ArticleFrontiers in oncology2024
Potential shared gene signatures and molecular mechanisms between recurrent pregnancy loss and ovarian cancer.
Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Ovarian cancer (OV) is the second most prevalent gynecological tumor. Recurrent pregnancy loss (RPL) refers to two or more spontaneous abortions. However, the molecular mechanisms underlying both OV and RPL remain poorly understood. This article focuses on the exploration of the common genetic characteristics of OV and RPL and their molecular mechanisms. Methods: The 71 differentially expressed genes associated with RPL and 1427 genes associated with OV survival were analyzed, among which 7 common genes were both important in the pathogenesis of RPL and OV. Then stepAIC analysis was performed to simplify the model and decrease the number of genes, which yielded a final set of 5 prognostic genes with coefficients to construct a prognostic risk scoring system. Univariate and multivariate Cox analyses were conducted to verify the independent prognostic factor for OV patients. GSEA and GO analysis results showed enriched biological pathways in the high/low risk groups, thereby revealing their biological characteristics. The effect of immunotherapy is better in LR patients. There was a significantly higher enrichment score of stemness and higher tumor aneuploidy score in the HR group. Results: A five-gene prognostic risk model provided a more accurate prognosis for OV, and this prognostic score system was validated using two external cohorts. The risk score was an independent prognostic index for OV patients. Based on levels of ICs, immune cell infiltration, and predicted response, low risk OV patients were more likely to benefit from immunotherapies. Conclusions: The 5-gene risk model can predict the prognosis of OV patients, which can draw the attention of clinicians and help stratify patients into high and low risk groups for management.
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