👤 Mingsong Liu

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3182
Articles
1983
Name variants
Also published as: A Liu, Ai Liu, Ai-Guo Liu, Aidong Liu, Aiguo Liu, Aihua Liu, Aijun Liu, Ailing Liu, Aimin Liu, Allen P Liu, Aman Liu, An Liu, An-Qi Liu, Ang-Jun Liu, Anjing Liu, Anjun Liu, Ankang Liu, Anling Liu, Anmin Liu, Annuo Liu, Anshu Liu, Ao Liu, Aoxing Liu, B Liu, Baihui Liu, Baixue Liu, Baiyan Liu, Ban Liu, Bang Liu, Bang-Quan Liu, Bao Liu, Bao-Cheng Liu, Baogang Liu, Baohui Liu, Baolan Liu, Baoli Liu, Baoning Liu, Baoxin Liu, Baoyi Liu, Bei Liu, Beibei Liu, Ben Liu, Bi-Cheng Liu, Bi-Feng Liu, Bihao Liu, Bilin Liu, Bin Liu, Bing Liu, Bing-Wen Liu, Bingcheng Liu, Bingjie Liu, Bingwen Liu, Bingxiao Liu, Bingya Liu, Bingyu Liu, Binjie Liu, Bo Liu, Bo-Gong Liu, Bo-Han Liu, Boao Liu, Bolin Liu, Boling Liu, Boqun Liu, Bowen Liu, Boxiang Liu, Boxin Liu, Boya Liu, Boyang Liu, Brian Y Liu, C Liu, C M Liu, C Q Liu, C-T Liu, C-Y Liu, Caihong Liu, Cailing Liu, Caiyan Liu, Can Liu, Can-Zhao Liu, Catherine H Liu, Chan Liu, Chang Liu, Chang-Bin Liu, Chang-Hai Liu, Chang-Ming Liu, Chang-Pan Liu, Chang-Peng Liu, Changbin Liu, Changjiang Liu, Changliang Liu, Changming Liu, Changqing Liu, Changtie Liu, Changya Liu, Changyun Liu, Chao Liu, Chao-Ming Liu, Chaohong Liu, Chaoqi Liu, Chaoyi Liu, Chelsea Liu, Chen Liu, Chenchen Liu, Chendong Liu, Cheng Liu, Cheng-Li Liu, Cheng-Wu Liu, Cheng-Yong Liu, Cheng-Yun Liu, Chengbo Liu, Chenge Liu, Chengguo Liu, Chenghui Liu, Chengkun Liu, Chenglong Liu, Chengxiang Liu, Chengyao Liu, Chengyun Liu, Chenmiao Liu, Chenming Liu, Chenshu Liu, Chenxing Liu, Chenxu Liu, Chenxuan Liu, Chi Liu, Chia-Chen Liu, Chia-Hung Liu, Chia-Jen Liu, Chia-Yang Liu, Chia-Yu Liu, Chiang Liu, Chin-Chih Liu, Chin-Ching Liu, Chin-San Liu, Ching-Hsuan Liu, Ching-Ti Liu, Chong Liu, Christine S Liu, ChuHao Liu, Chuan Liu, Chuanfeng Liu, Chuanxin Liu, Chuanyang Liu, Chun Liu, Chun-Chi Liu, Chun-Feng Liu, Chun-Lei Liu, Chun-Ming Liu, Chun-Xiao Liu, Chun-Yu Liu, Chunchi Liu, Chundong Liu, Chunfeng Liu, Chung-Cheng Liu, Chung-Ji Liu, Chunhua Liu, Chunlei Liu, Chunliang Liu, Chunling Liu, Chunming Liu, Chunpeng Liu, Chunping Liu, Chunsheng Liu, Chunwei Liu, Chunxiao Liu, Chunyan Liu, Chunying Liu, Chunyu Liu, Cici Liu, Clarissa M Liu, Cong Cong Liu, Cong Liu, Congcong Liu, Cui Liu, Cui-Cui Liu, Cuicui Liu, Cuijie Liu, Cuilan Liu, Cun Liu, Cun-Fei Liu, D Liu, Da Liu, Da-Ren Liu, Daiyun Liu, Dajiang J Liu, Dan Liu, Dan-Ning Liu, Dandan Liu, Danhui Liu, Danping Liu, Dantong Liu, Danyang Liu, Danyong Liu, Daoshen Liu, David Liu, David R Liu, Dawei Liu, Daxu Liu, Dayong Liu, Dazhi Liu, De-Pei Liu, De-Shun Liu, Dechao Liu, Dehui Liu, Deliang Liu, Deng-Xiang Liu, Depei Liu, Deping Liu, Derek Liu, Deruo Liu, Desheng Liu, Dewu Liu, Dexi Liu, Deyao Liu, Deying Liu, Dezhen Liu, Di Liu, Didi Liu, Ding-Ming Liu, Dingding Liu, Dinglu Liu, Dingxiang Liu, Dong Liu, Dong-Yun Liu, Dongang Liu, Dongbo Liu, Dongfang Liu, Donghui Liu, Dongjuan Liu, Dongliang Liu, Dongmei Liu, Dongming Liu, Dongping Liu, Dongxian Liu, Dongxue Liu, Dongyan Liu, Dongyang Liu, Dongyao Liu, Dongzhou Liu, Dudu Liu, Dunjiang Liu, Edison Tak-Bun Liu, En-Qi Liu, Enbin Liu, Enlong Liu, Enqi Liu, Erdong Liu, Erfeng Liu, Erxiong Liu, F Liu, F Z Liu, Fan Liu, Fan-Jie Liu, Fang Liu, Fang-Zhou Liu, Fangli Liu, Fangmei Liu, Fangping Liu, Fangqi Liu, Fangzhou Liu, Fani Liu, Fayu Liu, Fei Liu, Feifan Liu, Feilong Liu, Feiyan Liu, Feiyang Liu, Feiye Liu, Fen Liu, Fendou Liu, Feng Liu, Feng-Ying Liu, Fengbin Liu, Fengchao Liu, Fengen Liu, Fengguo Liu, Fengjiao Liu, Fengjie Liu, Fengjuan Liu, Fengqiong Liu, Fengsong Liu, Fonda Liu, Foqiu Liu, Fu-Jun Liu, Fu-Tong Liu, Fubao Liu, Fuhao Liu, Fuhong Liu, Fujun Liu, Gan Liu, Gang Liu, Gangli Liu, Ganqiang Liu, Gaohua Liu, Ge Liu, Ge-Li Liu, Gen Sheng Liu, Geng Liu, Geng-Hao Liu, Geoffrey Liu, George E Liu, George Liu, Geroge Liu, Gexiu Liu, Gongguan Liu, Guang Liu, Guangbin Liu, Guangfan Liu, Guanghao Liu, Guangliang Liu, Guangqin Liu, Guangwei Liu, Guangxu Liu, Guannan Liu, Guantong Liu, Gui Yao Liu, Gui-Fen Liu, Gui-Jing Liu, Gui-Rong Liu, Guibo Liu, Guidong Liu, Guihong Liu, Guiju Liu, Guili Liu, Guiqiong Liu, Guiquan Liu, Guisheng Liu, Guiyou Liu, Guiyuan Liu, Guning Liu, Guo-Liang Liu, Guochang Liu, Guodong Liu, Guohao Liu, Guojun Liu, Guoke Liu, Guoliang Liu, Guopin Liu, Guoqiang Liu, Guoqing Liu, Guoquan Liu, Guowen Liu, Guoyong Liu, H Liu, Hai Feng Liu, Hai-Jing Liu, Hai-Xia Liu, Hai-Yan Liu, Haibin Liu, Haichao Liu, Haifei Liu, Haifeng Liu, Hailan Liu, Hailin Liu, Hailing Liu, Haitao Liu, Haiyan Liu, Haiyang Liu, Haiying Liu, Haizhao Liu, Han Liu, Han-Fu Liu, Han-Qi Liu, Hancong Liu, Hang Liu, Hanhan Liu, Hanjiao Liu, Hanjie Liu, Hanmin Liu, Hanqing Liu, Hanxiang Liu, Hanyuan Liu, Hao Liu, Haobin Liu, Haodong Liu, Haogang Liu, Haojie Liu, Haokun Liu, Haoling Liu, Haowei Liu, Haowen Liu, Haoyue Liu, He-Kun Liu, Hehe Liu, Hekun Liu, Heliang Liu, Heng Liu, Hengan Liu, Hengru Liu, Hengtong Liu, Heyi Liu, Hong Juan Liu, Hong Liu, Hong Wei Liu, Hong-Bin Liu, Hong-Li Liu, Hong-Liang Liu, Hong-Tao Liu, Hong-Xiang Liu, Hong-Ying Liu, Hongbin Liu, Hongbing Liu, Hongfa Liu, Honghan Liu, Honghe Liu, Hongjian Liu, Hongjie Liu, Hongjun Liu, Hongli Liu, Hongliang Liu, Hongmei Liu, Hongqun Liu, Hongtao Liu, Hongwei Liu, Hongxiang Liu, Hongxing Liu, Hongyan Liu, Hongyang Liu, Hongyao Liu, Hongyu Liu, Hongyuan Liu, Houbao Liu, Hsiao-Ching Liu, Hsiao-Sheng Liu, Hsiaowei Liu, Hsu-Hsiang Liu, Hu Liu, Hua Liu, Hua-Cheng Liu, Hua-Ge Liu, Huadong Liu, Huaizheng Liu, Huan Liu, Huan-Yu Liu, Huanhuan Liu, Huanliang Liu, Huanyi Liu, Huatao Liu, Huawei Liu, Huayang Liu, Huazhen Liu, Hui Liu, Hui-Chao Liu, Hui-Fang Liu, Hui-Guo Liu, Hui-Hui Liu, Hui-Xin Liu, Hui-Ying Liu, Huibin Liu, Huidi Liu, Huihua Liu, Huihui Liu, Huijuan Liu, Huijun Liu, Huikun Liu, Huiling Liu, Huimao Liu, Huimin Liu, Huiming Liu, Huina Liu, Huiping Liu, Huiqing Liu, Huisheng Liu, Huiying Liu, Huiyu Liu, Hulin Liu, J Liu, J R Liu, J W Liu, J X Liu, J Z Liu, James K C Liu, Jamie Liu, Jay Liu, Ji Liu, Ji-Kai Liu, Ji-Long Liu, Ji-Xing Liu, Ji-Xuan Liu, Ji-Yun Liu, Jia Liu, Jia-Cheng Liu, Jia-Jun Liu, Jia-Qian Liu, Jia-Yao Liu, JiaXi Liu, Jiabin Liu, Jiachen Liu, Jiahao Liu, Jiahua Liu, Jiahui Liu, Jiajie Liu, Jiajuan Liu, Jiakun Liu, Jiali Liu, Jialin Liu, Jiamin Liu, Jiaming Liu, Jian Liu, Jian-Jun Liu, Jian-Kun Liu, Jian-hong Liu, Jian-shu Liu, Jianan Liu, Jianbin Liu, Jianbo Liu, Jiandong Liu, Jianfang Liu, Jianfeng Liu, Jiang Liu, Jiangang Liu, Jiangbin Liu, Jianghong Liu, Jianghua Liu, Jiangjiang Liu, Jiangjin Liu, Jiangling Liu, Jiangxin Liu, Jiangyan Liu, Jianhua Liu, Jianhui Liu, Jiani Liu, Jianing Liu, Jianjiang Liu, Jianjun Liu, Jiankang Liu, Jiankun Liu, Jianlei Liu, Jianmei Liu, Jianmin Liu, Jiannan Liu, Jianping Liu, Jiantao Liu, Jianwei Liu, Jianxi Liu, Jianxin Liu, Jianyong Liu, Jianyu Liu, Jianyun Liu, Jiao Liu, Jiaojiao Liu, Jiaoyang Liu, Jiaqi Liu, Jiaqing Liu, Jiawen Liu, Jiaxian Liu, Jiaxiang Liu, Jiaxin Liu, Jiayan Liu, Jiayi Liu, Jiayin Liu, Jiaying Liu, Jiayu Liu, Jiayun Liu, Jiazhe Liu, Jiazheng Liu, Jiazhuo Liu, Jidan Liu, Jie Liu, Jie-Qing Liu, Jierong Liu, Jiewei Liu, Jiewen Liu, Jieying Liu, Jieyu Liu, Jihe Liu, Jiheng Liu, Jin Liu, Jin-Juan Liu, Jin-Qing Liu, Jinbao Liu, Jinbo Liu, Jincheng Liu, Jindi Liu, Jinfeng Liu, Jing Liu, Jing Min Liu, Jing-Crystal Liu, Jing-Hua Liu, Jing-Ying Liu, Jing-Yu Liu, Jingbo Liu, Jingchong Liu, Jingfang Liu, Jingfeng Liu, Jingfu Liu, Jinghui Liu, Jingjie Liu, Jingjing Liu, Jingmeng Liu, Jingmin Liu, Jingqi Liu, Jingquan Liu, Jingqun Liu, Jingsheng Liu, Jingwei Liu, Jingwen Liu, Jingxing Liu, Jingyi Liu, Jingying Liu, Jingyun Liu, Jingzhong Liu, Jinjie Liu, Jinlian Liu, Jinlong Liu, Jinman Liu, Jinpei Liu, Jinpeng Liu, Jinping Liu, Jinqin Liu, Jinrong Liu, Jinsheng Liu, Jinsong Liu, Jinsuo Liu, Jinxiang Liu, Jinxin Liu, Jinxing Liu, Jinyue Liu, Jinze Liu, Jinzhao Liu, Jinzhi Liu, Jiong Liu, Jishan Liu, Jitao Liu, Jiwei Liu, Jixin Liu, Jonathan Liu, Joyce F Liu, Joyce Liu, Ju Liu, Ju-Fang Liu, Juan Liu, Juanjuan Liu, Juanxi Liu, Jue Liu, Jui-Tung Liu, Jun Liu, Jun O Liu, Jun Ting Liu, Jun Yi Liu, Jun-Jen Liu, Jun-Yan Liu, Jun-Yi Liu, Junbao Liu, Junchao Liu, Junfen Liu, Junhui Liu, Junjiang Liu, Junjie Liu, Junjin Liu, Junjun