👤 Chueh-Tan Chen

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1996
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Also published as: Ai-Qun Chen, Aiping Chen, Alex Chen, Alex F Chen, Alice P Chen, Alice Y Chen, Alice Ye A Chen, Allen Menglin Chen, Alon Chen, Alvin Chen, An Chen, Andrew Chen, Anqi Chen, Aoshuang Chen, Aozhou Chen, B Chen, B-S Chen, Baihua Chen, Ban Chen, Bang Chen, Bang-dang Chen, Bao-Bao Chen, Bao-Fu Chen, Bao-Sheng Chen, Bao-Ying Chen, Baofeng Chen, Baojiu Chen, Baolin Chen, Baosheng Chen, Baoxiang Chen, Beidong Chen, Beijian Chen, Ben-Kuen Chen, Benjamin Chen, Benjamin Jieming Chen, Benjamin P C Chen, Beth L Chen, Bihong T Chen, Bin Chen, Bing Chen, Bing-Bing Chen, Bing-Feng Chen, Bing-Huei Chen, Bingdi Chen, Bingqian Chen, Bingqing Chen, Bingyu Chen, Binlong Chen, Binzhen Chen, Bo Chen, Bo-Fang Chen, Bo-Jun Chen, Bo-Rui Chen, Bo-Sheng Chen, Bohe Chen, Bohong Chen, Bosong Chen, Bowang Chen, Bowei Chen, Bowen Chen, Boyu Chen, Brian Chen, C Chen, C Y Chen, C Z Chen, C-Y Chen, Cai-Long Chen, Caihong Chen, Can Chen, Cancan Chen, Canrong Chen, Canyu Chen, Caressa Chen, Carl Pc Chen, Carol Chen, Carol X-Q Chen, Catherine Qing Chen, Ceshi Chen, Chan Chen, Chang Chen, Chang-Lan Chen, Chang-Zheng Chen, Changjie Chen, Changya Chen, Changyan Chen, Chanjuan Chen, Chao Chen, Chao-Jung Chen, Chao-Wei Chen, Chaochao Chen, Chaojin Chen, Chaoli Chen, Chaoping Chen, Chaoqun Chen, Chaoran Chen, Chaoyi Chen, Chaoyue Chen, Chen Chen, Chen-Mei Chen, Chen-Sheng Chen, Chen-Yu Chen, Cheng Chen, Cheng-Fong Chen, Cheng-Sheng Chen, Cheng-Yi Chen, Cheng-Yu Chen, Chengchuan Chen, Chengchun Chen, Chengde Chen, Chengsheng Chen, Chengwei Chen, Chenyang Chen, Chi Chen, Chi-Chien Chen, Chi-Hua Chen, Chi-Long Chen, Chi-Yu Chen, Chi-Yuan Chen, Chi-Yun Chen, Chian-Feng Chen, Chider Chen, Chien-Hsiun Chen, Chien-Jen Chen, Chien-Lun Chen, Chien-Ting Chen, Chien-Yu Chen, Chih-Chieh Chen, Chih-Mei Chen, Chih-Ping Chen, Chih-Ta Chen, Chih-Wei Chen, Chih-Yi Chen, Chin-Chuan Chen, Ching Kit Chen, Ching-Hsuan Chen, Ching-Jung Chen, Ching-Wen Chen, Ching-Yi Chen, Ching-Yu Chen, Chiqi Chen, Chiung Mei Chen, Chiung-Mei Chen, Chixiang Chen, Chong Chen, Chongyang Chen, Christina Y Chen, Christina Yingxian Chen, Christopher S Chen, Chu Chen, Chu-Huang Chen, Chuanbing Chen, Chuannan Chen, Chuanzhi Chen, Chuck T Chen, Chujie Chen, Chun Chen, Chun-An Chen, Chun-Chi Chen, Chun-Fa Chen, Chun-Han Chen, Chun-Houh Chen, Chun-Wei Chen, Chun-Yuan Chen, Chung-Hao Chen, Chung-Hsing Chen, Chung-Hung Chen, Chung-Jen Chen, Chung-Yung Chen, Chunhai Chen, Chunhua Chen, Chunji Chen, Chunjie Chen, Chunlin Chen, Chunnuan Chen, Chunxiu Chen, Chuo Chen, Chuyu Chen, Cindi Chen, Constance Chen, Cuicui Chen, Cuie Chen, Cuilan Chen, Cuimin Chen, Cuncun Chen, D F Chen, D M Chen, D-F Chen, D. Chen, Dafang Chen, Daijie Chen, Daiwen Chen, Daiyu Chen, Dake Chen, Dali Chen, Dan Chen, Dan-Dan Chen, Dandan Chen, Danlei Chen, Danli Chen, Danmei Chen, Danna Chen, Danni Chen, Danxia Chen, Danxiang Chen, Danyang Chen, Danyu Chen, Daoyuan Chen, Dapeng Chen, Dawei Chen, Defang Chen, Dejuan Chen, Delong Chen, Denghui Chen, Dengpeng Chen, Deqian Chen, Dexi Chen, Dexiang Chen, Dexiong Chen, Deying Chen, Deyu Chen, Di Chen, Di-Long Chen, Dian Chen, Dianke Chen, Ding Chen, Diyun Chen, Dong Chen, Dong-Mei Chen, Dong-Yi Chen, Dongli Chen, Donglong Chen, Dongquan Chen, Dongrong Chen, Dongsheng Chen, Dongxue Chen, Dongyan Chen, Dongyin Chen, Du-Qun Chen, Duan-Yu Chen, Duo Chen, Duo-Xue Chen, Duoting Chen, E S Chen, Eleanor Y Chen, Elizabeth H Chen, Elizabeth S Chen, Elizabeth Suchi Chen, Emily Chen, En-Qiang Chen, Erbao Chen, Erfei Chen, Erqu Chen, Erzhen Chen, Everett H Chen, F Chen, F-K Chen, Fa Chen, Fa-Xi Chen, Fahui Chen, Fan Chen, Fang Chen, Fang-Pei Chen, Fang-Yu Chen, Fang-Zhi Chen, Fang-Zhou Chen, Fangfang Chen, Fangli Chen, Fangyan Chen, Fangyuan Chen, Faye H Chen, Fei Chen, Fei Xavier Chen, Feifan Chen, Feifeng Chen, Feilong Chen, Feixue Chen, Feiyang Chen, Feiyu Chen, Feiyue Chen, Feng Chen, Feng-Jung Chen, Feng-Ling Chen, Fenghua Chen, Fengju Chen, Fengling Chen, Fengming Chen, Fengrong Chen, Fengwu Chen, Fengyang Chen, Fred K Chen, Fu Chen, Fu-Shou Chen, Fumei Chen, Fusheng Chen, Fuxiang Chen, Gang Chen, Gao B Chen, Gao Chen, Gao-Feng Chen, Gaoyang Chen, Gaoyu Chen, Gaozhi Chen, Gary Chen, Gary K Chen, Ge Chen, Gen-Der Chen, Geng Chen, Gengsheng Chen, Ginny I Chen, Gong Chen, Gongbo Chen, Gonghai Chen, Gonglie Chen, Guan-Wei Chen, Guang Chen, Guang-Chao Chen, Guang-Yu Chen, Guangchun Chen, Guanghao Chen, Guanghong Chen, Guangjie Chen, Guangju Chen, Guangliang Chen, Guanglong Chen, Guangnan Chen, Guangping Chen, Guangquan Chen, Guangyao Chen, Guangyi Chen, Guangyong Chen, Guanjie Chen, Guanren Chen, Guanyu Chen, Guanzheng Chen, Gui Mei Chen, Gui-Hai Chen, Gui-Lai Chen, Guihao Chen, Guiqian Chen, Guiquan Chen, Guiying Chen, Guo Chen, Guo-Chong Chen, Guo-Jun Chen, Guo-Rong Chen, Guo-qing Chen, Guochao Chen, Guochong Chen, Guofang Chen, Guohong Chen, Guohua Chen, Guojun Chen, Guoliang Chen, Guopu Chen, Guoshun Chen, Guoxun Chen, Guozhong Chen, Guozhou Chen, H Chen, H Q Chen, H T Chen, Hai-Ning Chen, Haibing Chen, Haibo Chen, Haide Chen, Haifeng Chen, Haijiao Chen, Haimin Chen, Haiming Chen, Haining Chen, Haiqin Chen, Haiquan Chen, Haitao Chen, Haiyan Chen, Haiyang Chen, Haiyi Chen, Haiying Chen, Haiyu Chen, Haiyun Chen, Han Chen, Han-Bin Chen, Han-Chun Chen, Han-Hsiang Chen, Han-Min Chen, Hanbei Chen, Hang Chen, Hangang Chen, Hanjing Chen, Hanlin Chen, Hanqing Chen, Hanwen Chen, Hanxi Chen, Hanyong Chen, Hao Chen, Hao Yu Chen, Hao-Zhu Chen, Haobo Chen, Haodong Chen, Haojie Chen, Haoran Chen, Haotai Chen, Haotian Chen, Haoting Chen, Haoyun Chen, Haozhu Chen, Harn-Shen Chen, Haw-Wen Chen, He-Ping Chen, Hebing Chen, Hegang Chen, Hehe Chen, Hekai Chen, Heng Chen, Heng-Sheng Chen, Heng-Yu Chen, Hengsan Chen, Hengsheng Chen, Hengyu Chen, Heni Chen, Herbert Chen, Hetian Chen, Heye Chen, Hong Chen, Hong Yang Chen, Hong-Sheng Chen, Hongbin Chen, Hongbo Chen, Hongen Chen, Honghai Chen, Honghui Chen, Honglei Chen, Hongli Chen, Hongmei Chen, Hongmin Chen, Hongmou Chen, Hongqi Chen, Hongqiao Chen, Hongshan Chen, Hongxiang Chen, Hongxing Chen, Hongxu Chen, Hongyan Chen, Hongyu Chen, Hongyue Chen, Hongzhi Chen, Hou-Tsung Chen, Hou-Zao Chen, Hsi-Hsien Chen, Hsiang-Wen Chen, Hsiao-Jou Cortina Chen, Hsiao-Tan Chen, Hsiao-Wang Chen, Hsiao-Yun Chen, Hsin-Han Chen, Hsin-Hong Chen, Hsin-Hung Chen, Hsin-Yi Chen, Hsiu-Wen Chen, Hsuan-Yu Chen, Hsueh-Fen Chen, Hu Chen, Hua Chen, Hua-Pu Chen, Huachen Chen, Huafei Chen, Huaiyong Chen, Hualan Chen, Huali Chen, Hualin Chen, Huan Chen, Huan-Xin Chen, Huanchun Chen, Huang Chen, Huang-Pin Chen, Huangtao Chen, Huanhua Chen, Huanhuan Chen, Huanxiong Chen, Huaping Chen, Huapu Chen, Huaqiu Chen, Huatao Chen, Huaxin Chen, Huayu Chen, Huei-Rong Chen, Huei-Yan Chen, Huey-Miin Chen, Hui Chen, Hui Mei Chen, Hui-Chun Chen, Hui-Fen Chen, Hui-Jye Chen, Hui-Ru Chen, Hui-Wen Chen, Hui-Xiong Chen, Hui-Zhao Chen, Huichao Chen, Huijia Chen, Huijiao Chen, Huijie Chen, Huimei Chen, Huimin Chen, Huiqin Chen, Huiqun Chen, Huiru Chen, Huishan Chen, Huixi Chen, Huixian Chen, Huizhi Chen, Hung-Chang Chen, Hung-Chi Chen, Hung-Chun Chen, Hung-Po Chen, Hung-Sheng Chen, I-Chun Chen, I-M Chen, Ida Y-D Chen, Irwin Chen, Ivy Xiaoying Chen, J Chen, Jacinda Chen, Jack Chen, Jake Y Chen, Jason A Chen, Jeanne Chen, Jen-Hau Chen, Jen-Sue Chen, Jennifer F Chen, Jenny Chen, Jeremy J W Chen, Ji-ling Chen, Jia Chen, Jia Min Chen, Jia Wei Chen, Jia-De Chen, Jia-Feng Chen, Jia-Lin Chen, Jia-Mei Chen, Jia-Shun Chen, Jiabing Chen, Jiacai Chen, Jiacheng Chen, Jiade Chen, Jiahao Chen, Jiahua Chen, Jiahui Chen, Jiajia Chen, Jiajing Chen, Jiajun Chen, Jiakang Chen, Jiale Chen, Jiali Chen, Jialing Chen, Jiamiao Chen, Jiamin Chen, Jian Chen, Jian-Guo Chen, Jian-Hua Chen, Jian-Jun Chen, Jian-Kang Chen, Jian-Min Chen, Jian-Qiao Chen, Jian-Qing Chen, Jianan Chen, Jianfei Chen, Jiang Chen, Jiang Ye Chen, Jiang-hua Chen, Jianghua Chen, Jiangxia Chen, Jianhua Chen, Jianhui Chen, Jiani Chen, Jianjun Chen, Jiankui Chen, Jianlin Chen, Jianmin Chen, Jianping Chen, Jianshan Chen, Jiansu Chen, Jianxiong Chen, Jianzhong Chen, Jianzhou Chen, Jiao Chen, Jiao-Jiao Chen, Jiaohua Chen, Jiaping Chen, Jiaqi Chen, Jiaqing Chen, Jiaren Chen, Jiarou Chen, Jiawei Chen, Jiawen Chen, Jiaxin Chen, Jiaxu Chen, Jiaxuan Chen, Jiayao Chen, Jiaye Chen, Jiayi Chen, Jiayuan Chen, Jichong Chen, Jie Chen, Jie-Hua Chen, Jiejian Chen, Jiemei Chen, Jien-Jiun Chen, Jihai Chen, Jijun Chen, Jimei Chen, Jin