DNA methylation data-based molecular subtype classification related to the prognosis of patients with cervical cancer

Cervical cancer is one of the leading female health-killers among all types of malignancies globally. Human papillomavirus infection combined with genetic and epigenetic alterations have been indicated to be closely associated with the pathogenesis, progression, and malignant transformation of cervical cancer. Notably, during the complex tumorigenesis process, a series of DNA methylations occurs early and is the most frequent molecular behavior. In this study, to exploit the specific DNA methylation sites influencing the prognosis of patients with cervical cancer, 275 samples were downloaded from The Cancer Genome Atlas database and further analyzed. As a result, 1253 CpGs were found to have a significant correlation with patient prognosis and were further selected for the consistent clustering of samples into six subgroups. Specifically, the samples in every subgroup were different regarding the following: race, age, tumor stage, receptor status, histological type, metastasis status, and patient prognosis. In addition, we calculated the levels of methylation sites in all subgroups, with 79 methylation sites (corresponding to 81 genes) screened as the intrasubgroup-specific methylation sites. Moreover, signaling pathway enrichment analysis was conducted on the genes of the corresponding promoter regions of the above-described specific methylation sites, revealing that these genes were enriched in biological pathways closely associated with tumors, such as the cyclic guanosine monophosphate-dependent protein kinase and focal adhesion signaling pathways. Finally, the least absolute shrinkage and selection operator algorithm was employed to establish a prognostic prediction model for cervical cancer patients, with training and test sets used for testing and validation, respectively. In summary, the specific DNA methylation site-based classification is able to reflect the heterogeneity of cervical cancer tissue, contributing to the development of personalized therapy and the accurate prediction of patient prognosis.