πŸ‘€ Jing Min Liu

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3182
Articles
1983
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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-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, Mingsong 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

Expression, Characteristics, and Clinical Target Prediction of PIK3C3/ vps34 in Gastric Cancer.

Chenglou Zhu, Wenhan Liu, Mingxu Da Β· 2025 Β· Current cancer drug targets Β· Bentham Science Β· added 2026-04-24
This study aimed to investigate the expression pattern of phosphatidylinositol 3-kinase class III (PIK3C3/vps34) in gastric cancer (GC) tissues and their juxtaposed normal counterparts and its correla Show more
This study aimed to investigate the expression pattern of phosphatidylinositol 3-kinase class III (PIK3C3/vps34) in gastric cancer (GC) tissues and their juxtaposed normal counterparts and its correlation with the clinicopathological attributes and prognostic outlook of afflicted individuals. Immunohistochemical (IHC) staining was used to ascertain the expression levels of PIK3C3/vps34 across 60 GC tissues juxtaposed with their normal counterparts. Statistical methodologies were used to scrutinize the correlation between PIK3C3/vps34 expression and clinicopathological features, along with prognostic implications for GC patients. In GC tissues, the positive expression rate of PIK3C3/vps34 was 23.3% (14/60), which contrasted sharply with the markedly elevated rate of 66.7% (40/60) observed in adjacent tissues. The positive expression proportion of PIK3C3/vps34 within GC tissues exhibited a notable decrease than in adjacent tissues (P < 0.05). The expression of PIK3C3/vps34 inversely correlated with tumor size, degree of tissue differentiation, depth of tumor infiltration, and incidence of lymph node metastasis (P < 0.05), whereas no significant associations were found with patient sex, age, tumor location, TNM staging, or distant metastasis (P > 0.05). As the tumor diameter increases, the degree of tissue differentiation diminishes, tumor infiltration depth intensifies, lymph node metastasis emerges, the TNM stage progresses, and PIK3C3/vps34 expression level within GC tissues declines correspondingly. Kaplan-Meier survival analysis unveiled a prolonged survival duration among GC patients exhibiting heightened PIK3C3/vps34 expression than in their counterparts with diminished expression (HR=0.66, 95% CI: 0.55-0.80), demonstrating statistical significance (P < 0.05). Protein interaction analysis revealed noteworthy interactions involving PIK3C3 with Beclin 1, UVRAG, and ATG14. PIK3C3/vps34 is downregulated in GC tissues, exerting a pivotal role in tumorigenesis, and is intimately linked with the prognostic trajectory of GC patients. It may serve as a significant biomarker for prognostic evaluation and a promising molecular therapeutic target for GC. Show less
no PDF DOI: 10.2174/0115680096334160240916102105
PIK3C3

Association between polymorphisms of the adenylate cyclase 3 gene rs2241759 and the effect of high-intensity interval training on blood lipid profiles.

Junren Lai, Li Gong, Yan Liu +3 more Β· 2025 Β· PeerJ Β· added 2026-04-24
One of the recognized effects of systematic physical activity is the improvement of physical fitness, with a negative correlation found between physical fitness and cardiovascular and cardiometabolic Show more
One of the recognized effects of systematic physical activity is the improvement of physical fitness, with a negative correlation found between physical fitness and cardiovascular and cardiometabolic risk. The purpose of this study is to analyze the influence of single nucleotide polymorphisms (SNPs) of the adenylate cyclase 3 ( In the 12-week HIIT program, a total of 237 Chinese Han college students with non-regular exercise habits were recruited, and these volunteers participated in the training three times a week. Baseline and after the HIIT program, total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) were measured, respectively. DNA was extracted from the white blood cells of volunteers and genotyping was carried out. The PLINK v1.09 software was used to conduct quality control screening on the obtained SNPs, and a linear regression model was constructed to analyze the association between (1) Through the analysis of Illumina CGA chip scanning, a total of 22 SNPs of the (1) The implementation of a 12-week HIIT regimen can significantly enhance the blood lipid status of college students. (2) The locus rs2241759 of the Show less
πŸ“„ PDF DOI: 10.7717/peerj.19271
ADCY3

Oligodendrocyte-astrocyte crosstalk in Parkinson's disease mediates neuronal ferroptosis via the FGF signaling pathway.

Sen Zhang, Min Yan, Xing Jiang +7 more Β· 2025 Β· NPJ Parkinson's disease Β· Nature Β· added 2026-04-24
Parkinson's disease (PD), as a neurodegenerative disorder, is characterized primarily by damage to the central nervous system, accompanied by astrocyte dysfunction and the activation of ferroptosis. R Show more
Parkinson's disease (PD), as a neurodegenerative disorder, is characterized primarily by damage to the central nervous system, accompanied by astrocyte dysfunction and the activation of ferroptosis. Recent studies have shown that oligodendrocytes also exhibit functional abnormalities in the brains of PD patients and are involved in the ferroptotic process. However, it remains unclear whether there is an interaction between oligodendrocytes and astrocytes and how they induce neuronal ferroptosis. Here, we employed single-nucleus sequencing and spatial transcriptomics to characterize the intercellular communication network between oligodendrocytes and astrocytes in the PD environment. Among these, astrocytes are the primary recipients of signals sent by oligodendrocytes in the FGF (Fibroblast growth factors) signaling pathway. In PD, the communication intensity is weakened, involving FGF1 and FGF9 and their receptors FGFR1, FGFR2, and FGFR3. Subsequently, we further validated the significant activation of mitochondrial oxidative phosphorylation processes within oligodendrocytes and astrocytes in PD mice, and that astrocytes might also involve the interaction of Mt1 and Ca Show less
πŸ“„ PDF DOI: 10.1038/s41531-025-00995-0
FGFR1

Stigmasterol attenuates atherosclerosis by inhibiting inflammatory signaling and foam cell formation.

Baiyi Lu, Fan Xiao, Qinjun Zhang +8 more Β· 2025 Β· iMetaOmics Β· Wiley Β· added 2026-04-24
Foam cells derived from macrophages and smooth muscle cells (SMCs) play a pivotal role in the progression of atherosclerosis. While phytosterols (PS) have demonstrated cholesterol-lowering and anti-in Show more
Foam cells derived from macrophages and smooth muscle cells (SMCs) play a pivotal role in the progression of atherosclerosis. While phytosterols (PS) have demonstrated cholesterol-lowering and anti-inflammatory properties, their impact on foam cells remains elusive. Here, we investigated the effects of PS on foam cell formation, inflammatory responses, and lipid metabolism using both single-cell RNA sequencing (scRNA-seq) and functional assays. scRNA-seq of aortic tissue from Show less
πŸ“„ PDF DOI: 10.1002/imo2.70056
APOE

AMPK/SIRT1/PGC-1Ξ± Signaling Pathway: Molecular Mechanisms and Targeted Strategies From Energy Homeostasis Regulation to Disease Therapy.

