Qiu Jieping, Sun Mengyu, Wang Yaoqun, Chen Bo
1Department of Clinical Medicine, The First Clinical College, Anhui Medical University, Hefei, China.
2Department of Gastrointestinal Surgery Center, The First Affiliated Hospital of Anhui Medical University, NO. 218 Jixi Road, Hefei, Anhui 230000 China.
Cancer Cell Int. 2020 May 20;20:178. doi: 10.1186/s12935-020-01267-y. eCollection 2020.
The purpose of this study is to perform bioinformatics analysis of autophagy-related genes in gastric cancer, and to construct a multi-gene joint signature for predicting the prognosis of gastric cancer.
GO and KEGG analysis were applied for differentially expressed autophagy-related genes in gastric cancer, and PPI network was constructed in Cytoscape software. In order to optimize the prognosis evaluation system of gastric cancer, we established a prognosis model integrating autophagy-related genes. We used single factor Cox proportional risk regression analysis to screen genes related to prognosis from 204 autophagy-related genes in The Atlas Cancer Genome (TCGA) gastric cancer cohort. Then, the generated genes were applied to the Least Absolute Shrinkage and Selection Operator (LASSO). Finally, the selected genes were further included in the multivariate Cox proportional hazard regression analysis to establish the prognosis model. According to the median risk score, patients were divided into high-risk group and low-risk group, and survival analysis was conducted to evaluate the prognostic value of risk score. Finally, by combining clinic-pathological features and prognostic gene signatures, a nomogram was established to predict individual survival probability.
GO analysis showed that the 28 differently expressed autophagy-related genes was enriched in cell growth, neuron death, and regulation of cell growth. KEGG analysis showed that the 28 differently expressed autophagy-related genes were related to platinum drug resistance, apoptosis and p53 signaling pathway. The risk score was constructed based on 4 genes (GRID2, ATG4D,GABARAPL2, CXCR4), and gastric cancer patients were significantly divided into high-risk and low-risk groups according to overall survival. In multivariate Cox regression analysis, risk score was still an independent prognostic factor (HR = 1.922, 95% CI = 1.573-2.349, P < 0.001). Cumulative curve showed that the survival time of patients with low-risk score was significantly longer than that of patients with high-risk score (P < 0.001). The external data GSE62254 proved that nomograph had a great ability to evaluate the prognosis of individual gastric cancer patients.
This study provides a potential prognostic marker for predicting the prognosis of GC patients and the molecular biology of GC autophagy.
本研究旨在对胃癌中自噬相关基因进行生物信息学分析，并构建一个多基因联合特征用于预测胃癌预后。
对胃癌中差异表达的自噬相关基因进行基因本体（GO）和京都基因与基因组百科全书（KEGG）分析，并在Cytoscape软件中构建蛋白质-蛋白质相互作用（PPI）网络。为优化胃癌预后评估系统，我们建立了一个整合自噬相关基因的预后模型。我们使用单因素Cox比例风险回归分析从癌症基因组图谱（TCGA）胃癌队列中的204个自噬相关基因中筛选与预后相关的基因。然后，将生成的基因应用于最小绝对收缩和选择算子（LASSO）。最后，将选定的基因进一步纳入多因素Cox比例风险回归分析以建立预后模型。根据中位风险评分，将患者分为高风险组和低风险组，并进行生存分析以评估风险评分的预后价值。最后，通过结合临床病理特征和预后基因特征，建立了一个列线图来预测个体生存概率。
GO分析表明，28个差异表达的自噬相关基因富集于细胞生长、神经元死亡和细胞生长调控。KEGG分析表明，28个差异表达的自噬相关基因与铂类药物耐药、凋亡和p53信号通路有关。基于4个基因（GRID2、ATG4D、GABARAPL2、CXCR4）构建了风险评分，根据总生存期将胃癌患者显著分为高风险组和低风险组。在多因素Cox回归分析中，风险评分仍然是一个独立的预后因素（HR = 1.922，95%CI = 1.573 - 2.349，P < 0.001）。累积曲线显示，低风险评分患者的生存时间明显长于高风险评分患者（P < 0.001）。外部数据GSE62254证明列线图具有很强的评估个体胃癌患者预后的能力。
本研究为预测胃癌患者预后和胃癌自噬的分子生物学提供了一个潜在的预后标志物。
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