Construction and validation of a comprehensive metabolism-associated prognostic model for predicting survival and immunotherapy benefits in ovarian cancer.

Abstract

Background: Ovarian cancer (OV) is a prevalent malignancy among gynecological tumors. Numerous metabolic pathways play a significant role in various human diseases, including malignant tumors. Our study aimed to develop a prognostic signature for OV based on a comprehensive set of metabolism-related genes (MRGs). Method: To achieve this, a bioinformatics analysis was performed on the expression profiles of 51 MRGs. The OV individuals were subsequently categorized into two molecular clusters based on the expression levels of MRGs. Following this, differentially expressed genes (DEGs) were identified among these clusters. The DEGs aided in the classification of two gene clusters, with a total of 390 DEGs being identified between them. A prognostic signature, constructed using the DEGs, enabled the calculation of risk scores for OV patients. Results: This study revealed that patients classified as low-risk demonstrated a more favorable prognosis, increased immune cell infiltration, and superior response to chemotherapy in comparison to high-risk patients. Four signature genes, GDF6, KIF26A, P2RY14, and ALDH1A2, were identified as significant contributors to the prognostic signature. The expression levels of these signature genes were different between OV and normal ovary tissues through in vitro experiments. Additionally, P2RY14 protein was found to potentially influence the growth of OV cell lines. Conclusion: We have constructed a prognostic signature associated with MRGs that demonstrates exceptional efficacy in prognosis survival outcomes and therapeutic responses in patients diagnosed with OV. Downregulation of P2RY14 may contribute to an unfavorable prognosis in OV.