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Combining WGCNA and machine learning to identify mechanisms and biomarkers of hyperthyroidism and atrial fibrillation
Linyuan Wang1, Kun Yang1, Ruilong Kang1
1Department of Cardiovascular Surgery, The Affiliated Hospital of Shanxi Medical University, Shanxi Cardiovascular Hospital (Institute), Shanxi Clinical Medical Research Center for Cardiovascular Disease, Taiyuan, China.
Insights
CXCL16 and TMEM127 are identified as key genes linking hyperthyroidism and atrial fibrillation (AF). These biomarkers offer insights into shared disease mechanisms and potential new diagnostic and therapeutic strategies.
Area of Science:
- Cardiovascular Research
- Molecular Biology
- Biomarker Discovery
Background:
- Hyperthyroidism and atrial fibrillation (AF) share a clinical association, but underlying molecular mechanisms require elucidation.
- Identifying common molecular pathways and biomarkers is crucial for advancing understanding and therapeutic interventions.
- Cardiovascular complications associated with these conditions necessitate deeper investigation into their interrelationship.
Purpose of the Study:
- To identify shared molecular mechanisms and biomarkers between hyperthyroidism and atrial fibrillation (AF).
- To explore the potential of identified genes as diagnostic markers for these interrelated conditions.
- To investigate immune cell infiltration differences in hyperthyroidism and AF.
Main Methods:
- Utilized gene expression datasets (GSE71956 for hyperthyroidism, GSE115574 for AF) from the Gene Expression Omnibus (GEO) database.
- Applied differential gene analysis, weighted gene co-expression network analysis (WGCNA), and machine learning (LASSO, RF) to identify overlapping genes.
- Validated key gene expression via RT-qPCR and conducted immune infiltration analysis.
Main Results:
- Identified 23 overlapping genes between hyperthyroidism and AF; CXCL16 and TMEM127 were pinpointed as key genes using machine learning.
- CXCL16 and TMEM127 demonstrated diagnostic efficacy in validation datasets for both hyperthyroidism and AF.
- RT-qPCR confirmed significantly elevated expression of CXCL16 and TMEM127 in both conditions, with notable differences in immune cell infiltration.
Conclusions:
- CXCL16 and TMEM127 serve as promising biomarkers for hyperthyroidism and atrial fibrillation (AF).
- These genes provide insights into the shared pathogenesis of hyperthyroidism and AF.
- Findings support the development of novel diagnostic and therapeutic strategies for these interconnected cardiovascular conditions.
Background:
Hyperthyroidism and atrial fibrillation (AF) are interrelated conditions with significant cardiovascular impact. While their clinical association is established, the molecular mechanisms remain unclear. Identifying shared biomarkers and pathways can advance understanding and guide therapy.
Methods:
The hyperthyroidism dataset GSE71956 and the AF dataset GSE115574 were obtained from the Gene Expression Omnibus (GEO) database. Differential gene analysis was performed using the "limma" package, and overlapping genes shared by both diseases were identified through weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. Machine learning algorithms were also applied to identify key biomarkers. To validate the predictive results, peripheral blood samples were collected for real-time quantitative polymerase chain reaction (RT-qPCR) analysis. Finally, immune infiltration analysis was conducted to evaluate immune cell changes in hyperthyroidism and AF.
Results:
Through differential gene screening and WGCNA, 23 overlapping genes associated with hyperthyroidism and AF were identified. Using least absolute shrinkage and selection operator (LASSO) and random forest (RF) machine learning algorithms, CXCL16 and TMEM127 were ultimately identified as key genes. The two genes demonstrated good diagnostic efficacy in the hyperthyroidism validation set GSE276271 (AUC: TMEM127, 0.636; CXCL16, 0.591) and in the AF validation set GSE2240 (AUC: TMEM127, 0.745; CXCL16, 0.720). RT-qPCR analysis demonstrated that CXCL16 and TMEM127 expression levels were significantly elevated in both the hyperthyroidism and AF groups compared to the control group, aligning with the findings from our prior bioinformatics analysis. Immune analysis revealed significant differences in two immune cell types in both hyperthyroidism and AF.
Conclusion:
CXCL16 and TMEM127 are promising biomarkers, offering insights into the shared pathogenesis of hyperthyroidism and AF. These findings provide a foundation for novel diagnostic and therapeutic strategies targeting these conditions.
