👤 Qibing Li

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Also published as: A Li, Ai-Jun Li, Ai-Qin Li, Ailing Li, Aimin Li, Aixin Li, Alexander H Li, Alexander Li, Amy Li, An-Qi Li, AnHai Li, Anan Li, Andrew C Li, Ang Li, Anna Fen-Yau Li, Annie Li, Anqi Li, Anyao Li, Ao Li, Aowen Li, Aoxi Li, Audrey Li, Bai-Qiang Li, Baichuan Li, Baiqiang Li, Baixing Li, Baizhou Li, Bang-Yan Li, Bao Li, Bao-Shan Li, Baoguang Li, Baoguo Li, Baohong Li, Baohua Li, Baolin Li, Baoqi Li, Baoqing Li, Baosheng Li, Baoting Li, Bei Li, Bei-Bei Li, Beibei Li, Beixu Li, Ben Li, Ben-Shang Li, Benyi Li, Biao Li, Bichun Li, Bin Li, Bin-Kui Li, Binbin Li, Bing Li, Bing-Heng Li, Bing-Hui Li, Bing-Mei Li, Bingbing Li, Binghu Li, Binghua Li, Bingjie Li, Bingjue Li, Bingkun Li, Binglan Li, Bingong Li, Bingshan Li, Bingsheng Li, Bingsong Li, Bingxin Li, Binjun Li, Binkui Li, Binru Li, Binxing Li, Biyu Li, Bizhi Li, Bo Li, BoWen Li, Bohao Li, Bohua Li, Bolun Li, Boru Li, Botao Li, Boxuan Li, Boya Li, Boyang Li, Bugao Li, C H Li, C Li, C X Li, C Y Li, Caesar Z Li, Cai Li, Cai-Hong Li, Caihong Li, Caili Li, Caixia Li, Caiyu Li, Caiyun Li, Can Li, Cang Li, Caolong Li, Chang Li, Chang-Da Li, Chang-Ping Li, Chang-Sheng Li, Chang-Yan Li, Chang-hai Li, Changcheng Li, Changgui Li, Changhong Li, Changhui Li, Changjiang Li, Changkai Li, Changqing Li, Changwei Li, Changxian Li, Changyan Li, Changyu Li, Changzheng Li, Chanjuan Li, Chanyuan Li, Chao Bo Li, Chao Li, Chaochen Li, Chaojie Li, Chaonan Li, Chaoqian Li, Chaowei Li, Chaoying Li, Chen Li, Chen-Chen Li, Chen-Lu Li, Chen-Xi Li, Chenfeng Li, Cheng Li, Cheng-Lin Li, Cheng-Tian Li, Cheng-Wei Li, Chengbin Li, Chengcheng Li, Chenghao Li, Chenghong Li, Chengjian Li, Chengjun Li, Chenglan Li, Chenglong Li, Chengnan Li, Chengping Li, Chengqian Li, Chengquan Li, Chengsi Li, Chenguang Li, Chengwen Li, Chengxin Li, Chengyun Li, Chenhao Li, Chenjie Li, Chenli Li, Chenlin Li, Chenlong Li, Chenlu Li, Chenmeng Li, Chenrui Li, Chensheng Li, Chenwen Li, Chenxi Li, Chenxiao Li, Chenxin Li, Chenxuan Li, Chenyang Li, Chenyao Li, Chenyu Li, Cheung Li, Chi-Ming Li, Chi-Yuan Li, Chia Li, Chia-Yang Li, Chien-Feng Li, Chien-Hsiu Li, Chien-Te Li, Chih-Chi Li, Chitao Li, Chiyang Li, Chong Li, Chongyang Li, Chongyi Li, Chris Li, Chu-Qiao Li, Chuan F Li, Chuan Li, Chuan-Hai Li, Chuan-Yun Li, Chuanbao Li, Chuanfang Li, Chuang Li, Chuangpeng Li, Chuanning Li, Chuanyin Li, Chumei Li, Chun Li, Chun-Bo Li, Chun-Lai Li, Chun-Mei Li, Chun-Quan Li, Chun-Xiao Li, Chun-Xu Li, Chung-Hao Li, Chung-I Li, Chunhong Li, Chunhui Li, Chunjie Li, Chunjun Li, Chunlan Li, Chunlian Li, Chunliang Li, Chunlin Li, Chunmei Li, Chunmiao Li, Chunqing Li, Chunqiong Li, Chunshan Li, Chunsheng Li, Chunting Li, Chunxia Li, Chunxiao Li, Chunxing Li, Chunxue Li, Chunya Li, Chunyan Li, Chunyi Li, Chunying Li, Chunyu Li, Chunzhu Li, Chuzhong Li, Cien Li, Cong Li, Congcong Li, Congfa Li, Conghui Li, Congjiao Li, Conglin Li, Congxin Li, Congye Li, Cui Li, Cui-lan Li, Cuicui Li, Cuiguang Li, Cuilan Li, Cuiling Li, Cun Li, Cunxi Li, Cyril Li, D C Li, Da Li, Da-Hong Li, Da-Jin Li, Da-Lei Li, Da-wei Li, DaZhuang Li, Dacheng Li, Dai Li, Daiyue Li, Dalei Li, Dali Li, Dalin Li, Dan C Li, Dan Li, Dan-Dan Li, Dan-Ni Li, Dandan Li, Daniel Tian Li, Danjie Li, Danni Li, Danxi Li, Danyang Li, Daoyuan Li, Dapei Li, Dawei Li, Dayong Li, Dazhi Li, De-Jun Li, De-Tao Li, Dechao Li, Defa Li, Defeng Li, Defu Li, Dehai Li, Deheng Li, Dehua Li, Dejun Li, Demin Li, Deming Li, Dengfeng Li, Dengke Li, Dengxiong Li, Deqiang Li, Desen Li, Desheng Li, Dexiong Li, Deyu Li, Dezhi Li, Di Li, Di-Jie Li, Dianjie Li, Dijie Li, Ding Li, Ding Yang Li, Ding-Biao Li, Ding-Jian Li, Dingchen Li, Dingshan Li, Diyan Li, Dong Li, Dong Sheng Li, Dong-Jie Li, Dong-Ling Li, Dong-Run Li, Dong-Yun Li, Dong-fei Li, Dongbiao Li, Dongdong Li, Dongfang Li, Dongfeng Li, Donghe Li, Donghua Li, Dongliang Li, Dongmei Li, Dongmin Li, Dongnan Li, Dongtao Li, Dongyang Li, Dongye Li, Duan Li, Duanbin Li, Duanxiang Li, Dujuan Li, Duo Li, Duoyun Li, Ellen Li, En Li, En-Min Li, Enhao Li, Enhong Li, Enxiao Li, F Li, Fa-Hong Li, Fa-Hui Li, Fadi Li, Fan Li, Fang Li, Fangqi Li, Fangyan Li, Fangyong Li, Fangyuan Li, Fangzhou Li, Fei Li, Fei-Lin Li, Fei-feng Li, Feifei Li, Feilong Li, Fen Li, Feng Li, Feng-Feng Li, Fengfeng Li, Fengjuan Li, Fengli Li, Fengqi Li, Fengqiao Li, Fengqing Li, Fengxia Li, Fengxiang Li, Fengyi Li, Fengyuan Li, Fu-Rong Li, Fugen Li, Fuhai Li, Fujun Li, Fulun Li, Fuping Li, Fusheng Li, Fuyu Li, Fuyuan Li, G Li, G-P Li, Gaijie Li, Gaizhen Li, Gaizhi Li, Gan Li, Gang Li, Ganggang Li, Gao-Fei Li, Gaoyuan Li, Ge Li, Gen Li, Gen-Lin Li, Gerard Li, Gong-Hua Li, Gongda Li, Guanbin Li, Guandu Li, Guang Li, Guang Y Li, Guang-Li Li, Guang-Xi Li, Guangda Li, Guangdi Li, Guanghua Li, Guanghui Li, Guangjin Li, Guangli Li, Guanglu Li, Guanglve Li, Guangming Li, Guangping Li, Guangpu Li, Guangqiang Li, Guangquan Li, Guangwen Li, Guangxi Li, Guangxiao Li, Guangyan Li, Guangzhao Li, Guangzhen Li, Guannan Li, Guanqiao Li, Guanyu Li, Gui Lin Li, Gui-Bo Li, Gui-Hua Li, Gui-Rong Li, Gui-xing Li, Guigang Li, Guihua Li, Guilan Li, Guisen Li, Guixia Li, Guixin Li, Guiyang Li, Guiying Li, Guiyuan Li, Guo Li, Guo-Chun Li, Guo-Jian Li, Guo-Li Li, Guo-Ping Li, Guo-Qiang Li, Guobin Li, Guoge Li, Guohong Li, Guohua Li, Guohui Li, Guojin Li, Guojun Li, Guoli Li, Guoping Li, Guoqin Li, Guoqing Li, Guowei Li, Guoxi Li, Guoxiang Li, Guoxing Li, Guoyan Li, Guoyin Li, H J Li, H Li, H-F Li, H-H Li, H-J Li, Hai Li, Hai-Yun Li, Haibin Li, Haibo Li, Haifeng Li, Haihong Li, Haihua Li, Haijun Li, Hailong Li, Haimin Li, Haiming Li, Hainan Li, Haipeng Li, Hairong Li, Haitao Li, Haitong Li, Haixia Li, Haiyan Li, Haiyang Li, Haiying Li, Haiyu Li, Han Li, Han-Bing Li, Han-Bo Li, Han-Ni Li, Han-Ru Li, Han-Wei Li, Hanbin Li, Hanbing Li, Hanbo Li, Handong Li, Hang Li, Hangwen Li, Hanjun Li, Hankun Li, Hanlu Li, Hanmei Li, Hanqi Li, Hanqin Li, Hansen Li, Hanting Li, Hanxiao Li, Hanxue Li, Hao Li, Hao-Fei Li, Haojing Li, Haolong Li, Haomiao Li, Haoqi Li, Haoran Li, Haotong Li, Haoxian Li, Haoyu Li, Haying Li, He Li, He-Zhen Li, Hecheng Li, Hegen Li, Hehua Li, Heng Li, Heng-Zhen Li, Hengguo Li, Hengtong Li, Hengyu Li, Hening Li, Hewei Li, Hexin Li, Heying Li, Hong Li, Hong-Chun Li, Hong-Lan Li, Hong-Lian Li, Hong-Mei Li, Hong-Tao Li, Hong-Wen Li, Hong-Yan Li, Hong-Yu Li, Hong-Zheng Li, Hongbo Li, Hongchang Li, Hongde Li, Honggang Li, Hongguo Li, Honghua Li, Honghui Li, Hongjia Li, Hongjiang Li, Hongjuan Li, Honglei Li, Hongli Li, Honglian Li, Hongliang Li, Honglin Li, Hongling Li, Honglong Li, Hongmei Li, Hongmin Li, Hongming Li, Hongqin Li, Hongquan Li, Hongru Li, Hongsen Li, Hongwei Li, Hongxia Li, Hongxin Li, Hongxing Li, Hongxue Li, Hongyan Li, Hongye Li, Hongyi Li, Hongyu Li, Hongyun Li, Hongzhe K Li, Hongzheng Li, Hongzhi Li, Hsiao-Fen Li, Hsiao-Hui Li, Hsin-Hua Li, Hsin-Yun Li, Hu Li, Hua Li, Hua-Zhong Li, Huabin Li, Huafang Li, Huafu Li, Huaixing Li, Huaiyuan Li, Hualian Li, Hualing Li, Huamao Li, Huan Li, Huanan Li, Huang Li, Huangbao Li, Huangyuan Li, Huanhuan Li, Huanjun Li, Huanqing Li, Huanqiu Li, Huaping Li, Huashun Li, Huawei Li, Huayao Li, Huayin Li, Huaying Li, Hui Li, Hui-Jun Li, Hui-Long Li, Hui-Ping Li, Huibo Li, Huifang Li, Huifeng Li, Huihuang Li, Huihui Li, Huijie Li, Huijuan Li, Huijun Li, Huilan Li, Huili Li, Huiliang Li, Huilin Li, Huilong Li, Huimin Li, Huiping Li, Huiqin Li, Huiqing Li, Huiqiong Li, Huiting Li, Huixia Li, Huixue Li, Huiying Li, Huiyou Li, Huiyuan Li, Huizi Li, Hujie Li, Hulun Li, Hung Li, Hung-Yuan Li, Ivan Li, J Li, J T Li, Jason Li, Jen-Ming Li, Jenny J Li, Ji Li, Ji Xia Li, Ji-Cheng Li, Ji-Feng Li, Ji-Liang Li, Ji-Lin Li, Ji-Min Li, Jia Li, Jia Li Li, Jia-Da Li, Jia-Huan Li, Jia-Peng Li, Jia-Ru Li, Jia-Xin Li, Jiabei Li, Jiachen Li, Jiacheng Li, Jiafang Li, Jiafei Li, Jiahao Li, Jiahui Li, Jiajia Li, Jiajie Li, Jiajing Li, Jiajun Li, Jiajv Li, Jiali Li, Jialin Li, Jialing Li, Jialun Li, Jiaming Li, Jian Li, Jian'an Li, Jian-Jun Li, Jian-Mei Li, Jian-Qiang Li, Jian-Shuang Li, Jianan Li, Jianang Li, Jianbin Li, Jianbo Li, Jianchun Li, Jiandong Li, Jianfang Li, Jianfeng Li, Jiang Li, Jiangan Li, Jiangbo Li, Jiangchao Li, Jiangfeng Li, Jianglin Li, Jianglong Li, Jiangtao Li, Jiangui Li, Jianguo Li, Jiangxia Li, Jiangya Li, Jianhai Li, Jianhua Li, Jiani Li, Jianing Li, Jianliang Li, Jianlin Li, Jianmin Li, Jiannan Li, Jianping Li, Jianrong Li, Jianrui Li, Jiansheng Li, Jianshuang Li, Jianwei Li, Jianxin Li, Jianxiong Li, Jianye Li, Jianyi Li, Jianyong Li, Jianyu Li, Jianzhong Li, Jiao Li, Jiao-Jiao Li, Jiaomei Li, Jiaping Li, Jiaqi Li, Jiawei Li, Jiaxi Li, Jiaxin Li, Jiaxuan Li, Jiayan Li, Jiayang Li, Jiayi Li, Jiaying Li, Jiayu Li, Jiayuan Li, Jiazhou Li, Jicheng Li, Jie Li, Jie-Pin Li, Jie-Shou Li, Jiehan Li, Jiejia Li, Jiejie Li, Jiejing Li, Jieming Li, Jiequn Li, Jieshou Li, Jiexi Li, Jiexin Li, Jiezhen Li, Jifang Li, Jihua Li, Jin Li, Jin-Jiang Li, Jin-Liang Li, Jin-Long Li, Jin-Mei Li, Jin-Ping Li, Jin-Qiu Li, Jin-Wei Li, Jin-Xiu Li, Jinchen Li, Jinfang Li, Jinfeng Li, Jing Li, Jing-Jing Li, Jing-Ming Li, Jing-Yao Li, Jing-Yi Li, Jing-gao Li, Jingcheng Li, Jingchun Li, Jingfeng Li, Jinghao Li, Jinghui Li, Jingjing Li, Jingke Li, Jinglin Li, Jingmei Li, Jingming Li, Jingping Li, Jingqi Li, Jingshang Li, Jingshu Li, Jingtong Li, Jingui Li, Jingwen Li, Jingxia Li, Jingxiang Li, Jingxin Li, Jingya Li, Jingyi Li, Jingyong Li, Jingyu Li, Jingyun Li, Jinhua Li, Jinhui Li, Jinjie Li, Jinku Li, Jinlan Li, Jinliang Li, Jinlin Li, Jinman Li, Jinming Li, Jinping Li, Jinsong Li, Jinwei Li, Jinxia Li, Jinxin Li, Jinzhi Li, Jiong Li, Jiong-Ming Li, Jipeng Li, Jiqing Li, Jisen Li, Jisheng Li, Jiuke Li, Jiuyi Li, Jiwei Li, Jiwen Li, Jixi Li, Jixuan Li, Jiyang Li, Jiyuan Li, John Zhong Li, Jonathan Z Li, Joyce Li, Ju-Rong Li, Juan Li, Juan-Juan Li, Juanjuan Li, Juanling Li, Juanni Li, Jufang Li, Julia Li, Jun Li, Jun Z Li, Jun-Cheng Li, Jun-Jie Li, Jun-Ling Li, Jun-Ru Li, Jun-Yan Li, Jun-Ying Li, JunBo Li, Junfeng Li, Junhong Li, Junhui Li, Junjie Li, Junjun Li, Junming Li, Junping Li, Junqin Li, Junru Li, Junsheng Li, Juntong Li, Junxian Li, Junxin Li, Junxu Li, Junya Li, Junyi Li, Junying Li, Justin Li, Jutang Li, Juxue Li, K-L Li, Ka Li, Ka Wan Li, Kai Li, Kai-Wen Li, Kaibin Li, Kaibo Li, Kaifeng Li, Kailong Li, Kaimi Li, Kainan Li, Kaiwei Li, Kaixin Li, Kaiyi Li, Kaiyuan Li, Kang Li, Kangli Li, Kangyuan Li, Karen Li, Kathy H Li, Kawah Li, Ke Li, KeZhong Li, Keanning Li, Kecheng Li, Kechun Li, Keguo Li, Kejuan Li, Keke Li, Kening