👤 Serina Huang

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Also published as: Ai-Chun Huang, Ai-long Huang, Aijie Huang, Ailong 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, 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
Jing Huang, Zhi-Feng Xu, Feng Liu +3 more · 2023 · iScience · Elsevier · added 2026-04-24
Minichromosome maintenance 6 (MCM6) has been implicated in the progression of various malignant tumors; however, its exact physiological function in kidney diseases remains unclear. Here, we demonstra Show more
Minichromosome maintenance 6 (MCM6) has been implicated in the progression of various malignant tumors; however, its exact physiological function in kidney diseases remains unclear. Here, we demonstrated that MCM6 levels showed a significant increase in the proximal tubular cells during progressive renal fibrosis in two unrelated Show less
📄 PDF DOI: 10.1016/j.isci.2023.107940
DUSP6
Xiaoqing Wu, Zheng Jing, Tianpu Huang +1 more · 2023 · Archivos espanoles de urologia · added 2026-04-24
Nucleolar prominence is a biomarker of prostate cancer (CaP), and the nucleolar protein block of proliferation 1 (BOP1) participates in the development of CaP, which has great significance for CaP the Show more
Nucleolar prominence is a biomarker of prostate cancer (CaP), and the nucleolar protein block of proliferation 1 (BOP1) participates in the development of CaP, which has great significance for CaP therapy. Thus, this study explored the mechanism of BOP1 in CaP development. BOP1 expression levels in the tumor tissues of CaP patients and in PC3 tumor cells were determined. The viability, apoptosis rate of PC3 cells, and apoptosis-related proteins levels were determined to explore the effect of BOP1 on tumor-cell growth BOP1 expression was upregulated in the tumor tissues and PC3 cells of CaP patients. BOP1 knockout reduced the activity of PC3 cells and induced apoptosis, significantly inhibiting the metastasis of PC3 cells. DUSP6 was overexpressed in tumor tissues and PC3 cells. BOP1 knockout inhibited DUSP6 expression and the MAPK pathway. DUSP6 overexpression reversed the inhibition of BOP1 siRNA (si-BOP1) on PC3 cells and the activated MAPK signaling pathway. This finding demonstrated that BOP1 promoted CaP progression by regulating the DUSP6/MAPK pathway. Show less
no PDF DOI: 10.56434/j.arch.esp.urol.20237606.54
DUSP6
Xiaohu Jin, Zhiqi Huang, Peng Guo +1 more · 2023 · Translational cancer research · added 2026-04-24
Breast cancer (BC) ranks first in incidence among women, with approximately 2 million new cases per year. Therefore, it is essential to investigate emerging targets for BC patients' diagnosis and prog Show more
Breast cancer (BC) ranks first in incidence among women, with approximately 2 million new cases per year. Therefore, it is essential to investigate emerging targets for BC patients' diagnosis and prognosis. We analyzed gene expression data from 99 normal and 1,081 BC tissues in The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were identified using "limma" R package, and relevant modules were chosen through Weighted Gene Coexpression Network Analysis (WGCNA). Intersection genes were obtained by matching DEGs to WGCNA module genes. Functional enrichment studies were performed on these genes using Gene Ontology (GO), Disease Ontology (DO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Biomarkers were screened via Protein-Protein Interaction (PPI) networks and multiple machine-learning algorithms. The Gene Expression Profiling Interactive Analysis (GEPIA), The University of ALabama at Birmingham CANcer (UALCAN), and Human Protein Atlas (HPA) databases were employed to examine mRNA and protein expression of eight biomarkers. Kaplan-Meier mapper tool assessed their prognostic capabilities. Key biomarkers were analyzed via single-cell sequencing, and their relationship with immune infiltration was examined using Tumor Immune Estimation Resource (TIMER) database and "xCell" R package. Lastly, drug prediction was conducted based on the identified biomarkers. We identified 1,673 DEGs and 542 important genes through differential analysis and WGCNA, respectively. Intersection analysis revealed 76 genes, which play significant roles in immune-related viral infection and IL-17 signaling pathways. DIX domain containing 1 (DIXDC1), Dual specificity phosphatase 6 (DUSP6), Pyruvate dehydrogenase kinase 4 (PDK4), C-X-C motif chemokine ligand 12 (CXCL12), Interferon regulatory factor 7 (IRF7), Integrin subunit alpha 7 (ITGA7), NIMA related kinase 2 (NEK2), and Nuclear receptor subfamily 3 group C member 1 (NR3C1) were selected as BC biomarkers using machine-learning algorithms. NEK2 was the most critical gene for diagnosis. Prospective drugs targeting NEK2 include etoposide and lukasunone. Our study identified DIXDC1, DUSP6, PDK4, CXCL12, IRF7, ITGA7, NEK2, and NR3C1 as potential diagnostic biomarkers for BC, with NEK2 having the highest potential to aid in diagnosis and prognosis in clinical settings. Show less
📄 PDF DOI: 10.21037/tcr-23-3
DUSP6
Yi-Chinn Weng, Yu-Ting Huang, I-Chen Chiang +4 more · 2023 · International journal of molecular sciences · MDPI · added 2026-04-24
Transient global cerebral ischemia (tGCI) resulting from cardiac arrest causes selective neurodegeneration in hippocampal CA1 neurons. Although the effect is clear, the underlying mechanisms directing Show more
Transient global cerebral ischemia (tGCI) resulting from cardiac arrest causes selective neurodegeneration in hippocampal CA1 neurons. Although the effect is clear, the underlying mechanisms directing this process remain unclear. Previous studies have shown that phosphorylation of Erk1/2 promotes cell survival in response to tGCI. DUSP6 (also named MKP3) serves as a cytosolic phosphatase that dephosphorylates Erk1/2, but the role of DUSP6 in tGCI has not been characterized. We found that DUSP6 was specifically induced in the cytoplasm of hippocampal CA1 neurons 4 to 24 h after tGCI. DUSP6-deficient mice showed normal spatial memory acquisition and retention in the Barnes maze. Impairment of spatial memory acquisition and retention after tGCI was attenuated in DUSP6-deficient mice. Neurodegeneration after tGCI, revealed by Fluoro-Jade C and H&E staining, was reduced in the hippocampus of DUSP6-deficient mice and DUSP6 deficiency enhanced the phosphorylation and nuclear translocation of Erk1/2 in the hippocampal CA1 region. These data support the role of DUSP6 as a negative regulator of Erk1/2 signaling and indicate the potential of DUSP6 inhibition as a novel therapeutic strategy to treat neurodegeneration after tGCI. Show less
📄 PDF DOI: 10.3390/ijms24097690
DUSP6
Shih-Chang Hsu, Shan-Yueh Chang, Yi-Ting Hwang +5 more · 2023 · Scientific reports · Nature · added 2026-04-24
Malignant pleural effusions (MPE) commonly result from malignant tumors and represent advanced-stage cancers. Thus, in clinical practice, early recognition of MPE is valuable. However, the current dia Show more
