摘要
目的 筛选与SUMOylation过程密切相关的基因,并构建结直肠癌的预后基因表达模型.方法 从癌症基因组图谱(The Cancer Genome Atlas,TCGA)数据库中下载与结直肠癌相关的转录组数据和临床资料,结合基因集富集分析(Gene Set Enrichment Analysis,GSEA)数据库,筛选SUMOylation过程中与结直肠癌预后相关的基因.采用单因素COX回归和逐步回归进一步筛选关键基因,构建预后模型,并且计算模型风险评分,即SUMO-score.通过校准曲线和临床决策曲线评价该模型的可行性和准确性.结果 筛选获得SUMOylation过程中与结直肠癌预后相关的 18 个基因,并根据基因签名的风险评分将患者分为高风险组和低风险组.结果 显示,高风险组患者的预后显著差于低风险组(P<0.001).多因素分析表明,SUMO-score可作为结直肠癌的独立预测因素.将SUMO-score与临床病理特征相结合,构建列线图预测模型.校准曲线显示,该模型具有良好的拟合度,提示其具有较高的预测能力.结论 本研究构建了一个基于SUMOylation过程相关基因的风险模型,可用于预测结直肠癌预后.
Abstract
Objective To screen genes closely related to the SUMOylation process and to con-struct a prognostic prediction model for colorectal cancer(CRC).Methods Transcriptomic data and clinical information related to colorectal cancer were downloaded from The Cancer Ge-nome Atlas(TCGA)database and combined with the Gene Set Enrichment Analysis(GSEA)database to screen for genes in the SUMOylation process that are associated with CRC progno-sis.The prognostic model was developed through a rigorous process involving univariate COX regression and stepwise regression to identify the pivotal genes.Subsequently,a risk score termed SUMO-score was computed.The model's feasibility and accuracy were meticulously as-sessed using calibration curves and clinical decision cur.Results Eighteen genes in the SU-MOylation process associated with the prognosis of colorectal cancer were screened,and pa-tients were divided into high-risk and low-risk groups based on the risk score of the gene signa-ture.The results showed that the prognosis of patients in the high-risk group was significantly worse than that of the low-risk group(P<0.001).Multifactorial analysis showed that SUMO-score could be used as an independent predictor of colorectal cancer.Ultimately,SUMO-score was combined with clinicopathological features to construct a nomogram prediction model.The calibration curve showed that the model had a good fit,suggesting a high predictive ability.Conclusion A risk model based on genes related to the SUMOylation process is constructed for predicting colorectal cancer prognosis.
NETL
NSTL
万方数据