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A robust gene expression signature for NASH in liver expression data

Non-Alcoholic Fatty Liver Disease (NAFLD) is a progressive liver disease that affects up to 30% of worldwide population, of which up to 25% progress to Non-Alcoholic SteatoHepatitis (NASH), a severe form of the disease that involves inflammation and predisposes the patient to liver cirrhosis. Despit...

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Autores principales: Hasin-Brumshtein, Yehudit, Sakaram, Suraj, Khatri, Purvesh, He, Yudong D., Sweeney, Timothy E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8850484/
https://www.ncbi.nlm.nih.gov/pubmed/35173224
http://dx.doi.org/10.1038/s41598-022-06512-0
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author Hasin-Brumshtein, Yehudit
Sakaram, Suraj
Khatri, Purvesh
He, Yudong D.
Sweeney, Timothy E.
author_facet Hasin-Brumshtein, Yehudit
Sakaram, Suraj
Khatri, Purvesh
He, Yudong D.
Sweeney, Timothy E.
author_sort Hasin-Brumshtein, Yehudit
collection PubMed
description Non-Alcoholic Fatty Liver Disease (NAFLD) is a progressive liver disease that affects up to 30% of worldwide population, of which up to 25% progress to Non-Alcoholic SteatoHepatitis (NASH), a severe form of the disease that involves inflammation and predisposes the patient to liver cirrhosis. Despite its epidemic proportions, there is no reliable diagnostics that generalizes to global patient population for distinguishing NASH from NAFLD. We performed a comprehensive multicohort analysis of publicly available transcriptome data of liver biopsies from Healthy Controls (HC), NAFLD and NASH patients. Altogether we analyzed 812 samples from 12 different datasets across 7 countries, encompassing real world patient heterogeneity. We used 7 datasets for discovery and 5 datasets were held-out for independent validation. Altogether we identified 130 genes significantly differentially expressed in NASH versus a mixed group of NAFLD and HC. We show that our signature is not driven by one particular group (NAFLD or HC) and reflects true biological signal. Using a forward search we were able to downselect to a parsimonious set of 19 mRNA signature with mean AUROC of 0.98 in discovery and 0.79 in independent validation. Methods for consistent diagnosis of NASH relative to NAFLD are urgently needed. We showed that gene expression data combined with advanced statistical methodology holds the potential to serve basis for development of such diagnostic tests for the unmet clinical need.
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spelling pubmed-88504842022-02-17 A robust gene expression signature for NASH in liver expression data Hasin-Brumshtein, Yehudit Sakaram, Suraj Khatri, Purvesh He, Yudong D. Sweeney, Timothy E. Sci Rep Article Non-Alcoholic Fatty Liver Disease (NAFLD) is a progressive liver disease that affects up to 30% of worldwide population, of which up to 25% progress to Non-Alcoholic SteatoHepatitis (NASH), a severe form of the disease that involves inflammation and predisposes the patient to liver cirrhosis. Despite its epidemic proportions, there is no reliable diagnostics that generalizes to global patient population for distinguishing NASH from NAFLD. We performed a comprehensive multicohort analysis of publicly available transcriptome data of liver biopsies from Healthy Controls (HC), NAFLD and NASH patients. Altogether we analyzed 812 samples from 12 different datasets across 7 countries, encompassing real world patient heterogeneity. We used 7 datasets for discovery and 5 datasets were held-out for independent validation. Altogether we identified 130 genes significantly differentially expressed in NASH versus a mixed group of NAFLD and HC. We show that our signature is not driven by one particular group (NAFLD or HC) and reflects true biological signal. Using a forward search we were able to downselect to a parsimonious set of 19 mRNA signature with mean AUROC of 0.98 in discovery and 0.79 in independent validation. Methods for consistent diagnosis of NASH relative to NAFLD are urgently needed. We showed that gene expression data combined with advanced statistical methodology holds the potential to serve basis for development of such diagnostic tests for the unmet clinical need. Nature Publishing Group UK 2022-02-16 /pmc/articles/PMC8850484/ /pubmed/35173224 http://dx.doi.org/10.1038/s41598-022-06512-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Hasin-Brumshtein, Yehudit
Sakaram, Suraj
Khatri, Purvesh
He, Yudong D.
Sweeney, Timothy E.
A robust gene expression signature for NASH in liver expression data
title A robust gene expression signature for NASH in liver expression data
title_full A robust gene expression signature for NASH in liver expression data
title_fullStr A robust gene expression signature for NASH in liver expression data
title_full_unstemmed A robust gene expression signature for NASH in liver expression data
title_short A robust gene expression signature for NASH in liver expression data
title_sort robust gene expression signature for nash in liver expression data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8850484/
https://www.ncbi.nlm.nih.gov/pubmed/35173224
http://dx.doi.org/10.1038/s41598-022-06512-0
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