Liu, Junlin Liu, Junling Liu, Junnian Liu, Junpeng Liu, Junqi Liu, Junrong Liu, Juntao Liu, Juntian Liu, Junwen Liu, Junwu Liu, Junxi Liu, Junyan Liu, Junye Liu, Junying Liu, Junyu Liu, Juyao Liu, Kai Liu, Kai-Zheng Liu, Kaidong Liu, Kaijing Liu, Kaikun Liu, Kaiqi Liu, Kaisheng Liu, Kaitai Liu, Kaiwen Liu, Kang Liu, Kang-le Liu, Kangdong Liu, Kangwei Liu, Kathleen D Liu, Ke Liu, Ke-Tong Liu, Kechun Liu, Kehui Liu, Kejia Liu, Keng-Hau Liu, Keqiang Liu, Kexin Liu, Kiang Liu, Kuangyi Liu, Kun Liu, Kun-Cheng Liu, Kwei-Yan Liu, L L Liu, L Liu, L W Liu, Lan Liu, Lan-Xiang Liu, Lang Liu, Lanhao Liu, Le Liu, Lebin Liu, Lei Liu, Lele Liu, Leping Liu, Li Liu, Li-Fang Liu, Li-Min Liu, Li-Rong Liu, Li-Wen Liu, Li-Xuan Liu, Li-Ying Liu, Li-ping Liu, Lian Liu, Lianfei Liu, Liang Liu, Liang-Chen Liu, Liang-Feng Liu, Liangguo Liu, Liangji Liu, Liangjia Liu, Liangliang Liu, Liangyu Liu, Lianxin Liu, Lianyong Liu, Libin Liu, Lichao Liu, Lichun Liu, Lidong Liu, Liegang Liu, Lifang Liu, Ligang Liu, Lihua Liu, Lijuan Liu, Lijun Liu, Lili Liu, Liling Liu, Limin Liu, Liming Liu, Lin Liu, Lina Liu, Ling Liu, Ling-Yun Liu, Ling-Zhi Liu, Lingfei Liu, Lingjiao Liu, Lingjuan Liu, Linglong Liu, Lingyan Liu, Lining Liu, Linlin Liu, Linqing Liu, Linwen Liu, Liping Liu, Liqing Liu, Liqiong Liu, Liqun Liu, Lirong Liu, Liru Liu, Liu Liu, Liumei Liu, Liusheng Liu, Liwen Liu, Lixia Liu, Lixian Liu, Lixiao Liu, Liying Liu, Liyue Liu, Lizhen Liu, Long Liu, Longfei Liu, Longjian Liu, Longqian Liu, Longyang Liu, Longzhou Liu, Lu Liu, Luhong Liu, Lulu Liu, Luming Liu, Lunxu Liu, Luping Liu, Lushan Liu, Lv Liu, M L Liu, M Liu, Man Liu, Man-Ru Liu, Manjiao Liu, Manqi Liu, Manran Liu, Maolin Liu, Mei Liu, Mei-mei Liu, Meicen Liu, Meifang Liu, Meijiao Liu, Meijing Liu, Meijuan Liu, Meijun Liu, Meiling Liu, Meimei Liu, Meixin Liu, Meiyan Liu, Meng Han Liu, Meng Liu, Meng-Hui Liu, Meng-Meng Liu, Meng-Yue Liu, Mengduan Liu, Mengfan Liu, Mengfei Liu, Menggang Liu, Menghan Liu, Menghua Liu, Menghui Liu, Mengjia Liu, Mengjiao Liu, Mengke Liu, Menglin Liu, Mengling Liu, Mengmei Liu, Mengqi Liu, Mengqian Liu, Mengxi Liu, Mengxue Liu, Mengyang Liu, Mengying Liu, Mengyu Liu, Mengyuan Liu, Mengzhen Liu, Mi Liu, Mi-Hua Liu, Mi-Min Liu, Miao Liu, Miaoliang Liu, Min Liu, Minda Liu, Minetta C Liu, Ming Liu, Ming-Jiang Liu, Ming-Qi Liu, Mingcheng Liu, Mingchun Liu, Mingfan Liu, Minghui Liu, Mingjiang Liu, Mingjing Liu, Mingjun Liu, Mingli Liu, Mingming Liu, Mingna Liu, Mingqin Liu, Mingrui Liu, Mingsen Liu, Mingxiao Liu, Mingxing Liu, Mingxu Liu, Mingyang Liu, Mingyao Liu, Mingying Liu, Mingyu Liu, Minhao Liu, Minxia Liu, Mo-Nan Liu, Modan Liu, Mouze Liu, Muqiu Liu, Musang Liu, N A Liu, N Liu, Na Liu, Na-Nv Liu, Na-Wei Liu, Nai-feng Liu, Naihua Liu, Naili Liu, Nan Liu, Nan-Song Liu, Nana Liu, Nannan Liu, Nanxi Liu, Ni Liu, Nian Liu, Ning Liu, Ning'ang Liu, Ningning Liu, Niya Liu, Ou Liu, Ouxuan Liu, P C Liu, Pan Liu, Panhong Liu, Panting Liu, Paul Liu, Pei Liu, Pei-Ning Liu, Peijian Liu, Peijie Liu, Peijun Liu, Peilong Liu, Peiqi Liu, Peiqing Liu, Peiwei Liu, Peixi Liu, Peiyao Liu, Peizhong Liu, Peng Liu, Pengcheng Liu, Pengfei Liu, Penghong Liu, Pengli Liu, Pengtao Liu, Pengyu Liu, Pengyuan Liu, Pentao Liu, Peter S Liu, Piaopiao Liu, Pinduo Liu, Ping Liu, Ping-Yen Liu, Pinghuai Liu, Pingping Liu, Pingsheng Liu, Q Liu, Qi Liu, Qi-Xian Liu, Qian Liu, Qian-Wen Liu, Qiang Liu, Qiang-Yuan Liu, Qiangyun Liu, Qianjin Liu, Qianqi Liu, Qianshuo Liu, Qianwei Liu, Qiao-Hong Liu, Qiaofeng Liu, Qiaoyan Liu, Qiaozhen Liu, Qiji Liu, Qiming Liu, Qin Liu, Qinfang Liu, Qing Liu, Qing-Huai Liu, Qing-Rong Liu, Qingbin Liu, Qingbo Liu, Qingguang Liu, Qingguo Liu, Qinghao Liu, Qinghong Liu, Qinghua Liu, Qinghuai Liu, Qinghuan Liu, Qinglei Liu, Qingping Liu, Qingqing Liu, Qingquan Liu, Qingsong Liu, Qingxia Liu, Qingxiang Liu, Qingyang Liu, Qingyou Liu, Qingyun Liu, Qingzhuo Liu, Qinqin Liu, Qiong Liu, Qiu-Ping Liu, Qiulei Liu, Qiuli Liu, Qiulu Liu, Qiushi Liu, Qiuxu Liu, Qiuyu Liu, Qiuyue Liu, Qiwei Liu, Qiyao Liu, Qiye Liu, Qizhan Liu, Quan Liu, Quan-Jun Liu, Quanxin Liu, Quanying Liu, Quanzhong Liu, Quentin Liu, Qun Liu, Qunlong Liu, Qunpeng Liu, R F Liu, R Liu, R Y Liu, Ran Liu, Rangru Liu, Ranran Liu, Ren Liu, Renling Liu, Ri Liu, Rong Liu, Rong-Zong Liu, Rongfei Liu, Ronghua Liu, Rongxia Liu, Rongxun Liu, Rui Liu, Rui-Jie Liu, Rui-Tian Liu, Rui-Xuan Liu, Ruichen Liu, Ruihua Liu, Ruijie Liu, Ruijuan Liu, Ruilong Liu, Ruiping Liu, Ruiqi Liu, Ruitong Liu, Ruixia Liu, Ruiyi Liu, Ruizao Liu, Runjia Liu, Runjie Liu, Runni Liu, Runping Liu, Ruochen Liu, Ruotian Liu, Ruowen Liu, Ruoyang Liu, Ruyi Liu, Ruyue Liu, S Liu, Saiji Liu, Sasa Liu, Sen Liu, Senchen Liu, Senqi Liu, Sha Liu, Shan Liu, Shan-Shan Liu, Shandong Liu, Shang-Feng Liu, Shang-Xin Liu, Shangjing Liu, Shangxin Liu, Shangyu Liu, Shangyuan Liu, Shangyun Liu, Shanhui Liu, Shanling Liu, Shanshan Liu, Shao-Bin Liu, Shao-Jun Liu, Shao-Yuan Liu, Shaobo Liu, Shaocheng Liu, Shaohua Liu, Shaojun Liu, Shaoqing Liu, Shaowei Liu, Shaoying Liu, Shaoyou Liu, Shaoyu Liu, Shaozhen Liu, Shasha Liu, Sheng Liu, Shengbin Liu, Shengjun Liu, Shengnan Liu, Shengyang Liu, Shengzhi Liu, Shengzhuo Liu, Shenhai Liu, Shenping Liu, Shi Liu, Shi-Lian Liu, Shi-Wei Liu, Shi-Yong Liu, Shi-guo Liu, ShiWei Liu, Shih-Ping Liu, Shijia Liu, Shijian Liu, Shijie Liu, Shijun Liu, Shikai Liu, Shikun Liu, Shilin Liu, Shing-Hwa Liu, Shiping Liu, Shiqian Liu, Shiquan Liu, Shiru Liu, Shixi Liu, Shiyan Liu, Shiyang Liu, Shiying Liu, Shiyu Liu, Shiyuan Liu, Shou-Sheng Liu, Shouguo Liu, Shoupei Liu, Shouxin Liu, Shouyang Liu, Shu Liu, Shu-Chen Liu, Shu-Jing Liu, Shu-Lin Liu, Shu-Qiang Liu, Shu-Qin Liu, Shuai Liu, Shuaishuai Liu, Shuang Liu, Shuangli Liu, Shuangzhu Liu, Shuhong Liu, Shuhua Liu, Shui-Bing Liu, Shujie Liu, Shujing Liu, Shujun Liu, Shulin Liu, Shuling Liu, Shumin Liu, Shun-Mei Liu, Shunfang Liu, Shuning Liu, Shunming Liu, Shuqian Liu, Shuqing Liu, Shuwen Liu, Shuxi Liu, Shuxian Liu, Shuya Liu, Shuyan Liu, Shuyu Liu, Si-Jin Liu, Si-Xu Liu, Si-Yan Liu, Si-jun Liu, Sicheng Liu, Sidan Liu, Side Liu, Sihao Liu, Sijing Liu, Sijun Liu, Silvia Liu, Simin Liu, Sipu Liu, Siqi Liu, Siqin Liu, Siru Liu, Sirui Liu, Sisi Liu, Sitian Liu, Siwen Liu, Sixi Liu, Sixin Liu, Sixiu Liu, Sixu Liu, Siyao Liu, Siyi Liu, Siyu Liu, Siyuan Liu, Song Liu, Song-Fang Liu, Song-Mei Liu, Song-Ping Liu, Songfang Liu, Songhui Liu, Songqin Liu, Songsong Liu, Songyi Liu, Su Liu, Su-Yun Liu, Sudong Liu, Suhuan Liu, Sui-Feng Liu, Suling Liu, Suosi Liu, Sushuang Liu, Susu Liu, Szu-Heng Liu, T H Liu, T Liu, Ta-Chih Liu, Taihang Liu, Taixiang Liu, Tang Liu, Tao Liu, Taoli Liu, Taotao Liu, Te Liu, Teng Liu, Tengfei Liu, Tengli Liu, Teresa T Liu, Tian Liu, Tian Shu Liu, Tianhao Liu, Tianhu Liu, Tianjia Liu, Tianjiao Liu, Tianlai Liu, Tianlang Liu, Tianlong Liu, Tianqiang Liu, Tianrui Liu, Tianshu Liu, Tiantian Liu, Tianyao Liu, Tianyi Liu, Tianyu Liu, Tianze Liu, Tiemin Liu, Tina Liu, Ting Liu, Ting-Li Liu, Ting-Ting Liu, Ting-Yuan Liu, Tingjiao Liu, Tingting Liu, Tong Liu, Tonglin Liu, Tongtong Liu, Tongyan Liu, Tongyu Liu, Tongyun Liu, Tongzheng Liu, Tsang-Wu Liu, Tsung-Yun Liu, Vincent W S Liu, W Liu, W-Y Liu, Wan Liu, Wan-Chun Liu, Wan-Di Liu, Wan-Guo Liu, Wan-Ying Liu, Wang Liu, Wangrui Liu, Wanguo Liu, Wangyang Liu, Wanjun Liu, Wanli Liu, Wanlu Liu, Wanqi Liu, Wanqing Liu, Wanting Liu, Wei Liu, Wei-Chieh Liu, Wei-Hsuan Liu, Wei-Hua Liu, Weida Liu, Weifang Liu, Weifeng Liu, Weiguo Liu, Weihai Liu, Weihong Liu, Weijian Liu, Weijie Liu, Weijun Liu, Weilin Liu, Weimin Liu, Weiming Liu, Weina Liu, Weiqin Liu, Weiqing Liu, Weiren Liu, Weisheng Liu, Weishuo Liu, Weiwei Liu, Weiyang Liu, Wen Liu, Wen Yuan Liu, Wen-Chun Liu, Wen-Di Liu, Wen-Fang Liu, Wen-Jie Liu, Wen-Jing Liu, Wen-Qiang Liu, Wen-Tao Liu, Wen-ling Liu, Wenbang Liu, Wenbin Liu, Wenbo Liu, Wenchao Liu, Wenen Liu, Wenfeng Liu, Wenhan Liu, Wenhao Liu, Wenhua Liu, Wenjie Liu, Wenjing Liu, Wenlang Liu, Wenli Liu, Wenling Liu, Wenlong Liu, Wenna Liu, Wenping Liu, Wenqi Liu, Wenrui Liu, Wensheng Liu, Wentao Liu, Wenwu Liu, Wenxiang Liu, Wenxuan Liu, Wenya Liu, Wenyan Liu, Wenyi Liu, Wenzhong Liu, Wu Liu, Wuping Liu, Wuyang Liu, X C Liu, X Liu, X P Liu, X-D Liu, Xi Liu, Xi-Yu Liu, Xia Liu, Xia-Meng Liu, Xialin Liu, Xian Liu, Xianbao Liu, Xianchen Liu, Xianda