👤 Ailong Huang

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1004
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Also published as: Ai-Chun Huang, Ai-long Huang, Aijie Huang, Aimin Huang, Alden Y Huang, An-Fang Huang, Annie Huang, Aohuan Huang, Ariane Huang, Baihai Huang, Baisong Huang, Bao-Hua Huang, Bao-Yi Huang, Baoqin Huang, Baoying Huang, Benjamin J Huang, Benlin Huang, Bevan E Huang, Bi Huang, Biao Huang, Bin Huang, Binfang Huang, Bing Huang, Bingcang Huang, Bingkun Huang, Bizhi Huang, Bo Huang, Bo-Shih Huang, Bor-Ren Huang, Bowen Huang, Boyue Huang, C Y Huang, Caihong Huang, Caiyun Huang, Can Huang, Canhua Huang, Caoxin Huang, Cathelin Huang, Catherine Huang, Chang Ming Huang, Chang X Huang, Chang-Jen Huang, Changjiang Huang, Chao Huang, Chao Wei Huang, Chao-Wei Huang, Chao-Yuan Huang, Chaolin Huang, Chaoqun Huang, Chaowang Huang, Chaoyang Huang, Chen Huang, Chen-Na Huang, Chen-Ping Huang, Cheng Huang, Chengcheng Huang, Chengrui Huang, Chenshen Huang, Chenxiao Huang, Chi-Cheng Huang, Chi-Shuan Huang, Chia-Chang Huang, Chia-Wei Huang, Chieh-Cheng Huang, Chieh-Liang Huang, Chien-Hsun Huang, Chih-Chun Huang, Chih-Hsiang Huang, Chih-Jen Huang, Chih-Ting Huang, Chih-Yang Huang, Chin-Chang Huang, Chin-Chou Huang, Ching-Shan Huang, Ching-Shin Huang, Ching-Tang Huang, Ching-Wei Huang, Chiu-Ju Huang, Chiu-Jung Huang, Chiun-Sheng Huang, Chong Huang, Chongbiao Huang, Christine S Huang, Chuan Huang, Chuanbing Huang, Chuanhong Huang, Chuanjiang Huang, Chuanjun Huang, Chuansheng Huang, Chuiguo Huang, Chun Huang, Chun-Mei Huang, Chun-Yao Huang, Chun-Yin Huang, Chunfan Huang, Chung-Hsiung Huang, Chunhong Huang, Chunjian Huang, Chunkai Huang, Chunlan Huang, Chunling Huang, Chunshuai Huang, Chunxia Huang, Chunyao Huang, Chunyi Huang, Chunying Huang, Chunyu Huang, Chuxin Huang, Chuying Huang, Congcong Huang, Cuiyu Huang, Da Huang, Dajun Huang, Dan Huang, Dane Huang, Danqing Huang, Dantong Huang, David Huang, David J Huang, De Huang, De-Jun Huang, Dejia Huang, Dengjun Huang, Dianhua Huang, Dishu Huang, Dong Huang, Donglan Huang, Dongmei Huang, Dongni Huang, Dongqin Huang, Dongqing Huang, Dongsheng Huang, Dongyu Huang, Du-Juan Huang, Emily C Huang, Enhao Huang, Enping Huang, Eric Huang, Erya Huang, F Huang, Fan Huang, Fang Huang, Fang-Ling Huang, Fangling Huang, Fei Huang, Fei Wan Huang, Feiruo Huang, Feiteng Huang, Feizhou Huang, Feng Huang, Fengxian Huang, Fengyu Huang, Franklin W Huang, Fu-Chen Huang, Fu-Mei Huang, Fubiao Huang, Fude Huang, Fuhao Huang, Furong Huang, G Huang, Gairong Huang, Gang Huang, Gao-Zhong Huang, Gaoxingyu Huang, Ge Huang, Guang-Jian Huang, Guang-Yun Huang, Guangjian Huang, Guangming Huang, Guangqian Huang, Guangrui Huang, Guanhong Huang, Guanling Huang, Guanning Huang, Guanqun Huang, Guanrong Huang, Guicheng Huang, Guodong Huang, Guohong Huang, Guoping Huang, Guoqian Huang, Guowei Huang, Guoxing Huang, Guoying Huang, Guoyong Huang, Guoyuan Huang, H Huang, H S Huang, Hai Huang, Haigang Huang, Haihong Huang, Hailin Huang, Haimiao Huang, Haixin Huang, Haiyan Huang, Han-Chang Huang, Hanxia Huang, Hao Huang, Hao-Fei Huang, Haobo Huang, Haochu Huang, Haomin Huang, Haoyu Huang, Haoyue Huang, Haozhang Huang, Haozhong Huang, He Huang, Hefeng Huang, Heguang Huang, Helen Huang, Heming Huang, Hengbin Huang, Heqing Huang, Hete Huang, Hong Huang, Hongbiao Huang, Hongcan Huang, Hongda Huang, Hongfei Huang, Hongfeng Huang, Honghui Huang, Hongou Huang, Hongqiang Huang, Hongyan Huang, Hongyang Huang, Hongyi Huang, Hongying Huang, Hongyu Huang, Hongyun Huang, Hsi-Yuan Huang, Hsien-Da Huang, Hsing-Yen Huang, Hsu Chih Huang, Hsuan-Cheng Huang, Hsuan-Ying Huang, Hu Huang, Hua Huang, Huafei Huang, Huaju Huang, Huan Huang, Huanhuan Huang, Huanliang Huang, Huapin Huang, Huashan Huang, Huayun Huang, Hui Huang, Hui-Huang Huang, Hui-Kuang Huang, Hui-Yu Huang, Huibin Huang, Huifen Huang, Huiling Huang, Huimin Huang, Huina Huang, Huiqiao Huang, Huixian Huang, Huixin Huang, Huiyan Huang, Huiyu Huang, Huizhe Huang, Huizhen Huang, Hy Huang, I-Chieh Huang, J V Huang, Janice J Huang, Jasmin Huang, Jeffrey K Huang, Jia Huang, Jia-Jia Huang, Jiaan Huang, Jiahui Huang, Jiajin Huang, Jiajun Huang, Jian Huang, Jian-Dong Huang, Jiana Huang, Jianbiao Huang, Jianbing Huang, Jianfang Huang, Jianfeng Huang, Jiangfeng Huang, Jiangtao Huang, Jiangwei Huang, Jianhua Huang, Jianlu Huang, Jianmin Huang, Jianming Huang, Jiansheng Huang, Jianzhen Huang, Jiao-Qian Huang, Jiaoti Huang, Jiaotian Huang, Jiaqi Huang, Jiawen Huang, Jiaxing Huang, Jiayu Huang, Jiayue Huang, Jie Huang, Jie Qi Huang, Jiechun Huang, Jieli Huang, Jieling Huang, Jieping Huang, Jin Huang, Jin-Di Huang, Jin-Feng Huang, Jin-Hong Huang, Jin-Yan Huang, Jinbao Huang, Jinfang Huang, Jing Huang, Jing-Fei Huang, Jingang Huang, Jinghan Huang, Jingjing Huang, Jingkun Huang, Jinglong Huang, Jingtao Huang, Jingxian Huang, Jingyong Huang, Jingyuan Huang, Jingyue Huang, Jinhua Huang, Jinling Huang, Jinlu Huang, Jinshu Huang, Jinxing Huang, Jinyan Huang, Jinzhou Huang, Jiuhong Huang, Jiyu Huang, Ju Huang, Juan Huang, Jucun Huang, Jun Huang, Jun-Hua Huang, Jun-You Huang, Junhao Huang, Junhua Huang, Junjie Huang, Junming Huang, Junning Huang, Junqi Huang, Junwen Huang, Junyuan Huang, Junyun Huang, Juxiang Huang, K Huang, K N Huang, Kai Huang, Kaipeng Huang, Kang Huang, Kangbo Huang, Kate Huang, Katherine Huang, Ke Huang, Ke-Ke Huang, Ke-Pu Huang, Kevin Huang, Kevin Y Huang, Kuan-Chun Huang, Kui-Yuan Huang, Kuiyuan Huang, Kun Huang, Kuo-Hsiang Huang, Kuo-Hung Huang, L Huang, L-B Huang, Laiqiang Huang, Lan Huang, Lanlan Huang, Lei Huang, Leijuan Huang, Li Huang, Li-Hao Huang, Li-Jiang Huang, Li-Juan Huang, Li-Jun Huang, Li-Ping Huang, Li-Rung Huang, Li-Wei Huang, Li-Yun Huang, Lian Huang, Liang Huang, Liang-Yu Huang, Liangchong Huang, Lianggui Huang, Libin Huang, Lige Huang, Lihua Huang, Lijia Huang, Lijiang Huang, Lijuan Huang, Lijun Huang, Lili Huang, Limin Huang, Liming Huang, Lin Huang, Linchen Huang, Ling Huang, Ling-Chun Huang, Ling-Jin Huang, Lingling Huang, Lining Huang, Linjing Huang, Linsheng Huang, Linxue Huang, Linyuan Huang, Liping Huang, Liqiong Huang, Lixia Huang, Lixiang Huang, Lixuan Huang, Lixue Huang, Lizhen Huang, Longfei Huang, Lu Huang, Lu-Jie Huang, Lu-Qi Huang, Luanluan Huang, Luqi Huang, Luyang Huang, Luyao Huang, Lvzhen Huang, M C Huang, Man Huang, Manning Y Huang, Manyun Huang, Mao-Mao Huang, Mei Huang, Meihua Huang, Meina Huang, Meixiang Huang, Melissa Y Huang, Meng-Chuan Huang, Meng-Fan Huang, Meng-Na Huang, MengQian Huang, Menghao Huang, Mengjie Huang, Mengjun Huang, Mengnan Huang, Mengting Huang, Mengzhen Huang, Mia L Huang, Miao Huang, Min Huang, Ming-Lu Huang, Ming-Shyan Huang, Mingjian Huang, Mingjun Huang, Minglei Huang, Mingrui Huang, Mingwei Huang, Mingxuan Huang, Mingyu Huang, Mingyuan Huang, Minjun Huang, Minqi Huang, Minxuan Huang, Minyuan Huang, N Huang, Na Huang, Nian Huang, Nianyuan Huang, Ning-Na Huang, Ning-Ping Huang, Ninghao Huang, Nongyu Huang, Pan Huang, Pang-Shuo Huang, Paul L Huang, Pei Huang, Pei-Chi Huang, Pei-Ying Huang, Peiying Huang, Peng Huang, Peng-Fei Huang, Pengyu Huang, Piao-Piao Huang, Piaopiao Huang, Pin-Rui Huang, Ping Huang, Pingping Huang, Pintong Huang, Po-Hsun Huang, Po-Jung Huang, Poyao Huang, Qi Huang, Qi-Tao Huang, Qian Huang, Qiang Huang, Qianqian Huang, Qiaobing Huang, Qibin Huang, Qidi Huang, Qin Huang, Qing Huang, Qing-yong Huang, Qingjiang Huang, Qingke Huang, Qingling Huang, Qingqing Huang, Qingsong Huang, Qingxia Huang, Qingxing Huang, Qingyu Huang, Qingzhi Huang, Qinlou Huang, Qiong Huang, Qiubo Huang, Qiumin Huang, Qiuming Huang, Qiuru Huang, Qiuyin Huang, Qiuyue Huang, Qizhen Huang, Quanfang Huang, Qun Huang, R H Huang, R Stephanie Huang, Rae-Chi Huang, Ran Huang, Renbin Huang, Renhua Huang, Renli Huang, Richard Huang, Richard S P Huang, Riqing Huang, Ritai Huang, Robert J Huang, Rong Huang, Rong