Discovering Biomarkers for Non-Alcoholic Steatohepatitis Patients with and without Hepatocellular Carcinoma Using Fecal Metaproteomics

Abstract

High-calorie diets lead to hepatic steatosis and to the development of non-alcoholic fatty liver disease (NAFLD), which can evolve over many years into the inflammatory form of non-alcoholic steatohepatitis (NASH), posing a risk for the development of hepatocellular carcinoma (HCC). Due to diet and liver alteration, the axis between liver and gut is disturbed, resulting in gut microbiome alterations. Consequently, detecting these gut microbiome alterations represents a promising strategy for early NASH and HCC detection. We analyzed medical parameters and the fecal metaproteome of 19 healthy controls, 32 NASH patients, and 29 HCC patients, targeting the discovery of diagnostic biomarkers. Here, NASH and HCC resulted in increased inflammation status and shifts within the composition of the gut microbiome. An increased abundance of kielin/chordin, E3 ubiquitin ligase, and nucleophosmin 1 represented valuable fecal biomarkers, indicating disease-related changes in the liver. Although a single biomarker failed to separate NASH and HCC, machine learning-based classification algorithms provided an 86% accuracy in distinguishing between controls, NASH, and HCC. Fecal metaproteomics enables early detection of NASH and HCC by providing single biomarkers and machine learning-based metaprotein panels.

Microbiome Modeling: A Beginner’s Guide

“…The lack of mechanistic information is a disadvantage of statistical models because there is no information on the causal connection between input and output variables, models can be biased towards the structure of training data, and their range of validity is often limited [153]. Statistical modeling is, for example, applied in meta-proteomics to improve protein identification [154], predict disease states from meta-genomes [155], or for the detection of potential disease biomarkers [156] and biomarker panels [157].…”

Microbiome modeling: a beginner's guide

“…The lack of mechanistic information is a disadvantage of statistical models because no information on the causal connection between input and output variables is given, models can be biased toward the structure of training data, and their range of validity is often limited (Baker et al, 2018 ). Statistical modeling is, for example, applied in metaproteomics to improve protein identification (Bouwmeester et al, 2020 ), predict disease states from metagenomes (Pasolli et al, 2016 ), or for the detection of potential disease biomarkers (Tang et al, 2020 ) and biomarker panels (Sydor et al, 2022 ). A simple example of statistical modeling is fitting a calibration curve to data from a colorimetric protein assay by linear regression (Ninfa et al, 2009 ).…”

Comprehensive Profiling of the Human Fecal Proteome from IBD Patients with DIA-MS Enables Evaluation of Disease-Relevant Proteins

“…Recent studies indicate that fCAL measured at baseline and up to 14 weeks after treatment with biologics had long-term prognostic value; however, fCAL levels were affected by duration of disease, making it less reliable as an inflammatory signal indicative of flare or disease activity [19,20]. Furthermore, fCAL is also elevated in patients with NAFLD and obesity, and therefore, fCAL is not necessarily reflective of gut specific inflammation or IBD flare [21][22][23][24].…”