Multiple Machine Learning Identifies Key Gene PHLDA1 Suppressing NAFLD Progression

Miao, L., G. Targher, C.D. Byrne, Y.Y. Cao, and M.H. Zheng. 2024. Current status and future trends of the global burden of MASLD. Trends in Endocrinology and Metabolism: TEM. 35 (8): 697–707.

Article  PubMed  CAS  Google Scholar 

Lazarus, J.V., H.E. Mark, M. Villota-Rivas, A. Palayew, P. Carrieri, M. Colombo, et al. 2022. The global NAFLD policy review and preparedness index: Are countries ready to address this silent public health challenge? Journal of hepatology 76 (4): 771–780.

Article  PubMed  CAS  Google Scholar 

Byrne, C.D., and G. Targher. 2015. NAFLD: a multisystem disease. Journal of hepatology 62 (1): 47–64.

Article  Google Scholar 

Kabbany, M.N., P.K. Conjeevaram Selvakumar, K. Watt, R. Lopez, Z. Akras, N. Zein, et al. 2017. Prevalence of Nonalcoholic Steatohepatitis-Associated Cirrhosis in the United States: An Analysis of National Health and Nutrition Examination Survey Data. The American Journal of Gastroenterology 112 (4): 581–587.

Article  PubMed  Google Scholar 

Kendall, T.J., M. Jimenez-Ramos, F. Turner, P. Ramachandran, J. Minnier, M.D. McColgan, et al. 2023. An integrated gene-to-outcome multimodal database for metabolic dysfunction-associated steatotic liver disease. Nature medicine 29 (11): 2939–2953.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Sookoian, S., and C.J. Pirola. 2019. Review article: Shared disease mechanisms between non-alcoholic fatty liver disease and metabolic syndrome - translating knowledge from systems biology to the bedside. Alimentary pharmacology & therapeutics 49 (5): 516–527.

Article  Google Scholar 

Wang, X.J., and H. Malhi. 2018. Nonalcoholic Fatty Liver Disease. Annals of internal Medicine 169 (9): Itc65-itc80.

Article  PubMed  Google Scholar 

Wong, V.W., M. Ekstedt, G.L. Wong, and H. Hagström. 2023. Changing epidemiology, global trends and implications for outcomes of NAFLD. Journal of hepatology 79 (3): 842–852.

Article  PubMed  Google Scholar 

Loomba, R., V. Seguritan, W. Li, T. Long, N. Klitgord, A. Bhatt, et al. 2017. Gut Microbiome-Based Metagenomic Signature for Non-invasive Detection of Advanced Fibrosis in Human Nonalcoholic Fatty Liver Disease. Cell metabolism 25 (5): 1054-1062.e1055.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Meijnikman, A.S., M. Davids, H. Herrema, O. Aydin, V. Tremaroli, M. Rios-Morales, et al. 2022. Microbiome-derived ethanol in nonalcoholic fatty liver disease. Nature medicine 28 (10): 2100–2106.

Article  PubMed  CAS  Google Scholar 

Peverill, W., L.W. Powell, and R. Skoien. 2014. Evolving concepts in the pathogenesis of NASH: Beyond steatosis and inflammation. International Journal of Molecular Sciences 15 (5): 8591–8638.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Gao, R., J. Wang, X. He, T. Wang, L. Zhou, Z. Ren, et al. 2022. Comprehensive analysis of endoplasmic reticulum-related and secretome gene expression profiles in the progression of non-alcoholic fatty liver disease. Frontiers in Endocrinology 13: 967016.

Article  PubMed  PubMed Central  Google Scholar 

Sookoian, S., and C.J. Pirola. 2020. Precision medicine in nonalcoholic fatty liver disease: New therapeutic insights from genetics and systems biology. Clinical and molecular hepatology 26 (4): 461–475.

Article  PubMed  PubMed Central  Google Scholar 

Reel, P.S., S. Reel, E. Pearson, E. Trucco, and E. Jefferson. 2021. Using machine learning approaches for multi-omics data analysis: A review. Biotechnology advances 49: 107739.

Article  PubMed  CAS  Google Scholar 

Breiman, L. 2001. Random forests. Random forests 45: 5–32.

