# The Role of PANoptosis-Related Genes in Predicting Breast Cancer Survival and Immune Prospect

## TL;DR

This study explores how PANoptosis-related genes can predict breast cancer survival and immune response, helping identify high-risk patients for better treatment.

## Contribution

A novel nomogram model using PANoptosis-related genes to predict breast cancer prognosis and immune characteristics is developed.

## Key findings

- High-risk patients based on PANoptosis-related scores had worse overall survival.
- Differentially expressed genes were linked to immune system pathways and immune cell differences.
- Key genes like KLHDC7B and GNG8 were identified as important for prognosis and immune response.

## Abstract

Background: The function of PANoptosis in breast cancer (BC) remains indistinct. We constructed a nomogram model to predict the prognosis of BC to identify high-risk patients and help them receive more accurate treatment.

Method: We used Cox regression and least absolute shrinkage and selection operator (LASSO) algorithm to select PANoptosis-related genes (PRGs) and calculated the PANoptosis-related score (PRS) by LASSO coefficient. Through functional enrichment, somatic mutation, and tumor microenvironment (TME) analysis, we completed the identification of PANoptosis-related immune cells and difference analysis of drug sensitivity and then verified key genes by performing survival analysis.

Results: Patients were divided into low- and high-risk cohorts depending on PRS, and the negative association between risk scores and overall survival was disclosed. Analysis showed that differentially expressed genes in the two risk cohorts were mainly concentrated among pathways related to the immune system. Moreover, we detected distinguished differences in immune checkpoints, tumor mutation load, and TME in the two cohorts. Furthermore, KLHDC7B, GNG8, IGKV1OR2-108, and IGHD were identified as key genes. We also found that hub genes were highly expressed in tumor tissues, while B cells, CD4+, and CD8+ T cells pretended to be positive among the hub gene–negative cohort. Prognosis analysis showed that pivotal genes had adverse effects on survival over time.

Conclusion: We built a precise prediction model based on risk scores and proved the significance of PRGs in BC TME and medicine sensitivity regulation, providing key perception for subsequent molecular mechanism studies and contributing to more personalized treatment decisions in clinical practice.