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Identification and validation of the diagnostic biomarker MFAP5 for CAVD with type 2 diabetes by bioinformatics
Qiang Shen1, Lin Fan1, Chen Jiang1
1Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Introduction:
Calcific aortic valve disease (CAVD) is increasingly prevalent among the aging population, and there is a notable lack of drug therapies. Consequently, identifying novel drug targets will be of utmost importance. Given that type 2 diabetes is an important risk factor for CAVD, we identified key genes associated with diabetes - related CAVD via various bioinformatics methods, which provide further potential molecular targets for CAVD with diabetes.
Methods:
Three transcriptome datasets related to CAVD and two related to diabetes were retrieved from the Gene Expression Omnibus (GEO) database. To distinguish key genes, differential expression analysis with the "Limma" package and WGCNA was applied. Machine learning (ML) algorithms were employed to screen potential biomarkers. The receiver operating characteristic curve (ROC) and nomogram were then constructed. The CIBERSORT algorithm was utilized to investigate immune cell infiltration in CAVD. Lastly, the association between the hub genes and 22 types of infiltrating immune cells was evaluated.
Results:
By intersecting the results of the "Limma" and WGCNA analyses, 727 and 190 CAVD - related genes identified from the GSE76717 and GSE153555 datasets were obtained. Then, through differential analysis and interaction, 619 genes shared by the two diabetes mellitus datasets were acquired. Next, we intersected the differential genes and module genes of CAVD with the differential genes of diabetes, and the obtained genes were used for subsequent analysis. ML algorithms and the PPI network yielded a total of 12 genes, 10 of which showed a higher diagnostic value. Immune cell infiltration analysis revealed that immune dysregulation was closely linked to CAVD progression. Experimentally, we have verified the gene expression differences of MFAP5, which has the potential to serve as a diagnostic biomarker for CAVD.
Conclusion:
In this study, a multi-omics approach was used to identify 10 CAVD-related biomarkers (COL5A1, COL5A2, THBS2, MFAP5, BTG2, COL1A1, COL1A2, MXRA5, LUM, CD34) and to develop an exploratory risk model. Western blot (WB) and immunofluorescence experiments revealed that MFAP5 plays a crucial role in the progression of CAVD in the context of diabetes, offering new insights into the disease mechanism.
Insights
This study identifies 10 key genes, including MFAP5, as potential diagnostic biomarkers for calcific aortic valve disease (CAVD) in patients with type 2 diabetes. These findings offer new molecular targets for treating this prevalent condition.
Area of Science:
- Cardiovascular Biology
- Genomics and Bioinformatics
- Metabolic Diseases
Background:
- Calcific aortic valve disease (CAVD) is a growing concern in aging populations, with limited therapeutic options.
- Type 2 diabetes is a significant risk factor, highlighting the need to understand their molecular links.
Purpose of the Study:
- To identify novel molecular targets and diagnostic biomarkers for CAVD, particularly in the context of type 2 diabetes.
- To explore the role of immune cell infiltration in CAVD pathogenesis.
Main Methods:
- Utilized bioinformatics approaches including differential expression analysis (Limma), Weighted Gene Co-expression Network Analysis (WGCNA), and machine learning algorithms.
- Analyzed Gene Expression Omnibus (GEO) datasets for CAVD and diabetes.
- Performed immune cell infiltration analysis using CIBERSORT and constructed a risk model with ROC curves and nomograms.
Main Results:
- Identified 727 and 190 CAVD-related genes and 619 diabetes-related genes. Intersected these to find shared genes.
- Discovered 10 potential CAVD biomarkers (e.g., MFAP5, COL5A1, COL1A1) with high diagnostic value.
- Found immune dysregulation linked to CAVD progression and validated MFAP5 as a crucial gene in diabetes-related CAVD.
Conclusions:
- A multi-omics strategy successfully identified 10 key genes as potential biomarkers for CAVD in diabetic patients.
- MFAP5 was experimentally validated, showing a critical role in CAVD progression within a diabetic context.
- These findings provide novel insights into CAVD mechanisms and potential therapeutic targets.
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