Malignant pleural effusions (MPE) commonly result from malignant tumors and represent advanced-stage cancers. Thus, in clinical practice, early recognition of MPE is valuable. However, the current diagnosis of MPE is based on pleural fluid cytology or histologic analysis of pleural biopsies with a low diagnostic rate. This research aimed to assess the diagnostic ability of eight previously identified Non-Small Cell Lung Cancer (NSCLC)-associated genes for MPE. In the study, eighty-two individuals with pleural effusion were recruited. There were thirty-three patients with MPE and forty-nine patients with benign transudate. mRNA was isolated from the pleural effusion and amplified by Quantitative real-time PCR. The logistic models were further applied to evaluate the diagnostic performance of those genes. Four significant MPE-associated genes were discovered in our study, including Dual-specificity phosphatase 6 (DUSP6), MDM2 proto-oncogene (MDM2), Ring finger protein 4 (RNF4), and WEE1 G2 Checkpoint Kinase (WEE1). Pleural effusion with higher expression levels of MDM2 and WEE1 and lower expression levels of RNF4 and DUSP6 had a higher possibility of being MPE. The four-gene model had an excellent performance distinguishing MPE and benign pleural effusion, especially for pathologically negative effusions. Therefore, the gene combination is a suitable candidate for MPE screening in patients with pleural effusion. We also identified three survival-associated genes, WEE1, Neurofibromin 1 (NF1), and DNA polymerase delta interacting protein 2 (POLDIP2), which could predict the overall survival of patients with MPE. Show less
📄 PDF DOI: 10.1038/s41598-023-32872-2
DUSP6
Weiwei Zhang, Leping Liu, Xiangcheng Xiao +11 more · 2023 · Frontiers in immunology · Frontiers · added 2026-04-24
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the main cause of COVID-19, causing hundreds of millions of confirmed cases and more than 18.2 million deaths worldwide. Acute kidney in Show more
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the main cause of COVID-19, causing hundreds of millions of confirmed cases and more than 18.2 million deaths worldwide. Acute kidney injury (AKI) is a common complication of COVID-19 that leads to an increase in mortality, especially in intensive care unit (ICU) settings, and chronic kidney disease (CKD) is a high risk factor for COVID-19 and its related mortality. However, the underlying molecular mechanisms among AKI, CKD, and COVID-19 are unclear. Therefore, transcriptome analysis was performed to examine common pathways and molecular biomarkers for AKI, CKD, and COVID-19 in an attempt to understand the association of SARS-CoV-2 infection with AKI and CKD. Three RNA-seq datasets (GSE147507, GSE1563, and GSE66494) from the GEO database were used to detect differentially expressed genes (DEGs) for COVID-19 with AKI and CKD to search for shared pathways and candidate targets. A total of 17 common DEGs were confirmed, and their biological functions and signaling pathways were characterized by enrichment analysis. MAPK signaling, the structural pathway of interleukin 1 (IL-1), and the Toll-like receptor pathway appear to be involved in the occurrence of these diseases. Hub genes identified from the protein-protein interaction (PPI) network, including DUSP6, BHLHE40, RASGRP1, and TAB2, are potential therapeutic targets in COVID-19 with AKI and CKD. Common genes and pathways may play pathogenic roles in these three diseases mainly through the activation of immune inflammation. Networks of transcription factor (TF)-gene, miRNA-gene, and gene-disease interactions from the datasets were also constructed, and key gene regulators influencing the progression of these three diseases were further identified among the DEGs. Moreover, new drug targets were predicted based on these common DEGs, and molecular docking and molecular dynamics (MD) simulations were performed. Finally, a diagnostic model of COVID-19 was established based on these common DEGs. Taken together, the molecular and signaling pathways identified in this study may be related to the mechanisms by which SARS-CoV-2 infection affects renal function. These findings are significant for the effective treatment of COVID-19 in patients with kidney diseases. Show less
📄 PDF DOI: 10.3389/fimmu.2023.961642
DUSP6
Chi-Shuan Huang, Harn-Jing Terng, Yi-Ting Hwang · 2023 · Biomedicines · MDPI · added 2026-04-24
Colorectal cancer (CRC) is a complex disease characterized by dynamically deregulated gene expression and crosstalk between signaling pathways. In this study, a new approach based on gene-function-bas Show more
Colorectal cancer (CRC) is a complex disease characterized by dynamically deregulated gene expression and crosstalk between signaling pathways. In this study, a new approach based on gene-function-based clusters was introduced to explore the CRC-associated networks of gene expression. Each cluster contained genes involved in coordinated regulatory activity, such as RAS signaling, the cell cycle process, transcription, or translation. A retrospective case-control study was conducted with the inclusion of 119 patients with histologically confirmed colorectal cancer and 308 controls. The quantitative expression data of 15 genes were obtained from the peripheral blood samples of all participants to investigate cluster-gene and gene-gene interactions. Show less
📄 PDF DOI: 10.3390/biomedicines11010145
DUSP6
Tim Kong, Angelo B A Laranjeira, Kangning Yang +12 more · 2023 · Nature cancer · Nature · added 2026-04-24
Myeloproliferative neoplasms (MPNs) exhibit a propensity for transformation to secondary acute myeloid leukemia (sAML), for which the underlying mechanisms remain poorly understood, resulting in limit Show more
Myeloproliferative neoplasms (MPNs) exhibit a propensity for transformation to secondary acute myeloid leukemia (sAML), for which the underlying mechanisms remain poorly understood, resulting in limited treatment options and dismal clinical outcomes. Here, we performed single-cell RNA sequencing on serial MPN and sAML patient stem and progenitor cells, identifying aberrantly increased expression of DUSP6 underlying disease transformation. Pharmacologic dual-specificity phosphatase (DUSP)6 targeting led to inhibition of S6 and Janus kinase (JAK)-signal transducer and activator of transcription (STAT) signaling while also reducing inflammatory cytokine production. DUSP6 perturbation further inhibited ribosomal S6 kinase (RSK)1, which we identified as a second indispensable candidate associated with poor clinical outcome. Ectopic expression of DUSP6 mediated JAK2-inhibitor resistance and exacerbated disease severity in patient-derived xenograft (PDX) models. Contrastingly, DUSP6 inhibition potently suppressed disease development across Jak2 Show less
📄 PDF DOI: 10.1038/s43018-022-00486-8
DUSP6
Tian Ye, Mengya Jiang, Xueyan Zeng +5 more · 2023 · Lupus science & medicine · added 2026-04-24
This study aimed to investigate the clinical significance of exostosin 1 (EXT1) in confirmed and suspected lupus membranous nephropathy (LMN). EXT1 was detected in 67 renal tissues of M-type phospholi Show more
