A Medical Decision Support Platform for Identifying Thrombospondin 1 in Non-alcoholic Fatty Liver Disease via Immuno-Infiltration Analysis

Summary

This study investigated the relationships between non-alcoholic fatty liver disease (NAFLD) and myocardial infarction (MI) through co-expressed genes, identifying Thrombospondin 1 (THBS1) as a biomarker. Immuno-infiltration analysis revealed CD8+ T cells and neutrophils as key factors, with THBS1 showing potential as a diagnostic tool for NAFLD and MI.

Abstract

Non-alcoholic fatty liver disease (NAFLD) and myocardial infarction (MI) are two major health burdens with significant prevalence and mortality. This study aimed to explore the co-expressed genes to understand the relationship between NAFLD and MI and identify potential crucial biomarkers of NAFLD-related MI using bioinformatics and machine learning. Functional enrichment analysis was conducted, a co-protein-protein interaction (PPI) network diagram was constructed, and support vector machine-recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO) techniques were employed to identify one differentially expressed gene (DEG), Thrombospondin 1 (THBS1). THBS1 demonstrated strong performance in distinguishing NAFLD patients (AUC = 0.981) and MI patients (AUC = 0.900). Immuno-infiltration analysis revealed significantly lower CD8+ T cells and higher neutrophil levels in patients with NAFLD and MI. CD8+ T cells and neutrophils were effective in distinguishing NAFLD/MI from healthy controls. Correlation analysis showed that THBS1 was positively correlated with CCR (chemokine receptor), MHC class (major histocompatibility complex class), neutrophils, parainflammation, and Tfh (follicular helper T cells), and negatively correlated with CD8+ T cells, cytolytic activity, and TIL (tumor-infiltrating lymphocytes) in NAFLD and MI patients. THBS1 emerged as a novel biomarker for diagnosing NAFLD/MI in comparison to healthy controls. The results indicate that CD8+ T cells and neutrophils could serve as inflammatory immune features for differentiating patients with NAFLD/MI from healthy individuals.

Introduction

Non-alcoholic fatty liver disease (NAFLD) is a major public health issue with a prevalence of 25%-30%¹. It has been reported that the prevalence of NAFLD is high among patients with diabetes². However, the significance of NAFLD in non-diabetic patients is not yet clear. Studies have suggested that NAFLD plays an independent role in the pathogenesis of atherosclerosis^3,4. Additionally, a meta-analysis has shown that NAFLD is closely associated with coronary artery calcification, endothelial dysfunction, and atherosclerosis, and has emerged as an independent cardio....

Protocol

The details of the databases, weblinks, and software/packages used are listed in the Table of Materials. The simulation parameters used are provided in Table 1.

1. Obtaining RNA microarray datasets

1. Download the myocardial infarction (MI) dataset, GSE66360, from the NCBI-GEO database.
2. Download the non-alcoholic fatty liver disease (NAFLD) dataset, GSE89632, from the

Discussion

The method described in this study has significant implications for research into the molecular mechanisms underlying NAFLD and MI. By identifying key biomarkers such as THBS1, the proposed protocol offers potential targets for both diagnostic and therapeutic interventions. This approach can be extended to other complex diseases involving multiple pathways and immune responses, facilitating the discovery of novel biomarkers and therapeutic targets. Moreover, the integration of bioinformatics and machine learning techniqu.......

Materials

Name	Company	Catalog Number	Comments
Cytoscape	Cytoscape Consortium	Version 3.6.1	Used for visualizing protein-protein interaction (PPI) networks
MI dataset GSE66360 (https://www.ncbi.nlm.nih.gov/geo/).	NCBI-GEO database	-	To collect RNA microarray datasets for analysis
R package clusterProfiler	Bioconductor	-	Used for GO, KEGG, and DO enrichment analyses
R package ggplot2	CRAN	-	Used for creating Venn diagrams and other visualizations
R package GSEABase	Bioconductor	-	Used in conjunction with GSVA for gene set enrichment analysis
R package GSVA	Bioconductor	-	Used for single-sample gene set enrichment analysis (ssGSEA)
R package limma	Bioconductor	-	Used for identifying differentially expressed genes (DEGs)
R package pheatmap	CRAN	-	Used for generating heatmaps
R package venn	CRAN	-	Used for creating Venn diagrams
RNA microarray datasets (GSE66360, GSE89632)	NCBI-GEO	-	Publicly available RNA microarray datasets used for analysis
RStudio	RStudio, PBC	Version 1.4.1717	Integrated development environment for R
String database	STRING (www.string-db.org/)	-	Online tool for constructing PPI networks