Reply to correspondence on “Long-term gastrointestinal and hepatobiliary outcomes of COVID-19: A multinational population-based cohort study from South Korea, Japan, and the UK” Yang-Hyun Baek Clinical and Molecular Hepatology.2025; 31(1): e123. CrossRef
Correspondence to editorial on “Long-term gastrointestinal and hepatobiliary outcomes of COVID-19: a multinational population-based cohort study from South Korea, Japan, and the UK” Kwanjoo Lee, Jaeyu Park, Jinseok Lee, Hayeon Lee, Yeonjung Ha, Dong Keon Yon Clinical and Molecular Hepatology.2025; 31(1): e87. CrossRef
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.
Citations
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