This study aimed to investigate the clinical significance of exostosin 1 (EXT1) in confirmed and suspected lupus membranous nephropathy (LMN). EXT1 was detected in 67 renal tissues of M-type phospholipase A2 receptor (PLA2R)-negative and ANA-positive membranous nephropathy by immunohistochemistry, and cases were divided into confirmed LMN and suspected LMN. The clinicopathological data were compared among the above groups, as well as EXT1-positive group and EXT1-negative group. Twenty-two cases (73.3%) of confirmed LMN and six cases (16.2%) of suspected LMN exhibited EXT1 expression on the glomerular basement membrane and/or mesangium area, showing a significant difference (p<0.001). Concurrently, lupus nephritis (LN) of pure class V demonstrated a lower frequency of EXT1 positivity compared with mixed class V LN in the confirmed LMN group (31.8% vs 68.2%, p=0.007). EXT1-positive patients in the confirmed and suspected LMN group showed significant differences in some clinicopathological data comparing with EXT1-negative patients (p<0.05). Follow-up data revealed that a greater proportion of patients in the EXT1-positive group achieved complete remission post-treatment (p<0.05). Cox regression analysis showed that EXT1 positivity was significantly correlated with complete remission across the entire study cohort (HR 5.647; 95% CI, 1.323 to 12.048; p=0.019). Kaplan-Meier analysis indicated that the EXT1-positive group had a higher rate of accumulated nephrotic remission compared with the EXT1-negative group in the whole study cohort (p=0.028). The EXT1-positive group exhibited a higher active index and a more favourable renal outcome than the EXT1-negative group. It would be better to recognise suspected LMN with EXT1 positivity as a potential autoimmune disease and maintain close follow-up due to its similarities with confirmed LMN. Show less
📄 PDF DOI: 10.1136/lupus-2023-001051
EXT1
Chaoyun Yang, Zengwen Huang, Cuili Pan +1 more · 2023 · PloS one · PLOS · added 2026-04-24
Feed efficiency is a major constraint in the beef industry and has a significant negative correlation with residual feed intake (RFI). RFI is widely used as a measure of feed efficiency in beef cattle Show more
Feed efficiency is a major constraint in the beef industry and has a significant negative correlation with residual feed intake (RFI). RFI is widely used as a measure of feed efficiency in beef cattle and is independent of economic traits such as body weight and average daily gain. However, key traits with commonality or specificity among beef cattle breeds at the same level of RFI have not been reported. Accordingly, the present study hypothesized that signatures associated with feed efficiency would have commonality or specificity in the liver of cattle breeds at the same RFI level. By comparing and integrating liver transcriptome data, we investigated the critical signatures closely associated with RFI in beef cattle using weighted co-expression network analysis, consensus module analysis, functional enrichment analysis and protein network interaction analysis. The results showed that the consensus modules in Angus and Charolais cattle were negatively correlated, and four (turquoise, red, tan, yellow) were significantly positively correlated in Angus liver, while (turquoise, red) were significantly negatively correlated in Charolais liver. These consensus modules were found to be primarily involved in biological processes such as substance metabolism, energy metabolism and gene transcription, which may be one of the possible explanations for the difference in feed efficiency between the two beef breeds. This research also identified five key candidate genes, PLA2G12B, LCAT, MTTP, LCAT, ABCA1 and FADS1, which are closely associated with hepatic lipid metabolism. The present study has identified some modules, genes and pathways that may be the major contributors to the variation in feed efficiency among different cattle breeds, providing a new perspective on the molecular mechanisms of feed efficiency in beef cattle and a research basis for investigating molecular markers associated with feed efficiency in beef cattle. Show less
📄 PDF DOI: 10.1371/journal.pone.0289939
FADS1
Jianxin Shi, Kouya Shiraishi, Jiyeon Choi +219 more · 2023 · Nature communications · Nature · added 2026-04-24
Jianxin Shi, Kouya Shiraishi, Jiyeon Choi, Keitaro Matsuo, Tzu-Yu Chen, Juncheng Dai, Rayjean J Hung, Kexin Chen, Xiao-Ou Shu, Young Tae Kim, Maria Teresa Landi, Dongxin Lin, Wei Zheng, Zhihua Yin, Baosen Zhou, Bao Song, Jiucun Wang, Wei Jie Seow, Lei SONG, I-Shou Chang, Wei Hu, Li-Hsin Chien, Qiuyin Cai, Yun-Chul Hong, Hee Nam Kim, Yi-Long Wu, Maria Pik Wong, Brian Douglas Richardson, Karen M Funderburk, Shilan Li, Tongwu Zhang, Charles Breeze, Zhaoming Wang, Batel Blechter, Bryan A Bassig, Jin Hee Kim, Demetrius Albanes, Jason Y Y Wong, Min-Ho Shin, Lap Ping Chung, Yang Yang, She-Juan An, Hong Zheng, Yasushi Yatabe, Xu-Chao Zhang, Young-Chul Kim, Neil E Caporaso, Jiang Chang, James Chung Man Ho, Michiaki Kubo, Yataro Daigo, Minsun Song, Yukihide Momozawa, Yoichiro Kamatani, Masashi Kobayashi, Kenichi Okubo, Takayuki Honda, Dean H Hosgood, Hideo Kunitoh, Harsh Patel, Shun-Ichi Watanabe, Yohei Miyagi, Haruhiko Nakayama, Shingo Matsumoto, Hidehito Horinouchi, Masahiro Tsuboi, Ryuji Hamamoto, Koichi Goto, Yuichiro Ohe, Atsushi Takahashi, Akiteru Goto, Yoshihiro Minamiya, Megumi Hara, Yuichiro Nishida, Kenji Takeuchi, Kenji Wakai, Koichi Matsuda, Yoshinori Murakami, Kimihiro Shimizu, Hiroyuki Suzuki, Motonobu Saito, Yoichi Ohtaki, Kazumi Tanaka, Tangchun Wu, Fusheng Wei, Hongji Dai, Mitchell J Machiela, Jian Su, Yeul Hong Kim, In-Jae Oh, Victor Ho Fun Lee, Gee-Chen Chang, Ying-Huang Tsai, Kuan-Yu Chen, Ming-Shyan Huang, Wu-Chou Su, Yuh-Min Chen, Adeline Seow, Jae Yong Park, Sun-Seog Kweon, Kun-Chieh Chen, Yu-Tang Gao, Biyun Qian, Chen Wu, Daru Lu, Jianjun Liu, Ann G Schwartz, Richard Houlston, Margaret R Spitz, Ivan P Gorlov, Xifeng Wu, Ping Yang, Stephen Lam, Adonina Tardon, Chu Chen, Stig E Bojesen, Mattias Johansson, Angela Risch, Heike Bickeböller, Bu-Tian Ji, H-Erich Wichmann, David C Christiani, Gadi Rennert, Susanne Arnold, Paul Brennan, James McKay, John K Field, Sanjay S Shete, Loic Le Marchand, Geoffrey Liu, Angeline Andrew, Lambertus A Kiemeney, Shan Zienolddiny-Narui, Kjell Grankvist, Mikael Johansson, Angela Cox, Fiona Taylor, Jian-Min Yuan, Philip Lazarus, Matthew B Schabath, Melinda C Aldrich, Hyo-Sung Jeon, Shih Sheng Jiang, Jae Sook Sung, Chung-Hsing Chen, Chin-Fu Hsiao, Yoo Jin Jung, Huan Guo, Zhibin Hu, Laurie Burdett, Meredith Yeager, Amy Hutchinson, Belynda Hicks, Jia Liu, Bin Zhu, Sonja I Berndt, Wei Wu, Junwen Wang, Yuqing Li, Jin Eun Choi, Kyong Hwa Park, Sook Whan Sung, Li Liu, Chang Hyun Kang, Wen-Chang Wang, Jun Xu, Peng Guan, Wen Tan, Chong-Jen Yu, Gong Yang, Alan Dart Loon Sihoe, Ying Chen, Yi Young Choi, Jun Suk Kim, Ho-Il Yoon, In Kyu Park, Ping Xu, Qincheng He, Chih-Liang Wang, Hsiao-Han Hung, Roel C H Vermeulen, Iona Cheng, Junjie Wu, Wei-Yen Lim, Fang-Yu Tsai, John K C Chan, Jihua Li, Hongyan Chen, Hsien-Chih Lin, Li Jin, Jie Liu, Norie Sawada, Taiki Yamaji, Kathleen Wyatt, Shengchao A Li, Hongxia Ma, Meng Zhu, Zhehai Wang, Sensen Cheng, Xuelian Li, Yangwu Ren, Ann Chao, Motoki Iwasaki, Junjie Zhu, Gening Jiang, Ke Fei, Guoping Wu, Chih-Yi Chen, Chien-Jen Chen, Pan-Chyr Yang, Jinming Yu, Victoria L Stevens, Joseph F Fraumeni, Nilanjan Chatterjee, Olga Y Gorlova, Chao Agnes Hsiung, Christopher I Amos, Hongbing Shen, Stephen J Chanock, Nathaniel Rothman, Takashi Kohno, Qing Lan Show less
Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide associatio Show more
Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (P Show less
📄 PDF DOI: 10.1038/s41467-023-38196-z
FADS1
Tingting Huang, Yichen Long, Yang Ou +3 more · 2023 · BMC medical genomics · BioMed Central · added 2026-04-24
Fatty acids are involved in a wide range of immunological responses in humans. Supplementation of polyunsaturated fatty acids has been reported to help alleviate symptoms and airway inflammation in as Show more
Fatty acids are involved in a wide range of immunological responses in humans. Supplementation of polyunsaturated fatty acids has been reported to help alleviate symptoms and airway inflammation in asthma patients, whereas the effects of fatty acids on the actual risk of asthma remain controversial. This study comprehensively investigated the causal effects of serum fatty acids on asthma risk using two-sample bidirectional Mendelian Randomization (MR) analysis. Genetic variants strongly associated with 123 circulating fatty acid metabolites were extracted as instrumental variables, and a large GWAS data of asthma was used to test effects of the metabolites on this outcome. The inverse-variance weighted method was used for primary MR analysis. The weighted median, MR-Egger regression, MR-PRESSO, and leave-one-out analyses were utilized to evaluate heterogeneity and pleiotropy. Potential confounders were adjusted by performing multivariable MR analyses. Reverse MR analysis was also conducted to estimate the causal effect of asthma on candidate fatty acid metabolites. Further, we performed colocalization analysis to examine the pleiotropy of variants within the fatty acid desaturase 1 (FADS1) locus between the significant metabolite traits and the risk of asthma. Cis-eQTL-MR and colocalization analysis were also performed to determine the association between RNA expression of FADS1 and asthma. Genetically instrumented higher average number of methylene groups was causally associated with a lower risk of asthma in primary MR analysis, while inversely, the higher ratio of bis-allylic groups to double bonds and the higher ratio of bis-allylic groups to total fatty acids, were associated with higher probabilities of asthma. Consistent results were obtained in multivariable MR when adjusted for potential confounders. However, these effects were completely eliminated after SNPs correlated with the FADS1 gene were excluded. The reverse MR also found no causal association. The colocalization analysis suggested that the three candidate metabolite traits and asthma likely share causal variants within the FADS1 locus. In addition, the cis-eQTL-MR and colocalization analyses demonstrated a causal association and shared causal variants between FADS1 expression and asthma. Our study supports a negative association between several PUFA traits and the risk of asthma. However, this association is largely attributed to the influence of FADS1 polymorphisms. The results of this MR study should be carefully interpreted given the pleiotropy of SNPs associated with FADS1. Show less
📄 PDF DOI: 10.1186/s12920-023-01545-4
FADS1
Iain Mathieson, Felix R Day, Nicola Barban +122 more · 2023 · Nature human behaviour · Nature · added 2026-04-24
Iain Mathieson, Felix R Day, Nicola Barban, Felix C Tropf, David M Brazel, eQTLGen Consortium, BIOS Consortium, Ahmad Vaez, Natalie van Zuydam, Bárbara D Bitarello, Eugene J Gardner, Evelina T Akimova, Ajuna Azad, Sven Bergmann, Lawrence F Bielak, Dorret I Boomsma, Kristina Bosak, Marco Brumat, Julie E Buring, David Cesarini, Daniel I Chasman, Jorge E Chavarro, Massimiliano Cocca, Maria Pina Concas, George Davey Smith, Gail Davies, Ian J Deary, Tõnu Esko, Jessica D Faul, FinnGen Study, Oscar Franco, Andrea Ganna, Audrey J Gaskins, Andrea Gelemanovic, Eco J C de Geus, Christian Gieger, Giorgia Girotto, Bamini Gopinath, Hans Jörgen Grabe, Erica P Gunderson, Caroline Hayward, Chunyan He, Diana van Heemst, W David Hill, Eva R Hoffmann, Georg Homuth, Jouke Jan Hottenga, Hongyang Huang, Elina Hyppӧnen, M Arfan Ikram, Rick Jansen, Magnus Johannesson, Zoha Kamali, Sharon L R Kardia, Maryam Kavousi, Annette Kifley, Tuomo Kiiskinen, Peter Kraft, Brigitte Kühnel, Claudia Langenberg, Gerald Liew, LifeLines Cohort Study, Penelope A Lind, Jian'an Luan, Reedik Mägi, Patrik K E Magnusson, Anubha Mahajan, Nicholas G Martin, Hamdi Mbarek, Mark I McCarthy, George McMahon, Sarah E Medland, Thomas Meitinger, Andres Metspalu, Evelin Mihailov, Lili Milani, Stacey A Missmer, Paul Mitchell, Stine Møllegaard, Dennis O Mook-Kanamori, Anna Morgan, Peter J van der Most, Renée de Mutsert, Matthias Nauck, Ilja M Nolte, Raymond Noordam, Brenda W J H Penninx, Annette Peters, Patricia A Peyser, Ozren Polašek, Chris Power, Ajka Pribisalic, Paul Redmond, Janet W Rich-Edwards, Paul M Ridker, Cornelius A Rietveld, Susan M Ring, Lynda M Rose, Rico Rueedi, Vallari Shukla, Jennifer A Smith, Stasa Stankovic, Kári Stefánsson, Doris Stöckl, Konstantin Strauch, Morris A Swertz, Alexander Teumer, Gudmar Thorleifsson, Unnur Thorsteinsdottir, A Roy Thurik, Nicholas J Timpson, Constance Turman, André G Uitterlinden, Melanie Waldenberger, Nicholas J Wareham, David R Weir, Gonneke Willemsen, Jing Hau Zhao, Wei Zhao, Yajie Zhao, Harold Snieder, Marcel den Hoed, Ken K Ong, Melinda C Mills, John R B Perry Show less
Identifying genetic determinants of reproductive success may highlight mechanisms underlying fertility and identify alleles under present-day selection. Using data in 785,604 individuals of European a Show more
Identifying genetic determinants of reproductive success may highlight mechanisms underlying fertility and identify alleles under present-day selection. Using data in 785,604 individuals of European ancestry, we identified 43 genomic loci associated with either number of children ever born (NEB) or childlessness. These loci span diverse aspects of reproductive biology, including puberty timing, age at first birth, sex hormone regulation, endometriosis and age at menopause. Missense variants in ARHGAP27 were associated with higher NEB but shorter reproductive lifespan, suggesting a trade-off at this locus between reproductive ageing and intensity. Other genes implicated by coding variants include PIK3IP1, ZFP82 and LRP4, and our results suggest a new role for the melanocortin 1 receptor (MC1R) in reproductive biology. As NEB is one component of evolutionary fitness, our identified associations indicate loci under present-day natural selection. Integration with data from historical selection scans highlighted an allele in the FADS1/2 gene locus that has been under selection for thousands of years and remains so today. Collectively, our findings demonstrate that a broad range of biological mechanisms contribute to reproductive success. Show less
📄 PDF DOI: 10.1038/s41562-023-01528-6
FADS1
Wen-Chieh Wu, Pei-Yu Wu, Chien-Yi Chan +2 more · 2023 · Advances in nutrition (Bethesda, Md.) · Elsevier · added 2026-04-24
PUFA status is highly implicated in cognitive development and metabolic disorder-related diseases. Genetic variants of FADS genes encoding enzymes that catalyze the rate-limiting steps of PUFA biosynt Show more
