一种基于13个基因的结直肠癌预后风险预测模型的开发和验证

Development and Validation of a 13-gene-based Prognostic Risk Prediction Model for Colorectal Cancer

结直肠癌(colorectal cancer,CRC)是全球范围内最常见的消化道恶性肿瘤之一,鉴于基因的异常表达在CRC发病机制中的关键作用,本研究旨在开发一种多基因预后风险预测模型以对CRC患者的生存结果进行分层和预测。在3个独立的转录组数据集中鉴定人类CRC肿瘤组织样本和正常结直肠组织样本之间的显著差异表达基因(differentially expressed genes,DEGs);基于219个重叠的DEGs,通过单因素Cox回归分析鉴定出31个具有预后价值的基因;采用LASSO Cox回归分析构建一个由13个基因组成的预后模型,其预后预测效果通过KM(Kaplan-Meier)生存分析和时间依赖性ROC分析在验证集中得到验证。多因素Cox回归分析表明,基于该模型的风险评分可以作为CRC患者生存结局[包括总生存期(overall survival,OS)、无病生存期(disease free survival,DFS)和疾病特异性生存期(disease special survival,DSS)]的独立预测因子。功能分析表明,该模型与CRC患者的免疫状态和化疗反应密切相关。此外,联合基于该模型的风险评分和常见临床病理因素构建的列线图对CRC患者的预后也具有较强的预测能力。本研究构建的基于13个基因的预后模型有望为CRC患者风险分层、生存预测和治疗评估提供有力的工具。

Colorectal cancer(CRC)is one of the most prevalent gastrointestinal cancer worldwide.Given the critical role of abnormal gene expression in CRC pathogenesis,the study aims develop a multi-gene prognostic risk prediction model that can stratify and predict survival outcome for patients with CRC.Differentially expressed genes(DEGs)were identified between human CRC tumor tissue samples and normal colorectal samples in three independent transcriptome datasets.Based on the 219 overlapping DEGs,31 genes with prognostic value were identified by univariate Cox regression analysis.A 13-gene prognostic model was constructed by LASSO Cox regression analysis,and its predictive performance was validated by KM(Kaplan-Meier)survival analysis and time-dependent ROC analysis in the validation sets.Multivariate Cox regression analysis indicated that the model could serve as an independent predictor of survival outcomes,including overall survival(OS),disease free survival(DFS)and disease special survival(DSS).Functional analysis demonstrated that the prognostic model was strongly connected with immune status and chemotherapeutic response of CRC patients.Besides,the nomogram based on the risk score of the model and common clinicopathological factors also exhibited powerful predictive performance.The 13-gene prognostic model constructed in this study is expected to provide a powerful tool for risk stratification,survival prediction and treatment evaluation of CRC patients.