Liu, Xiang Liu, Xiang-Qian Liu, Xiang-Yu Liu, Xiangchen Liu, Xiangfei Liu, Xianglan Liu, Xiangli Liu, Xiangliang Liu, Xianglu Liu, Xiangning Liu, Xiangping Liu, Xiangsheng Liu, Xiangtao Liu, Xiangting Liu, Xiangxiang Liu, Xiangxuan Liu, Xiangyong Liu, Xiangyu Liu, Xiangyun Liu, Xianli Liu, Xianling Liu, Xiansheng Liu, Xianyang Liu, Xiao Dong Liu, Xiao Liu, Xiao Yan Liu, Xiao-Cheng Liu, Xiao-Dan Liu, Xiao-Gang Liu, Xiao-Guang Liu, Xiao-Huan Liu, Xiao-Jiao Liu, Xiao-Li Liu, Xiao-Ling Liu, Xiao-Ning Liu, Xiao-Qiu Liu, Xiao-Qun Liu, Xiao-Rong Liu, Xiao-Song Liu, Xiao-Xiao Liu, Xiao-lan Liu, Xiaoan Liu, Xiaobai Liu, Xiaobei Liu, Xiaobing Liu, Xiaocen Liu, Xiaochuan Liu, Xiaocong Liu, Xiaodan Liu, Xiaoding Liu, Xiaodong Liu, Xiaofan Liu, Xiaofang Liu, Xiaofei Liu, Xiaogang Liu, Xiaoguang Liu, Xiaoguang Margaret Liu, Xiaohan Liu, Xiaoheng Liu, Xiaohong Liu, Xiaohua Liu, Xiaohuan Liu, Xiaohui Liu, Xiaojie Liu, Xiaojing Liu, Xiaoju Liu, Xiaojun Liu, Xiaole Shirley Liu, Xiaolei Liu, Xiaoli Liu, Xiaolin Liu, Xiaoling Liu, Xiaoman Liu, Xiaomei Liu, Xiaomeng Liu, Xiaomin Liu, Xiaoming Liu, Xiaona Liu, Xiaonan Liu, Xiaopeng Liu, Xiaoping Liu, Xiaoqian Liu, Xiaoqiang Liu, Xiaoqin Liu, Xiaoqing Liu, Xiaoran Liu, Xiaosong Liu, Xiaotian Liu, Xiaoting Liu, Xiaowei Liu, Xiaoxi Liu, Xiaoxia Liu, Xiaoxiao Liu, Xiaoxu Liu, Xiaoxue Liu, Xiaoya Liu, Xiaoyan Liu, Xiaoyang Liu, Xiaoye Liu, Xiaoying Liu, Xiaoyong Liu, Xiaoyu Liu, Xiawen Liu, Xibao Liu, Xibing Liu, Xie-hong Liu, Xiehe Liu, Xiguang Liu, Xijun Liu, Xili Liu, Xin Liu, Xin-Hua Liu, Xin-Yan Liu, Xinbo Liu, Xinchang Liu, Xing Liu, Xing-De Liu, Xing-Li Liu, Xing-Yang Liu, Xingbang Liu, Xingde Liu, Xinghua Liu, Xinghui Liu, Xingjing Liu, Xinglei Liu, Xingli Liu, Xinglong Liu, Xinguo Liu, Xingxiang Liu, Xingyi Liu, Xingyu Liu, Xinhua Liu, Xinjun Liu, Xinlei Liu, Xinli Liu, Xinmei Liu, Xinmin Liu, Xinran Liu, Xinru Liu, Xinrui Liu, Xintong Liu, Xinxin Liu, Xinyao Liu, Xinyi Liu, Xinying Liu, Xinyong Liu, Xinyu Liu, Xinyue Liu, Xiong Liu, Xiqiang Liu, Xiru Liu, Xishan Liu, Xiu Liu, Xiufen Liu, Xiufeng Liu, Xiuheng Liu, Xiuling Liu, Xiumei Liu, Xiuqin Liu, Xiyong Liu, Xu Liu, Xu-Dong Liu, Xu-Hui Liu, Xuan Liu, Xuanlin Liu, Xuanyu Liu, Xuanzhu Liu, Xue Liu, Xue-Lian Liu, Xue-Min Liu, Xue-Qing Liu, Xue-Zheng Liu, Xuefang Liu, Xuejing Liu, Xuekui Liu, Xuelan Liu, Xueling Liu, Xuemei Liu, Xuemeng Liu, Xuemin Liu, Xueping Liu, Xueqin Liu, Xueqing Liu, Xueru Liu, Xuesen Liu, Xueshibojie Liu, Xuesong Liu, Xueting Liu, Xuewei Liu, Xuewen Liu, Xuexiu Liu, Xueying Liu, Xueyuan Liu, Xuezhen Liu, Xuezheng Liu, Xuezhi Liu, Xufeng Liu, Xuguang Liu, Xujie Liu, Xulin Liu, Xuming Liu, Xunhua Liu, Xunyue Liu, Xuxia Liu, Xuxu Liu, Xuyi Liu, Xuying Liu, Y H Liu, Y L Liu, Y Liu, Y Y Liu, Ya Liu, Ya-Jin Liu, Ya-Kun Liu, Ya-Wei Liu, Yadong Liu, Yafei Liu, Yajing Liu, Yajuan Liu, Yaling Liu, Yalu Liu, Yan Liu, Yan-Li Liu, Yanan Liu, Yanchao Liu, Yanchen Liu, Yandong Liu, Yanfei Liu, Yanfen Liu, Yanfeng Liu, Yang Liu, Yange Liu, Yangfan Liu, Yangfan P Liu, Yangjun Liu, Yangkai Liu, Yangruiyu Liu, Yangyang Liu, Yanhong Liu, Yanhua Liu, Yanhui Liu, Yanjie Liu, Yanju Liu, Yanjun Liu, Yankuo Liu, Yanli Liu, Yanliang Liu, Yanling Liu, Yanman Liu, Yanmin Liu, Yanping Liu, Yanqing Liu, Yanqiu Liu, Yanquan Liu, Yanru Liu, Yansheng Liu, Yansong Liu, Yanting Liu, Yanwu Liu, Yanxiao Liu, Yanyan Liu, Yanyao Liu, Yanying Liu, Yanyun Liu, Yao Liu, Yao-Hui Liu, Yaobo Liu, Yaoquan Liu, Yaou Liu, Yaowen Liu, Yaoyao Liu, Yaozhong Liu, Yaping Liu, Yaqiong Liu, Yarong Liu, Yaru Liu, Yating Liu, Yaxin Liu, Ye Liu, Ye-Dan Liu, Yehai Liu, Yen-Chen Liu, Yen-Chun Liu, Yen-Nien Liu, Yeqing Liu, Yi Liu, Yi-Chang Liu, Yi-Chien Liu, Yi-Han Liu, Yi-Hung Liu, Yi-Jia Liu, Yi-Ling Liu, Yi-Meng Liu, Yi-Ming Liu, Yi-Yun Liu, Yi-Zhang Liu, YiRan Liu, Yibin Liu, Yibing Liu, Yicun Liu, Yidan Liu, Yidong Liu, Yifan Liu, Yifu Liu, Yihao Liu, Yiheng Liu, Yihui Liu, Yijing Liu, Yilei Liu, Yili Liu, Yilin Liu, Yimei Liu, Yiming Liu, Yin Liu, Yin-Ping Liu, Yinchu Liu, Yinfang Liu, Ying Liu, Ying Poi Liu, Yingchun Liu, Yinghua Liu, Yinghuan Liu, Yinghui Liu, Yingjun Liu, Yingli Liu, Yingwei Liu, Yingxia Liu, Yingyan Liu, Yingyi Liu, Yingying Liu, Yingzi Liu, Yinhe Liu, Yinhui Liu, Yining Liu, Yinjiang Liu, Yinping Liu, Yinuo Liu, Yiping Liu, Yiqing Liu, Yitian Liu, Yiting Liu, Yitong Liu, Yiwei Liu, Yiwen Liu, Yixiang Liu, Yixiao Liu, Yixuan Liu, Yiyang Liu, Yiyi Liu, Yiyuan Liu, Yiyun Liu, Yizhi Liu, Yizhuo Liu, Yong Liu, Yong Mei Liu, Yong-Chao Liu, Yong-Hong Liu, Yong-Jian Liu, Yong-Jun Liu, Yong-Tai Liu, Yong-da Liu, Yongchao Liu, Yonggang Liu, Yonggao Liu, Yonghong Liu, Yonghua Liu, Yongjian Liu, Yongjie Liu, Yongjun Liu, Yongli Liu, Yongmei Liu, Yongming Liu, Yongqiang Liu, Yongshuo Liu, Yongtai Liu, Yongtao Liu, Yongtong Liu, Yongxiao Liu, Yongyue Liu, You Liu, You-ping Liu, Youan Liu, Youbin Liu, Youdong Liu, Youhan Liu, Youlian Liu, Youwen Liu, Yu Liu, Yu Xuan Liu, Yu-Chen Liu, Yu-Ching Liu, Yu-Hui Liu, Yu-Li Liu, Yu-Lin Liu, Yu-Peng Liu, Yu-Wei Liu, Yu-Zhang Liu, YuHeng Liu, Yuan Liu, Yuan-Bo Liu, Yuan-Jie Liu, Yuan-Tao Liu, YuanHua Liu, Yuanchu Liu, Yuanfa Liu, Yuanhang Liu, Yuanhui Liu, Yuanjia Liu, Yuanjiao Liu, Yuanjun Liu, Yuanliang Liu, Yuantao Liu, Yuantong Liu, Yuanxiang Liu, Yuanxin Liu, Yuanxing Liu, Yuanying Liu, Yuanyuan Liu, Yubin Liu, Yuchen Liu, Yue Liu, Yuecheng Liu, Yuefang Liu, Yuehong Liu, Yueli Liu, Yueping Liu, Yuetong Liu, Yuexi Liu, Yuexin Liu, Yuexing Liu, Yueyang Liu, Yueyun Liu, Yufan Liu, Yufei Liu, Yufeng Liu, Yuhao Liu, Yuhe Liu, Yujia Liu, Yujiang Liu, Yujie Liu, Yujun Liu, Yulan Liu, Yuling Liu, Yulong Liu, Yumei Liu, Yumiao Liu, Yun Liu, Yun-Cai Liu, Yun-Qiang Liu, Yun-Ru Liu, Yun-Zi Liu, Yunfen Liu, Yunfeng Liu, Yuning Liu, Yunjie Liu, Yunlong Liu, Yunqi Liu, Yunqiang Liu, Yuntao Liu, Yunuan Liu, Yunuo Liu, Yunxia Liu, Yunyun Liu, Yuping Liu, Yupu Liu, Yuqi Liu, Yuqiang Liu, Yuqing Liu, Yurong Liu, Yuru Liu, Yusen Liu, Yutao Liu, Yutian Liu, Yuting Liu, Yutong Liu, Yuwei Liu, Yuxi Liu, Yuxia Liu, Yuxiang Liu, Yuxin Liu, Yuxuan Liu, Yuyan Liu, Yuyi Liu, Yuyu Liu, Yuyuan Liu, Yuzhen Liu, Yv-Xuan Liu, Z H Liu, Z Q Liu, Z Z Liu, Zaiqiang Liu, Zan Liu, Zaoqu Liu, Ze Liu, Zefeng Liu, Zekun Liu, Zeming Liu, Zengfu Liu, Zeyu Liu, Zezhou Liu, Zhangyu Liu, Zhangyuan Liu, Zhansheng Liu, Zhao Liu, Zhaoguo Liu, Zhaoli Liu, Zhaorui Liu, Zhaotian Liu, Zhaoxiang Liu, Zhaoxun Liu, Zhaoyang Liu, Zhe Liu, Zhekai Liu, Zheliang Liu, Zhen Liu, Zhen-Lin Liu, Zhendong Liu, Zhenfang Liu, Zhenfeng Liu, Zheng Liu, Zheng-Hong Liu, Zheng-Yu Liu, ZhengYi Liu, Zhengbing Liu, Zhengchuang Liu, Zhengdong Liu, Zhenghao Liu, Zhengkun Liu, Zhengtang Liu, Zhengting Liu, Zhenguo Liu, Zhengxia Liu, Zhengye Liu, Zhenhai Liu, Zhenhao Liu, Zhenhua Liu, Zhenjiang Liu, Zhenjiao Liu, Zhenjie Liu, Zhenkui Liu, Zhenlei Liu, Zhenmi Liu, Zhenming Liu, Zhenna Liu, Zhenqian Liu, Zhenqiu Liu, Zhenwei Liu, Zhenxing Liu, Zhenxiu Liu, Zhenzhen Liu, Zhenzhu Liu, Zhi Liu, Zhi Y Liu, Zhi-Fen Liu, Zhi-Guo Liu, Zhi-Jie Liu, Zhi-Kai Liu, Zhi-Ping Liu, Zhi-Ren Liu, Zhi-Wen Liu, Zhi-Ying Liu, Zhicheng Liu, Zhifang Liu, Zhigang Liu, Zhiguo Liu, Zhihan Liu, Zhihao Liu, Zhihong Liu, Zhihua Liu, Zhihui Liu, Zhijia Liu, Zhijie Liu, Zhikui Liu, Zhili Liu, Zhiming Liu, Zhipeng Liu, Zhiping Liu, Zhiqian Liu, Zhiqiang Liu, Zhiru Liu, Zhirui Liu, Zhishuo Liu, Zhitao Liu, Zhiteng Liu, Zhiwei Liu, Zhixiang Liu, Zhixue Liu, Zhiyan Liu, Zhiying Liu, Zhiyong Liu, Zhiyuan Liu, Zhong Liu, Zhong Wu Liu, Zhong-Hua Liu, Zhong-Min Liu, Zhong-Qiu Liu, Zhong-Wu Liu, Zhong-Ying Liu, Zhongchun Liu, Zhongguo Liu, Zhonghua Liu, Zhongjian Liu, Zhongjuan Liu, Zhongmin Liu, Zhongqi Liu, Zhongqiu Liu, Zhongwei Liu, Zhongyu Liu, Zhongyue Liu, Zhongzhong Liu, Zhou Liu, Zhou-di Liu, Zhu Liu, Zhuangjun Liu, Zhuanhua Liu, Zhuo Liu, Zhuoyuan Liu, Zi Hao Liu, Zi-Hao Liu, Zi-Lun Liu, Zi-Ye Liu, Zi-wen Liu, Zichuan Liu, Zihang Liu, Zihao Liu, Zihe Liu, Ziheng Liu, Zijia Liu, Zijian Liu, Zijing J Liu, Zimeng Liu, Ziqian Liu, Ziqin Liu, Ziteng Liu, Zitian Liu, Ziwei Liu, Zixi Liu, Zixuan Liu, Ziyang Liu, Ziying Liu, Ziyou Liu, Ziyuan Liu, Ziyue Liu, Zong-Chao Liu, Zong-Yuan Liu, Zonghua Liu, Zongjun Liu, Zongtao Liu, Zongxiang Liu, Zu-Guo Liu, Zuguo Liu, Zuohua Liu, Zuojin Liu, Zuolu Liu, Zuyi Liu, Zuyun Liu
articles