PUFA status is highly implicated in cognitive development and metabolic disorder-related diseases. Genetic variants of FADS genes encoding enzymes that catalyze the rate-limiting steps of PUFA biosynthesis appear to be associated with n-3 and n-6 PUFA contents. Therefore, we conducted the first systematic review and meta-analysis to explore the association of the A-allele carriers of the FADS1 rs174556 with PUFA status. The PRISMA guidelines were followed. The literature search was conducted up to November 2022 in PubMed, Web of Science, Embase, Cochrane Library, Airiti Library, and CINAHL. The Joanna Briggs Institute checklists were used to assess the methodological quality. The correlation with 95% CIs was determined by a random-effect meta-analysis. Eleven studies that met the inclusion criteria and acceptable quality were included in this systematic review. The data on PUFA contents were collected when they were mainly analyzed using blood samples and breast milk. Results of the meta-analysis on eight studies (one randomized controlled trial, one cohort study, and six cross-sectional studies) showed that the A-allele carriers of rs174556 were significantly negatively correlated with the concentrations of AA (P = 0.001), EPA (P = 0.004), and DHA (P = 0.025). However, ALA and LA were not associated with the A-allele carriers. To clarify the discrepancy, we further divided the studies into blood samples and breast milk subgroups. The subgroup analysis revealed that the A-allele carriers of rs174556 were significantly positively correlated with LA (P = 0.031) and negatively correlated with AA (P = 0.001), EPA (P = 0.036), and DHA (P < 0.001) in the blood sample group, but not in the breast milk group. The current meta-analysis proved that the A-allele carriers of the FADS1 rs174556 appeared to be highly associated with lower concentrations of AA, EPA, and DHA but higher LA in the blood samples. The study has been registered on the International Prospective Register of Systematic Reviews (PROSPERO:CRD42022363978). Adv Nutr 2023;x:xx-xx. Show less
📄 PDF DOI: 10.1016/j.advnut.2023.01.007
FADS1
Pei-Chi Huang, Hsuan Cheng, Yu-Ting Su +5 more · 2023 · Journal of diabetes investigation · Blackwell Publishing · added 2026-04-24
Fatty acid desaturase (FADS) genetic polymorphisms are strongly correlated with the risk of dyslipidemia and cardiovascular disease. In this study, we examined the impact of FADS1 and FADS2 genetic va Show more
Fatty acid desaturase (FADS) genetic polymorphisms are strongly correlated with the risk of dyslipidemia and cardiovascular disease. In this study, we examined the impact of FADS1 and FADS2 genetic variants on plasma lipid status, and assessed interactions between FADS genetic polymorphisms and plasma n-3/n-6 fatty acids regarding lipid status within a population of 816 Taiwanese patients with type 2 diabetes. Selected tag single-nucleotide polymorphisms (FADS1 rs174546 [T/C]; FADS2 rs174602 [A/G] and rs2072114 [A/G]) were genotyped (n = 816). The distribution of genotypes were compared with reports publicly available in the Genome Aggregation Database for East Asian populations (https://gnomad.broadinstitute.org). In the subgroup of patients not taking lipid-lowering medications (n = 192), we observed that the G allele of FADS2 rs174602 was statistically significantly correlated with lower low-density lipoprotein cholesterol (LDL-C) concentrations (P = 0.001), whereas the G allele of rs2072114 was marginally associated with LDL-C concentrations (P = 0.091). Using a general linear model adjusted for confounding factors, statistically significant interactions (P = 0.016) between single-nucleotide polymorphisms in rs2072114 and a low alpha-linolenic acid (18:3n-3)/linoleic acid (18:2n-6) ratio; the G allele correlated with lower LDL-C levels among individuals with a low alpha-linolenic acid/linoleic acid ratio. Interaction between rs174602 single-nucleotide polymorphisms and low alpha-linolenic acid/linoleic acid values on LDL-C was only marginally significant (P = 0.063). Our results show the role of n-3/n-6 dietary polyunsaturated fatty acids in modifying the effects of genetic susceptibility on lipoprotein concentrations in patients with type 2 diabetes. Our findings highlight the potential of interventions with dietary polyunsaturated fatty acids regarding developing individualized prevention strategies for type 2 diabetes presenting with co-occurring dyslipidemia and cardiovascular diseases. Show less
📄 PDF DOI: 10.1111/jdi.13944
FADS1
Weilin Zhang, Zhongcheng Huang, Zhigang Xiao +6 more · 2023 · Acta biochimica et biophysica Sinica · added 2026-04-24
Despite substantial advancements in screening, surgery, and chemotherapy, colorectal cancer remains the second most lethal form of the disease. Nuclear factor kappa B (NF-κB) signaling is a critical d Show more
Despite substantial advancements in screening, surgery, and chemotherapy, colorectal cancer remains the second most lethal form of the disease. Nuclear factor kappa B (NF-κB) signaling is a critical driver facilitating the malignant transformation of chronic inflammatory bowel diseases. In this study, deregulated miRNAs that could play a role in colon cancer are analyzed and investigated for specific functions Show less
📄 PDF DOI: 10.3724/abbs.2023235
FGFR1
Hong Huang, Qingyi Chen, Zhengang Xu +1 more · 2023 · International journal of molecular sciences · MDPI · added 2026-04-24
The thalamus plays a crucial role in ensuring the faithful transfer of sensory information, except olfactory signals, to corresponding cortical areas. However, thalamic function is not simply restrict Show more
The thalamus plays a crucial role in ensuring the faithful transfer of sensory information, except olfactory signals, to corresponding cortical areas. However, thalamic function is not simply restricted to relaying information to and from the cerebral cortex. The ability to modulate the flow of sensory information is supported by a second abundant neuronal type in the prethalamus, the inhibitory gamma-aminobutyric acid (GABAergic) neurons, which project inhibitory GABAergic axons to dorsal thalamic glutamatergic neurons. Interestingly, during the trajectory of pioneer prethalamic axons, morphogen fibroblast growth factor (FGF)-3 is expressed in the ventral chick hypothalamus. Using in vitro analyses in chick explants, we identify a chemorepellent effect of FGF3 on nearby prethalamic GABAergic axons. Furthermore, inhibition of FGF3 guidance functions indicates that FGF3 signaling is necessary to navigate prethalamic axons correctly. Gene expression analyses and loss of function studies demonstrate that FGF3 mediates prethalamic axonal guidance through the downstream pathway of the FGF receptor (FGFR)-1. Together, these results suggest that FGF3 expressed in the hypothalamus functions as a chemorepellent molecule to direct the pathway selection of neighboring GABAergic axons. Show less
📄 PDF DOI: 10.3390/ijms241914998
FGFR1
Yu-Ching Huang, Wei-Cheng Chen, Chen-Lin Yu +5 more · 2023 · Biochemical pharmacology · Elsevier · added 2026-04-24