Li, Kenli Li, Kenneth Kai Wang Li, Keqing Li, Keshen Li, Keying Li, Keyuan Li, Kezhen Li, Kongdong Li, Kuan Li, Kui Li, Kuiliang Li, Kun Li, Kun-Peng Li, Kun-Ping Li, Kun-Xin Li, Kunlin Li, Kunlong Li, Kunlun Li, Kunpeng Li, L I Li, L K Li, L Li, L P Li, L-Y Li, Lai K Li, Laiqing Li, Lamei Li, Lan Li, Lan-Juan Li, Lan-Lan Li, Lanfang Li, Lang Li, Lanjuan Li, Lanlan Li, Lanzhou Li, Le Li, Le-Le Li, Le-Ying Li, Lei Li, Leilei Li, Leipeng Li, Letai Li, Leyao Li, Li Li, Li-Min Li, Li-Na Li, Lian Li, Lianbing Li, Liang Li, Liangdong Li, Liangji Li, Liangkui Li, Liangqian Li, Lianhong Li, Lianjian Li, Lianyong Li, Liao-Yuan Li, Lieyou Li, Liguo Li, Lihong Li, Lihua Li, Lijia Li, Lijuan Li, Lijun Li, Lili Li, Liliang Li, Liling Li, Liming Li, Lin Li, Lin-Feng Li, Linchuan Li, Linfeng Li, Ling Li, Ling-Jie Li, Ling-Ling Li, Ling-Zhi Li, Lingjiang Li, Lingjie Li, Lingjun Li, Lingling Li, Lingxi Li, Lingyan Li, Lingyi Li, Lingzhi Li, Linhong Li, Linke Li, Linlin Li, Linqi Li, Linqing Li, Linsheng Li, Linting Li, Linxin Li, Linyan Li, Linying Li, Lipeng Li, Liping Li, Liqin Li, Liqun Li, Lirong Li, Lisha Li, Litao Li, Liuzheng Li, Liwei Li, Lixi Li, Lixia Li, Lixiang Li, Liyan Li, Long Li, Long Shan Li, Long-Yan Li, Longhui Li, Longxuan Li, Longyu Li, Lu Li, Lu-Yun Li, Lucia M Li, Lucy Li, Luhan Li, Lujiao Li, Lujie Li, Lulu Li, Luquan Li, Luxuan Li, Luyao Li, Luying Li, M D Li, M Li, M V Li, M-J Li, Man Li, Man-Xiang Li, Man-Zhi Li, Mangmang Li, Manjiang Li, Manna Li, Manru Li, Manxia Li, Mao Li, Maogui Li, Maolin Li, Maoquan Li, Maosheng Li, Marilyn Li, Mei Li, Mei-Lan Li, Mei-Ya Li, Mei-Zhen Li, Meifang Li, Meifen Li, Meijia Li, Meilan Li, Meiqing Li, Meitao Li, Meiting Li, Meiyan Li, Meiying Li, Meiyue Li, Meizi Li, Melody M H Li, Meng Li, Meng-Hua Li, Meng-Jun Li, Meng-Meng Li, Meng-Miao Li, Meng-Yang Li, Meng-Yao Li, Meng-Yue Li, MengGe Li, Mengfan Li, Menghua Li, Mengjiao Li, Mengjuan Li, Mengling Li, Menglu Li, Mengmeng Li, Mengqing Li, Mengqiu Li, Mengsen Li, Mengshi Li, Mengxi Li, Mengxia Li, Mengxuan Li, Mengyang Li, Mengyao Li, Mengying Li, Mengyuan Li, Mengyun Li, Mengze Li, Mi Li, Mian Li, Miao Li, Miao X Li, Miaoxin Li, Michelle Li, Mimi Li, Min Li, Min-Dian Li, Min-Rui Li, Min-jun Li, Minerva X Li, Ming D Li, Ming Li, Ming V Li, Ming Xing Li, Ming Zhou Li, Ming-Han Li, Ming-Hao Li, Ming-Jiang Li, Ming-Kai Li, Ming-Qing Li, Ming-Wei Li, Ming-Xing Li, Ming-Yang Li, Mingdan Li, Mingfang Li, Mingfei Li, Minghao Li, Minghua Li, Minghui Li, Mingjiang Li, Mingjie Li, Mingjun Li, Mingke Li, Mingkun Li, Mingli Li, Minglong Li, Minglun Li, Mingna Li, Mingqiang Li, Mingquan Li, Mingrui Li, Mingwei Li, Mingxi Li, Mingxia Li, Mingxing Li, Mingxu Li, Mingxuan Li, Mingyang Li, Mingyao Li, Mingyue Li, Mingzhe Li, Mingzhou Li, Minhui Li, Minle Li, Minmin Li, Minqi Li, Minyue Li, Minze Li, Minzhe Li, Miyang Li, Mo Li, Mohan Li, Monica M Li, Moyi Li, Mufan Li, Mulin Jun Li, Muzi Li, N Li, Na Li, Naishi Li, Nan Li, Nan-Nan Li, Nana Li, Nanjun Li, Nanlong Li, Nanxing Li, Nanzhen Li, Ni Li, Nianfu Li, Nianyu Li, Nien Li, Nien-Chen Li, Nien-Chi Li, Ning Li, Ningyan Li, Ningyang Li, Niu Li, Nuomin Li, O Li, P H Li, P Li, Pan Li, Panlong Li, Panyuan Li, Pei Li, Pei-Lin Li, Pei-Qin Li, Pei-Shan Li, Pei-Ying Li, Pei-Zhi Li, PeiQi Li, Peibo Li, Peifen Li, Peifeng Li, Peihong Li, Peihua Li, Peilin Li, Peilong Li, Peining Li, Peipei Li, Peiqin Li, Peiran Li, Peiwu Li, Peixin Li, Peiyu Li, Peiyuan Li, Peiyun Li, Peng Li, Peng Peng Li, Peng-li Li, Pengcui Li, Penghui Li, Pengjie Li, Pengju Li, Pengsong Li, Pengyang Li, Pengyu Li, Pengyun Li, Pik Yi Li, Pilong Li, Pindong Li, Ping Li, Ping'an Li, Pinghua Li, Pingping Li, Pu Li, Pu-Yu Li, Q Li, Qi Li, Qi-Fu Li, Qi-Jing Li, Qian Li, Qian-Qian Li, Qiang Li, Qiang-Ming Li, Qiankun Li, Qianqian Li, Qiao Li, Qiao-Xin Li, Qiaolian Li, Qiaoqiao Li, Qifang Li, Qihang Li, Qihua Li, Qiji Li, Qijun Li, Qilan Li, Qilong Li, Qin Li, Qiner Li, Qing Li, Qing Run Li, Qing-Chang Li, Qing-Fang Li, Qing-Min Li, Qing-Wei Li, Qingchao Li, Qingfang Li, Qingfeng Li, Qinggang Li, Qinghe Li, Qinghong Li, Qinghua Li, Qingjie Li, Qinglan Li, Qingli Li, Qinglin Li, Qingling Li, Qingqin S Li, Qingrun Li, Qingshang Li, Qingsheng Li, Qingxian Li, Qingyang Li, Qingyu Li, Qingyuan Li, Qingyun Li, Qinqin Li, Qinrui Li, Qintong Li, Qiong Li, Qionghua Li, Qipei Li, Qiqiong Li, Qiu Li, Qiufeng Li, Qiuhong Li, Qiusheng Li, Qiuxuan Li, Qiuya Li, Qiuyan Li, Qiwei Li, Qiyong Li, Qizhai Li, Quan Li, Quan-Zhong Li, Quanpeng Li, Quanshun Li, Quanzhang Li, Qun Li, R H L Li, R Li, Ran Li, Ranchang Li, Ranran Li, Ranwei Li, Ren Li, Ren-Ke Li, Rena Li, Roger Li, Ronald Li, Rong Li, Rong-Bing Li, Ronggui Li, Rongkai Li, Rongling Li, Rongqing Li, Rongsong Li, Rongxia Li, Rongyao Li, Rosa J W Li, Ru Li, Ru-Hao Li, Rui Li, Rui-Fang Li, Rui-Han Li, Rui-Jún Eveline Li, Ruibing Li, Ruidong Li, Ruifang Li, Ruihuan Li, Ruijia Li, Ruijin Li, Ruikai Li, Ruitong Li, Ruiwen Li, Ruixi Li, Ruixia Li, Ruixue Li, Ruiyang Li, Rujia Li, Rulin Li, Rumei Li, Runbing Li, Runwen Li, Runzhao Li, Runzhen Li, Runzhi Li, Ruobing Li, Ruolin Li, Ruonan Li, Ruotai Li, Ruotian Li, Ruotong Li, Ruyi Li, Ruyue Li, S A Li, S E Li, S L Li, S Li, S S Li, S-C Li, Sai Li, Saijuan Li, Sainan Li, San-Feng Li, Sanqiang Li, Senlin Li, Senmao Li, Sha Li, Sha-Sha Li, Shan Li, Shan-Shan Li, Shangjia Li, Shanglai Li, Shangming Li, Shanhang Li, Shanpeng Li, Shanshan Li, Shanyi Li, Shao-Dan Li, Shaobin Li, Shaodan Li, Shaofei Li, Shaoguang Li, Shaojian Li, Shaojing Li, Shaoliang Li, Shaomin Li, Shaoqi Li, Shaoyong Li, Shasha Li, Shawn S C Li, Shawn Shun-Cheng Li, Shen Li, Sheng Li, Sheng-Fu Li, Sheng-Jie Li, Sheng-Qing Li, Sheng-Tien Li, Shengbiao Li, Shengbin Li, Shengchao A Li, Shenghao Li, Shengjie Li, Shengli Li, Shengliang Li, Shengsheng Li, Shengwen Li, Shengxian Li, Shengxu Li, Shengze Li, Sherly X Li, Shi Li, Shi-Fang Li, Shi-Guang Li, Shi-Hong Li, Shi-Ying Li, Shibao Li, Shibo Li, Shichao Li, Shigang Li, Shihao Li, Shiheng Li, Shihong Li, Shijie Li, Shijun Li, Shikang Li, Shilan Li, Shili Li, Shiliang Li, Shilin Li, Shilun Li, Shiqi Li, Shiquan Li, Shisheng Li, Shishi Li, Shitao Li, Shiya Li, Shiyan Li, Shiyang Li, Shiyi Li, Shiying Li, Shiyu Li, Shiyue Li, Shiyun Li, Shu Li, Shu-Fang Li, Shu-Fen Li, Shu-Feng Li, Shu-Hong Li, Shu-Qi Li, Shu-Xin Li, Shuai Li, Shuaicheng Li, Shuang Li, Shuang-Ling Li, Shuangding Li, Shuangfei Li, Shuanglong Li, Shuangmei Li, Shuangshuang Li, Shuangxiu Li, Shubo Li, Shude Li, Shufen Li, Shugang Li, Shuguang Li, Shuhao Li, Shuhua Li, Shuhui Li, Shujiao Li, Shujie Li, Shujin Li, Shujing Li, Shulin Li, Shun Li, Shunhua Li, Shunle Li, Shunqin Li, Shunqing Li, Shunwang Li, Shuo Li, Shupeng Li, Shuqiang Li, Shuwei Li, Shuwen Li, Shuying Li, Shuyu D Li, Shuyu Dan Li, Shuyuan Li, Shuyue Li, Si Li, Si-Wei Li, Si-Xing Li, Si-Ying Li, Si-Yuan Li, Sibing Li, Sichen Li, Sichong Li, Side Li, Siguang Li, Sijie Li, Simin Li, Siming Li, Sin-Lun Li, Siqi Li, Sitao Li, Siting Li, Siwen Li, Siyi Li, Siyu Li, Siyue Li, Song Li, Song-Chao Li, Songhan Li, Songlin Li, Songtao Li, Songyu Li, Songyun Li, Stephen Li, Su Li, SuYun Li, Suchun Li, Suheng Li, Suhong Li, Suiyan Li, Sujing Li, Suk-Yee Li, Sumei Li, Sunan Li, Sung-Chou Li, Supeng Li, Suping Li, Suran Li, Suwei Li, Suwen Li, Suyan Li, T Li, Taibo Li, Taiwen Li, Taixu Li, Tao Li, Taoyingnan Li, Teng Li, Tengyan Li, Thomas Li, Tian Li, Tian-Yi Li, Tian-chang Li, Tian-wang Li, Tianchang Li, Tiandong Li, Tianfeng Li, Tiange Li, Tianjiao Li, Tianjun Li, Tianming Li, Tiansen Li, Tiantian Li, Tianxiang Li, Tianyao Li, Tianye Li, Tianyi Li, Tianyou Li, Tie Li, Tiegang Li, Tiehua Li, Tiewei Li, Timmy Li, Ting Li, Tingguang Li, Tinghao Li, Tinghua Li, Tingsong Li, Tingting Li, Tong Li, Tong-Ruei Li, Tongyao Li, Tongzheng Li, Tsai-Kun Li, Tuojian Li, Tuoping Li, Vivian Li, Vivian S W Li, W H Li, W J Li, W Li, W W Li, W Y Li, W-B Li, Wan Jie Li, Wan Li, Wan-Hong Li, Wan-Shan Li, Wan-Xin Li, Wang Li, Wanling Li, Wanni Li, Wanqian Li, Wanru Li, Wanshi Li, Wanshun Li, Wanting Li, Wanwan Li, Wanxin Li, Wanyan Li, Wanyi Li, Wei Li, Wei-Bo Li, Wei-Dong Li, Wei-Jun Li, Wei-Li Li, Wei-Ming Li, Wei-Na Li, Wei-Ping Li, Wei-Qin Li, Wei-Yang Li, Weidong Li, Weifeng Li, Weiguang Li, Weiguo Li, Weihai Li, Weiheng Li, Weihua Li, Weijian Li, Weijie Li, Weijun Li, Weike Li, Weiling Li, Weimin Li, Weina Li, Weining Li, Weiping Li, Weiqin Li, Weirong Li, Weisong Li, Weiyang Li, Weiye Li, Weiyong Li, Weizu Li, Wen Lan Li, Wen Li, Wen-Chao Li, Wen-Jie Li, Wen-Ting Li, Wen-Wen Li, Wen-Xi Li, Wen-Xing Li, Wen-Ya Li, Wen-Ying Li, Wen-juan Li, Wenbo Li, Wenchao Li, Wende Li, Wendeng Li, Wenfang Li, Wenfeng Li, Wenge Li, Wenguo Li, Wenhao Li, Wenhong Li, Wenhua Li, Wenhui Li, Wenjia Li, Wenjian Li, Wenjie Li, Wenjing Li, Wenjuan Li, Wenjun Li, Wenke Li, Wenlei Li, Wenli Li, Wenlong Li, Wenming Li, Wenqi Li, Wenqiang Li, Wenqing Li, Wenqun Li, Wenrui Li, Wensheng Li, Wentao Li, Wenwen Li, Wenxi Li, Wenxia Li, Wenxiang Li, Wenxin Li, Wenxiu Li, Wenxue Li, Wenyan Li, Wenyang Li, Wenyi Li, Wenying Li, Wenyong Li, Wenyu Li, Wenzhe Li, Wenzhuo Li, Wu-Jun Li, Wuguo Li, Wulan Li, Wuyan Li, X B Li, X L Li, X Li, X Y Li, X-H Li, X-L Li, Xi Li, Xi-Hai Li, Xi-Xi Li, Xia Li, Xian Li, Xiancheng Li, Xiang Li, Xiang-Dong Li, Xiang-Jun Li, Xiang-Ping Li, Xiang-Yu Li, Xiangcheng Li, Xiangchun Li, Xiangdong Li, Xiangfei Li, Xiangjun Li, Xiangling Li, Xianglong Li, Xiangnan Li, Xiangpan Li, Xiangping Li, Xiangqi Li, Xiangrui Li, Xiangwei Li, Xiangyan Li, Xiangyang Li, Xiangyun Li, Xiangzhe Li, Xiankai Li, Xiankun Li, Xianlin Li, Xianlong Li, Xianlu Li, Xianlun Li, Xianrui Li, Xianyong Li, Xiao Li, Xiao-Cheng Li, Xiao-Dong Li, Xiao-Feng Li, Xiao-Gang Li, Xiao-Guang Li, Xiao-Hong Li, Xiao-Hui Li, Xiao-Jiao Li, Xiao-Jing Li, Xiao-Jun Li, Xiao-Kang Li, Xiao-Li Li, Xiao-Lin Li, Xiao-Long Li, Xiao-Min Li, Xiao-Na Li, Xiao-Qiang Li, Xiao-Qin Li, Xiao-Qiu Li, Xiao-Sa Li, Xiao-Tong Li, Xiao-Yao Li, Xiao-Yun Li, Xiao-kun Li, Xiao-mei Li, Xiao-xu Li, Xiao-yu Li, XiaoQiu Li, Xiaobai Li, Xiaobin Li, Xiaobing Li, Xiaobo Li, Xiaochen Li, Xiaochun Li, Xiaocun Li, Xiaodong Li, Xiaofang Li, Xiaofei Li, Xiaofeng Li, Xiaoguang Li, Xiaohan Li, Xiaoheng Li, Xiaohong Li, Xiaohu Li, Xiaohua Li, Xiaohuan Li, Xiaohui Li, Xiaojiao Li, Xiaojiaoyang Li, Xiaojing Li, Xiaoju Li, Xiaojuan Li, Xiaokun Li, Xiaolei Li, Xiaoli Li, Xiaolian Li, Xiaoliang Li, Xiaolin Li, Xiaoling Li, Xiaolong Li, Xiaoman Li, Xiaomei Li, Xiaomeng Li, Xiaomin Li, Xiaoming Li, Xiaona Li, Xiaonan Li, Xiaoning Li, Xiaopeng Li, Xiaoping