Skip to main content
Log in

Identification and validation of cuproptosis-related genes for diagnosis and therapy in nonalcoholic fatty liver disease

  • Published:
Molecular and Cellular Biochemistry Aims and scope Submit manuscript

Abstract

In recent years, nonalcoholic fatty liver disease (NAFLD) has become a more serious public health issue worldwide. This study strived to investigate the molecular mechanism of pathogenesis of NAFLD and explore promising diagnostic and therapeutic targets for NAFLD. Raw data from GSE130970 were downloaded from the Gene Expression Omnibus database. We used the dataset to analyze the expression levels of cuproptosis-related genes in NAFLD patients and healthy controls to identify the differentially expressed cuproptosis-related genes (DECRGs). The relationship and potential mechanism between DECRGs and clinicopathological factors were examined by enrichment analysis and two consensus clustering methods. We screened key DECRGs based on Random Forest (RF), and then verified the key DECRGs in NAFLD patients, high-fat diet (HFD)–fed mice, and palmitic acid–induced AML12 cells. ROC analysis showed good diagnostic function of DECRGs in normal and NAFLD liver tissue. Two consensus clusters indicated the important role of cuproptosis in the development of NAFLD. We screened for key DECRGs (DLD, DLAT) based on RF and found a close relationship between the DECRGs and clinicopathological factors. We collected clinical blood samples to verify the differences in gene expression levels by qPCR. In addition, we further verified the expression levels of DLD and DLAT in HFD mice and AML12 cells, which showed the same results. This study provides a novel perspective on the pathogenesis of NAFLD. We identified two cuproptosis-related genes that are closely related to NAFLD. These genes may play a significant role in the molecular pathogenesis of NAFLD, which may be useful to make progress in the diagnosis and treatment of NAFLD.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: GSE130970; further inquiries can be directed to the corresponding author.

References

  1. Sanyal AJ (2019) Past, present and future perspectives in nonalcoholic fatty liver disease. Nat Rev Gastroenterol Hepatol 16:377–386. https://doi.org/10.1038/s41575-019-0144-8

    Article  PubMed  Google Scholar 

  2. Younossi ZM, Koenig AB, Abdelatif D, Fazel Y, Henry L, Wymer M (2016) Global epidemiology of nonalcoholic fatty liver disease-meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology 64:73–84. https://doi.org/10.1002/hep.28431

    Article  PubMed  Google Scholar 

  3. Abdelmalek MF (2021) Nonalcoholic fatty liver disease: another leap forward. Nat Rev Gastroenterol Hepatol 18:85–86. https://doi.org/10.1038/s41575-020-00406-0

    Article  PubMed  PubMed Central  Google Scholar 

  4. Chalasani N, Younossi Z, Lavine JE, Charlton M, Cusi K, Rinella M, Harrison SA, Brunt EM, Sanyal AJ (2018) The diagnosis and management of nonalcoholic fatty liver disease: practice guidance from the American association for the study of liver diseases. Hepatology 67:328–357. https://doi.org/10.1002/hep.29367

    Article  PubMed  Google Scholar 

  5. Galluzzi L, Vitale I, Aaronson SA, Abrams JM, Adam D, Agostinis P, Alnemri ES, Altucci L, Amelio I, Andrews DW, Annicchiarico-Petruzzelli M, Antonov AV, Arama E, Baehrecke EH, Barlev NA, Bazan NG, Bernassola F, Bertrand MJM, Bianchi K, Blagosklonny MV, Blomgren K, Borner C, Boya P, Brenner C, Campanella M, Candi E, Carmona-Gutierrez D, Cecconi F, Chan FKM, Chandel NS, Cheng EH, Chipuk JE, Cidlowski JA, Ciechanover A, Cohen GM, Conrad M, Cubillos-Ruiz JR, Czabotar PE, D’Angiolella V, Dawson TM, Dawson VL, De Laurenzi V, De Maria R, Debatin K-M, DeBerardinis RJ, Deshmukh M, Di Daniele N, Di Virgilio F, Dixit VM, Dixon SJ, Duckett CS, Dynlacht BD, El-Deiry WS, Elrod JW, Fimia GM, Fulda S, García-Sáez AJ, Garg AD, Garrido C, Gavathiotis E, Golstein P, Gottlieb E, Green DR, Greene LA, Gronemeyer H, Gross A, Hajnoczky G, Hardwick JM, Harris IS, Hengartner MO, Hetz C, Ichijo H, Jäättelä M, Joseph B, Jost PJ, Juin PP, Kaiser WJ, Karin M, Kaufmann T, Kepp O, Kimchi A, Kitsis RN, Klionsky DJ, Knight RA, Kumar S, Lee SW, Lemasters JJ, Levine B, Linkermann A, Lipton SA, Lockshin RA, López-Otín C, Lowe SW, Luedde T, Lugli E, MacFarlane M, Madeo F, Malewicz M, Malorni W, Manic G, Marine J-C, Martin SJ, Martinou J-C, Medema JP, Mehlen P, Meier P, Melino S, Miao EA, Molkentin JD, Moll UM, Muñoz-Pinedo C, Nagata S, Nuñez G, Oberst A, Oren M, Overholtzer M, Pagano M, Panaretakis T, Pasparakis M, Penninger JM, Pereira DM, Pervaiz S, Peter ME, Piacentini M, Pinton P, Prehn JHM, Puthalakath H, Rabinovich GA, Rehm M, Rizzuto R, Rodrigues CMP, Rubinsztein DC, Rudel T, Ryan KM, Sayan E, Scorrano L, Shao F, Shi Y, Silke J, Simon H-U, Sistigu A, Stockwell BR, Strasser A, Szabadkai G, Tait SWG, Tang D, Tavernarakis N, Thorburn A, Tsujimoto Y, Turk B, Vanden Berghe T, Vandenabeele P, Vander Heiden MG, Villunger A, Virgin HW, Vousden KH, Vucic D, Wagner EF, Walczak H, Wallach D, Wang Y, Wells JA, Wood W, Yuan J, Zakeri Z, Zhivotovsky B, Zitvogel L, Melino G, Kroemer G (2018) Molecular mechanisms of cell death: recommendations of the nomenclature committee on cell death 2018. Cell Death Differ 25:486–541. https://doi.org/10.1038/s41418-017-0012-4

