近日,，國(guó)際著名雜志《臨床腫瘤學(xué)雜志》Journal of Clinical Oncology刊登了來自中山大學(xué)腫瘤防治中心,、中國(guó)科學(xué)院,、香港中文大學(xué)、斯坦福大學(xué)醫(yī)學(xué)院新加坡國(guó)立綜合醫(yī)院等機(jī)構(gòu)的研究人員的最新研究成果“Eight-signature classifier for prediction of nasopharnyngeal carcinoma survival.。”,，研究者合作，通過對(duì)大樣本量鼻咽癌回顧性分析研究,，發(fā)現(xiàn)了7個(gè)鼻咽癌相關(guān)基因,。
領(lǐng)導(dǎo)這一研究的是中山大學(xué)腫瘤防治中心的邵建永教授，其早年畢業(yè)于安徽省蚌埠醫(yī)學(xué)院,，后于中山大學(xué)獲取醫(yī)學(xué)博士學(xué)位。曾留學(xué)瑞典卡羅林斯卡醫(yī)學(xué)院從事腫瘤分子生物學(xué)研究,。臨床主攻腫瘤病理診斷,，專長(zhǎng)鼻咽癌和肝癌的分子生物學(xué)研究。曾獲得國(guó)家自然科學(xué)獎(jiǎng)二等獎(jiǎng),、中華醫(yī)學(xué)科技獎(jiǎng)一等獎(jiǎng)等獎(jiǎng)項(xiàng),。已在權(quán)威的專業(yè)雜志如Int. J Oncology, Cancer，Cancer Biology & Therapy等刊物發(fā)表論文近60篇,。
鼻咽癌是一種發(fā)生于鼻咽粘膜的惡性腫瘤,。其惡性程度較高，且具有極高的癌細(xì)胞轉(zhuǎn)移率,?！∥覈?guó)是鼻咽癌發(fā)病率最高的國(guó)家，而廣東,、廣西,、海南等地都是高發(fā)區(qū)，發(fā)病率比其他大部分國(guó)家,、地區(qū)高100倍以上,，因此鼻咽癌有“廣東癌”之稱。
自2005年開始,，邵建永課題組,，采用免疫組織化學(xué)染色技術(shù)，對(duì)來自廣東,、廣西,、福建、香港和新加坡等地區(qū)的1268個(gè)鼻咽癌腫瘤組織標(biāo)本進(jìn)行研究,，在18個(gè)前期研究或文獻(xiàn)報(bào)道過的與鼻咽癌病因,、浸潤(rùn)和轉(zhuǎn)移、腫瘤血管生成等相關(guān)基因中,，篩選出EB病毒潛伏膜蛋白1等7個(gè)與鼻咽癌病人生存預(yù)后最為密切的基因,，結(jié)合鼻咽癌患者的性別參數(shù)，應(yīng)用生物信息學(xué)方法,，建立數(shù)學(xué)預(yù)測(cè)模型,，篩選出431名高?；颊撸渌麣w為低危組,。研究人員臨床5年隨訪追蹤發(fā)現(xiàn),，兩組患者的生存狀況存在顯著差異，被歸類為低危組的鼻咽癌患者5年生存率達(dá)到87%,，而高危組鼻咽癌患者5年生存率僅為37.7%,。
新研究確定的7個(gè)鼻咽癌相關(guān)基因不僅能夠幫助從普通患者中檢測(cè)出高危鼻咽癌患者，還可以預(yù)測(cè)鼻咽癌患者復(fù)發(fā)風(fēng)險(xiǎn)和生存預(yù)后,，指導(dǎo)臨床實(shí)施更有效的治療方案,。邵建永教授表示接下來將進(jìn)一步開展前瞻性臨床研究進(jìn)一步確認(rèn)其對(duì)鼻咽癌病人個(gè)體化治療的臨床應(yīng)用價(jià)值。（生物谷Bioon.com）
doi:10.1200/JCO.2010.33.7741
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Eight-signature classifier for prediction of nasopharnyngeal carcinoma survival.
Hu CF,Zhang JX,Chen FL,et al.
Purpose Currently, nasopharyngeal carcinoma (NPC) prognosis evaluation is based primarily on the TNM staging system. This study aims to identify prognostic markers for NPC. Patients and Methods We detected expression of 18 biomarkers by immunohistochemistry in NPC tumors from 209 patients and evaluated the association between gene expression level and disease-specific survival (DSS). We used support vector machine (SVM) –based methods to develop a prognostic classifier for NPC (NPC-SVM classifier). Further validation of the NPC-SVM classifier was performed in an independent cohort of 1,059 patients. Results The NPC-SVM classifier integrated patient sex and the protein expression level of seven genes, including Epstein-Barr virus latency membrane protein 1, CD147, caveolin-1, phospho-P70S6 kinase, matrix metalloproteinase 11, survivin, and secreted protein acidic and rich in cysteine. The NPC-SVM classifier distinguished patients with NPC into low- and high-risk groups with significant differences in 5-year DSS in the evaluated patients (87% v 37.7%; P < .001) in the validation cohort. In multivariate analysis adjusted for age, TNM stage, and histologic subtype, the NPC-SVM classifier was an independent predictor of 5-year DSS in the evaluated patients (hazard ratio, 4.9; 95% CI, 3.0 to 7.9) in the validation cohort. Conclusion As a powerful predictor of 5-year DSS among patients with NPC, the newly developed NPC-SVM classifier based on tumor-associated biomarkers will facilitate patient counseling and individualize management of patients with NPC.
