Clin Mol Hepatol.  2024 Apr;30(2):247-262. 10.3350/cmh.2023.0449.

Identification of signature gene set as highly accurate determination of metabolic dysfunction-associated steatotic liver disease progression

Affiliations
  • 1Laboratory of Biomedical Genomics, Department of Biological Sciences, Sookmyung Women’s University, Seoul, Korea
  • 2Research Institute of Women’s Health, Sookmyung Women’s University, Seoul, Korea
  • 3Liver Center, Department of Internal Medicine, Dong-A University College of Medicine, Busan, Korea
  • 4Department of Biological Sciences and Institute of Molecular Biology and Genetics, Seoul National University, Seoul, Korea
  • 5Bio-MAX Institute, Seoul National University, Seoul, Korea
  • 6Division of Rare Cancer, Research Institute, National Cancer Center, Goyang, Korea
  • 7Department of Biological Sciences, Sookmyung Women’s University, Seoul, Korea
  • 8Liver Center, On Hospital, Busan, Korea
  • 9Department of Pathology, Dong-A University Medical Center, Busan, Korea
  • 10Department of Diagnostic Radiology, Dong-A University Medical Center, Busan, Korea
  • 11Department of Surgery, Dong-A University Medical Center, Busan, Korea
  • 12Liver Center, Department of Internal Medicine, Dong-A University Medical Center, Busan, Korea
  • 13Department of Radiology, On Hospital, Busan, Korea
  • 14Department of Cancer Biomedical Science, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Korea
  • 15Department of Internal Medicine, Seoul National University College of Medicine, Seoul Metropolitan Government Boramae Medical Center, Seoul, Korea

Abstract

Background/Aims
Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by fat accumulation in the liver. MASLD encompasses both steatosis and MASH. Since MASH can lead to cirrhosis and liver cancer, steatosis and MASH must be distinguished during patient treatment. Here, we investigate the genomes, epigenomes, and transcriptomes of MASLD patients to identify signature gene set for more accurate tracking of MASLD progression.
Methods
Biopsy-tissue and blood samples from patients with 134 MASLD, comprising 60 steatosis and 74 MASH patients were performed omics analysis. SVM learning algorithm were used to calculate most predictive features. Linear regression was applied to find signature gene set that distinguish the stage of MASLD and to validate their application into independent cohort of MASLD.
Results
After performing WGS, WES, WGBS, and total RNA-seq on 134 biopsy samples from confirmed MASLD patients, we provided 1,955 MASLD-associated features, out of 3,176 somatic variant callings, 58 DMRs, and 1,393 DEGs that track MASLD progression. Then, we used a SVM learning algorithm to analyze the data and select the most predictive features. Using linear regression, we identified a signature gene set capable of differentiating the various stages of MASLD and verified it in different independent cohorts of MASLD and a liver cancer cohort.
Conclusions
We identified a signature gene set (i.e., CAPG, HYAL3, WIPI1, TREM2, SPP1, and RNASE6) with strong potential as a panel of diagnostic genes of MASLD-associated disease.

Keyword

MASLD; Multi-omics; Machine learning; Signature gene set; Biomarker
Full Text Links
  • CMH
Actions
Cited
CITED
export Copy
Close
Share
  • Twitter
  • Facebook
Similar articles
Copyright © 2024 by Korean Association of Medical Journal Editors. All rights reserved.     E-mail: koreamed@kamje.or.kr