Abstract

Background: Ovarian cancer is the most malignant gynecological disease, which seriously threatens female physical and mental health. Paclitaxel is a first-line chemotherapy drug in the clinical treatment of ovarian cancer, but drug resistance has become an important factor affecting the survival of ovarian cancer patients. However, the main mechanism of chemotherapy resistance in ovarian cancer remains unclear. In this study, we analyzed the Integrated Gene Expression Database (GEO) dataset using comprehensive bioinformatics tools to provide new therapeutic strategies and search for prognostic targets for ovarian cancer. Methods: Ovarian cancer related genes were extracted from GSE18520 by bioinformatics method. Differentially expressed genes (DEGs) were obtained by differential analysis, and related genes and functions were elucidated. The key gene CRTC2 was identified by prognostic analysis. Immunohistochemistry was used to detect the expression of CRTC2 in chemotherapy-resistant and chemotherapy-sensitive ovarian cancer tissues. Functional analysis (cell assay) confirmed the role of CRTC2 in paclitaxel resistance. Autophagy related proteins were detected by Western blot. Autophagy flux analysis was performed using the GFP/RFP-LC3 adenovirus reporter. Results: A total of 3,852 DEGs were identified in the GEO microarray dataset. Key genes were screened by prognostic analysis. We found that CRTC2 was highly expressed in chemoresistant tissues of ovarian cancer. In 110 patients with ovarian cancer, high expression of CRTC2 was associated with poorer prognostic factors and shorter survival. At the same time, we found that CRTC2 can promote the proliferation and invasion ability of ovarian cancer cells. In addition, CRTC2 can affect the expression of PI3K, AKT, autophagic flux and sensitivity to paclitaxel chemotherapy in ovarian cancer. Conclusion: CRTC2 can affect autophagy partially through PI3K-AKT signaling pathway, and then affect the sensitivity of ovarian cancer to paclitaxel chemotherapy. CRTC2 may be a potential predictor or target for ovarian cancer therapy.

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