Development of an adenosine-related RiskScore model to predict the prognosis of patients with ovarian cancer

Abstract Objectives This study aims to explore the crucial functions of adenosine-related genes (ARGs) in immune activity and the development of ovarian cancer (OC). Methods Adenosine score was calculated by “GSVA” package. Key module genes were identified by weighted correlation network analysis (WGCNA). Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to select ARGs, which were combined to develop a RiskScore model. Gene set enrichment analysis (GSEA) was conducted for functional enrichment analysis, and immune infiltration in different risk groups was assessed. Somatic mutation was analyzed by MuTect2 and visualized by “maftools” package. The viability, migration, and invasion of two OC cell lines (A2780 and SK-OV-3) were measured by cell counting kit-8 (CCK-8), wound healing, and Transwell assays. Results To accurately predict the prognosis of OC, 9 ARGs ( PIK3CG , VSIG4 , MATK , PIEZO1 , RARRES1 , SELL , S1PR4 , IL18BP and CD40LG ) were identified to construct a RiskScore model. Functional enrichment analysis revealed that immunity-correlated pathways were predominantly enriched in the low-risk group, whereas inflammation-associated pathways were significantly enriched in the high-risk group. In vitro assays revealed a high expression of most of the 9 ARGs in OC cells, except MATK and SELL . Notably, silencing VSIG4 markedly suppressed the proliferation, migration, and invasion of the OC cells. Conclusions This study not only provides novel insights into the prognosis evaluation of patients with OC but also offers possibilities for formulating targeted therapeutic strategies and personalized treatment plans.