Chronic Obstructive Pulmonary Disease Molecular Subtyping and Pathway Deviation-Based Candidate Gene Identification

Area of Science:

• Genomics and Bioinformatics
• Pulmonary Medicine
• Systems Biology

Background:

• Chronic obstructive pulmonary disease (COPD) is a complex respiratory disease with heterogeneous clinical presentations.
• Current therapeutic strategies for COPD lack personalization, leading to variable treatment efficacy.

Purpose of the Study:

• To identify novel molecular subtypes of COPD using bioinformatics approaches.
• To prioritize candidate genes associated with COPD pathogenesis.
• To explore the potential for subtype-specific therapeutic interventions.

Main Methods:

• Utilized the Gene Expression Omnibus (GEO) dataset GSE76705 (229 COPD samples) and OMIM database for COPD-related genes.
• Applied hierarchical clustering to divide COPD samples into molecular subtypes based on gene expression.
• Conducted functional enrichment analysis and pathway deviation analysis on identified gene sets.

Main Results:

• Identified 213 COPD-related genes, leading to the classification of samples into three distinct molecular subtypes.
• Discovered 160 common genes, including TGFB1, EGFR, and IL13, implicated in COPD.
• Validated the three molecular subtypes in an independent dataset (GSE71220) with high accuracy (>80% ROC curves).

Conclusions:

• COPD can be further subclassified into distinct molecular subtypes.
• These molecular subtypes hold potential for guiding personalized therapeutic strategies in COPD management.
• The identified subtypes and associated genes offer new targets for COPD research and drug development.