroxisome proliferator-activated receptors (PPARs) are essential for cellular physiological processes. However, there is less research on the PPAR-related genes in lung adenocarcinoma (LUAD). Open-access data were get from the cancer genome atlas (TCGA) and gene expression omnibus (GEO) databases. All the analysis were conducted in the R software based on different R packages. In this study, we gauged the PPAR score employing a set of 72 PPAR-associated genes and probed the biological impact of this score on lung adenocarcinoma (LUAD). Subsequently, we established a unique signature composed of eight PPAR-related genes (ANGPTL4, ACSL3, ADIPOQ, FABP1, SLC27A1, ACOX2, PPARD and OLR1) to forecast the prognosis of LUAD. The signature's effectiveness in predicting survival was validated through the receiver operating characteristic curve in the TCGA-LUAD cohort. As per the pathway enrichment analysis, several crucial oncogenic pathways and metabolic processes were enriched in high-risk individuals. Further, we observed that these high-risk patients exhibited heightened genomic instability. Additionally, compared to the low-risk cohort, high-risk patients demonstrated diminished immune components and function. Intriguingly, high-risk patients exhibited a potential heightened sensitivity to immunotherapy and certain drugs, including Gefitinib, Afatinib, Erlotinib, IAP₅₆₂₀, Sapitinib, LCL161, Lapatinib and AZD3759. The prognosis model based on eight PPAR-related genes has satisfactory prognosis prediction efficiency. Meanwhile, our results can provide direction for future studies in the relevant aspects. Show less
📄 PDF DOI: 10.1111/jcmm.17877
ANGPTL4
Guoyi Wang, Jinwen Zhao, Min Zhou +2 more · 2024 · Aging · Impact Journals · added 2026-04-24
Diabetic nephropathy (DN) is a severe complication of diabetes that affects the kidneys. Disulfidptosis, a newly defined type of programmed cell death, has emerged as a potential area of interest, yet Show more
Diabetic nephropathy (DN) is a severe complication of diabetes that affects the kidneys. Disulfidptosis, a newly defined type of programmed cell death, has emerged as a potential area of interest, yet its significance in DN remains unexplored. This study utilized single-cell sequencing data GSE131882 from GEO database combined with bulk transcriptome sequencing data GSE30122, GSE30528 and GSE30529 to investigate disulfidptosis in DN. Single-cell sequencing analysis was performed on samples from DN patients and healthy controls, focusing on cell heterogeneity and communication. Weighted gene co-expression network analysis (WGCNA) and gene set enrichment analysis (GSEA) were employed to identify disulfidptosis-related gene sets and pathways. A diagnostic model was constructed using machine learning techniques based on identified genes, and immunocorrelation analysis was conducted to explore the relationship between key genes and immune cells. PCR validation was performed on blood samples from DN patients and healthy controls. The study revealed significant disulfidptosis heterogeneity and cell communication differences in DN. Specific targets related to disulfidptosis were identified, providing insights into the pathogenesis of DN. The diagnostic model demonstrated high accuracy in distinguishing DN from healthy samples across multiple datasets. Immunocorrelation analysis highlighted the complex interactions between immune cells and key disulfidptosis-related genes. PCR validation supported the differential expression of model genes VEGFA, MAGI2, THSD7A and ANKRD28 in DN. This research advances our understanding of DN by highlighting the role of disulfidptosis and identifying potential biomarkers for early detection and personalized treatment. Show less
📄 PDF DOI: 10.18632/aging.205982
ANKRD28