Osteosarcoma is a malignant tumor with high metastatic potential, such that the overall 5-year survival rate of patients with metastatic osteosarcoma is only 20%. Therefore, it is necessary to unravel Show more
Osteosarcoma is a malignant tumor with high metastatic potential, such that the overall 5-year survival rate of patients with metastatic osteosarcoma is only 20%. Therefore, it is necessary to unravel the mechanisms of osteosarcoma metastasis to identify predictors of metastasis by which to develop new therapies. Fibroblast growth factor 2 (FGF2) is a growth factor involved in embryonic development, cell migration, and proliferation. The overexpression of FGF2 and FGF receptors (FGFRs) has been shown to enhance cancer cell proliferation in lung, breast, gastric, and prostate cancers as well as melanoma. Nonetheless, the roles of FGF2 and FGFRs in human osteosarcoma cells remain unknown. In the present study, we found that FGF2 was overexpressed in human osteosarcoma sections and correlated with lung metastasis. Treatment of FGF2 induced migration activity, invasion activity, and intercellular adhesion molecule (ICAM)-1 expression in osteosarcoma cells. In particular, the downregulation or antagonism of FGFR1-4 suppressed FGF2-induced ICAM-1 expression and cancer cell migration. Furthermore, FGFR1, FGFR2, FGFR3, and FGFR4 were involved in FGF2-induced the phospholipase Cβ/protein kinase Cα/proto-oncogene c-Src signaling pathway and triggered c-Jun nuclear translocation. Subsequent c-Jun upregulation of activator protein-1 transcription activity on the ICAM-1 promoter led to an increased migration of osteosarcoma cells. Moreover, the knockdown of endogenous FGF2 suppressed ICAM-1 expression and migration of osteosarcoma cells. These findings suggest that FGF2/FGFR1-4 signaling promotes metastasis via its direct downstream target gene ICAM-1, revealing a novel potential therapeutic target for osteosarcoma. Show less
no PDF DOI: 10.1016/j.bcp.2023.115853
FGFR1
Ying Huang, Chenchen Wei, Ping Li +8 more · 2023 · Free radical biology & medicine · Elsevier · added 2026-04-24
Fibroblast growth factor 21 (FGF21) regulates glycolipid metabolism and insulin homeostasis and acts as a cardioprotective factor by protecting against myocardial ischemia/reperfusion injury, hyperten Show more
Fibroblast growth factor 21 (FGF21) regulates glycolipid metabolism and insulin homeostasis and acts as a cardioprotective factor by protecting against myocardial ischemia/reperfusion injury, hypertension, and vascular dysfunction. FGF21 has been reported to prevent Doxorubicin (Dox)-induced cardiotoxicity, and the related signaling pathway is worthy of further study. Connexin43 (Cx43) protein was reduced by Dox treatment, especially low phosphorylated form of Cx43. Thus the aim of study is to explore the protection effect of FGF21 on Dox induced cardiotoxicity by improving the expression of Cx43 and the involved signaling pathway. FGF21 inhibited apoptosis in Dox-treated mice and cardiomyocytes. FGF21 increased the levels of connexin43 phosphorylated at serine (S) 282 (p-Cx43 S282) and total Cx43 to inhibit Dox-induced apoptosis. By RNA sequencing, we found that deubiquitinase monocyte chemoattractant protein-induced protein 1 (MCPIP1) expression was increased by FGF21. We further found that FGF21 induced the phosphorylation of fibroblast growth factor receptor 1 (FGFR1), extracellular signal-regulated kinase 1 and 2 (Erk1/2), and Elk. Phosphorylated Elk translocated to the nucleus and increased the expression of MCPIP1. Then, MCPIP1 bound neural precursor cell expressed developmentally downregulated protein 4 (Nedd4), an E3 ubiquitination ligase, as shown by co-immunoprecipitation (Co-IP), and suppressed Cx43 ubiquitination and degradation, competitively inhibiting the binding of Cx43 with Nedd4. Thus Nedd4 could not bind and ubiquitinate Cx43, leading to the up-regulation of Cx43 and phosphorylation of Cx43 at S282. FGF21 inhibited the effects of Dox on cardiomyocytes by elevating the phosphorylation of Cx43 at S282 and total Cx43 expression. This study suggests a previously unknown mechanism for the FGF21-mediated enhancement of cardiomyocyte survival and provides an effective approach to protect against the adverse cardiac effects of Dox. Show less
no PDF DOI: 10.1016/j.freeradbiomed.2023.09.033
FGFR1
Oliver Sartor, Elisa Ledet, Minqi Huang +10 more · 2023 · Journal of nuclear medicine : official publication, Society of Nuclear Medicine · added 2026-04-24
no PDF DOI: 10.2967/jnumed.123.266167
FGFR1
Qing Liu, Jiyu Huang, Weiwei Yan +3 more · 2023 · MedComm · Wiley · added 2026-04-24
There are five fibroblast growth factor receptors (FGFRs), namely, FGFR1-FGFR5. When FGFR binds to its ligand, namely, fibroblast growth factor (FGF), it dimerizes and autophosphorylates, thereby acti Show more
There are five fibroblast growth factor receptors (FGFRs), namely, FGFR1-FGFR5. When FGFR binds to its ligand, namely, fibroblast growth factor (FGF), it dimerizes and autophosphorylates, thereby activating several key downstream pathways that play an important role in normal physiology, such as the Ras/Raf/mitogen-activated protein kinase kinase/extracellular signal-regulated kinase, phosphoinositide 3-kinase (PI3K)/AKT, phospholipase C gamma/diacylglycerol/protein kinase c, and signal transducer and activator of transcription pathways. Furthermore, as an oncogene, FGFR genetic alterations were found in 7.1% of tumors, and these alterations include gene amplification, gene mutations, gene fusions or rearrangements. Therefore, FGFR amplification, mutations, rearrangements, or fusions are considered as potential biomarkers of FGFR therapeutic response for tyrosine kinase inhibitors (TKIs). However, it is worth noting that with increased use, resistance to Show less
📄 PDF DOI: 10.1002/mco2.367
FGFR1
Jen-Chieh Lee, Tsung-Han Hsieh, Yu-Chien Kao +17 more · 2023 · Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · Elsevier · added 2026-04-24
Phosphaturic mesenchymal tumors (PMT) are uncommon neoplasms that cause hypophosphatemia/osteomalacia mainly by secreting fibroblast growth factor 23. We previously identified FN1::FGFR1/FGF1 fusions Show more
Phosphaturic mesenchymal tumors (PMT) are uncommon neoplasms that cause hypophosphatemia/osteomalacia mainly by secreting fibroblast growth factor 23. We previously identified FN1::FGFR1/FGF1 fusions in nearly half of the PMTs and frequent KL (Klotho or α-Klotho) overexpression in only those with no known fusion. Here, we studied a larger cohort of PMTs for KL expression and alterations. By FN1 break-apart fluorescence in situ hybridization (FISH) and reappraisal of previous RNA sequencing data, 6 tumors previously considered "fusion-negative" (defined by negative results of FISH for FN1::FGFR1 fusion and FGF1 break-apart and/or of RNA sequencing) were reclassified as fusion-positive PMTs, including 1 containing a novel FN1::ZACN fusion. The final cohort of fusion-negative PMTs included 33 tumors from 32 patients, which occurred in the bone (n = 18), soft tissue (n = 10), sinonasal tract (n = 4), and brain (n = 1). In combination with previous work, RNA sequencing, RNA in situ hybridization, and immunohistochemistry showed largely concordant results and demonstrated KL/α-Klotho