Chen, Jin-An Chen, Jin-Ran Chen, Jin-Shuen Chen, Jin-Wu Chen, Jin-Xia Chen, Jina Chen, Jinbo Chen, Jindong Chen, Jing Chen, Jing-Hsien Chen, Jing-Wen Chen, Jing-Xian Chen, Jing-Yuan Chen, Jing-Zhou Chen, Jingde Chen, Jinghua Chen, Jingjing Chen, Jingli Chen, Jinglin Chen, Jingming Chen, Jingnan Chen, Jingqing Chen, Jingshen Chen, Jingteng Chen, Jinguo Chen, Jingxuan Chen, Jingyao Chen, Jingyi Chen, Jingyuan Chen, Jingzhao Chen, Jingzhou Chen, Jinhao Chen, Jinhuang Chen, Jinli Chen, Jinlun Chen, Jinquan Chen, Jinsong Chen, Jintian Chen, Jinxuan Chen, Jinyan Chen, Jinyong Chen, Jion Chen, Jiong Chen, Jiongyu Chen, Jishun Chen, Jiu-Chiuan Chen, Jiujiu Chen, Jiwei Chen, Jiyan Chen, Jiyuan Chen, Jonathan Chen, Joy J Chen, Juan Chen, Juan-Juan Chen, Juanjuan Chen, Juei-Suei Chen, Juhai Chen, Jui-Chang Chen, Jui-Yu Chen, Jun Chen, Jun-Long Chen, Junchen Chen, Junfei Chen, Jung-Sheng Chen, Junhong Chen, Junhui Chen, Junjie Chen, Junling Chen, Junmin Chen, Junming Chen, Junpan Chen, Junpeng Chen, Junqi Chen, Junqin Chen, Junsheng Chen, Junshi Chen, Junyang Chen, Junyi Chen, Junyu Chen, K C Chen, Kai Chen, Kai-En Chen, Kai-Ming Chen, Kai-Ting Chen, Kai-Yang Chen, Kaifu Chen, Kaijian Chen, Kailang Chen, Kaili Chen, Kaina Chen, Kaiquan Chen, Kan Chen, Kang Chen, Kang-Hua Chen, Kangyong Chen, Kangzhen Chen, Katharine Y Chen, Katherine C Chen, Ke Chen, Kecai Chen, Kehua Chen, Kehui Chen, Kelin Chen, Ken Chen, Kenneth L Chen, Keping Chen, Kequan Chen, Kevin Chen, Kewei Chen, Kexin Chen, Keyan Chen, Keyang Chen, Keying Chen, Keyu Chen, Keyuan Chen, Kuan-Jen Chen, Kuan-Ling Chen, Kuan-Ting Chen, Kuan-Yu Chen, Kuangyang Chen, Kuey Chu Chen, Kui Chen, Kun Chen, Kun-Chieh Chen, Kunmei Chen, Kunpeng Chen, L B Chen, L F Chen, Lan Chen, Lang Chen, Lankai Chen, Lanlan Chen, Lanmei Chen, Le Chen, Le Qi Chen, Lei Chen, Lei-Chin Chen, Lei-Lei Chen, Leijie Chen, Lena W Chen, Leqi Chen, Letian Chen, Lexia Chen, Li Chen, Li Jia Chen, Li-Chieh Chen, Li-Hsien Chen, Li-Hsin Chen, Li-Hua Chen, Li-Jhen Chen, Li-Juan Chen, Li-Mien Chen, Li-Nan Chen, Li-Tzong Chen, Li-Zhen Chen, Li-hong Chen, Lian Chen, Lianfeng Chen, Liang Chen, Liang-Kung Chen, Liangkai Chen, Liangsheng Chen, Liangwan Chen, Lianmin Chen, Liaobin Chen, Lichang Chen, Lichun Chen, Lidian Chen, Lie Chen, Liechun Chen, Lifang Chen, Lifen Chen, Lifeng Chen, Ligang Chen, Lihong Chen, Lihua Chen, Lijin Chen, Lijuan Chen, Lili Chen, Limei Chen, Limin Chen, Liming Chen, Lin Chen, Lina Chen, Linbo Chen, Ling Chen, Ling-Yan Chen, Lingfeng Chen, Lingjun Chen, Lingli Chen, Lingxia Chen, Lingxue Chen, Lingyi Chen, Linjie Chen, Linlin Chen, Linna Chen, Linxi Chen, Linyi Chen, Liping Chen, Liqiang Chen, Liugui Chen, Liujun Chen, Liutao Chen, Lixia Chen, Lixian Chen, Liyun Chen, Lizhen Chen, Lizhu Chen, Lo-Yun Chen, Long Chen, Long-Jiang Chen, Longqing Chen, Longyun Chen, Lu Chen, Lu Hua Chen, Lu-Biao Chen, Lu-Zhu Chen, Lulu Chen, Luming Chen, Luyi Chen, Luzhu Chen, M Chen, M L Chen, Man Chen, Man-Hua Chen, Mao Chen, Mao-Yuan Chen, Maochong Chen, Maorong Chen, Marcus Y Chen, Mark I-Cheng Chen, Max Jl Chen, Mechi Chen, Mei Chen, Mei-Chi Chen, Mei-Chih Chen, Mei-Hsiu Chen, Mei-Hua Chen, Mei-Jie Chen, Mei-Ling Chen, Mei-Ru Chen, Meilan Chen, Meilin Chen, Meiling Chen, Meimei Chen, Meiting Chen, Meiyang Chen, Meiyu Chen, Meizhen Chen, Meng Chen, Meng Xuan Chen, Meng-Lin Chen, Meng-Ping Chen, Mengdi Chen, Menglan Chen, Mengling Chen, Mengping Chen, Mengqing Chen, Mengting Chen, Mengxia Chen, Mengyan Chen, Mengying Chen, Mian-Mian Chen, Miao Chen, Miao-Der Chen, Miao-Hsueh Chen, Miao-Yu Chen, Miaomiao Chen, Miaoran Chen, Michael C Chen, Michelle Chen, Mien-Cheng Chen, Min Chen, Min-Hsuan Chen, Min-Hu Chen, Min-Jie Chen, Ming Chen, Ming-Fong Chen, Ming-Han Chen, Ming-Hong Chen, Ming-Huang Chen, Ming-Huei Chen, Ming-Yu Chen, Mingcong Chen, Mingfeng Chen, Minghong Chen, Minghua Chen, Minglang Chen, Mingling Chen, Mingmei Chen, Mingxia Chen, Mingxing Chen, Mingyang Chen, Mingyi Chen, Mingyue Chen, Minjian Chen, Minjiang Chen, Minjie Chen, Minyan Chen, Mo Chen, Mu-Hong Chen, Muh-Shy Chen, Mulan Chen, Mystie X Chen, Na Chen, Naifei Chen, Naisong Chen, Nan Chen, Ni Chen, Nian-Ping Chen, Ning Chen, Ning-Bo Chen, Ning-Hung Chen, Ning-Yuan Chen, Ningbo Chen, Ningning Chen, Nuan Chen, On Chen, Ou Chen, Ouyang Chen, P P Chen, Pan Chen, Paul Chih-Hsueh Chen, Pei Chen, Pei-Chen Chen, Pei-Chun Chen, Pei-Lung Chen, Pei-Yi Chen, Pei-Yin Chen, Pei-zhan Chen, Peihong Chen, Peipei Chen, Peiqin Chen, Peixian Chen, Peiyou Chen, Peiyu Chen, Peize Chen, Peizhan Chen, Peng Chen, Peng-Cheng Chen, Pengxiang Chen, Ping Chen, Ping-Chung Chen, Ping-Kun Chen, Pingguo Chen, Po-Han Chen, Po-Ju Chen, Po-Min Chen, Po-See Chen, Po-Sheng Chen, Po-Yu Chen, Qi Chen, Qi-An Chen, Qian Chen, Qianbo Chen, Qianfen Chen, Qiang Chen, Qiangpu Chen, Qiankun Chen, Qianling Chen, Qianming Chen, Qianping Chen, Qianqian Chen, Qianxue Chen, Qianyi Chen, Qianyu Chen, Qianyun Chen, Qianzhi Chen, Qiao Chen, Qiao-Yi Chen, Qiaoli Chen, Qiaoling Chen, Qichen Chen, Qifang Chen, Qihui Chen, Qili Chen, Qinfen Chen, Qing Chen, Qing-Hui Chen, Qing-Juan Chen, Qing-Wei Chen, Qingao Chen, Qingchao Chen, Qingchuan Chen, Qingguang Chen, Qinghao Chen, Qinghua Chen, Qingjiang Chen, Qingjie Chen, Qingliang Chen, Qingmei Chen, Qingqing Chen, Qingqiu Chen, Qingshi Chen, Qingxing Chen, Qingyang Chen, Qingyi Chen, Qinian Chen, Qinsheng Chen, Qinying Chen, Qiong Chen, Qiongyun Chen, Qiqi Chen, Qitong Chen, Qiu Jing Chen, Qiu-Jing Chen, Qiu-Sheng Chen, Qiuchi Chen, Qiuhong Chen, Qiujing Chen, Qiuli Chen, Qiuwen Chen, Qiuxia Chen, Qiuxiang Chen, Qiuxuan Chen, Qiuyun Chen, Qiwei Chen, Qixian Chen, Qu Chen, Quan Chen, Quanjiao Chen, Quanwei Chen, Qunxiang Chen, R Chen, Ran Chen, Ranyun Chen, Ray-Jade Chen, Ren-Hui Chen, Renjin Chen, Renwei Chen, Renyu Chen, Robert Chen, Roger Chen, Rong Chen, Rong-Hua Chen, Rongfang Chen, Rongfeng Chen, Rongrong Chen, Rongsheng Chen, Rongyuan Chen, Roufen Chen, Rouxi Chen, Ru Chen, Rucheng Chen, Ruey-Hwa Chen, Rui Chen, Rui-Fang Chen, Rui-Min Chen, Rui-Pei Chen, Rui-Zhen Chen, Ruiai Chen, Ruibing Chen, Ruijing Chen, Ruijuan Chen, Ruilin Chen, Ruimin Chen, Ruiming Chen, Ruiqi Chen, Ruisen Chen, Ruixiang Chen, Ruixue Chen, Ruiying Chen, Rujun Chen, Runfeng Chen, Runsen Chen, Runsheng Chen, Ruofan Chen, Ruohong Chen, Ruonan Chen, Ruoyan Chen, Ruoying Chen, S Chen, S N Chen, S Pl Chen, S-D Chen, Sai Chen, San-Yuan Chen, Sean Chen, Sen Chen, Shali Chen, Shan Chen, Shanchun Chen, Shang-Chih Chen, Shang-Hung Chen, Shangduo Chen, Shangsi Chen, Shangwu Chen, Shangzhong Chen, Shanshan Chen, Shanyuan Chen, Shao-Ke Chen, Shao-Peng Chen, Shao-Wei Chen, Shao-Yu Chen, Shao-long Chen, Shaofei Chen, Shaohong Chen, Shaohua Chen, Shaokang Chen, Shaokun Chen, Shaoliang Chen, Shaotao Chen, Shaoxing Chen, Shaoze Chen, Shasha Chen, She Chen, Shen Chen, Shen-Ming Chen, Sheng Chen, Sheng-Xi Chen, Sheng-Yi Chen, Shengdi Chen, Shenghui Chen, Shenglan Chen, Shengnan Chen, Shengpan Chen, Shengyu Chen, Shengzhi Chen, Shi Chen, Shi-Qing Chen, Shi-Sheng Chen, Shi-Yi Chen, Shi-You Chen, Shibo Chen, Shih-Jen Chen, Shih-Pin Chen, Shih-Yin Chen, Shih-Yu Chen, Shilan Chen, Shiming Chen, Shin-Wen Chen, Shin-Yu Chen, Shipeng Chen, Shiqian Chen, Shiqun Chen, Shirui Chen, Shiuhwei Chen, Shiwei Chen, Shixuan Chen, Shiyan Chen, Shiyao Chen, Shiyi Chen, Shiyu Chen, Shou-Tung Chen, Shoudeng Chen, Shoujun Chen, Shouzhen Chen, Shu Chen, Shu-Fen Chen, Shu-Gang Chen, Shu-Hua Chen, Shu-Jen Chen, Shuai Chen, Shuai-Bing Chen, Shuai-Ming Chen, Shuaijie Chen, Shuaijun Chen, Shuaiyin Chen, Shuaiyu Chen, Shuang Chen, Shuangfeng Chen, Shuanghui Chen, Shuchun Chen, Shuen-Ei Chen, Shufang Chen, Shufeng Chen, Shuhai Chen, Shuhong Chen, Shuhuang Chen, Shuhui Chen, Shujuan Chen, Shuliang Chen, Shuming Chen, Shunde Chen, Shuntai Chen, Shunyou Chen, Shuo Chen, Shuo-Bin Chen, Shuoni Chen, Shuqin Chen, Shuqiu Chen, Shuting Chen, Shuwen Chen, Shuyi Chen, Shuying Chen, Si Chen, Si-Ru Chen, Si-Yuan Chen, Si-Yue Chen, Si-guo Chen, Sien-Tsong Chen, Sifeng Chen, Sihui Chen, Sijia Chen, Sijuan Chen, Sili Chen, Silian Chen, Siping Chen, Siqi Chen, Siqin Chen, Sisi Chen, Siteng Chen, Siting Chen, Siyi Chen, Siyu Chen, Siyu S Chen, Siyuan Chen, Siyue Chen, Size Chen, Song Chen, Song-Mei Chen, Songfeng Chen, Suet N Chen, Suet Nee Chen, Sufang Chen, Suipeng Chen, Sulian Chen, Suming Chen, Sun Chen, Sung-Fang Chen, Suning Chen, Sunny Chen, Sy-Jou Chen, Syue-Ting Chen, Szu-Chi Chen, Szu-Chia Chen, Szu-Chieh Chen, Szu-Han Chen, Szu-Yun Chen, T Chen, Tai-Heng Chen, Tai-Tzung Chen, Tailai Chen, Tan-Huan