HBx induces hepatocellular carcinogenesis through ARRB1-mediated autophagy to drive the G

Yiming Lei, Xuan Xu, Huiling Liu +5 more · 2021 · Autophagy · Taylor & Francis · added 2026-04-24
The hepatitis B virus X protein (HBx) is involved in the process of hepatocellular carcinoma via the activation of various oncogenes. Our previous study indicated that ARBB1 (arrestin beta 1) promotes Show more
The hepatitis B virus X protein (HBx) is involved in the process of hepatocellular carcinoma via the activation of various oncogenes. Our previous study indicated that ARBB1 (arrestin beta 1) promotes hepatocellular carcinogenesis (HCC). However, the role of ARRB1 in HBx-related HCC remains unclear. Herein, we identified that ARRB1 was upregulated by HBx Show less
no PDF DOI: 10.1080/15548627.2021.1917948
PIK3C3

The Role of

Li Shu, Neng Xiao, Jiong Qin +9 more · 2021 · Frontiers in molecular neuroscience · Frontiers · added 2026-04-24
📄 PDF DOI: 10.3389/fnmol.2021.775479
MAST3

Screening of potential biomarkers for distinguishing between latent and active tuberculosis in children using bioinformatics analysis.

Meng Shao, Fang Wu, Jie Zhang +8 more · 2021 · Medicine · added 2026-04-24
Tuberculosis (TB) is one of the leading causes of childhood morbidity and death globally. Lack of rapid, effective non-sputum diagnosis and prediction methods for TB in children are some of the challe Show more
Tuberculosis (TB) is one of the leading causes of childhood morbidity and death globally. Lack of rapid, effective non-sputum diagnosis and prediction methods for TB in children are some of the challenges currently faced. In recent years, blood transcriptional profiling has provided a fresh perspective on the diagnosis and predicting the progression of tuberculosis. Meanwhile, combined with bioinformatics analysis can help to identify the differentially expressed genes (DEGs) and functional pathways involved in the different clinical stages of TB. Therefore, this study investigated potential diagnostic markers for use in distinguishing between latent tuberculosis infection (LTBI) and active TB using children's blood transcriptome data.From the Gene Expression Omnibus database, we downloaded two gene expression profile datasets (GSE39939 and GSE39940) of whole blood-derived RNA sequencing samples, reflecting transcriptional signatures between latent and active tuberculosis in children. GEO2R tool was used to screen for DEGs in LTBI and active TB in children. Database for Annotation, Visualization and Integrated Discovery tools were used to perform Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis. STRING and Cytoscape analyzed the protein-protein interaction network and the top 15 hub genes respectively. Receiver operating characteristics curve was used to estimate the diagnostic value of the hub genes.A total of 265 DEGs were identified, including 79 upregulated and 186 downregulated DEGs. Further, 15 core genes were picked and enrichment analysis revealed that they were highly correlated with neutrophil activation and degranulation, neutrophil-mediated immunity and in defense response. Among them TLR2, FPR2, MMP9, MPO, CEACAM8, ELANE, FCGR1A, SELP, ARG1, GNG10, HP, LCN2, LTF, ADCY3 had significant discriminatory power between LTBI and active TB, with area under the curves of 0.84, 0.84, 0.84, 0.80, 0.87, 0.78, 0.88, 0.84, 0.86, 0.82, 0.85, 0.85, 0.79, and 0.88 respectively.Our research provided several genes with high potential to be candidate gene markers for developing non-sputum diagnostic tools for childhood Tuberculosis. Show less
📄 PDF DOI: 10.1097/MD.0000000000023207
ADCY3

Transcriptome-wide association study identifies new susceptibility genes and pathways for depression.

