Integrated Analysis of Single-Cell RNA Sequencing and Machine Learning Reveals a T Cell-Specific PANoptosis Signature Predicting Prognosis and Immunotherapy in Prostate Cancer.

BACKGROUND

Prostate cancer (PCa) ranks among the most prevalent malignancies, with prognosis heavily influenced by diagnostic stage. The role of PANoptosis in T cell-based immunotherapy has garnered growing attention recently. This study is aimed at establishing a T cell-specific PANoptosis signature (TSPS) to predict prognosis and immunotherapy response in patients with PCa.

METHODS

Single-cell RNA sequencing (scRNA-seq) data from the GSE185344 dataset were used to identify T cell-specific genes. A comprehensive machine learning pipeline incorporating 10 distinct algorithms was employed to construct a consensus prognostic TSPS.

RESULTS

The scRNA-seq analysis identified T cells as the predominant cell type, and cell-cell communication analysis indicated heightened activation of specific immune-related signaling pathways in PCa. A consensus prognostic signature comprising nine key genes was developed, demonstrating superior predictive accuracy for clinical outcomes compared to conventional clinical variables. A TSPS-based nomogram was also constructed, displaying strong predictive capability for survival outcomes in patients with PCa. Patients in the high-risk group exhibited greater intratumor heterogeneity, increased immune infiltration, and higher immunosuppression scores, suggesting reduced immunotherapy benefits. Validation with four independent immunotherapy cohorts verified that patients in the low-risk group exhibited more favorable immunotherapy responses. Additionally, 18 compounds were determined as therapeutic options for high-risk patients with PCa. In vitro experiments demonstrated that UBB expression was upregulated in PCa, and UBB knockdown significantly inhibited PCa cell proliferation and invasion.

CONCLUSION

We established a consensus prognostic TSPS for PCa, offering a potential foundation for future personalized approaches in risk stratification, prognostic evaluation, and treatment selection for patients with PCa.