Chen, Tan-Zhou Chen, Tania Chen, Tao Chen, Tian Chen, Tianfeng Chen, Tianhang Chen, Tianhong Chen, Tianhua Chen, Tianpeng Chen, Tianran Chen, Tianrui Chen, Tiantian Chen, Tianzhen Chen, Tielin Chen, Tien-Hsing Chen, Ting Chen, Ting-Huan Chen, Ting-Tao Chen, Ting-Ting Chen, Tingen Chen, Tingtao Chen, Tingting Chen, Tom Wei-Wu Chen, Tong Chen, Tongsheng Chen, Tse-Ching Chen, Tse-Wei Chen, TsungYen Chen, Tuantuan Chen, Tzu-An Chen, Tzu-Chieh Chen, Tzu-Ju Chen, Tzu-Ting Chen, Tzu-Yu Chen, Tzy-Yen Chen, Valerie Chen, W Chen, Wai Chen, Wan Jun Chen, Wan-Tzu Chen, Wan-Yan Chen, Wan-Yi Chen, Wanbiao Chen, Wanjia Chen, Wanjun Chen, Wanling Chen, Wantao Chen, Wanting Chen, Wanyin Chen, Wei Chen, Wei J Chen, Wei Ning Chen, Wei-Cheng Chen, Wei-Cong Chen, Wei-Fei Chen, Wei-Hao Chen, Wei-Hui Chen, Wei-Kai Chen, Wei-Kung Chen, Wei-Lun Chen, Wei-Min Chen, Wei-Peng Chen, Wei-Ting Chen, Wei-Wei Chen, Wei-Yu Chen, Wei-xian Chen, Weibo Chen, Weican Chen, Weichan Chen, Weicong Chen, Weihao Chen, Weihong Chen, Weihua Chen, Weijia Chen, Weijie Chen, Weili Chen, Weilun Chen, Weina Chen, Weineng Chen, Weiping Chen, Weiqin Chen, Weiqing Chen, Weirui Chen, Weisan Chen, Weitao Chen, Weitian Chen, Weiwei Chen, Weixian Chen, Weixin Chen, Weiyi Chen, Weiyong Chen, Wen Chen, Wen-Chau Chen, Wen-Jie Chen, Wen-Pin Chen, Wen-Qi Chen, Wen-Tsung Chen, Wen-Yi Chen, Wenbiao Chen, Wenbing Chen, Wenfan Chen, Wenfang Chen, Wenhao Chen, Wenhua Chen, Wenjie Chen, Wenjun Chen, Wenlong Chen, Wenqin Chen, Wensheng Chen, Wenshuo Chen, Wentao Chen, Wenting Chen, Wentong Chen, Wenwen Chen, Wenwu Chen, Wenxi Chen, Wenxing Chen, Wenxu Chen, Willian Tzu-Liang Chen, Wu-Jun Chen, Wu-Xian Chen, Wuyan Chen, X Chen, X R Chen, X Steven Chen, Xi Chen, Xia Chen, Xia-Fei Chen, Xiaguang Chen, Xiameng Chen, Xian Chen, Xian-Kai Chen, Xianbo Chen, Xiancheng Chen, Xianfeng Chen, Xiang Chen, Xiang-Bin Chen, Xiang-Mei Chen, XiangFan Chen, Xiangding Chen, Xiangjun Chen, Xiangli Chen, Xiangliu Chen, Xiangmei Chen, Xiangna Chen, Xiangning Chen, Xiangqiu Chen, Xiangyu Chen, Xiankai Chen, Xianmei Chen, Xianqiang Chen, Xianxiong Chen, Xianyue Chen, Xianze Chen, Xianzhen Chen, Xiao Chen, Xiao-Chen Chen, Xiao-Hui Chen, Xiao-Jun Chen, Xiao-Lin Chen, Xiao-Qing Chen, Xiao-Quan Chen, Xiao-Wei Chen, Xiao-Yang Chen, Xiao-Ying Chen, Xiao-chun Chen, Xiao-he Chen, Xiao-ping Chen, Xiaobin Chen, Xiaobo Chen, Xiaochang Chen, Xiaochun Chen, Xiaodong Chen, Xiaofang Chen, Xiaofen Chen, Xiaofeng Chen, Xiaohan Chen, Xiaohong Chen, Xiaohua Chen, Xiaohui Chen, Xiaojiang S Chen, Xiaojie Chen, Xiaojing Chen, Xiaojuan Chen, Xiaojun Chen, Xiaokai Chen, Xiaolan Chen, Xiaole L Chen, Xiaolei Chen, Xiaoli Chen, Xiaolin Chen, Xiaoling Chen, Xiaolong Chen, Xiaolu Chen, Xiaomeng Chen, Xiaomin Chen, Xiaona Chen, Xiaonan Chen, Xiaopeng Chen, Xiaoping Chen, Xiaoqian Chen, Xiaoqing Chen, Xiaorong Chen, Xiaoshan Chen, Xiaotao Chen, Xiaoting Chen, Xiaowan Chen, Xiaowei Chen, Xiaowen Chen, Xiaoxiang Chen, Xiaoxiao Chen, Xiaoyan Chen, Xiaoyang Chen, Xiaoyin Chen, Xiaoyong Chen, Xiaoyu Chen, Xiaoyuan Chen, Xiaoyun Chen, Xiatian Chen, Xihui Chen, Xijun Chen, Xikun Chen, Ximei Chen, Xin Chen, Xin-Jie Chen, Xin-Ming Chen, Xin-Qi Chen, Xinan Chen, Xing Chen, Xing-Lin Chen, Xing-Long Chen, Xing-Zhen Chen, Xingdong Chen, Xinghai Chen, Xingxing Chen, Xingyi Chen, Xingyong Chen, Xingyu Chen, Xinji Chen, Xinlin Chen, Xinpu Chen, Xinqiao Chen, Xinwei Chen, Xinyan Chen, Xinyang Chen, Xinyi Chen, Xinyu Chen, Xinyuan Chen, Xinyue Chen, Xinzhuo Chen, Xiong Chen, Xiqun Chen, Xiu Chen, Xiu-Juan Chen, Xiuhui Chen, Xiujuan Chen, Xiuli Chen, Xiuping Chen, Xiuxiu Chen, Xiuyan Chen, Xixi Chen, Xiyao Chen, Xiyu Chen, Xu Chen, Xuan Chen, Xuancai Chen, Xuanjing Chen, Xuanli Chen, Xuanmao Chen, Xuanwei Chen, Xuanxu Chen, Xuanyi Chen, Xue Chen, Xue-Mei Chen, Xue-Qing Chen, Xue-Xin Chen, Xue-Yan Chen, Xue-Ying Chen, XueShu Chen, Xuechun Chen, Xuefei Chen, Xuehua Chen, Xuejiao Chen, Xuejun Chen, Xueli Chen, Xueling Chen, Xuemei Chen, Xuemin Chen, Xueqin Chen, Xueqing Chen, Xuerong Chen, Xuesong Chen, Xueting Chen, Xueyan Chen, Xueying Chen, Xufeng Chen, Xuhui Chen, Xujia Chen, Xun Chen, Xuxiang Chen, Xuxin Chen, Xuzhuo Chen, Y Chen, Y D I Chen, Y Eugene Chen, Y M Chen, Y P Chen, Y S Chen, Y U Chen, Y-D I Chen, Y-D Ida Chen, Ya Chen, Ya-Chun Chen, Ya-Nan Chen, Ya-Peng Chen, Ya-Ting Chen, Ya-xi Chen, Yafang Chen, Yafei Chen, Yahong Chen, Yajie Chen, Yajing Chen, Yajun Chen, Yalan Chen, Yali Chen, Yan Chen, Yan Jie Chen, Yan Q Chen, Yan-Gui Chen, Yan-Jun Chen, Yan-Ming Chen, Yan-Qiong Chen, Yan-yan Chen, Yanan Chen, Yananlan Chen, Yanbin Chen, Yanfei Chen, Yanfen Chen, Yang Chen, Yang-Ching Chen, Yang-Yang Chen, Yangchao Chen, Yanghui Chen, Yangxin Chen, Yanhan Chen, Yanhua Chen, Yanjie Chen, Yanjing Chen, Yanli Chen, Yanlin Chen, Yanling Chen, Yanming Chen, Yann-Jang Chen, Yanping Chen, Yanqiu Chen, Yanrong Chen, Yanru Chen, Yanting Chen, Yanyan Chen, Yanyun Chen, Yanzhu Chen, Yanzi Chen, Yao Chen, Yao-Shen Chen, Yaodong Chen, Yaosheng Chen, Yaowu Chen, Yau-Hung Chen, Yaxi Chen, Yayun Chen, Yazhuo Chen, Ye Chen, Ye-Guang Chen, Yeh Chen, Yelin Chen, Yen-Chang Chen, Yen-Chen Chen, Yen-Cheng Chen, Yen-Ching Chen, Yen-Fu Chen, Yen-Hao Chen, Yen-Hsieh Chen, Yen-Jen Chen, Yen-Ju Chen, Yen-Lin Chen, Yen-Ling Chen, Yen-Ni Chen, Yen-Rong Chen, Yen-Teen Chen, Yewei Chen, Yi Chen, Yi Feng Chen, Yi-Bing Chen, Yi-Chun Chen, Yi-Chung Chen, Yi-Fei Chen, Yi-Guang Chen, Yi-Han Chen, Yi-Hau Chen, Yi-Heng Chen, Yi-Hong Chen, Yi-Hsuan Chen, Yi-Hui Chen, Yi-Jen Chen, Yi-Lin Chen, Yi-Ru Chen, Yi-Ting Chen, Yi-Wen Chen, Yi-Yung Chen, YiChung Chen, YiPing Chen, Yian Chen, Yibing Chen, Yibo Chen, Yidan Chen, Yiding Chen, Yidong Chen, Yiduo Chen, Yifa Chen, Yifan Chen, Yifang Chen, Yifei Chen, Yih-Chieh Chen, Yihao Chen, Yihong Chen, Yii-Der Chen, Yii-Der I Chen, Yii-Derr Chen, Yii-der Ida Chen, Yijiang Chen, Yijun Chen, Yike Chen, Yilan Chen, Yilei Chen, Yili Chen, Yilin Chen, Yiming Chen, Yin-Huai Chen, Ying Chen, Ying-Cheng Chen, Ying-Hsiang Chen, Ying-Jie Chen, Ying-Jung Chen, Ying-Lan Chen, Ying-Ying Chen, Yingchun Chen, Yingcong Chen, Yinghui Chen, Yingji Chen, Yingjie Chen, Yinglian Chen, Yingting Chen, Yingxi Chen, Yingying Chen, Yingyu Chen, Yinjuan Chen, Yintong Chen, Yinwei Chen, Yinzhu Chen, Yiru Chen, Yishan Chen, Yisheng Chen, Yitong Chen, Yixin Chen, Yiyin Chen, Yiyun Chen, Yizhi Chen, Yong Chen, Yong-Jun Chen, Yong-Ping Chen, Yong-Syuan Chen, Yong-Zhong Chen, YongPing Chen, Yongbin Chen, Yongfa Chen, Yongfang Chen, Yongheng Chen, Yonghui Chen, Yongke Chen, Yonglu Chen, Yongmei Chen, Yongming Chen, Yongning Chen, Yongqi Chen, Yongshen Chen, Yongshuo Chen, Yongxing Chen, Yongxun Chen, You-Ming Chen, You-Xin Chen, You-Yue Chen, Youhu Chen, Youjia Chen, Youmeng Chen, Youran Chen, Youwei Chen, Yu Chen, Yu-Bing Chen, Yu-Cheng Chen, Yu-Chi Chen, Yu-Chia Chen, Yu-Chuan Chen, Yu-Fan Chen, Yu-Fen Chen, Yu-Fu Chen, Yu-Gen Chen, Yu-Han Chen, Yu-Hui Chen, Yu-Ling Chen, Yu-Ming Chen, Yu-Pei Chen, Yu-San Chen, Yu-Si Chen, Yu-Ting Chen, Yu-Tung Chen, Yu-Xia Chen, Yu-Xin Chen, Yu-Yang Chen, Yu-Ying Chen, Yuan Chen, Yuan-Hua Chen, Yuan-Shen Chen, Yuan-Tsong Chen, Yuan-Yuan Chen, Yuan-Zhen Chen, Yuanbin Chen, Yuanhao Chen, Yuanjia Chen, Yuanjian Chen, Yuanli Chen, Yuanqi Chen, Yuanwei Chen, Yuanwen Chen, Yuanyu Chen, Yuanyuan Chen, Yubin Chen, Yucheng Chen, Yue Chen, Yue-Lai Chen, Yuebing Chen, Yueh-Peng Chen, Yuelei Chen, Yuewen Chen, Yuewu Chen, Yuexin Chen, Yuexuan Chen, Yufei Chen, Yufeng Chen, Yuh-Lien Chen, Yuh-Ling Chen, Yuh-Min Chen, Yuhan Chen, Yuhang Chen, Yuhao Chen, Yuhong Chen, Yuhui Chen, Yujie Chen, Yule Chen, Yuli Chen, Yulian Chen, Yulin Chen, Yuling Chen, Yulong Chen, Yulu Chen, Yumei Chen, Yun Chen, Yun-Ju Chen, Yun-Tzu Chen, Yun-Yu Chen, Yundai Chen, Yunfei Chen, Yunfeng Chen, Yung-Hsiang Chen, Yung-Wu Chen, Yunjia Chen, Yunlin Chen, Yunn-Yi Chen, Yunqin Chen, Yunshun Chen, Yunwei Chen, Yunyun Chen, Yunzhong Chen, Yunzhu Chen, Yupei Chen, Yupeng Chen, Yuping Chen, Yuqi Chen, Yuqin Chen, Yuqing Chen, Yuquan Chen, Yurong Chen, Yushan Chen, Yusheng Chen, Yusi Chen, Yuting Chen, Yutong Chen, Yuxi Chen, Yuxian Chen, Yuxiang Chen, Yuxin Chen, Yuxing Chen, Yuyan Chen, Yuyang Chen, Yuyao Chen, Z Chen, Zan Chen, Zaozao Chen, Ze-Hui Chen, Ze-Xu Chen, Zechuan Chen, Zemin Chen, Zetian Chen, Zexiao Chen, Zeyu Chen, Zhanfei Chen, Zhang-Liang Chen, Zhang-Yuan Chen, Zhangcheng Chen, Zhanghua Chen, Zhangliang Chen, Zhanglin Chen, Zhangxin Chen, Zhanjuan Chen, Zhao Chen, Zhao-Xia Chen, ZhaoHui Chen, Zhaojun Chen, Zhaoli Chen, Zhaolin Chen, Zhaoran Chen, Zhaowei