    Article  PubMed  PubMed Central  Google Scholar 

  6. Tan Y, Chen Q, Li X, Zeng Z, Xiong W, Li G, Li X, Yang J, Xiang B, Yi M (2021) Pyroptosis: a new paradigm of cell death for fighting against cancer. J Exp Clin Cancer Res 40:153. https://doi.org/10.1186/s13046-021-01959-x

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Tsvetkov P, Coy S, Petrova B, Dreishpoon M, Verma A, Abdusamad M, Rossen J, Joesch-Cohen L, Humeidi R, Spangler RD, Eaton JK, Frenkel E, Kocak M, Corsello SM, Lutsenko S, Kanarek N, Santagata S, Golub TR (2022) Copper induces cell death by targeting lipoylated TCA cycle proteins. Science 375:1254–1261. https://doi.org/10.1126/science.abf0529

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  8. Chen L, Min J, Wang F (2022) Copper homeostasis and cuproptosis in health and disease. Signal Transduct Target Ther 7:378. https://doi.org/10.1038/s41392-022-01229-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Ma C, Han L, Zhu Z, Heng Pang C, Pan G (2022) Mineral metabolism and ferroptosis in non-alcoholic fatty liver diseases. Biochem Pharmacol 205:115242. https://doi.org/10.1016/j.bcp.2022.115242

    Article  CAS  PubMed  Google Scholar 

  10. Hoang SA, Oseini A, Feaver RE, Cole BK, Asgharpour A, Vincent R, Siddiqui M, Lawson MJ, Day NC, Taylor JM, Wamhoff BR, Mirshahi F, Contos MJ, Idowu M, Sanyal AJ (2019) Gene expression predicts histological severity and reveals distinct molecular profiles of nonalcoholic fatty liver disease. Sci Rep 9:12541. https://doi.org/10.1038/s41598-019-48746-5

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  11. Clough E, Barrett T (2016) The gene expression omnibus database. Methods Mol Biol 1418:93–110. https://doi.org/10.1007/978-1-4939-3578-9_5

    Article  PubMed  PubMed Central  Google Scholar 

  12. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, Müller M (2011) pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 12:77. https://doi.org/10.1186/1471-2105-12-77

    Article  PubMed  PubMed Central  Google Scholar 

  13. Wilkerson MD, Hayes DN (2010) ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 26:1572–1573. https://doi.org/10.1093/bioinformatics/btq170

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ringnér M (2008) What is principal component analysis? Nat Biotechnol 26:303–304. https://doi.org/10.1038/nbt0308-303

    Article  CAS  PubMed  Google Scholar 

  15. Hänzelmann S, Castelo R, Guinney J (2013) GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14:7. https://doi.org/10.1186/1471-2105-14-7

    Article  PubMed  PubMed Central  Google Scholar 

  16. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:e47. https://doi.org/10.1093/nar/gkv007

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30. https://doi.org/10.1093/nar/28.1.27

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. da Huang W, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4:44–57. https://doi.org/10.1038/nprot.2008.211

    Article  CAS  PubMed  Google Scholar 

  19. Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, Franz M, Grouios C, Kazi F, Lopes CT, Maitland A, Mostafavi S, Montojo J, Shao Q, Wright G, Bader GD, Morris Q (2010) The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res 38:W214–W220. https://doi.org/10.1093/nar/gkq537

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Qin Z, Xi Y, Zhang S, Tu G, Yan A (2019) Classification of cyclooxygenase-2 inhibitors using support vector machine and random forest methods. J Chem Inf Model 59:1988–2008. https://doi.org/10.1021/acs.jcim.8b00876

    Article  CAS  PubMed  Google Scholar 

  21. Lau JK, Zhang X, Yu J (2017) Animal models of non-alcoholic fatty liver disease: current perspectives and recent advances. J Pathol 241:36–44. https://doi.org/10.1002/path.4829

