Development and Validation of a Lipid Metabolism-Related Gene Prognostic Model for Breast Cancer: Integrating Risk Score and Nomogram.

Authors

Ying Yin, Jianming Yan, Jie Wang, Mingjie Yuan

Published in

Breast care (Basel, Switzerland). Oct 12, 2026. Epub Oct 12, 2026.

Abstract

Breast cancer is a leading cause of cancer death globally. Altered lipid metabolism drives tumor progression and therapy resistance. This study aimed to evaluate the prognostic value of lipid metabolism-related genes and build a predictive model for breast cancer.
We integrated transcriptomic data from The Cancer Genome Atlas (TCGA) breast cancer cohort (n = 1,106) and normal breast tissue samples from both TCGA (n = 113) and the Genotype-Tissue Expression database (n = 162). After rigorous batch effect correction, we identified lipid metabolism-related differentially expressed genes (DEGs) by intersecting DEGs with a curated gene set from the Molecular Signatures Database. Univariate Cox regression analysis was employed to identify prognosis-associated genes, based on which a risk-scoring model was constructed. The cohort was randomly divided into training (70%, n = 738) and validation (30%, n = 311) sets. A nomogram was developed by integrating the risk score with key clinicopathological variables. Model performance was assessed using the concordance index, receiver operating characteristic curves, and calibration plots. Functional enrichment analyses were conducted to explore the biological significance of the identified genes.
From 169 lipid metabolism-related DEGs, 14 were linked to overall survival (OS). The risk model stratified patients into high- and low-risk groups with distinct survival (5-year OS: 72.1% vs. 91.2% in training; 77.2% vs. 89.9% in validation). The risk score was an independent prognostic factor (HR = 1.72). The nomogram showed good accuracy for 3-/5-year survival prediction (training area under the curves: 0.815, 0.797; validation: 0.805, 0.713). Protein-protein interaction analysis revealed a core protein module (SCARB2, AUH, ACADM, ACSF2) that was enriched in functional enrichment primarily in fatty acid β-oxidation and branched-chain amino acid degradation pathways.
We developed a validated lipid metabolism-related gene signature and nomogram that reliably predicts OS in breast cancer, offering prognostic insights, though further external validation is warranted.

PMID:
42927407
Bibliographic data and abstract were imported from PubMed on 25 Oct 2026.

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