Integrative analysis reveals early epigenetic alterations in high-grade serous ovarian carcinomas.


Journal

Experimental & molecular medicine
ISSN: 2092-6413
Titre abrégé: Exp Mol Med
Pays: United States
ID NLM: 9607880

Informations de publication

Date de publication:
10 2023
Historique:
received: 04 01 2023
accepted: 06 07 2023
revised: 02 06 2023
medline: 2 11 2023
pubmed: 2 10 2023
entrez: 1 10 2023
Statut: ppublish

Résumé

High-grade serous ovarian carcinoma (HGSOC) is the most lethal gynecological malignancy. To date, the profiles of gene mutations and copy number alterations in HGSOC have been well characterized. However, the patterns of epigenetic alterations and transcription factor dysregulation in HGSOC have not yet been fully elucidated. In this study, we performed integrative omics analyses of a series of stepwise HGSOC model cells originating from human fallopian tube secretory epithelial cells (HFTSECs) to investigate early epigenetic alterations in HGSOC tumorigenesis. Assay for transposase-accessible chromatin using sequencing (ATAC-seq), chromatin immunoprecipitation sequencing (ChIP-seq), and RNA sequencing (RNA-seq) methods were used to analyze HGSOC samples. Additionally, protein expression changes in target genes were confirmed using normal HFTSECs, serous tubal intraepithelial carcinomas (STICs), and HGSOC tissues. Transcription factor motif analysis revealed that the DNA-binding activity of the AP-1 complex and GATA family proteins was dysregulated during early tumorigenesis. The protein expression levels of JUN and FOSL2 were increased, and those of GATA6 and DAB2 were decreased in STIC lesions, which were associated with epithelial-mesenchymal transition (EMT) and proteasome downregulation. The genomic region around the FRA16D site, containing a cadherin cluster region, was epigenetically suppressed by oncogenic signaling. Proteasome inhibition caused the upregulation of chemokine genes, which may facilitate immune evasion during HGSOC tumorigenesis. Importantly, MEK inhibitor treatment reversed these oncogenic alterations, indicating its clinical effectiveness in a subgroup of patients with HGSOC. This result suggests that MEK inhibitor therapy may be an effective treatment option for chemotherapy-resistant HGSOC.

Identifiants

pubmed: 37779141
doi: 10.1038/s12276-023-01090-1
pii: 10.1038/s12276-023-01090-1
pmc: PMC10618212
doi:

Substances chimiques

Proteasome Endopeptidase Complex EC 3.4.25.1
Transcription Factors 0
Mitogen-Activated Protein Kinase Kinases EC 2.7.12.2

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

2205-2219

Informations de copyright

© 2023. The Author(s).

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Auteurs

Hidenori Machino (H)

Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan. hidenori.machino@riken.jp.
Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan. hidenori.machino@riken.jp.

Ai Dozen (A)

Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
Department of Obstetrics and Gynecology, Keio University School of Medicine, 35 Shinanomachi, Shinjyuku-ku, Tokyo, 160-8582, Japan.

Mariko Konaka (M)

Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan.

Masaaki Komatsu (M)

Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan.
Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.

Kohei Nakamura (K)

Genomics Unit, Keio Cancer Center, Keio University School of Medicine, 35 Shinanomachi, Shinjyuku-ku, Tokyo, 160-8582, Japan.

Noriko Ikawa (N)

Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.

Kanto Shozu (K)

Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
Department of Obstetrics and Gynecology, University of Toyama, 2630 Sugitani, Toyama-shi, Toyama, 930-0152, Japan.

Ken Asada (K)

Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan.
Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.

Syuzo Kaneko (S)

Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan.
Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.

Hiroshi Yoshida (H)

Division of Diagnostic Pathology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.

Tomoyasu Kato (T)

Department of Gynecology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.

Kentaro Nakayama (K)

Department of Obstetrics and Gynecology, Shimane University Faculty of Medicine, 89-1 Enyacho, Izumo-shi, Shimane, 693-8501, Japan.

Vassiliki Saloura (V)

Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892, USA.

Satoru Kyo (S)

Department of Obstetrics and Gynecology, Shimane University Faculty of Medicine, 89-1 Enyacho, Izumo-shi, Shimane, 693-8501, Japan.

Ryuji Hamamoto (R)

Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan. rhamamot@ncc.go.jp.
Division of Medical AI Research and Development, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan. rhamamot@ncc.go.jp.

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