Junyang Chen, Boya Liu, Xinlei Yao +8 more Β· 2025 Β· CNS neuroscience & therapeutics Β· Blackwell Publishing Β· added 2026-04-24
The AMPK/SIRT1/PGC-1Ξ± pathway serves as a central regulator of cellular energy homeostasis, coordinating metabolic stress responses, epigenetic modifications, and transcriptional programs. Its dysfunc Show more
The AMPK/SIRT1/PGC-1Ξ± pathway serves as a central regulator of cellular energy homeostasis, coordinating metabolic stress responses, epigenetic modifications, and transcriptional programs. Its dysfunction is implicated in the pathogenesis of a wide spectrum of complex modern diseases, spanning neurodegeneration, metabolic syndromes, and chronic inflammatory conditions. This review examines the pathway's role as an integrative hub and its potential as a therapeutic target. We synthesize current mechanistic evidence from molecular, cellular, and preclinical studies to elucidate the pathway's operational logic and the consequences of its dysregulation. The analysis is structured around key disease paradigms-including Alzheimer's disease, Parkinson's disease, diabetes, cardiovascular injury, stroke, and chronic kidney disease-to dissect its tissue-specific pathophysiological impacts. The AMPK/SIRT1/PGC-1Ξ± axis operates through a core positive feedback loop: AMPK activation elevates NAD+, thereby activating SIRT1, which in turn deacetylates and activates PGC-1Ξ± to drive mitochondrial biogenesis and function, further reinforcing SIRT1 activity. Disruption of this cascade manifests in disease-specific mechanisms: promoting AΞ² production via BACE1/Ξ³-secretase in Alzheimer's; impairing Ξ±-synuclein clearance in Parkinson's; disrupting GLUT4 translocation and insulin signaling in diabetes; exacerbating oxidative damage and mitochondrial dysfunction in cardiovascular and neuronal injury; and accelerating fibrosis and sustained inflammation in renal and pulmonary diseases via NLRP3 and TGF-Ξ²/Smad3 signaling. The AMPK/SIRT1/PGC-1Ξ± pathway represents a cornerstone target at the intersection of metabolism, aging, and disease. Current therapeutic strategies-including pharmacological activators (e.g., metformin, SRT1720), natural compounds (e.g., resveratrol), lifestyle interventions (e.g., exercise, caloric restriction), and emerging technologies (e.g., gene editing, exosomal miRNAs)-offer multidimensional avenues for intervention. Future research must prioritize elucidating tissue-specific regulatory mechanisms, such as AMPK isoform diversity and PGC-1Ξ± interactome dynamics, to enable precision therapeutics and successful clinical translation for a range of complex disorders. Show less
πŸ“„ PDF DOI: 10.1111/cns.70657
BACE1

NSP6 regulates calcium overload-induced autophagic cell death and is regulated by KLHL22-mediated ubiquitination.

Xingyu Tao, Yanan Wang, Jiangbo Jin +10 more Β· 2025 Β· Journal of advanced research Β· Elsevier Β· added 2026-04-24
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a substantial global threat. SARS-CoV-2 nonstructural proteins (NSPs) are essential for impeding the host replication mechanism while Show more
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a substantial global threat. SARS-CoV-2 nonstructural proteins (NSPs) are essential for impeding the host replication mechanism while also assisting in the production and organization of new viral components. However, NSPs are not incorporated into viral particles, and their subsequent fate within host cells remains poorly understood. Additionally, their role in viral pathogenesis requires further investigation. This study aimed to discover the ultimate fate of NSP6 in host cells and to elucidate its role in viral pathogenesis. We investigated the effects of NSP6 on cell death and explored the underlying mechanism; moreover, we examined the degradation mechanism of NSP6 in human cells, along with analysing its correlation with coronavirus disease 2019 (COVID-19) severity in patient peripheral blood mononuclear cells (PBMCs). NSP6 was demonstrated to induce cell death. Specifically, NSP6 interacted with EI24 autophagy-associated transmembrane protein (EI24) to increase intracellular Ca This study reveals that KLHL22-mediated ubiquitination controls NSP6 stability and that NSP6 induces autophagic cell death via calcium overload, highlighting its cytotoxic role and suggesting therapeutic strategies that target calcium signaling or promote NSP6 degradation as potential interventions against COVID-19. Show less
no PDF DOI: 10.1016/j.jare.2025.05.031
PIK3C3

PNPLA3-I148M is a neomorph that interferes with two primary hepatic triglyceride clearance pathways.

David J Sherman, Lei Liu, Edward L LaGory +8 more Β· 2025 Β· Cell reports Β· Elsevier Β· added 2026-04-24
The common variant PNPLA3-I148M, globally, is the most significant genetic risk factor for fatty liver disease. However, it is unclear precisely how I148M drives disease risk. Using human hepatoma cel Show more
The common variant PNPLA3-I148M, globally, is the most significant genetic risk factor for fatty liver disease. However, it is unclear precisely how I148M drives disease risk. Using human hepatoma cells expressing endogenous I148M, we find that the variant impairs cellular secretion of apolipoprotein B (ApoB), the scaffolding protein of very-low-density lipoprotein (VLDL). This is not due to loss-of-function of wild-type PNPLA3. Expression of human I148M in primary hepatocytes and mice also hinders VLDL secretion. Lipidomic profiling reveals a shift from polyunsaturated phosphatidylcholine to polyunsaturated triglycerides in I148M cells, reducing membrane fluidity and, concomitantly, VLDL biogenesis. ApoB secretion is substantially rescued in I148M cells overexpressing ABHD5/CGI-58, an I148M-binding partner that normally activates ATGL/PNPLA2-mediated triglyceride lipolysis. Conversely, knocking down CGI-58 or PNPLA2 mimics I148M. We propose that I148M is a neomorph that exacerbates fatty liver risk by simultaneously impeding two major CGI-58-dependent pathways for liver triglyceride clearance: lipolysis and secretion. Show less
no PDF DOI: 10.1016/j.celrep.2025.116371
APOB

Sleep hygiene profiling, dysfunctional sleep beliefs, and their impacts on cancer-related fatigue in lung cancer patients undergoing chemotherapy.