Li, Xiaoqi Li, Xiaoqiang Li, Xiaoqin Li, Xiaoqing Li, Xiaoqiong Li, Xiaoquan Li, Xiaoran Li, Xiaorong Li, Xiaotian Li, Xiaoting Li, Xiaotong Li, Xiaowei Li, Xiaoxia Li, Xiaoxiao Li, Xiaoxiong Li, Xiaoxuan Li, Xiaoya Li, Xiaoyan Li, Xiaoyao Li, Xiaoyi Li, Xiaoying Li, Xiaoyong Li, Xiaoyu Li, Xiaoyuan Li, Xiaoyun Li, Xiaozhao Li, Xiaozhen Li, Xiaozheng Li, Xiatian Li, Xiawei Li, Xiaxia Li, Xiayu Li, Xidan Li, Xihao Li, Xihe Li, Xijing Li, Xikun Li, Xiliang Li, Ximei Li, Xin Li, Xin-Chang Li, Xin-Jian Li, Xin-Ping Li, Xin-Tao Li, Xin-Ya Li, Xin-Yu Li, Xin-Yue Li, Xin-Zhu Li, Xinbin Li, Xing Li, Xing-Wang Li, Xingchen Li, Xingcheng Li, Xingfang Li, Xinghuan Li, Xinghui Li, Xingli Li, Xinglong Li, Xingwang Li, Xingxing Li, Xingya Li, Xingye Li, Xingyu Li, Xingyuan Li, Xinhai Li, Xinhua Li, Xinhui Li, Xining Li, Xinjia Li, Xinjian Li, Xinke Li, Xinle Li, Xinli Li, Xinlin Li, Xinmei Li, Xinmiao Li, Xinmin Li, Xinming Li, Xinpeng Li, Xinping Li, Xinrong Li, Xinrui Li, Xinsheng Li, Xinwei Li, Xinxin Li, Xinxiu Li, Xinyan Li, Xinyang Li, Xinyao Li, Xinye Li, Xinyi Li, Xinyu Li, Xinyuan Li, Xinzhi Li, Xinzhong Li, Xiong Bing Li, Xiong Li, Xiongfeng Li, Xionghao Li, Xionghui Li, Xiu-Ling Li, Xiucui Li, Xiufeng Li, Xiujuan Li, Xiuli Li, Xiuling Li, Xiumei Li, Xiuqi Li, Xiurong Li, Xiushen Li, Xiushi Li, Xiuzhen Li, Xixi Li, Xiying Li, Xiyue Li, Xiyun Li, Xu Li, Xu-Bo Li, Xu-Wei Li, Xu-Zhao Li, Xuan Li, Xuan-Ling Li, Xuanfei Li, Xuanxuan Li, Xuanzheng Li, Xudong Li, Xue Cheng Li, Xue Li, Xue-Er Li, Xue-Fei Li, Xue-Hua Li, Xue-Lian Li, Xue-Min Li, Xue-Nan Li, Xue-Peng Li, Xue-Yan Li, Xue-Ying Li, Xue-jing Li, Xue-zhi Li, Xuebiao Li, Xueer Li, Xuefei Li, Xuefeng Li, Xuehua Li, Xuejie Li, Xuejun Li, Xuekun Li, Xuelian Li, Xuelin Li, Xueling Li, Xuemei Li, Xuemin Li, Xuening Li, Xuepeng Li, Xueqin Li, Xueren Li, Xueshan Li, Xuesong Li, Xueting Li, Xuewang Li, Xuewei Li, Xuewen Li, Xueyang Li, Xueyi Li, Xueying Li, Xuezhong Li, Xuhang Li, Xuhong Li, Xuhua Li, Xujun Li, Xun Li, Xunjia Li, Xuri Li, Xutong Li, Xuyi Li, Xuze Li, Y H Li, Y L Li, Y Li, Y M Li, Y X Li, Y-Y Li, Ya Li, Ya-Feng Li, Ya-Ge Li, Ya-Jun Li, Ya-Li Li, Ya-Pei Li, Ya-Qiang Li, Ya-Ting Li, Ya-Zhou Li, YaJie Li, Yadong Li, Yahui Li, Yajiao Li, Yajing Li, Yajuan Li, Yajun Li, Yakui Li, Yalan Li, Yali Li, Yalin Li, Yan Bing Li, Yan Li, Yan Ning Li, Yan-Chun Li, Yan-Guang Li, Yan-Hong Li, Yan-Hua Li, Yan-Li Li, Yan-Nan Li, Yan-Xue Li, Yan-Yan Li, Yan-Yu Li, Yanan Li, Yanbin Li, Yanbing Li, Yanbo Li, Yanchang Li, Yanchuan Li, Yanchun Li, Yandong Li, Yanfeng Li, Yang Li, Yangxue Li, Yangyang Li, Yanhui Li, Yani Li, Yanjiao Li, Yanjie Li, Yanjing Li, Yanjun Li, Yanli Li, Yanlin Li, Yanling Li, Yanlong Li, Yanmei Li, Yanmin Li, Yanming Li, Yanni Li, Yanping Li, Yanqing Li, Yansen Li, Yanshu Li, Yansong Li, Yantao Li, Yanwei Li, Yanwu Li, Yanxi Li, Yanxiang Li, Yanxin Li, Yanyan Li, Yanying Li, Yanze Li, Yanzhong Li, Yao Li, Yaobo Li, Yaochen Li, Yaodong Li, Yaofu Li, Yaojia Li, Yaokun Li, Yaoqi Li, Yaoyao Li, Yaqi Li, Yaqiang Li, Yaqiao Li, Yaqin Li, Yaqing Li, Yaqiong Li, Yarong Li, Yawei Li, Yaxi Li, Yaxian Li, Yaxiong Li, Yaxuan Li, Yaying Li, Yayu Li, Yazhou Li, Ye Li, Yehong Li, Yeshan Li, Yetian Li, Yi Li, Yi-Heng Li, Yi-Ling Li, Yi-Ning Li, Yi-Shuan J Li, Yi-Ting Li, Yi-Wen Li, Yi-Yang Li, Yi-Ying Li, Yi-Yun Li, YiPing Li, YiQing Li, Yibo Li, Yiche Li, Yicun Li, Yifan Li, Yifei Li, Yifeng Li, Yige Li, Yihan Li, Yihao Li, Yiheng Li, Yihong Li, Yijian Li, Yijie Li, Yijing Li, Yiju Li, Yikang Li, Yike Li, Yilang Li, Yiliang Li, Yilong Li, Yimei Li, Yimeng Li, Yiming Li, Yin Li, Yinan Li, Ying Li, Ying-Bo Li, Ying-Lan Li, Ying-Qin Li, Ying-Qing Li, Ying-na Li, Yinggao Li, Yinghao Li, Yinghua Li, Yinghui Li, Yingjian Li, Yingjie Li, Yingjun Li, Yinglin Li, Yingnan Li, Yingpu Li, Yingqin Li, Yingrui Li, Yingshuo Li, Yingxi Li, Yingxia Li, Yingyi Li, Yingying Li, Yinhao Li, Yining Li, Yinliang Li, Yinxiong Li, Yinyan Li, Yinzhen Li, Yipeng Li, Yiqiang Li, Yirun Li, Yitong Li, Yiwei Li, Yiwen Li, Yixi Li, Yixiang Li, Yixiao Li, Yixin Li, Yixing Li, Yixuan Li, Yixue Li, Yiyang Li, Yizhe Li, Yong Li, Yong-Jian Li, Yong-Jun Li, Yong-Liang Li, Yongchao Li, Yonghao Li, Yonghe Li, Yongjia Li, Yongjiang Li, Yongjin Li, Yongjing Li, Yongjun Li, Yongkai Li, Yongle Li, Yongli Li, Yongmei Li, Yongnan Li, Yongpeng Li, Yongping Li, Yongqi Li, Yongqiang Li, Yongqiu Li, Yongsen Li, Yongsheng Li, Yongting Li, Yongxiang Li, Yongxin Li, Yongxue Li, Yongze Li, Yongzhe Li, Yongzhen Li, Yongzheng Li, You Li, You Ran Li, You-Mei Li, Youchen Li, Youjun Li, Youming Li, Youran Li, Yousheng Li, Youwei Li, Yu Li, Yu-Cheng Li, Yu-Chia Li, Yu-Hang Li, Yu-Hao Li, Yu-He Li, Yu-Hui Li, Yu-I Li, Yu-Jin Li, Yu-Jui Li, Yu-Kun Li, Yu-Lin Li, Yu-Sheng Li, Yu-Xiang Li, Yu-Ye Li, Yu-Ying Li, Yu-quan Li, Yuan Hao Li, Yuan Li, Yuan-Hai Li, Yuan-Jing Li, Yuan-Tao Li, Yuan-Yuan Li, Yuan-hao Li, Yuanchang Li, Yuanchuang Li, Yuancong Li, Yuandong Li, Yuanfang Li, Yuanfei Li, Yuanhao Li, Yuanhe Li, Yuanheng Li, Yuanhong Li, Yuanhua Li, Yuanjing Li, Yuanmei Li, Yuanyou Li, Yuanyuan Li, Yuanze Li, Yubin Li, Yubo Li, Yuchan Li, Yuchao Li, Yucheng Li, Yuchuan Li, Yuchun Li, Yudong Li, Yue Li, Yue-Chun Li, Yue-Jia Li, Yue-Ming Li, Yue-Rui Li, Yue-Ting Li, Yue-Ying Li, YueQiang Li, Yuefei Li, Yuefeng Li, Yueguo Li, Yuehua Li, Yuemei Li, Yueping Li, Yueqi Li, Yueting Li, Yuezheng Li, Yufan Li, Yufen Li, Yufeng Li, Yuguang Li, Yuhan Li, Yuhang Li, Yuhong Li, Yuhua Li, Yuhuang Li, Yuhui Li, Yujie Li, Yujun Li, Yukun Li, Yuli Li, Yulin Li, Yuling Li, Yulong Li, Yumao Li, Yumei Li, Yumiao Li, Yumin Li, Yun Li, Yun-Da Li, Yun-Lin Li, Yun-Peng Li, Yun-tian Li, Yuna Li, Yunan Li, Yunchu Li, Yunfeng Li, Yunjiu Li, Yunlong Li, Yunlun Li, Yunman Li, Yunmin Li, Yunpeng Li, Yunqi Li, Yunrui Li, Yunshen Li, Yunsheng Li, Yunting Li, Yunxi Li, Yunxiao Li, Yunxu Li, Yunyun Li, Yunze Li, Yuping Li, Yuqi Li, Yuqian Li, Yuqing Li, Yuqiu Li, Yuquan Li, Yushan Li, Yutang Li, Yutian Li, Yuting Li, Yutong Li, Yuwei Li, Yuxi Li, Yuxiang Li, Yuxin Li, Yuxiu Li, Yuxuan Li, Yuyan Li, Yuying Li, Yuyun Li, Yuzhe Li, Yvonne Li, Z Li, Z-H Li, Zaibo Li, Ze Li, Ze-An Li, Zecai Li, Zechuan Li, Zehan Li, Zehua Li, Zejian Li, Zemin Li, Zengyang Li, Zequn Li, Zesong Li, Zexu Li, Zeyu Li, Zeyuan Li, Zezhi Li, Zhan Li, Zhandong Li, Zhang Li, Zhanjun Li, Zhankui Li, Zhanquan Li, Zhantao Li, Zhao Li, Zhao-Cong Li, Zhao-Yang Li, Zhaobing Li, Zhaohan Li, Zhaojin Li, Zhaoliang Li, Zhaolun Li, Zhaoping Li, Zhaosha Li, Zhaoshui Li, Zhaoyong Li, Zhe Li, Zhehui Li, Zhen Li, Zhen-Hua Li, Zhen-Jia Li, Zhen-Li Li, Zhen-Xi Li, Zhen-Yu Li, Zhen-Yuan Li, Zhenbei Li, Zhencheng Li, Zhencong Li, Zhenfei Li, Zhenfen Li, Zheng Li, Zheng-Dao Li, Zhengda Li, Zhenghao Li, Zhenghui Li, Zhengjie Li, Zhengliang Li, Zhenglong Li, Zhengnan Li, Zhengpeng Li, Zhengrui Li, Zhenguang Li, Zhengwei Li, Zhengyang Li, Zhengyao Li, Zhengying Li, Zhengyu Li, Zhenhao Li, Zhenhua Li, Zhenhui Li, Zhenjia Li, Zhenjun Li, Zhenli Li, Zhenlu Li, Zhenming Li, Zhenshu Li, Zhenyan Li, Zhenyu Li, Zhenzhe Li, Zhenzhou Li, Zheyun Li, Zhi Li, Zhi-Bin Li, Zhi-Gang Li, Zhi-Jian Li, Zhi-Peng Li, Zhi-Wei Li, Zhi-Xing Li, Zhi-Yong Li, Zhi-Yuan Li, Zhi-qiang Li, Zhibin Li, Zhichao Li, Zhifan Li, Zhifei Li, Zhigang Li, Zhigao Li, Zhihao Li, Zhihong Li, Zhihua Li, Zhihui Li, Zhijia Li, Zhijie Li, Zhijun Li, Zhilei Li, Zhimei Li, Zhiming Li, Zhipeng Li, Zhiping Li, Zhiqiang Li, Zhiqiong Li, Zhiquan Li, Zhirong Li, Zhisheng Li, Zhiwei Li, Zhixiong Li, Zhixuan Li, Zhiyang Li, Zhiyi Li, Zhiyong Li, Zhiyu Li, Zhiyuan Li, Zhizhong Li, Zhizong Li, Zhong Li, Zhong-Xin Li, Zhongcai Li, Zhongding Li, Zhonggen Li, Zhonghua Li, Zhongjie Li, Zhonglian Li, Zhonglin Li, Zhongwen Li, Zhongxia Li, Zhongxian Li, Zhongxuan Li, Zhongyu Li, Zhongzhe Li, Zhou Li, Zhouhua Li, Zhouxiang Li, Zhu Li, Zhuang Li, Zhuangzhuang Li, Zhuanjian Li, Zhuo Li, Zhuo-Rong Li, Zhuoran Li, Zhuorong Li, Zi-Zhan Li, Zichao Li, Zihai Li, Zihan Li, Zihao Li, 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articles
Shiqi Wu, Hening Li, Pintian Wang +1 more · 2025 · Medicine · added 2026-04-24
Natural killer (NK) cells are an integral component of the tumor microenvironment, and their role in immune checkpoint inhibitors (ICI) therapy has garnered increasing attention. However, comprehensiv Show more
Natural killer (NK) cells are an integral component of the tumor microenvironment, and their role in immune checkpoint inhibitors (ICI) therapy has garnered increasing attention. However, comprehensive studies on NK cells across cancers, especially their impact on immunotherapy response, remain limited. We used machine learning algorithms to establish a pan-cancer natural killer cell immunotherapy predictive model (NKCIPM) by combining single-cell RNA sequencing data from 164 samples across 6 cancer types and bulk RNA-seq data from different tumor samples. Tumor immune cell infiltration analysis, drug sensitivity analysis, and cell-cell communication were also further conducted. An upregulation of NK cell proportions post-immunotherapy and the identification of 188 NK cell differentially expressed genes were observed through single-cell RNA sequencing analysis. By integrating bulk RNA-seq data and applying machine learning algorithms, 7 key hub genes were identified, ultimately leading to the construction of NKCIPM, with APOE emerging as the most influential hub gene. Further analysis using the CIBERSORT algorithm revealed that the signature genes within this model were significantly associated with immune cell infiltration and response to ICI. Additionally, therapeutic evaluation of CHEK1 and CHEK2 targets demonstrated potential significance in the communication between B cells, NK cells, and mast cells within the context of ICI therapy. In summary, the NKCIPM model offers a valuable tool for predicting immunotherapy outcomes and informing clinical decision-making, highlighting the potential of NK cell signature genes as therapeutic targets. Show less
📄 PDF DOI: 10.1097/MD.0000000000045753
APOE
Chen Yao, Geng Wang, Quanhui Wu +6 more · 2025 · Medicine · added 2026-04-24
Aortic dissection (AD) involves complex interactions among amino acid, glucose, and lipid metabolism, exacerbating aortic inflammation and extracellular matrix (ECM) degradation, coupled with smooth m Show more
