Horm Metab Res 2023; 55(08): 546-554
DOI: 10.1055/a-2081-1098
Original Article: Endocrine Care

Identification of Basement Membrane Genes and Related Molecular Subtypes in Nonalcoholic Fatty Liver Disease

1   Department of Endocrinology, Affiliated Hospital of Jiangsu University, Zhenjiang, China
,
Huijuan Qin
1   Department of Endocrinology, Affiliated Hospital of Jiangsu University, Zhenjiang, China
,
Qichao Yang
2   Department of Endocrinology, Jiangsu University Affiliated Wujin Hospital, Changzhou, China
,
Jue Jia
1   Department of Endocrinology, Affiliated Hospital of Jiangsu University, Zhenjiang, China
,
Ling Yang
1   Department of Endocrinology, Affiliated Hospital of Jiangsu University, Zhenjiang, China
,
Shao Zhong
3   Department of Endocrinology, Affiliated Kunshan Hospital of Jiangsu University, Kunshan, China
,
Guoyue Yuan
1   Department of Endocrinology, Affiliated Hospital of Jiangsu University, Zhenjiang, China
› Author Affiliations
Funding Information Natural Science Foundation of Jiangsu Province — http://dx.doi. org/10.13039/501100004608; BK20191222; Jiangsu Province Sixth Phase “333 Talents” — ; National Natural Science Foundation of China — http://dx.doi.org/10.13039/501100001809; 81570721; Youth Medical Talent Project of Jiangsu Province — QNRC2016842; Suzhou Science and Technology Planning Project — STL2021006; Social Development Project of Jiangsu Province — BE2018692

Abstract

Basement membranes (BMs) are widely distributed and highly specialized extracellular matrix (ECM). The goal of this study was to explore novel genes associated with nonalcoholic fatty liver disease (NAFLD) from the perspective of BMs. Sequencing results of 304 liver biopsy samples about NAFLD were systematically obtained from the Gene Expression Omnibus (GEO) database. Biological changes during NAFLD progression and hub BM-associated genes were investigated by differential gene analysis and weighted gene co-expression network analysis (WGCNA), respectively. The nonalcoholic steatohepatitis (NASH) subgroups were identified based on hub BM-associated genes expression, as well as the differences in Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways and immune microenvironment between different subgroups were compared. Extracellular matrix (ECM) seems to play an important role in the development of NAFLD. Three representative BM-associated genes (ADAMTS2, COL5A1, and LAMC3) were finally identified. Subgroup analysis results suggested that there were significant changes in KEGG signaling pathways related to metabolism, extracellular matrix, cell proliferation, differentiation, and death. There were also changes in macrophage polarization, neutrophils, and dendritic cells abundance, and so on. In conclusion, the present study identified novel potential BM-associated biomarkers and further explored the heterogeneity of NASH that might provide new insights into the diagnosis, assessment, management, and personalized treatment of NAFLD.



