A depression-related lncRNA signature predicts the clinical outcome and immune characteristics of gastric cancer

Background: Depression and long non-coding RNA (lncRNA) have been linked to tumor progression and prognosis in gastric cancer (GC). This study aims to develop a risk classification and prognosis model for GC based on depression-related lncRNAs (DRLs).

Methods: RNA sequencing data from The Cancer Genome Atlas (TCGA) and depression-related genes (DRGs) from previous studies were used to construct the model. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were employed to identify key DRLs. Various bioinformatics methods were applied, including Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve analysis, nomogram development, pathway enrichment analysis, immune profiling, and drug sensitivity testing.

Results: A prognostic model consisting of seven DRLs was developed and validated in an internal cohort. High-risk scores were associated with shorter overall survival (OS). Both univariate and multivariate analyses confirmed the risk score as an independent prognostic factor. The ROC curve demonstrated that the risk score outperformed conventional clinicopathological features in diagnostic accuracy. The nomogram showed high reliability, as indicated by calibration curve analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed distinct digestive and nervous system pathways between the high- and low-risk groups. Tumor mutational burden (TMB) and tumor immune dysfunction and exclusion (TIDE) analyses suggested that low-risk patients responded better to immunotherapy. Additionally, drug sensitivity analysis indicated that the risk score influenced sensitivity to the drug EHT 1864 in GC.

Conclusion: This study successfully developed and validated a 7-DRL-based prognostic model that can predict outcomes, assess immune characteristics, and support personalized treatment strategies for GC patients.