Aortic dissection (AD) involves complex interactions among amino acid, glucose, and lipid metabolism, exacerbating aortic inflammation and extracellular matrix (ECM) degradation, coupled with smooth muscle cell (SMC) dysfunction (phenotypic alteration, aging, apoptosis). To explore AD pathogenesis, we integrated single-cell RNA sequencing (scRNA-seq), metabolomics, machine learning, and Mendelian randomization to investigate SMC changes and gene-metabolite interactions. ScRNA-seq data (GSE213740, GSE155468) were analyzed for cell clustering and pseudo-time trajectories via Seurat and Monocle2. Metabolomics (9 samples: 6 AD, 3 controls) and machine learning validated key genes/metabolites, with Mendelian randomization assessing causal links. Nine cell subsets and 2000 variable genes were identified, with SMCs central to AD via cholesterol metabolism. APOE and PLTP were key genes; metabolomics highlighted cholesterol esters (CEs) and triglycerides (TGs) as critical metabolites. Machine learning confirmed APOE/PLTP's high predictive accuracy (AUC: 0.796-0.989). Mendelian randomization linked elevated CEs and TGs to increased AD risk (IVW: P = .04 and P = .02, respectively). This study establishes a gene-metabolite network where APOE and PLTP regulate CEs/TGs, influencing SMC function and AD progression, offering potential therapeutic targets. Show less
📄 PDF DOI: 10.1097/MD.0000000000045846
APOE
Hao Xu, Junjie Ma, Nanjun Li +6 more · 2025 · NPJ precision oncology · Nature · added 2026-04-24
Thyroid cancer, the most common endocrine malignancy, is characterized by a unique and complex tumor microenvironment (TME). To unravel the high tumor heterogeneity and molecular mechanisms driving ca Show more
Thyroid cancer, the most common endocrine malignancy, is characterized by a unique and complex tumor microenvironment (TME). To unravel the high tumor heterogeneity and molecular mechanisms driving cancer progression, we performed single-cell RNA sequencing (scRNA-seq) analysis, enabling a comprehensive exploration of cellular diversity and molecular dynamics at single-cell resolution. We employed Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction and subsequent identification of cellular clusters. Differential gene expression analysis across subclusters was conducted using the FindAllMarkers function, while the DoHeatmap function was utilized to visualize the distribution of differentially expressed genes. The AUCell algorithm was applied to evaluate pathway enrichment within specific cell subtypes. To decipher cellular communication networks, we integrated the CellChat and NicheNet algorithms, which revealed intricate intercellular signaling interactions. Finally, multiplex immunohistochemistry (mIHC) was performed to validate key cellular interactions identified in silico. By analyzing 405,077 single cells from 50 thyroid cancer samples (including papillary, anaplastic, and metastatic tumors) and 14 normal thyroid tissues, we identified four major cellular subpopulations through unbiased clustering based on gene expression patterns and representative cellular markers. The TME was found to encompass diverse immune, endothelial, and mesenchymal cell subtypes, including novel populations such as CD4 + HSPA1A + T cells. Functional pathway enrichment analysis highlighted the roles of abundant cell types in tumor progression. Cell-cell communication analysis uncovered potential immunotherapeutic targets and revealed critical crosstalk among hub niche cells, including APOE+ macrophages, EMT-like cancer-associated fibroblasts (CAFs), and RBP7+ endothelial cells. These findings were further validated by multiplex immunohistochemistry, confirming the spatial organization and interactions of these cell populations within the TME. Our study provides a comprehensive single-cell transcriptomic atlas of thyroid cancer, offering profound insights into tumor heterogeneity, the functional roles of key niche cells, and potential biomarkers for anticancer therapy. These findings not only enhance our understanding of thyroid cancer biology but also pave the way for the development of novel therapeutic strategies targeting the TME. Show less
📄 PDF DOI: 10.1038/s41698-025-00924-7
APOE
Yuemei Zhang, Yuxin Cao, Yongxin Sun +12 more · 2025 · Proceedings of the National Academy of Sciences of the United States of America · National Academy of Sciences · added 2026-04-24
The activation of blood monocytes and the infiltration of monocyte-derived macrophages into the vessel walls are the central part of atherosclerosis. However, the mechanisms underlying the processes r Show more
The activation of blood monocytes and the infiltration of monocyte-derived macrophages into the vessel walls are the central part of atherosclerosis. However, the mechanisms underlying the processes remain unclear. Here, we report that G-protein signaling modulator 1 (GPSM1) plays a critical role in atherogenesis. We found that GPSM1 expression in lesional macrophages was increased during atherosclerosis development both in mice and humans. Myeloid-specific GPSM1 ablation protects mice against atherosclerosis and reduces aortic inflammation in both Show less
no PDF DOI: 10.1073/pnas.2517531122
APOE
Jing Liu, Junshuang Wang, Shuang Lv +7 more · 2025 · PloS one · PLOS · added 2026-04-24
Radiation-induced brain injury (RIBI) is a significant complication following radiotherapy for brain tumors, leading to neurocognitive deficits and other neurological impairments. This study aims to i Show more
Radiation-induced brain injury (RIBI) is a significant complication following radiotherapy for brain tumors, leading to neurocognitive deficits and other neurological impairments. This study aims to identify potential biomarkers and therapeutic targets for RIBI by utilizing advanced proteomic techniques to explore the molecular mechanisms underlying RIBI. A rat model of RIBI was established and subjected to whole-brain irradiation (30 Gy). Tandem mass tagging (TMT)-based quantitative proteomics, combined with high-resolution mass spectrometry, was used to identify differentially expressed proteins (DEPs) in the brain tissues of irradiated rats. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to identify the biological processes and pathways involved. Protein-protein interaction (PPI) networks were constructed to identify key hub proteins. A total of 35 DEPs were identified, including PHLDA3, APOE and CPE. GO enrichment analysis revealed that the DEPs were mainly involved in lipid transport, cell adhesion, and metabolic processes. KEGG analysis highlighted the enrichment of pathways related to metabolism, tight junctions, and PPAR signaling. APOE was identified as a key hub protein through PPI network analysis, indicating its potential role in RIBI pathophysiology. Immunohistochemistry further validated the increased expression of PHLDA3, APOE, and CPE in the brain tissue of irradiated rats. This study provides valuable insights into the molecular mechanisms of RIBI by identifying key proteins and their associated pathways. The findings suggest that these proteins, particularly APOE and PHLDA3, could serve as potential biomarkers and therapeutic targets for clinical intervention in RIBI. These results not only enhance our understanding of RIBI's molecular pathology but also open new avenues for the development of targeted therapies to mitigate radiation-induced neurotoxicity. Show less
📄 PDF DOI: 10.1371/journal.pone.0337608
APOE
Bo Lin, Mengsen Li · 2025 · Frontiers in immunology · Frontiers · added 2026-04-24
Abnormalities in the Wnt/β-catenin pathway promote the development of hepatocellular carcinoma (HCC). Mutations in CTNNB1, which encodes β-catenin, are frequently found in clinical HCC samples, as are Show more
Abnormalities in the Wnt/β-catenin pathway promote the development of hepatocellular carcinoma (HCC). Mutations in CTNNB1, which encodes β-catenin, are frequently found in clinical HCC samples, as are loss-of-function mutations in signaling pathway regulators such as axis inhibition protein 1 (Axin1) and adenomatous polyposis coli (APC). The activation of the Wnt/β-catenin pathway synergizes with other oncogenic signal molecules such as c-Met or glypican-3, contributing to HCC development. Furthermore, Wnt/β-catenin pathway activation in the tumour microenvironment (TME) leads to cold tumour and resistance to immunotherapy. In this review, we discuss two models of Wnt/β-catenin signaling activation, role of Wnt/β-catenin signaling pathway in the development of HCC, the association between Wnt/β-catenin pathway and tumour angiogenesis, metastasis, and immune escape in the TME, and the targeting of this signaling pathway for HCC treatment. Show less
📄 PDF DOI: 10.3389/fimmu.2025.1691297
AXIN1
Li Niu, Yubo Li, Hao Wu +7 more · 2025 · Journal of Alzheimer's disease reports · SAGE Publications · added 2026-04-24
Neuroinflammation represents a central pathological mechanism in Alzheimer's disease (AD). Lipopolysaccharide (LPS) is a potent inducer of neuroinflammation and demonstrates elevated circulating level Show more
Neuroinflammation represents a central pathological mechanism in Alzheimer's disease (AD). Lipopolysaccharide (LPS) is a potent inducer of neuroinflammation and demonstrates elevated circulating levels in AD patients. This study aims to investigate the genetic association between serum LPS activity level, inflammatory proteins and AD. A two-sample mendelian randomization (MR) analysis was performed to explore the causal effect of serum LPS activity level and 91 inflammatory proteins on AD, including 1, 260, 136 sporadic AD and 2, 838, 825 familial AD patients, respectively. Meta-analysis was conducted on multiple datasets to determine statistically significant results that was initially observed in one dataset. Serum LPS activity level is a risk factor for early onset sporadic AD with OR = 1.392, 95% CI: 1.038-1.869. In most other sporadic AD datasets, LPS shows a trend of increasing the risk of AD onset. After meta-analysis in 10 independent datasets, no association between LPS and sporadic AD was observed. In most familial AD datasets, LPS level demonstrated a trend of decreasing AD risk in MR analysis, however, meta-analysis of the combined 8 datasets showed no statistically significant difference. Two inflammatory proteins, AXIN1 and IL-1 alpha, were identified as significant risk factors for sporadic AD. This study suggested that serum LPS activity level may present a risk effect in early onset sporadic AD. Two inflammatory proteins AXIN1 and IL-1 alpha were associated with the risk of sporadic AD. These findings provide a new perspective for the early diagnosis and treatment of sporadic and familial AD. Show less
