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The following is a summary of “Comprehensive analysis of metabolism-related gene biomarkers reveals their impact on the diagnosis and prognosis of triple-negative breast cancer,” published in the April 2025 issue of the BMC Cancer by Ren et al.

Triple-negative breast cancer (TNBC) represents one of the most aggressive subtypes of breast cancer, marked by the absence of estrogen receptors, progesterone receptors, and HER2 expression. This phenotype is associated with poor clinical outcomes and limited therapeutic options, highlighting the need for novel biomarkers to guide prognosis and treatment. Given the critical role of metabolic reprogramming in cancer development and progression, this study aimed to identify metabolism-related genes that could serve as prognostic indicators in TNBC.

To identify potential prognostic biomarkers, transcriptomic data and corresponding clinical information were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differential gene expression analyses were conducted to isolate metabolism-associated genes significantly altered in TNBC. Subsequent enrichment analysis provided insights into the biological pathways involved. Prognostically relevant genes were identified through univariate and multivariate Cox regression analyses, supplemented by machine learning algorithms to enhance predictive accuracy. A gene-based prognostic model was constructed and validated using an independent patient dataset. In vitro experiments were performed to further validate the biological relevance of candidate genes. Additionally, the study examined immune cell infiltration, immune checkpoint expression, mutational profiles, and drug sensitivity in relation to the established risk score.

Five metabolism-related genes—SDS, RDH12, IDO1, GLDC, and ALOX12B—were identified as key prognostic indicators. These genes were used to construct a risk prediction model, which effectively stratified patients with TNBC into high- and low-risk groups. Patients in the high-risk group experienced significantly worse overall survival compared to those in the low-risk group. The model’s prognostic performance was validated across independent cohorts, confirming its clinical utility. Further analysis revealed that the high-risk group displayed distinct mutational characteristics, reduced immune cell infiltration, and a lower likelihood of response to immune checkpoint blockade therapy. Drug sensitivity analyses also indicated differential responsiveness to chemotherapeutic agents based on the risk score, offering potential guidance for personalized treatment strategies.

This study presents a robust prognostic model based on five metabolism-related genes with significant predictive value for overall survival and metastatic potential in TNBC. The model not only stratifies patient risk but also provides insights into the tumor microenvironment and therapeutic susceptibility. These findings support the integration of metabolic biomarkers into clinical decision-making for more effective and individualized management of TNBC.

Source: bmccancer.biomedcentral.com/articles/10.1186/s12885-025-14053-8