Integrative analysis of epigenetic and transcriptional interrelations identifies histotype-specific biomarkers in early-stage ovarian carcinoma
Abstract Background Epithelial ovarian cancer (EOC) is a deadly and heterogenous disease comprising five major histotypes: clear cell carcinoma (CCC), endometrioid carcinoma (EC), low- and high-grade serous carcinoma (LGSC, HGSC), and mucinous carcinoma (MC). Despite this heterogeneity, EOC is often...
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2025-05-01
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| Series: | Journal of Ovarian Research |
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| Online Access: | https://doi.org/10.1186/s13048-025-01676-5 |
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| author | Hugo Swenson Ella Ittner Lucas Werner Elisabeth Werner Rönnerman Claudia Mateoiu Anikó Kovács Pernilla Dahm-Kähler Ghassan M. Saed Szilárd Nemes Per Karlsson Toshima Z. Parris Khalil Helou |
| author_facet | Hugo Swenson Ella Ittner Lucas Werner Elisabeth Werner Rönnerman Claudia Mateoiu Anikó Kovács Pernilla Dahm-Kähler Ghassan M. Saed Szilárd Nemes Per Karlsson Toshima Z. Parris Khalil Helou |
| author_sort | Hugo Swenson |
| collection | DOAJ |
| description | Abstract Background Epithelial ovarian cancer (EOC) is a deadly and heterogenous disease comprising five major histotypes: clear cell carcinoma (CCC), endometrioid carcinoma (EC), low- and high-grade serous carcinoma (LGSC, HGSC), and mucinous carcinoma (MC). Despite this heterogeneity, EOC is often treated as a homogenous disease, and reliable screening tests are lacking. Although progress has been made, there is a pressing need for biomarkers to refine patient stratification, guide treatment, and improve outcomes. Here, we elucidated the relationship between DNA methylation and gene expression patterns in EOC to identify histotype-specific biomarkers. Methods Differential DNA methylation and gene expression analyses were performed for 86 early-stage EOC samples after histopathological reclassification stratified by histotype. The correlation between DNA methylation and gene expression was examined, and histotype-specific biomarkers were identified. Hierarchical clustering and predictive machine learning modeling were employed to assess the performance of the histotype-specific biomarkers using four external cohorts. Results EOC histotypes exhibited distinct epigenetic, transcriptional, and functional profiles, with candidate histotype-specific biomarkers such as CTSE and VCAN effectively distinguishing CCC, HGSC, and MC on the transcriptional level. Gene expression for the candidate biomarkers was found to be reproducible across external cohorts, with histotype-specific differences remaining homogenous. Conclusions This study identified promising histotype-specific biomarkers for EOC using integrative transcriptomic and epigenomic analysis. Furthermore, these findings indicate that additional stratification or potential reclassification of the EC histotype is warranted in future studies. |
| format | Article |
| id | doaj-art-dc3b3e4d79c6498ebd032b2f7b4e204f |
| institution | Kabale University |
| issn | 1757-2215 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Ovarian Research |
| spelling | doaj-art-dc3b3e4d79c6498ebd032b2f7b4e204f2025-08-20T03:48:18ZengBMCJournal of Ovarian Research1757-22152025-05-0118111510.1186/s13048-025-01676-5Integrative analysis of epigenetic and transcriptional interrelations identifies histotype-specific biomarkers in early-stage ovarian carcinomaHugo Swenson0Ella Ittner1Lucas Werner2Elisabeth Werner Rönnerman3Claudia Mateoiu4Anikó Kovács5Pernilla Dahm-Kähler6Ghassan M. Saed7Szilárd Nemes8Per Karlsson9Toshima Z. Parris10Khalil Helou11Department of Oncology, Sahlgrenska Academy, Institute of Clinical Sciences, University of GothenburgDepartment of Oncology, Sahlgrenska Academy, Institute of Clinical Sciences, University of GothenburgDepartment of Oncology, Sahlgrenska Academy, Institute of Clinical Sciences, University of GothenburgDepartment of Clinical Pathology, Region Västra Götaland, Sahlgrenska University HospitalDepartment of Clinical Pathology, Region Västra