📄 PDF DOI: 10.1177/25424823251385589
AXIN1
Mei Lu, Xiaohui Li, Lin Ma +4 more · 2025 · IUBMB life · Wiley · added 2026-04-24
Muscle wasting, characterized by loss of muscle mass and strength, severely impacts patient quality of life and is associated with numerous chronic diseases and aging. The molecular mechanisms are com Show more
Muscle wasting, characterized by loss of muscle mass and strength, severely impacts patient quality of life and is associated with numerous chronic diseases and aging. The molecular mechanisms are complex, involving protein synthesis/degradation imbalance. Dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) and ubiquitin-specific peptidase 7 (USP7) have diverse cellular roles, but their coordinated function in skeletal muscle homeostasis remains poorly understood. DYRK1A overexpression in vivo induced muscle atrophy phenotypes, including reduced muscle mass, grip strength, fiber cross-sectional area (CSA), altered fiber type composition, and neuromuscular junction integrity, accompanied by elevated atrophy markers: muscle atrophy F-box protein (Atrogin-1), muscle ring finger 1 (MuRF-1), myostatin and suppressed myogenic markers: myoblast determination protein 1 (MyoD), myogenin (MyoG), myocyte enhancer factor 2C (Mef2c), myogenic factor 5 (Myf5). Conversely, pharmacological inhibition of DYRK1A with Harmine ameliorated these atrophy phenotypes in transgenic DYRK1A overexpressing (TgD) mice. In vivo, USP7 deficiency resulted in similar muscle wasting phenotypes. In vitro, DYRK1A overexpression or USP7 overexpression inhibited C2C12 myoblast proliferation and differentiation, effects rescued by Wnt3a treatment or USP7 knockdown, respectively. Mechanistically, DYRK1A activity suppressed active β-catenin levels. USP7 was found to interact with and deubiquitinate axis inhibition protein 1 (Axin1), leading to its stabilization. Knockdown of USP7 increased Axin1 ubiquitination and degradation, thereby promoting β-catenin signaling and myogenesis, counteracting the effects of DYRK1A. Our findings reveal a novel signaling axis where DYRK1A and USP7 cooperatively suppress Wnt/β-catenin signaling to promote muscle wasting. DYRK1A likely acts upstream, potentially phosphorylating pathway components, whereas USP7 stabilizes the β-catenin destruction complex scaffold protein Axin1 through deubiquitination. This coordinated action inhibits myogenesis and activates atrophy pathways. Targeting DYRK1A or USP7 could represent promising therapeutic strategies for muscle wasting disorders. Show less
no PDF DOI: 10.1002/iub.70061
AXIN1
Xian Chen, Hui Wang, Qianqian Li +4 more · 2025 · Discover oncology · Springer · added 2026-04-24
Renal clear cell carcinoma (RCC) is the most common type of kidney cancer, and its relationship with kidney fibrosis and inflammatory responses has attracted considerable attention. However, whether c Show more
Renal clear cell carcinoma (RCC) is the most common type of kidney cancer, and its relationship with kidney fibrosis and inflammatory responses has attracted considerable attention. However, whether causal relationships exist among these associations remains unclear, as traditional observational studies are susceptible to confounding factors. To evaluate causal relationships between kidney cancer, kidney fibrosis, and inflammatory factors using Mendelian randomization, and explore tumor microenvironment heterogeneity through single-cell analysis. Based on large-scale GWAS data, bidirectional Mendelian randomization analysis was performed to assess causal relationships between kidney cancer and kidney fibrosis, using MR Egger, inverse variance weighted (IVW), and weighted mode methods. Causal associations between kidney cancer and inflammatory factors including Axin-1, C-C motif chemokine 28, and interleukin-10 receptor subunit were analyzed. Single-cell RNA sequencing data from the GEO database (GSM4819725) was integrated for tumor microenvironment analysis. Bidirectional Mendelian randomization analysis revealed no significant causal relationship between kidney cancer and kidney fibrosis [kidney cancer→kidney fibrosis: IVW OR=0.992(95%CI: 0.913-1.077, P=0.842); kidney fibrosis→kidney cancer: IVW OR=0.922(95%CI: 0.824-1.030, P=0.151)]. However, significant positive causal associations were identified between kidney cancer and multiple inflammatory factors: Axin-1 levels [OR=1.448(95%CI: 1.107-1.894, P=0.007)], C-C motif chemokine 28 [OR=1.287(95%CI: 1.076-1.540, P=0.006)], and interleukin-10 receptor subunit [OR=1.135(95%CI: 1.032-1.248, P=0.009)]. Sensitivity analyses confirmed the robustness of results. Single-cell analysis revealed cellular heterogeneity in the tumor microenvironment, including various cell types such as immune cells, T cells, and NK cells, with pseudotime analysis demonstrating cell differentiation trajectories and dynamic gene expression changes. Mendelian randomization analysis provides genetic evidence for causal relationships between kidney cancer and inflammatory factors, while excluding direct causal associations between kidney cancer and kidney fibrosis. Show less
📄 PDF DOI: 10.1007/s12672-025-03343-z
AXIN1
Renjing Lin, Jinyin Xiao, Yanjie Chen +4 more · 2025 · Journal of cellular and molecular medicine · Blackwell Publishing · added 2026-04-24
This study aimed to investigate the anti-tumour effect and the possible molecular mechanism of Tianma granules on colorectal cancer (CRC). The therapeutic effect of Tianma granules on CRC cell lines ( Show more
This study aimed to investigate the anti-tumour effect and the possible molecular mechanism of Tianma granules on colorectal cancer (CRC). The therapeutic effect of Tianma granules on CRC cell lines (HT116 and SW480) and AOM/DSS-induced CRC mouse models was evaluated. Tianma granules can attenuate weight loss and increase the survival rate of CRC mice, restore reduced colon length, reduce tumour numbers and increase goblet cell numbers in CRC mice. Tianma granules also downregulated the level of CRC-specific markers (COX2 and MUC2), inhibited the inflammation (decreased TNF-α, IL-1β, IL-6 levels and increased INF-γ level), and promoted apoptosis (decreased TUNEL positive cell rate; decreased Bax and Cleaved caspase3 protein levels and increased Bcl2 level) in CRC mice. In vitro, Tianma granules can inhibit the viability, proliferation, migration and invasion of CRC cells, while promoting cell apoptosis, cell cycle arrest and cell senescence. Tianma granules promoted AXIN1 protein levels and inhibited p-GSK-3β, β-catenin, Wnt5a and Cyclin D1 and c-Myc protein levels. Moreover, the network pharmacology analysis and in vitro validation revealed berberine might be the key compound responsible for Tianma granules' pharmacological actions. In conclusion, Tianma granules can inhibit inflammation and tumour progression in AOM/DSS-induced CRC mice, as well as inhibit CRC cell malignant phenotype. The protection of Tianma granules against CRC may be achieved by inhibiting the Wnt signalling pathway. Show less
📄 PDF DOI: 10.1111/jcmm.70772
AXIN1
Xin Liu, Ting Du, Ruofan Xi +7 more · 2025 · Drug design, development and therapy · added 2026-04-24
Cyclophosphamide (CTX), a cornerstone in breast cancer combination chemotherapy, frequently induces adverse effects including myelosuppression, gastrointestinal disturbances, hepatic impairment, and a Show more
Cyclophosphamide (CTX), a cornerstone in breast cancer combination chemotherapy, frequently induces adverse effects including myelosuppression, gastrointestinal disturbances, hepatic impairment, and alopecia. Chemotherapy-induced alopecia severely impacts patients' quality of life and psychological well-being. Modified Huanjingjian (MHJJ), a traditional Chinese herbal formula, demonstrates clinical efficacy in alleviating chemotherapy-related side effects, yet its mechanisms against CTX-induced alopecia remain uncharacterized. And our main aim was to explore the efficacy and the mechanism of MHJJ in mice. UPLC-QE-Orbitrap-MS characterized MHJJ's chemical composition. A CTX-induced alopecia murine model was established. Systemic toxicity was evaluated through body weight monitoring, automated biochemical analysis (ALT/AST levels), and hematological profiling (WBC/PLT counts). Hair follicle histopathology was assessed via H&E staining. IHC and IF staining quantified proliferation markers and hair follicle stem cell (HFSC) biomarkers. Reduced representation bisulfite sequencing (RRBS) was used to map DNA methylation patterns. Wnt pathway dynamics were analyzed through qRT-PCR and IF staining. We identified 110 bioactive compounds in MHJJ. MHJJ intervention attenuated alopecia severity, restored follicular architecture, and increased follicular density compared to CTX monotherapy (p<0.05). HFSC proliferation markers (Ki67/CD34) showed significant upregulation, while apoptosis markers (Caspase-3) were suppressed. RRBS revealed MHJJ-mediated hypomethylation in differentially methylated regions, with gene body methylation constituting 60% of total methylation changes. Methylation-modulated genes predominantly localized to Wnt signaling pathways: MHJJ enhanced Wnt3/Wnt10a expression while suppressing Cer1/Axin1. Corresponding methylation reductions at promoter and gene body regions were confirmed at mRNA and protein levels. MHJJ mitigates CTX-induced alopecia through epigenetic regulation of HFSCs, specifically via DNA hypomethylation-mediated activation of Wnt3/Wnt10a and suppression of Cer1/Axin1. This mechanism promotes follicular regeneration by restoring Wnt signaling homeostasis, positioning MHJJ as a promising adjuvant for chemotherapy-induced alopecia management. Show less
📄 PDF DOI: 10.2147/DDDT.S523809
AXIN1
Wenzhe Li, Gaosi Xu, Manna Li · 2025 · Frontiers in medicine · Frontiers · added 2026-04-24
This paper aims to investigate m6A modification during DKD progression. We evaluated m6A regulators expression in peripheral blood mononuclear cells, whole kidney tissue, glomerular, and tubulointerst Show more
