Development and Validation of a Prognostic Classifier Based on Lipid Metabolism-Related Genes for Breast Cancer

Abstract

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.

Expression Variation of CPT1A Induces Lipid Reconstruction in Goat Intramuscular Precursor Adipocytes

“…This suggests that AP2S1 is involved in the lipoprotein metabolism and AP2S1 is linked with CPT1A. SORBS1 has been shown to be involved in the lipid metabolism [44,45] and other studies have proven that SORBS1 is associated with obesity and insulin signaling [46]. In addition, it has been shown that SORBS1 is a candidate gene that affects fatty acid traits in milk [47], thus we presume that it may affect the fatty acid composition of goat meat in IMF deposition.…”

SORBS1 inhibits epithelial to mesenchymal transition (EMT) of breast cancer cells by regulating PI3K/AKT signaling and macrophage phenotypic polarization

“…ROC analysis confirmed the diagnostic utility of SORBS1 in patients with BRCA with high confidence scores. Similarly, many previous studies have demonstrated the importance of SORBS1 as a possible prognostic predictor in patients with various solid malignancies such as metastatic prostate [ 22 ], cervical [ 23 ] and breast cancers [ 24 ]. We performed KEGG analysis of SORBS1-correlated genes using TCGA data and found that the PI3K/AKT signaling pathway was the most significantly enriched signaling pathway.…”