DNA methylation-based profiling reveals distinct clusters with survival heterogeneity in high-grade serous ovarian cancer

Abstract

High-grade serous ovarian cancer (HGSOC) is the most common type of epigenetically heterogeneous ovarian cancer. Methylation typing has previously been used in many tumour types but not in HGSOC. Methylation typing in HGSOC may promote the development of personalized care. The present study used DNA methylation data from The Cancer Genome Atlas database and identified four unique methylation subtypes of HGSOC. With the poorest prognosis and high frequency of residual tumours, cluster 4 featured hypermethylation of a panel of genes, which indicates that demethylation agents may be tested in this group and that neoadjuvant chemotherapy may be used to reduce the possibility of residual lesions. Cluster 1 and cluster 2 were significantly associated with metastasis genes and metabolic disorders, respectively. Two feature CpG sites, cg24673765 and cg25574024, were obtained through Cox proportional hazards model analysis of the CpG sites. Based on the methylation level of the two CpG sites, the samples were classified into high- and low-risk groups to identify the prognostic information. Similar results were obtained in the validation set. Taken together, these results explain the epigenetic heterogeneity of HGSOC and provide guidance to clinicians for the prognosis of HGSOC based on DNA methylation sites.

Modbamtools: Analysis of single-molecule epigenetic data for long-range profiling, heterogeneity, and clustering

“…This is a useful feature for regions without many SNPs for phasing reads into haplotypes (Gershman et al 2022). Clustering can also be used to quantify different cell types or to profile early cancer detection from a heterogeneous sample (Wang et al 2021; Houseman et al 2008; Gkountela et al 2019; Tian et al 2020). Clustering can be performed either as a part of the plotting command or separately (‘--cluster’ command) with the input of a batch file for locations used for the clustering.…”

Overview of Tumor Heterogeneity in High-Grade Serous Ovarian Cancers

“…Considering the global methylation status detected in CpG sites in both island and open-sea regions, there is nearly universal hypomethylation in HGSOC compared with normal controls [59]. However, DNA hypomethylation at specific chromosomal sites distinguished three groups of patients with different prognoses and better survival than hypermethylated HGSOC [63]. Of course, the variation of methylation profiles is highly dependent on the genes' functions; for instance, hypomethylation of APOBEC3A but hypermethylation of NKAPL were associated with Pt-resistance in HGSOC [61].…”

Methods in DNA methylation array dataset analysis: A review

“…These algorithms leverage information from the analysis of overall survival on samples to accomplish this task. For instance, Consensus clustering is employed by Wang et al, 2021 for subgrouping prognostic methylated CpG site into four methylation clusters, reflecting the variation in molecular genetic features (determined by hypermethylated/hypomethylated loci) concerning the prognostic behaviour of the cluster group [85] . Similarly, Yin, X. et al, 2021 evaluate such molecular subgroups of pancreatic cancer samples, for poorer prognosis and its associated clinicopathological features [86] .…”

“…Algorithms such as Comb-p, DMRcate, Bumphunter, and probe lasso suggest its identification in the promoter region with the decreasing order of their performance evaluation in terms of power, sensitivity, DMR size, DMR overlap, and the simulated time consumption [102] , [103] . Therefore, a recent study done by Zhang W et al, 2023 implemented the use of Comb-p for identifying DMRs related to the CSF biomarker in Alzheimer’s patient's blood samples, to collectively identify the regions showcasing the adjacent low p-values [104] , [85] .…”