This paper aims to investigate m6A modification during DKD progression. We evaluated m6A regulators expression in peripheral blood mononuclear cells, whole kidney tissue, glomerular, and tubulointerstitial samples. CIBERSORT and single-sample gene set enrichment analysis analyzed glomerular immune characteristics. Logistic-LASSO regression were used to develop the m6A regulators model that can identify early DKD. Consensus clustering algorithms were used to classify DKD in glomerular samples into m6A modified subtypes based on the expression of m6A regulators. Gene set variation analysis algorithm was used to evaluate the functional pathway enrichment of m6A modified subtypes. Weighted gene co-expression network analysis and protein-protein interaction networks identified m6A modified subtype marker genes. The Nephroseq V5 tool was used to evaluate the correlation between m6A modified subtypes marker genes and renal function. DKD patients' m6A regulators expression differed from the control group in various tissue types. DKD stages have various immune characteristics. The m6A regulators model with YTHDC1, METTL3, and ALKBH5 better identified early DKD. DKD was divided into two subtypes based on the expression of 26 m6A regulators. Subtype 1 was enriched in myogenesis, collagen components, and cytokine receptor interaction, while subtype 2 was enriched in protein secretion, proliferation, apoptosis, and various signaling pathways (e.g., TGFβ signaling pathway, PI3K/AKT/mTOR pathway, and etc.). Finally, AXIN1 and GOLGA4 were identified as possible biomarkers associated with glomerular filtration rate. From the viewpoint of m6A modification, the immune characteristics and molecular mechanisms of DKD at various stages are different, and targeted treatment would improve efficacy. Show less
📄 PDF DOI: 10.3389/fmed.2025.1494162
AXIN1
Baolin Qian, Bing Yin, Hongjun Yu +12 more · 2025 · Nature communications · Nature · added 2026-04-24
Hepatic ischemia‒reperfusion injury (HIRI) is a common pathological phenomenon after hepatectomy and liver transplantation. Here, we aim to explore the role of Axin formation inhibitor 1 (Axin1) in HI Show more
Hepatic ischemia‒reperfusion injury (HIRI) is a common pathological phenomenon after hepatectomy and liver transplantation. Here, we aim to explore the role of Axin formation inhibitor 1 (Axin1) in HIRI. In this work, we find that the expression of Axin1 is upregulated after HIRI. Cellular experiments confirme that Axin1 knockdown alleviated hypoxia/reoxygenation (H/R)-induced inflammation and apoptosis. Subsequently, we construct a HIRI model based on transgenic hepatocellular-specific Axin1 knockout and overexpression male mice and find that Axin1 deletion alleviated inflammation and apoptosis. Transcriptome sequencing reveal that the genes whose expression differed after Axin1 overexpression are significantly enriched in the PPAR signaling pathway. Furthermore, we demonstrate that Axin1 negatively regulates the expression of PPARβ, thereby activating the NF-κB pathway. Mechanistically, Axin1 binds to PPARβ to enhance the ubiquitination-mediated degradation of PPARβ by the E3 ubiquitin ligase RBBP6. Notably, adenovirus-mediated Axin1 knockdown block I/R damage in mice. Our study results demonstrate that Axin1 exacerbates HIRI by promoting the ubiquitination and degradation of PPARβ, which in turn activates the NF-κB signaling pathway. These results suggest that Axin1 may be a potential therapeutic target for HIRI. Show less
📄 PDF DOI: 10.1038/s41467-025-56967-8
AXIN1
Hui Lian, Yujie Zhang, Zhao Zhu +11 more · 2025 · Life science alliance · added 2026-04-24
Idiopathic pulmonary fibrosis is a progressive and lethal interstitial lung disease with an unclear etiology and limited treatment options. Fatty acid synthase (FASN) plays various roles in metabolic- Show more
Idiopathic pulmonary fibrosis is a progressive and lethal interstitial lung disease with an unclear etiology and limited treatment options. Fatty acid synthase (FASN) plays various roles in metabolic-related diseases. This study demonstrates that FASN expression is increased in fibroblasts from the lung tissues of patients with idiopathic pulmonary fibrosis and in bleomycin-treated mice. In MRC-5 cells, the inhibition of FASN using shRNA or the pharmacological inhibitor C75 resulted in the increased mRNA and protein expression of glycogen synthase kinase 3β and Axin1, both negative regulators of the Wnt/β-catenin signaling pathway, and promoted autophagy. This outcome led to a decrease in β-catenin protein and mRNA levels, effectively inhibiting the proliferation, migration, and differentiation of lung fibroblasts into myofibroblasts, while inducing the differentiation of fibroblasts into adipofibroblasts. In vivo experiments showed that C75 alleviated bleomycin-induced lung fibrosis in mice by inhibiting β-catenin. In conclusion, these findings suggest that inhibiting FASN in fibroblasts may diminish the activity of the Wnt/β-catenin signaling pathway, providing a potential therapeutic avenue for pulmonary fibrosis. Show less
📄 PDF DOI: 10.26508/lsa.202402805
AXIN1
Sirui Fan, Hongqing Zhao, Cheng Li +8 more · 2025 · Biochemical genetics · Springer · added 2026-04-24
As a member of Rho GAPs family, Rho GTPase-Activating Protein 17 (ARHGAP17) regulates cytoskeletal recombination, cell polarity, cell proliferation and cell migration. ARHGAP17 is identified as a tumo Show more
As a member of Rho GAPs family, Rho GTPase-Activating Protein 17 (ARHGAP17) regulates cytoskeletal recombination, cell polarity, cell proliferation and cell migration. ARHGAP17 is identified as a tumor suppressor in numerous cancer types. Current study intends to examine ARHGAP17 expression and its possible influence on the progression of hepatocellular carcinoma (HCC). ARHGAP17 expression in HCC cells was verified by RT-PCR and western blot. The proliferation and invasion of HCC cells were evaluated by CCK8 assay and transwell assay, respectively. The mRNA expression of ARHGAP17, PCNA, E-cadherin, N-cadherin, β-catenin, GSK-3β, Axin1, and APC were detected by RT-PCR. The protein expression of ARHGAP17, PCNA, E-cadherin, N-cadherin, β-catenin, p-β-catenin, GSK-3β, p-GSK-3β, Axin1, and APC were detected by western blot. ARHGAP17 staining was evaluated by immunohistochemistry and immunofluorescence. ARHGAP17 expression decreased significantly in HCC tumors and HCC cells after EMT. In response to overexpression of ARHGAP17, the capacities of HCC cell proliferation and invasion were reduced significantly, which were also confirmed by tumorigenesis experiments in vivo. With overexpression of ARHGAP17 in HCC cells, the p-GSK3β/GSK3β decreased, while the p-β-catenin/β-catenin, Axin1 and APC increased. In conclusion, ARHGAP17 inhibits HCC progression by inactivating the Wnt/β-catenin signaling pathway. Show less
📄 PDF DOI: 10.1007/s10528-024-10822-5
AXIN1
Ruijun Sun, Yuchi Zhang, Jingying Xu +7 more · 2025 · Archiv der Pharmazie · Wiley · added 2026-04-24
Acetylcholinesterase (AChE) inhibitors are crucial for the symptomatic management of Alzheimer's disease (AD), with natural products-particularly botanical sources like Yellow Gastrodia elata (YGE)-se Show more
Acetylcholinesterase (AChE) inhibitors are crucial for the symptomatic management of Alzheimer's disease (AD), with natural products-particularly botanical sources like Yellow Gastrodia elata (YGE)-serving as promising reservoirs of such inhibitors. Nevertheless, comprehensive screening and mechanistic characterization of their inhibitory potential remain limited. This study sought to identify potent AChE inhibitors from YGE, investigate their mechanisms of action, and assess their therapeutic prospects for AD. Methodologically, an integrated approach was employed, combining ultrafiltration-liquid chromatography (UF-LC) for rapid inhibitor screening, molecular docking and dynamics simulations for mechanistic insight, two-stage high-speed countercurrent chromatography for compound isolation, enzyme kinetics to delineate inhibition modalities, and network pharmacology to uncover relevant AD-related targets. The findings identified seven active constituents with notable AChE inhibition, among which parishins A and G were obtained at high purity (98.26% and 97.26%, respectively) and exhibited mixed-type inhibition with low IC Show less
no PDF DOI: 10.1002/ardp.70174
BACE1
Lu Wang, Xiao-Yong Xie, Qiu-Ling Pan +13 more · 2025 · Nature communications · Nature · added 2026-04-24
Biomolecular condensates, membrane-less assemblies formed by phase separation, are implicated in neurodegenerative disease, but their role in Alzheimer's disease (AD) remains unclear. Here, we report Show more
Biomolecular condensates, membrane-less assemblies formed by phase separation, are implicated in neurodegenerative disease, but their role in Alzheimer's disease (AD) remains unclear. Here, we report that in the brain of AD patients and animal models, an elevation of poly(C)-binding protein 2 (PCBP2) correlates with biomolecular condensation that involves phase separation. These condensates sequester large numbers of mitochondrial and mRNA-binding proteins, leading to the outside impairment of mitochondrial morphology and function, and BACE1 mRNA decay relative to amyloid deposition. We then identify a small molecule CN-0928 that inhibits the condensates by reducing PCBP2 protein level and mitigates AD pathology and cognitive decline, in which CN-0928 binding to a target protein integrator complex subunit 1 (INTS1) allows to regulate PCBP2 expression. Our findings place PCBP2 condensates as a key player that cooperates the seemingly disparate but important pathways, and show pharmacological modulation of PCBP2 as an effective approach for treating AD. Show less
📄 PDF DOI: 10.1038/s41467-025-65547-9
BACE1
Song Li, Dezhong Wang, Anqi Li +7 more · 2025 · Molecular neurobiology · Springer · added 2026-04-24
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by amyloid-β (Aβ) accumulation, tau hyperphosphorylation, synaptic dysfunction, and chronic neuroinflammation. Curren Show more
