基于失巢凋亡基因构建直肠癌预后预测模型

Construction of a prognostic model for rectal cancer based on anoikis related genes

目的分析失巢凋亡相关基因对直肠癌患者预后的影响。方法从TCGA数据库获得直肠癌患者的基因表达数据,通过最小绝对收缩和选择算法(least absolute shrinkage and selection operator,LASSO)构建直肠癌-失巢凋亡基因预后预测模型。使用Kaplan-Meier生存曲线,受试者工作特征(receiver operating characteristic,ROC)曲线,单因素和多因素分析等方法对预后预测模型进行评估。通过蛋白相互作用网络筛选与预后模型相关的基因,并进行富集分析。结果6个失巢凋亡基因:酪蛋白激酶2 Alpha 1(casein kinase 2-α1,CSNK2A1)、抑制素Bβ亚基(inhibin subunit beta B,INHBB)、p21活化激酶1(p21-activated kinase 1,PAK1)、CD63分子(CD63 molecule,CD63)、细胞间黏附分子β-1(catenin beta-1,CTNNB1)、促凝集素蛋白(clusterin,CLU)被确定为直肠癌预后相关基因。生存分析结果显示,低风险组预后较高风险组好,且差异有统计学意义(P<0.05),ROC曲线显示预后模型曲线下面积(area under the curve,AUC)在1年内为0.84,在3年内为0.86,在5年内为0.92。富集分析提示预后模型相关基因功能与肿瘤免疫相关。结论通过多种生物信息学方法,构建了基于6个失巢凋亡相关基因的直肠癌预后预测模型,预后模型的风险评分可以作为影响直肠癌预后的独立因素,可能为直肠癌患者的个体化治疗和评估提供参考。

Objective Analyzing the impact of anoikis related genes on the prognosis of rectal cancer.Methods Gene expression data of rectal cancer patients was obtained from the TCGA database.A prognostic model for rectal cancer based on anoikis related genes was constructed using the Least Absolute Shrinkage and Selection Operator(LASSO)algorithm.The prognostic model was evaluated using methods such as Kaplan-Meier survival curves,receiver operating characteristic(ROC)curves,univariate and multivariate analyses.Protein-protein interaction networks were utilized to identify genes associated with the prognostic model,and enrichment analysis was performed to gain insights into their functional relevance.Results Six anoikis related genes,including casein kinase 2 alpha 1(CSNK2A1),inhibin subunit beta B(INHBB),p21-activated kinase 1(PAK1),CD63 molecule(CD63),catenin beta-1(CTNNB1),and clusterin(CLU),were identified as prognostic genes in rectal cancer.Survival analysis revealed that the low-risk group had better prognosis compared to the high-risk group,and the difference was statistically significant(P<0.05).The ROC curve analysis demonstrated that the area under the curve(AUC)for the prognostic model was 0.84 at 1 year,0.86 at 3 years,and 0.92 at 5 years.Enrichment analysis indicated that the prognostic genes in the model were functionally associated with tumor immunity.Conclusion A prognostic model for rectal cancer was constructed using bioinformatics methods,incorporating six anoikis related genes.The risk score derived from the prognostic model serves as an independent factor influencing the prognosis of rectal cancer.This model has the potential to provide valuable insights for personalized treatment and evaluation of rectal cancer patients.