overexpression in 17 of the 28 fusion-negative and none of the 10 fusion-positive PMTs studied. Prompted by a patient in this cohort harboring germline KL upstream translocation with systemic α-Klotho overexpression and multifocal PMTs, FISH was performed and revealed KL rearrangement in 16 of the 33 fusion-negative PMTs (one also with amplification), including 14 of the 17 cases with KL/α-Klotho overexpression and none of the 11 KL/α-Klotho-low fusion-negative and 11 fusion-positive cases studied. Whole genomic sequencing confirmed translocation and inversion in 2 FISH-positive cases involving the KL upstream region, warranting further investigation into the mechanism whereby these rearrangements may lead to KL upregulation. Methylated DNA immunoprecipitation and sequencing suggested no major role of promoter methylation in KL regulation in PMT. Interestingly, KL-high/-rearranged cases seemed to form a clinicopathologically homogeneous group, showing a predilection for skeletal/sinonasal locations and typically matrix-poor, cellular solitary fibrous tumor-like morphology. Importantly, FGFR1 signaling pathways were upregulated in fusion-negative PMTs regardless of the KL status compared with non-PMT mesenchymal tumors by gene set enrichment analysis, perhaps justifying FGFR1 inhibition in treating this subset of PMTs. Show less
no PDF DOI: 10.1016/j.modpat.2023.100336
FGFR1
Tianli Xu, Qiancheng Zhu, Qun Huang +9 more · 2023 · Brain research bulletin · Elsevier · added 2026-04-24
Spinal cord injury (SCI) is a kind of traumatic nervous system disease caused by neuronal death, causing symptoms like sensory, motor, and autonomic nerve dysfunction. The recovery of neurological fun Show more
Spinal cord injury (SCI) is a kind of traumatic nervous system disease caused by neuronal death, causing symptoms like sensory, motor, and autonomic nerve dysfunction. The recovery of neurological function has always been a intractable problem that has greatly distressed individuals and society. Although the involvement of iron-dependent lipid peroxidation leading to nerve cell ferroptosis in SCI progression has been reported, the underlying mechanisms remain unaddressed. Thus, this study aimed to investigate the potential of recombinant human FGF21 (rhFGF21) in inhibiting ferroptosis of nerve cells and improving limb function after SCI, along with its underlying mechanisms. In vivo animal model showed that FGFR1, p-FGFR1, and β-Klotho protein gradually increased over time after injury, reaching a peak on the third day. Moreover, rhFGF21 treatment significantly reduced ACSL4, increased GPX4 expression, reduced iron deposition, and inhibited ferroptosis. Meanwhile, rhFGF21 decreased cell apoptosis following acute spinal cord damage. In contrast, FGFR1 inhibitor PD173074 partially reversed the rhFGF21-induced therapeutic effects. Overall, this work revealed that rhFGF21 activates the FGFR1/β-Klotho pathway to decrease ferroptosis of nerve cells, suggesting that FGF21 could be a new therapeutic target for SCI neurological rehabilitation. Show less
no PDF DOI: 10.1016/j.brainresbull.2023.110753
FGFR1
Chunsik Lee, Rongyuan Chen, Guangli Sun +45 more · 2023 · Signal transduction and targeted therapy · Nature · added 2026-04-24
Although VEGF-B was discovered as a VEGF-A homolog a long time ago, the angiogenic effect of VEGF-B remains poorly understood with limited and diverse findings from different groups. Notwithstanding, Show more
Although VEGF-B was discovered as a VEGF-A homolog a long time ago, the angiogenic effect of VEGF-B remains poorly understood with limited and diverse findings from different groups. Notwithstanding, drugs that inhibit VEGF-B together with other VEGF family members are being used to treat patients with various neovascular diseases. It is therefore critical to have a better understanding of the angiogenic effect of VEGF-B and the underlying mechanisms. Using comprehensive in vitro and in vivo methods and models, we reveal here for the first time an unexpected and surprising function of VEGF-B as an endogenous inhibitor of angiogenesis by inhibiting the FGF2/FGFR1 pathway when the latter is abundantly expressed. Mechanistically, we unveil that VEGF-B binds to FGFR1, induces FGFR1/VEGFR1 complex formation, and suppresses FGF2-induced Erk activation, and inhibits FGF2-driven angiogenesis and tumor growth. Our work uncovers a previously unrecognized novel function of VEGF-B in tethering the FGF2/FGFR1 pathway. Given the anti-angiogenic nature of VEGF-B under conditions of high FGF2/FGFR1 levels, caution is warranted when modulating VEGF-B activity to treat neovascular diseases. Show less
📄 PDF DOI: 10.1038/s41392-023-01539-9
FGFR1
Min Peng, Hui Li, Huan Cao +4 more · 2023 · Journal of gastroenterology · Springer · added 2026-04-24
Therapies for cholangiocarcinoma are largely limited and ineffective. Herein, we examined the role of the FGF and VEGF pathways in regulating lymphangiogenesis and PD-L1 expression in intrahepatic cho Show more
Therapies for cholangiocarcinoma are largely limited and ineffective. Herein, we examined the role of the FGF and VEGF pathways in regulating lymphangiogenesis and PD-L1 expression in intrahepatic cholangiocarcinoma (iCCA). The lymphangiogenic functions of FGF and VEGF were evaluated in lymphatic endothelial cells (LECs) and iCCA xenograft mouse models. The relationship between VEGF and hexokinase 2 (HK2) was validated in LECs by western blot, immunofluorescence, ChIP and luciferase reporter assays. The efficacy of the combination therapy was assessed in LECs and xenograft models. Microarray analysis was used to evaluate the pathological relationships of FGFR1 and VEGFR3 with HK2 in human lymphatic vessels. FGF promoted lymphangiogenesis through c-MYC-dependent modulation of HK2 expression. VEGFC also upregulated HK2 expression. Mechanistically, VEGFC phosphorylated components of the PI3K/Akt/mTOR axis to upregulate HIF-1α expression at the translational level, and HIF-1α then bound to the HK2 promoter region to activate its transcription. More importantly, dual FGFR and VEGFR inhibition with infigratinib and SAR131675 almost completely inhibited lymphangiogenesis, and significantly suppressed iCCA tumor growth and progression by reducing PD-L1 expression in LECs. Dual FGFR and VEGFR inhibition inhibits lymphangiogenesis through suppression of c-MYC-dependent and HIF-1α-mediated HK2 expression, respectively. HK2 downregulation decreased glycolytic activity and further attenuated PD-L1 expression. Our findings suggest that dual FGFR and VEGFR blockade is an effective novel combination strategy to inhibit lymphangiogenesis and improve immunocompetence in iCCA. Show less
📄 PDF DOI: 10.1007/s00535-023-02012-8
FGFR1
Lingfeng Chen, Lili Fu, Jingchuan Sun +11 more · 2023 · Nature · Nature · added 2026-04-24
α/βKlotho coreceptors simultaneously engage fibroblast growth factor (FGF) hormones (FGF19, FGF21 and FGF23)
📄 PDF DOI: 10.1038/s41586-023-06155-9
FGFR1
Yubin Tang, Peng Yang, Min Jin +5 more · 2023 · Bone · Elsevier · added 2026-04-24
Osteoporosis (OP) is the most common skeletal disease in middle-aged and elderly people. A comprehensive understanding of the pathogenesis of osteoporosis is important. Fibroblast growth factor recept Show more
