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

## TL;DR

This study identifies a T cell-specific PANoptosis signature in prostate cancer that predicts prognosis and immunotherapy response, offering a new tool for personalized treatment.

## Contribution

A novel T cell-specific PANoptosis signature (TSPS) was developed using single-cell RNA sequencing and machine learning to predict prognosis and immunotherapy outcomes in prostate cancer.

## Key findings

- A TSPS comprising nine key genes was developed with superior predictive accuracy for clinical outcomes.
- High-risk patients showed increased immune infiltration and reduced immunotherapy benefits.
- Eighteen compounds were identified as potential therapies for high-risk prostate cancer patients.

## Abstract

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.

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.

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.

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.