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ORIGINAL RESEARCH article
Front. Genet.
Sec. Human and Medical Genomics
Volume 16 - 2025 | doi: 10.3389/fgene.2025.1569122
This article is part of the Research TopicInsights in Human and Medical Genomics 2024View all 4 articles
Integrative analysis of DNA methylation, RNA sequencing, and genomic variants in The Cancer Genome Atlas (TCGA) to predict endometrial cancer recurrence
Provisionally accepted
Jin Hwa Hong1Yung Taek Ouh1
Sohyeon Jeong1Yoonji Oh1Hyun Woong Cho1Jae Kwan Lee1Hayeon Kim1Chungyeul Kim1
Sanghyun Roh2
Eun Na Kim3Yikyeong Chun1
Jeong-An Gim2*- 1Korea University Medical Center, Seoul, Republic of Korea
- 2Soonchunhyang University, Asan, Republic of Korea
- 3Seoul National University Hospital, Seoul, Republic of Korea
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Endometrial cancer (EC) is classified into four molecular subtypes, each with unique prognostic characteristics.The prognosis within each subtype varies due to histological and molecular factors. This study leverages omics datasets and machine learning to identify biomarkers associated with EC recurrence in different molecular subtypes. Utilizing DNA methylation, RNA-sequencing, and common variant data from 116 EC samples in The Cancer Genome Atlas (TCGA), differentially expressed genes (DEGs) and differentially methylated regions (DMRs) were identified using the Wilcoxon test between recurrence and non-recurrence groups. These were visualized through volcano plots and heat maps, while decision trees and random forests classified and stratified the samples. A machine learning analysis combined with box plots showed that in the copy number-high (CN-H) recurrence group, PARD6G-AS1 had decreased methylation, CSMD1 had increased methylation, and TESC expression was higher than the non-recurrence group. In the copy number-low (CN-L) recurrence group, CD44 expression was elevated. Further validation using TCGA clinical data confirmed PARD6G-AS1 hypomethylation and CD44 overexpression as significant indicators of recurrence (p=0.006 and p=0.02, respectively), and both were linked to advanced stage and lymph node metastasis. The study concludes that PARD6G-AS1 hypomethylation and CD44 overexpression are potential predictors of recurrence in CN-H and CN-L EC patients, respectively.
Keywords: endometrial cancer, machine-learning, Recurrence, The Cancer Genome Atlas, Multiomics analysis
Received: 31 Jan 2025; Accepted: 09 Apr 2025.
Copyright: © 2025 Hong, Ouh, Jeong, Oh, Cho, Lee, Kim, Kim, Roh, Kim, Chun and Gim. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jeong-An Gim, Soonchunhyang University, Asan, Republic of Korea
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