Stephanie Huang, Ronghua Huang, Ronghui Huang, Rongjie Huang, Rongrong Huang, Rongxiang Huang, Ru-Ting Huang, Ruby Yun-Ju Huang, Rui Huang, Ruihua Huang, Ruijin Huang, Ruina Huang, Ruiyan Huang, Ruizhen Huang, Runyue Huang, Ruo-Hui Huang, S Huang, S Y Huang, S Z Huang, Saisai Huang, San-Yuan Huang, See-Chang Huang, Sen Huang, Serina Huang, Shan Huang, Shang-Ming Huang, Shanhe Huang, Shanshan Huang, Shaojun Huang, Shaoxin Huang, Shaoze Huang, Shau Ku Huang, Shau-Ku Huang, Shenan Huang, Sheng-He Huang, Shengfeng Huang, Shengjie Huang, Shengnan Huang, Shengyan Huang, Shengyun Huang, Shi-Feng Huang, Shi-Shi Huang, Shi-Ying Huang, Shiang-Suo Huang, Shichao Huang, Shih-Chiang Huang, Shih-Wei Huang, Shih-Yi Huang, Shihao Huang, Shijing Huang, Shilu Huang, Shixia Huang, Shiya Huang, Shiying Huang, Shiyun Huang, Shoucheng Huang, Shu Huang, Shu-Pang Huang, Shu-Pin Huang, Shu-Qiong Huang, Shu-Wei Huang, Shu-Yi Huang, Shu-ying Huang, Shuai Huang, Shuang Huang, Shungen Huang, Shuo Huang, Shushu Huang, Shutong Huang, Shuwen Huang, Si-Yang Huang, Sidong Huang, Sihua Huang, Sijia Huang, Sinchun Huang, Sisi Huang, Sixiu Huang, Song Bin Huang, Song-Mei Huang, Songmei Huang, Songming Huang, Songqian Huang, Steven Huang, Steven Kuan-Hua Huang, Suli Huang, Sung-Ying Huang, Susan M Huang, Suwen Huang, Taiqi Huang, Tang-Hsiu Huang, Tao Huang, Te-Hsuan Huang, Tengda Huang, Tengfei Huang, Tian Hao Huang, Tianhao Huang, Tianpu Huang, Tiantian Huang, Tieqiu Huang, Tim H Huang, Ting Huang, Tinghua Huang, Tingping Huang, Tingqin Huang, Tingting Huang, Tingxuan Huang, Tingyun Huang, Tong Huang, Tongsheng Huang, Tongtong Huang, Tony T Huang, Tse-Shun Huang, Tseng-Yu Huang, Tsung-Wei Huang, Tzu-Rung Huang, Wan-Ping Huang, Way-Ren Huang, Wei Huang, Wei-Chi Huang, Weibin Huang, Weicheng Huang, Weifeng Huang, Weihua Huang, Weijun Huang, Weiqi Huang, Weisu Huang, Weiwei Huang, Weixue Huang, Weizhen Huang, Wen Huang, Wen-yu Huang, Wenbin Huang, Wenda Huang, Wenfang Huang, Wenfeng Huang, Wenhua Huang, Wenji Huang, Wenjie Huang, Wenjun Huang, Wenqiao Huang, Wenqing Huang, Wenqiong Huang, Wenshan Huang, Wentao Huang, Wenxin Huang, Wenya Huang, Wenying Huang, Wunan Huang, Wuqing Huang, X F Huang, X Huang, Xi Huang, Xian-sheng HUANG, Xiang Huang, Xianghua Huang, Xianglong Huang, Xiangming Huang, Xianping Huang, Xianqing Huang, Xiansheng Huang, Xianwei Huang, Xianxi Huang, Xianxian Huang, Xianying Huang, Xianzhang Huang, Xiao Huang, Xiao-Fang Huang, Xiao-Fei Huang, Xiao-Ming Huang, Xiao-Song Huang, Xiao-Yan Huang, Xiao-Yong Huang, Xiao-Yu Huang, XiaoFang Huang, Xiaochun Huang, Xiaofei Huang, Xiaofeng Huang, Xiaohong Huang, Xiaohua Huang, Xiaojie Huang, Xiaojing Huang, Xiaojuan Huang, Xiaolan Huang, Xiaoli Huang, Xiaolin Huang, Xiaoman Huang, Xiaomin Huang, Xiaoqing Huang, Xiaoshuai Huang, Xiaowen Huang, Xiaowu Huang, Xiaoxia Huang, Xiaoyan Huang, Xiaoying Huang, Xiaoyu Huang, Xiaoyuan Huang, Xiaoyun Huang, Xiaozhun Huang, Xiayang Huang, Xichang Huang, Xie-Lin Huang, Xin Huang, Xin-Di Huang, Xinen Huang, Xinfeng Huang, Xingguo Huang, Xingming Huang, Xingqin Huang, Xingru Huang, Xingxu Huang, Xingya Huang, Xingzhen Huang, Xinwen Huang, Xinyi Huang, Xinying Huang, Xinyue Huang, Xinzhu Huang, Xiongfeng Huang, Xionggao Huang, Xiuju Huang, Xiuyun Huang, Xiuzhen Huang, Xiwen Huang, Xu Huang, Xu-Feng Huang, Xuan Huang, Xuanzhang Huang, Xucong Huang, Xudong Huang, Xue-Ying Huang, Xue-shuang Huang, Xuehong Huang, Xuejie Huang, Xuejing Huang, Xuejun Huang, Xuemei Huang, Xueming Huang, Xueqi Huang, Xuewei Huang, Xuezhe Huang, Xuhui Huang, Xuliang Huang, Xun Huang, Xuxiong Huang, Y Huang, Y Joyce Huang, Y S Huang, Ya-Chih Huang, Ya-Dong Huang, Ya-Fang Huang, Ya-Ru Huang, Yabo Huang, Yadong Huang, Yafang Huang, Yajiao Huang, Yajuan Huang, Yali Huang, Yamei Huang, Yan Huang, Yan-Lin Huang, Yan-Qing Huang, Yan-Ting Huang, Yang Huang, Yang Zhong Huang, Yangqing Huang, Yangyang Huang, Yanhao Huang, Yani Huang, Yanjun Huang, Yanlong Huang, Yanna Huang, Yanping Huang, Yanqin Huang, Yanqing Huang, Yanqun Huang, Yanru Huang, Yanshan Huang, Yansheng Huang, Yanxia Huang, Yanyan Huang, Yanyao Huang, Yao Huang, Yao-Kuang Huang, Yaowei Huang, Yatian Huang, Yating Huang, Ye Huang, Yechao Huang, Yen-Chu Huang, Yen-Ning Huang, Yen-Tsung Huang, Yeqing Huang, Yewei Huang, Yi Huang, Yi-Chun Huang, Yi-Jan Huang, Yi-Jia Huang, Yi-Wen Huang, Yi-ping Huang, Yichao Huang, Yichuan Huang, Yicong Huang, Yifan Huang, Yihao Huang, Yiheng Huang, Yihong Huang, Yikeng Huang, Yilin Huang, Yin Huang, Yin-Tsen Huang, Ying Huang, Ying-Hsuan Huang, Ying-Jung Huang, Ying-Zhi Huang, Yinghua Huang, Yingying Huang, Yingzhen Huang, Yingzhi Huang, Yiping Huang, Yiquan Huang, Yishan Huang, Yiwei Huang, Yixian Huang, Yizhou Huang, Yong Huang, Yong-Fu Huang, Yongbiao Huang, Yongcan Huang, Yongjie Huang, Yongqi Huang, Yongsheng Huang, Yongtong Huang, Yongye Huang, Yongyi Huang, Yongzhen Huang, Youheng Huang, Youyang Huang, Yu Huang, Yu-Ching Huang, Yu-Chu Huang, Yu-Chuen Huang, Yu-Chyi Huang, Yu-Fang Huang, Yu-Han Huang, Yu-Jie Huang, Yu-Lei Huang, Yu-Ren Huang, Yu-Shu Huang, Yu-Ting Huang, Yuan Huang, Yuan-Lan Huang, Yuan-Li Huang, Yuan-Lu Huang, Yuancheng Huang, Yuanpeng Huang, Yuanshuai Huang, Yuanyu Huang, Yuanyuan Huang, Yue Huang, Yue-Hua Huang, Yuedi Huang, Yueh-Hsiang Huang, Yuehong Huang, Yuejun Huang, Yueye Huang, Yuezhen Huang, Yufang Huang, Yufen Huang, Yuguang Huang, Yuh-Chin T Huang, Yuhong Huang, Yuhua Huang, Yuhui Huang, Yujia Huang, Yujie Huang, Yulin Huang, Yumei Huang, Yumeng Huang, Yun Huang, Yun-Juan Huang, Yunchao Huang, Yung-Hsin Huang, Yung-Yu Huang, Yunmao Huang, Yunpeng Huang, Yunru Huang, Yunyan Huang, Yuping Huang, Yuqi Huang, Yuqiang Huang, Yuqiong Huang, Yusi Huang, Yutang Huang, Yuting Huang, Yutong Huang, Yuxian Huang, Yuxin Huang, Yuxuan Huang, Yuyang Huang, Yuying Huang, Z Huang, Z Z Huang, Z-Y Huang, Zebin Huang, Zebo Huang, Zehua Huang, Zeling Huang, Zengwen Huang, Zhang Huang, Zhao Huang, Zhaoxia Huang, Zhe Huang, Zhen Huang, Zhenfei Huang, Zheng Huang, Zheng-Xiang Huang, Zhengwei Huang, Zhengxian Huang, Zhengxiang Huang, Zhengyang Huang, Zhenlin Huang, Zhenrui Huang, Zhenyao Huang, Zhenyi Huang, Zhi Huang, Zhi-Ming Huang, Zhi-Qiang Huang, Zhi-Xin Huang, Zhi-xiang Huang, Zhican Huang, Zhicong Huang, Zhifang Huang, Zhifeng Huang, Zhigang Huang, Zhihong Huang, Zhilin Huang, Zhilong Huang, Zhipeng Huang, Zhiping Huang, Zhiqi Huang, Zhiqiang Huang, Zhiqin Huang, Zhiqing Huang, Zhitong Huang, Zhiwei Huang, Zhixiang Huang, Zhiying Huang, Zhiyong Huang, Zhiyu Huang, Zhongbin Huang, Zhongcheng Huang, Zhongfeng Huang, Zhonglu Huang, Zhouyang Huang, Zi-Xin Huang, Zi-Ye Huang, Zicheng Huang, Zichong Huang, Zihan Huang, Zihao Huang, Ziheng Huang, Ziling Huang, Zini Huang, Zirui Huang, Zizhan Huang, Zongjian Huang, Zongliang Huang, Zunnan Huang, Zuotian Huang, Zuxian Huang, Zuyi Huang
articles
Chong-Hui Zhang, Liang-Yu Huang, Yu-Gong Feng +3 more · 2026 · Journal of the American Heart Association · added 2026-04-24
Stroke and This prospective cohort study included 336 903 participants (mean age: 56.3 years, stroke history: 1.3%, Either ischemic or hemorrhagic stroke was significantly associated with elevated ris Show more
Stroke and This prospective cohort study included 336 903 participants (mean age: 56.3 years, stroke history: 1.3%, Either ischemic or hemorrhagic stroke was significantly associated with elevated risk of ACD and Alzheimer disease ( Stroke interacts with Show less
📄 PDF DOI: 10.1161/JAHA.125.043446
APOE
Yi-Wen Huang, Hua-Chen Chan, Jing-Yi Khoo +5 more · 2026 · Neurochemistry international · Elsevier · added 2026-04-24
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by amyloid-β (Aβ) deposition, tau hyperphosphorylation, and synaptic loss. Emerging evidence indicates that apolipopr Show more
