Identifying disease progression biomarkers in metabolic associated steatotic liver disease (MASLD) through weighted gene co-expression network analysis and machine learning

被引：0
作者：
Weiliang Zhang [1 ]
Weirong Lu [1 ]
Yaqi Jiao [1 ]
Tianhao Li [1 ]
Haining Wang [2 ]
Chunhua Wan [1 ]
机构：
[1] Department of Nutrition and Food Hygiene, School of Public Health, Nantong University, Jiangsu, Nantong
[2] Department of Cardiovascular Medicine, First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang
来源：
Journal of Translational Medicine | / 23卷 / 1期
基金：
中国国家自然科学基金;
关键词：
Bioinformatics; Disease biomarker; Inflammation; Metabolic associated steatotic liver disease; Weighted gene co-expression network analysis;
D O I：
10.1186/s12967-025-06490-7
中图分类号：
学科分类号：
摘要：
Background: Metabolic Associated Steatotic Liver Disease (MASLD), encompassing conditions simple liver steatosis (MAFL) and metabolic associated steatohepatitis (MASH), is the most prevalent chronic liver disease. Currently, the management of MASLD is impeded by the lack of reliable diagnostic biomarkers and effective therapeutic strategies. Methods: We analyzed eight independent clinical MASLD datasets from the GEO database. Differential expression and weighted gene co-expression network analyses (WGCNA) were used to identify 23 genes related to inflammation. Five hub genes were selected using machine learning techniques (SVM-RFE, LASSO, and RandomForest) combined with a literature review. Nomograms were created to predict MASLD incidence, and the diagnostic potential of the hub genes was evaluated through receiver operating characteristic (ROC) curves. Additionally, Protein-Protein Interaction (PPI) networks, functional enrichment, and immune infiltration analyses were performed. Potential transcription factors and therapeutic agents were also explored. Finally, the expression and biological significance of these hub genes were validated using MASLD animal model, histological examination and transcriptomic profiles. Results: We identified five hub genes—UBD/FAT10, STMN2, LYZ, DUSP8, and GPR88—that are potential biomarkers for MASLD. These genes exhibited strong diagnostic potential, either individually or in combination. Conclusion: This study highlights five key biomarkers as promising candidates for understanding MASLD. These findings offer new insights into the disease’s pathophysiology and may contribute to the development of better diagnostic and therapeutic approaches. © The Author(s) 2025.
引用
收藏
相关论文
共 83 条
  • [1] Younossi Z.M., Koenig A.B., Abdelatif D., Fazel Y., Henry L., Wymer M., Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes, Hepatol Baltim Md, 64, pp. 73-84, (2016)
  • [2] Huang D.Q., El-Serag H.B., Loomba R., Global epidemiology of NAFLD-related HCC: trends, predictions, risk factors and prevention, Nat Rev Gastroenterol Hepatol, 18, pp. 223-238, (2021)
  • [3] Mittal S., El-Serag H.B., Epidemiology of hepatocellular carcinoma: consider the population, J Clin Gastroenterol, 47, pp. S2-S6, (2013)
  • [4] Cassidy S., Syed B.A., Nonalcoholic steatohepatitis (NASH) drugs market, Nat Rev Drug Discov, 15, pp. 745-746, (2016)
  • [5] Wong R.J., Aguilar M., Cheung R., Perumpail R.B., Harrison S.A., Younossi Z.M., Ahmed A., Nonalcoholic steatohepatitis is the second leading etiology of liver disease among adults awaiting liver transplantation in the united States, Gastroenterology, 148, pp. 547-555, (2015)
  • [6] Aghaei M., Khademi R., Bahreiny S.S., Saki N., The need to Establish and recognize the field of clinical laboratory science (CLS) as an essential field in advancing clinical goals, Health Sci Rep, 7, (2024)
  • [7] Fouad Y., Waked I., Bollipo S., Gomaa A., Ajlouni Y., Attia D., What’s in a name? Renaming NAFLD to MAFLD, Liver Int Off J Int Assoc Study Liver, 40, pp. 1254-1261, (2020)
  • [8] Hammerich L., Tacke F., Hepatic inflammatory responses in liver fibrosis, Nat Rev Gastroenterol Hepatol, 20, pp. 633-646, (2023)
  • [9] Saki N., Haybar H., Aghaei M., Subject: motivation can be suppressed, but scientific ability cannot and should not be ignored, J Transl Med, 21, (2023)
  • [10] Leek J.T., Johnson W.E., Parker H.S., Jaffe A.E., Storey J.D., The Sva package for removing batch effects and other unwanted variation in high-throughput experiments, Bioinforma Oxf Engl, 28, pp. 882-883, (2012)
123456789