Screening of potential targets and small-molecule drugs related to lipid metabolism in ovarian cancer based on bioinformatics.

BACKGROUND

about 70 % of ovarian cancer (OC) patients with postoperative chemotherapy relapse within 2-3 years due to drug resistance and metastasis, and the 5-year survival rate is only about 30 %. Lipid metabolism plays an important role in OC. We try to explore the potential targets and drugs related to lipid metabolism to provide clues for the treatment of OC.

METHODS

the gene expression profiles of OC and normal ovarian tissue samples were obtained from the cancer genome atlas (TCGA) and genotype-tissue expression databases (GTEx). The differentially expressed genes (DEGs) were analyzed. Lipid metabolism related genes (LMRGs) were downloaded from MSigDB database. The DEGs related to lipid metabolism in OC was obtained by intersection. And gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analyses were performed. The protein-protein interaction (PPI) network of lipid metabolism related DEGs was constructed, and seven algorithms were used to screen core potential target genes. Its expression in OC and prognostic ability were analyzed by Univariate Cox. Cmap database mining OC lipid metabolism related potential small-molecular drugs and docking. CCK8, scratch assay, transwell test and free fatty acid (FFA) assay, fluorescence detection of cellular fatty acid uptake, and the reactivity assay of CPT1A were used to detect the biological effects of drugs on OC cell.Rreverse transcription PCR(RT-qPCR) and WesternBlot were performed to measure the expression of core targets.

RESULTS

437 DEGs related to lipid metabolism of OC were screened. GO and KEGG analysis showed that these DEGs were lipid metabolism, fatty acid metabolism, sphingolipid metabolism, PPAR signal pathway and so on. The PPI network based on lipid metabolism DEGs consists of 301 nodes and 1107 interaction pairs, and 6 core target genes were screened. ROC curve analysis showed that all of the 6 genes could predict the prognosis of OC. Three small molecular drugs Cephaeline, AZD8055 and GSK-1059615 were found by cmap and molecular docking showed that they all had good binding ability to target gene. Cephaeline has the strongest inhibitory effect on SKOV3 cells of OC, and could significantly inhibit cell migration and invasion regulate the mRNA and protein expression of some targets, and inhibit lipid metabolism process in ovarian cancer cells.

CONCLUSION

six OC potential genes related to lipid metabolism were identified and verified, which can be used as potential biomarkers and therapeutic targets to evaluate the prognostic risk of OC patients. In addition, three small-molecular drugs that may be effective in the treatment of OC were unearthed, among which Cephaeline has the most potential. We speculate that Cephaeline may target six genes to inhibit progression of OC by affecting lipid metabolism.