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Published on: July 20, 2022
Construction and validation of a cuproptosis-related diagnostic gene signature for atrial fibrillation based on
Yixin Wang1, Qiaozhu Wang1, Peng Liu1
1Department of Cardiology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Insights
This study identifies two cuproptosis-related genes, SLC31A1 and LIAS, for diagnosing atrial fibrillation (AF). The findings suggest cuproptosis interacts with immune cells in AF pathogenesis.
Area of Science:
- Cardiovascular Research
- Molecular Biology
- Computational Biology
Background:
- Atrial fibrillation (AF) is a common arrhythmia with diagnostic challenges.
- Cell death, including cuproptosis, is implicated in AF pathogenesis.
- Novel diagnostic markers for AF are needed.
Purpose of the Study:
- To identify a novel gene signature for AF diagnosis related to cuproptosis.
- To explore the connection between cuproptosis and AF using computational methods.
- To develop a diagnostic model for AF based on cuproptosis-related genes.
Main Methods:
- Ensemble learning (Random Forests, eXtreme Gradient Boosting) for gene selection.
- Light Gradient Boosting Machine (LightGBM) for diagnostic model construction.
- microRNA-transcription factor-messenger RNA network analysis and CIBERSORT for immunocyte infiltration analysis.
Main Results:
- Two cuproptosis-related genes, SLC31A1 and LIAS, were identified as potential diagnostic markers for AF.
- A diagnostic model using these genes achieved an AUC > 0.75.
- HOXA9 and TET1 were predicted to regulate SLC31A1 and LIAS in AF.
- Cuproptosis was linked to immunocyte activities, with increased neutrophils in AF patients.
- Specific correlations between SLC31A1/LIAS and various immune cells were observed.
Conclusions:
- A validated cuproptosis-related diagnostic model for AF was successfully developed using the LightGBM algorithm.
- HOXA9 and TET1 may regulate cuproptosis in AF.
- Cuproptosis potentially interacts with infiltrating immunocytes in the context of AF.
Background:
Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. Nonetheless, the accurate diagnosis of this condition continues to pose a challenge when relying on conventional diagnostic techniques. Cell death is a key factor in the pathogenesis of AF. Existing investigations suggest that cuproptosis may also contribute to AF. This investigation aimed to identify a novel diagnostic gene signature associated with cuproptosis for AF using ensemble learning methods and discover the connection between AF and cuproptosis.
Results:
Two genes connected to cuproptosis, including solute carrier family 31 member 1 (SLC31A1) and lipoic acid synthetase (LIAS), were selected by integration of random forests and eXtreme Gradient Boosting algorithms. Subsequently, a diagnostic model was constructed that includes the two genes for AF using the Light Gradient Boosting Machine (LightGBM) algorithm with good performance (the area under the curve value > 0.75). The microRNA-transcription factor-messenger RNA network revealed that homeobox A9 (HOXA9) and Tet methylcytosine dioxygenase 1 (TET1) could target SLC31A1 and LIAS in AF. Functional enrichment analysis indicated that cuproptosis might be connected to immunocyte activities. Immunocyte infiltration analysis using the CIBERSORT algorithm suggested a greater level of neutrophils in the AF group. According to the outcomes of Spearman's rank correlation analysis, there was a negative relation between SLC31A1 and resting dendritic cells and eosinophils. The study found a positive relationship between LIAS and eosinophils along with resting memory CD4+ T cells. Conversely, a negative correlation was detected between LIAS and CD8+ T cells and regulatory T cells.
Conclusions:
This study successfully constructed a cuproptosis-related diagnostic model for AF based on the LightGBM algorithm and validated its diagnostic efficacy. Cuproptosis may be regulated by HOXA9 and TET1 in AF. Cuproptosis might interact with infiltrating immunocytes in AF.