Google Scholar 

Tibshirani, R. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology 58 (1): 267–288.

Article  Google Scholar 

Huang, M.L., Y.H. Hung, W.M. Lee, R.K. Li, and B.R. Jiang. 2014. SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier. TheScientificWorldJournal 2014: 795624.

Article  PubMed  PubMed Central  Google Scholar 

Yao, K., E. Tarabra, D. Sia, R. Morotti, R. Fawaz, P. Valentino, et al. 2022. Transcriptomic profiling of a multiethnic pediatric NAFLD cohort reveals genes and pathways associated with disease. Hepatology communications 6 (7): 1598–1610.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Pfister, D., N.G. Núñez, R. Pinyol, O. Govaere, M. Pinter, M. Szydlowska, et al. 2021. NASH limits anti-tumour surveillance in immunotherapy-treated HCC. Nature 592 (7854): 450–456.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Arendt, B.M., E.M. Comelli, D.W. Ma, W. Lou, A. Teterina, T. Kim, et al. 2015. Altered hepatic gene expression in nonalcoholic fatty liver disease is associated with lower hepatic n-3 and n-6 polyunsaturated fatty acids. Hepatology (Baltimore, MD) 61 (5): 1565–1578.

Article  PubMed  CAS  Google Scholar 

Chen, H., and P.C. Boutros. 2011. VennDiagram: A package for the generation of highly-customizable Venn and Euler diagrams in R. BMC Bioinformatics 12: 35.

Article  PubMed  PubMed Central  Google Scholar 

Yu, G., L.G. Wang, Y. Han, and Q.Y. He. 2012. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics : a Journal of Integrative Biology 16 (5): 284–287.

Article  PubMed  CAS  Google Scholar 

Langfelder, P., and S. Horvath. 2008. WGCNA: an R package for weighted correlation network analysis. BMC bioinformatics 9: 559.

Article  PubMed  PubMed Central  Google Scholar 

Robin, X., N. Turck, A. Hainard, N. Tiberti, F. Lisacek, J.C. Sanchez, et al. 2011. pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 12: 77.

Article  PubMed  PubMed Central  Google Scholar 

Lu, Y., X. Liu, Y. Jiao, X. Xiong, E. Wang, X. Wang, et al. 2014. Periostin promotes liver steatosis and hypertriglyceridemia through downregulation of PPARα. The Journal of clinical investigation 124 (8): 3501–3513.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Tryndyak, V., A. de Conti, T. Kobets, K. Kutanzi, I. Koturbash, T. Han, et al. 2012. Interstrain differences in the severity of liver injury induced by a choline- and folate-deficient diet in mice are associated with dysregulation of genes involved in lipid metabolism. FASEB journal : Official publication of the Federation of American Societies for Experimental Biology 26 (11): 4592–4602.

Article  PubMed  CAS  Google Scholar 

Han, M., W. Piorońska, S. Wang, Z.C. Nwosu, C. Sticht, S. Wang, et al. 2020. Hepatocyte caveolin-1 modulates metabolic gene profiles and functions in non-alcoholic fatty liver disease. Cell death & disease 11 (2): 104.

Article  CAS  Google Scholar 

Newman, A.M., C.L. Liu, M.R. Green, A.J. Gentles, W. Feng, Y. Xu, et al. 2015. Robust enumeration of cell subsets from tissue expression profiles. Nature methods 12 (5): 453–457.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Butler, A., P. Hoffman, P. Smibert, E. Papalexi, and R. Satija. 2018. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nature biotechnology 36 (5): 411–420.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Ramachandran, P., R. Dobie, J.R. Wilson-Kanamori, E.F. Dora, B.E.P. Henderson, N.T. Luu, et al. 2019. Resolving the fibrotic niche of human liver cirrhosis at single-cell level. Nature 575 (7783): 512–518.

Article  PubMed  PubMed Central  CAS  Google Scholar 

Subramanian, A., P. Tamayo, V.K. Mootha, S. Mukherjee, B.L. Ebert, M.A. Gillette, et al. 2005. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences of the United States of America 102 (43): 15545–15550.

Article  PubMed 

留言 (0)

沒有登入
gif