Chen, Zhaoyao Chen, Zhe Chen, Zhe-Ling Chen, Zhe-Sheng Chen, Zhe-Yu Chen, Zhebin Chen, Zhehui Chen, Zhelin Chen, Zhen Bouman Chen, Zhen Chen, Zhen-Hua Chen, Zhen-Yu Chen, Zhencong Chen, Zhenfeng Chen, Zheng Chen, Zheng-Zhen Chen, Zhenghong Chen, Zhengjun Chen, Zhengling Chen, Zhengming Chen, Zhenguo Chen, Zhengwei Chen, Zhengzhi Chen, Zhenlei Chen, Zhenyi Chen, Zhenyue Chen, Zheping Chen, Zheren Chen, Zhesheng Chen, Zheyi Chen, Zhezhe Chen, Zhi Bin Chen, Zhi Chen, Zhi-Hao Chen, Zhi-bin Chen, Zhi-zhe Chen, Zhiang Chen, Zhichuan Chen, Zhifeng Chen, Zhigang Chen, Zhigeng Chen, Zhiguo Chen, Zhihai Chen, Zhihang Chen, Zhihao Chen, Zhiheng Chen, Zhihong Chen, Zhijian Chen, Zhijian J Chen, Zhijing Chen, Zhijun Chen, Zhimin Chen, Zhinan Chen, Zhiping Chen, Zhiqiang Chen, Zhiquan Chen, Zhishi Chen, Zhitao Chen, Zhiting Chen, Zhiwei Chen, Zhixin Chen, Zhixuan Chen, Zhixue Chen, Zhiyong Chen, Zhiyu Chen, Zhiyuan Chen, Zhiyun Chen, Zhizhong Chen, Zhong Chen, Zhongbo Chen, Zhonghua Chen, Zhongjian Chen, Zhongliang Chen, Zhongxiu Chen, Zhongzhu Chen, Zhou Chen, Zhouji Chen, Zhouliang Chen, Zhoulong Chen, Zhouqing Chen, Zhuchu Chen, Zhujun Chen, Zhuo Chen, Zhuo-Yuan Chen, ZhuoYu Chen, Zhuohui Chen, Zhuojia Chen, Zi-Jiang Chen, Zi-Qing Chen, Zi-Yang Chen, Zi-Yue Chen, Zi-Yun Chen, Zian Chen, Zifan Chen, Zihan Chen, Zihang Chen, Zihao Chen, Zihe Chen, Zihua Chen, Zijie Chen, Zike Chen, Zilin Chen, Zilong Chen, Ziming Chen, Zinan Chen, Ziqi Chen, Ziqing Chen, Zitao Chen, Zixi Chen, Zixin Chen, Zixuan Chen, Ziying Chen, Ziyuan Chen, Zoe Chen, Zongming E Chen, Zongnan Chen, Zongyou Chen, Zongzheng Chen, Zugen Chen, Zuolong Chen
articles
Tibor V Varga, Azra Kurbasic, Mattias Aine +21 more · 2017 · International journal of epidemiology · Oxford University Press · added 2026-04-24
Cross-sectional genome-wide association studies have identified hundreds of loci associated with blood lipids and related cardiovascular traits, but few genetic association studies have focused on lon Show more
Cross-sectional genome-wide association studies have identified hundreds of loci associated with blood lipids and related cardiovascular traits, but few genetic association studies have focused on long-term changes in blood lipids. Participants from the GLACIER Study (Nmax = 3492) were genotyped with the MetaboChip array, from which 29 387 SNPs (single nucleotide polymorphisms; replication, fine-mapping regions and wildcard SNPs for lipid traits) were extracted for association tests with 10-year change in total cholesterol (ΔTC) and triglycerides (ΔTG). Four additional prospective cohort studies (MDC, PIVUS, ULSAM, MRC Ely; Nmax = 8263 participants) were used for replication. We conducted an in silico look-up for association with coronary artery disease (CAD) in the Coronary ARtery DIsease Genome-wide Replication and Meta-analysis (CARDIoGRAMplusC4D) Consortium (N ∼ 190 000) and functional annotation for the top ranking variants. In total, 956 variants were associated (P < 0.01) with either ΔTC or ΔTG in GLACIER. In GLACIER, chr19:50121999 at APOE was associated with ΔTG and multiple SNPs in the APOA1/A4/C3/A5 region at genome-wide significance (P < 5 × 10-8), whereas variants in four loci, DOCK7, BRE, SYNE1 and KCNIP1, reached study-wide significance (P < 1.7 × 10-6). The rs7412 variant at APOE was associated with ΔTC in GLACIER (P < 1.7 × 10-6). In pooled analyses of all cohorts, 139 SNPs at six and five loci were associated with ΔTC and for ΔTG, respectively (P < 10-3). Of these, a variant at CAPN3 (P = 1.2 × 10-4), multiple variants at HPR (Pmin = 1.5 × 10-6) and a variant at SIX5 (P = 1.9 × 10-4) showed evidence for association with CAD. We identified seven novel genomic regions associated with long-term changes in blood lipids, of which three also raise CAD risk. Show less
no PDF DOI: 10.1093/ije/dyw245
DOCK7
Rong-Quan He, Xiao-Jiao Li, Lu Liang +6 more · 2017 · BMC cancer · BioMed Central · added 2026-04-24
Non-small cell lung cancer (NSCLC) has led to the highest cancer-related mortality for decades. To enhance the efficiency of early diagnosis and therapy, more efforts are urgently needed to reveal the Show more
Non-small cell lung cancer (NSCLC) has led to the highest cancer-related mortality for decades. To enhance the efficiency of early diagnosis and therapy, more efforts are urgently needed to reveal the origins of NSCLC. In this study, we explored the effect of miR-542-5p in NSCLC with clinical samples and in vivo models and further explored the prospective function of miR-542-5p though bioinformatics methods. A total of 125 NSCLC tissue samples were collected, and the expression of miR-542-5p was detected by qRT-PCR. The relationship between miR-542-5p level and clinicopathological features was analyzed. The effect of miR-542-5p on survival time was also explored with K-M survival curves and Cox's regression. The effect of miR-542-5p on the tumorigenesis of NSCLC was verified with a chick chorioallantoic membrane (CAM) model. The potential target genes were predicted by bioinformatics tools, and relevant pathways were analyzed by GO and KEGG. Several hub genes were validated by Proteinatlas. The expression of miR-542-5p was down-regulated in NSCLC tissues, and consistent results were also found in the subgroups of adenocarcinoma and squamous cell carcinoma. Down-regulation of miR-542-5p was found to be connected with advanced TNM stage, vascular invasion, lymphatic metastasis and EGFR. Survival analyses showed that patients with lower miR-542-5p levels had markedly poorer prognosis. Both tumor growth and angiogenesis were significantly suppressed by miR-542-5p mimic in the CAM model. The potential 457 target genes of miR-542-5p were enriched in several key cancer-related pathways, such as morphine addiction and the cAMP signaling pathway from KEGG. Interestingly, six genes (GABBR1, PDE4B, PDE4C, ADCY6, ADCY1 and GIPR) from the cAMP signaling pathway were confirmed to be overexpressed in NSCLCs tissues. This evidence suggests that miR-542-5p is a potential tumor-suppressed miRNA in NSCLC, which has the potential to act as a diagnostic and therapeutic target of NSCLC. Show less
📄 PDF DOI: 10.1186/s12885-017-3646-1
GIPR
Dapeng Jin, Yong-Guo Zhang, Shaoping Wu +6 more · 2017 · The Journal of steroid biochemistry and molecular biology · Elsevier · added 2026-04-24
Axin1 is a scaffold protein in the β-catenin destruction complex, which, if disrupted, contributes to pathogenesis of various human diseases, including colorectal carcinogenesis and inflammatory bowel Show more
Axin1 is a scaffold protein in the β-catenin destruction complex, which, if disrupted, contributes to pathogenesis of various human diseases, including colorectal carcinogenesis and inflammatory bowel diseases (IBD). We have previously demonstrated that Salmonella infection promotes the degradation and plasma sequestration of Axin1, leading to bacterial invasiveness and inflammatory responses. Vitamin D and the vitamin D receptor (VDR) appear to be important regulators of IBD and colon cancer. Although VDR and Axin1 are all involved in intestinal inflammation, it remains unclear whether these processes are related or function independently. In the current study, we hypothesize that VDR is an important regulator for the maintenance of physiological level of Axin1. Using the intestinal epithelial conditional VDR knockout mouse model (VDR We found that VDR deletion led to lower protein and mRNA levels of Axin1, whereas knockdown of Axin1 did not change the expression level of VDR protein. Immunoprecipitation data did not support physical interaction between VDR and Axin1. The VDR regulation of Axin1 was through a VDR genomic binding site for Axin1 gene on the regulatory region. Fractionation data showed that cytosolic Axin1 was significantly reduced due to VDR deletion, leaving the nuclear fraction unchanged. In ileum, Axin1 was distributed in the cytosol of apical epithelium and crypts. VDR is important for the maintenance of physiological level of Axin1. The discovery of Axin1 as a VDR target gene provides novel and fundamental insights into the interactions between the VDR and β-catenin signaling pathways. Show less
📄 PDF DOI: 10.1016/j.jsbmb.2016.09.002
AXIN1
Xiansheng Huang, Rong Li, Luzhu Chen +1 more · 2017 · Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · added 2026-04-24
To investigate the role of apolipoprotein A5 (apoA5) in the pathogenesis of obesity-related hypertriglyceridemia and the related therapeutic effects of metformin.
