Bioinformatics analysis of drug-resistant ceRNA in epithelial ovarian cancer

Abstract

Objective: To explore the possible biological function of long-chain non-coding RNA (lncRNA) on epithelial ovarian cancer (EOC) drug resistance and the value of new diagnostic markers through bioinformatics analysis, clinical testing and verification methods. Methods: (1) Mining the lncRNA related to EOC and constructing the competing endogenous RNA (ceRNA) regulatory network: comprehensively apply text mining, data prediction and network construction and other bioinformatics methods to establish a potential ceRNA regulatory network related to EOC drug resistance, namely lncRNA-microRNA (miRNA)-mRNA regulatory network. (2) Clinical verification: a total of 95 cancer tissue specimens were collected from EOC patients who underwent cytoreductive surgery at the Affiliated Tumor Hospital of Guangxi Medical University from June 2008 to October 2016, of which 54 were platinum-resistant patients (resistance group), 41 platinum-based drug-sensitive patients (sensitive group). Real-time fluorescent quantitative PCR was used to detect the expression of lncRNA in EOC tissues of the two groups, the effect of lncRNA expression on the prognosis of EOC patients, and the diagnostic efficacy of lncRNA expression on resistance to EOC were also analyzed. Results: (1) Text mining preliminarily screened out 25 differentially expressed lncRNA related to the occurrence and development of EOC, and further subcellular localization analysis found that 8 lncRNA exist in the cytoplasm. Through further data mining, collinear literature analysis and construction of ceRNA, the regulatory network predicts that the two lncRNA molecules, GAS5 and HOTAIR, could serve as key ceRNA molecules. (2) Through real-time fluoressent quantitative PCR verification, it was found that both GAS5 and HOTAIR were highly expressed in drug-resistant EOC tissues, which affects the progression-free survival (PFS) and overall survival (OS) time of patients with drug-resistant EOC independent risk factors (P<0.05). The receiver operating characteristic (ROC) area under the curve (AUC) of GAS5 alone was 0.678, the AUC of HOTAIR alone was 0.863, and the AUC of GAS5 combined with HOTAIR was 0.871, and there were statistically significant differences (all P<0.05). Conclusions: The high expression of GAS5 and HOTAIR is closely related to the drug resistance of EOC, which could be used as a potential predictor of response to chemotherapy. At the same time, the combined detection of GAS5 and HOTAIR has a certain diagnostic efficiency for patients with platinum-resistant EOC. This method of using the ceRNA regulatory network to predict key molecules will provide new ideas for the diagnosis and treatment of EOC.