Discovering and Validating Cuproptosis-Associated Marker Genes for Accurate Keloid Diagnosis Through Multiple Machine...

Area of Science:

• Biomedical research
• Genetics and genomics
• Cell biology

Background:

• Keloids are abnormal skin scars affecting many individuals globally.
• Keloid formation may involve reduced cell death, with recent discoveries highlighting a new cell death mode dependent on copper ions.
• Understanding these mechanisms is key to developing effective keloid treatments.

Purpose of the Study:

• To identify novel cuproptosis-related genes (CRGs) associated with keloid diagnosis.
• To explore the molecular underpinnings of keloid development.
• To provide potential targets for novel keloid therapies.

Main Methods:

• Utilized multiple gene expression datasets (GSE44270, GSE145725, GSE7890, GSE92566, GSE121618).
• Integrated machine learning models (SVM, RF, GLM, XGB) to identify diagnostic CRGs.
• Validated findings using RT-qPCR, Western blotting, IHC, and independent datasets.

Main Results:

• Identified 10 CRGs for keloid diagnosis and categorized keloid samples into two clusters based on CRG expression.
• WGCNA analysis revealed 110 candidate cuproptosis-associated genes.
• Functional enrichment analysis implicated the MAPK pathway in keloid cell growth; GSVA indicated CRG influence on ECM receptor interaction.

Conclusions:

• Successfully identified 10 CRGs with diagnostic value for keloids.
• These CRGs offer insights into keloid pathogenesis.
• The findings may pave the way for developing targeted keloid therapies.