Identification and verification of a novel anoikis-related gene signature with prognostic significance in clear cell renal cell carcinoma.

Abstract

Clear cell renal cell carcinomas (ccRCCs) are the most common form of renal cancer in the world. The loss of extracellular matrix (ECM) stimulates cell apoptosis, known as anoikis. A resistance to anoikis in cancer cells is believed to contribute to tumor malignancy, particularly metastasis; however, the potential influence of anoikis on the prognosis of ccRCC patients is not fully understood. In this study, anoikis-related genes (ARGs) with discrepant expression were selected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The anoikis-related gene signature (ARS) was built using a combination of the univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses. ARS was also evaluated for their prognostic value. We explored the tumor microenvironment and enrichment pathways between different clusters of ccRCC. We also examined differences in clinical characteristics, immune cell infiltration and drug sensitivity between the high- and low-risk sets. In addition, we utilized three external databases and quantitative real-time polymerase chain reaction (qRT-PCR) to validate the expression and prognosis of ARGs. Eight ARGs (PLAUR, HMCN1, CDKN2A, BID, GLI2, PLG, PRKCQ and IRF6) were identified as anoikis-related prognostic factors. According to Kaplan-Meier (KM) analysis, ccRCC patients with high-risk ARGs have a worse prognosis. The risk score was found to be a significant independent prognostic indicator. According to tumor microenvironment (TME) scores, stromal score, immune score, and estimated score of the high-risk group were superior to those of the low-risk group. There were significant differences between the two groups regarding the amount of infiltrated immune cells, immune checkpoint expression as well as drug sensitivity. A nomogram was constructed using ccRCC clinical features and risk scores. The signature and the nomogram both performed well in predicting overall survival (OS) for ccRCC patients. According to a decision curve analysis (DCA), clinical treatment options for patients with ccRCC could be improved using this model. The results of validation from external databases and qRT-PCR were basically agreement with findings in TCGA and GEO databases. The ARS serving as biomarkers may provide an important reference for individual therapy of ccRCC patients.