Tincopa, M. A., Anstee, Q. M. & Loomba, R. New and emerging treatments for metabolic dysfunction-associated steatohepatitis. Cell Metab. 36, 912–926. (2024).
Google Scholar
Lazarus, J. V. et al. Advancing the global public health agenda for NAFLD: A consensus statement. Nat. Rev. Gastroenterol. Hepatol. 19, 60–78. (2022).
Google Scholar
Sumida, Y., Nakajima, A. & Itoh, Y. Limitations of liver biopsy and non-invasive diagnostic tests for the diagnosis of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis. World J. Gastroenterol. 20, 475–485. (2014).
Google Scholar
Castera, L., Friedrich-Rust, M. & Loomba, R. Noninvasive assessment of liver disease in patients with nonalcoholic fatty liver disease. Gastroenterology 156, 1264-1281 e1264. (2019).
Google Scholar
Sveinbjornsson, G. et al. Multiomics study of nonalcoholic fatty liver disease. Nat. Genet. 54, 1652–1663. (2022).
Google Scholar
Tun, K. M., Noureddin, N. & Noureddin, M. Noninvasive tests in the evaluation of nonalcoholic fatty liver disease: A review. Clin. Liver Dis. (Hoboken) 22, 103–112. (2023).
Google Scholar
Verschuren, L. et al. Development of a novel non-invasive biomarker panel for hepatic fibrosis in MASLD. Nat. Commun. 15, 4564. (2024).
Google Scholar
Odenkirk, M. T., Reif, D. M. & Baker, E. S. Multiomic big data analysis challenges: Increasing confidence in the interpretation of artificial intelligence assessments. Anal. Chem. 93, 7763–7773. (2021).
Google Scholar
Slobodyanyuk, M. et al. Directional integration and pathway enrichment analysis for multi-omics data. Nat. Commun. 15, 5690. (2024).
Google Scholar
Greener, J. G., Kandathil, S. M., Moffat, L. & Jones, D. T. A guide to machine learning for biologists. Nat. Rev. Mol. Cell Biol. 23, 40–55. (2022).
Google Scholar
Camacho, D. M., Collins, K. M., Powers, R. K., Costello, J. C. & Collins, J. J. Next-generation machine learning for biological networks. Cell 173, 1581–1592. (2018).
Google Scholar
Perez-Lopez, R., Ghaffari Laleh, N., Mahmood, F. & Kather, J. N. A guide to artificial intelligence for cancer researchers. Nat. Rev. Cancer 24, 427–441. (2024).
Google Scholar
Santos, A. et al. A knowledge graph to interpret clinical proteomics data. Nat. Biotechnol. 40, 692–702. (2022).
Google Scholar
Niu, L. et al. Noninvasive proteomic biomarkers for alcohol-related liver disease. Nat. Med. 28, 1277–1287. (2022).
Google Scholar
Peng, H., Wang, H., Kong, W., Li, J. & Goh, W. W. B. Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference. Nat. Commun. 15, 3922. (2024).
Google Scholar
Zhang, X., Li, X. & Xiong, X. Applying proteomics in metabolic dysfunction-associated steatotic liver disease: From mechanism to biomarkers. Clin. Res. Hepatol. Gastroenterol. 47, 102230. (2023).
Google Scholar
Jimenez Ramos, M., Kendall, T. J., Drozdov, I. & Fallowfield, J. A. A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease. Ann. Hepatol. 29, 101278. (2024).
Google Scholar
Thery, C. et al. Minimal information for studies of extracellular vesicles 2018 (MISEV2018): A position statement of the International Society for Extracellular Vesicles and update of the MISEV2014 guidelines. J. Extracell. Vesicles 7, 1535750. (2018).
Google Scholar
Povero, D. et al. Characterization and proteome of circulating extracellular vesicles as potential biomarkers for NASH. Hepatol. Commun. 4, 1263–1278. (2020).
Google Scholar
Garcia, N. A., Mellergaard, M., Gonzalez-King, H., Salomon, C. & Handberg, A. Comprehensive strategy for identifying extracellular vesicle surface proteins as biomarkers for non-alcoholic fatty liver disease. Int. J. Mol. Sci. (2023).
Google Scholar
Guo, Q., Jian, Z., Jia, B. & Chang, L. CXCL7 promotes proliferation and invasion of cholangiocarcinoma cells. Oncol. Rep. 37, 1114–1122. (2017).