Xiaoyan Li, Xi Su, Jiewei Liu +5 more · 2021 · Translational psychiatry · Nature · added 2026-04-24
Depression is the most prevalent mental disorder with substantial morbidity and mortality. Although genome-wide association studies (GWASs) have identified multiple risk variants for depression, due t Show more
Depression is the most prevalent mental disorder with substantial morbidity and mortality. Although genome-wide association studies (GWASs) have identified multiple risk variants for depression, due to the complicated gene regulatory mechanisms and complexity of linkage disequilibrium (LD), the biological mechanisms by which the risk variants exert their effects on depression remain largely unknown. Here, we perform a transcriptome-wide association study (TWAS) of depression by integrating GWAS summary statistics from 807,553 individuals (246,363 depression cases and 561,190 controls) and summary-level gene-expression data (from the dorsolateral prefrontal cortex (DLPFC) of 1003 individuals). We identified 53 transcriptome-wide significant (TWS) risk genes for depression, of which 23 genes were not implicated in risk loci of the original GWAS. Seven out of 53 risk genes (B3GALTL, FADS1, TCTEX1D1, XPNPEP3, ZMAT2, ZNF501 and ZNF502) showed TWS associations with depression in two independent brain expression quantitative loci (eQTL) datasets, suggesting that these genes may represent promising candidates. We further conducted conditional analyses and identified the potential risk genes that driven the TWAS association signal in each locus. Finally, pathway enrichment analysis revealed biologically pathways relevant to depression. Our study identified new depression risk genes whose expression dysregulation may play a role in depression. More importantly, we translated the GWAS associations into risk genes and relevant pathways. Further mechanistic study and functional characterization of the TWS depression risk genes will facilitate the diagnostics and therapeutics for depression. Show less
📄 PDF DOI: 10.1038/s41398-021-01411-w
FADS1

FADS1 is a Prognostic Biomarker in Bladder Cancer: A Study Based on TCGA Data.

Ying Lu, Jing Shao, Xu Shu +4 more · 2021 · Combinatorial chemistry & high throughput screening · Bentham Science · added 2026-04-24
Fatty acid desaturase 1 (FADS1) has been reported to be a potential biomarker in various cancers. However, no study has explored the relationship between FADS1 expression and bladder cancer. Our study Show more
Fatty acid desaturase 1 (FADS1) has been reported to be a potential biomarker in various cancers. However, no study has explored the relationship between FADS1 expression and bladder cancer. Our study aimed to investigate the role of FADS1 in bladder cancer prognosis via The Cancer Genome Atlas (TCGA). RNA-Seq expression of 414 tumor tissues and 19 paired normal tissues, as well as corresponding clinical data, were downloaded from the TCGA database. Two cancer cases were excluded due to a lack of clinical information. The association between FADS1 and the clinicopathological features of bladder cancer was analyzed. This study was conducted in October 2019 in China. The high expression of FADS1 in bladder cancer was significantly related to histological grade (OR = 0.155 for low vs. high), clinical stage (OR=2.074 for III or IV vs. I or II), T classification (OR=2.326 for T3 or T4 vs. T1 or T2), lymphatic metastasis (OR=1.923 for N1 or N2 or N3 vs. N0) and distant metastasis (OR=4.883 for yes vs. no) (all p-values <0.05). Bladder cancer with high FADS1 levels was related to a worse prognosis than bladder cancer with low FADS1 levels (p= 1.626*10-5), according to median expression value 3.622. FADS1 was an independent factor of overall survival in bladder cancer, with a hazard ratio of 1.048 (95%CI: 1.020-1.077, p = 0.001). Increased FADS1 expression in bladder cancer is associated with advanced clinicopathological features and may be a potential biomarker for poor prognosis. Show less
no PDF DOI: 10.2174/1386207323666200925104911
FADS1

A novel long non-coding RNA RP11-286H15.1 represses hepatocellular carcinoma progression by promoting ubiquitination of PABPC4.

Xiang Jiang, Ganggang Wang, Yingyi Liu +8 more · 2021 · Cancer letters · Elsevier · added 2026-04-24
Hepatocellular carcinoma (HCC) is a malignancy found at high frequency around the world. Unfortunately, the scarcity of effective early diagnostic methods invariably results in poor outcomes. Long non Show more
Hepatocellular carcinoma (HCC) is a malignancy found at high frequency around the world. Unfortunately, the scarcity of effective early diagnostic methods invariably results in poor outcomes. Long noncoding RNAs (lncRNAs) are known to regulate the progression of hepatocellular carcinoma (HCC). A novel lncRNA RP11-286H15.1(OTTHUMG00000186042) has been identified and associated with HCC; however, the potential role of RP11-286H15.1 in HCC remains undefined. The transcript abundance of RP11-286H15.1 in 80 pairs of HCC samples and cell lines was evaluated by qRT-PCR analysis. The functional role of RP11-286H15.1 in HCC was tested in vivo and in vitro. The mechanisms underlying the role of RP11-286H15.1 in HCC were explored by RNA pulldown, transcriptome sequencing, and RNA immunoprecipitation (RIP), ubiquitination and fluorescence in situ hybridization (FISH) assays as well as Western blot analysis. The qRT-PCR and FISH assays revealed that RP11-286H15.1 was significantly decreased in HCC, and implied a shorter survival time. RP11-286H15.1 overexpression inhibited HCC cell proliferation and metastasis in vitro and in vivo, whereas RP11-286H15.1 knockdown produced the opposite results. Furthermore, we confirmed that RP11-286H15.1 (620-750 nucleotides) binds to poly(A) binding protein 4 (PABPC4) and promotes its ubiquitination, thus, reducing the stability of TRIM37 and CDC27 mRNAs. Our study demonstrates that a novel lncRNA, RP11-286H15.1, represses HCC progression by promoting PABPC4 ubiquitination. These findings highlight potential therapeutic targets for HCC. Show less
no PDF DOI: 10.1016/j.canlet.2020.11.038
PABPC4

AKR1B10 promotes breast cancer cell proliferation and migration via the PI3K/AKT/NF-κB signaling pathway.

Jiayao Qu, Jia Li, Yaming Zhang +9 more · 2021 · Cell & bioscience · BioMed Central · added 2026-04-24
Aberrant expression of Aldo-Keto reductase family 1 member B10 (AKR1B10) was associated with tumor size and metastasis of breast cancer in our published preliminary studies. However, little is known a Show more
Aberrant expression of Aldo-Keto reductase family 1 member B10 (AKR1B10) was associated with tumor size and metastasis of breast cancer in our published preliminary studies. However, little is known about the detailed function and underlying molecular mechanism of AKR1B10 in the pathological process of breast cancer. The relationship between elevated AKR1B10 expression and the overall survival and disease-free survival of breast cancer patients was analyzed by Kaplan-Meier Plotter database. Breast cancer cell lines overexpressing AKR1B10 (MCF-7/AKR1B10) and breast cancer cell lines with knockdown of AKR1B10 (BT-20/shAKR1B10) were constructed to analyze the impact of AKR1B10 expression on cell proliferation and migration of breast cancer. The expression levels of AKR1B10 were detected and compared in the breast cancer cell lines and tissues by RT-qPCR, western blot and immunohistochemistry. The proliferation of breast cancer cells was monitored by CCK8 cell proliferation assay, and the migration and invasion of breast cancer cells was observed by cell scratch test and transwell assay. The proliferation- and EMT-related proteins including cyclinD1, c-myc, Survivin, Twist, SNAI1, SLUG, ZEB1, E-cadherin, PI3K, p-PI3K, AKT, p-AKT, IKBα, p-IKBα, NF-κB p65, p-NF-κB p65 were detected by western blot in breast cancer cells. MCF-7/AKR1B10 cells were treated with LY294002, a PI3K inhibitor, to consider the impact of AKR1B10 overexpression on the PI3K/AKT/NF-κB signal cascade and the presence of NF-κB p65 in nuclear. In vivo tumor xenograft experiments were used to observe the role of AKR1B10 in breast cancer growth in mice. AKR1B10 expression was significantly greater in breast cancer tissue compared to paired non-cancerous tissue. The expression of AKR1B10 positively correlated with lymph node metastasis, tumor size, Ki67 expression, and p53 expression, but inversely correlated with overall and disease-free survival rates. Gene Ontology analysis showed that AKR1B10 activity contributes to cell proliferation. Overexpression of AKR1B10 facilitated the proliferation of MCF-7 cells, and induced the migration and invasion of MCF-7 cells in vitro in association with induction of epithelial-mesenchymal transition (EMT). Conversely, knockdown of AKR1B10 inhibited these effects in BT-20 cells. Mechanistically, AKR1B10 activated PI3K, AKT, and NF-κB p65, and induced nuclear translocation of NF-κB p65, and expression of proliferation-related proteins including c-myc, cyclinD1, Survivin, and EMT-related proteins including ZEB1, SLUG, Twist, but downregulated E-cadherin expression in MCF-7 cells. AKR1B10 silencing reduced the phosphorylation of PI3K, AKT, and NF-κB p65, the nuclear translocation of NF-κB p65, and the expression of proliferation- and migration-related proteins in BT-20 cells. LY294002, a PI3K inhibitor, attenuated the phosphorylation of PI3K, AKT, and NF-κB p65, and the nuclear translocation of NF-κB p65. In vivo tumor xenograft experiments confirmed that AKR1B10 promoted breast cancer growth in mice. AKR1B10 promotes the proliferation, migration and invasion of breast cancer cells via the PI3K/AKT/NF-κB signaling pathway and represents a novel prognostic indicator as well as a potential therapeutic target in breast cancer. Show less
no PDF DOI: 10.1186/s13578-021-00677-3
SNAI1

Screening of non-alcoholic steatohepatitis (NASH)-related datasets and identification of NASH-related genes.

Ming-Jiang Liu, Hu Jin, Yu-Bing Chen +4 more · 2021 · International journal of clinical and experimental pathology · added 2026-04-24
Non-alcoholic steatohepatitis (NASH) is a common liver disease in the western world. The mechanisms behind NASH formation are poorly understood, but there may be multiple targets considering the disea Show more
Non-alcoholic steatohepatitis (NASH) is a common liver disease in the western world. The mechanisms behind NASH formation are poorly understood, but there may be multiple targets considering the disease's multifactorial nature. To explore the genes related to the pathogenesis of NASH, we downloaded clinical data and gene expression of NASH patients from the Gene Expression Omnibus database (GEO). We identified 281 genes with a common expression in two NASH-related datasets (GSE89632 and GSE83452), suggesting that they may be related to NASH. Further study showed that Show less
no PDF
ANGPTL4

WLS/wntless is essential in controlling dendritic cell homeostasis via a WNT signaling-independent mechanism.

Li-Ting Wang, Ming-Hong Lin, Kwei-Yan Liu +9 more · 2021 · Autophagy · Taylor & Francis · added 2026-04-24
We propose that beyond its role in WNT secretion, WLS/GPR177 (wntless, WNT ligand secretion mediator) acts as an essential regulator controlling protein glycosylation, endoplasmic reticulum (ER) homeo Show more
We propose that beyond its role in WNT secretion, WLS/GPR177 (wntless, WNT ligand secretion mediator) acts as an essential regulator controlling protein glycosylation, endoplasmic reticulum (ER) homeostasis, and dendritic cell (DC)-mediated immunity. WLS deficiency in bone marrow-derived DCs (BMDCs) resulted in poor growth and an inability to mount cytokine and T-cell responses Show less
no PDF DOI: 10.1080/15548627.2021.1907516
PIK3C3

Tear dynamics testing and quantitative proteomics analysis in patients with chronic renal failure.