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by amyloid-β (Aβ) accumulation, tau hyperphosphorylation, synaptic dysfunction, and chronic neuroinflammation. Current single-target interventions fail to halt disease progression, highlighting the need for multi-target strategies. This study investigates the therapeutic potential and mechanisms of ZuoGui Pill (ZGP), a traditional Chinese medicine formula, in a transgenic AD mouse model. 3xTg-AD mice were treated with ZGP for 60 days. Behavioral performance was assessed using the Morris water maze, novel object recognition, and open field test. Aβ deposition, tau phosphorylation, and synaptic integrity were evaluated via immunohistochemistry, Western blotting, RT-qPCR, and Golgi staining. Neuroinflammation and RAGE/NF-κB signaling were analyzed by ELISA and protein expression profiling. Statistical analyses included ANOVA with post hoc Tukey or Bonferroni tests following Shapiro-Wilk and Bartlett's validation. ZGP significantly improved cognitive performance, reduced hippocampal Aβ deposition and BACE1 expression, and suppressed tau phosphorylation at multiple pathological sites (T205, S396, S404). Synaptic markers (Syn, PSD95) were restored, accompanied by increased dendritic spine density. ZGP also reduced hippocampal IL-1β, IL-6, and TNF-α levels and inhibited the RAGE/p-NF-κB pathway. ZGP exerts multi-target neuroprotective effects in 3xTg-AD mice by modulating Aβ and tau pathologies, preserving synaptic structure, and attenuating RAGE-mediated neuroinflammation. These findings support ZGP as a promising integrative therapeutic strategy for AD. Show less
📄 PDF DOI: 10.1007/s12035-025-05336-z
BACE1
Hongqin Li, Rong Xu, Liquan Xie +3 more · 2025 · Journal of interferon & cytokine research : the official journal of the International Society for Interferon and Cytokine Research · SAGE Publications · added 2026-04-24
Bushen Huoxue Acupuncture shows potential in treating neurodegenerative diseases, but its mechanisms remain incompletely understood. Using the senescence-accelerated mouse-prone 8 (SAMP8) mouse model, Show more
Bushen Huoxue Acupuncture shows potential in treating neurodegenerative diseases, but its mechanisms remain incompletely understood. Using the senescence-accelerated mouse-prone 8 (SAMP8) mouse model, we assessed cognitive function via the Morris water maze test, hippocampal neuronal apoptosis with terminal deoxynucleotidyl transferase dUTP nick end labeling staining, and microglial activation through immunohistochemistry. Serum levels of inflammatory cytokines [tumor necrosis factor-alpha, interleukin (IL)-1β, and IL-6] were quantified by enzyme-linked immunosorbent assay. The expression of SIRT2 pathway-related proteins, along with Aβ deposition, was analyzed using Western blotting, immunohistochemistry, and immunofluorescence. The results demonstrated that Bushen Huoxue Acupuncture improved cognitive function in SAMP8 mice, reducing hippocampal neuronal apoptosis and decreasing serum levels of pro-inflammatory cytokines. Additionally, it reduced the levels of Aβ42, a more aggregation-prone and toxic Aβ subtype, in both hippocampal tissues and serum, as well as the number of CD68-positive cells in hippocampal tissues, suggesting the inhibition of amyloid pathology and neuroinflammatory. The treatment also downregulated SIRT2, BACE1, and APP-CTF while increasing RTN4B expression. Notably, Bushen Huoxue Acupuncture outperformed non-acupoint acupuncture in enhancing cognitive function and reducing inflammation. Our findings indicate that Bushen Huoxue Acupuncture alleviates cognitive deficits and neuroinflammation by suppressing the SIRT2-mediated RTN4B/BACE1 pathway, highlighting acupuncture as a promising therapy for neurodegenerative diseases. Show less
no PDF DOI: 10.1177/10799907251391519
BACE1
Ye Huang, Min Han, Yinglin Fu +6 more · 2025 · European journal of pharmacology · Elsevier · added 2026-04-24
Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an unclear pathogenesis and no effective treatment methods. HY-021068 (HY), a novel class I drug, exhibits significant neuropr Show more
Alzheimer's disease (AD) is a progressive neurodegenerative disorder with an unclear pathogenesis and no effective treatment methods. HY-021068 (HY), a novel class I drug, exhibits significant neuroprotective properties in ischemic brain injury. Recent studies suggest that neuronal ferroptosis may be a critical contributor to the onset and progression of AD. However, it is still unclear whether HY treatment has protective effects on AD by inhibiting ferroptosis. In this study, APP/PS1 double transgenic mice were used to investigate the effect and mechanism of HY in AD. In vitro, HT22 cells were stimulated with Amyloid β Show less
no PDF DOI: 10.1016/j.ejphar.2025.178349
BACE1
Beiyu Zhang, Yunan Li, Huan Li +2 more · 2025 · Brain sciences · MDPI · added 2026-04-24
Alzheimer's disease (AD) is the most common cause of dementia, characterized by progressive cognitive decline and neuropathological hallmarks, including amyloid-β (Aβ) plaques, neurofibrillary tangles Show more
Alzheimer's disease (AD) is the most common cause of dementia, characterized by progressive cognitive decline and neuropathological hallmarks, including amyloid-β (Aβ) plaques, neurofibrillary tangles (NFTs), and neurodegeneration. Since the amyloid cascade hypothesis was proposed, Aβ has remained a central therapeutic target, with interventions aiming to reduce Aβ production, aggregation, or downstream toxicity. This review first outlines the historical development of the Aβ hypothesis and the two major APP processing pathways (α-cleavage and β-cleavage), highlighting the role of biomarkers in early diagnosis, patient stratification, and regulatory approval. We then summarize the development and clinical outcomes of anti-Aβ small-molecule drugs, including β-secretase inhibitors, γ-secretase modulators, Aβ aggregation inhibitors, receptor/synapse modulators, and metabolic or antioxidant modalities. We further review the progression of biologic therapies, with a particular focus on monoclonal antibodies, vaccines, and emerging gene-silencing strategies, such as small interfering RNA (siRNA) and antisense oligonucleotides. Finally, we discuss future perspectives, including next-generation biologics, multi-target approaches, optimized delivery platforms, and early-prevention strategies. Collectively, these efforts underscore both the challenges and opportunities in translating anti-Aβ therapies into meaningful clinical benefits for patients with AD. Show less
📄 PDF DOI: 10.3390/brainsci15101101
BACE1
Jin Li, Jiawen Wang, Yaodong Li +7 more · 2025 · Biology · MDPI · added 2026-04-24
Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia, with current therapies offering only limited symptomatic relief and lacking disease-modifying ef Show more
Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia, with current therapies offering only limited symptomatic relief and lacking disease-modifying efficacy. Addressing this critical therapeutic gap, natural multi-target compounds like mulberroside A (MsA)-a bioactive glycoside from Show less
📄 PDF DOI: 10.3390/biology14091114
BACE1
Huijun Li, Yawei Fan, Chan Chen +3 more · 2025 · Biochemistry and biophysics reports · Elsevier · added 2026-04-24
Synaptic dysfunction and synapse loss occur in Alzheimer's disease (AD). The current study aimed to identify synaptic-related genes with diagnostic potential for AD. Differentially expressed genes (DE Show more
Synaptic dysfunction and synapse loss occur in Alzheimer's disease (AD). The current study aimed to identify synaptic-related genes with diagnostic potential for AD. Differentially expressed genes (DEGs) were overlapped with phenotype-associated module selected through weighted gene co-expression network analysis (WGCNA), and synaptic-related genes. The overlapped hub genes were further processed using machine learning algorithms, intersected with module gene from protein-protein interaction (PPI) network constructed with DEGs, to yield co-hub genes. The diagnostic potentials of the co-hub genes were examined by receiver operating characteristic (ROC) analysis. Correlation between co-hub genes with clinical features and immune cell infiltration was analyzed. Finally, the expression of co-hub genes was analyzed in several datasets and validated in AD transgenic mice. A total of three co-hub genes were identified, including MAP1B, L1CAM, and GABBR2. GABBR2 showed area under the curve (AUC) values of 0.98, 0.81, and 0.88 in the training and two external validation datasets. GABBR2 was negatively correlate with beta- and gamma-secretase activities, and infiltration of natural killer T cells and effector memory CD8 T cells. Finally, GABBR2 was validated to be downregulated in AD transgenic mice, aligning with bioinformatic findings. GABBR2 overexpression in N2a/APP cells increased ADAM10 while decreased of BACE1, leading to upregulation of sAPPα while downregulation of sAPPβ. In conclusion, GABBR2 acts as a novel biomarker for the diagnosis of AD and negatively correlated with Aβ in AD. Show less
📄 PDF DOI: 10.1016/j.bbrep.2025.102035
BACE1
Yang Yu, Wenjun Xiao, Zhixin Ma +3 more · 2025 · Journal of neuroinflammation · BioMed Central · added 2026-04-24
Alzheimer’s disease (AD) is the most common type of dementia. A major pathological feature of AD is the aggregation of amyloid-β (Aβ), primarily driven by β-secretase (BACE1) activity. However, the me Show more
Alzheimer’s disease (AD) is the most common type of dementia. A major pathological feature of AD is the aggregation of amyloid-β (Aβ), primarily driven by β-secretase (BACE1) activity. However, the mechanisms underlying continuous Aβ accumulation remain unclear. Circulating extracellular vesicles (EVs) may play a crucial role in AD progression. Here, we investigate whether circulating EVs in AD promote Aβ generation and aggregation. In this study, we found that compared to WTEVs (circulating EVs isolated from WT mice), APPEVs (circulating EVs isolated from APP/PS1 mice) showed higher concentrations and activated the JAK2-STAT1 pathway in neurons, upregulating BACE1 expression and activity. This cascade promoted amyloid precursor protein (APP) β-cleavage in lipid rafts, inducing substantial Aβ generation. Proteomic analysis revealed complement C1q in APPEVs as a key protein activating the JAK2-STAT1-BACE1 pathway. Furthermore, in vivo experiments demonstrated that intravenously injected APPEVs crossed the blood-brain barrier without damaged the epithelial tight junction, promoting BACE1 expression in neurons, and enhancing Aβ production and aggregation in brain. Inhibition of C1q mitigated these effects in both in vitro and in vivo experiments. In conclusion, during the progression of AD, circulating EVs containing complement C1q are delivered to neurons, activating their JAK2-STAT1 signaling pathway. This activation upregulates the expression of BACE1, subsequently enhancing the β-cleavage of APP in lipid rafts. These events lead to a substantial increase in Aβ production, exacerbating the pathological progression of AD. The online version contains supplementary material available at 10.1186/s12974-025-03528-x. Show less
