Modeling of senescence-related chemoresistance in ovarian cancer using data analysis and patient-derived organoids.

Background: Ovarian cancer (OC) is a malignant tumor associated with poor prognosis owing to its susceptibility to chemoresistance. Cellular senescence, an irreversible biological state, is intricately linked to chemoresistance in cancer treatment. We developed a senescence-related gene signature for prognostic prediction and evaluated personalized treatment in patients with OC. Methods: We acquired the clinical and RNA-seq data of OC patients from The Cancer Genome Atlas and identified a senescence-related prognostic gene set through differential and cox regression analysis in distinct chemotherapy response groups. A prognostic senescence-related signature was developed and validated by OC patient-derived-organoids (PDOs). We leveraged gene set enrichment analysis (GSEA) and ESTIMATE to unravel the potential functions and immune landscape of the model. Moreover, we explored the correlation between risk scores and potential chemotherapeutic agents. After confirming the congruence between organoids and tumor tissues through immunohistochemistry, we measured the IC Results: We got 2740 differentially expressed genes between two chemotherapy response groups including 43 senescence-related genes. Model prognostic genes were yielded through univariate cox analysis, and multifactorial cox analysis. Our work culminated in a senescence-related prognostic model based on the expression of SGK1 and VEGFA. Simultaneously, we successfully constructed and propagated three OC PDOs for drug screening. PCR and WB from PDOs affirmed consistent expression trends as those of our model genes derived from comprehensive data analysis. Specifically, SGK1 exhibited heightened expression in cisplatin-resistant OC organoids, while VEGFA manifested elevated expression in the sensitive group ( Conclusion: Through the formulation of a senescence-related signature comprising SGK1 and VEGFA, we established a promising tool for prognosticating chemotherapy reactions, predicting outcomes, and steering therapeutic strategies. Patients with high VEGFA and low SGK1 expression levels exhibit heightened sensitivity to chemotherapy.