Evidence map›Paper›PMID 35655171›Full record

ArticleBMC gastroenterology2022

Two differentially methylated region networks in nonalcoholic fatty liver disease, viral hepatitis, and hepatocellular carcinoma.

Suguru Kurokawa, Masato Yoneda, Yuji Ogawa, Yasushi Honda, Takaomi Kessoku, Kento Imajo, Satoru Saito, Atsushi Nakajima, Kikuko Hotta

Open access · goldAbstract read
In one paragraph

Article in BMC gastroenterology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.9field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 12 citations in OpenAlex.

  1. Review
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  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 1 country.

Suguru KurokawaLaboratory of Pathophysiology and Pharmacotherapeutics, Faculty of Pharmacy, Osaka Ohtani University, 3-11-1 Nishikiori-kita, Tondabayashi, Osaka, 584-8540, Japan.
Masato YonedaDepartment of Gastroenterology and Hepatology, Yokohama City University Graduate School of Medicine, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, 236-0004, Japan.
Yuji OgawaDepartment of Gastroenterology, National Hospital Organization Yokohama Medical Center, 3-60-2 Harajyuku, Totsuka, Yokohama, 245-8675, Japan.
Yasushi HondaDepartment of Gastroenterology and Hepatology, Yokohama City University Graduate School of Medicine, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, 236-0004, Japan.
Takaomi KessokuDepartment of Gastroenterology and Hepatology, Yokohama City University Graduate School of Medicine, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, 236-0004, Japan.
Kento ImajoDepartment of Gastroenterology, Shin-yurigaoka General Hospital, 255 Furusawatsuko, Asao, Kawasaki, 2150-0026, Japan.
Satoru SaitoDepartment of Gastroenterology and Hepatology, Yokohama City University Graduate School of Medicine, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, 236-0004, Japan.
Atsushi NakajimaDepartment of Gastroenterology and Hepatology, Yokohama City University Graduate School of Medicine, 3-9 Fukuura, Kanazawa-ku, Yokohama, Kanagawa, 236-0004, Japan.
Kikuko HottaLaboratory of Pathophysiology and Pharmacotherapeutics, Faculty of Pharmacy, Osaka Ohtani University, 3-11-1 Nishikiori-kita, Tondabayashi, Osaka, 584-8540, Japan. hottakiku@osaka-ohtani.ac.jp.
Yokohama City University · JPOsaka Ohtani University · JPNational Hospital Organization · JP

Funding

Japan Society for the Promotion of Science 19K08979
6 · The paper itself

Abstract

backgroundWe previously reported that two differentially methylated region (DMR) networks identified by DMR and co-methylation analyses are strongly correlated with the fibrosis stages of nonalcoholic fatty liver disease (NAFLD). In the current study, we examined these DMR networks in viral hepatitis and hepatocellular carcinoma (HCC).

methodsWe performed co-methylation analysis of DMRs using a normal dataset (GSE48325), two NAFLD datasets (JGAS000059 and GSE31803), and two HCC datasets (GSE89852 and GSE56588). The dataset GSE60753 was used for validation.

resultsOne DMR network was clearly observed in viral hepatitis and two HCC populations. Methylation levels of genes in this network were higher in viral hepatitis and cirrhosis, and lower in HCC. Fatty acid binding protein 1 (FABP1), serum/glucocorticoid regulated kinase 2 (SGK2), and hepatocyte nuclear factor 4 α (HNF4A) were potential hub genes in this network. Increased methylation levels of the FABP1 gene may be correlated with reduced protection of hepatocytes from oxidative metabolites in NAFLD and viral hepatitis. The decreased methylation levels of SGK2 may facilitate the growth and proliferation of HCC cells. Decreased methylation levels of HNF4A in HCC may be associated with tumorigenesis. The other DMR network was observed in NAFLD, but not in viral hepatitis or HCC. This second network included genes involved in transcriptional regulation, cytoskeleton organization, and cellular proliferation, which are specifically related to fibrosis and/or tumorigenesis in NAFLD.

conclusionsOur results suggest that one DMR network was associated with fibrosis and tumorigenesis in both NAFLD and viral hepatitis, while the other network was specifically associated with NAFLD progression. Furthermore, FABP1, SGK2, and HNF4A are potential candidate targets for the prevention and treatment of HCC.

Indexed as

Carcinoma, HepatocellularHepatitis, Viral, HumanLiver NeoplasmsNon-alcoholic Fatty Liver DiseaseCell Transformation, NeoplasticHumansLiver CirrhosisDNA methylationEpigeneticsHepatocellular carcinomaNAFLDNetwork modeling

Identifiers

PMID35655171
PMCPMC9164838
OpenAlexW4281772226

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.