Datasheet1_Identification of hub genes and key signaling pathways by weighted gene co-expression network analysis for human aortic stenosis and insufficiency.docx

BackgroundHuman aortic valve stenosis (AS) and insufficiency (AI) are common diseases in aging population. Identifying the molecular regulatory networks of AS and AI is expected to offer novel perspectives for AS and AI treatment.
MethodsHighly correlated modules with the progression of AS and AI were identified by weighted genes co-expression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by the clusterProfiler program package. Differentially expressed genes (DEGs) were identified by the DESeqDataSetFromMatrix function of the DESeq2 program package. The protein-protein interaction (PPI) network analyses were implemented using the STRING online tool and visualized with Cytoscape software. The DEGs in AS and AI groups were overlapped with the top 30 genes with highest connectivity to screen out ten hub genes. The ten hub genes were verified by analyzing the data in high throughput RNA-sequencing dataset and real-time PCR assay using AS and AI aortic valve samples.
ResultsBy WGCNA algorithm, 302 highly correlated genes with the degree of AS, degree of AI, and heart failure were identified from highly correlated modules. GO analyses showed that highly correlated genes had close relationship with collagen fibril organization, extracellular matrix organization and extracellular structure organization. KEGG analyses also manifested that protein digestion and absorption, and glutathione metabolism were probably involved in AS and AI pathological courses. Moreover, DEGs were picked out for 302 highly correlated genes in AS and AI groups relative to the normal control group. The PPI network analyses indicated the connectivity among these highly correlated genes. Finally, ten hub genes (CD74, COL1A1, TXNRD1, CCND1, COL5A1, SERPINH1, BCL6, ITGA10, FOS, and JUNB) in AS and AI were found out and verified.
ConclusionOur study may provide the underlying molecular targets for the mechanism research, diagnosis, and treatment of AS and AI in the future.

研究背景：人类主动脉瓣狭窄（Aortic Stenosis, AS）与主动脉瓣关闭不全（Aortic Insufficiency, AI）是老年人群中的常见疾病。阐明AS与AI的分子调控网络，有望为二者的诊疗提供全新的研究视角与治疗思路。
研究方法：通过加权基因共表达网络分析（Weighted Gene Co-expression Network Analysis, WGCNA），筛选出与AS、AI进展密切相关的共表达模块；采用clusterProfiler程序包开展基因本体（Gene Ontology, GO）与京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）富集分析；借助DESeq2程序包的DESeqDataSetFromMatrix函数，筛选得到差异表达基因（Differentially Expressed Genes, DEGs）；利用STRING在线数据库构建蛋白质相互作用（Protein-Protein Interaction, PPI）网络，并通过Cytoscape软件完成可视化；将AS组与AI组中的差异表达基因，与连接度排名前30的基因取交集，最终筛选出10个核心基因（hub genes）；收集AS与AI患者的主动脉瓣样本，通过高通量RNA测序数据集与实时荧光定量PCR实验，对这10个核心基因进行验证。
研究结果：通过WGCNA算法，从核心共表达模块中筛选出302个与AS严重程度、AI严重程度及心力衰竭程度密切相关的高相关性基因。GO富集分析结果显示，上述高相关性基因主要参与胶原纤维组装、细胞外基质组装及细胞外结构组装等生物学过程。KEGG富集分析结果亦表明，蛋白质消化吸收、谷胱甘肽代谢等通路可能参与AS与AI的病理进程。此外，以正常对照组为参照，分别在AS组与AI组中，针对上述302个高相关性基因筛选得到差异表达基因。PPI网络分析揭示了这些高相关性基因间的相互作用关联。最终筛选并验证得到AS与AI相关的10个核心基因：CD74、COL1A1、TXNRD1、CCND1、COL5A1、SERPINH1、BCL6、ITGA10、FOS及JUNB。
研究结论：本研究可为未来AS与AI的发病机制研究、临床诊断及治疗提供潜在的分子靶点。