Characterization of prognostic signature related with twelve types of programmed cell death in lung squamous cell carcinoma.

OBJECTIVE

This study aimed to develop a prognostic cell death index (CDI) based on the expression of genes related with various types of programmed cell death (PCD), and to assess its clinical relevance in lung squamous cell carcinoma (LUSC).

METHODS

PCD-related genes were gathered and analyzed in silico using the transcriptomic data from the LUSC cohorts of The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC). Differentially expressed PCD genes were analyzed, and a prognostic model was subsequently constructed. CDI scores were calculated for each patient, and their correlations with clinical features, survival outcomes, tumor mutation burden, gene clusters, and tumor microenvironment were investigated. Unsupervised consensus clustering was performed based on CDI model genes. Furthermore, the correlation of CDI for sensitivity of targeted drugs, chemotherapy efficacy, and immunotherapy responses was assessed.

RESULTS

Based on 351 differentially expressed PCD genes in LUSC, a CDI signature comprising FGA, GAB2, JUN, and CDKN2A was identified. High CDI scores were significantly associated with poor survival outcomes (p < 0.05). Unsupervised clustering revealed three distinct patient subsets with varying survival rates. CDKN2A exhibited significantly different mutation patterns between patients with high and low CDI scores (p < 0.01). High CDI scores were also linked to increased immune cell infiltration of specific subsets and altered expression of immune-related genes. Patients with high-CDI showed reduced sensitivity to several chemotherapeutic drugs and a higher Tumor Immune Dysfunction and Exclusion (TIDE) score, indicating potential resistance to immunotherapy.

CONCLUSION

The CDI signature based on PCD genes offers valuable prognostic insights into LUSC, reflecting molecular heterogeneity, immune microenvironment associations, and potential therapeutic challenges. The CDI holds potential clinical utility in predicting treatment responses and guiding the selection of appropriate therapies for patients with LUSC. Future studies are warranted to further validate the prognostic value of CDI in combination with clinical factors and to explore its application across diverse patient cohorts.