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Updated: Sep 7, 2025
Exploring the Regulation of Lipid Droplet Catabolism through Lipophagy
Published on: January 31, 2025
Development and Validation of a Prognostic Classifier Based on Lipid Metabolism-Related Genes for Breast Cancer
Nan Wang1, Yuanting Gu1, Lin Li1
1Department of Breast Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China.
Background:
The changes of lipid metabolism have been implicated in the development of many tumors, but its role in breast invasive carcinoma (BRCA) remains to be fully established. Here, we attempted to ascertain the prognostic value of lipid metabolism-related genes in BRCA.
Methods:
We obtained RNA expression data and clinical information for BRCA and normal samples from public databases and downloaded a lipid metabolism-related gene set. Ingenuity Pathway Analysis (IPA) was applied to identify the potential pathways and functions of Differentially Expressed Genes (DEGs) related to lipid metabolism. Subsequently, univariate and multivariate Cox regression analyses were utilized to construct the prognostic gene signature. Functional enrichment analysis of prognostic genes was achieved by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Kaplan-Meier analysis, Receiver Operating Characteristic (ROC) curves, clinical follow-up results were employed to assess the prognostic potency. Potential compounds targeting prognostic genes were screened by Connectivity Map (CMap) database and a prognostic gene-drug interaction network was constructed using Comparative Toxicogenomics Database (CTD). Furthermore, we separately validated the selected marker genes in BRCA samples and human breast cancer cell lines (MCF-7, MDA-MB-231).
Results:
IPA and functional enrichment analysis demonstrated that the 162 lipid metabolism-related DEGs we obtained were involved in many lipid metabolism and BRCA pathological signatures. The prognostic classifier we constructed comprising SDC1 and SORBS1 can serve as an independent prognostic marker for BRCA. CMap filtered 37 potential compounds against prognostic genes, of which 16 compounds could target both two prognostic genes were identified by CTD. The functions of the two prognostic genes in breast cancer cells were verified by cell function experiments.
Conclusion:
Within this study, we identified a novel prognostic classifier based on two lipid metabolism-related genes: SDC1 and SORBS1. This result highlighted a new perspective on the metabolic exploration of BRCA.