 Methods: The ob/ob mice were treated Show more
To investigate the role of apolipoprotein A5 (apoA5) in the pathogenesis of obesity-related hypertriglyceridemia and the related therapeutic effects of metformin.
 Methods: The ob/ob mice were treated with regular chow diet and metformin for 4 weeks, and the levels of hepatic triglyceride (TG) and apoA5 were measured. Hepatic IAR20 cells were treated with metformin and/or apoA5 siRNAs, and then cellular TG contents and apoA5 expression were determined.
 Results: High plasma and hepatic levels of apoA5 and TG were found in ob/ob mice. The plasma levels of apoA5 were positively correlated with plasma TG in these mice. Metformin could dose-dependently decrease the plasma and hepatic levels of apoA5 and TG in ob/ob mice. Metformin could also dose-dependently reduce cellular TG contents and apoA5 expression, these effects were attenuated by knockdown of apoA5.
 Conclusion: Hepatic apoA5 is up-regulated in ob/ob mice, which contributes to the elevation of plasma TG. Metformin could inhibit hepatic apoA5 expression, leading to the reduction of the plasma level of TG. Show less
no PDF DOI: 10.11817/j.issn.1672-7347.2017.12.006
APOA5
Minzeng Sun, Lin Chen, Hui Liu +3 more · 2017 · Lipids in health and disease · BioMed Central · added 2026-04-24
The SstI polymorphism in the apolipoprotein 3 gene (apoC3) has been identified in many ethnic groups. In addition, the S2 allele of the SstI polymorphism is shown to be associated with increased plasm Show more
The SstI polymorphism in the apolipoprotein 3 gene (apoC3) has been identified in many ethnic groups. In addition, the S2 allele of the SstI polymorphism is shown to be associated with increased plasma triglyceride (TG) levels. Plasma apoCIII is an important atherogenic factor, which interrupts lipid metabolism and is positively associated with plasma TG levels. However, the existence of the SstI polymorphism in the Li ethnic group in China remains to be confirmed. The relationship between the S2 allele of the SstI polymorphism and plasma apoCIII or TG and their roles in atherosclerosis are also unknown. A cohort of 628 participants was recruited (316 atherosclerotic patients and 312 healthy controls) from both the Li and Han ethnic groups. Blood samples were obtained to evaluate the SstI polymorphism in the apoC3 and lipid profiles. Chi-squared and t-tests and multiple unconditional logistic regression were employed to analyze the genotypic and allelic frequencies and lipid profiles using SPSS version 20.0 software. The SstI polymorphism in the apoC3 was identified in the Li ethnic group. The S2 allele and plasma apoCIII and TG levels were associated with the development of atherosclerosis (P < 0.01, S2 allele and apoCIII; P < 0.05, TG) in the Li ethnic group. The S2 allele was associated with increased plasma apoCIII levels in the atherosclerotic group (P < 0.01), but with increased plasma apoCIII and TG levels in control group (both P < 0.01). In addition to the increases in the S2 allele frequency and plasma TG and apoCIII levels, atherosclerotic patients in the Li ethnic group also exhibited increased apoB, decreased HDL-C and apoAI and a lower apoAI:apoB ratio (all P < 0.01). Our results indicate that the S2 allele of the SstI polymorphism in the apoC3 gene is associated with plasma apoCIII levels in the Li population. In combination with unfavorable lipid profiles, this might contribute to susceptibility to atherosclerosis. Show less
📄 PDF DOI: 10.1186/s12944-017-0614-3
APOC3
Lu Hua Chen, Yan Hui Fan, Patrick Yu Ping Kao +4 more · 2017 · Journal of the American Geriatrics Society · Blackwell Publishing · added 2026-04-24
To investigate whether genetic variations on the estrogen metabolic pathway would be associated with risk of Alzheimer's disease (AD). Cross-sectional study. Individuals were recruited at the Memory C Show more
To investigate whether genetic variations on the estrogen metabolic pathway would be associated with risk of Alzheimer's disease (AD). Cross-sectional study. Individuals were recruited at the Memory Clinic, Queen Mary Hospital, Hong Kong. Chinese individuals with (n = 426) and without (n = 350) AD. All subjects underwent a standardized cognitive assessment and genotyping of four candidate genes on the estrogen metabolic pathway (estrogen receptor α gene (ESR1), estrogen receptor β gene (ESR2), cytochrome P450 19A1 gene (CYP19A1), cytochrome P450 11A1 gene (CYP11A1)). Apart from consistent results showing an association between apolipoprotein (APO)E and AD, strong evidence of disease associations were found for polymorphisms in ESR2 and CYP11A1 based on the entire data set. For ESR2, significant protective effects were found for A alleles of rs4986938 (permuted P = .02) and rs867443 (permuted P = .02). For CYP11A1, significant risk effects were found for G alleles of rs11638442 (permuted P = .03) and rs11632698 (permuted P = .03). Stratifying subjects according to APOE ε4 status, their genetic effects continued to be significant in the APOE ε4-negative subgroup. Associations between CYP11A1 polymorphisms (rs2279357, rs2073475) and risk of AD were detected in women but not men. Further gene-level analysis confirmed the above association between ESR2 and CYP11A1, and pathway-level analysis highlighted the genetic effect of the estrogen metabolic pathway on disease susceptibility (permuted pathway-level P = .03). Consistent with previous biological findings for sex steroid hormones in the central nervous system, genetic alterations on the estrogen metabolic pathway were revealed in the Chinese population. Confirmation of these present findings in an independent population is warranted to elucidate disease pathogenesis and to explore the potential of hormone therapy in the treatment of AD. Show less
no PDF DOI: 10.1111/jgs.14537
APOA4
Cuicui Li, Lei Chang, Zhiquan Chen +3 more · 2017 · International journal of molecular medicine · added 2026-04-24
Exploring the biological functions of long non-coding RNAs (lncRNAs) has come to the foreground in recent years. Studies have indicated that the lncRNA metastasis‑associated lung adenocarcinoma transc Show more
Exploring the biological functions of long non-coding RNAs (lncRNAs) has come to the foreground in recent years. Studies have indicated that the lncRNA metastasis‑associated lung adenocarcinoma transcript 1 (MALAT1) not only regulates tumorigenesis in hepatocellular carcinoma, but also controls cell cycle progression in hematopoietic cells. The present study was designed to investigate the biological role of lncRNA MALAT1 in liver regeneration. We carried out a series of assays during liver regeneration following 2/3 partial hepatectomy in mice. We explored the functions of lncRNA MALAT1 with a series of functional analyses in vitro. We found that MALAT1 was upregulated during liver regeneration. Moreover, MALAT1 accelerated hepatocyte proliferation by stimulating cell cycle progression from the G1 to the S phase and inhibiting apoptosis in vitro. In addition, our findings also demonstrated that MALAT1 was regulated by p53 during liver regeneration, and that p53 may be a key upstream regulator of MALAT1 activity. Mechanistically, we found that MALAT1 activated the Wnt/β‑catenin pathway by inhibiting the expression of Axin1 and adenomatous polyposis coli (APC), and subsequently promoting the expression of cyclin D1. On the whole, the findings of this study suggest that MALAT1 is a critical molecule for liver regeneration. Pharmacological interventions targeting MALAT1 may thus prove to be therapeutically beneficial in liver failure or liver transplantation by promoting liver regeneration. Show less
📄 PDF DOI: 10.3892/ijmm.2017.2854
AXIN1
Jiali Zhu, Xuemei Zhang, Xiu Chen +5 more · 2017 · Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · Elsevier · added 2026-04-24
Usnea is a lichen of Usnea diffracta Vain and Usnea longissima Ach, which belongs to the genus Usnea Adans of Usneaceae. Usnea exerts numerous pharmacological activities, while its lipid regulatory ac Show more
Usnea is a lichen of Usnea diffracta Vain and Usnea longissima Ach, which belongs to the genus Usnea Adans of Usneaceae. Usnea exerts numerous pharmacological activities, while its lipid regulatory activities remain unreported. This study aims to evaluate the effects of aqueous and ethanol extracts of Usnea on the regulation of lipid metabolism and to explore the possible mechanism. Hyperlipidemia rat model was established by feeding with high-fat diet for 45days. Therapy rats were intragastrically administered with simvastatin (0.004g/kg/d), Usnea aqueous extract (2.766g/kg/d), or Usnea ethanol extract (2.766g/kg/d) for 20days. Pharmacodynamic effects, including body weight, serum and liver lipid levels, total bile acid (TBA), aspartate aminotransferase (AST), alanine aminotransferase (ALT), liver index, and hepatic morphological changes were evaluated. To explore the mechanisms, the lipase activities and protein expressions related to lipid metabolism were detected. Compared with the model group, aqueous and ethanol extracts of Usnea can slow down the weight gain of rats, significantly reduce the serum levels of total cholesterol, triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and the liver contents of TG, LDL-C, as well as significantly increase the contents of high-density lipoprotein cholesterol in serum. In addition, aqueous and ethanol extracts of Usnea can significantly reduce the serum contents of AST and ALT. Furthermore, ethanol extract of Usnea can also significantly reduce the TBA content in serum and liver index. Liver tissue pathological observation showed that aqueous and ethanol extracts of Usnea can improve cell degeneration to a certain extent. Aqueous and ethanol extracts of Usnea can significantly reduce sterol regulatory element-binding proteins-1c, and liver X receptor α (LXR-α) protein expressions. Furthermore, aqueous extract of Usnea can significantly increase hepatic lipase activity and promote apoprotein A5 (ApoA5) protein expression. These findings strongly suggest that the aqueous and ethanol extracts of Usnea play significant roles in regulating lipid metabolism, and the ethanol extract exhibits higher activity than the aqueous extract. The mechanism of the regulation of lipid metabolism by Usnea aqueous extract may involve the increased ApoA5 protein expression via inhibition of the LXR-α signal pathway; however, the mechanism of the regulation of lipid metabolism by Usnea ethanol extract remains to be further studied. Show less
no PDF DOI: 10.1016/j.biopha.2017.08.012
APOA5
Yimin Zhu, DanDan Zhang, Dan Zhou +31 more · 2017 · Journal of cellular and molecular medicine · Blackwell Publishing · added 2026-04-24
Metabolic syndrome (MetS), a cluster of metabolic disturbances that increase the risk for cardiovascular disease and diabetes, was because of genetic susceptibility and environmental risk factors. To Show more
Metabolic syndrome (MetS), a cluster of metabolic disturbances that increase the risk for cardiovascular disease and diabetes, was because of genetic susceptibility and environmental risk factors. To identify the genetic variants associated with MetS and metabolic components, we conducted a genome-wide association study followed by replications in totally 12,720 participants from the north, north-eastern and eastern China. In combined analyses, independent of the top known signal at rs651821 on APOA5, we newly identified a secondary triglyceride-associated signal at rs180326 on BUD13 (P Show less
📄 PDF DOI: 10.1111/jcmm.13042
APOA5
Jiping Yue, Yao Zhang, Wenguang G Liang +9 more · 2016 · Nature communications · Nature · added 2026-04-24
Turnover of focal adhesions allows cell retraction, which is essential for cell migration. The mammalian spectraplakin protein, ACF7 (Actin-Crosslinking Factor 7), promotes focal adhesion dynamics by Show more
Turnover of focal adhesions allows cell retraction, which is essential for cell migration. The mammalian spectraplakin protein, ACF7 (Actin-Crosslinking Factor 7), promotes focal adhesion dynamics by targeting of microtubule plus ends towards focal adhesions. However, it remains unclear how the activity of ACF7 is regulated spatiotemporally to achieve focal adhesion-specific guidance of microtubule. To explore the potential mechanisms, we resolve the crystal structure of ACF7's NT (amino-terminal) domain, which mediates F-actin interactions. Structural analysis leads to identification of a key tyrosine residue at the calponin homology (CH) domain of ACF7, whose phosphorylation by Src/FAK (focal adhesion kinase) complex is essential for F-actin binding of ACF7. Using skin epidermis as a model system, we further demonstrate that the phosphorylation of ACF7 plays an indispensable role in focal adhesion dynamics and epidermal migration in vitro and in vivo. Together, our findings provide critical insights into the molecular mechanisms underlying coordinated cytoskeletal dynamics during cell movement. Show less
📄 PDF DOI: 10.1038/ncomms11692
