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Screening of immune-related secretory proteins linking chronic kidney disease with calcific aortic valve
Enyi Zhu1, Xiaorong Shu2, Zi Xu3
1Department of Nephrology, Center of Kidney and Urology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China.
Insights
This study identifies key genes and biomarkers linking chronic kidney disease (CKD) to calcific aortic valve disease (CAVD). A diagnostic nomogram using SLPI/MMP9 shows promise for identifying CKD with CAVD.
Area of Science:
- Cardiovascular Research
- Nephrology
- Genomics
Background:
- Chronic kidney disease (CKD) is a significant risk factor for cardiovascular diseases, including calcific aortic valve disease (CAVD).
- Understanding the genetic links between CKD and CAVD is crucial for developing diagnostic and therapeutic strategies.
Purpose of the Study:
- To identify genes associated with CKD that may contribute to CAVD pathogenesis.
- To discover candidate biomarkers for diagnosing the co-occurrence of CKD and CAVD.
Main Methods:
- Utilized gene expression datasets (CAVD, CKD-PBMC, CKD-Kidney) from the GEO database.
- Applied differential expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), protein-protein interaction (PPI), and cMAP analyses.
- Employed machine learning (LASSO, random forest) for biomarker screening and nomogram construction, validated with ROC, calibration, and decision curve analyses. CIBERSORT algorithm assessed immune cell infiltration.
Main Results:
- Identified 124 key CAVD genes and 983 CKD-associated secretory proteins.
- Discovered 76 CKD-related pathogenic genes in CAVD, enriched in inflammatory and immune pathways.
- Developed a diagnostic nomogram using SLPI/MMP9 with high diagnostic performance for CKD with CAVD, confirmed in an external cohort. Metyrapone identified as a potential drug for CAVD.
Conclusions:
- Elucidated inflammatory-immune pathways in CKD-related CAVD.
- Developed a novel SLPI/MMP9-based diagnostic nomogram for CKD with CAVD.
- The findings offer new avenues for serum-based diagnosis and therapeutic interventions for patients with both conditions.
Background:
Chronic kidney disease (CKD) is one of the most significant cardiovascular risk factors, playing vital roles in various cardiovascular diseases such as calcific aortic valve disease (CAVD). We aim to explore the CKD-associated genes potentially involving CAVD pathogenesis, and to discover candidate biomarkers for the diagnosis of CKD with CAVD.
Methods:
Three CAVD, one CKD-PBMC and one CKD-Kidney datasets of expression profiles were obtained from the GEO database. Firstly, to detect CAVD key genes and CKD-associated secretory proteins, differentially expressed analysis and WGCNA were carried out. Protein-protein interaction (PPI), functional enrichment and cMAP analyses were employed to reveal CKD-related pathogenic genes and underlying mechanisms in CKD-related CAVD as well as the potential drugs for CAVD treatment. Then, machine learning algorithms including LASSO regression and random forest were adopted for screening candidate biomarkers and constructing diagnostic nomogram for predicting CKD-related CAVD. Moreover, ROC curve, calibration curve and decision curve analyses were applied to evaluate the diagnostic performance of nomogram. Finally, the CIBERSORT algorithm was used to explore immune cell infiltration in CAVD.
Results:
The integrated CAVD dataset identified 124 CAVD key genes by intersecting differential expression and WGCNA analyses. Totally 983 CKD-associated secretory proteins were screened by differential expression analysis of CKD-PBMC/Kidney datasets. PPI analysis identified two key modules containing 76 nodes, regarded as CKD-related pathogenic genes in CAVD, which were mostly enriched in inflammatory and immune regulation by enrichment analysis. The cMAP analysis exposed metyrapone as a more potential drug for CAVD treatment. 17 genes were overlapped between CAVD key genes and CKD-associated secretory proteins, and two hub genes were chosen as candidate biomarkers for developing nomogram with ideal diagnostic performance through machine learning. Furthermore, SLPI/MMP9 expression patterns were confirmed in our external cohort and the nomogram could serve as novel diagnosis models for distinguishing CAVD. Finally, immune cell infiltration results uncovered immune dysregulation in CAVD, and SLPI/MMP9 were significantly associated with invasive immune cells.
Conclusions:
We revealed the inflammatory-immune pathways underlying CKD-related CAVD, and developed SLPI/MMP9-based CAVD diagnostic nomogram, which offered novel insights into future serum-based diagnosis and therapeutic intervention of CKD with CAVD.
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