Construction of a novel five programmed cell death-related gene signature as a promising prognostic model for triple negative breast cancer

TL;DR

This study identifies a new five-gene model to predict outcomes in triple negative breast cancer patients, offering potential for better clinical management.

Contribution

A novel five-gene signature based on programmed cell death-related genes is proposed for TNBC prognosis.

Findings

The five-gene signature (SEPTIN3, SCARB1, CHML, SYNM, COL5A3) effectively stratifies TNBC patients into high- and low-risk groups.

The model shows strong survival prediction performance across different cohorts and is linked to tumor metabolism and metastasis.

The nomogram incorporating the gene signature improves clinical prognostic accuracy for TNBC.

Abstract

Triple negative breast cancer (TNBC) is a more aggressive subtype of breast cancer that usually progresses rapidly, develops drug resistance, metastasis, and relapses, and remains a challenge for clinicians to treat. Programmed cell death (PCD), a conserved mechanism of cell suicide controlled by various pathways, contributed to carcinogenesis and cancer progression. Nevertheless, the prognostic significance of PCD-related genes in TNBC remains largely unclear, and more accurate prognostic models are urgently needed.

Gene expression profiles and clinical information of TNBC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis were used to establish the PCD-related gene signature. Kaplan-Meier plotter, receiver operating characteristic curves, and nomogram were applied to validate the prognostic value of the gene signature. Gene set enrichment analysis was carried out to investigate the pathways and molecular functions.

Five PCD-related genes including SEPTIN3, SCARB1, CHML, SYNM, and COL5A3 were identified to establish the PCD-related risk score for TNBC patients. Patients stratified into high-risk or low-risk group showed significantly different survival outcome, immune infiltration, and drug susceptibility. Kaplan-Meier and receiver operating characteristic curves showed a good performance for survival prediction in different cohorts. Gene set enrichment analysis revealed that the five-gene signature was associated with tumor metabolism, cancer cell proliferation, invasion and metastasis, and tumor microenvironment. Nomogram including the five-gene signature was established.

A novel five PCD-related gene signature and nomogram could be used for prognostic prediction in TNBC. The present work might offer useful insights in digging sensitive and effective biomarkers for TNBC prognosis prediction and establishing accurate prognostic model in clinical management.

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