📄 PDF DOI: 10.1186/s12974-025-03528-x
BACE1
Zhaohan Li, Jun Yang, Jianan Li +10 more · 2025 · Translational neurodegeneration · BioMed Central · added 2026-04-24
The deposition of toxic aggregated amyloid-β (Aβ), resulting from continuous cleavage of amyloid precursor protein (APP) by β-site APP cleaving enzyme 1 (BACE1) and γ-secretase, is a key pathogenic ev Show more
The deposition of toxic aggregated amyloid-β (Aβ), resulting from continuous cleavage of amyloid precursor protein (APP) by β-site APP cleaving enzyme 1 (BACE1) and γ-secretase, is a key pathogenic event in Alzheimer's disease (AD). Small interfering RNAs (siRNA) have shown great potential for disease treatment by specifically silencing target genes. However, the poor brain delivery efficiency of siRNAs limits their therapeutic efficacy against AD. We designed a simplified and effective BACE1 siRNA (siBACE1) delivery system, namely, dendritic polyamidoamine modified with the neurotropic virus-derived peptide RVG29 and polyethylene glycol (PPR@siBACE1). PPR@siBACE1 crossed the blood-brain barrier efficiently and entered brain parenchyma in large amount, with subsequent neurotropism and potential microglia-targeting ability. Both in vitro and in vivo studies validated the effective brain delivery of siBACE1 and strong BACE1 silencing efficiency. Treatment of AD mice with PPR@siBACE1 inhibited the production of Aβ, potentiated Aβ phagocytosis by microglia, improved the memory deficits and reduced neuroinflammatory response in AD mice. This study provides a reliable delivery platform for gene therapies for AD. Show less
📄 PDF DOI: 10.1186/s40035-025-00503-7
BACE1
Qinze Yu, Chang Zhou, Jiyue Jiang +2 more · 2025 · Bioinformatics (Oxford, England) · Oxford University Press · added 2026-04-24
Accurate and generalizable prediction of drug-target interactions (DTIs) remains a critical challenge for drug discovery, particularly when addressing underexplored targets and compounds. Recent advan Show more
Accurate and generalizable prediction of drug-target interactions (DTIs) remains a critical challenge for drug discovery, particularly when addressing underexplored targets and compounds. Recent advances in graph neural networks and large-scale pre-trained models offer new opportunities to capture rich structural and functional features essential for DTI prediction while enhancing the generalization ability. We present GS-DTI, a graph structure-based DTI prediction framework that integrates molecular graph transformers, protein language models, and protein tertiary structure. Our method achieved robust and interpretable DTI predictions. GS-DTI extracts drug features from SMILES-derived molecular graphs using a knowledge-guided pre-trained transformer, while protein features are derived from both sequence and predicted 3D structure for comprehensive representation. A multi-task loss function equipped with contrastive learning is adopted to enhance generalization and functional interpretability. Extensive experiments on the benchmarks and challenging cross-domain settings demonstrate that GS-DTI achieves state-of-the-art performance. Notably, our model improves the MCC by over 10% compared to previous methods in the drug-target pair cold start test. The model can pinpoint the binding pockets of the targets, offering robust interpretability, and case studies show GS-DTI's promising potential in virtual screening for new candidate drugs of BACE1. The GS-DTI source code and processed datasets are available at https://github.com/purvavideha/GSDTI. All experimental data are derived from public sources. Show less
📄 PDF DOI: 10.1093/bioinformatics/btaf445
BACE1
Nan Wang, Xin-Zhu Li, Xiao-Wen Jiang +10 more · 2025 · Molecular neurobiology · Springer · added 2026-04-24
no PDF DOI: 10.1007/s12035-025-05265-x
BACE1
Weiyao Zhu, Yu Wang, Ming Qin +3 more · 2025 · Aging and disease · added 2026-04-24
Alzheimer's disease (AD) represents a neurodegenerative condition characterized by steadily increasing prevalence and incidence, arising significant challenge to both patients and social insurance. Ho Show more
Alzheimer's disease (AD) represents a neurodegenerative condition characterized by steadily increasing prevalence and incidence, arising significant challenge to both patients and social insurance. However, the etiology of AD remains controversial so far, and pathogenesis is far more complicated. Presently, no definitive therapeutic methodologies were available for AD, and only partial symptomatic relief can be achieved. Consequently, early diagnosis and intervention are emergently needed for AD patients. The diagnostic criteria for AD are continuously evolving, and biomarker testing is becoming increasingly critical for diagnosis. Currently, the diagnosis of AD primarily relies on the detection of pathological proteins through cerebrospinal fluid (CSF) testing and positron emission tomography (PET). However, factors such as high costs, operational contraindications, and invasiveness limited the application of these technologies, making them particularly challenging to implement in large-scale clinical trials and screenings. Core fluid biomarkers for AD including β-amyloid (Aβ), phosphorylated tau protein (p-tau), total tau protein (t-tau), and their combinations were found in CSF. Although these biomarkers were demonstrated with significant specificity and sensitivity, challenges remain high concerning the collection of CSF. Blood-derived biomarkers for Aβ and tau proteins are essential for preliminary screening, diagnosis, and monitoring of AD. Additionally, other bodily fluids such as saliva, urine, and tears have been investigated for their potential as biomarkers, offering unique characteristics and applications. Emerging biomarkers, including neurofilament light chain (NfL), neurogranin (Ng), Beta-site APP cleaving enzyme 1 (BACE1), synaptosome associated protein 25 (SNAP-25), as well as inflammation-related and gene-related factors, provided valuable insights into the diagnosis and pathogenesis of AD from diverse perspectives. Despite the substantial progress made in AD biomarker research, there are still baskets of limitations concerning the complication of the disease. The current review focused on the reported literature to summarize the biomarkers associated with AD. By critically analyzing studies published over the past decade, we aimed to strengthen the recent research progress, theoretical frameworks, and unresolved challenges related to AD biomarkers. Show less
no PDF DOI: 10.14336/AD.2025.0761
BACE1
Weiwei Qi, Yanlan Long, Ziming Li +11 more · 2025 · eLife · added 2026-04-24
Accumulation of amyloid-β (Aβ) peptides and hyperphosphorylated tau proteins in the hippocampus triggers cognitive memory decline in Alzheimer's disease (AD). The incidence and mortality of sporadic A Show more
Accumulation of amyloid-β (Aβ) peptides and hyperphosphorylated tau proteins in the hippocampus triggers cognitive memory decline in Alzheimer's disease (AD). The incidence and mortality of sporadic AD were tightly associated with diabetes and hyperlipidemia, while the exact linked molecular mechanism is uncertain. Here, the present investigation identified significantly elevated serum Kallistatin levels in AD patients concomitant with hyperglycemia and hypertriglyceridemia, suggesting potential crosstalk between neuroendocrine regulation and metabolic dysregulation in AD pathophysiology. In addition, the constructed Kallistatin-transgenic (KAL-TG) mice defined its cognitive memory impairment phenotype and lower long-term potentiation in hippocampal CA1 neurons accompanied by increased Aβ deposition and tau phosphorylation. Mechanistically, Kallistatin could directly bind to the Notch1 receptor and thereby upregulate BACE1 expression by inhibiting PPARγ signaling, resulting in Aβ cleavage and production. Besides, Kallistatin could promote the phosphorylation of tau by activating GSK-3β. Fenofibrate, a hypolipidemic drug, could alleviate cognitive memory impairment by downregulating Aβ and tau phosphorylation of KAL-TG mice. Collectively, the experiments clarified a novel mechanism for Aβ accumulation and tau protein hyperphosphorylation regulation by Kallistatin, which might play a crucial role in linking metabolic syndromes and cognitive memory deterioration, and suggested that fenofibrate might have the potential for treating metabolism-related AD. Show less
📄 PDF DOI: 10.7554/eLife.99462
BACE1
Ruoping Yanzhang, Zhaojie Yang, Xiangping Li +5 more · 2025 · Discover oncology · Springer · added 2026-04-24
Osteosarcoma (OS) is an invasive and lethal malignancy showing a low 5 year survival rate, underscoring the need for identifying new therapeutic targets and their inhibitors to enhance prevention and Show more
Osteosarcoma (OS) is an invasive and lethal malignancy showing a low 5 year survival rate, underscoring the need for identifying new therapeutic targets and their inhibitors to enhance prevention and treatment strategies. In this study, in vitro experiments including CCK-8 assay, anchorage-independent growth assays, and plate cloning assays were used to detect the anti-proliferation ability of natural compound tangeretin towards OS cells. An integrated approach was performed including WGCNA and network pharmacology to identify the key genes of tangeretin for the treatment of OS. Multigene diagnostic model, reverse transcription quantitative polymerase chain reaction (RT-qPCR) analysis along with molecular docking analysis were further conducted to validate the reliability of the targets obtained by bioinformatics methods. Single-cell and gene enrichment analyses were chosen to explore the mechanism of tangeretin in OS. Hub genes identified by the bioinformatics strategy included ABCC1, AKR1C3, BACE1, and CA12. RT-qPCR validation and molecular docking analysis confirmed that ABCC1 and BACE1 were the most likely potential targets. A multigene diagnostic model for OS demonstrated moderate accuracy of the hub genes. Single-cell sequencing results indicated that these two hub targets were closely related to OS and provided more potential mechanisms for targeting OS. Our research highlights the therapeutic potential of the natural compound tangeretin and its antineoplastic mechanisms in OS. It offers new insights into the molecular mechanisms of tangeretin, paving the way for the development of effective OS treatments. Show less
📄 PDF DOI: 10.1007/s12672-025-03221-8
BACE1