Publication History

Received: 05 February 2023

Accepted after revision: 17 April 2023

Article published online:
02 June 2023

© 2023. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 
  • References

  • 1 Powell EE, Wong VW, Rinella M. Non-alcoholic fatty liver disease. Lancet 2021; 397: 2212-2224
  • 2 Sheka AC, Adeyi O, Thompson J. et al. Nonalcoholic steatohepatitis: a review. JAMA 2020; 323: 1175-1183
  • 3 Pozzi A, Yurchenco PD, Iozzo RV. The nature and biology of basement membranes. Matrix Biol 2017; 57–58: 1-11
  • 4 Yurchenco PD. Basement membranes: cell scaffoldings and signaling platforms. Cold Spring Harb Perspect Biol 2011; 3
  • 5 Jayadev R, Morais M, Ellingford JM. et al. A basement membrane discovery pipeline uncovers network complexity, regulators, and human disease associations. Sci Adv 2022; 8: eabn2265
  • 6 Jayadev R, Sherwood DR. Basement membranes. Curr Biol 2017; 27: R207-R211
  • 7 Li S, Qi Y, McKee K. et al. Integrin and dystroglycan compensate each other to mediate laminin-dependent basement membrane assembly and epiblast polarization. Matrix Biol 2017; 57-58: 272-284
  • 8 Goddi A, Schroedl L, Brey EM. et al. Laminins in metabolic tissues. Metabolism 2021; 120: 154775
  • 9 Xu T, Lu Z, Xiao Z. et al. Myofibroblast induces hepatocyte-to-ductal metaplasia via laminin-βv β 6 integrin in liver fibrosis. Cell Death Dis 2020; 11: 199
  • 10 Bianchi FB, Biagini G, Ballardini G. et al. Basement membrane production by hepatocytes in chronic liver disease. Hepatology 1984; 4: 1167-1172
  • 11 Barrett T, Wilhite SE, Ledoux P. et al. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res 2013; 41: D991-D995
  • 12 Zhang Y, Parmigiani G, Johnson WE. ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genom Bioinform 2020; 2 lqaa078
  • 13 McDermaid A, Monier B, Zhao J. et al. Interpretation of differential gene expression results of RNA-seq data: review and integration. Brief Bioinform 2019; 20: 2044-2054
  • 14 Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008; 9: 559
  • 15 Yu G, Wang LG, Han Y. et al. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 2012; 16: 284-287
  • 16 McEligot AJ, Poynor V, Sharma R. et al. Logistic LASSO regression for dietary Intakes and breast cancer. Nutrients 2020; 12
  • 17 Subramanian A, Tamayo P, Mootha VK. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005; 102: 15545-15550
  • 18 Van Calster B, Wynants L, Verbeek JFM. et al. Reporting and interpreting decision curve analysis: a guide for investigators. Eur Urol 2018; 74: 796-804
  • 19 Wilkerson MD, Hayes DN. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 2010; 26: 1572-1573
  • 20 Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 2013; 14: 7
  • 21 Newman AM, Liu CL, Green MR. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 2015; 12: 453-457
  • 22 Gao R, Wang J, He X. et al. Comprehensive analysis of endoplasmic reticulum-related and secretome gene expression profiles in the progression of non-alcoholic fatty liver disease. Front Endocrinol (Lausanne) 2022; 13: 967016
  • 23 Wang Z, Zhao Z, Xia Y. et al. Potential biomarkers in the fibrosis progression of nonalcoholic steatohepatitis (NASH). J Endocrinol Invest 2022; 45: 1379-1392
  • 24 Liu L, Liu C, Zhao M. et al. The pharmacodynamic and differential gene expression analysis of PPAR α/δ agonist GFT505 in CDAHFD-induced NASH model. PLoS One 2020; 15: e0243911
  • 25 Schwabe RF, Tabas I, Pajvani UB. Mechanisms of fibrosis development in nonalcoholic steatohepatitis. Gastroenterology 2020; 158: 1913-1928
  • 26 Zhu C, Tabas I, Schwabe RF. et al. Maladaptive regeneration - the reawakening of developmental pathways in NASH and fibrosis. Nat Rev Gastroenterol Hepatol 2021; 18: 131-142
  • 27 Bonnans C, Chou J, Werb Z.. Remodelling the extracellular matrix in development and disease. Nat Rev Mol Cell Biol 2014; 15: 786-801
  • 28 Bekhouche M, Colige A. The procollagen N-proteinases ADAMTS2, 3 and 14 in pathophysiology. Matrix Biol 2015; 44-46: 46-53
  • 29 Schwettmann L, Wehmeier M, Jokovic D. et al. Hepatic expression of A disintegrin and metalloproteinase (ADAM) and ADAMs with thrombospondin motives (ADAM-TS) enzymes in patients with chronic liver diseases. J Hepatol 2008; 49: 243-250
  • 30 Kesteloot F, Desmoulière A, Leclercq I. et al. ADAM metallopeptidase with thrombospondin type 1 motif 2 inactivation reduces the extent and stability of carbon tetrachloride-induced hepatic fibrosis in mice. Hepatology 2007; 46: 1620-1631
  • 31 Birk DE, Fitch JM, Babiarz JP. et al. Collagen fibrillogenesis in vitro: interaction of types I and V collagen regulates fibril diameter. . J Cell Sci 1990; 95: 649-657
  • 32 Gu S, Peng Z, Wu Y. et al. COL5A1 Serves as a biomarker of tumor progression and poor prognosis and may be a potential therapeutic target in gliomas. Front Oncol 2021; 11: 752694
  • 33 He YH, Deng YS, Peng PX. et al. A novel messenger RNA and long noncoding RNA signature associated with the progression of nonmuscle invasive bladder cancer. J Cell Biochem 2019; 120: 8101-8109
  • 34 Friedman SL, Neuschwander-Tetri BA, Rinella M. et al. Mechanisms of NAFLD development and therapeutic strategies. Nat Med 2018; 24: 908-922
  • 35 Loomba R, Schork N, Chen CH. et al. Heritability of hepatic fibrosis and steatosis based on a prospective twin study. Gastroenterology 2015; 149: 1784-1793
  • 36 Caussy C, Soni M, Cui J. et al. Nonalcoholic fatty liver disease with cirrhosis increases familial risk for advanced fibrosis. J Clin Invest 2017; 127: 2697-2704
  • 37 Ramachandran P, Matchett KP, Dobie R. et al. Single-cell technologies in hepatology: new insights into liver biology and disease pathogenesis. Nat Rev Gastroenterol Hepatol 2020; 17: 457-472
  • 38 Peiseler M, Schwabe R, Hampe J. et al. Immune mechanisms linking metabolic injury to inflammation and fibrosis in fatty liver disease - novel insights into cellular communication circuits. J Hepatol 2022; 77: 1136-1160
  • 39 Morabito S, Miyoshi E, Michael N. et al. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease. Nat Genet 2021; 53: 1143-1155
  • 40 Xu M, Xu HH, Lin Y. et al. LECT2, a ligand for tie1, plays a crucial role in liver Ffibrogenesis. Cell 2019; 1478-1492.e1420