Zhige Yan, Xiajun Guo, Ying Hu +2 more Β· 2025 Β· Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer Β· Springer Β· added 2026-04-24
To elucidate the accurate roles of dysfunctional sleep beliefs in modulating cancer-related fatigue (CRF), identify distinct sleep hygiene profiles, and assess whether and how these profiles serve as Show more
To elucidate the accurate roles of dysfunctional sleep beliefs in modulating cancer-related fatigue (CRF), identify distinct sleep hygiene profiles, and assess whether and how these profiles serve as mediators in lung cancer patients undergoing chemotherapy. This study recruited 396 lung cancer patients receiving chemotherapy between May and December 2023. Participants completed the Sleep Hygiene Index, Brief Fatigue Inventory, and Dysfunctional Beliefs and Attitudes about Sleep Scale. Latent profile analysis (LPA) was conducted to identify profiles of sleep hygiene, and mediation analysis was performed to explore the impacts of sleep hygiene profiles and dysfunctional sleep beliefs on CRF. LPA revealed three distinct sleep hygiene profiles: normal (33.3%), excellent (50.3%), and poor (16.4%). Family monthly disposable income, radiotherapy, and performance status were identified as influential factors distinguishing these profiles. Additionally, the dimensions of dysfunctional sleep beliefs and sleep hygiene profiles showed different correlations with CRF. With the normal sleep hygiene group as reference, mediation analysis revealed that poor sleep hygiene serves as a mediator between sleep worry of dysfunctional sleep beliefs and CRF (SE = 0.010, 95% CI [0.006, 0.047]). This study contributes to understanding the heterogeneity in sleep hygiene in lung cancer patients undergoing chemotherapy and elucidates the underlying mechanisms of the relationship between sleep worry of dysfunctional cognitions and CRF. Clinical healthcare providers developing targeted interventions in terms of sleep beliefs and sleep hygiene might be helpful to alleviate CRF in this population. Show less
no PDF DOI: 10.1007/s00520-025-10109-4
LPA

TAC-ECG: A task-adaptive classification method for electrocardiogram based on cross-modal contrastive learning and low-rank convolutional adapter.

Rongjia Wang, Xunde Dong, Xiuling Liu +5 more Β· 2025 Β· Computer methods and programs in biomedicine Β· Elsevier Β· added 2026-04-24
Cardiovascular diseases are one of the major health threats to humans. Researchers have proposed numerous deep learning-based methods for the automatic analysis of electrocardiogram (ECG), achieving e Show more
Cardiovascular diseases are one of the major health threats to humans. Researchers have proposed numerous deep learning-based methods for the automatic analysis of electrocardiogram (ECG), achieving encouraging results. However, many existing methods are limited to task-specific model training and require retraining or full fine-tuning when confronted with a new ECG classification task, thus lacking flexibility in clinical applications. In this study, we propose a Task-Adaptive Classification method for ECG (TAC-ECG) based on cross-modal contrastive learning and low-rank convolutional adapters. TAC-ECG comprises two main phases. In the first phase, inspired by the Contrastive Language-Image Pre-training, we design the Contrastive ECG-Text Pre-training (CETP) to pre-train a robust ECG encoder. In the second phase, the pre-trained ECG encoder is frozen and integrated with a lightweight plug-in, the Low-Rank Convolutional Adapter (LRC-Adapter), forming an extensible ECG classification model. The frozen encoder extracts more discriminative features from the ECG signal, while the LRC-Adapter enables task-specific adaptation. For diverse ECG classification tasks, TAC-ECG only requires training the LRC-Adapter. This mechanism enables TAC-ECG to efficiently perform different ECG classification tasks, significantly reducing resource consumption and deployment costs in multi-tasking scenarios compared to traditional fully fine-tuned methods. We conducted extensive experiments using six different network architectures as ECG encoders. Specifically, we performed ECG classification experiments on four datasets: CPSC2018, Cinc2017, PTB-XL, and Chapman, targeting 9-category, 3-category, 5-category, and 4-category classifications respectively. The TAC-ECG achieved highly competitive results using only approximately 3% of the trainable parameters and approximately 25% of the total parameters compared to the fully fine-tuned method. These results demonstrates the effectiveness and practicality of the TAC-ECG method. The TAC-ECG offers a flexible and efficient method for ECG classification, enabling rapid adaptation to diverse tasks and enhancing clinical diagnostic practicality. Show less
no PDF DOI: 10.1016/j.cmpb.2025.108918
CETP

Genetic associations of metabolic factors and therapeutic drug targets with polycystic ovary syndrome.

Changlong Zhang, Yuxuan Li, Yang Wang +6 more Β· 2025 Β· Journal of advanced research Β· Elsevier Β· added 2026-04-24
Polycystic ovary syndrome (PCOS) is frequently accompanied with metabolic dysfunctions, yet the causal relationships between metabolic factors and PCOS remain to be conclusively established and etiolo Show more
Polycystic ovary syndrome (PCOS) is frequently accompanied with metabolic dysfunctions, yet the causal relationships between metabolic factors and PCOS remain to be conclusively established and etiology-based therapies are lacking. To comprehensively identify the metabolic causal factors and potential drug targets for PCOS. This genetic association study was conducted using bidirectional two-sample Mendelian Randomization (MR), multivariable MR (MVMR) and drug-target MR. Considering metabolic sexual dimorphism, female-specific genome-wide association studies (GWASs) for metabolic factors were obtained. To ensure the robustness of the findings, an additional independent PCOS GWAS dataset was utilized for replication. The PCOS cohort included 10,074 PCOS cases (mean age 28 to 45Β years) and 103,164 controls (mean age 27 to 60Β years) of European ancestry. All participants were female. Employing two-sample MR analysis, we found that genetically proxied body mass index (BMI) (OR = 3.40 [95Β % CI, 2.65-4.36]), triglyceride (TG) (OR = 1.54 [95Β % CI, 1.17-2.04]), low-density lipoprotein cholesterol (LDL-c) (OR = 1.37 [95Β % CI, 1.07-1.76]), and type 2 diabetes (T2D) (OR = 1.24 [95Β % CI, 1.09-1.41]) were significantly associated with an increased risk of PCOS, whereas genetically predicted high-density lipoprotein cholesterol (HDL-c) (OR = 0.61 [95Β % CI, 0.47-0.80]) decreased the odds of PCOS. Stepwise MVMR established a hierarchy of interactions among these metabolic factors, identifying BMI and HDL-c as the most prominent causal factors. Notably, drug-target MR analysis identified incretin-based therapeutics, PCSK9 inhibitors, LPL gene therapy, sulfonylureas, and thiazolidinediones as potential therapeutics for PCOS. All these findings were validated in an independent dataset. This study offered insights into the roles of obesity, diabetes, and dyslipidemia in PCOS etiology and therapeutics, underscoring the necessity for managing metabolic health in women and paving the way for tailored therapeutic strategies for PCOS based on its metabolic underpinnings. Show less
πŸ“„ PDF DOI: 10.1016/j.jare.2024.10.038
LPL