Google Scholar
Zajkowska, M. & Mroczko, B. Chemokines in primary liver cancer. Int. J. Mol. Sci. (2022).
Google Scholar
Maneerat, Y., Prasongsukarn, K., Benjathummarak, S. & Dechkhajorn, W. PPBP and DEFA1/DEFA3 genes in hyperlipidaemia as feasible synergistic inflammatory biomarkers for coronary heart disease. Lipids Health Dis. 16, 80. (2017).
Google Scholar
Welton, J. L. et al. Proteomics analysis of vesicles isolated from plasma and urine of prostate cancer patients using a multiplex, aptamer-based protein array. J. Extracell. Vesicles 5, 31209. (2016).
Google Scholar
Zhang, Z. & Zhan, F. Type 2 cystatins and their roles in the regulation of human immune response and cancer progression. Cancers (Basel) (2023).
Google Scholar
Zhou, T., Chang, L., Luo, Y., Zhou, Y. & Zhang, J. Mst1 inhibition attenuates non-alcoholic fatty liver disease via reversing Parkin-related mitophagy. Redox Biol. 21, 101120. (2019).
Google Scholar
Moller, S. & Becker, U. Insulin-like growth factor 1 and growth hormone in chronic liver disease. Dig. Dis. 10, 239–248. (1992).
Google Scholar
Korol, C. B. et al. Fulminant viral hepatitis in two siblings with inherited IL-10RB deficiency. J. Clin. Immunol. 43, 406–420. (2023).
Google Scholar
Zhu, J. et al. Glycopeptide biomarkers in serum haptoglobin for hepatocellular carcinoma detection in patients with nonalcoholic steatohepatitis. J. Proteome Res. 19, 3452–3466. (2020).
Google Scholar
Sanyal, A. J. et al. Defining the serum proteomic signature of hepatic steatosis, inflammation, ballooning and fibrosis in non-alcoholic fatty liver disease. J. Hepatol. 78, 693–703. (2023).
Google Scholar
Le Roith, D. Seminars in medicine of the Beth Israel Deaconess Medical Center. Insulin-like growth factors. N. Engl. J. Med. 336, 633–640. (1997).
Google Scholar
Jorgensen, J. O., Moller, L., Krag, M., Billestrup, N. & Christiansen, J. S. Effects of growth hormone on glucose and fat metabolism in human subjects. Endocrinol. Metab. Clin. North Am. 36, 75–87. (2007).
Google Scholar
Kumar, M. A. et al. Extracellular vesicles as tools and targets in therapy for diseases. Signal Transduct. Target. Ther. 9, 27. (2024).
Google Scholar
Ichikawa, T. et al. Role of growth hormone, insulin-like growth factor 1 and insulin-like growth factor-binding protein 3 in development of non-alcoholic fatty liver disease. Hepatol. Int. 1, 287–294. (2007).
Google Scholar
Liu, P. et al. Non-canonical NRF2 activation promotes a pro-diabetic shift in hepatic glucose metabolism. Mol. Metab. 51, 101243. (2021).
Google Scholar
Li, Z. et al. Arginase: Shedding light on the mechanisms and opportunities in cardiovascular diseases. Cell Death Discov. 8, 413. (2022).
Google Scholar
Roderfeld, M. Matrix metalloproteinase functions in hepatic injury and fibrosis. Matrix Biol. 68, 452–462. (2018).
Google Scholar
Dibra, D. et al. Mutant p53 in concert with an interleukin-27 receptor alpha deficiency causes spontaneous liver inflammation, fibrosis, and steatosis in mice. Hepatology 63, 1000–1012. (2016).
Google Scholar
Guan, H., Guo, Y., Zhu, L., Jiao, Y. & Liu, X. Peroxisome deficiency dysregulates fatty acid oxidization and exacerbates lipotoxicity in beta cells. Oxid. Med. Cell. Longev. 2021, 7726058. (2021).
Google Scholar
Rubin, J., Mariani, L., Smith, A. & Zee, J. Ridge regression for functional form identification of continuous predictors of clinical outcomes in glomerular disease. Glomerular Dis. 3, 47–55. (2023).
Google Scholar
Linardatos, P., Papastefanopoulos, V. & Kotsiantis, S. Explainable AI: A review of machine learning interpretability methods. Entropy (2020).