    Article  PubMed  Google Scholar 

  22. Van Herck MA, Vonghia L, Francque SM (2017) Animal models of nonalcoholic fatty liver disease-a starter’s guide. Nutrients. https://doi.org/10.3390/nu9101072

    Article  PubMed  PubMed Central  Google Scholar 

  23. Friedman SL, Neuschwander-Tetri BA, Rinella M, Sanyal AJ (2018) Mechanisms of NAFLD development and therapeutic strategies. Nat Med 24:908–922. https://doi.org/10.1038/s41591-018-0104-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. He F, Ru X, Wen T (2020) NRF2, a transcription factor for stress response and beyond. Int J Mol Sci. https://doi.org/10.3390/ijms21134777

    Article  PubMed  PubMed Central  Google Scholar 

  25. Mohs A, Otto T, Schneider KM, Peltzer M, Boekschoten M, Holland CH, Hudert CA, Kalveram L, Wiegand S, Saez-Rodriguez J, Longerich T, Hengstler JG, Trautwein C (2021) Hepatocyte-specific NRF2 activation controls fibrogenesis and carcinogenesis in steatohepatitis. J Hepatol 74:638–648. https://doi.org/10.1016/j.jhep.2020.09.037

    Article  CAS  PubMed  Google Scholar 

  26. Cai Y, He Q, Liu W, Liang Q, Peng B, Li J, Zhang W, Kang F, Hong Q, Yan Y, Peng J, Xu Z, Bai N (2022) Comprehensive analysis of the potential cuproptosis-related biomarker LIAS that regulates prognosis and immunotherapy of pan-cancers. Front Oncol 12:952129. https://doi.org/10.3389/fonc.2022.952129

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Chen Y (2022) Identification and validation of cuproptosis-related prognostic signature and associated regulatory axis in uterine corpus endometrial carcinoma. Front Genet 13:912037. https://doi.org/10.3389/fgene.2022.912037

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Huster D, Kühne A, Bhattacharjee A, Raines L, Jantsch V, Noe J, Schirrmeister W, Sommerer I, Sabri O, Berr F, Mössner J, Stieger B, Caca K, Lutsenko S (2012) Diverse functional properties of Wilson disease ATP7B variants. Gastroenterology. https://doi.org/10.1053/j.gastro.2011.12.048

    Article  PubMed  Google Scholar 

  29. Jiang X, Ji S, Yuan F, Li T, Cui S, Wang W, Ye X, Wang R, Chen Y, Zhu S (2023) Pyruvate dehydrogenase B regulates myogenic differentiation via the FoxP1-Arih2 axis. J Cachexia Sarcopenia Muscle 14:606–621. https://doi.org/10.1002/jcsm.13166

    Article  PubMed  Google Scholar 

  30. Wu C, Liu X, Zhong L, Zhou Y, Long L, Yi T, Chen S, Li Y, Chen Y, Shen L, Zeng Q, Tang S (2023) Identification of cuproptosis-related genes in nonalcoholic fatty liver disease. Oxid Med Cell Longev 2023:9245667. https://doi.org/10.1155/2023/9245667

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Chen S, Liu X, Peng C, Tan C, Sun H, Liu H, Zhang Y, Wu P, Cui C, Liu C, Yang D, Li Z, Lu J, Guan J, Ke X, Wang R, Bo X, Xu X, Han J, Liu J (2021) The phytochemical hyperforin triggers thermogenesis in adipose tissue via a Dlat-AMPK signaling axis to curb obesity. Cell Metab. https://doi.org/10.1016/j.cmet.2021.02.007

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors all would like to thank the GEO database and the authors who provided their platforms and contributors for uploading their meaningful datasets. We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

Funding

This work was supported by the Shandong Provincial Natural Science Foundation (Grant No. ZR2022MH182 and Grant No. ZR2021QH119), and the National Natural Science Foundation of China (Grant No. 82300892).

Author information

Authors and Affiliations

Authors

Contributions

JL carried out experiments and wrote the manuscript, YZ, XM, RL, CX downloaded, arranged, analyzed, and validated the data. MD, QH designed the experiment and reviewed and approved the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Qin He or Ming Dong.

Ethics declarations

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Ethical approval

Gene Expression Omnibus (GEO) database belongs to public databases. Patients / participants provided written informed consent to participate in this study. Individual written informed consent has been obtained for publishing any potentially identifiable images or data contained in this article, our study is based on open-source data with no ethical issues and other conflicts of interest. This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Qilu Hospital of Shandong University.(KYLL-202210–070-1). All animal experimental protocols were approved by the Animal Ethics Committee of Qilu Hospital of Shandong University.(DWLL-2022–090).

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, J., Zhang, Y., Ma, X. et al. Identification and validation of cuproptosis-related genes for diagnosis and therapy in nonalcoholic fatty liver disease. Mol Cell Biochem (2024). https://doi.org/10.1007/s11010-024-04957-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11010-024-04957-7

Keywords

Navigation