Association of

Na Liu, Hongli Zeng, Xiangsheng Cai +6 more Β· 2025 Β· Frontiers in genetics Β· Frontiers Β· added 2026-04-24
To investigate the association between polymorphisms of the A case-control study was conducted, enrolling 100 HTG patients and 100 age-matched controls with normal triglyceride levels from the physica Show more
To investigate the association between polymorphisms of the A case-control study was conducted, enrolling 100 HTG patients and 100 age-matched controls with normal triglyceride levels from the physical examination cohort at Guangzhou 11th People's Hospital (January-December 2023) The observation group showed significant differences in genotype frequencies of Show less
πŸ“„ PDF DOI: 10.3389/fgene.2025.1654501
APOA5

Study on the Efficacy and Pharmacological Mechanism of Innate Immune STING Pathway Regulators in the Treatment of Ischemic Brain Injury.

Chang Liu, Xiaoqing Wang, Yueru Zhang +2 more Β· 2025 Β· Pharmaceuticals (Basel, Switzerland) Β· MDPI Β· added 2026-04-24
πŸ“„ PDF DOI: 10.3390/ph18121775
BDNF

Unveiling Biomarkers and Therapeutic Targets in Systemic Sclerosis and Lupus Erythematosus Through Transcriptomic Profiling.

Ang-Jun Liu, Jian-Ruei Ciou, Po-Chang Wu +3 more Β· 2025 Β· International journal of rheumatic diseases Β· Blackwell Publishing Β· added 2026-04-24
This study aims to investigate the molecular differences and commonalities between systemic sclerosis (SSc) and systemic lupus erythematosus (SLE) by analyzing RNA-sequencing (RNA-seq) data. By focusi Show more
This study aims to investigate the molecular differences and commonalities between systemic sclerosis (SSc) and systemic lupus erythematosus (SLE) by analyzing RNA-sequencing (RNA-seq) data. By focusing on differentially expressed genes and enriched pathways, the investigation seeks to identify unique biomarkers, shared pathways, and potential therapeutic targets for these autoimmune diseases. This study involved 10 patients with SSc and 24 with SLE who did not receive immunosuppressants. RNA-seq data from patients with SSc and SLE were analyzed using DESeq2 to identify differentially expressed genes. Functional and pathway enrichment analyses were conducted and comparative analyses were performed. We identified 2055 differentially expressed genes (DEGs) between patients with SSc and controls. Notably, the expression of the shared gene RGS5 was significantly downregulated in both SLE and SSc, with a more pronounced downregulation in SSc. Additionally, the expression of the key transcription factor EGR1 was upregulated in SSc, whereas that of BLK, ITGAM, and IFNG was upregulated in SLE. Network analysis identified hub genes-AP3D1, FTX, USP47, CUX1, ZC3H4, CAND1, INTS1, TRNT1, MTERF1, and SETD1B-that may play critical roles in the progression of both SLE and SSc. These findings suggest that RGS5 could serve as a shared biomarker for vascular dysfunction, while EGR1 and BLK may represent therapeutic targets in SSc and SLE. Overall, this analysis enhances understanding of distinct and overlapping gene expression signatures in SSc and SLE, providing a foundation for future targeted treatment strategies and requiring further validation in larger cohorts. Show less
no PDF DOI: 10.1111/1756-185X.70308
ZC3H4

Discovery of First Branched-Chain Ketoacid Dehydrogenase Kinase (BDK) Inhibitor Clinical Candidate PF-07328948.

Kevin J Filipski, Luis A Martinez-Alsina, Matthew R Reese +31 more Β· 2025 Β· Journal of medicinal chemistry Β· ACS Publications Β· added 2026-04-24
Inhibition of branched-chain ketoacid dehydrogenase kinase (BDK or BCKDK), a negative regulator of branched-chain amino acid (BCAA) metabolism, is hypothesized to treat cardio-metabolic diseases. From Show more
Inhibition of branched-chain ketoacid dehydrogenase kinase (BDK or BCKDK), a negative regulator of branched-chain amino acid (BCAA) metabolism, is hypothesized to treat cardio-metabolic diseases. From a starting point with potential idiosyncratic toxicity risk, modification to a benzothiophene core and discovery of a cryptic pocket allowed for improved potency with 3-aryl substitution to arrive at PF-07328948, which was largely devoid of protein covalent binding liability. This BDK inhibitor was shown also to be a BDK degrader in cells and in vivo rodent studies. Plasma biomarkers, including BCAAs and branched-chain ketoacids (BCKAs), were lowered in vivo with enhanced pharmacodynamic effect upon chronic dosing due to BDK degradation. This molecule improves metabolic and heart failure end points in rodent models. PF-07328948 is the first known selective BDK inhibitor candidate to be examined in clinical studies, with Phase 1 single ascending dose data showing good tolerability and a pharmacokinetic profile commensurate with once-daily dosing. Show less
no PDF DOI: 10.1021/acs.jmedchem.4c02230
BCKDK

BACE1 inhibition via miR-6838-5p overexpression inhibits insulin resistance and the immune response in HFD-induced obesity in mice model.

