Screening of immune-related secretory proteins linking chronic kidney disease with calcific aortic valve disease based on comprehensive bioinformatics analysis and machine learning

Abstract

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.

Causal relationship between systemic lupus erythematosus and primary liver cirrhosis based on two-sample bidirectional Mendelian randomization and transcriptome overlap analysis

“…Transcriptome overlap analysis is a genetic validation method for analyzing the relationship between two diseases. For example, based on three datasets of polycystic ovary syndrome and recurrent implantation failure, 12 disease overlapping genes were identified using weighted gene co expression networks, functional enrichment analysis, and three machine learning algorithms, which potential pathogenic associations and mechanisms between the two were explored [ 28 ]; The inflammatory immune pathway of calcified aortic valve disease (CAVD) related to chronic kidney disease (CKD) had been revealed, providing new insights for future serum based diagnosis and treatment interventions for CKD combined with CAVD [ 29 ]. In this study, 418 upregulated overlapping genes were identified using the GEO dataset of SLE and PBC, and their biological functions were found to be related to TNF signaling pathway and JAK-STAT signaling pathway, which was similar to previous reports [ 7 ].…”

Identifying and Validating GSTM5 as an Immunogenic Gene in Diabetic Foot Ulcer Using Bioinformatics and Machine Learning

“…Subsequently, the limma package is utilized to analyze the differential expression matrix comparing the control group and DFU. To identify genes related to immune infiltration in DFU patients, we employ the criteria of |log FC| > 1 and an adjusted p-value < 0.05 16 as thresholds for detecting differentially expressed genes (DEGs).…”

Identification of renal ischemia reperfusion injury-characteristic genes, pathways and immunological micro-environment features through bioinformatics approaches

“…Besides, gene correlation networks have been widely constructed to extensively understand the mechanism underlying the high-throughput sequencing data [3]. Weighted gene coexpression network analysis (WGCNA) was a common gene correlation analysis method [4], which supplied complete interpretation into the detail of molecular interactions [5][6][7][8]. In this study, RNA sequencing datasets referred to kidney IRI were collected from the Gene Expression Omnibus (GEO) database.…”