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by amyloid-β (Aβ) deposition, tau hyperphosphorylation, and synaptic loss. Emerging evidence indicates that apolipoprotein E (APOE) polymorphism and dysregulated ceramide metabolism are critical links among these pathogenic processes. Ceramide accumulation in the brain contributes to Aβ generation, tau phosphorylation, and neuronal apoptosis. Elevated ceramide levels have been observed in plasma, cerebrospinal fluid, and peripheral organs such as the liver, reflecting systemic lipid dysregulation. Lipoproteins-particularly low-density lipoprotein (LDL) and very low-density lipoprotein (VLDL)-transport ceramide across the blood-brain barrier, while apoE4 isoforms exacerbate this process by disrupting vascular integrity and lipid homeostasis. In addition, hepatic and gut-derived ceramides may influence neurodegeneration through the liver-gut-brain axis. Therapeutic interventions targeting ceramide synthesis (serine palmitoyltransferase inhibitors), production (neutral sphingomyelinase inhibitors), and the ceramide/sphingosine-1-phosphate (S1P) balance show potential in preclinical models for reducing Aβ pathology, tau aggregation, and neuroinflammation. These findings position ceramide metabolism as a critical mediator of AD pathogenesis and a promising target for diagnosis and treatment. Modulating ceramide and S1P signaling could complement current amyloid- and tau-directed therapies, offering new opportunities for disease modification and early intervention. Show less
no PDF DOI: 10.1016/j.neuint.2025.106104
APOE
Chenglin Chi, Xiaoli Yang, Can Li +5 more · 2026 · Fitoterapia · Elsevier · added 2026-04-24
The Tetradium ruticarpum (E)-Zingiber officinale Roscoe (Z) herb pair is a well-known herbal formulation with multiple beneficial cardiovascular pharmacological activities. Therefore, E and Z are pote Show more
The Tetradium ruticarpum (E)-Zingiber officinale Roscoe (Z) herb pair is a well-known herbal formulation with multiple beneficial cardiovascular pharmacological activities. Therefore, E and Z are potentially natural products for Atherosclerosis (AS). However, it is not clear whether E and Z work synergistically in the treatment of AS and which of their components is responsible. This study was to determine the synergistic effect of E and Z in the treatment of AS, to identify the active ingredient combination (AIC) that exerts the action of the original formula and to determine its molecular mechanism. First, the combined effects of E and Z were assessed in an ApoE Show less
no PDF DOI: 10.1016/j.fitote.2025.107030
APOE
Dong Huang, Chengyong Yin, Di Wang · 2026 · Experimental animals · added 2026-04-24
Atherosclerosis (AS) is a chronic inflammatory disorder underlying most cardiovascular events sialic acid (SIA), a terminal metabolite of glycolipid catabolism, modulates vascular injury, but its role Show more
Atherosclerosis (AS) is a chronic inflammatory disorder underlying most cardiovascular events sialic acid (SIA), a terminal metabolite of glycolipid catabolism, modulates vascular injury, but its role in endothelial dysfunction remains unclear. To investigate whether N-acetylneuraminic acid (Neu5Ac) accelerates AS development. ApoE Show less
📄 PDF DOI: 10.1538/expanim.25-0100
APOE
Xu Chen, Xueying Jiang, Siyu Hou +12 more · 2026 · Metabolism: clinical and experimental · Elsevier · added 2026-04-24
Vascular smooth muscle cell (VSMC)-derived foam cell formation is a major contributor to atherosclerosis progression and plaque instability. Meteorin-like protein (METRNL), a secreted organokine with Show more
Vascular smooth muscle cell (VSMC)-derived foam cell formation is a major contributor to atherosclerosis progression and plaque instability. Meteorin-like protein (METRNL), a secreted organokine with known metabolic and anti-inflammatory effects, has been linked to cardiovascular protection, but its role in atherosclerosis is not well defined. This study investigated the function of METRNL in VSMC-derived foam cell formation and atherosclerosis and explored the underlying signaling mechanisms. ApoE METRNL levels declined during atherosclerosis progression and were restored during regression. METRNL selectively inhibited foam cell formation in VSMCs-but not in macrophages-by downregulating CD36-mediated cholesterol uptake and suppressing endoplasmic reticulum stress through KIT signaling. Deletion of KIT specifically in smooth muscle cells abolished these protective effects. The transcription factor SP1 was found to bind directly to the METRNL promoter and enhance its expression. Clinically, lower serum METRNL levels were independently associated with increased risk and severity of acute coronary syndrome. METRNL protects against VSMC foam cell formation and atherosclerosis by enhancing KIT signaling, thereby reducing ER stress and subsequent cholesterol uptake. These findings position METRNL as a potential therapeutic target and biomarker for atherosclerotic cardiovascular disease. Show less
no PDF DOI: 10.1016/j.metabol.2025.156459
APOE
Te-Hsuan Huang, Yen-Ching Chen, Ching-Yu Lin +4 more · 2026 · Mechanisms of ageing and development · Elsevier · added 2026-04-24
Limited studies have explored the link between metabolic profiles and cognitive frailty, its temporal relationship is especially lacking. This study aimed to identify metabolic patterns associated wit Show more
Limited studies have explored the link between metabolic profiles and cognitive frailty, its temporal relationship is especially lacking. This study aimed to identify metabolic patterns associated with cognitive frailty over time. This eight-year prospective cohort study (2011-2019) recruited 605 nondemented community-dwelling older adults at baseline. Cognitive frailty, assessed biennially, was defined as physical frailty and mild cognitive impairment. Baseline plasma metabolites were evaluated using Show less
no PDF DOI: 10.1016/j.mad.2025.112130
APOE
Wenyu Gao, Hao Chen, Fangyu Lin +7 more · 2026 · FASEB journal : official publication of the Federation of American Societies for Experimental Biology · added 2026-04-24
Gastric cancer (GC) is a leading cause of cancer-related deaths and has high recurrence rate. Although fibronectin domain-containing protein 1 (FNDC1) is implicated in GC progression, its molecular me Show more
Gastric cancer (GC) is a leading cause of cancer-related deaths and has high recurrence rate. Although fibronectin domain-containing protein 1 (FNDC1) is implicated in GC progression, its molecular mechanisms remain unclear. Multi-omics analyses (TCGA, GEO datasets) were used to assess FNDC1 expression and clinical correlation. In vitro (cell proliferation, invasion, EMT markers) and in vivo (xenograft) experiments, combined with molecular assays (Co-IP, WB, ChIP), explored FNDC1's function and mechanism. FNDC1 was significantly upregulated in GC, correlating with advanced clinicopathological features and poor prognosis. Knockdown of FNDC1 suppressed GC cell proliferation, invasion, and metastasis by inhibiting EMT and Wnt/β-catenin signaling. Mechanistically, FNDC1 competitively bound the WD5 domain (residues 224-254) of Gβ2, disrupting Gβγ-Dvl1 interaction. This prevented Dvl1 degradation, promoted Axin1 ubiquitination, and destabilized the β-catenin-destruction complex (GSK3 β-APC-Axin1), leading to β-catenin accumulation and Wnt pathway activation. FNDC1 drives GC malignancy by targeting the Gβ2-Dvl1 axis to activate Wnt/β-catenin signaling, suggesting FNDC1 as a novel prognostic biomarker and therapeutic target. Show less
📄 PDF DOI: 10.1096/fj.202503587R
AXIN1
Meimei Chen, Ruina Huang, Zhaoyang Yang · 2026 · Nan fang yi ke da xue xue bao = Journal of Southern Medical University · added 2026-04-24
To investigate the causal relationship between inflammatory proteins and Alzheimer's disease (AD) and the mediating role of plasma metabolites therein. Using Mendelian mandomization (MR) methods and p Show more
