Exploring Co-expression Modules-Traits Correlation through Weighted Gene Co-expression Network Analysis: A Promising...

Area of Science:

• Genomics and Bioinformatics
• Molecular Biology
• Dermatology

Background:

• Skin serves vital protective and regulatory functions, but wound development can impede healing.
• Chronic wounds present significant challenges, including persistent infections and impaired angiogenesis, impacting patient well-being and healthcare costs.
• Understanding genetic pathways is crucial for developing effective wound healing therapies and reducing socio-economic burdens.

Purpose of the Study:

• To identify key genes and co-expression modules associated with wound healing processes using Weighted Gene Co-Expression Network Analysis (WGCNA).
• To uncover potential therapeutic targets for enhancing tissue repair and promoting efficient wound healing.
• To elucidate the genetic underpinnings of chronic wound challenges.

Main Methods:

• Utilized 65 microarray gene expression datasets from the Gene Expression Omnibus (GEO) database.
• Applied Robust MultiArray Averaging (RMA) for data preprocessing, followed by WGCNA for network construction and module identification.
• Conducted functional enrichment analysis and identified hub genes using GeneMANIA and KEGG databases.

Main Results:

• Identified significant correlations between wound healing and specific gene modules (black, brown, light green).
• Uncovered 16 hub genes within the brown and black modules critical for wound healing.
• Highlighted key signaling pathways including MAPK, EGFR, and ErbB, alongside cellular processes like autophagy and mitophagy.

Conclusions:

• Consensus modules and 16 hub genes related to wound healing were identified across multiple datasets.
• KEGG enrichment analysis pinpointed autophagy and mitophagy as significant pathways in wound healing.
• The identified hub genes and pathways offer potential targets for future research and therapeutic interventions in wound care.