Google Scholar
AlMansoori, M. E., Jemimah, S., Abuhantash, F. & AlShehhi, A. Predicting early Alzheimer’s with blood biomarkers and clinical features. Sci. Rep. 14, 6039. (2024).
Google Scholar
Ramirez-Garrastacho, M. et al. Extracellular vesicles as a source of prostate cancer biomarkers in liquid biopsies: A decade of research. Br. J. Cancer 126, 331–350. (2022).
Google Scholar
Adhit, K. K., Wanjari, A., Menon, S. & K, S. Liquid biopsy: An evolving paradigm for non-invasive disease diagnosis and monitoring in medicine. Cureus 15, e50176. (2023).
Google Scholar
Eguchi, A., Kostallari, E., Feldstein, A. E. & Shah, V. H. Extracellular vesicles, the liquid biopsy of the future. J. Hepatol. 70, 1292–1294. (2019).
Google Scholar
Eguchi, A. et al. Extracellular vesicles released by hepatocytes from gastric infusion model of alcoholic liver disease contain a MicroRNA barcode that can be detected in blood. Hepatology 65, 475–490. (2017).
Google Scholar
Boursier, J. et al. Practical diagnosis of cirrhosis in non-alcoholic fatty liver disease using currently available non-invasive fibrosis tests. Nat. Commun. 14, 5219. (2023).
Google Scholar
Zhou, E. et al. Circulating extracellular vesicles are effective biomarkers for predicting response to cancer therapy. EBioMedicine 67, 103365. (2021).
Google Scholar
Abozaid, Y. J. et al. Plasma proteomic signature of fatty liver disease: The Rotterdam Study. Hepatology 78, 284–294. (2023).
Google Scholar
Niu, L. et al. Defining NASH from a multi-omics systems biology perspective. J. Clin. Med. (2021).
Google Scholar
Luo, Y. et al. SOMAscan proteomics identifies serum biomarkers associated with liver fibrosis in patients with NASH. Hepatol. Commun. 5, 760–773. (2021).
Google Scholar
Brown, E. A. et al. Effect of pegbelfermin on NASH and fibrosis-related biomarkers and correlation with histological response in the FALCON 1 trial. JHEP Rep. 5, 100661. (2023).
Google Scholar
Zarghamravanbakhsh, P., Frenkel, M. & Poretsky, L. Metabolic causes and consequences of nonalcoholic fatty liver disease (NAFLD). Metabol. Open 12, 100149. (2021).
Google Scholar
Huby, T. & Gautier, E. L. Immune cell-mediated features of non-alcoholic steatohepatitis. Nat. Rev. Immunol. 22, 429–443. (2022).
Google Scholar
Christensen, T. D. et al. Development and validation of circulating protein signatures as diagnostic biomarkers for biliary tract cancer. JHEP Rep. 5, 100648. (2023).
Google Scholar
Kostadinova, R. et al. Digital pathology with artificial intelligence analysis provides insight to the efficacy of anti-fibrotic compounds in human 3D MASH model. Sci. Rep. 14, 5885. (2024).
Google Scholar
Stiglund, N., Hagstrom, H., Stal, P., Cornillet, M. & Bjorkstrom, N. K. Dysregulated peripheral proteome reveals NASH-specific signatures identifying patient subgroups with distinct liver biology. Front. Immunol. 14, 1186097. (2023).
Google Scholar
Vilar-Gomez, E. & Chalasani, N. Non-invasive assessment of non-alcoholic fatty liver disease: Clinical prediction rules and blood-based biomarkers. J. Hepatol. 68, 305–315. (2018).
Google Scholar
Skrede, O. J. et al. Deep learning for prediction of colorectal cancer outcome: A discovery and validation study. Lancet 395, 350–360. (2020).
Google Scholar
Lee, S. H. & Jang, H. J. Deep learning-based prediction of molecular cancer biomarkers from tissue slides: A new tool for precision oncology. Clin. Mol. Hepatol. 28, 754–772. (2022).
Google Scholar
Mann, M., Kumar, C., Zeng, W. F. & Strauss, M. T. Artificial intelligence for proteomics and biomarker discovery. Cell Syst. 12, 759–770. (2021).
