Prognostic implications of ERLncRNAs in ccRCC: a novel risk score model and its association with tumor mutation burden and immune microenvironment.

Abstract

Introduction/background: The specific role of efferocytosis-related long noncoding RNAs (ERLncRNAs) in Clear Cell Renal Cell Carcinoma (ccRCC) has not been thoroughly examined. This study aims to identify and validate a signature of ERLncRNAs for prognostic prediction and characterization of the immune landscape in individuals with ccRCC.

Materials and methods: Analysis of ccRCC samples was conducted by utilizing clinical and RNA sequencing information obtained from The Cancer Genome Atlas (TCGA). Pearson correlation analysis was utilized to identify lncRNAs associated with efferocytosis, which was then used to create a new prognostic model through univariate Cox regression, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and stepwise multivariate Cox analysis. In order to investigate the biological significance, we performed a functional enrichment analysis to assess how well the model predicts outcomes. Differences in the immune landscape were observed through a comparison of immune cell infiltration, tumor mutational burden (TMB), and tumor microenvironment (TME) characteristics. Following this, drug sensitivity analysis was conducted.

Results: This led to the identification of a unique signature consisting of seven ERLncRNAs (LINC01615, RUNX3-AS1, FOXD2-AS1, AC002070.1, LINC02747, LINC00944, and AC092296.1). Model performance was measured by Kaplan-Meier curves and receiver operating characteristic (ROC) curves. The nomogram and C-index provided additional validation of the strong correlation between the risk signature and clinical decision-making.

Conclusion: On the whole, our innovative signature exhibits potential for prognostic prediction and assessment of immunotherapeutic response in patients with ccRCC.