To investigate the causal relationship between inflammatory proteins and Alzheimer's disease (AD) and the mediating role of plasma metabolites therein. Using Mendelian mandomization (MR) methods and publicly available genome-wide association study (GWAS) data, we selected 91 single nucleotide polymorphisms (SNPs) that were strongly linked to inflammatory proteins without reverse causality with AD as the outcome. A bidirectional two-sample MR analysis was performed. Inflammatory proteins with causal links to AD were identified via inverse variance weighted (IVW) analysis. A mediation MR analysis was then performed using 1400 plasma metabolites to assess their mediating role in this causal pathway. The preliminary bidirectional MR analysis identified 3 inflammatory proteins that had a potential positive causal association with AD without reverse causality: Axin-1, C-X-C motif chemokine ligand 11 (CXCL11), and interleukin-12β (IL-12β). Elevated levels of Axin-1 were positively causally associated with AD risk (OR=1.082, 95% This study reveals how specific inflammatory proteins influence AD risk via plasma metabolites and provides genetic evidence for inflammatory-metabolic interactions in AD to facilitate the identification of potential biomarkers and targets for early detection and intervention of AD. Show less
no PDF DOI: 10.12122/j.issn.1673-4254.2026.02.05
AXIN1
Ni-Xue Song, Yan-Chun Wang, Tong Zhao +6 more · 2026 · Acta pharmacologica Sinica · Nature · added 2026-04-24
Diabetic peripheral neuropathy (DPN), a severe complication of diabetes, is a key risk factor for diabetic foot (DF) that contributes highly to amputation and mortality. The pathogenesis of DPN remain Show more
Diabetic peripheral neuropathy (DPN), a severe complication of diabetes, is a key risk factor for diabetic foot (DF) that contributes highly to amputation and mortality. The pathogenesis of DPN remains unclear and complex, with no effective treatments currently available. Monoamine oxidase (MAO), a flavin adenine dinucleotide (FAD)-dependent enzyme, catalyzes the oxidative deamination of critical biogenic amines. The MAO family comprises two subtypes, MAOA and MAOB, which play distinct roles in pathophysiology. In this study, we identified that MAOB but not MAOA is pathologically upregulated in the sciatic nerve (SN) tissues of DPN patients and in the SN/dorsal root ganglion (DRG) tissues of DPN model mice. Notably, the selective MAOB inhibitor Khellin (Khe) effectively alleviated DPN-like pathology in mice. To explore the mechanistic role of MAOB in DPN, we performed proteomic profiling of DRG tissues from DPN mice and validated the findings using a MAOB-specific knockdown DPN mice model treated with adeno-associated virus (AAV) 8-MAOB-RNAi. Our results demonstrate that Khe targets MAOB to mitigate DPN pathology through HIF-1α/BACE1/Aβ/NLRP3/tau pathway, mediated by Schwann cell/DRG neuron crosstalk. All findings suggest that selective MAOB inhibition represents a promising therapeutic strategy for DPN, with Khe as a potential candidate for clinical translation against this disease. Show less
📄 PDF DOI: 10.1038/s41401-026-01764-2
BACE1
Minghua Li, Aijun Shen, Xiaolong Gao +11 more · 2026 · Advanced science (Weinheim, Baden-Wurttemberg, Germany) · Wiley · added 2026-04-24
Beta-site APP-cleaving enzyme 1 (BACE1), a critical rate-limiting enzyme that synthesizes β-amyloid peptide (Aβ), is an important marker of early pathological changes in Alzheimer's disease (AD). Earl Show more
Beta-site APP-cleaving enzyme 1 (BACE1), a critical rate-limiting enzyme that synthesizes β-amyloid peptide (Aβ), is an important marker of early pathological changes in Alzheimer's disease (AD). Early small plaques cannot be accurately detected using traditional Magnetic resonance imaging (MRI) probes. Therefore, magnetic resonance tuning (MRET) and susceptibility weighted imaging (SWI)-based smart responsive MR nanoprobes are designed to achieve the sensitive detection of BACE1 and Aβ plaques. This probe is modified with a blood-brain barrier-penetrating targeting peptide that enables its reach to the AD microenvironment. The enhancement of T1WI signals owing to the MRET effect caused by the separation of probes in response to BACE1 is used to reflect real-time BACE1 changes. When Aβ plaques are present, the remaining probes that bound around Aβ plaques underwent in situ thiol cross-linking under the action of peroxynitrite (ONOO Show less
📄 PDF DOI: 10.1002/advs.202510298
BACE1
Huixian Huang, Wensi Lu, Yusi Huang +6 more · 2026 · Journal of ethnopharmacology · Elsevier · added 2026-04-24
Bazi Bushen (BZBS), a Traditional Chinese Medicine (TCM) formula, is composed of fourteen herbal ingredients, including classic tonics such as Ginseng Radix et Rhizoma and Cistanches Herba. Traditiona Show more
Bazi Bushen (BZBS), a Traditional Chinese Medicine (TCM) formula, is composed of fourteen herbal ingredients, including classic tonics such as Ginseng Radix et Rhizoma and Cistanches Herba. Traditionally used to combat fatigue and promote vitality in aging individuals, BZBS is rooted in TCM principles of kidney essence replenishment and brain function enhancement. Recent pharmacological studies have begun to validate its efficacy in age-related cognitive decline, but its effects and mechanisms in Alzheimer's disease (AD) remain unclear. This study aimed to evaluate the potential therapeutic effects of BZBS in 5 × FAD transgenic mice, a commonly used Alzheimer's disease model, and to shed light on its possible mechanisms of action. Four- and six-month-old 5 × FAD mice were treated with BZBS to examine how it might influence cognitive performance. Behavioral assessments were carried out using Y-Maze and the Morris Water Maze. To investigate the biological changes and uncover the mechanisms involved, we used a range of techniques-Thioflavin S staining, immunofluorescence, Western blotting, and qPCR-to look at Aβ plaque accumulation, Amyloid Precursor Protein C-terminal Fragments (APP-CTF) and β-secretase 1 (BACE1) expression levels, markers of inflammation, and indicators of cellular aging in hippocampus and motor cortex. In the 4-month group, where treatment was started before severe pathology developed, BZBS improved learning and memory performance. It also reduced amyloid deposition in the cortex and hippocampus, and lowered the levels of APP-CTFs and BACE1. In addition, we observed decreased mRNA expression of IL-1α, IL-6, and NF-κB, along with reduced microglial activation in the hippocampus of BZBS-treated mice. Similarly BZBS downregulated key markers of cellular senescence, including p16, p21, and senescence-associated β galactosidase (SA-β-gal) activity. In the 6-month group, which already showed signs of amyloid pathology, BZBS still had beneficial effects-improving cognition, lowering Aβ load, and reducing microglial activity-suggesting that it may be effective even after disease onset. These findings demonstrate that BZBS exerts significant therapeutic effects in 5 × FAD mice, including improved cognitive improvement, reduced Aβ deposition, suppressed microglial activation, and attenuated hippocampal cellular senescence. Notably, BZBS was effective whether administered from the early stage of pathology (at four months of age) or after established amyloidosis (at six months of age), highlighting its dual potential as both a preventive and disease-modifying intervention for Alzheimer's disease (AD). Show less
no PDF DOI: 10.1016/j.jep.2025.120586
BACE1
Thanh-Tung Ho, Hai Huang, Yi-Ling Li +6 more · 2026 · Biological trace element research · Springer · added 2026-04-24
Manganese and iron are essential trace elements involved in critical neuronal processes; however, excessive exposure to these metals is a significant risk factor for Alzheimer's disease (AD). While mo Show more
