Mining of Subtype Markers for the Prognosis of Ovarian Cancer based on Methylation Data
Abstract Aims: Ovarian cancer is one of three major malignancies involving the female reproductive system, and its morbidity and mortality are ranked number 3 and number 1 among gynecological tumors, respectively. DNA methylation (MET), as one of the main epigenetic modes, is closely related to the occurrence and development of ovarian cancer. To guide individualized treatment and improve the prognosis in ovarian cancer patients, it is of great significance to elucidate effective MET subtype markers. Methods: A total of 571 ovarian cancer MET samples were downloaded from the Cancer Genome Atlas (TCGA), and a COX proportional hazards model was established using the MET spectrum and clinically pathological parameters. Subsequently, the consensus clustering of CpG loci with a significant difference in both univariate and multivariate analyses was performed to screen the molecular subtypes, and these CpG loci were subjected to gene function annotation. Finally, CpG MET loci associated with poor prognosis in ovarian cancer patients were further screened by constructing a weighted gene co-expression network analysis (WGCNA). Results: A total of 250 prognosis-related MET loci were obtained by COX regression and 6 molecular subtypes were screened by clustering. There was a remarkable MET difference between most subtypes, of which Cluster 2 had the highest MET level and demonstrated the best prognosis in patients, while Cluster 4 and Cluster 5 had a MET level significantly lower than that of the other subtypes and demonstrated a very poor prognosis. All Cluster 5 samples were at a high grade, while the percentage of Stage IV samples in Cluster 4 was evidently greater than that in the other subtypes. Using the co-expression network, 5 CpG loci were eventually obtained: cg27625732, cg00431050, cg22197830, cg03152385, and cg22809047. The clustering analysis shows that the prognosis in patients with hypomethylation was significantly worse than that in patients with hypermethylation. Conclusions: These MET molecular subtypes can be used not only to evaluate the prognosis in ovarian cancer patients but also to fully distinguish the tumor stage and histological grade in these patients. Prognosis-related CpG loci can be applied as biomarkers for individualized treatment in ovarian cancer patients.