MACF1
Gu Jing, Junqin Chen, Guanlan Xu +1 more · 2016 · Molecular metabolism · Elsevier · added 2026-04-24
Carbohydrate-response element-binding protein (ChREBP) is the major transcription factor conferring glucose-induced gene expression in pancreatic islets, liver and adipose tissue. Recently, a novel Ch Show more
Carbohydrate-response element-binding protein (ChREBP) is the major transcription factor conferring glucose-induced gene expression in pancreatic islets, liver and adipose tissue. Recently, a novel ChREBP isoform, ChREBP-β, was identified in adipose tissue and found to be also expressed in islets and involved in glucose-induced beta cell proliferation. However, the physiological function of this less abundant β-isoform in the islet, and in diabetes, is largely unknown. The aims of the present study, therefore, were to determine how diabetes affects ChREBP-β and elucidate its physiological role in pancreatic beta cells. Non-obese diabetic and obese, diabetic ob/ob mice were used as models of T1D and T2D and human islets and the rat INS-1 beta cell line were exposed to low/high glucose and used for ChREBP isoform-specific gain-and-loss-of-function experiments. Changes in ChREBP-β and ChREBP-α were assessed by qRT-PCR, immunoblotting, promoter luciferase, and chromatin immunoprecipitation studies. Expression of the ChREBP-β isoform was highly induced in diabetes and by glucose, whereas ChREBP-α was downregulated. Interestingly, ChREBP-β gain-of-function experiments further revealed that it was ChREBP-β that downregulated ChREBP-α through a negative feedback loop. On the other hand, ChREBP-β knockdown led to unabated ChREBP-α activity and glucose-induced expression of target genes, suggesting that one of the physiological roles of this novel β-isoform is to help keep glucose-induced and ChREBP-α-mediated gene expression under control. We have identified a previously unappreciated negative feedback loop by which glucose-induced ChREBP-β downregulates ChREBP-α-signaling providing new insight into the physiological role of islet ChREBP-β and into the regulation of glucose-induced gene expression. Show less
📄 PDF DOI: 10.1016/j.molmet.2016.09.010
MLXIPL
Tze-Kiong Er, Yu-Fa Su, Chun-Chieh Wu +9 more · 2016 · Journal of molecular medicine (Berlin, Germany) · Springer · added 2026-04-24
Recent molecular and pathological studies suggest that endometriosis may serve as a precursor of ovarian cancer (endometriosis-associated ovarian cancer, EAOC), especially of the endometrioid and clea Show more
Recent molecular and pathological studies suggest that endometriosis may serve as a precursor of ovarian cancer (endometriosis-associated ovarian cancer, EAOC), especially of the endometrioid and clear cell subtypes. Accordingly, this study had two cardinal aims: first, to obtain mutation profiles of EAOC from Taiwanese patients; and second, to determine whether somatic mutations present in EAOC can be detected in preneoplastic lesions. Formalin-fixed paraffin-embedded (FFPE) tissues were obtained from ten endometriosis patients with malignant transformation. Macrodissection was performed to separate four different types of cells from FFPE sections in six patients. The four types of samples included normal endometrium, ectopic endometriotic lesion, atypical endometriosis, and carcinoma. Ultra-deep (>1000×) targeted sequencing was performed on 409 cancer-related genes to identify pathogenic mutations associated with EAOC. The most frequently mutated genes were PIK3CA (6/10) and ARID1A (5/10). Other recurrently mutated genes included ETS1, MLH1, PRKDC (3/10 each), and AMER1, ARID2, BCL11A, CREBBP, ERBB2, EXT1, FANCD2, MSH6, NF1, NOTCH1, NUMA1, PDE4DIP, PPP2R1A, RNF213, and SYNE1 (2/10 each). Importantly, in five of the six patients, identical somatic mutations were detected in atypical endometriosis and tumor lesions. In two patients, genetic alterations were also detected in ectopic endometriotic lesions, indicating the presence of genetic alterations in preneoplastic lesion. Genetic analysis in preneoplastic lesions may help to identify high-risk patients at early stage of malignant transformation and also shed new light on fundamental aspects of the molecular pathogenesis of EAOC. Molecular characterization of endometriosis-associated ovarian cancer genes by targeted NGS. Candidate genes predictive of malignant transformation were identified. Chromatin remodeling, PI3K-AKT-mTOR, Notch signaling, and Wnt/β-catenin pathway may promote cell malignant transformation. Show less
no PDF DOI: 10.1007/s00109-016-1395-2
EXT1
Yao Hu, Huaixing Li, Ling Lu +11 more · 2016 · Human molecular genetics · Oxford University Press · added 2026-04-24
Epidemiological studies suggest that levels of n-3 and n-6 long-chain polyunsaturated fatty acids are associated with risk of cardio-metabolic outcomes across different ethnic groups. Recent genome-wi Show more
Epidemiological studies suggest that levels of n-3 and n-6 long-chain polyunsaturated fatty acids are associated with risk of cardio-metabolic outcomes across different ethnic groups. Recent genome-wide association studies in populations of European ancestry have identified several loci associated with plasma and/or erythrocyte polyunsaturated fatty acids. To identify additional novel loci, we carried out a genome-wide association study in two population-based cohorts consisting of 3521 Chinese participants, followed by a trans-ethnic meta-analysis with meta-analysis results from 8962 participants of European ancestry. Four novel loci (MYB, AGPAT4, DGAT2 and PPT2) reached genome-wide significance in the trans-ethnic meta-analysis (log10(Bayes Factor) ≥ 6). Of them, associations of MYB and AGPAT4 with docosatetraenoic acid (log10(Bayes Factor) = 11.5 and 8.69, respectively) also reached genome-wide significance in the Chinese-specific genome-wide association analyses (P = 4.15 × 10(-14) and 4.30 × 10(-12), respectively), while associations of DGAT2 with gamma-linolenic acid (log10(Bayes Factor) = 6.16) and of PPT2 with docosapentaenoic acid (log10(Bayes Factor) = 6.24) were nominally significant in both Chinese- and European-specific genome-wide association analyses (P ≤ 0.003). We also confirmed previously reported loci including FADS1, NTAN1, NRBF2, ELOVL2 and GCKR. Different effect sizes in FADS1 and independent association signals in ELOVL2 were observed. These results provide novel insight into the genetic background of polyunsaturated fatty acids and their differences between Chinese and European populations. Show less
no PDF DOI: 10.1093/hmg/ddw002
FADS1
Changming Liu, Liangen Mao, Zepeng Ping +5 more · 2016 · Evidence-based complementary and alternative medicine : eCAM · added 2026-04-24
Yin-deficiency-heat (YDH) syndrome is a concept in Traditional Chinese Medicine (TCM) for describing subhealth status. However, there are few efficient diagnostic methods available for confirming YDH Show more
Yin-deficiency-heat (YDH) syndrome is a concept in Traditional Chinese Medicine (TCM) for describing subhealth status. However, there are few efficient diagnostic methods available for confirming YDH syndrome. To explore the novel method for diagnosing YDH syndrome, we applied iTRAQ to observe the serum protein profiles in YDH syndrome rats and confirmed protein levels by ELISA. A total of 92 differentially expressed proteins (63 upregulated proteins and 29 downregulated proteins), which were mainly involved in complement and coagulation cascades and glucose metabolism pathway, were identified by the proteomic experiments. Kininogen 1 (KNG1) was significantly increased ( Show less
📄 PDF DOI: 10.1155/2016/5176731
APOC3
Jue Wang, Zhizhong Ye, Shuhui Zheng +4 more · 2016 · Brain research · Elsevier · added 2026-04-24
Determination of the exogenous factors that regulate differentiation of neural stem/progenitor cells into neurons, oligodendrocytes and astrocytes is an important step in the clinical therapy of spina Show more
Determination of the exogenous factors that regulate differentiation of neural stem/progenitor cells into neurons, oligodendrocytes and astrocytes is an important step in the clinical therapy of spinal cord injury (SCI). The Notch pathway inhibits the differentiation of neural stem/progenitor cells and Lingo-1 is a strong negative regulator for myelination and axon growth. While Lingo-1 shRNA and N-[N-(3, 5-difluorophenacetyl)-1-alanyl]-S-Phenylglycinet-butylester (DAPT), a Notch pathway inhibitor, have been used separately to help repair SCI, the results have been unsatisfactory. Here we investigated and elucidated the preliminary mechanism for the effect of Lingo-1 shRNA and DAPT on neural stem/progenitor cells differentiation. We found that neural stem/progenitor cells from E14 rat embryos expressed Nestin, Sox-2 and Lingo-1, and we optimized the transduction of neural stem/progenitor cells using lentiviral vectors encoding Lingo-1 shRNA. The addition of DAPT decreased the expression of Notch intracellular domain (NICD) as well as the downstream genes Hes1 and Hes5. Expression of NeuN, CNPase and GFAP in DAPT treated cells and expression of NeuN in Lingo-1 shRNA treated cells confirmed differentiation of neural stem/progenitor cells into neurons, oligodendrocytes and astrocytes. These results revealed that while Lingo-1 shRNA and Notch signaling inhibitor DAPT both promoted differentiation of neural stem cells into neurons, only DAPT was capable of driving neural stem/progenitor cells differentiation into oligodendrocytes and astrocytes. Since we were able to show that both Lingo-1 shRNA and DAPT could drive neural stem/progenitor cells differentiation, our data might aid the development of more effective SCI therapies using Lingo-1 shRNA and DAPT. Show less
no PDF DOI: 10.1016/j.brainres.2015.11.029
LINGO1
Lucas Goedert, Cristiano G Pereira, Jason Roszik +8 more · 2016 · Oncotarget · Impact Journals · added 2026-04-24
Previous work identified RMEL3 as a lncRNA with enriched expression in melanoma. Analysis of The Cancer Genome Atlas (TCGA) data confirmed RMEL3 enriched expression in melanoma and demonstrated its as Show more
Previous work identified RMEL3 as a lncRNA with enriched expression in melanoma. Analysis of The Cancer Genome Atlas (TCGA) data confirmed RMEL3 enriched expression in melanoma and demonstrated its association with the presence of BRAFV600E. RMEL3 siRNA-mediated silencing markedly reduced (95%) colony formation in different BRAFV600E melanoma cell lines. Multiple genes of the MAPK and PI3K pathways found to be correlated with RMEL3 in TCGA samples were experimentally confirmed. RMEL3 knockdown led to downregulation of activators or effectors of these pathways, including FGF2, FGF3, DUSP6, ITGB3 and GNG2. RMEL3 knockdown induces gain of protein levels of tumor suppressor PTEN and the G1/S cyclin-Cdk inhibitors p21 and p27, as well as a decrease of pAKT (T308), BRAF, pRB (S807, S811) and cyclin B1. Consistently, knockdown resulted in an accumulation of cells in G1 phase and subG0/G1 in an asynchronously growing population. Thus, TCGA data and functional experiments demonstrate that RMEL3 is required for MAPK and PI3K signaling, and its knockdown decrease BRAFV600E melanoma cell survival and proliferation. Show less
📄 PDF DOI: 10.18632/oncotarget.9164
DUSP6
Zong-Bo Wei, Ye-Feng Yuan, Florence Jaouen +8 more · 2016 · Autophagy · Taylor & Francis · added 2026-04-24
Searching for new regulators of autophagy involved in selective dopaminergic (DA) neuron loss is a hallmark in the pathogenesis of Parkinson disease (PD). We here report that an endoplasmic reticulum Show more
Searching for new regulators of autophagy involved in selective dopaminergic (DA) neuron loss is a hallmark in the pathogenesis of Parkinson disease (PD). We here report that an endoplasmic reticulum (ER)-associated transmembrane protein SLC35D3 is selectively expressed in subsets of midbrain DA neurons in about 10% TH (tyrosine hydroxylase)-positive neurons in the substantia nigra pars compacta (SNc) and in about 22% TH-positive neurons in the ventral tegmental area (VTA). Loss of SLC35D3 in ros (roswell mutant) mice showed a reduction of 11.9% DA neurons in the SNc and 15.5% DA neuron loss in the VTA with impaired autophagy. We determined that SLC35D3 enhanced the formation of the BECN1-ATG14-PIK3C3 complex to induce autophagy. These results suggest that SLC35D3 is a new regulator of tissue-specific autophagy and plays an important role in the increased autophagic activity required for the survival of subsets of DA neurons. Show less
no PDF DOI: 10.1080/15548627.2016.1179402
PIK3C3
Aihui Fan, Qian Wang, Yongjun Yuan +7 more · 2016 · American journal of physiology. Cell physiology · added 2026-04-24
Recent studies have shown that activation of liver X receptors (LXRs) attenuates the development of atherosclerosis, not only by regulating lipid metabolism but also by suppressing inflammatory signal Show more