Manganese and iron are essential trace elements involved in critical neuronal processes; however, excessive exposure to these metals is a significant risk factor for Alzheimer's disease (AD). While most previous studies have focused on single-metal neurotoxicity, the mechanisms underlying combined manganese and iron exposure remain unclear. In this study, we investigated the effects of manganese and iron exposure, both individually and in combination, on tau hyperphosphorylation, β-amyloid (Aβ) accumulation (particularly Aβ Show less
📄 PDF DOI: 10.1007/s12011-025-04681-3
BACE1
Yue Sun, Xinping Pang, Xudong Huang +5 more · 2026 · Neural regeneration research · added 2026-04-24
Alzheimer's disease, a progressively degenerative neurological disorder, is the most common cause of dementia in the elderly. While its precise etiology remains unclear, researchers have identified di Show more
Alzheimer's disease, a progressively degenerative neurological disorder, is the most common cause of dementia in the elderly. While its precise etiology remains unclear, researchers have identified diverse pathological characteristics and molecular pathways associated with its progression. Advances in scientific research have increasingly highlighted the crucial role of non-coding RNAs in the progression of Alzheimer's disease. These non-coding RNAs regulate several biological processes critical to the advancement of the disease, offering promising potential as therapeutic targets and diagnostic biomarkers. Therefore, this review aims to investigate the underlying mechanisms of Alzheimer's disease onset, with a particular focus on microRNAs, long non-coding RNAs, and circular RNAs associated with the disease. The review elucidates the potential pathogenic processes of Alzheimer's disease and provides a detailed description of the synthesis mechanisms of the three aforementioned non-coding RNAs. It comprehensively summarizes the various non-coding RNAs that have been identified to play key regulatory roles in Alzheimer's disease, as well as how these non-coding RNAs influence the disease's progression by regulating gene expression and protein functions. For example, miR-9 targets the UBE4B gene, promoting autophagy-mediated degradation of Tau protein, thereby reducing Tau accumulation and delaying Alzheimer's disease progression. Conversely, the long non-coding RNA BACE1-AS stabilizes BACE1 mRNA, promoting the generation of amyloid-β and accelerating Alzheimer's disease development. Additionally, circular RNAs play significant roles in regulating neuroinflammatory responses. By integrating insights from these regulatory mechanisms, there is potential to discover new therapeutic targets and potential biomarkers for early detection and management of Alzheimer's disease. This review aims to enhance the understanding of the relationship between Alzheimer's disease and non-coding RNAs, potentially paving the way for early detection and novel treatment strategies. Show less
📄 PDF DOI: 10.4103/NRR.NRR-D-24-00696
BACE1
Mitchell J Rechtzigel, Brittany Lee, Christine Neville +9 more · 2026 · Communications medicine · Nature · added 2026-04-24
Development of therapies for CLN3 disease, a rare pediatric lysosomal storage disorder, has been hindered by the lack of etiological insights and translatable biomarkers to clinics. We used a deep mul Show more
Development of therapies for CLN3 disease, a rare pediatric lysosomal storage disorder, has been hindered by the lack of etiological insights and translatable biomarkers to clinics. We used a deep multi-omics approach to discover blood-based biomarkers using longitudinal serum samples from a porcine model of CLN3 disease. Comprehensive metabolomics was combined with a nanoparticle-based LC-MS-based proteomic profiling coupled with TMTpro 18-plex to generate quantitative data on 769 metabolites and 2634 proteins, collectively the most exhaustive multi-omics profile conducted on serum from a porcine model. This was previously impossible due to lack of efficient deep serum proteome profiling technologies compatible with model organisms. Here we show that the presymptomatic disease state is characterized by elevations in glycerophosphodiester species and lysosomal proteases, while later timepoints are enriched with species involved in immune cell activation and sphingolipid metabolism. Cathepsin S (CTSS), Cathepsin B (CTSB), glycerophosphoinositol, and glycerophosphoethanolamine captured a large portion of the genotype-correlated variation between healthy and diseased animals, suggesting that an index score based on these analytes could have great utility in the clinic. This study's findings demonstrate the potential of deep multi-omics profiling for uncovering disease-specific biomarkers, providing valuable insights for understanding disease and facilitating the identification of potential drug targets, thus offering valuable insights for therapeutic interventions. Show less
📄 PDF DOI: 10.1038/s43856-025-01227-5
CLN3
Jiaqi Fang, Jing Ling, Xinyue Liu +5 more · 2026 · Amino acids · Springer · added 2026-04-24
Nitrogen metabolism plays a key role in maintaining normal physiological functions of the organism and cell proliferation and differentiation. Nitrogen metabolism in normal human body maintains a dyna Show more
Nitrogen metabolism plays a key role in maintaining normal physiological functions of the organism and cell proliferation and differentiation. Nitrogen metabolism in normal human body maintains a dynamic balance to meet the body's demand for synthesis of biological macromolecules such as proteins and nucleic acids. However, in the process of tumor development, the nitrogen metabolism of tumor cells is reprogrammed to meet the demand of rapid proliferation, showing significantly different metabolic characteristics from normal cells. Key enzymes in the tumor microenvironment affect nitrogen metabolism through multiple mechanisms, providing essential nitrogen sources and energy for tumor cells. In-depth exploration of the regulatory mechanisms of tumor nitrogen metabolism not only helps to reveal the molecular basis of tumor development, but also provides a theoretical basis for the development of new tumor therapeutic strategies. In this paper, the relationship between nitrogen metabolism and tumors is systematically elaborated from the characteristics of nitrogen metabolism in normal people, the reprogramming of nitrogen metabolism in tumor patients, the influence of key enzymes on nitrogen metabolism in the tumor microenvironment, as well as the mechanism of tumor nitrogen metabolism regulation, etc., so as to provide references for the related research. Show less
no PDF DOI: 10.1007/s00726-026-03517-1
CPS1
Hao-Ran Geng, Yu-Ling Chen, Lei Huang +7 more · 2026 · Journal of advanced research · Elsevier · added 2026-04-24
Congenital heart disease (CHD) is the most common birth defect worldwide, with over half of cases lacking a defined etiology. Maternal metabolic dysregulation has been implicated in CHD risk, but the Show more