Rong-Rong Zong, Fei-Feng Zhu, Wei Han +8 more · 2021 · Journal of proteomics · Elsevier · added 2026-04-24
Ocular surface changes may develop in patients with chronic renal failure (CRF) undergoing hemodialysis. In recent years, an association of CRF with dry eye syndrome has been emphasized. However, tear Show more
Ocular surface changes may develop in patients with chronic renal failure (CRF) undergoing hemodialysis. In recent years, an association of CRF with dry eye syndrome has been emphasized. However, tear proteomics of CRF patients has not been analyzed. Here, we performed systematic profiling of the tear film proteins in CRF patients through use of isobaric tags for relative and absolute quantitative (iTRAQ) MS/MS, aiming to identify associations between dry eye symptoms and expression of tear proteomic changes in patients with CRF undergoing hemodialysis. Twenty CRF patients and ten healthy subjects underwent a series of ophthalmic examinations. Tear samples from the participants were analyzed by iTRAQ approach. A total of 1139 tear proteins were screened, and 212 differentially expressed proteins were identified. The pattern changes included 77 whose expression levels were upregulated (fold increase >1.2) whereas 135 others that were downregulated (fold decrease <1/1.2). Bioinformatics analysis showed that these proteins were significantly enriched in lipid metabolism, inflammatory, and immune response pathways. Furthermore, APOA1, APOA4, APOB, APOE, S100A8, S100A9, S100A4, HSP90B and other molecules were significantly changed. Our study elucidated the characteristics of tear dynamics and protein markers in CRF patients undergoing hemodialysis. Significance: Despite the association of chronic renal failure (CRF) with dry eye disease, there are no reports describing potentially important differentially expressed tear proteins in CRF patients undergoing hemodialysis. It is still a challenge to obtain a comprehensive description of the pathogenesis of dry eye in CRF patients which hinders establishing a patient specific therapeutic scheme. Our study is the first iTRAQ proteomics analysis of the tears of patients with CRF, which reveals the changes in the protein expression profile in CRF patients afflicted with dry eye disease. The identity was verified of some relevant differentially expressed proteins, and they may be candidate diagnostic markers of dry eye disease in patients with CRF. These tear film protein constituents found in hemodialysis patients can be of important clinical significance in treating this condition. SIGNIFICANCE: Despite the association of chronic renal failure (CRF) with dry eye disease, there are no reports describing potentially important differentially expressed tear proteins in CRF patients undergoing hemodialysis. It is still a challenge to obtain a comprehensive description of the pathogenesis of dry eye in CRF patients which hinders establishing a patient specific therapeutic scheme. Our study is the first iTRAQ proteomics analysis of the tears of patients with CRF, which reveals the changes in the protein expression profile in CRF patients afflicted with dry eye disease. The identity was verified of some relevant differentially expressed proteins, and they may be candidate diagnostic markers of dry eye disease in patients with CRF. These tear film protein constituents found in hemodialysis patients can be of important clinical significance in treating this condition. Show less
no PDF DOI: 10.1016/j.jprot.2021.104351
APOA4

Generation and characterization of an induced pluripotent stem cell line SDQLCHi018-A from a congenital myasthenic syndrome patient carrying compound heterozygote mutations in RAPSN gene.

Haiyan Zhang, Huawei Zhang, Yanyan Ma +3 more · 2021 · Stem cell research · Elsevier · added 2026-04-24
Mutations in RAPSN are an important cause of congenital myasthenic syndrome (CMS). In this study, we generated an induced pluripotent stem cell line (iPSC) derived from a 14-day-old male CMS patient c Show more
Mutations in RAPSN are an important cause of congenital myasthenic syndrome (CMS). In this study, we generated an induced pluripotent stem cell line (iPSC) derived from a 14-day-old male CMS patient carrying compound heterozygote mutations (c.532-2A > G and c.264C > A/p.Asn88Lys) in RAPSN gene. The established iPSC line harboring the original mutations, possessing a normal karyotype, is able to differentiate into all three germ layers in vitro and expresses pluripotency markers. Show less
no PDF DOI: 10.1016/j.scr.2021.102160
RAPSN

Effects of Gene-Environment Interaction on Obesity among Chinese Adults Born in the Early 1960s.

Weiyan Gong, Hui Li, Chao Song +6 more · 2021 · Genes · MDPI · added 2026-04-24
The prevalence of obesity has been increasing sharply and has become a serious public health problem worldwide. Gene-environment interaction in obesity is a relatively new field, and little is known a Show more
The prevalence of obesity has been increasing sharply and has become a serious public health problem worldwide. Gene-environment interaction in obesity is a relatively new field, and little is known about it in Chinese adults. This study aimed to provide the effects of gene-environment interaction on obesity among Chinese adults. A stratified multistage cluster sampling method was conducted to recruit participants from 150 surveillance sites. Subjects born in 1960, 1961 and 1963 were selected. An exploratory factor analysis was used to classify the environmental factors. The interaction of single nucleotide polymorphisms (SNPs) and environmental factors on body mass index (BMI) and waist circumference were analyzed using a general linear model. A multiple logistic regression model combined with an additive model was performed to analyze the interaction between SNPs and environmental factors in obesity and central obesity. A total of 2216 subjects were included in the study (mean age, 49.7 years; male, 39.7%, female, 60.3%). Engaging in physical activity (PA) could reduce the effect of Show less
📄 PDF DOI: 10.3390/genes12020270
MC4R

Prognosis and Immune Infiltration of Chromobox Family Genes in Sarcoma.

Jian Zhou, Ziyuan Chen, Ming Zou +6 more · 2021 · Frontiers in oncology · Frontiers · added 2026-04-24
Chromobox family genes (CBXs) are known to play roles in numerous modifications of the chromatin in order to inhibit the transcription of target genes. CBXs have been shown to be expressed at high lev Show more
Chromobox family genes (CBXs) are known to play roles in numerous modifications of the chromatin in order to inhibit the transcription of target genes. CBXs have been shown to be expressed at high levels in many types of cancer and can also serve as a target gene for therapeutic purposes. However, little is known about the expression and prognostic value of CBXs in human sarcomas. The transcription level of CBXs was analyzed using the Oncomine dataset, and the differential expression of CBXs in sarcoma was reported by the Gene Expression Profiling Interactive Analysis (GEPIA) dataset. We also used the CCLE dataset to evaluate the expression of CBXs in a sarcoma cell line. The prognostic value of CBXs was analyzed using GEPIA and Kaplan-Meier analysis. In addition, the corrections between CBXs and their co-expressed genes were reported using Oncomine and GEPIA datasets. DAVID was used to perform GO function enrichment analysis for the CBXs and their co-expression genes. Finally, TIMER was used to analyze the immune cell infiltration of CBXs in patients with sarcoma. HP1- The results from the present study indicated that CBXs were significantly associated with prognosis and immunological status in sarcoma. These data suggest that CBXs could serve as potential biomarkers for prognosis and immune infiltration in human sarcoma. Show less
📄 PDF DOI: 10.3389/fonc.2021.657595
CBX1

Kynurenine derivative 3-HAA is an agonist ligand for transcription factor YY1.

Zhaopeng Shi, Guifang Gan, Xiang Xu +13 more · 2021 · Journal of hematology & oncology · BioMed Central · added 2026-04-24
The 3-hydroxyanthranilic acid (3-HAA), a derivative of kynurenine, was reported to suppress tumor growth. However, the function of 3-HAA largely remains unclear. Here, we report that 3-hydroxyanthrani Show more
The 3-hydroxyanthranilic acid (3-HAA), a derivative of kynurenine, was reported to suppress tumor growth. However, the function of 3-HAA largely remains unclear. Here, we report that 3-hydroxyanthranilic acid (3-HAA) is lower in tumor cells, while adding exogenous 3-HAA induces apoptosis in hepatocellular carcinoma by binding YY1. This 3-HAA binding of YY1 leads to phosphorylation of YY1 at the Thr 398 by PKCζ, concomitantly enhances YY1 chromatin binding activity to increase expression of target genes. These findings demonstrate that 3-HAA is a ligand of YY1, suggesting it is a promising therapeutic candidate for HCC. Show less
📄 PDF DOI: 10.1186/s13045-021-01165-4
DUSP6

LUBAC and OTULIN regulate autophagy initiation and maturation by mediating the linear ubiquitination and the stabilization of ATG13.

Yuanyuan Chu, Yingjin Kang, Cong Yan +4 more · 2021 · Autophagy · Taylor & Francis · added 2026-04-24
Macroautophagy/autophagy is a membrane-mediated intracellular degradation pathway, through which bulky cytoplasmic content is digested in lysosomes. How the autophagy initiation and maturation steps a Show more
Macroautophagy/autophagy is a membrane-mediated intracellular degradation pathway, through which bulky cytoplasmic content is digested in lysosomes. How the autophagy initiation and maturation steps are regulated is not clear. In this study, we found an E3 ubiquitin ligase complex, linear ubiquitin chain assembly complex (LUBAC) and a deubiquitinating enzyme (DUB) OTULIN localize to the phagophore area to control autophagy initiation and maturation. LUBAC key component RNF31/HOIP translocates to the LC3 puncta area when autophagy is induced. Show less
no PDF DOI: 10.1080/15548627.2020.1781393
PIK3C3

Urinary Proteomic Characteristics of Hyperuricemia and Their Possible Links with the Occurrence of Its Concomitant Diseases.

Shuai Huo, Hongxin Wang, Meixia Yan +12 more · 2021 · ACS omega · ACS Publications · added 2026-04-24
Hyperuricemia (HUA), a chronic disease caused by metabolic disorders of purine, is often accompanied by other diseases such as gout, type 2 diabetes mellitus (T2DM), and hyperlipidemia. However, littl Show more
Hyperuricemia (HUA), a chronic disease caused by metabolic disorders of purine, is often accompanied by other diseases such as gout, type 2 diabetes mellitus (T2DM), and hyperlipidemia. However, little is known about the relationship between HUA and these diseases on the protein level. We performed label-free liquid chromatography MS/MS spectrometry analysis of urine samples from 26 HUA patients and 25 healthy controls, attempting to establish the possible protein links between HUA and these diseases by profiling urine proteome. A total of 2119 proteins were characterized in sample proteomes. Among them, 11 were found decreased and 2 were found increased in HUA samples. Plausible pathways found by enrichment analysis of these differentially expressed proteins (DEPs) include the processes for insulin receptor recycling and lipid metabolism, suggesting potential links between HUA and T2DM and hyperlipidemia. The abundance changes of three key proteins (VATB1, CFAD, and APOC3) involved in these processes were validated by enzyme-linked immunosorbent assay (ELISA). In conclusion, our result provides proteomic evidence, for the first time, that the aberrant pathways enriched by described key DEPs are closely related to the incidence of HUA and its concomitant diseases. Show less
📄 PDF DOI: 10.1021/acsomega.0c06229
APOC3

PRKAA/AMPKα phosphorylation switches the role of RASAL2 from a suppressor to an activator of autophagy.

Yong Bao, Christopher Qian, Meng-Yue Liu +10 more · 2021 · Autophagy · Taylor & Francis · added 2026-04-24
RASAL2 (RAS protein activator like 2), a RASGTPase activating protein, can catalyze the hydrolysis of RAS-GTP into RAS-GDP to inactivate the RAS pathway in various types of cancer cells. However, the Show more
RASAL2 (RAS protein activator like 2), a RASGTPase activating protein, can catalyze the hydrolysis of RAS-GTP into RAS-GDP to inactivate the RAS pathway in various types of cancer cells. However, the cellular function of RASAL2 remains elusive. Here we showed that RASAL2 can attenuate PRKAA/AMPKα phosphorylation by recruiting phosphatase PPM1B/pp2cβ, thus inhibiting the initiation of basal autophagy under normal conditions. In addition, we found that glucose starvation could induce dissociation of PPM1B from RASAL2 and then RASAL2 at S351 be phosphorylated by PRKAA, followed by the binding of phosphorylated-RASAL2 with to PIK3C3/VPS34-ATG14-BECN1/Beclin1 complex to increase PIK3C3 activity and autophagy. Furthermore, RASAL2 S351 phosphorylation facilitated breast tumor growth and correlated to poor clinical outcomes in breast cancer patients. Our study demonstrated that the phosphorylation status of RASAL2 S351 can function as a molecular switch to either suppress or promote AMPK-mediated autophagy. Inhibition of RASAL2 S351 phosphorylation might be a potential therapeutic strategy to overcome the resistance of AMPK-activation agents. Show less
no PDF DOI: 10.1080/15548627.2021.1886767
PIK3C3

IL-27 Negatively Regulates Tip-DC Development during Infection.