Recent studies have shown that activation of liver X receptors (LXRs) attenuates the development of atherosclerosis, not only by regulating lipid metabolism but also by suppressing inflammatory signaling. Sphingosine 1-phosphate receptor 2 (S1PR2), an important inflammatory gene product, plays a role in the development of various inflammatory diseases. It was proposed that S1PR2 might be regulated by LXR-α. In the present study, the effect of LXR-α on tumor necrosis factor-α (TNF-α)-induced S1PR2 expression in human umbilical vein endothelial cells (HUVECs) was investigated and the underlying mechanism was explored. The results demonstrated that TNF-α led to an increase in S1PR2 expression and triggered a downregulation of LXR-α expression in HUVECs as well. Downregulation of LXR-α with specific small interfering RNA (siRNA) remarkably enhanced the primary as well as TNF-α-induced expression of S1PR2 in HUVECs. Activation of LXR-α by agonist GW3965 inhibited both primary and TNF-α-induced S1PR2 expression. GW3965 also attenuated S1PR2-induced endothelial barrier dysfunction. The data further showed that TNF-α induced a significant decrease in miR-130a-3p expression. Overexpression of miR-130a-3p with mimic product reduced S1PR2 protein expression, and inhibition of miR-130a-3p by specific inhibitor resulted in an increase in S1PR2 protein expression. Furthermore, activation of LXRs with agonist enhanced the expression of miR-130a-3p, and knockdown of LXR-α by siRNA suppressed miR-130a-3p expression. These results suggest that LXR-α might downregulate S1PR2 expression via miR-130a-3p in quiescent HUVECs. Stimulation of TNF-α attenuates the activity of LXR-α and results in enhanced S1PR2 expression. Show less
no PDF DOI: 10.1152/ajpcell.00102.2015
NR1H3
Ye Tian, Wei Zhang, Shigang Zhao +11 more · 2016 · Scientific reports · Nature · added 2026-04-24
Dyslipidemia is common in polycystic ovary syndrome (PCOS). This study was aimed to investigate whether fatty acid desaturase genes (FADS), a dyslipidemia-related gene cluster, are associated with PCO Show more
Dyslipidemia is common in polycystic ovary syndrome (PCOS). This study was aimed to investigate whether fatty acid desaturase genes (FADS), a dyslipidemia-related gene cluster, are associated with PCOS. We scanned variations of FADS genes using our previous data of genome-wide association study (GWAS) for PCOS and selected rs174570 for further study. The case-control study was conducted in an independent cohort of 1918 PCOS cases and 1889 age-matched controls and family-based study was conducted in a set of 243 core family trios with PCOS probands. Minor allele frequency (allele T) of rs174570 was significantly lower in PCOS cases than that in age-matched controls (P = 2.17E-03, OR = 0.85), even after adjustment of BMI and age. PCOS subjects carrying CC genotype had higher testosterone level and similar lipid/glucose level compared with those carrying TT or TC genotype. In trios, transmission disequilibrium test (TDT) analysis revealed risk allele C of rs174570 was significantly over-transmitted (P = 2.00E-04). Decreased expression of FADS2 was detected in PCOS cases and expression quantitative trait loci (eQTL) analysis revealed the risk allele C dosage was correlated with the decline of FADS2 expression (P = 0.002). Our results demonstrate that FADS1-FADS2 are susceptibility genes for PCOS. Show less
📄 PDF DOI: 10.1038/srep21195
FADS1
Salman M Tajuddin, Ursula M Schick, John D Eicher +94 more · 2016 · American journal of human genetics · Elsevier · added 2026-04-24
Salman M Tajuddin, Ursula M Schick, John D Eicher, Nathalie Chami, Ayush Giri, Jennifer A Brody, W David Hill, Tim Kacprowski, Jin Li, Leo-Pekka Lyytikäinen, Ani Manichaikul, Evelin Mihailov, Michelle L O'Donoghue, Nathan Pankratz, Raha Pazoki, Linda M Polfus, Albert Vernon Smith, Claudia Schurmann, Caterina Vacchi-Suzzi, Dawn M Waterworth, Evangelos Evangelou, Lisa R Yanek, Amber Burt, Ming-Huei Chen, Frank J A van Rooij, James S Floyd, Andreas Greinacher, Tamara B Harris, Heather M Highland, Leslie A Lange, Yongmei Liu, Reedik Mägi, Mike A Nalls, Rasika A Mathias, Deborah A Nickerson, Kjell Nikus, John M Starr, Jean-Claude Tardif, Ioanna Tzoulaki, Digna R Velez Edwards, Lars Wallentin, Traci M Bartz, Lewis C Becker, Joshua C Denny, Laura M Raffield, John D Rioux, Nele Friedrich, Myriam Fornage, He Gao, Joel N Hirschhorn, David C M Liewald, Stephen S Rich, Andre Uitterlinden, Lisa Bastarache, Diane M Becker, Eric Boerwinkle, Simon de Denus, Erwin P Bottinger, Caroline Hayward, Albert Hofman, Georg Homuth, Ethan Lange, Lenore J Launer, Terho Lehtimäki, Yingchang Lu, Andres Metspalu, Chris J O'Donnell, Rakale C Quarells, Melissa Richard, Eric S Torstenson, Kent D Taylor, Anne-Claire Vergnaud, Alan B Zonderman, David R Crosslin, Ian J Deary, Marcus Dörr, Paul Elliott, Michele K Evans, Vilmundur Gudnason, Mika Kähönen, Bruce M Psaty, Jerome I Rotter, Andrew J Slater, Abbas Dehghan, Harvey D White, Santhi K Ganesh, Ruth J F Loos, Tõnu Esko, Nauder Faraday, James G Wilson, Mary Cushman, Andrew D Johnson, Todd L Edwards, Neil A Zakai, Guillaume Lettre, Alex P Reiner, Paul L Auer Show less
White blood cells play diverse roles in innate and adaptive immunity. Genetic association analyses of phenotypic variation in circulating white blood cell (WBC) counts from large samples of otherwise Show more
White blood cells play diverse roles in innate and adaptive immunity. Genetic association analyses of phenotypic variation in circulating white blood cell (WBC) counts from large samples of otherwise healthy individuals can provide insights into genes and biologic pathways involved in production, differentiation, or clearance of particular WBC lineages (myeloid, lymphoid) and also potentially inform the genetic basis of autoimmune, allergic, and blood diseases. We performed an exome array-based meta-analysis of total WBC and subtype counts (neutrophils, monocytes, lymphocytes, basophils, and eosinophils) in a multi-ancestry discovery and replication sample of ∼157,622 individuals from 25 studies. We identified 16 common variants (8 of which were coding variants) associated with one or more WBC traits, the majority of which are pleiotropically associated with autoimmune diseases. Based on functional annotation, these loci included genes encoding surface markers of myeloid, lymphoid, or hematopoietic stem cell differentiation (CD69, CD33, CD87), transcription factors regulating lineage specification during hematopoiesis (ASXL1, IRF8, IKZF1, JMJD1C, ETS2-PSMG1), and molecules involved in neutrophil clearance/apoptosis (C10orf54, LTA), adhesion (TNXB), or centrosome and microtubule structure/function (KIF9, TUBD1). Together with recent reports of somatic ASXL1 mutations among individuals with idiopathic cytopenias or clonal hematopoiesis of undetermined significance, the identification of a common regulatory 3' UTR variant of ASXL1 suggests that both germline and somatic ASXL1 mutations contribute to lower blood counts in otherwise asymptomatic individuals. These association results shed light on genetic mechanisms that regulate circulating WBC counts and suggest a prominent shared genetic architecture with inflammatory and autoimmune diseases. Show less
no PDF DOI: 10.1016/j.ajhg.2016.05.003
JMJD1C
Deyi Zhang, Wei Wang, Xiujie Sun +8 more · 2016 · Autophagy · Taylor & Francis · added 2026-04-24
Macroautophagy/autophagy is a conserved catabolic process that recycles cytoplasmic material during low energy conditions. BECN1/Beclin1 (Beclin 1, autophagy related) is an essential protein for funct Show more
Macroautophagy/autophagy is a conserved catabolic process that recycles cytoplasmic material during low energy conditions. BECN1/Beclin1 (Beclin 1, autophagy related) is an essential protein for function of the class 3 phosphatidylinositol 3-kinase (PtdIns3K) complexes that play a key role in autophagy nucleation and elongation. Here, we show that AMP-activated protein kinase (AMPK) regulates autophagy by phosphorylating BECN1 at Thr388. Phosphorylation of BECN1 is required for autophagy upon glucose withdrawal. BECN1(T388A), a phosphorylation defective mutant, suppresses autophagy through decreasing the interaction between PIK3C3 (phosphatidylinositol 3-kinase catalytic subunit type 3) and ATG14 (autophagy-related 14). The BECN1(T388A) mutant has a higher affinity for BCL2 than its wild-type counterpart; the mutant is more prone to dimer formation. Conversely, a BECN1 phosphorylation mimic mutant, T388D, has stronger binding to PIK3C3 and ATG14, and promotes higher autophagy activity than the wild-type control. These findings uncover a novel mechanism of autophagy regulation. Show less
no PDF DOI: 10.1080/15548627.2016.1185576
PIK3C3
Wen-li Song, Yu Tian, Xian-e Wang +7 more · 2016 · Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · added 2026-04-24
To investigate the potential association between FADS1 rs174537 polymorphism and serum proteins in patients with aggressive periodontitis, which may provide benefits for diagnosis and treatment of agg Show more
To investigate the potential association between FADS1 rs174537 polymorphism and serum proteins in patients with aggressive periodontitis, which may provide benefits for diagnosis and treatment of aggressive periodontitis. A total of 353 patients with aggressive periodontitis (group AgP) and 125 matched controls (group HP) were recruited in the study. Genotyping of FADS1 rs174537 and serum biochemical indexes were tested at the study's start. The relationships between the levels of TP, GLB, ALB, A/G and genotyping were analyzed. (1) The detection rate of allele G in group AgP was higher than that in group HP(68.1% vs. 61.2%, P=0.046,OR=1.35,95% CI 1.00-1.83); the detection rate of genotype GG in group AgP was higher than in group HP(45.5% vs. 34.4%,P=0.029, OR=1.60, 95% CI 1.05-2.44). (2) In group AgP, the patients with GG genotype exhibited significantly lower TP, GLB than the patients with GT+TT genotype [(77.08 ± 7.88) g/L vs. (79.00 ± 4.66) g/L, P=0.007; (28.17 ± 7.63) g/L vs.(29.88 ± 3.49) g/L,P=0.007) and the higher A/G(1.72 ± 0.22 vs.1.67 ± 0.22, P=0.040), but there was no significant difference in ALB between the patients with GG genotype and the patients with GT+TT genotype. In group HP, there were no significant differences in TP, GLB, A/G and ALB between individuals with genotype GT+TT and with genotype GG. (3)Compared with individuals with genotype GT+TT in group HP, the AgP patients with genotype GT+TT exhibited significantly higher TP, GLB [(79.00 ± 4.66) g/L vs. (75.20 ± 4.53) g/L, P<0.01; (29.88 ± 3.49) g/L vs.(26.55 ± 2.94) g/L, P<0.01) and the lower A/G(1.67 ± 0.22 vs. 1.88 ± 0.30, P<0.01), but there was no significant difference in ALB. There were no significant differences in TP, GLB, A/G and ALB the between the AgP patients with genotype GG and the healthy subjects with the same genotype either. FADS1 rs174537 polymorphism is associated with aggressive periodontitis. The patients with genotype GG in group AgP had relatively lower TP,GLB and higher A/G. Genotype GG might be a risk indicator for aggressive periodontitis by reducing host defense capability and contributing to inflammatory response in the occurrence and development of aggressive periodontitis. Show less
no PDF
FADS1
Jiali Zhu, Keke Xu, Xuemei Zhang +7 more · 2016 · Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · Elsevier · added 2026-04-24
Valeriana jatamansi Jones, a plant with heart-shaped leaves in the Valeriana genus of Valerianaceae, is widely used in Chinese folk medicine. Iridoid is an important constituent of V. jatamansi that c Show more
Valeriana jatamansi Jones, a plant with heart-shaped leaves in the Valeriana genus of Valerianaceae, is widely used in Chinese folk medicine. Iridoid is an important constituent of V. jatamansi that contributes to the pharmacological efficacy of the herb. This study aims to investigate the regulation of lipid metabolism and its mechanism of the iridoids rich fraction in V. jatamansi (IRFV). A high fat diet was used to establish the hyperlipidemia rat model, with 2mg/kg/d of simvastatin as a positive control, fed with 7.5, 15, and 30mg/kg/d of IRFV for 20days to investigate the lipid regulation activity and mechanism of IRFV. Body weight, liver index, total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) in both serum and liver, as well as total bile acid (TBA), aspartate aminotransferase (AST), and alanine aminotransferase (ALT) in serum were measured. The lipoprotein lipase (LPL) and hepatic lipase (HL) activities and the apoprotein A5 (ApoA5), peroxisome proliferator-activated receptor α (PPAR-α), sterol regulatory element-binding proteins (SREBP-1c), and liver X receptor α (LXR-α) protein expressions were observed. Liver pathology was described through hematoxylin-eosin (HE) staining. Compared with the model group, three different IRFV dosages can slow down the weight gain of rats, reduce the contents of TG, and increase the contents of HDL-C in serum. Low IRFV dosage can significantly reduce the AST and ALT contents in serum, liver index, and the TG contents in liver, enhance LPL activity. Medium IRFV dosage can significantly decrease the TG and LDL-C contents in liver. High IRFV dosage can significantly reduce LDL-C, TBA, AST, and ALT contents in serum, and enhance HL activity. Three different IRFV dosages can significantly increase the ApoA5 and PPAR-α protein expression and decrease the SREBP-1c protein expression. Furthermore, the LXR-α protein expression decreased in low- and high-dose groups. Liver tissue pathological observation showed that IRFV can improve cell degeneration to a certain extent. These results strongly suggest that IRFV play significant roles in regulating lipid metabolism, the mechanism may be related to the increased ApoA5 protein expression. Show less
