生物谷報道：來自臺灣大學醫(yī)學院臨床實驗科學，醫(yī)學生物技術系,，NTU基因組醫(yī)學中心（NTU Center for Genomic Medicine,，生物谷注），中央研究院（Academia Sinica）,，臺中榮民總醫(yī)院（Taichung Veterans General Hospital）,，加州大學洛杉磯分校的研究人員利用現(xiàn)代生物技術手段，發(fā)現(xiàn)了5個能預測肺癌存活及復發(fā)的miRNA信號,，對于癌癥的預測與治療提供了新的資料,。這一研究成果公布在Cell子刊Cancer Cell雜志上。
領導這一研究的是臺灣大學醫(yī)學院的楊泮池教授,，其早年畢業(yè)于臺灣大學,，獲得了碩士及博士學位，現(xiàn)任臺灣大學醫(yī)學院基因組醫(yī)學中心執(zhí)行秘書,，芯片核心實驗室（Microarray Core Facility,，生物谷注）項目主任等。
在最新的一份報告中指出（美國癌癥協(xié)會）,，2007年全球的新增癌癥病例將超過1200萬,，并且有760萬人死于癌癥,，即每天有2萬人死于癌癥。在發(fā)展中國家,，男性最常診斷出的癌癥類型分別為肺癌,、胃癌和肝癌；女性最常診斷出的前三位癌癥分別為乳腺癌,、宮頸癌和胃癌,。作為癌癥死亡率已占癌癥死亡率之首的一種常見的肺部惡性腫瘤，肺癌在國內(nèi)的發(fā)病率和病死率均迅速上升,。
肺癌共有四種不同類型的肺癌：小細胞肺癌,、非小細胞肺癌，后者包括鱗癌,、腺癌和支氣管肺泡癌,，其中非小細胞肺癌（Non-Small Cell Lung Cancer，NSCLC,，生物谷注）占肺癌的85％以上,，而非小細胞肺癌中85％以上又都屬中晚期肺癌而失去根治性手術治療的機會，因此這部分肺癌研究尤其吸引研究人員,。
在這篇文章中,，研究人員對患有NSCLC的病患進行了檢測，分析miRNA表達是否能預測NSCLC的臨床效果,，利用實時RT-PCR手段，研究人員獲得了112個病患的miRNA表達數(shù)據(jù),。通過Cox模型（Cox regression）和風險評分（risk-score,，生物谷注）分析，研究人員發(fā)現(xiàn)了5個預測NSCLC治療效果的miRNA信號,。帶有這些miRNA信號的高風險基數(shù)的病人相對于風險基數(shù)低的病人,，治療效果較差。因此研究人員認為這些miRNA信號是NSCLC病患癌癥復發(fā)和存活的獨立預測因子,。
微小RNA (microRNA,，簡稱miRNA)是生物體內(nèi)源長度約為20－23個核苷酸的非編碼小RNA，通過與靶mRNA的互補配對而在轉(zhuǎn)錄后水平上對基因的表達進行負調(diào)控,，導致mRNA的降解或翻譯抑制,。到目前為止，已報道有幾千種miRNA存在于動物,、植物,、真菌等多細胞真核生物中，進化上高度保守,。
許多研究證明,，miRNA可以用以標記癌癥,，比如今年三月，俄亥俄州立大學的Stefano Volinia等人通過分析來自肺部,、胸部,、胃部、前列腺,、結腸和胰腺等處的癌細胞樣品540份,，發(fā)現(xiàn)了由過量表達的大部分miRNAs組成的實體癌癥miRNA信號，在這些miRNA中包括miR-17-5p,、miR-20a,、miR-21,、miR-92、miR-106a和miR-155,。而對于編碼蛋白的腫瘤抑制子（protein-coding tumor suppressors）和致癌基因，這些miRNAs差異表達作用的靶目標會大量富集,，其中得到證實的是腫瘤抑制子RB1（Retinoblastoma 1）和TGFBR2 (transforming growth factor, beta receptor II),。這說明miRNAs在實體腫瘤的癌癥發(fā)病機理中發(fā)揮著重要的作用。
生物谷推薦英文原文：
Copyright © 2008 Cell Press. All rights reserved.
Cancer Cell, Vol 13, 48-57, 08 January 2008
MicroRNA Signature Predicts Survival and Relapse in Lung Cancer
Sung-Liang Yu,1,2,3 Hsuan-Yu Chen,2,6,7 Gee-Chen Chang,9,11 Chih-Yi Chen,10,12 Huei-Wen Chen,13 Sher Singh,14 Chiou-Ling Cheng,2 Chong-Jen Yu,4 Yung-Chie Lee,5 Han-Shiang Chen,15,16 Te-Jen Su,2,11 Ching-Cheng Chiang,2 Han-Ni Li,2 Qi-Sheng Hong,2 Hsin-Yuan Su,2 Chun-Chieh Chen,2 Wan-Jiun Chen,13 Chun-Chi Liu,11 Wing-Kai Chan,3 Wei J. Chen,2,6 Ker-Chau Li,7,17,18 Jeremy J.W. Chen,2,11,18 and Pan-Chyr Yang2,4,8,18,
1 Department of Clinical Laboratory Sciences and Medical Biotechnology, National Taiwan University College of Medicine, Taipei, Taiwan 100, Republic of China
2 NTU Center for Genomic Medicine, National Taiwan University College of Medicine, Taipei, Taiwan 100, Republic of China
3 Department of Medical Research, National Taiwan University Hospital, Taipei, Taiwan 100, Republic of China
4 Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan 100, Republic of China
5 Department of Surgery, National Taiwan University Hospital, Taipei, Taiwan 100, Republic of China
6 Graduate Institute of Epidemiology, National Taiwan University, Taipei, Taiwan 100, Republic of China
7 Institute of Statistical Science, Academia Sinica, Taipei, Taiwan 115, Republic of China
8 Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan 115, Republic of China
9 Division of Chest Medicine, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan 407, Republic of China
10 Division of Thoracic Surgery, Department of Surgery, Taichung Veterans General Hospital, Taichung, Taiwan 407, Republic of China
11 Institutes of Biomedical Sciences and Molecular Biology, National Chung Hsing University, Taichung, Taiwan 402, Republic of China
12 Division of Thoracic Surgery, Department of Surgery, School of Medicine, China Medical University Hospital, Taichung, Taiwan 404, Republic of China
13 Institute of Pharmacology, College of Medicine, National Yang-Ming University, Taipei, Taiwan 112, Republic of China
14 Department of Life Science, National Taiwan Normal University, Taipei, Taiwan 116, Republic of China
15 Department of Colon & Rectal Surgery, Hualien Tzu Chi Medical Center, Hualien, Taiwan 970, Republic of China
16 Department of Surgery, Hualien Tzu Chi Medical Center, Hualien, Taiwan 970, Republic of China
17 Department of Statistics, University of California, Los Angeles, Los Angeles, California, CA 90095, USA
Corresponding author
Pan-Chyr Yang
[email protected]
Summary
We investigated whether microRNA expression profiles can predict clinical outcome of NSCLC patients. Using real-time RT-PCR, we obtained microRNA expressions in 112 NSCLC patients, which were divided into the training and testing sets. Using Cox regression and risk-score analysis, we identified a five-microRNA signature for the prediction of treatment outcome of NSCLC in the training set. This microRNA signature was validated by the testing set and an independent cohort. Patients with high-risk scores in their microRNA signatures had poor overall and disease-free survivals compared to the low-risk-score patients. This microRNA signature is an independent predictor of the cancer relapse and survival of NSCLC patients.