Gongguan Liu, Osama Abas, Yong Fu +4 more · 2021 · mBio · added 2026-04-24
Tumor necrosis factor (TNF)/inducible nitric oxide synthase (iNOS)-producing dendritic cells (Tip-DCs) have profound impacts on host immune responses during infections. The mechanisms regulating Tip-D Show more
Tumor necrosis factor (TNF)/inducible nitric oxide synthase (iNOS)-producing dendritic cells (Tip-DCs) have profound impacts on host immune responses during infections. The mechanisms regulating Tip-DC development remain largely unknown. Here, using a mouse model of infection with African trypanosomes, we show that a deficiency in interleukin-27 receptor (IL-27R) signaling results in escalated intrahepatic accumulation of Ly6C-positive (Ly6C Show less
📄 PDF DOI: 10.1128/mBio.03385-20
IL27

Aberrant Activation of Notch1 Signaling in Glomerular Endothelium Induces Albuminuria.

Liqun Li, Qiang Liu, Tongyao Shang +7 more · 2021 · Circulation research · added 2026-04-24
Glomerular capillaries are lined with a highly specialized fenestrated endothelium and contribute to the glomerular filtration barrier. The Notch signaling pathway is involved in regulation of glomeru Show more
Glomerular capillaries are lined with a highly specialized fenestrated endothelium and contribute to the glomerular filtration barrier. The Notch signaling pathway is involved in regulation of glomerular filtration barrier, but its role in glomerular endothelium has not been investigated due to the embryonic lethality of animal models with genetic modification of Notch pathway components in the endothelium. To determine the effects of aberrant activation of the Notch signaling in glomerular endothelium and the underlying molecular mechanisms. We established the Our results reveal novel regulatory mechanisms whereby endothelial Notch1 signaling dictates the level of VE-cadherin through the transcription factors SNAI1 and ERG, leading to dysfunction of glomerular filtration barrier and induction of albuminuria. Graphic Abstract: A graphic abstract is available for this article. Show less
no PDF DOI: 10.1161/CIRCRESAHA.120.316970
SNAI1

Long-Term Effects of Maternal Low-Protein Diet and Post-weaning High-Fat Feeding on Glucose Metabolism and Hypothalamic POMC Promoter Methylation in Offspring Mice.

Jia Zheng, Ling Zhang, Jiayi Liu +2 more · 2021 · Frontiers in nutrition · Frontiers · added 2026-04-24
Substantial evidence indicated that maternal malnutrition could increase the susceptibility to obesity, insulin resistance, and type 2 diabetes in adulthood. It is increasingly apparent that the brain Show more
Substantial evidence indicated that maternal malnutrition could increase the susceptibility to obesity, insulin resistance, and type 2 diabetes in adulthood. It is increasingly apparent that the brain, especially the hypothalamus, plays a critical role in glucose homeostasis. However, little information is known about the mechanisms linking maternal protein restriction combined with post-weaning high-fat (HF) feeding with altered expression of brain neurotransmitters, and investigations into the epigenetic modifications of hypothalamus in offspring have not been fully elucidated. Our objective was to explore the effects of maternal protein restriction combined with post-weaning HF feeding on glucose metabolism and hypothalamic POMC methylation in male offspring mice. C57/BL6 mice were fed on either low-protein (LP) or normal chow (NC) diet throughout gestation and lactation. Then, the male offspring were randomly weaned to either NC or high-fat (HF) diet until 32 weeks of age. Gene expressions and DNA methylation of hypothalamic proopiomelanocortin (POMC) and melanocortin receptor 4 (MC4R) were determined in male offspring. The results showed that birth weights and body weights at weaning were both significantly lower in male offspring mice of the dams fed with a LP diet. Maternal protein restriction combined with post-weaning high-fat feeding, predisposes higher body weight, persistent glucose intolerance (from weaning to 32 weeks of age), hyperinsulinemia, and hyperleptinemia in male offspring mice. POMC and MC4R expressions were significantly increased in offspring mice fed with maternal LP and postnatal high-fat diet ( Show less
📄 PDF DOI: 10.3389/fnut.2021.657848
MC4R

Association between VPS13C rs2414739 polymorphism and Parkinson's disease risk: A meta-analysis.

Xinling Bai, Xian Liu, Xiaoyuan Li +2 more · 2021 · Neuroscience letters · Elsevier · added 2026-04-24
We aimed to estimate the role of vacuolar protein sorting 13C (VPS13C) gene single nucleotide polymorphism (SNP) rs2414739 variant in the risk of PD by meta-analysis. Five eligible case-control studie Show more
We aimed to estimate the role of vacuolar protein sorting 13C (VPS13C) gene single nucleotide polymorphism (SNP) rs2414739 variant in the risk of PD by meta-analysis. Five eligible case-control studies including 2796 PD cases and 4138 health controls involved in this meta-analysis. The fixed or random effect model was selected based on the heterogeneity of the included studies which detected by I The results of our meta-analysis showed a significant correlation between VPS13C rs2424739 gene polymorphism and PD susceptibility in Allele model (A versus vs. G: OR = 1.14, 95 %CI = 1.05-1.23, p = 0.002), dominant model (GG + AG vs. AA: OR = 0.86, 95 %CI = 0.78-0.95, p = 0.004), heterozygote model (AG vs. AA: OR = 0.87, 95 %CI = 0.77-0.99, p = 0.04), homozygote model (GG vs. AA: OR = 0.76, 95 %CI = 0.60-0.96, p = 0.02). Surprisingly, we did not find a significant statistical difference between VPS13C rs2414739 polymorphism and PD risk in Chinese cohort in the regional stratified analysis. This meta-analysis suggests that VPS13C rs2414739 polymorphism might act as a genetic predisposition factor for PD, whereas does not include Chinese population. Show less
no PDF DOI: 10.1016/j.neulet.2021.135879
VPS13C

Caspase-Dependent Cleavage of DDX21 Suppresses Host Innate Immunity.

Wei Wu, Yang Qu, Shengqing Yu +12 more · 2021 · mBio · added 2026-04-24
DEAD (Glu-Asp-Ala-Glu) box RNA helicases have been proven to contribute to antiviral innate immunity. The DDX21 RNA helicase was identified as a nuclear protein involved in rRNA processing and RNA unw Show more
DEAD (Glu-Asp-Ala-Glu) box RNA helicases have been proven to contribute to antiviral innate immunity. The DDX21 RNA helicase was identified as a nuclear protein involved in rRNA processing and RNA unwinding. DDX21 was also proven to be the scaffold protein in the complex of DDX1-DDX21-DHX36, which senses double-strand RNA and initiates downstream innate immunity. Here, we identified that DDX21 undergoes caspase-dependent cleavage after virus infection and treatment with RNA/DNA ligands, especially for RNA virus and ligands. Caspase-3/6 cleaves DDX21 at D126 and promotes its translocation from the nucleus to the cytoplasm in response to virus infection. The cytoplasmic cleaved DDX21 negatively regulates the interferon beta (IFN-β) signaling pathway by suppressing the formation of the DDX1-DDX21-DHX36 complex. Thus, our data identify DDX21 as a regulator of immune balance and most importantly uncover a potential role of DDX21 cleavage in the innate immune response to virus. Show less
📄 PDF DOI: 10.1128/mBio.01005-21
DHX36

Hepatic autophagy and mitophagy status in dairy cows with subclinical and clinical ketosis.

Taiyu Shen, Feng Xu, Zhiyuan Fang +14 more · 2021 · Journal of dairy science · added 2026-04-24
Severe negative energy balance around parturition is an important contributor to ketosis, a metabolic disorder that occurs most frequently in the peripartal period. Autophagy and mitophagy are importa Show more
Severe negative energy balance around parturition is an important contributor to ketosis, a metabolic disorder that occurs most frequently in the peripartal period. Autophagy and mitophagy are important processes responsible for breaking down useless or toxic cellular material, and in particular damaged mitochondria. However, the role of autophagy and mitophagy during the occurrence and development of ketosis is unclear. The objective of this study was to investigate autophagy and mitophagy in the livers of cows with subclinical ketosis (SCK) and clinical ketosis (CK). We assessed autophagy by measuring the protein abundance of microtubule-associated protein 1 light chain 3-II (LC3-II; encoded by MAP1LC3) and sequestosome-1 (p62, encoded by SQSTM1), as well as the mRNA abundance of autophagy-related genes 5 (ATG5), 7 (ATG7), and 12 (ATG12), beclin1 (BECN1), and phosphatidylinositol 3-kinase catalytic subunit type 3 (PIK3C3). Mitophagy was evaluated by measuring the protein abundance of the mitophagy upstream regulators PTEN-induced putative kinase 1 (PINK1) and Parkin. Liver and blood samples were collected from healthy cows [n = 15; blood β-hydroxybutyrate (BHB) concentration <1.2 mM], cows with SCK (n = 15; blood BHB concentration 1.2 to 3.0 mM) and cows with CK (n = 15; blood BHB concentration >3.0 mM with clinical signs) with similar lactation numbers (median = 3, range = 2 to 4) and days in milk (median = 6, range = 3 to 9). The serum activity of aspartate aminotransferase and alanine aminotransferase was greater in cows with CK than in healthy cows. Levels of oxidative stress biomarkers malondialdehyde and hydrogen peroxide were also higher in liver tissue from ketotic cows (SCK and CK) than from healthy cows. Compared with cows with CK and healthy cows, the hepatic mRNA abundance of MAP1LC3, SQSTM1, ATG5, ATG7, ATG12, and PIK3C3 was upregulated in cows with SCK. Compared with healthy cows, cows with SCK had a lower abundance of p62 and a greater abundance of LC3-II, but levels of both were higher in cows with CK. The mRNA abundance of ATG12 was lower in cows with CK than in healthy cows. Furthermore, the hepatic protein abundance of PINK1 and Parkin was greater in cows with SCK and slightly lower in cows with CK than in healthy cows. These data demonstrated differences in the hepatic activities of autophagy and mitophagy in cows with SCK compared with cows with CK. Although the precise mechanisms for these differences could not be discerned, autophagy and mitophagy seem to be involved in ketosis. Show less
no PDF DOI: 10.3168/jds.2020-19150
PIK3C3

Low-density lipoprotein receptor-related protein 1 (LRP1) is a novel receptor for apolipoprotein A4 (APOA4) in adipose tissue.

Jie Qu, Sarah Fourman, Maureen Fitzgerald +8 more · 2021 · Scientific reports · Nature · added 2026-04-24
Apolipoprotein A4 (APOA4) is one of the most abundant and versatile apolipoproteins facilitating lipid transport and metabolism. APOA4 is synthesized in the small intestine, packaged onto chylomicrons Show more
Apolipoprotein A4 (APOA4) is one of the most abundant and versatile apolipoproteins facilitating lipid transport and metabolism. APOA4 is synthesized in the small intestine, packaged onto chylomicrons, secreted into intestinal lymph and transported via circulation to several tissues, including adipose. Since its discovery nearly 4 decades ago, to date, only platelet integrin αIIbβ3 has been identified as APOA4 receptor in the plasma. Using co-immunoprecipitation coupled with mass spectrometry, we probed the APOA4 interactome in mouse gonadal fat tissue, where ApoA4 gene is not transcribed but APOA4 protein is abundant. We demonstrate that lipoprotein receptor-related protein 1 (LRP1) is the cognate receptor for APOA4 in adipose tissue. LRP1 colocalized with APOA4 in adipocytes; it interacted with APOA4 under fasting condition and their interaction was enhanced during lipid feeding concomitant with increased APOA4 levels in plasma. In 3T3-L1 mature adipocytes, APOA4 promoted glucose uptake both in absence and presence of insulin in a dose-dependent manner. Knockdown of LRP1 abrogated APOA4-induced glucose uptake as well as activation of phosphatidylinositol 3 kinase (PI3K)-mediated protein kinase B (AKT). Taken together, we identified LRP1 as a novel receptor for APOA4 in promoting glucose uptake. Considering both APOA4 and LRP1 are multifunctional players in lipid and glucose metabolism, our finding opens up a door to better understand the molecular mechanisms along APOA4-LRP1 axis, whose dysregulation leads to obesity, cardiovascular disease, and diabetes. Show less
📄 PDF DOI: 10.1038/s41598-021-92711-0
APOA4

Single-cell analysis reveals the intra-tumor heterogeneity and identifies MLXIPL as a biomarker in the cellular trajectory of hepatocellular carcinoma.

