Identification of a novel anoikis-related gene signature and molecular subtype to predict prognosis and tumor microenvironment in clear cell renal cell carcinoma

Abstract

Abstract Background Anoikis, an alternative form of programmed cell death, plays a pivotal role in cancer invasion and metastasis, preventing the detached cancer cells from readhering to other substrates for abnormal proliferation. However, the mechanism of anoikis in clear cell renal cell carcinoma (ccRCC) remains unknown. Methods ARGs(anoikis-related gene) were selected from The Cancer Genome Atlas (TCGA) database and Genecards dataset using differential expression analysis. We used an unsupervised consensus clustering algorithm to classify ccRCC patients. Gene set enrichment analysis (GSVA) and single sample gene set enrichment analysis (ssGSEA) were utilized to investigate the molecular mechanism of patients in the different subgroup. The signature incorporating ARGs was identified using univariate Cox regression analysis and LASSO regression analysis. Furthermore, a nomogram containing the signature and clinical information was developed through univariate and multivariate Cox regression analysis. Kaplan– Meier survival analysis and receiver operating characteristic (ROC) curves were applied to evaluate the predictive validity of these risk models. Finally, CIBERSOT, ESTIMATE and drug sensitivity analysis were also conducted. Results Our results showed that the TCGA cohorts could be divided into three subgroups which we named Group A, Group B and Group C, with a remarkable difference in immune infiltration landscape and prognosis. A fresh risk model was constructed based on the 5 prognostic ARGs (BIRC5, EDA2R, PLG, OCLN and SLPI). Kaplan-Meier survival analysis showed that the overall surviva(OS) rate of patients with low risk score was significantly higher than that of patients with high risk score. Moreover, the prognostic risk model effectively predicted OS, which was validated using train datasets. The nomogram results illustrated that the prognostic risk model was an independent prognostic predictor that distinguished it from other clinical characteristics. The CIBERSORT and ESTIMATE results illustrated a significant gap in immune infiltration landscape of patients in the low- and high-risk group. TIDE score showed a more promising immunotherapy response of ccRCC patients in low risk groups. Our drug sensitivity analysis data showed significant differences in sensitivity to different chemotherapy agents by risk group. Conclusion In this study, we identified anoikis-related subgroups and prognostic genes in ccRCC and integrated multiple ARGs to establish a risk-predictive model, which could be significant for understanding the molecular mechanisms and treatment of ccRCC.