Yubo Han, Zhenhua Quan, Miao Tian +4 more Β· 2025 Β· Immunopharmacology and immunotoxicology Β· Taylor & Francis Β· added 2026-04-24
Obesity is a chronic inflammatory disorder, which promotes the progression of metabolic disorders. MicroRNA (miR)-6838-5p is dysregulated and participates in the progression of several disorder models Show more
Obesity is a chronic inflammatory disorder, which promotes the progression of metabolic disorders. MicroRNA (miR)-6838-5p is dysregulated and participates in the progression of several disorder models. To explore the role and mechanism of miR-6838-5p in insulin resistance. Mice were fed with high-fat diet (HFD) to construct an obesity animal model. The role of miR-6838-5p was evaluated by insulin tolerance test (ITT), glucose tolerance test (GTT), homeostasis model assessment of insulin resistance (HOMA-IR) analysis, enzyme-linked immunosorbent assay (ELISA) and western blot assays. The potential target of miR-6838-5p was screened through the starBase online website and confirmed by the luciferase assay. HFD supply induced a prominent increase in the body weight, white adipose tissue (WAT) weight, the area under the curve (AUC) of GTT and ITT, HOMA-IR, the serum level of insulin and the serum concentrations and relative protein levels of interleukin (IL)-1Ξ², IL-6 and monocyte chemoattractant protein-1 (MCP-1) accompanied with reduced levels of IL-10 in mice. The level of miR-6838-5p was reduced in HFD-fed mice. Upregulation of miR-6838-5p partly reversed the above-mentioned indicators. Moreover, miR-6838-5p directly targeted to Ξ²-site amyloid precursor protein cleaving enzyme1 (BACE1) and negatively regulated the BACE1 expression. Downregulation of BACE1 improved insulin sensitivity and inflammatory mediators release involving in AKT/GSK3Ξ² signaling pathway in HFD-fed mice. Besides, overexpression of BACE1 counteracted the depressant role of miR-6838-5p overexpression in insulin resistance and inflammatory factors release in HFD-fed mice. MiR-6838-5p/BACE1 axis regulated insulin resistance and inflammatory factors release in HFD-fed mice. Show less
no PDF DOI: 10.1080/08923973.2024.2430668
BACE1

Insights Into Causal Effects of Genetically Proxied Lipids and Lipid-Modifying Drug Targets on Cardiometabolic Diseases.

Liwan Fu, Qin Liu, Hong Cheng +3 more Β· 2025 Β· Journal of the American Heart Association Β· added 2026-04-24
The differential impact of serum lipids and their targets for lipid modification on cardiometabolic disease risk is debated. This study used Mendelian randomization to investigate the causal relations Show more
The differential impact of serum lipids and their targets for lipid modification on cardiometabolic disease risk is debated. This study used Mendelian randomization to investigate the causal relationships and underlying mechanisms. Genetic variants related to lipid profiles and targets for lipid modification were sourced from the Global Lipids Genetics Consortium. Summary data for 10 cardiometabolic diseases were compiled from both discovery and replication data sets. Expression quantitative trait loci data from relevant tissues were employed to evaluate significant lipid-modifying drug targets. Comprehensive analyses including colocalization, mediation, and bioinformatics were conducted to validate the results and investigate potential mediators and mechanisms. Significant causal associations were identified between lipids, lipid-modifying drug targets, and various cardiometabolic diseases. Notably, genetic enhancement of LPL (lipoprotein lipase) was linked to reduced risks of myocardial infarction (odds ratio [OR] The study substantiates the causal role of lipids in specific cardiometabolic diseases, highlighting LPL as a potent drug target. The effects of LPL are suggested to be influenced by changes in glucose and blood pressure, providing insights into its mechanism of action. Show less
πŸ“„ PDF DOI: 10.1161/JAHA.124.038857
LPL

PTPRN promotes glioma cell proliferation, migration, and invasion by regulating the EMT pathway.

Shichao Wang, Xing Zhao, Mingyang Li +2 more Β· 2025 Β· International journal of clinical and experimental pathology Β· added 2026-04-24
Glioma is a highly aggressive malignancy with no effective treatment. This study investigates the role of protein tyrosine phosphatase receptor type N (PTPRN) in glioma progression. The U87 human glio Show more
Glioma is a highly aggressive malignancy with no effective treatment. This study investigates the role of protein tyrosine phosphatase receptor type N (PTPRN) in glioma progression. The U87 human glioma cell line was used to monitor proliferation, invasion, and migration during PTPRN knockdown. The viability, migration, and invasion were analyzed using the Cell Counting Kit-8 assay, transwell migration, and invasion assays. Additionally, the expression of proteins associated with the cell cycle was examined using western blotting. The knockdown of PTPRN resulted in a reduction in glioma cell proliferation, migration, and invasion, as well as the expression of cell cycle markers like Show less
no PDF DOI: 10.62347/GABE3607
SNAI1

Dietary Tannic Acid Promotes Growth Performance and Resistance Against

Liqin Ji, Yisen Shangguan, Chen Chen +6 more Β· 2025 Β· Antioxidants (Basel, Switzerland) Β· MDPI Β· added 2026-04-24
To investigate the effect of tannic acid (TA) on the growth, disease resistance, and intestinal health of Chinese soft-shelled turtles, individual turtles were fed with 0 g/kg (CG), 0.5 g/kg, 1 g/kg, Show more
To investigate the effect of tannic acid (TA) on the growth, disease resistance, and intestinal health of Chinese soft-shelled turtles, individual turtles were fed with 0 g/kg (CG), 0.5 g/kg, 1 g/kg, 2 g/kg, and 4 g/kg TA diets for 98 days. Afterwards, the turtles' disease resistance was tested using Show less
πŸ“„ PDF DOI: 10.3390/antiox14010112
APOA5

Single-cell transcriptomic profiling reveals liver fibrosis in colorectal cancer liver metastasis.

Yiqiao Deng, Chengyao Guo, Xiaomeng Liu +14 more Β· 2025 Β· Experimental & molecular medicine Β· Nature Β· added 2026-04-24
Tumor fibrosis is recognized as a malignant hallmark in various solid tumors; however, the clinical importance and associated molecular characteristics of tumor fibrosis in liver metastases (LM) from Show more
Tumor fibrosis is recognized as a malignant hallmark in various solid tumors; however, the clinical importance and associated molecular characteristics of tumor fibrosis in liver metastases (LM) from colorectal cancer (CRLM) remain poorly understood. Here we show that patients with CRLM whose liver metastases (LM) exhibited tumor fibrosis (Fibrosis+ LM) had significantly worse progression-free survival (P = 0.025) and overall survival (P = 0.008). Single-cell RNA sequencing revealed that the tumor microenvironment of the Fibrosis+ LM was characterized by T cells with an exhausted phenotype, macrophages displaying a profibrotic and suppressive phenotype and fibrosis-promoting fibroblasts. Further investigation highlighted the pivotal role of VCAN_eCAF in remodeling the tumor fibrosis in the tumor microenvironment of Fibrosis+ LM, emphasizing potential targetable interactions such as FGF23 or FGF3-FGFR1. Validation through multiplex immunohistochemistry/immunofluorescence and spatial transcriptomics supported these findings. Here we present a comprehensive single-cell atlas of tumor fibrosis in LM, revealing the intricate multicellular environment and molecular features associated with it. These insights deepen our understanding of tumor fibrosis mechanisms and inform improved clinical diagnosis and treatment strategies. Show less
πŸ“„ PDF DOI: 10.1038/s12276-025-01573-3
FGFR1

MicroRNA-6084 orchestrates angiogenesis and liver metastasis in colorectal cancer via extracellular vesicles.