Congenital heart disease (CHD) is the most common birth defect worldwide, with over half of cases lacking a defined etiology. Maternal metabolic dysregulation has been implicated in CHD risk, but the specific metabolites and mechanisms involved in embryonic heart development remain poorly understood. Carbamoyl phosphate (CP), a key urea cycle intermediate, has not previously been linked to cardiac morphogenesis. This study aimed to identify maternal metabolites associated with offspring CHD risk and to elucidate the role of CP in regulating cardiac development. Untargeted metabolomic profiling was performed on early-pregnancy serum from 98 mothers of CHD offspring and 50 age-matched controls. Functional validation was performed using two pregnant mouse models: pharmacological inhibition of glutamine metabolism via BPTES and Cps1 heterozygous knockout (Cps1 Maternal serum CP levels were significantly reduced in CHD cases and negatively correlated with upstream nutrient levels. In mice, both BPTES treatment and maternal Cps1 knockdown increased CHD incidence in offspring. Conversely, NCG supplementation reduced CHD risk in Cps1 Maternal CP deficiency increases offspring CHD risk by disrupting TET2-mediated DNA demethylation through impaired lysine carbamylation. These findings highlight maternal CP and TET2 carbamylation as potential metabolic-epigenetic targets for CHD prevention. Show less
no PDF DOI: 10.1016/j.jare.2026.02.021
CPS1
Xiaohua Huang, Wei Lu, Dandan Jiang +2 more · 2026 · Genes & diseases · Elsevier · added 2026-04-24
📄 PDF DOI: 10.1016/j.gendis.2025.101671
DUSP6
Cheng Huang, Haowen Liu, Bao Jiang +6 more · 2026 · Bioelectrochemistry (Amsterdam, Netherlands) · Elsevier · added 2026-04-24
Acute kidney injury (AKI), a critical clinical syndrome marked by high incidence and mortality, is currently diagnosed mainly by serum creatinine (SCr) and blood urea nitrogen (BUN), which have high m Show more
Acute kidney injury (AKI), a critical clinical syndrome marked by high incidence and mortality, is currently diagnosed mainly by serum creatinine (SCr) and blood urea nitrogen (BUN), which have high miss rates. This study innovatively proposes using urinary hydrogen peroxide (H Show less
no PDF DOI: 10.1016/j.bioelechem.2025.109173
DYM
Daniel Owrang, Aboulfazl Rad, Constantin Cretu +10 more · 2026 · QJM : monthly journal of the Association of Physicians · Oxford University Press · added 2026-04-24
The relationship between observed clinical phenotypes and underlying genotypes is blended or skewed in multiple molecular diagnoses, complicating a comprehensive molecular genetic diagnosis. We report Show more
The relationship between observed clinical phenotypes and underlying genotypes is blended or skewed in multiple molecular diagnoses, complicating a comprehensive molecular genetic diagnosis. We report two families with dual diagnoses, using the deafness-associated gene, COL4A6, to exemplify its contribution to blended, complex clinical presentations. This is an observational study within a large, ethnically diverse rare disease cohort, focusing on families with hearing loss and suspected dual diagnoses, followed by functional and structural studies of novel variants. Families were identified through a large rare disease sequencing initiative. Exome or genome sequencing was performed, with follow-up RNA studies for a synonymous COL4A6 variant. Spatial and temporal expression analysis in zebrafish traced col4a6 expression in the otic vesicle and ear from 1 to 5 days post-fertilization. Structural modeling was used to estimate variant impact on protein structure. We identified two families affected by multiple genetic disorders. The first family presented a missense COL4A6 variant (NM₀₃₃₆₄₁.4: c.1480G>A p.(Gly494Arg)), accounting for hearing loss, while a likely pathogenic HEXA variant (NM₀₀₀₅₂₀.6: c.902T>G p.(Met301Arg)) explained Tay-Sachs disease features. The second family exhibited a synonymous COL4A6 variant (NM₀₃₃₆₄₁.4: c.1767G>A p.(Pro589=)), leading to partial exon skipping and hearing loss, along with a pathogenic splice-site variant in DYM (NM₀₀₁₃₅₃₂₁₄.3: c.1125 + 1G>T p.?), causing the Dyggve-Melchior-Clausen disease. Our findings highlight the importance of recognizing dual molecular diagnoses to untangle blended phenotypes, as well as the diagnostic relevance of synonymous variants with predicted splicing effects. Show less
📄 PDF DOI: 10.1093/qjmed/hcaf246
DYM
Jinfeng Cui, Zhenyi Peng, Yuanyuan Chen +6 more · 2026 · Stem cell research & therapy · BioMed Central · added 2026-04-24
Acute respiratory distress syndrome (ARDS) has a high clinical mortality rate and continues to draw research attention regarding its mechanisms and potential treatments. Disruption of the endothelial Show more
Acute respiratory distress syndrome (ARDS) has a high clinical mortality rate and continues to draw research attention regarding its mechanisms and potential treatments. Disruption of the endothelial barrier is a primary pathological feature, and glycocalyx degradation is a key factor contributing to this disruption. Human umbilical cord mesenchymal stem cells (hucMSCs) exhibit strong anti-inflammatory and immunomodulatory effects, making their application in ARDS treatment an area of increasing interest. Proteomic screening identified Cxcl12 as a protein secreted by hucMSCs. In male C57 mice and cell models, lipopolysaccharide (LPS) was used to induce injury, followed by interventions with hucMSCs or hucMSCs with silenced Cxcl12 to assess glycocalyx-related proteins SDC-1, HS, and the repair marker EXT-1. To evaluate downstream signaling, the CXCR4 receptor was inhibited and related indicators were examined. Silencing Cxcl12 reduced the therapeutic effect of hucMSCs on LPS-induced glycocalyx damage. Inhibition of CXCR4 also weakened the effect of Cxcl12. These findings indicate that hucMSCs alleviate LPS-induced glycocalyx damage in pulmonary vascular endothelial cells by secreting Cxcl12, which activates the downstream receptor CXCR4, providing a therapeutic effect for ARDS. Show less
no PDF DOI: 10.1186/s13287-026-05024-2
EXT1
Yao Xie, Tieqiu Huang, He Wang +2 more · 2026 · Journal of cardiovascular pharmacology · added 2026-04-24
Heart failure (HF), with varied symptoms caused by cardiac strain or damage, has high morbidity and mortality. Protein lactylation, a post-translational modification, regulates immune and cardiovascul Show more
Heart failure (HF), with varied symptoms caused by cardiac strain or damage, has high morbidity and mortality. Protein lactylation, a post-translational modification, regulates immune and cardiovascular processes, but its role in HF's immune microenvironment remains underexplored. Differentially expressed lactylation-related genes (LacRGs) were identified by intersecting HF differentially expressed genes with LacRG data sets. Unsupervised clustering categorized patients with HF into LacRG-based subgroups. An LacRG diagnostic model was developed to assess associations with immune cell infiltration, immunotherapy potential, and single-cell RNA sequencing expression patterns. HF mouse models were constructed and verified for LacRG expression. In 200 HF versus 166 non-HF samples, 38 differentially expressed LacRGs were identified, revealing distinct immune landscapes. Two LacRG clusters exhibited unique functional enrichment and immunologic features. A 14-gene LacRG signature distinguished HF from controls with high accuracy (area under the curve: 0.999, 1.000, 0.744). Single-cell RNA sequencing (GSE145154) revealed reduced lactylation scores in fibroblast, macrophage, T-cell, and NK-cell subsets in HF, alongside characterization of altered cellular subtypes and activated signaling pathways within these populations. External data sets (GSE46224, GSE116250) identified 6 hub genes-HBB, EXT1, CENPA, NT5E, STAT4, and CAPN5, which were validated in HF mouse models. In addition, analysis of HF dataset further indicated higher LacRG scores in heart failure with preserved ejection fraction than in reduced ejection fraction. Lactylation modification is closely linked to HF's immune microenvironment. A 14-gene LacRG signature and 6 hub genes provide novel insights into HF pathophysiology and potential therapeutic avenues. Further studies are warranted to validate their regulatory roles in HF through immune microenvironmental mechanisms. Show less
no PDF DOI: 10.1097/FJC.0000000000001775
EXT1
Devendra Meena, Michele Pansini, Alessandro Fichera +5 more · 2026 · Human genomics · BioMed Central · added 2026-04-24