Google Scholar
Pasini, A. Artificial neural networks for small dataset analysis. J. Thorac. Dis. 7, 953–960. (2015).
Google Scholar
Vali, Y. et al. Biomarkers for staging fibrosis and non-alcoholic steatohepatitis in non-alcoholic fatty liver disease (the LITMUS project): A comparative diagnostic accuracy study. Lancet Gastroenterol. Hepatol. 8, 714–725. (2023).
Google Scholar
Pearce, S. G., Thosani, N. C. & Pan, J. J. Noninvasive biomarkers for the diagnosis of steatohepatitis and advanced fibrosis in NAFLD. Biomark. Res. 1, 7. (2013).
Google Scholar
Gilca-Blanariu, G. E. et al. Advances in noninvasive biomarkers for nonalcoholic fatty liver disease. Metabolites (2023).
Google Scholar
Caussy, C., Aubin, A. & Loomba, R. The relationship between type 2 diabetes, NAFLD, and cardiovascular risk. Curr. Diab. Rep. 21, 15. (2021).
Google Scholar
En Li Cho, E. et al. Global prevalence of non-alcoholic fatty liver disease in type 2 diabetes mellitus: An updated systematic review and meta-analysis. Gut 72, 2138–2148. (2023).
Google Scholar
Ajmera, V. et al. A prospective study on the prevalence of NAFLD, advanced fibrosis, cirrhosis and hepatocellular carcinoma in people with type 2 diabetes. J. Hepatol. 78, 471–478. (2023).
Google Scholar
Poulsen, K. L., Cajigas-Du Ross, C. K., Chaney, J. K. & Nagy, L. E. Role of the chemokine system in liver fibrosis: A narrative review. Dig. Med. Res. (2022).
Google Scholar
Basset, L. et al. Interleukin-27 and IFNgamma regulate the expression of CXCL9, CXCL10, and CXCL11 in hepatitis. J. Mol. Med. (Berl.) 93, 1355–1367. (2015).
Google Scholar
Starlinger, P. et al. The profile of platelet alpha-granule released molecules affects postoperative liver regeneration. Hepatology 63, 1675–1688. (2016).
Google Scholar
Wu, Q., Tu, H. & Li, J. Multifaceted roles of chemokine C-X-C motif ligand 7 in inflammatory diseases and cancer. Front. Pharmacol. 13, 914730. (2022).
Google Scholar
Gleissner, C. A., von Hundelshausen, P. & Ley, K. Platelet chemokines in vascular disease. Arterioscler. Thromb. Vasc. Biol. 28, 1920–1927. (2008).
Google Scholar
Naryzny, S. N. & Legina, O. K. Haptoglobin as a biomarker. Biochemistry (Mosc.) Suppl. Ser. B Biomed. Chem. 15, 184–198. (2021).
Google Scholar
Shih, A. W., McFarlane, A. & Verhovsek, M. Haptoglobin testing in hemolysis: Measurement and interpretation. Am. J. Hematol. 89, 443–447. (2014).
Google Scholar
Kamada, Y. et al. Serum fucosylated haptoglobin as a novel diagnostic biomarker for predicting hepatocyte ballooning and nonalcoholic steatohepatitis. PLoS ONE 8, e66328. (2013).
Google Scholar
Shirai, K. et al. Fucosylated haptoglobin is a novel predictive marker of hepatocellular carcinoma after hepatitis C virus elimination in patients with advanced liver fibrosis. PLoS ONE 17, e0279416. (2022).
Google Scholar
Harrison, S. A. et al. Simtuzumab is ineffective for patients with bridging fibrosis or compensated cirrhosis caused by nonalcoholic steatohepatitis. Gastroenterology 155, 1140–1153. (2018).
Google Scholar
Povero, D. et al. Human induced pluripotent stem cell-derived extracellular vesicles reduce hepatic stellate cell activation and liver fibrosis. JCI Insight (2019).
Google Scholar
Rohloff, J. C. et al. Nucleic acid ligands with protein-like side chains: Modified aptamers and their use as diagnostic and therapeutic agents. Mol. Ther. Nucl. Acids 3, e201. (2014).
Google Scholar
Gold, L. et al. From oligonucleotide shapes to genomic SELEX: Novel biological regulatory loops. Proc. Natl. Acad. Sci. U.S.A. 94, 59–64. (1997).
Google Scholar