Xiao Dong, Fan Wang, Chuan Liu +7 more · 2021 · Cell death discovery · Nature · added 2026-04-24
Hepatocellular carcinoma (HCC) is a globally prevailing cancer with a low 5-year survival rate. Little is known about its intricate gene expression profile. Single-cell RNA sequencing is an indispensa Show more
Hepatocellular carcinoma (HCC) is a globally prevailing cancer with a low 5-year survival rate. Little is known about its intricate gene expression profile. Single-cell RNA sequencing is an indispensable tool to explore the genetic characteristics of HCC at a more detailed level. In this study, we profiled the gene expression of single cells from human HCC tumor and para-tumor tissues using the Smart-seq 2 sequencing method. Based on differentially expressed genes, we identified heterogeneous subclones in HCC tissues, including five HCC and two hepatocyte subclones. We then carried out hub-gene co-network and functional annotations analysis followed pseudo-time analysis with regulated transcriptional factor co-networks to determine HCC cellular trajectory. We found that MLX interacting protein like (MLXIPL) was commonly upregulated in the single cells and tissues and associated with a poor survival rate in HCC. Mechanistically, MLXIPL activation is crucial for promoting cell proliferation and inhibits cell apoptosis by accelerating cell glycolysis. Taken together, our work identifies the heterogeneity of HCC subclones, and suggests MLXIPL might be a promising therapeutic target for HCC. Show less
📄 PDF DOI: 10.1038/s41420-021-00403-5
MLXIPL

The long non-coding RNA

Changlin Qian, Weiqing Qiu, Jie Zhang +3 more · 2021 · PeerJ · added 2026-04-24
Cholesterol gallstone (CG) is the most common gallstone disease, which is induced by biliary cholesterol supersaturation. The purpose of this study is to investigate the pathogenesis of CG. Sixteen mi Show more
Cholesterol gallstone (CG) is the most common gallstone disease, which is induced by biliary cholesterol supersaturation. The purpose of this study is to investigate the pathogenesis of CG. Sixteen mice were equally and randomly divided into model group and normal control group. The model group was fed with lithogenic diets to induce CG, and then gallbladder bile lipid analysis was performed. After RNA-seq library was constructed, differentially expressed mRNAs (DE-mRNAs) and differentially expressed lncRNAs (DE-lncRNAs) between model group and normal control group were analyzed by DESeq2 package. Using the cluster Profiler package, enrichment analysis for the DE-mRNAs was carried out. Based on Cytoscape software, the protein-protein interaction (PPI) network and competing endogenous RNA (ceRNA) network were built. Using quantitative real-time reverse transcription-PCR (qRT-PCR) analysis, the key RNAs were validated. The mouse model of CG was suc cessfully established, and then 181 DE-mRNAs and 33 DE-lncRNAs between model and normal groups were obtained. Moreover, KDM4A was selected as a hub node in the PPI network, and lncRNA These RNAs might be related to the pathogenesis of CG. Show less
no PDF DOI: 10.7717/peerj.10803
PABPC4

Seasonal regulation of singing-driven gene expression associated with song plasticity in the canary, an open-ended vocal learner.

Shin Hayase, Chengru Shao, Masahiko Kobayashi +3 more · 2021 · Molecular brain · BioMed Central · added 2026-04-24
Songbirds are one of the few animal taxa that possess vocal learning abilities. Different species of songbirds exhibit species-specific learning programs during song acquisition. Songbirds with open-e Show more
Songbirds are one of the few animal taxa that possess vocal learning abilities. Different species of songbirds exhibit species-specific learning programs during song acquisition. Songbirds with open-ended vocal learning capacity, such as the canary, modify their songs during adulthood. Nevertheless, the neural molecular mechanisms underlying open-ended vocal learning are not fully understood. We investigated the singing-driven expression of neural activity-dependent genes (Arc, Egr1, c-fos, Nr4a1, Sik1, Dusp6, and Gadd45β) in the canary to examine a potential relationship between the gene expression level and the degree of seasonal vocal plasticity at different ages. The expression of these genes was differently regulated throughout the critical period of vocal learning in the zebra finch, a closed-ended song learner. In the canary, the neural activity-dependent genes were induced by singing in the song nuclei throughout the year. However, in the vocal motor nucleus, the robust nucleus of the arcopallium (RA), all genes were regulated with a higher induction rate by singing in the fall than in the spring. The singing-driven expression of these genes showed a similar induction rate in the fall between the first year juvenile and the second year adult canaries, suggesting a seasonal, not age-dependent, regulation of the neural activity-dependent genes. By measuring seasonal vocal plasticity and singing-driven gene expression, we found that in RA, the induction intensity of the neural activity-dependent genes was correlated with the state of vocal plasticity. These results demonstrate a correlation between vocal plasticity and the singing-driven expression of neural activity-dependent genes in RA through song development, regardless of whether a songbird species possesses an open- or closed-ended vocal learning capacity. Show less
📄 PDF DOI: 10.1186/s13041-021-00869-5
DUSP6

IL-27 as a potential biomarker for distinguishing between necrotising enterocolitis and highly suspected early-onset food protein-induced enterocolitis syndrome with abdominal gas signs.

Yuhong Qi, Chan Liu, Xin Zhong +7 more · 2021 · EBioMedicine · Elsevier · added 2026-04-24
The initial clinical manifestations and abdominal imaging findings of neonates with necrotising enterocolitis (NEC) and food protein-induced enterocolitis syndrome (FPIES) are sometimes similar; howev Show more
The initial clinical manifestations and abdominal imaging findings of neonates with necrotising enterocolitis (NEC) and food protein-induced enterocolitis syndrome (FPIES) are sometimes similar; however, their prognosis and therapies are different. We aimed to evaluate the utility of interleukin (IL)-27 as a differentiation marker between NEC and highly suspected early onset (HSEO)-FPIES. All samples used in this study were obtained from the neonatal diagnosis centre of Children's Hospital of Chongqing Medical University. In the case-control study, neonates with NEC (n = 13), HSEO-FPIES (n = 9), and jaundice (control, n = 8) were enroled to determine the serum IL-27 levels using commercial enzyme-linked immunosorbent assay (ELISA) kits. In the validation cohort study, the NEC (n = 87), HSEO-FPIES (n = 62), and jaundice (control, n = 54) groups were included to analyse the diagnostic efficiency of IL-27 for discriminating between NEC and HSEO-FPIES using a receiver operating characteristic (ROC) curve. In the case-control study, IL-27 levels were higher in the NEC group than in the HSEO-FPIES group (p = 0·005). In the cohort study, the area under the ROC curve (AUC) of IL-27 for differentiating NEC from HSEO-FPIES was 0·878, which was higher than the AUCs of IL-6 (0·761), C-reactive protein (0·800), white blood cell count (0·637), neutrophils (0·765), lymphocytes (0·782), neutrophil to lymphocyte ratio (0·781), and platelet count (0·729). Serum IL-27 is a novel biomarker that may potentially discriminate NEC from HSEO-FPIES in neonates. None. Show less
📄 PDF DOI: 10.1016/j.ebiom.2021.103607
IL27

Sulfatase 2-Induced Cancer-Associated Fibroblasts Promote Hepatocellular Carcinoma Progression via Inhibition of Apoptosis and Induction of Epithelial-to-Mesenchymal Transition.

Cong Wang, Chuzhi Shang, Xiaohong Gai +4 more · 2021 · Frontiers in cell and developmental biology · Frontiers · added 2026-04-24
Sulfatase 2 (SULF2) removes the 6- The clinical relevance of SULF2 and CAFs was examined using The Cancer Genome Atlas (TCGA) database and IHC analyses revealed that the expression of CAF markers, whi Show more
Sulfatase 2 (SULF2) removes the 6- The clinical relevance of SULF2 and CAFs was examined using The Cancer Genome Atlas (TCGA) database and IHC analyses revealed that the expression of CAF markers, which was positively correlated with that of SULF2 in the HCC tissues, predicted unfavorable postsurgical outcomes. Co-culturing HSCs with HCC cells expressing SULF2 promoted CAF differentiation. Additionally, CAFs repressed HCC cell apoptosis by activating the SDF-1/CXCR4/PI3K/AKT signaling pathway. Meanwhile, SULF2-induced CAFs promoted epithelial-to-mesenchymal transition (EMT) of HCC cells by modulating the SDF-1/CXCR4/OIP5-AS1/miR-153-3p/SNAI1 axis. Studies using HCC xenograft mouse models demonstrated that OIP5-AS1 induced EMT by upregulating SNAI1 and promoted HCC growth These data indicated that SULF2 secreted by the HCC cells induced the differentiation of HSCs into CAFs through the TGFβ1/SMAD3 signaling pathway. SULF2-induced CAFs attenuated HCC apoptosis by activating the SDF-1/CXCR4/PI3K/AKT signaling pathway and induced EMT through the SDF-1/CXCR4/OIP5-AS1/miR-153-3p/SNAI1 axis. This study revealed a novel mechanism involved in the crosstalk between HCC cells and CAFs in the tumor microenvironment, which can aid in the development of novel and efficient therapeutic strategies for primary liver cancer. Show less
no PDF DOI: 10.3389/fcell.2021.631931
SNAI1

Rare Variants Analysis of Lysosomal Related Genes in Early-Onset and Familial Parkinson's Disease in a Chinese Cohort.

Yong-Ping Chen, Xiao-Jing Gu, Wei Song +10 more · 2021 · Journal of Parkinson's disease · added 2026-04-24
Genetic studies have indicated that variants in several lysosomal genes are risk factors for idiopathic Parkinson's disease (PD). However, the role of lysosomal genes in PD in Asian populations is lar Show more
Genetic studies have indicated that variants in several lysosomal genes are risk factors for idiopathic Parkinson's disease (PD). However, the role of lysosomal genes in PD in Asian populations is largely unknown. This study aimed to analyze rare variants in lysosomal related genes in Chinese population with early-onset and familial PD. In total, 1,136 participants, including 536 and 600 patients with sporadic early-onset PD (SEOPD) and familial PD, respectively, underwent whole-exome sequencing to assess the genetic etiology. Rare variants in PD were investigated in 67 candidate lysosomal related genes (LRGs), including 15 lysosomal function-related genes and 52 lysosomal storage disorder genes. Compared with the autosomal dominant PD (ADPD) or SEOPD cohorts, a much higher proportion of patients with multiple rare damaging variants of LRGs were found in the autosomal recessive PD (ARPD) cohort. At a gene level, rare damaging variants in GBA and MAN2B1 were enriched in PD, but in SCARB2, MCOLN1, LYST, VPS16, and VPS13C were much less in patients. At an allele level, GBA p. Leu483Pro was found to increase the risk of PD. Genotype-phenotype correlation showed no significance in the clinical features among patients carrying a discrepant number of rare variants in LRGs. Our study suggests rare variants in LRGs might be more important in the pathogenicity of ARPD cases compared with ADPD or SEOPD. We further confirm rare variants in GBA are involve in PD pathogenecity and other genes associated with PD identified in this study should be supported with more evidence. Show less
no PDF DOI: 10.3233/JPD-212658
VPS13C