New perspectives on the mechanisms and treatment of valvular heart disease: Mendelian randomization and systematic analysis based on plasma proteins

瓣膜性心脏病机制及治疗的新视角:基于血浆蛋白的孟德尔随机化和系统分析

阅读:4

Abstract

Valvular heart disease (VHD) is a common cardiovascular disorder with insidious early symptoms and can progress to heart failure or sudden death. Pharmacological options remain limited, and severe disease often requires surgical intervention. This study aimed to identify key molecular targets and potential therapeutic candidates for VHD using Mendelian randomization (MR) and integrative analyses. A 2-sample MR design was used to evaluate the association between genetically predicted exposures and VHD using publicly available genome-wide association study summary statistics. Downstream analyses included functional enrichment, drug repurposing with molecular docking, protein-protein interaction network construction with hub-gene identification, and single-cell RNA sequencing-based analysis to examine the cell-type distribution of candidate gene expression. MR analysis identified 76 genes associated with VHD, including stathmin 1, ribosomal protein S5, and mitogen-activated protein kinase 8. Enrichment analysis suggested that these genes were involved in multiple signaling pathways potentially related to disease progression. Drug prediction and molecular docking prioritized razoxane, reserpine, and bisindolylmaleimide I as candidate compounds targeting key molecules. Protein-protein interaction network analysis further identified 10 hub genes, such as DEAD-box helicase 6 and apolipoprotein E. Single-cell sequencing showed high expression of these genes in cardiomyocytes, fibroblasts, and smooth muscle cells. This study identified candidate genes and potential drug leads for VHD through an MR-based integrative analysis, providing targets for subsequent mechanistic and experimental validation.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。