Liver steatosis, fibroinflammation, and iron overload, are growing global health concerns, yet the genetic architecture and causal pathways linking liver pathology to systemic disease remain incomplet Show more
Liver steatosis, fibroinflammation, and iron overload, are growing global health concerns, yet the genetic architecture and causal pathways linking liver pathology to systemic disease remain incompletely understood. We analysed MRI-derived liver traits—corrected T1 (cT1), proton density fat fraction (PDFF), and liver iron—in 37,626 UK Biobank participants. Genome-wide (GWAS), transcriptome-wide (TWAS), and GWAS identified 18 loci for cT1, 15 for PDFF, and 5 for liver iron, including six not previously reported. TWAS, This integrative imaging-genetics study reveals 13 potentially novel genes and several protein candidates implicated in hepatic steatosis, inflammation, and iron homeostasis. These findings enhance understanding of liver disease biology and may help identify new targets for early detection or treatment. This large imaging-genetics study in over 37,000 people identifies genetic and protein factors linked to liver fat, fibroinflammation, and iron levels. It shows that higher liver fat and inflammation are associated with increased cardiometabolic risk, while higher liver iron appears inversely linked to risk of heart disease. These findings highlight molecular targets such as The online version contains supplementary material available at 10.1186/s40246-026-00913-2. Show less
📄 PDF DOI: 10.1186/s40246-026-00913-2
FADS1
Ruirui Zhu, Hao Tian, Fangfang Zou +7 more · 2026 · iScience · Elsevier · added 2026-04-24
The intramuscular fat content and the unsaturated fatty acid (UFA) composition are both critical indicators of buffalo meat quality. While microRNAs regulate fatty acid metabolism, their specific role Show more
The intramuscular fat content and the unsaturated fatty acid (UFA) composition are both critical indicators of buffalo meat quality. While microRNAs regulate fatty acid metabolism, their specific roles in buffaloes remain unclear. Our previous WGCNA identified bta-miR-30f as a hub miRNA positively correlated with UFA levels. In the present study, bta-miR-30f was found to be highly expressed in sternum subcutaneous adipose tissue and mature adipocytes. Functional studies indicated that bta-miR-30f increased lipid accumulation via enhanced adipogenesis and UFA levels, upregulating key genes including Show less
📄 PDF DOI: 10.1016/j.isci.2025.114578
FADS1
Rong Huang, Jinyue Ma, Jiaxin Yao +8 more · 2026 · Ecotoxicology and environmental safety · Elsevier · added 2026-04-24
Hepatocellular carcinoma (HCC) is a major malignancy with rising global incidence and mortality. Clinical treatment is limited by molecular heterogeneity and drug resistance. In recent years, endocrin Show more
Hepatocellular carcinoma (HCC) is a major malignancy with rising global incidence and mortality. Clinical treatment is limited by molecular heterogeneity and drug resistance. In recent years, endocrine-disrupting chemicals (EDCs) have attracted attention as emerging risk factors, but systematic pathogenic evidence for their roles in HCC initiation and progression remains insufficient. First, we predicted potential targets of EDCs using SwissTargetPrediction, STITCH, and ChEMBL, and intersected them with differentially expressed genes and key module genes from WGCNA in the GEO database to screen candidate key genes. Second, based on these candidates, we constructed diagnostic models using 14 machine-learning algorithms and evaluated feature importance via the SHAP framework to identify key biomarkers and their functional contributions. Molecular docking and molecular dynamics simulations were used to validate interaction mechanisms between EDCs and key target proteins. We then built a multivariable Cox proportional hazards model in the TCGA-LIHC cohort and performed stratified survival analysis, somatic mutation profiling, and immune evasion characterization. Subsequently, we evaluated the tumor immune microenvironment using CIBERSORT and ssGSEA, and integrated single-cell transcriptomic data to resolve cell-subtype heterogeneity, target expression distributions, and cell-cell communication. Meanwhile, we integrated the GDSC drug-sensitivity database to evaluate associations between risk scores and drug response, and conducted pan-cancer analyses to examine cross-cancer applicability. We identified 18 genes jointly associated with EDCs and HCC, significantly enriched in AMPK, p53, and FoxO signaling pathways and cell cycle-related pathways. Among models built with 14 machine-learning algorithms, CatBoost showed the best discriminative performance and identified CCNB2 and AKR1C3 as core driver genes. Docking and dynamics simulations indicated strong binding affinities and stable binding conformations between EDCs and target proteins including CCNB1 (-8.9 kcal/mol), AKR1C3 (-8.4 kcal/mol), and FADS1 (-8.5 kcal/mol). A multivariable Cox risk model based on nine key genes served as an independent prognostic predictor for HCC (HR = 1.746, 95% CI: 1.477-2.064, P < 0.001). The nomogram achieved AUCs of 0.836, 0.810, and 0.788 at 1, 3, and 5 years, respectively, indicating good predictive performance. The high-risk group was significantly associated with high tumor mutational burden (TMB), TP53 mutations, and low immune evasion scores. Regarding the tumor immune microenvironment, CIBERSORT and ssGSEA analyses showed marked enrichment of Tregs and M0 macrophages, while most effector immune cells and functions were suppressed. Single-cell transcriptomics further showed enrichment of endothelial cells, fibroblasts, hepatocytes, and macrophages in HCC tissues, with notable reductions in T cells, B cells, NK cells, and neutrophils, indicating an immunosuppressive microenvironment with stromal remodeling. Cell-cell communication analysis indicated that the MIF-CD74 receptor axis is central in immune-cell interactions. Drug-sensitivity analysis suggested that the high-risk group was more sensitive to GDC0810, BPD-00008900, and Fulvestrant, indicating potential beneficiary populations. Pan-cancer analysis showed that the risk model also had diagnostic and prognostic value in LUAD, KIRP, KIRC, and KICH, suggesting cross-cancer generalizability. This study systematically reveals that EDCs promote HCC initiation and progression by perturbing cell cycle, metabolic, and immune homeostasis through multi-target, multi-pathway mechanisms. The nine-gene risk model demonstrates superior performance in HCC diagnosis and prognosis and shows potential clinical translational value in drug-sensitivity prediction and pan-cancer analyses. This work provides a new perspective at the intersection of environmental toxicology and precision oncology and informs individualized therapeutic strategies. Show less
no PDF DOI: 10.1016/j.ecoenv.2025.119519
FADS1
Weijie Guo, Jingyun Luan, Xuejie Huang +17 more · 2026 · Cancer cell · Elsevier · added 2026-04-24
The heterogeneous nature of tumor-associated neutrophils (TANs) has been recognized, but how different cell states of TANs emerge, evolve, distribute, and impact cancer immunotherapy efficacy remain e Show more
The heterogeneous nature of tumor-associated neutrophils (TANs) has been recognized, but how different cell states of TANs emerge, evolve, distribute, and impact cancer immunotherapy efficacy remain elusive. Using single-cell RNA sequencing, spatial transcriptomics, and genetic manipulations, we show that anti-PDL1 + CD40 agonist immunotherapy can induce interferon responses in TANs, allowing them to regain anti-tumor activities in squamous cell carcinomas (SCCs). In contrast, TANs residing at the tumor-stroma interface can preserve their immune-suppressive state. Importantly, we identify a group of SOX2 Show less
