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

被引：11

作者：

Oh, Sumin ^{[1
,2
]}

Baek, Yang-Hyun ^{[3
]}

Jung, Sungju ^{[1
]}

Yoon, Sumin ^{[1
]}

Kang, Byeonggeun ^{[4
,5
]}

Han, Su-hyang ^{[1
]}

Park, Gaeul ^{[6
]}

Ko, Je Yeong ^{[7
]}

Han, Sang -Young ^{[8
]}

Jeong, Jin-Sook ^{[9
]}

Cho, Jin-Han ^{[10
]}

Roh, Young-Hoon ^{[11
]}

Lee, Sung-Wook ^{[11
,12
]}

Choi, Gi-Bok ^{[13
]}

Lee, Yong Sun ^{[6
]}

Kim, Won ^{[15
]}

Seong, Rho Hyun ^{[4
]}

Park, Jong Hoon ^{[7
]}

Lee, Yeon-Su ^{[6
,14
]}

Yoo, Kyung Hyun ^{[1
,2
,12
]}

机构：

[1] Sookmyung Womens Univ, Dept Biol Sci, Lab Biomed Genom, Seoul, South Korea

[2] Sookmyung Womens Univ, Res Inst Womens Hlth, Seoul, South Korea

[3] Dong A Univ, Liver Ctr, Dept Internal Med, Coll Med, Busan, South Korea

[4] Seoul Natl Univ, Inst Mol Biol & Genet, Dept Biol Sci, Seoul, South Korea

[5] Seoul Natl Univ, Biomax Inst, Seoul, South Korea

[6] Natl Canc Ctr, Res Inst, Div Rare Canc, 323 Ilsan Ro, Goyang 10408, South Korea

[7] Sookmyung Womens Univ, Dept Biol Sci, 100 Cheongpa Ro 47 Gil, Seoul 04310, South Korea

[8] On Hosp, Liver Ctr, Busan, South Korea

[9] Dong A Univ, Dept Pathol, Med Ctr, Busan, South Korea

[10] Dong A Univ, Dept Diagnost Radiol, Med Ctr, Busan, South Korea

[11] Dong A Univ, Dept Surg, Med Ctr, Busan, South Korea

[12] Dong A Univ, Liver Ctr, Dept Internal Med, Med Ctr, Busan, South Korea

[13] On Hosp, Dept Radiol, Busan, South Korea

[14] Natl Canc Ctr, Grad Sch Canc Sci & Policy, Dept Canc Biomed Sci, Goyang, South Korea

[15] Seoul Natl Univ, Seoul Metropolitan Govt Boramae Med Ctr, Dept Internal Med, Coll Med, Seoul, South Korea

来源：

CLINICAL AND MOLECULAR HEPATOLOGY | 2024年 / 30卷 / 02期

基金：

新加坡国家研究基金会;

关键词：

MASLD; Multi-omics; Machine learning; Signature gene set; Biomarker; FIBROSIS; REVEALS; MODEL;

D O I：

10.3350/cmh.2023.0449

中图分类号：

R57 [消化系及腹部疾病];

学科分类号：

摘要：

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. (Clin Mol Hepatol 2024;30:247-262)

引用

页码：247 / 262

页数：17

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