Rong Li, Huang Wei, Tian Shangkun, Chi Xiangbo, Zhao Pan, Liu Fengfeng
Department of gastroenterology, Chongqing Infectious Disease Medical Center, 1#Huangjuewan, Xiaolongkan, Shapingba District, Chongqing, 400030, People's Republic of China.
Health Center of Southwest University of Political Science and Law, Chongqing, 401120, People's Republic of China.
Pathol Oncol Res. 2018 Jan;24(1):129-134. doi: 10.1007/s12253-017-0223-5. Epub 2017 Apr 11.
Gastric cancer is the third most common cause of cancer-related death in worldwide. It is crucial to target the key genes controlling pathogenesis in the early stage of gastric cancer. This study describes an integrated bioinformatics to identify molecular biomarkers for gastric cancer in patients' cancer tissues. We reports differently expression genes in large gastric cancer cohorts from Gene Expression Ominus (GEO). Our findings revealed that 433 genes were significantly different expressed in human gastric cancer. Differently expression gene profile in gastric cancer was further validated by bioinformatic analyses, co-expression network construction. Based on the co-expression network and top-ranked genes, we identified collagen type I alpha 2 (COL1A2) which encodes the pro-alpha2 chain of type I collagen whose triple helix comprises two alpha1 chains and one alpha2 chain, was the key gene in a 37-gene network that modulates cell motility by interacting with the cytoskeleton. Furthermore, the prognostic role of COL1A2 was determined by use of immunohistochemistry on human gastric cancer tissue. COL1A2 was highly expressed in human gastric cancer as compared with normal gastric tissues. Statistical analysis showed COL1A2 expression level was significantly associated with histological type and lymph node status. However, there were no correlations between COL1A2 expression and age, lymph node numbers, tumor size, or clinical stage. In conclusion, the novel bioinformatics used in this study has led to identification of improving diagnostic biomarkers for human gastric cancer and could benefit further analyses of the key alteration during its progression.
胃癌是全球癌症相关死亡的第三大常见原因。针对胃癌早期控制发病机制的关键基因至关重要。本研究描述了一种综合生物信息学方法,以在患者癌组织中鉴定胃癌的分子生物标志物。我们报告了来自基因表达综合数据库(GEO)的大型胃癌队列中的差异表达基因。我们的研究结果显示,433个基因在人类胃癌中存在显著差异表达。通过生物信息学分析、共表达网络构建进一步验证了胃癌中的差异表达基因谱。基于共表达网络和排名靠前的基因,我们鉴定出编码I型胶原蛋白原α2链的I型胶原蛋白α2(COL1A2),其三重螺旋由两条α1链和一条α2链组成,是一个由37个基因组成的网络中的关键基因,该网络通过与细胞骨架相互作用来调节细胞运动。此外,通过对人类胃癌组织进行免疫组织化学检测确定了COL1A2的预后作用。与正常胃组织相比,COL1A2在人类胃癌中高表达。统计分析表明,COL1A2表达水平与组织学类型和淋巴结状态显著相关。然而,COL1A2表达与年龄、淋巴结数量、肿瘤大小或临床分期之间没有相关性。总之,本研究中使用的新型生物信息学方法已导致鉴定出用于人类胃癌的改进诊断生物标志物,并可能有益于进一步分析其进展过程中的关键改变。