Identification of molecular subtypes and prognostic risk model of glucocorticoid-related lncRNAs in bladder cancer to evaluate prognosis and immunological characteristics.

BACKGROUND

Bladder cancer (BC) heterogeneity presents significant challenges in prognosis and personalized therapy. Long non-coding RNAs (lncRNAs) have been increasingly considered to be critical regulatory elements in tumor biology, and glucocorticoids exert complex effects on tumor biology. This investigation aimed to recognize glucocorticoid-related lncRNAs (GR-lncRNAs) and construct a prognostic signature for BC, elucidating their roles in subtype identification, prognosis, and immune characteristics.

METHODS

We retrieved messenger RNA (mRNA) expression profiles, mutation information, and clinical data for BC from The Cancer Genome Atlas (TCGA) repository. Glucocorticoid-associated genes were identified from GeneCards. Differentially expressed GR-lncRNAs were screened employing limma and Spearman correlation. We utilized univariate Cox regression, the least absolute shrinkage and selection operator (LASSO) method, and multivariate Cox regression analyses to generate the prognostic signature. We performed immune infiltration analysis [single-sample gene set enrichment analysis (ssGSEA), CIBERSORT, ESTIMATE, immunophenoscore (IPS), and Tumor Immune Dysfunction and Exclusion (TIDE)], enrichment analysis [gene set enrichment analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG)], tumor mutational burden (TMB) assessment, and drug sensitivity prediction. Furthermore, molecular subtypes relying on GR-lncRNAs were classified through non-negative matrix factorization (NMF) clustering.

RESULTS

Seven GR-lncRNAs were identified to establish a prognostic framework with strong predictive capability. Patients with high-risk status had significantly unfavorable survival prognoses, higher immune checkpoint (ICP) expression, and suppressed immune infiltration. Functional enrichment analysis revealed distinct biological processes (BPs) and pathways between risk groups. Two molecular subtypes based on these lncRNAs displayed divergent survival patterns and immune profiles, indicating potential therapeutic implications.

CONCLUSIONS

Our study presents a novel GR-lncRNA-based prognostic model and molecular subtypes for BC, providing valuable insights into disease heterogeneity and offering potential biomarkers for improved prognostic assessment and personalized therapeutic strategies.