Yang Zhang, Xuyang Yang, Su Zhang +10 more Β· 2025 Β· JCI insight Β· added 2026-04-24
The prognosis for colorectal cancer (CRC) patients with liver metastasis remains poor, and the molecular mechanisms driving CRC liver metastasis are not fully understood. Tumor-derived hypoxia-induced Show more
The prognosis for colorectal cancer (CRC) patients with liver metastasis remains poor, and the molecular mechanisms driving CRC liver metastasis are not fully understood. Tumor-derived hypoxia-induced extracellular vesicles have emerged as key players in inducing angiogenesis by transferring noncoding RNAs. However, the specific role of CRC-derived hypoxic extracellular vesicles (H-EVs) in regulating premetastatic microenvironment (PMN) formation by inducing angiogenesis remains unclear. Our study demonstrates that H-EVs induce angiogenesis and liver metastasis. Through microRNA microarray analysis, we identified a reduction in miR-6084 levels within H-EVs. We found that miR-6084 inhibited angiogenesis by being transferred to endothelial cells via EVs. In endothelial cells, miR-6084 directly targeted angiopoietin like 4 (ANGPTL4) mRNA, thereby suppressing angiogenesis through the ANGPTL4-mediated JAK2/STAT3 pathway. Furthermore, we uncovered that specificity protein 1 (SP1) acted as a transcription factor regulating miR-6084 transcription, while hypoxia-inducible factor 1A (HIF1A) decreased miR-6084 expression by promoting SP1 protein dephosphorylation and facilitating ubiquitin-proteasome degradation in SW620 cells. In clinical samples, we observed low expression of miR-6084 in plasma-derived EVs from CRC patients with liver metastasis. In summary, our findings suggest that CRC-derived H-EVs promote angiogenesis and liver metastasis through the HIF1A/SP1/miR-6084/ANGPTL4 axis. Additionally, miR-6084 holds promise as a diagnostic and prognostic biomarker for CRC liver metastasis. Show less
πŸ“„ PDF DOI: 10.1172/jci.insight.189503
ANGPTL4

Targeting ApoA5-Associated Hypertriglyceridemia to Ameliorate Acute Pancreatitis: Insights From a Knockout Hamster Model.

Sijing Shi, Kaikai Lu, Yijun Tao +6 more Β· 2025 Β· MedComm Β· Wiley Β· added 2026-04-24
πŸ“„ PDF DOI: 10.1002/mco2.70555
APOA5

The therapeutic potential of recombinant ANGPTL4 in Parkinson's disease: Evidence from in vivo and in vitro studies.

Hualing Li, Junjie Wei, Zhiyi Zheng +6 more Β· 2025 Β· Free radical biology & medicine Β· Elsevier Β· added 2026-04-24
The established body of knowledge attests to the pivotal influence of ANGPTL4 on lipid metabolism and vascular biology. Nevertheless, its potential implication in neurodegenerative disease remains to Show more
The established body of knowledge attests to the pivotal influence of ANGPTL4 on lipid metabolism and vascular biology. Nevertheless, its potential implication in neurodegenerative disease remains to be fully characterized. The present investigation delves into the involvement of ANGPTL4 in the pathological progression of PD, both in vitro and in vivo. PD models were induced by intraperitoneal administration of MPTP and LPS in WT and ANGPTL4 The observations unveiled that ANGPTL4 deficiency exacerbated behavioral aberrations, intensified dopaminergic neuron loss, and stimulated microglial activation along with p21-dependent senescence. There was an elevation in the expression of proinflammatory cytokines in the PD model. Furthermore, the administration of rANGPTL4 protein reversed the observed phenotypes in ANGPTL4 Our findings posit a salutary role for ANGPTL4 in counteracting PD, rendering it a prospective therapeutic target for the development of innovative drugs aimed at treating neuroinflammation-associated neurological diseases, including PD. Show less
no PDF DOI: 10.1016/j.freeradbiomed.2024.12.009
ANGPTL4

Integrative analysis of serum lipids and chronic gastritis: causal insights from mendelian randomization and experimental models.

Xinqiao Chu, Yaning Biao, Hongzheng Li +9 more Β· 2025 Β· Lipids in health and disease Β· BioMed Central Β· added 2026-04-24
Lipid metabolism may be linked to chronic gastritis, but its causal role remains unclear. While current research emphasizes inflammation, mucosal changes, immune regulation, genetics, and the gut micr Show more
Lipid metabolism may be linked to chronic gastritis, but its causal role remains unclear. While current research emphasizes inflammation, mucosal changes, immune regulation, genetics, and the gut microbiota, the contribution of lipid metabolism is understudied. This study aims to evaluate the impact of serum lipids and the mechanistic roles of lipid-lowering drug targets in chronic gastritis. We conducted a cross-sectional study using data from real world. Multivariable logistic regression was performed to assess the association between serum lipid profiles and gastritis. Mendelian randomization (MR) analyses based on genome-wide association study (GWAS) datasets were performed to detect the causal relationship of serum lipids, plasma lipid species, and lipid-lowering drug targets. Experimental validation was conducted using high-fat diet (HFD)-fed mice and chemically induced CAG rat models. Four thousand sixty one person, including 1,023 patients with chronic atrophic gastritis (CAG), 1,742 with non-atrophic gastritis (NAG), and 1,296 as healthy population were included in the analysis. Through covariates adjustment, TC, ApoA1, and HDL-C showed to be associated with an increased risk of chronic gastritis, whereas TG exhibited a protective effect. MR analysis confirmed a significant inverse causal relationship between TG and gastritis (OR = 0.889, 95% CI: 0.825-0.958). Ten plasma lipid species and lipid-lowering gene targets, including LPL and APOC3, were identified as causally associated with disease risk. Mediation analysis revealed six plasma lipid species as potential intermediaries linking genetic variation to gastritis. In vivo experiments demonstrated progressive hepatic steatosis and mild gastric mucosal changes in HFD-fed mice. Immunohistochemical analysis further revealed a significant reduction in LPL and APOC3 expression in gastric tissue (P < 0.05). In the CAG rat model, histological analysis revealed hepatocyte disarray, edema, and gastric mucosal atrophy. Elevated levels of TNF-Ξ±, IL-6, IL-1Ξ² and decreased levels of GAS-17 and PG I/II were also observed (P < 0.05). Western blot analyses further confirmed the downregulation of LPL and APOC3 expression in gastric tissue (P < 0.05). This study provides genetic and experimental evidence, supporting a causal role of lipid metabolism in chronic gastritis. LPL and APOC3 are implicated in its pathogenesis, highlighting potential lipid-targeted strategies for prevention and treatment. Show less
πŸ“„ PDF DOI: 10.1186/s12944-025-02782-5
APOC3