Osteoporosis (OP) is the most common skeletal disease in middle-aged and elderly people. A comprehensive understanding of the pathogenesis of osteoporosis is important. Fibroblast growth factor receptor 1 (FGFR1) is an important molecule for skeletal development and bone remodeling. Osteocytes are the most numerous cells in bone and play critical roles in bone homeostasis, however the effect of FGFR1 on osteocytes is still unclear. To clarify the direct effects of FGFR1 on osteocytes, we conditionally deleted Fgfr1 in osteocytes with Dentin matrix protein 1 (Dmp1)-Cre. We found that mice lacking Fgfr1 in osteocytes (Fgfr1 Show less
no PDF DOI: 10.1016/j.bone.2023.116817
FGFR1
Siyu Yang, Lianggui Huang, Huiling Liang +4 more · 2023 · Biology open · added 2026-04-24
As a member of the fibronectin leucine-rich transmembrane (flrt) gene family, fibronectin leucine-rich transmembrane 2 (flrt2) is strongly expressed in a subset of sclerotome cells, and the resultant Show more
As a member of the fibronectin leucine-rich transmembrane (flrt) gene family, fibronectin leucine-rich transmembrane 2 (flrt2) is strongly expressed in a subset of sclerotome cells, and the resultant protein interacts with FGFR1 in the FGF signaling pathway during development. Studies on flrt2 have focused mainly on its roles in the brain, heart and chondrogenesis. However, reports on its expression and function in the zebrafish retina are lacking. Here, we detected the high expression of flrt2 in zebrafish retina using in situ hybridization technique and developed an flrt2-knockout (KO) zebrafish line using CRISPR/Cas9 genome editing. Quantitative real-time PCR was used to measure the expression levels of flrt2, which results in an approximately 60% mRNA reduction. The flrt2-KO zebrafish eyes' altered morphological, cellular, and molecular events were identified using BrdU labeling, TUNEL assay, immunofluorescent staining, fluorescent dye injection and RNA sequencing. Abnormal eye development, known as microphthalmia, was found in flrt2-KO larvae, and the retinal progenitor cells exhibited increased apoptosis, perhaps owing to the combined effects of crx, neurod4, atoh7, and pcdh8 downregulation and Casp3a and Caspbl upregulation. In contrast, the retinal neural development, as well as retinal progenitor cell differentiation and proliferation, were not affected by the flrt2 deletion. Thus, flrt2 appears to play important roles in retinal development and function, which may provide the basis for further investigations into the molecular mechanisms of retinal development and evolution. Show less
📄 PDF DOI: 10.1242/bio.059784
FGFR1
Xichun Xia, Hai Yu, Yanxiang Li +3 more · 2023 · Clinical, cosmetic and investigational dermatology · added 2026-04-24
Psoriasis is a systemic inflammatory disease, and the mechanism that links psoriasis to depression is still elusive. Hence, this study aimed to elucidate the potential pathogenesis of psoriasis and de Show more
Psoriasis is a systemic inflammatory disease, and the mechanism that links psoriasis to depression is still elusive. Hence, this study aimed to elucidate the potential pathogenesis of psoriasis and depression comorbidity. The gene expression profiles of psoriasis (GSE34248, GSE78097 and GSE161683) and depression (GSE39653) were downloaded from the Gene Expression Omnibus (GEO) DataSets. Functional annotation, protein-protein interaction (PPI) network and module construction, and hub gene identification and co-expression analysis were performed, following identification of the common differentially expressed genes (DEGs) of psoriasis and depression. A total of 115 common DEGs (55 up-regulated and 60 down-regulated) were identified between psoriasis and depression. Functional analysis indicated that T cell activation and differentiation were predominantly implicated in the potential pathogenesis of these two diseases. In addition, Th17 cell differentiation and cytokines is closely related to both. Finally, 17 hub genes were screened, including CTLA4, LCK, ITK, IL7R, CD3D, SOCS1, IL4R, PRKCQ, SOCS3, IL23A, PDGFB, PAG1, TGFA, FGFR1, RELN, ITGB5 and TNXB, which re-emphasized the importance of the immune system in psoriasis and depression. Our study reveals the common pathogenesis of psoriasis and depression. These common pathways and hub genes may apply to a molecular screening tool for depression in psoriasis patients, which could help dermatologists optimize patient management in routine care. Show less
📄 PDF DOI: 10.2147/CCID.S413887
FGFR1
Maroun Bou Zerdan, Gennady Bratslavsky, Joseph Jacob +3 more · 2023 · Molecular diagnosis & therapy · Springer · added 2026-04-24
Genomic alterations in fibroblast growth factor receptor (FGFR) genes have been linked to a reduced response to immune checkpoint inhibitors. Some of the immune microenvironment of urothelial bladder Show more
Genomic alterations in fibroblast growth factor receptor (FGFR) genes have been linked to a reduced response to immune checkpoint inhibitors. Some of the immune microenvironment of urothelial bladder cancer (UBC) could be distorted because of the inhibition of interferon signaling pathways. We present a landscape of FGFR genomic alterations in distorted UBC to evaluate the immunogenomic mechanisms of resistance and response. There were 4035 UBCs that underwent hybrid, capture-based comprehensive genomic profiling. Tumor mutational burden was determined in up to 1.1 Mbp of sequenced DNA and microsatellite instability was determined in 114 loci. Programmed death ligand expression in tumor cells was assessed by immunohistochemistry (Dako 22C3). The FGFR tyrosine kinases were altered in 894 (22%) UBCs. The highest frequency of alterations was in FGFR genomic alterations with FGFR3 at 17.4% followed by FGFR1 at 3.7% and FGFR2 at 1.1%. No FGFR4 genomic alterations were identified. The age and sex distribution were similar in all groups. Urothelial bladder cancers that featured FGFR3 genomic alterations were associated with lower driver genomic alterations/tumors. 14.7% of the FGFR3 genomic alterations were FGFR3 fusions. Other findings included a significantly higher frequency of ERBB2 amplification in FGFR1/2-altered UBCs compared with FGFR3-altered UBCs. Urothelial bladder cancers with FGFR3 genomic alterations also had the highest frequency of the activating mTOR pathway. FGFR3-altered UBCs also featured significantly higher frequencies of biomarkers associated with a lack of response to immune checkpoint inhibitors including a lower tumor mutational burden, lower programmed death-ligand 1 expression, and higher frequencies of genomic alterations in MDM2. Also linked to IO drug resistance, CDKN2A/B loss and MTAP loss were observed at a higher frequency in FGFR3-driven UBC. An increased frequency of genomic alterations is observed in UBC FGFR. These have been linked to immune checkpoint inhibitor resistance. Clinical trials are needed to evaluate UBC FGFR-based biomarkers prognostic of an immune checkpoint inhibitor response. Only then can we successfully incorporate novel therapeutic strategies into the evolving landscape of UBC treatment. Show less
no PDF DOI: 10.1007/s40291-023-00647-0
FGFR1