Identification of tumor immune microenvironment of clear cell renal cell carcinoma and construction of prognostic model
Objective To screen immune-related prognostic genes of clear cell renal cell carcinoma(ccRCC)by bioinformat-ics methods and to explore the infiltration degree of each various cells in the tumor immune microenvironment,and to as-sess the prognostic value of immune related prognostic genes.Methods Totally 532 ccRCC patients with complete gene data in the cancer genome atlas(TCGA)database were selected,and immune-related genes were screened according to differential gene expression analysis.Least absolute shrinkage and selection operator(Lasso)-Cox analysis was used to screen immune-related prognostic genes,and a prognostic risk score formula was established.Consistent clustering was used to divide 532 ccRCC patients from the TCGA into different immune subgroups,and the survival status and immune microenvironmental differences between these subgroups were compared.Immune-related genes were identified according to immune scores and matrix scores.We further applied consensus clustering to explore immunological subgroup among included patients and difference in survival and tumor immune microenvironment between different immunological sub-groups.Finally,1-year,3-year and 5-year prognostic models were constructed based on prognostic risk score,age and clinical staging,and the receiver operating characteristic(ROC)curve was drawn to evaluate the predictive ability of the risk model.Results A total of 1 276 immune-related genes were screened by differential analysis,and the subjects were clustered based on immune-related genes,including 281 subjects in immunological subgroup one and 251 subjects in im-munological subgroup two.Patients in immunological subgroup two had significantly better prognosis than patients in im-munological subgroup one.We also found significant difference in the immune infiltrating cells and immune checkpoints between two immunological subgroups(all P＜0.05).Nine immune-related prognostic genes were identified using LAS-SO-Cox analysis to construct the prognostic risk score,including IGHV1.3,IGHV1.69D,MANCR,LINC00460,LINC00942,NMB,CPA4,TMEM158 and PI3K.Multivariate Cox regression analysis showed that risk score(HR=9.564,95％CI:3.439-26.600,P＜0.001),clinical stage(HR=1.788,95％CI:1.562-2.047,P＜0.001),age(HR=1.032,95％CI:1.017-1.047,P＜0.001),and they allwere associated with prognosis.ROC analysis showed prognostic risk score had excellent prediction performance,with area under curve(AUC)=0.866(95％CI:0.823-0.908),AUC=0.799(95％CI:0.752-0.847),AUC=0.765(95％CI:0.709-0.821)for 1-year,3-year and 5-year prognostic models,respectively.Conclusions We divided renal clear cell carcinoma patients into two immunological sub-groups,and further identified nine9 immune-related prognostic genes.The prognostic risk score could act as an independ-ent predictor to predict the prognosis of ccRCC patients and guide clinical treatment.
clear cell renal cell carcinomatumor immune microenvironmentprognostic risk scoreprognostic model
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