A novel DNA methylation-related gene signature for the prediction of overall survival and immune characteristics of ovarian cancer patients

• Main Authors: Sixue Wang (Author), Jie Fu (Author), Xiaoling Fang (Author)
• Format: Book
• Published: BMC, 2023-03-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s13048-023-01142-0; 1757-2215
• Keywords: DNA methylation; Risk score; Prognosis; Immunotherapy; Ovarian cancer; Gynecology and obstetrics; RG1-991
• Source: Journal of Ovarian Research, Vol 16, Iss 1, Pp 1-14 (2023)

Abstract Background Ovarian cancer (OC) is one of the most life-threatening cancers affecting women worldwide. Recent studies have shown that the DNA methylation state can be used in the diagnosis, treatment and prognosis prediction of diseases. Meanwhile, it has been reported that the DNA methylation state can affect the function of immune cells. However, whether DNA methylation-related genes can be used for prognosis and immune response prediction in OC remains unclear. Methods In this study, DNA methylation-related genes in OC were identified by an integrated analysis of DNA methylation and transcriptome data. Prognostic values of the DNA methylation-related genes were investigated through least absolute shrinkage and selection operator (LASSO) and Cox progression analyses. Immune characteristics were investigated by CIBERSORT, correlation analysis and weighted gene co-expression network analysis (WGCNA). Results Twelve prognostic genes (CA2, CD3G, HABP2, KCTD14, PI3, SERPINB5, SLAMF7, SLC9A2, STC2, TBP, TREML2 and TRIM27) were identified and a risk score signature and a nomogram based on prognostic genes and clinicopathological features were constructed for the survival prediction of OC patients in the training and two validation cohorts. Subsequently, the differences in the immune landscape between the high- and low-risk score groups were systematically investigated. Conclusions Taken together, our study explored a novel efficient risk score signature and a nomogram for the survival prediction of OC patients. In addition, the differences of the immune characteristics between the two risk groups were clarified preliminarily, which will guide the further exploration of synergistic targets to improve the efficacy of immunotherapy in OC patients.