Götaland, Sahlgrenska University HospitalDepartment of Clinical Pathology, Region Västra Götaland, Sahlgrenska University HospitalDepartment of Obstetrics and Gynecology, Sahlgrenska Academy, Institute of Clinical Sciences, University of GothenburgDepartment of Obstetrics and Gynecology, Wayne State University School of MedicineAstraZenecaDepartment of Oncology, Sahlgrenska Academy, Institute of Clinical Sciences, University of GothenburgDepartment of Oncology, Sahlgrenska Academy, Institute of Clinical Sciences, University of GothenburgDepartment of Oncology, Sahlgrenska Academy, Institute of Clinical Sciences, University of GothenburgAbstract Background Epithelial ovarian cancer (EOC) is a deadly and heterogenous disease comprising five major histotypes: clear cell carcinoma (CCC), endometrioid carcinoma (EC), low- and high-grade serous carcinoma (LGSC, HGSC), and mucinous carcinoma (MC). Despite this heterogeneity, EOC is often treated as a homogenous disease, and reliable screening tests are lacking. Although progress has been made, there is a pressing need for biomarkers to refine patient stratification, guide treatment, and improve outcomes. Here, we elucidated the relationship between DNA methylation and gene expression patterns in EOC to identify histotype-specific biomarkers. Methods Differential DNA methylation and gene expression analyses were performed for 86 early-stage EOC samples after histopathological reclassification stratified by histotype. The correlation between DNA methylation and gene expression was examined, and histotype-specific biomarkers were identified. Hierarchical clustering and predictive machine learning modeling were employed to assess the performance of the histotype-specific biomarkers using four external cohorts. Results EOC histotypes exhibited distinct epigenetic, transcriptional, and functional profiles, with candidate histotype-specific biomarkers such as CTSE and VCAN effectively distinguishing CCC, HGSC, and MC on the transcriptional level. Gene expression for the candidate biomarkers was found to be reproducible across external cohorts, with histotype-specific differences remaining homogenous. Conclusions This study identified promising histotype-specific biomarkers for EOC using integrative transcriptomic and epigenomic analysis. Furthermore, these findings indicate that additional stratification or potential reclassification of the EC histotype is warranted in future studies.https://doi.org/10.1186/s13048-025-01676-5Ovarian cancerGene expressionDNA methylationBioinformaticsMachine learning |
| spellingShingle | Hugo Swenson Ella Ittner Lucas Werner Elisabeth Werner Rönnerman Claudia Mateoiu Anikó Kovács Pernilla Dahm-Kähler Ghassan M. Saed Szilárd Nemes Per Karlsson Toshima Z. Parris Khalil Helou Integrative analysis of epigenetic and transcriptional interrelations identifies histotype-specific biomarkers in early-stage ovarian carcinoma Journal of Ovarian Research Ovarian cancer Gene expression DNA methylation Bioinformatics Machine learning |
| title | Integrative analysis of epigenetic and transcriptional interrelations identifies histotype-specific biomarkers in early-stage ovarian carcinoma |
| title_full | Integrative analysis of epigenetic and transcriptional interrelations identifies histotype-specific biomarkers in early-stage ovarian carcinoma |
| title_fullStr | Integrative analysis of epigenetic and transcriptional interrelations identifies histotype-specific biomarkers in early-stage ovarian carcinoma |
| title_full_unstemmed | Integrative analysis of epigenetic and transcriptional interrelations identifies histotype-specific biomarkers in early-stage ovarian carcinoma |
| title_short | Integrative analysis of epigenetic and transcriptional interrelations identifies histotype-specific biomarkers in early-stage ovarian carcinoma |
| title_sort | integrative analysis of epigenetic and transcriptional interrelations identifies histotype specific biomarkers in early stage ovarian carcinoma |
| topic | Ovarian cancer Gene expression DNA methylation Bioinformatics Machine learning |
| url | https://doi.org/10.1186/s13048-025-01676-5 |
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