摘要
目的筛选结肠癌(CC)关键预后相关免疫基因(KIGs),构建免疫相关预后模型。方法从癌症基因组图谱(TCGA)数据库中下载CC患者的基因表达数据和临床数据,基于免疫分型筛选差异表达基因(DEGs),将DEGs与ImmPort数据库中免疫基因取交集得到差异表达免疫基因(DEIGs)。通过单因素Cox回归分析、Lasso-Cox回归分析筛选出KIGs,建立免疫相关预后模型,并验证预后模型的预测性能。分析风险评分与临床特征、免疫浸润细胞、肿瘤突变负荷、微卫星不稳定性的相关性。应用单因素和多因素Cox回归分析筛选CC的独立预后因素,进一步构建用于预测CC患者总生存率(OS)的列线图。结果基于免疫分型筛选出1439个DEGs,DEGs与ImmPort数据库免疫基因取交集得到379个DEIGs,通过单因素Cox回归分析、Lasso-Cox回归分析筛选出12个KIGs,构建免疫相关预后模型。高风险评分(高风险组)患者和低风险评分(低风险组)患者的OS差异有统计学意义(P<0.05);预后模型预测1、3、5年OS的受试者工作特征曲线的曲线下面积(AUC)分别为0.735、0.725、0.699,表明预后模型的预测效果较好。不同疾病分期、不同TNM分期患者的风险评分比较,差异有统计学意义(P<0.05);KIGs与众多免疫浸润细胞均存在相关性(P<0.05);高风险组与低风险组的肿瘤突变负荷比较,差异有统计学意义(P<0.05);高风险组的微卫星不稳定性比低风险组更强。单因素和多因素Cox回归分析发现,风险评分是CC的独立预后因素(P<0.001),进一步构建的列线图对CC患者生存状态具有良好的预测能力和准确性。结论本研究构建了基于12个KIGs的免疫相关预后模型,并建立了可用于预测CC患者OS的列线图,有助于医生做出个体化治疗决策。
Objective To screen key prognosis-related immune genes(KIGs)in colon cancer(CC)and construct immune-related prognostic model.Methods The gene expression data and clinical data of CC patients were downloaded from the Cancer Genome Atlas(TCGA),differential expressed genes(DEGs)were screened based on immune typing,and the intersection of DEGs and immune genes of ImmPort database was used to obtain differentially expressed immune genes(DEIGs).Univariate Cox analysis and Lasso-Cox analysis were used to screen KIGs to establish immune-related prognostic models.Next,the predictive performance of the prognostic model was verified.The correlations of risk score with clinical features,immune infiltrating cells,tumor mutation burden,and microsatellite instability were analyzed.Independent prognostic factors of CC were screened by univariate and multivariate Cox analysis,and a nomogram related to overall survival rate(OS)was constructed to predict the overall survival rate of CC patients.Results A total of 1,439 DEGs were screened out based on immune typing,and 379 DEIGs were obtained by the intersection of DEGs and immune genes of ImmPort database.A total of 12 KIGs were screened out by univariate Cox analysis and Lasso-Cox analysis,and an immune-related prognostic model was constructed.There was a significant difference in OS between high-risk score patients(high-risk group)and low-risk score patients(low-risk group)(P<0.05).The area under the receiver operating characteristic curve(AUC)for 1-,3-and 5-year were 0.735,0.725 and 0.699,respectively,which showed that the model had a good predictive effect.There were significant differences in risk scores of patients with different disease stages and different TNM stages(P<0.05);KIGs was correlated with numerous immune infiltrating cells(P<0.05);micro-satellite instability was greater in the high-risk group than in the low-risk group.Univariate and multivariate Cox regression analysis showed that risk score was an independent prognostic factor for CC(P<0.001),and the further constructed nomogram had good predictive power and accuracy for the survival status of CC patients.Conclusion An immune-related prognostic model based on 12 KIGs is constructed in the study,and a nomogram is developed to predict overall survival in CC patients,which could help physicians make individualized treatment decisions.
作者
齐荣暄
刘格良
穆俊芳
李晨龙
李渊
贺培凤
于琦
QI Rongxuan;LIU Geliang;MU Junfang;LI Chenlong;LI Yuan;HE Peifeng;YU Qi(School of Management,Shanxi Medical University,Taiyuan,Shanxi,030001;Shanxi Key Laboratory of Big Data for Clinical Decision Research,Taiyuan,Shanxi,030001;School of Basic Medical Sciences,Shanxi Medical University,Taiyuan,Shanxi,030001)
出处
《实用临床医药杂志》
2023年第14期63-71,77,共10页
Journal of Clinical Medicine in Practice
基金
山西省重点研发计划项目(202102130501003,201903D311011)
山西省回国留学人员科研资助项目(HGKY2019057)
山西医科大学省级博士基金项目(SD2229)
山西省研究生教育创新项目(2022Y426)。
关键词
结肠癌
癌症基因组图谱
转录组
免疫基因
肿瘤微环境
预后模型
colon cancer
cancer genome atlas
transcriptome
immune genes
tumor microenvironment
prognosis model