Shared Plasma Metabolites Mediate Causal Effects of Metabolic Diseases on Colorectal Cancer: A Two-Step Mendelian Randomization Study.

Xinyi Shi, Yuxin Tang, Yu Zhang +4 more Β· 2025 Β· Biomedicines Β· MDPI Β· added 2026-04-24
πŸ“„ PDF DOI: 10.3390/biomedicines13102433
FADS1

ApoB/ApoA-Ξ™ is associated with major cardiovascular events and readmission risk of patients after percutaneous coronary intervention in one year.

Jie Zhang, Mengyu Liu, Ju Gao +4 more Β· 2025 Β· Scientific reports Β· Nature Β· added 2026-04-24
Percutaneous coronary intervention (PCI) is a practical and effective method for treating coronary heart disease (CHD). This study aims to explore the influencing factors of major cardiovascular event Show more
Percutaneous coronary intervention (PCI) is a practical and effective method for treating coronary heart disease (CHD). This study aims to explore the influencing factors of major cardiovascular events (MACEs) and hospital readmission risk within one year following PCI treatment. Additionally, it seeks to assess the clinical value of Apolipoprotein B/Apolipoprotein A-I (ApoB/ApoA-I) in predicting the risk of one-year MACEs and readmission post-PCI. A retrospective study included 1938 patients who underwent PCI treatment from January 2010 to December 2018 at Shandong Provincial Hospital affiliated with Shandong First Medical University. Patient demographics, medications, and biochemical indicators were recorded upon admission, with one-year follow-up post-operation. Univariate and multivariate Cox proportional hazards regression models were utilized to establish the relationship between ApoB/ApoA-I levels and MACEs/readmission. Predictive nomograms were constructed to forecast MACEs and readmission, with the accuracy of the nomograms assessed using the concordance index. Subgroup analyses were conducted to explore the occurrence of MACEs and readmission. We observed a correlation between ApoB/ApoA-I and other lipid indices, including total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) (P < 0.001). Univariate and multivariate Cox regression analyses demonstrated that ApoB/ApoA-I is an independent risk factor for MACEs in post-PCI patients (P = 0.038). Within one year, the incidence of MACEs significantly increased in the high-level ApoB/ApoA-I group (ApoB/ApoA-I ratio β‰₯ 0.824) (P = 0.038), while the increase in readmission incidence within one year was not statistically significant. Furthermore, a nomogram predicting one-year MACEs was established (Concordance Index: 0.668). Subgroup analysis revealed that ApoB/ApoA-I was associated with the occurrence of both MACEs and readmission in male patients, those using CCB/ARB/ACEI, those without multivessel diseases, or those with LDL-C < 2.6Β mmol/L. The ApoB/ApoA-I ratio serves as an independent risk factor for one-year MACEs in post-PCI patients and correlates closely with other blood lipid indicators. ApoB/ApoA-I demonstrates significant predictive value for the occurrence of MACEs within one year.Trial registration Chinese clinical trial registry: No.ChiCTR22000597-23. Show less
πŸ“„ PDF DOI: 10.1038/s41598-024-84092-x
APOB

The macrophage sterol transport protein ORP2 promotes cholesterol efflux and prevents foam cell formation and atherosclerosis.

Xiaowei Wang, Kenan Peng, Yudi Zhao +11 more Β· 2025 Β· The Journal of biological chemistry Β· Elsevier Β· added 2026-04-24
Cholesterol-loaded macrophage foam cells are a key feature of atherosclerotic plaques. Oxysterol-binding protein-related protein 2 (ORP2) facilitates the transport of cholesterol from lysosomes to the Show more
Cholesterol-loaded macrophage foam cells are a key feature of atherosclerotic plaques. Oxysterol-binding protein-related protein 2 (ORP2) facilitates the transport of cholesterol from lysosomes to the plasma membrane in cultured cell lines. However, the role of ORP2 in macrophages and its involvement in atherosclerosis remain unclear. In this study, we found ORP2 expression was reduced in atherosclerotic vessels and in macrophages exposed to oxidized LDL (ox-LDL). Myeloid-specific human ORP2 overexpression (hORP2 Show less
no PDF DOI: 10.1016/j.jbc.2025.110228
NR1H3

Regulation of partial endothelial-to-mesenchymal transition by circATXN1 in ischemic diseases.

Yue Li, Zhe Zheng, Yanze Li +14 more Β· 2025 Β· Nature communications Β· Nature Β· added 2026-04-24
Ischemic injury induces a partial mesenchymal shift in endothelial cells (ECs), contributing to impaired vascular regeneration. However, the molecular regulators of this transitional state remain poor Show more
Ischemic injury induces a partial mesenchymal shift in endothelial cells (ECs), contributing to impaired vascular regeneration. However, the molecular regulators of this transitional state remain poorly defined. To address this, we performed circular RNA profiling of endothelial cells under ischemic-like conditions and identified a marked upregulation of a circular RNA, named circATXN1. Functional studies revealed that circATXN1 knockdown modulates endothelial phenotype and vascular response after ischemia. Functional studies have shown that knockdown of circATXN1 can regulate the endothelial cell phenotype and vascular response after ischemia. Mechanistically, circATXN1 knockdown enhances the demethylase protein ALKBH5 to reduce the RNA methylation level of the key transcription factor SLUG, thereby stabilizing SLUG. In animal models, suppression of circATXN1 enhances angiogenesis and improves recovery following ischemic injury. Here, we show that circATXN1 regulates partial endothelial-to-mesenchymal transition (EndMT) and angiogenesis by controlling SLUG mRNA methylation dynamics, highlighting its potential as a therapeutic target in ischemic disease. Show less
no PDF DOI: 10.1038/s41467-025-61596-2
SNAI1

ENO1-related gene signature predicts prognosis and therapeutic response in diffuse large B-cell lymphoma.

