Dataset Information

Comparative total proteome analysis for nash biomarker

ABSTRACT: Nonalcoholic fatty liver disease (NAFLD) is a hepatic condition characterized by excessive fat accumulation, leading to liver fibrosis, cirrhosis, and cancer. Currently, invasive liver biopsy is the only method for identifying and classifying NASH. Mass spectrometry-based proteomics can detect crucial proteins and pathways involved in NASH development. A study using liver and serum samples from mice and humans found altered proteins involved in detoxification, fibrosis, inflammation, and fatty acid metabolism. These findings provide insights into NASH molecular mechanisms and potential diagnostic biomarkers for early diagnosis.

INSTRUMENT(S):

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Hepatocyte, Liver

DISEASE(S): Non-alcoholic Fatty Liver

SUBMITTER: Eda ATES

LAB HEAD: Min-Jung Kang

PROVIDER: PXD053187 | Pride | 2025-05-07

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	20230620_sample_W6_10.mzML	Mzml
	20230620_sample_W6_10.mzid.gz	Mzid
	20230620_sample_W6_10.raw	Raw
	20230620_sample_W6_4.mzML	Mzml
	20230620_sample_W6_4.mzid.gz	Mzid

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Publications

Comparative Analysis of the Total Proteome in Nonalcoholic Steatohepatitis: Identification of Potential Biomarkers.

Ates Eda E My Ong Hien Thi HT Yu Seung-Min SM Kim Ji-Hoon JH Kang Min-Jung MJ

Molecular & cellular proteomics : MCP 20250131 3

Nonalcoholic fatty liver disease is a hepatic condition characterized by excessive fat accumulation in the liver with advanced stage nonalcoholic steatohepatitis (NASH), potentially leading to liver fibrosis, cirrhosis, and cancer. Currently, the identification and classification of NASH require invasive liver biopsy, which has certain limitations. Mass spectrometry-based proteomics can detect crucial proteins and pathways implicated in NASH development and progression. We collected the liver an ...[more]

PMID: 39894410

Similar Datasets

Project description:SUMMARY: This article presents a predictive molecular signature that marks the early onset of fibrosis in a translational nonalcoholic steatohepatitis mouse model. Overlap of genes and processes with human nonalcoholic steatohepatitis and a list of top candidate biomarkers for early fibrosis are described. BACKGROUND & AIMS: The incidence of nonalcoholic steatohepatitis (NASH) is increasing. The pathophysiological mechanisms of NASH and the sequence of events leading to hepatic fibrosis are incompletely understood. The aim of this study was to gain insight into the dynamics of key molecular processes involved in NASH and to rank early markers for hepatic fibrosis. METHODS: A time-course study in low-density lipoprotein–receptor knockout. Leiden mice on a high-fat diet was performed to identify the temporal dynamics of key processes contributing to NASH and fibrosis. An integrative systems biology approach was used to elucidate candidate markers linked to the active fibrosis process by combining transcriptomics, dynamic proteomics, and histopathology. The translational value of these findings were confirmed using human NASH data sets. RESULTS: High-fat-diet feeding resulted in obesity, hyperlipidemia, insulin resistance, and NASH with fibrosis in a time-dependent manner. Temporal dynamics of key molecular processes involved in the development of NASH were identified, including lipid metabolism, inflammation, oxidative stress, and fibrosis. A data-integrative approach enabled identification of the active fibrotic process preceding histopathologic detection using a novel molecular fibrosis signature. Human studies were used to identify overlap of genes and processes and to perform a network biology-based prioritization to rank top candidate markers representing the early manifestation of fibrosis. CONCLUSIONS: An early predictive molecular signature was identified that marked the active profibrotic process before histopathologic fibrosis becomes manifest. Early detection of the onset of NASH and fibrosis enables identification of novel blood-based biomarkers to stratify patients at risk, development of new therapeutics, and help shorten (pre)clinical experimental time frames. Keywords: Systems Biology; Metabolic Syndrome; Liver Disease; Diagnosis.