no PDF DOI: 10.1016/j.biopha.2016.10.099
APOA5
Tong-Hong Wang, Cheng-Chia Yu, Yong-Shiang Lin +6 more · 2016 · Oncotarget · Impact Journals · added 2026-04-24
Recently, increasing numbers of long noncoding RNAs (lncRNAs), with both oncogenic and tumor-suppressive potential, have been found to be aberrantly expressed in various human cancers. However, the fu Show more
Recently, increasing numbers of long noncoding RNAs (lncRNAs), with both oncogenic and tumor-suppressive potential, have been found to be aberrantly expressed in various human cancers. However, the function of lncRNAs in hepatocellular carcinoma (HCC) progression remains largely unknown. In this study, we performed a comprehensive microarray analysis of lncRNA expression using human HCC specimens. After validation in 119 human HCC tissues, we identified a novel tumor suppressor lncRNA, CPS1 intronic transcript 1 (CPS1-IT1). To elucidate the clinical significance of CPS1-IT1 in HCC, correlations between CPS1-IT1 levels, clinical parameters, and survival outcomes were analyzed. In vitro and in vivo functional assays were also performed to dissect the potential underlying mechanisms. Expression of CPS1-IT1 was significantly decreased in 73% of HCC tissues, and patients with low CPS1-IT1 expression had poor survival outcomes. Furthermore, in vitro functional assays indicated that CPS1-IT1 significantly reduced cell proliferation, migration and invasion capacities through reduced Hsp90 binding to and activation of HIF-1α, thereby suppressing the epithelial-mesenchymal transition (EMT). An in vivo animal model also demonstrated the tumor suppressor role of CPS1- IT1 via decreased tumor growth and metastasis. In conclusion, lncRNA CPS1-IT1 acts as a tumor suppressor in HCC by reducing HIF-1α activation and suppressing EMT. The findings of this study establish a function for CPS1-IT1 in HCC progression and suggest its potential as a new prognostic biomarker and target for HCC therapy. Show less
📄 PDF DOI: 10.18632/oncotarget.9635
CPS1
Haiying Chen, Hongli Yang, Chong Xu +8 more · 2016 · Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · Elsevier · added 2026-04-24
Pulmonary arterial hypertension (PAH) is associated with sustained vasoconstriction, inflammation and suppressed apoptosis of smooth muscle cells. Our previous studies have found that rat bone marrow- Show more
Pulmonary arterial hypertension (PAH) is associated with sustained vasoconstriction, inflammation and suppressed apoptosis of smooth muscle cells. Our previous studies have found that rat bone marrow-derived mesenchymal stem cells (rBMSCs) transduced with a mutant caveolin-1(F92A-Cav1) could enhance endothelial nitric oxide synthase (eNOS) activity and improve pulmonary vascular remodeling, but the potential mechanism is not yet fully explored. The present study was to investigate the gene expression profile upon rBMSCs/F92A-Cav1delivered to PAH rat to evaluate the role of F92A-Cav1 in its regulation. PAH was induced with monocrotaline (MCT, 60mg/kg) prior to delivery of lentiviral vector transduced rBMSCs expressing Cav1 or F92A-Cav1. Gene expression profiling was performed using Rat Signal Transduction PathwayFinder array. The expression changes of 84 key genes representing 10 signal transduction pathways in rat following rBMSCs/F92A-Cav1 treatment was examined. Screening with the Rat Signal Transduction PathwayFinder R rBMSCs/F92A-Cav1 inhibits inflammation and cell proliferation by regulating signaling pathways that related to inflammation, proliferation, cell cycle and oxidative stress. Show less
no PDF DOI: 10.1016/j.biopha.2016.06.028
HEY2
Junxiong Pang, Anna Lindblom, Thomas Tolfvenstam +8 more · 2016 · PloS one · PLOS · added 2026-04-24
Dengue results in a significant public health burden in endemic regions. The World Health Organization (WHO) recommended the use of warning signs (WS) to stratify patients at risk of severe dengue dis Show more
Dengue results in a significant public health burden in endemic regions. The World Health Organization (WHO) recommended the use of warning signs (WS) to stratify patients at risk of severe dengue disease in 2009. However, WS is limited in stratifying adult dengue patients at early infection (Day 1-3 post fever), who require close monitoring in hospitals to prevent severe dengue. The aim of this study is to identify and validate prognostic models, built with differentially expressed biomarkers, that enable the early identification of those with early dengue infection that require close clinical monitoring. RNA microarray and protein assays were performed to identify differentially expressed biomarkers of severity among 92 adult dengue patients recruited at early infection from years 2005-2008. This comprised 47 cases who developed WS after first presentation and required hospitalization (WS+Hosp), as well as 45 controls who did not develop WS after first presentation and did not require hospitalization (Non-WS+Non-Hosp). Independent validation was conducted with 80 adult dengue patients recruited from years 2009-2012. Prognostic models were developed based on forward stepwise and backward elimination estimation, using multiple logistic regressions. Prognostic power was estimated by the area under the receiver operating characteristic curve (AUC). The WS+Hosp group had significantly higher viral load (P<0.001), lower platelet (P<0.001) and lymphocytes counts (P = 0.004) at early infection compared to the Non-WS+Non-Hosp group. From the RNA microarray and protein assays, the top single RNA and protein prognostic models at early infection were CCL8 RNA (AUC:0.73) and IP-10 protein (AUC:0.74), respectively. The model with CCL8, VPS13C RNA, uPAR protein, and with CCL8, VPS13C RNA and platelets were the best biomarker models for stratifying adult dengue patients at early infection, with sensitivity and specificity up to 83% and 84%, respectively. These results were tested in the independent validation group, showing sensitivity and specificity up to 96% and 54.6%, respectively. At early infection, adult dengue patients who later presented WS and require hospitalization have significantly different pathophysiology compared with patients who consistently presented no WS and / or require no hospitalization. The molecular prognostic models developed and validated here based on these pathophysiology differences, could offer earlier and complementary indicators to the clinical WHO 2009 WS guide, in order to triage adult dengue patients at early infection. Show less
no PDF DOI: 10.1371/journal.pone.0155993
VPS13C
Yan-Bei Yang, Jian-Qing Chen, Yu-Lin Zhao +6 more · 2016 · Frontiers in microbiology · Frontiers · added 2026-04-24
📄 PDF DOI: 10.3389/fmicb.2016.01659
CPS1
Dechang Diao, Lei Wang, Jin Wan +6 more · 2016 · BMC cancer · BioMed Central · added 2026-04-24
Mitogen/extracellular signal-regulated kinase kinase-5 (MEK5) has been confirmed to play a pivotal role in tumor carcinogenesis and progression. However, few studies have investigated the role of MEK5 Show more
Mitogen/extracellular signal-regulated kinase kinase-5 (MEK5) has been confirmed to play a pivotal role in tumor carcinogenesis and progression. However, few studies have investigated the role of MEK5 in colorectal cancer (CRC). MEK5 expression was determined by immunohistochemistry (IHC) in tissue microarrays (TMAs) containing 2 groups of tissues, and western blotting was used to confirm MEK5 expression in 8 cases of primary CRC tissues and paired normal mucosa. RNA interference was used to verify the biological function of MEK5 gene in the development of CRC. IHC revealed the expression of MEK5 was higher in tumor tissues (38.1 %), compared with adjacent normal tissue (8.3 %). Western blot showed that, MEK5 expression was upregulated in CRC tumor tissues compared with normal tissue. Analysis of clinical pathology parameters indicated MEK5 overexpression was significantly correlated with the depth of invasion, lymph node metastasis, distant metastasis and histological grade. Survival analysis revealed that MEK5 overexpression negatively correlated with cancer-free survival (hazard ratio 1.64, P = 0.017). RNA interference-mediated knockdown of MEK5 in SW480 colon cancer cells decreased their proliferation, division, migration and invasiveness in vitro and slowed down tumors growth in mice engrafted with the cells. MEK5 plays an important role in CRC progression and may be a potential molecular target for the treatment of CRC. Show less
📄 PDF DOI: 10.1186/s12885-016-2327-9
MAP2K5
Ruiyang Zhang, Congle Shen, Lijun Zhao +4 more · 2016 · International journal of cancer · Wiley · added 2026-04-24
Integration of human papillomavirus (HPV) viral DNA into the human genome has been postulated as an important etiological event during cervical carcinogenesis. Several recent reports suggested a possi Show more
Integration of human papillomavirus (HPV) viral DNA into the human genome has been postulated as an important etiological event during cervical carcinogenesis. Several recent reports suggested a possible role for such integration-targeted cellular genes (ITGs) in cervical carcinogenesis. Therefore, a comprehensive analysis of HPV integration events was undertaken using data collected from 14 publications, with 499 integration loci on human chromosomes included. It revealed that HPV DNA preferred to integrate into intragenic regions and gene-dense regions of human chromosomes. Intriguingly, the host cellular genes nearby the integration sites were found to be more transcriptionally active compared with control. Furthermore, analysis of the integration sites in the human genome revealed that there were several integration hotspots although all chromosomes were represented. The ITGs identified were found to be enriched in tumor-related terms and pathways using gene ontology and KEGG analysis. In line with this, three of six ITGs tested were found aberrantly expressed in cervical cancer tissues. Among them, it was demonstrated for the first time that MPPED2 could induce HeLa cell and SiHa cell G1/S transition block and cell proliferation retardation. Moreover, "knocking out" the integrated HPV fragment in HeLa cell line decreased expression of MYC located ∼500 kb downstream of the integration site, which provided the first experimental evidence supporting the hypothesis that integrated HPV fragment influence MYC expression via long distance chromatin interaction. Overall, the results of this comprehensive analysis implicated that dysregulation of ITGs caused by viral integration as possibly having an etiological involvement in cervical carcinogenesis. Show less
📄 PDF DOI: 10.1002/ijc.29872
MPPED2
Linda M Polfus, Rajiv K Khajuria, Ursula M Schick +53 more · 2016 · American journal of human genetics · Elsevier · added 2026-04-24
Circulating blood cell counts and indices are important indicators of hematopoietic function and a number of clinical parameters, such as blood oxygen-carrying capacity, inflammation, and hemostasis. Show more
Circulating blood cell counts and indices are important indicators of hematopoietic function and a number of clinical parameters, such as blood oxygen-carrying capacity, inflammation, and hemostasis. By performing whole-exome sequence association analyses of hematologic quantitative traits in 15,459 community-dwelling individuals, followed by in silico replication in up to 52,024 independent samples, we identified two previously undescribed coding variants associated with lower platelet count: a common missense variant in CPS1 (rs1047891, MAF = 0.33, discovery + replication p = 6.38 × 10(-10)) and a rare synonymous variant in GFI1B (rs150813342, MAF = 0.009, discovery + replication p = 1.79 × 10(-27)). By performing CRISPR/Cas9 genome editing in hematopoietic cell lines and follow-up targeted knockdown experiments in primary human hematopoietic stem and progenitor cells, we demonstrate an alternative splicing mechanism by which the GFI1B rs150813342 variant suppresses formation of a GFI1B isoform that preferentially promotes megakaryocyte differentiation and platelet production. These results demonstrate how unbiased studies of natural variation in blood cell traits can provide insight into the regulation of human hematopoiesis. Show less
no PDF DOI: 10.1016/j.ajhg.2016.06.016
CPS1