Wenli Yan, Xiaoxi Liu, Beibei Gao +6 more Β· 2025 Β· Frontiers in immunology Β· Frontiers Β· added 2026-04-24
Alpha-enolase (ENO1), the enzyme catalyzing 2-phosphoglycerate conversion to phosphoenolpyruvate, is highly expressed in diffuse large B-cell lymphoma (DLBCL) and correlates with adverse clinical outc Show more
Alpha-enolase (ENO1), the enzyme catalyzing 2-phosphoglycerate conversion to phosphoenolpyruvate, is highly expressed in diffuse large B-cell lymphoma (DLBCL) and correlates with adverse clinical outcomes. Thus, understanding the relationship between ENO1-related gene (ERG) network and DLBCL is imperative. Here, we integrated multi-omics profiling (RIP-seq, RNA-seq, and protein interactome analysis) to identify ERGs and established a prognostic model by machine learning algorithms. We identified eleven hub genes (CHERP, SYNE2, INTS1, FAP, MMP9, LRP5, RBM8A, PRMT5, SLC25A6, PABPC4, PSTPIP2) using RNA sequencing, RNA immunoprecipitation sequencing, and protein interaction profiling. A prognostic model was constructed using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression in the GSE10846 dataset and validated in two independent cohorts. DLBCL patients were stratified into high- and low-risk groups based on the model, and clinical characteristics were compared. The tumor immune microenvironment (TIME) was analyzed using CIBERSORT and xCell algorithms to explore correlations with the ERG score. Drug sensitivity assays in DLBCL cell lines were performed to validate the model's predictive capacity for chemotherapy response. Furthermore, the functional role of PABPC4, a key gene in the scoring system, was investigated through A prognostic model including 11 hub genes was established. Patients in the high-risk group exhibited worse clinical outcomes and an immunosuppressive TIME, characterized by altered expression of immune checkpoint-related proteins. This group demonstrated increased sensitivity to vincristine, etoposide, and oxaliplatin. Knockdown of PABPC4 significantly inhibited cell proliferation, reduced colony formation, and delayed tumor growth The ERG scoring system offers a robust and precise tool for predicting survival and guiding personalized treatment in DLBCL patients. Show less
no PDF DOI: 10.3389/fimmu.2025.1644020
PABPC4

Systemic angiogenic protein changes following propranolol therapy in infantile hemangioma: a multi-target perspective.

Lijun Liang, Yan Zhang, Yan Ma +3 more Β· 2025 Β· Frontiers in pharmacology Β· Frontiers Β· added 2026-04-24
Infantile hemangioma (IH) is a common benign vascular tumor in infants, often requiring intervention due to potential functional impairment and cosmetic concerns. Propranolol, a nonselective Ξ²-adrener Show more
Infantile hemangioma (IH) is a common benign vascular tumor in infants, often requiring intervention due to potential functional impairment and cosmetic concerns. Propranolol, a nonselective Ξ²-adrenergic receptor blocker, is the first-line therapy for IH, yet its mechanisms remain incompletely elucidated. This prospective study investigated the systemic angiogenic protein profile changes in response to propranolol in 14 treatment-naΓ―ve IH infants compared to 14 healthy controls using antibody array analysis. We identified twenty-six angiogenic proteins significantly downregulated in pretreatment IH patients compared to healthy controls. After 3 months of propranolol treatment, six proteins including HB-EGF, TGFΞ±, ANGPTL4, Follistatin, Tie-1 and PLGF were significantly upregulated. Bioinformatic enrichment analysis revealed that these proteins are involved in key biological processes and signaling pathways, including epithelial cell proliferation, angiogenesis regulation, VEGF signaling, ERBB-EGFR axis, Ras-MAPK, and PI3K-Akt pathways. These results suggest that propranolol treatment is associated with a rebalancing of dysregulated angiogenic proteins in IH, through modulating both pro- and anti-angiogenic factors to rebalance vascular homeostasis. Our study provides novel insights into the systems-level pharmacological actions of propranolol and proposes potential biomarkers for treatment response evaluation. Show less
πŸ“„ PDF DOI: 10.3389/fphar.2025.1706048
ANGPTL4

C6orf223 promotes colorectal cancer growth and metastasis by facilitating PRMT5-MEP50 multiprotein complex assembling.

Yufeng Qiao, Zhenzhen Wu, Peng Wang +18 more Β· 2025 Β· The Journal of clinical investigation Β· added 2026-04-24
Protein arginine methyltransferase 5 (PRMT5) complexes with methylosome protein 50 (MEP50) play crucial roles in tumor progress. However, the regulatory mechanism of governing the PRMT5-MEP50 hetero-o Show more
Protein arginine methyltransferase 5 (PRMT5) complexes with methylosome protein 50 (MEP50) play crucial roles in tumor progress. However, the regulatory mechanism of governing the PRMT5-MEP50 hetero-octameric complex remains unclear. Here, we demonstrate that C6orf223, to our knowledge an uncharacterized protein, facilitates PRMT5-MEP50 multiprotein complex assembling, thereby promoting colorectal cancer (CRC) growth and metastasis. C6orf223 forms dimers through disulfide bonds, with its N-terminal arginine-enriched region binding to the C-terminal negatively charged groove of PRMT5, thus stabilizing PRMT5-MEP50 multiprotein and enhancing PRMT5 methyltransferase activity. Consequently, PRMT5-mediated H4R3me2s substantially decreases the expression of the tumor suppressor GATA5, leading to the upregulation of multiple oncogenic target genes including WWTR1, FGFR1, and CLU. Targeting C6orf223 using siRNAs encapsulated in ferritin protein shells effectively suppresses CRC tumor growth and metastasis. Collectively, our findings characterize the role of C6orf223 in facilitating PRMT5-MEP50 hetero-octameric complex assembling and suggest that C6orf223 could serve as a potential therapeutic target for CRC. Show less
πŸ“„ PDF DOI: 10.1172/JCI186052
FGFR1