Objective: Increasing evidence has indicated an association between immune micro-environment in clear cell renal cell carcinoma (ccRCC) and clinical outcomes. The aim of this research is to comprehensively investigate the effect of tumor immune genes on the prognosis of ccRCC patients.
Methods: 2498 immune genes were downloaded from ImmPort database. Additionally, we identified and downloaded the transcriptome data of patients with ccRCC from the TCGA database through the R package, as well as relevant clinical information. We apply certain survival R package to analyse the survival of hub-genes before analyzing the effect of immune genes on the prognosis of clear cell renal cell carcinoma (ccRCC) utilizing Cox regression analysis. Based on the statistical correlation between hub immune gene and survival ,immune risk score model was set up.We finally constructed a nomogram to predict the survival rate of ccRCC overally. In addition, whether the immune gene risk score model is an independent prognostic factor for ccRCC is comprehensively considered applying multivariate cox regression analysis. It is worth noting that throughout the data analysis, P< 0.05 was recognized to be of significance statistically.
Results: The results of the difference analysis showed that 556 immune genes exhibited differential expression between normal and ccRCC tissues (p<0. 05). Univariate cox regression analysis revealed 43 immune genes statistically correlated with ccRCC related survival risk (P<0.05). In addition, a 18-genes based immune genes risk scoring model was constructed through lasso COX regression analysis. KM curve indicated that patients in high-risk were associated with poor outcomes (p<0.001). ROC curve indicated that the immune risk score model was reliable in predicting survival risk (5-year OS, AUC=0.802). Our model showed satisfying AUC and survival correlation in the validation dataset ( 5-year OS AUC=0.705, P<0.001). Furthermore, multivariate cox regression analysis confirmed that the immune risk score model was an independent factor for predicting the prognosis of ccRCC. A nomogram was established to comprehensively predict the survival of ccRCC patients with the results of multivariate cox regression analysis. Finally, we found that 15 immune genes and risk scores were significantly associated with clinical factors and prognosis, and were involved in multiple oncogenic pathways.
Conclusion: Collectively, tumor immune genes played an essential role in the prognosis of ccRCC. Furthermore, immune risk score was an independent predictive factor of ccRCC, indicating a poor survival.