Integrated Machine Learning Approaches to Explore the Role of Glycosylation-Related Genes in Idiopathic Pulmonary Fibrosis

Summary

This study used integrated transcriptomic analysis, machine learning (LASSO, SVM-RFE, and XGBoost), immune infiltration analysis, molecular docking, and RT-qPCR validation to identify glycosylation-related genes associated with idiopathic pulmonary fibrosis. Ten candidate diagnostic genes were identified, and a diagnostic model with strong performance in lung tissue datasets was developed and validated.

Abstract

Idiopathic pulmonary fibrosis (IPF) is a progressive chronic lung disease with an unclear etiology, and the contribution of glycosylation-related genes (GRGs) to its pathogenesis remains poorly understood. This study focuses on elucidating the potential mechanisms of GRGs in IPF, identifying key biomarkers, and developing a diagnostic model using bioinformatics and machine learning. Transcriptomic data from the GEO database were integrated with GRGs to investigate their role in IPF. Differentially expressed genes (DEGs) were identified and subjected to functional enrichment and protein-protein interaction (PPI) analyses. A machine learning workflow combining LASSO regression, support vector machine-recursive feature elimination (SVM-RFE), and XGBoost was applied to identify key genes and construct a diagnostic model using the GSE150910 training dataset. Model performance was subsequently evaluated in four independent validation datasets. Additional analyses, including gene set enrichment analysis (GSEA), immune infiltration analysis, drug-gene interaction analysis, molecular docking, and RT-qPCR validation using peripheral blood samples from IPF patients and healthy controls, were performed to investigate the biological relevance of the identified genes. A total of 126 glycosylation-related DEGs were identified, and 10 key genes were selected. The diagnostic model achieved area under the curve (AUC) values of 0.963 and 0.814 on lung tissue datasets, and 0.681 and 0.706 on blood datasets. Immune infiltration analysis revealed differences in B-cell abundance between patient subgroups, and RT-qPCR validation confirmed the differential expression of selected genes in clinical samples. These findings provide insight into the potential involvement of GRGs in IPF and support their relevance as candidate diagnostic genes.

Introduction

Idiopathic pulmonary fibrosis (IPF) is a progressive, fibrosing interstitial lung disease characterized by a heterogeneous pathogenesis, which involves genetic predisposition, environmental triggers, and immune dysfunction^1,2. The disease presents significant clinical challenges, primarily due to difficulties in early diagnosis, limited therapeutic efficacy, and a generally poor prognosis. The prevalence of IPF ranges from 20 to 80 patients per 100,000, and without treatment, the median survival time after diagnosis is typically 3–5 years^3,4. Ide....

Protocol

This study was approved by the Institutional Review Board of The First Affiliated Hospital of Bengbu Medical University (Approval Number: 2023YJS162). Written informed consent was obtained from all participants prior to sample collection.

Data collection

The transcriptome data used in this study were obtained from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/; RRID: SCR_005012). The primary training dataset, GSE150910, was generated using the Illumina NovaSeq 6000 platform (GPL24676) and comprised 103 IPF and 103 normal lung tissue samples. To validate the findings, independent datasets ....

Results

Functional analysis and interaction network analysis of GR-DEGs  

To investigate gene involvement in IPF, differential expression analysis of GSE150910 was conducted, identifying 4,216 DEGs (Figure 2A). By intersecting these DEGs with 636 GRGs, 126 disease-associated glycosylation genes were identified (Figure 2B). Furthermore, to evaluate the potential biological functions of intersection genes, we c.......

Discussion

IPF is a chronic, progressive disease of unknown etiology that significantly reduces patients' quality of life and leads to earlier mortality⁴. Despite advances in therapeutic strategies, the median survival time remains low post-diagnosis, and the pathogenesis of IPF is still not fully understood²². Therefore, elucidating the molecular mechanisms underlying IPF is both urgent and critical. Previous studies have established a link between deregulated glycosylation and p.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Caret	R Foundation for Statistical Computing	Version 6.0.94	SVM-RFE
ClusterProfiler	R Foundation for Statistical Computing	Version 4.12.6	GSEA, GO and KEGG enrichment analysis
DESeq2	R Foundation for Statistical Computing	Version 1.44.0	Differential expression analysis
Fast First-Strand cDNA Synthesis Mix	Goonie Biotech Co., Ltd.	500-101	cDNA synthesis for RT-qPCR
FastTaq qPCR SYBR Green Master Mix	Gooniebio	500-102	Quantitative PCR amplification
Fluorescent Quantitative PCR System	Bio-Rad Laboratories, Inc.	CFX96	Quantitative PCR amplification
GEO Database	NCBI	https://www.ncbi.nlm.nih.gov/geo/	Transcriptome data source
ggpubr	R Foundation for Statistical Computing	Version 0.6.0	Box plots generation
GSVA	R Foundation for Statistical Computing	Version 1.52.3	ssGSEA
MSigDB	Broad Institute	https://www.gsea-msigdb.org/	GSEA analysis
PCR System	Bio-Rad Laboratories, Inc.	PTC Tempo	cDNA synthesis for RT-qPCR
Primers	Tsingke Biotechnology Co., Ltd.	N/A	Custom synthesis of qPCR primers
R software	R Foundation for Statistical Computing	Version 4.4.1	Statistical analysis and visualization
RNA Isolater Total RNA Extraction Reagent	Vazyme Biotech Co., Ltd.	R401-01	Total RNA extraction from whole blood
STRING Database	STRING Consortium	https://string-db.org/	PPI network construction
Synergy HTX Multi-Mode Reader	BioTek Instruments, Inc.	Synergy HTX	RNA concentration measurement