Developing and experimentally validating a glucocorticoid signaling-related gene signature to evaluate the prognosis and immunotherapeutic response in kidney renal clear cell carcinoma - Yu Zhang; Chen Chen; Tianhang Zhu; Wei Luo; Ranran Zhou; Wanlong Tan (PLoS ONE, 2025)
Glucocorticoids play a pivotal role in tumorigenesis and cancer progression. However, the prognostic significance of glucocorticoid signaling-related genes remains poorly understood, particularly in kidney renal clear cell carcinoma (KIRC). Collected samples indicated KIRC patients exhibited elevated serum glucocorticoid levels compared to healthy donors (P < 0.05). Glucocorticoid signaling-related genes were curated from the MSigDB database. The TCGA-KIRC cohort was utilized for training, while 7 independent public KIRC cohorts and local samples were employed for validation. Through LASSO and random forest analyses, ACADM, ANGPTL4, and NFKB2 were identified and subsequently incorporated into a multivariate Cox regression model. This gene signature emerged as a robust prognostic indicator across multiple cohorts (pooled hazard ratio [HR] = 2.73, 95% confidence interval [CI] = 2.05–3.65). In local samples, KIRC tissues exhibited increased infiltration of NFKB2+ cells and decreased levels of ACADM+ and ANGPTL4+ cells (all P < 0.05). Meta-analyses and spatial transcriptomics revealed a positive association between the signature and CD8+ T cell infiltration. Furthermore, the signature was associated with T cell exhaustion levels and could predict immunotherapeutic responses in both computational simulations and real-world clinical settings (all P < 0.05). In vivo experiments showed that NFKB2 knockdown inhibited tumor growth and the expansion of CD8+PDCD1+ T cells, effects that were reversible with corticosterone treatment (all P < 0.05). Collectively, a glucocorticoid signaling-related gene signature was developed and rigorously validated as a predictive tool for prognosis and immunotherapeutic response in KIRC, offering valuable insights for guiding personalized treatment strategies.