构建SUMOylation相关基因的结直肠癌患者临床预后模型

Construction of a clinical prognostic model for colorectal cancer patients with SUMOylation-related genes

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。

Objective To screen genes closely related to the SUMOylation process and to construct a prognostic prediction model for colorectal cancer(CRC).Methods Transcriptomic data and clinical information related to colorectal cancer were downloaded from The Cancer Genome 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 prognosis.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 assessed using calibration curves and clinical decision cur.Results Eighteen genes in the SUMOylation process associated with the prognosis of colorectal cancer were screened,and patients 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 SUMOscore 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.