Identification and validation of prognostic biomarkers in ccRCC: immune-stromal score and survival prediction.

BACKGROUND

The tumor microenvironment (TME) is integral to tumor progression. However, its prognostic implications and underlying mechanisms in clear cell renal cell carcinoma (ccRCC) are not yet fully elucidated. This study aims to examine the prognostic significance of genes associated with immune-stromal scores and to explore their underlying mechanisms in ccRCC.

METHODS

Data from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) were subjected to analysis to compute immune and stromal scores utilizing the ESTIMATE algorithm. The weighted gene co-expression network analysis (WGCNA) was employed to identify gene modules associated with these scores. Differentially expressed genes were assessed using the limma package. Prognostic biomarkers were subsequently identified through univariate, LASSO, and multivariate Cox regression analyses, culminating in the development of a risk score model. Gene expression was confirmed in ccRCC cell lines (786-O, Caki-1) and tumor tissues. Functional assays, such as wound healing and Transwell assays, were employed to evaluate tumor invasion and migration. The prognostic accuracy was assessed through ROC curve analysis, and a nomogram integrating risk scores with clinical variables was constructed. Analyses of immune infiltration, human leukocyte antigen (HLA) expression, immune checkpoint expression, immunophenoscore (IPS), tumor immune dysfunction and exclusion (TIDE) scores, and responses to six targeted therapies were conducted across different risk groups.

RESULTS

Twelve critical prognostic markers, including CAPRIN1, CXCR3, FERMT3, HAPLN3, HBP1, MACF1, MPEG1, OSCAR, STAT1, UBA7, VAMP1, and VSIG4, were identified. The risk score model exhibited a high degree of predictive accuracy for survival outcomes in ccRCC. Immune profiling revealed significant differences in the TME between risk groups, with high-risk patients displaying elevated expression of HLA and immune checkpoints. Drug sensitivity analyses suggested that high-risk patients had a better response to erlotinib, temsirolimus, axitinib, and sunitinib, whereas low-risk patients demonstrated greater sensitivity to pazopanib. Variability in immunotherapy responsiveness between groups was observed based on IPS and TIDE analyses.

CONCLUSION

This study highlights the prognostic value and TME-related mechanisms of immune-stromal score signatures in ccRCC, developing a risk score model and nomogram for predicting patient prognosis.