Caspase-7 and Vitamin D Receptor Gene as Key Genes of Hypertension Caused by Pyroptosis in Human

Abstract

This comprehensive investigation endeavors to unravel the intricate mechanisms underlying the phenomenon of pyroptosis, a distinct form of programmed inflammatory cell death, within the context of hypertension. To achieve this ambitious aim, a multi-faceted approach leveraging advanced bioinformatics and sophisticated machine learning methodologies was meticulously employed. The R programming language served as the foundational computational tool, facilitating the systematic integration of differentially expressed genes (DEGs) identified between peripheral blood samples obtained from hypertensive subjects and healthy control individuals. These gene expression profiles were sourced from two publicly available and extensively utilized Gene Expression Omnibus (GEO) datasets: GSE24752 and GSE75360. Following the robust integration of these datasets, the identified DEGs were subjected to detailed functional annotation through Gene Ontology (GO) analysis, which categorizes genes based on their biological processes, cellular components, and molecular functions. Concurrently, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed to identify enriched signaling pathways and disease associations, providing a broader biological context. Furthermore, Gene Set Enrichment Analysis (GSEA) was utilized to detect coordinated changes in predefined sets of genes, offering insights into subtle yet significant pathway alterations.

A crucial phase of the study involved the judicious screening for key genes from the pool of identified DEGs, employing a trio of powerful machine learning algorithms. Logistic regression was initially applied to model the probability of hypertension based on gene expression profiles. This was followed by LASSO (Least Absolute Shrinkage and Selection Operator) regression, a technique particularly adept at performing feature selection and regularization, which helps to identify a parsimonious set of highly predictive genes by shrinking less important coefficients to zero. Finally, a support vector machine (SVM) model was utilized, a robust supervised learning algorithm widely used for classification tasks, to further refine and validate the selection of critical genes. Upon the identification of these key genes, a visualized protein-protein interaction (PPI) regulatory network was meticulously constructed. This network graphically represents the complex web of interactions among the proteins encoded by the identified key genes, offering a systemic perspective on their functional interplay. Complementing this, a comprehensive immune cell infiltration analysis was meticulously performed on the integrated GEO datasets derived from hypertensive samples. This analysis aimed to quantify and characterize the proportions of various immune cell types present within the peripheral blood samples, thereby shedding light on the immunological landscape in hypertension and its potential connection to pyroptosis. To bridge the gap between computational predictions and biological validation, serum samples were collected from a distinct cohort of hypertensive subjects and healthy control individuals. These samples were then subjected to quantitative real-time polymerase chain reaction (RT-qPCR) for the precise detection and validation of the expression levels of the screened key genes.

The rigorous bioinformatics pipeline successfully identified a total of 1005 differentially expressed genes from the peripheral blood samples of 13 hypertension cases and 14 healthy control samples, indicating a substantial alteration in gene expression profiles associated with the disease. The subsequent GO analysis, KEGG enrichment analysis, and GSEA collectively revealed that these identified DEGs do not operate in isolation but rather function synergistically across a multitude of interconnected biological pathways, hinting at the systemic nature of hypertension. Applying the powerful machine learning algorithms—LASSO regression and SVM—enabled the precise identification of six key genes that demonstrated significant relevance to the pyroptosis pathway. These crucial genes include CASP7 (encoding caspase-7), CYBB, NEK7, NLRP2, RAB5A, and VDR (encoding the vitamin D receptor), suggesting their potential involvement in driving pyroptotic processes in hypertension. The immune infiltration analysis provided valuable insights into the cellular immune landscape, indicating that activated B cells, effector memory CD8 T cells, immature B cells, myeloid-derived suppressor cells (MDSCs), and T follicular helper cells constituted the largest proportions of immune cells within the hypertensive peripheral blood samples. This shift in immune cell composition points towards a complex immunological dysregulation in hypertension. Finally, the validation experiments using RT-qPCR on independent serum samples yielded compelling results, demonstrating significantly higher relative expression levels of both caspase-7 (encoded by CASP7) and the vitamin D receptor gene (VDR) in the hypertensive subjects when compared to the healthy control group.

These collective findings offer profound implications, suggesting that CASP7 and the vitamin D receptor gene, given their altered expression in hypertension and their established roles in cellular processes, may represent novel and promising research targets for the future development of diagnostic biomarkers and therapeutic interventions for hypertension. Furthermore, TVB-3166 the robust evidence presented in this study provides fresh and compelling support for the involvement of pyroptosis as a significant underlying pathological mechanism in the progression and manifestation of hypertension, opening new avenues for mechanistic investigations and targeted therapeutic strategies.