📄 PDF DOI: 10.1016/j.ccell.2025.11.001
FADS1
Fan Jiang, Huaju Huang, Zhe Dong +6 more · 2026 · Cell death discovery · Nature · added 2026-04-24
Ovarian cancer (OC) is an aggressive gynecological malignancy with poor prognosis, largely due to late-stage diagnosis and high metastatic potential. However, the functional role and regulatory mechan Show more
Ovarian cancer (OC) is an aggressive gynecological malignancy with poor prognosis, largely due to late-stage diagnosis and high metastatic potential. However, the functional role and regulatory mechanisms of fibroblast growth factor receptor 1 (FGFR1) in OC remain incompletely understood. In this study, we investigated the expression pattern and biological function of FGFR1 in OC and explored its underlying molecular mechanisms. FGFR1 expression was analyzed using TCGA, GTEx, and tissue microarray datasets, and its prognostic significance was evaluated by Kaplan-Meier survival analysis. Functional assays were performed in OVCAR-3 and SK-OV-3 cells following FGFR1 knockdown or overexpression to assess cell proliferation, migration, invasion, and metabolic activity, including extracellular acidification rate (ECAR) and oxygen consumption rate (OCR). Lactate production and histone lactylation were measured by biochemical assays and Western blotting. Protein interaction between FGFR1 and SIRT3 was examined by co-immunoprecipitation and immunofluorescence, and rescue experiments were conducted to determine SIRT3 dependency. In vivo subcutaneous xenograft models were used to evaluate the role of FGFR1 in tumor growth. We found that FGFR1 expression was significantly reduced in OC tissues and that low FGFR1 levels were associated with unfavorable clinical outcomes. Functionally, FGFR1 silencing promoted OC cell proliferation, migration, invasion, and metabolic activity, whereas FGFR1 overexpression exerted inhibitory effects. Mechanistically, FGFR1 interacted with SIRT3 and stabilized its protein expression. Importantly, SIRT3 knockdown abrogated the FGFR1-mediated reductions in lactate production, glycolytic enzyme expression, ATP levels, and histone lactylation, indicating that FGFR1 regulates metabolic reprogramming through a SIRT3-dependent mechanism. Consistently, FGFR1 knockdown promoted the formation of larger and more invasive tumors in vivo. Collectively, these findings demonstrate that FGFR1 functions as a context-dependent tumor suppressor in OC by modulating SIRT3-mediated metabolic reprogramming and histone lactylation, suggesting that targeting the FGFR1-SIRT3 axis may represent a potential therapeutic strategy for ovarian cancer. Show less
no PDF DOI: 10.1038/s41420-026-03054-6
FGFR1
Zhe Zhang, Yili Xiong, Mingyang Li +9 more · 2026 · International journal of biological sciences · added 2026-04-24
High mobility group AT-hook 1 (HMGA1) is a chromatin regulator overexpressed in various cancers, often predicting poor outcomes. However, its role in head and neck squamous cell carcinoma (HNSCC) rema Show more
High mobility group AT-hook 1 (HMGA1) is a chromatin regulator overexpressed in various cancers, often predicting poor outcomes. However, its role in head and neck squamous cell carcinoma (HNSCC) remains unclear. A hallmark of HNSCC is the rapid growth of its vasculature. Here, we identify an epigenetic mechanism whereby HMGA1 promotes tumor progression and angiogenesis via upregulation of fibroblast growth factor-binding protein 1 (FGFBP1). Show less
📄 PDF DOI: 10.7150/ijbs.109079
FGFR1

2

Pinglian Wu, Zhaodi Tian, Weizhong Shen +9 more · 2026 · Journal of enzyme inhibition and medicinal chemistry · Taylor & Francis · added 2026-04-24
Although FGFR2 is a well-validated oncogenic target, no selective FGFR2 inhibitors have been approved for clinical use. In this study, we report the discovery of 2
📄 PDF DOI: 10.1080/14756366.2026.2647526
FGFR1
Yu Feng, Ningning Jia, Peng Huang +2 more · 2026 · Molecular psychiatry · Nature · added 2026-04-24
Psychiatric disorders, including bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia (SCZ), share substantial genetic overlap. We conducted a cross-ancestry multivariate genome-w Show more
Psychiatric disorders, including bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia (SCZ), share substantial genetic overlap. We conducted a cross-ancestry multivariate genome-wide association study (GWAS) integrating European and East Asian populations to uncover shared genetic underpinnings. Our analyses identified 403 loci associated with shared polygenic liability to psychiatric disorders, including 88 novel regions. Cross-ancestry fine-mapping highlighted robust shared signals, notably at VRK2 (rs7596038), consistently significant across ancestries. Gene prioritization revealed 90 high-confidence candidate genes enriched in neurodevelopmental pathways. Single-nucleus RNA sequencing implicated excitatory neurons and astrocytes as key cellular contexts, emphasizing NCAM1-FGFR1 and NEGR1-NEGR1 signaling pathways. Mendelian randomization analyses provided causal evidence linking shared genetic liability to structural brain alterations, particularly in regions crucial for emotion and cognition. Polygenic risk scores derived from shared genetic liability substantially enhanced predictive accuracy for BD and SCZ, demonstrating strong trans-ancestry validity. These results advance understanding of shared genetic architecture in psychiatric disorders, highlighting potential therapeutic targets and emphasizing the critical importance of diverse ancestry studies in precision psychiatry. Show less
no PDF DOI: 10.1038/s41380-026-03541-3
FGFR1
Siddhartha Yadav, Sonya Reid, Binyam Yilma +13 more · 2026 · Journal of the National Cancer Institute · Oxford University Press · added 2026-04-24
The association of germline pathogenic and likely pathogenic variants (GPVs) in hereditary breast cancer genes with underlying tumor biology and clinical outcomes remain incompletely understood. This Show more
The association of germline pathogenic and likely pathogenic variants (GPVs) in hereditary breast cancer genes with underlying tumor biology and clinical outcomes remain incompletely understood. This study characterized differences in somatic alterations and intrinsic subtypes between sporadic and hereditary breast cancers associated with GPVs in ATM, BRCA1, BRCA2, CHEK2, or PALB2. This retrospective cohort study included women with breast cancer and an ATM, BRCA1, BRCA2, CHEK2, or PALB2 GPV who underwent tumor sequencing and whole transcriptome RNA expression analysis. Clinicopathologic features, intrinsic subtypes, somatic alterations, and survival were compared by GPV status and immunohistochemistry-defined subtype, and to sporadic cases. All significance tests were 2-sided. 4,988 women with breast cancer included 98 BRCA1, 126 BRCA2, 74 PALB2, 54 ATM, and 83 CHEK2 GPVs. Compared to sporadic cases, HR+/HER2- tumors in BRCA1 GPVs were significantly enriched for basal subtype (45.5% vs 11.4%, p < 0.001), while CHEK2 carriers had a higher prevalence of luminal A subtype (80.4% vs 60.3%, p = 0.006). In HR+/HER2- breast cancers, BRCA1 GPVs were enriched for TP53 alterations (84.6% vs 29.8%, q < 0.001), ATM GPVs with FGFR1 alterations (35.4% vs 12.7%, q = 0.04), and BRCA2 GPVs with APC alterations (10.1% vs 1.5%, q = 0.004). Conversely, BRCA2 GPVs were inversely associated with PIK3CA alterations (13.0% vs 34.1%, q = 0.005), and CHEK2 GPVs with TP53 alterations (8.0% vs 29.8%, q = 0.02). GPVs in BRCA1, BRCA2, ATM, CHEK2, and PALB2 are associated with distinct intrinsic breast cancer subtypes and somatic genomic alterations. These findings may enhance precision in risk stratification and guide personalized treatment strategies. Show less
no PDF DOI: 10.1093/jnci/djag070
FGFR1