Background: Alterations in lipid metabolism have been implicated in the development of many tumors. However, the contribution of different lipid metabolism pathways to 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 to harvest lipid metabolism-related genes. IPA was applied to identify the potential pathways and functions of DEGs related to lipid metabolism. Subsequently, univariate and multivariate Cox regression analyses were utilized to construct the prognostic gene signature and independent prognostic analyses. Thereafter, the differential expression of the selected marker genes SDC1 and SORBS1 in clinical tissue samples was verified by qRT-PCR, western blotting, and immunohistochemical experiments. Functional enrichment analysis of prognostic genes was achieved by the GO and KEGG databases. Moreover, Kaplan-Meier analysis, ROC curves, clinical immunohistochemistry conditions and follow-up results were employed to assess the prognostic potency. Potential compounds targeting prognostic genes were then screened by CMap database and a prognostic gene-drug interaction network was constructed using Comparative Toxicogenomics Database.
Results: IPA demonstrated that the 162 lipid metabolism-related DEGs we obtained were involved in a variety of lipid metabolism and BRCA pathological signatures. Subsequent functional enrichment analysis of candidate prognostic lipid metabolism DEGs also revealed a similar outcome. The prognostic classifier we constructed comprising SDC1 and SORBS1 has a strong prognostic potency that was verified by the clinical conditions and follow-up results, it also can serve as an independent prognostic marker for BRCA. CMap filtered 37 potential compounds against prognostic genes. CTD indicated that the two prognostic genes had 16 drugs in common.
Conclusion: Within this study, we identified a novel prognostic classifier based on two lipid metabolism-related genes: SDC1 and SORBS1. This classifier had accurately predicted the prognosis of our follow-up BRCA patients and this result highlighted a new perspective on the metabolic exploration of BRCA. In addition, SDC1 and SORBS1 could serve as a possible new target for the synthesis of BRCA drugs.