Identification of Cardiovascular Disease-Related Genes Based on the Co-Expression Network Analysis of Genome-Wide Blood Transcriptome

Abstract

Inference of co-expression network and identification of disease-related modules and gene sets can help us understand disease-related molecular pathophysiology. We aimed to identify a cardiovascular disease (CVD)-related transcriptomic signature, specifically, in peripheral blood tissue, based on differential expression (DE) and differential co-expression (DcoE) analyses. Publicly available blood sample datasets for coronary artery disease (CAD) and acute coronary syndrome (ACS) statuses were integrated to establish a co-expression network. A weighted gene co-expression network analysis was used to construct modules that include genes with highly correlated expression values. The DE criterion is a linear regression with module eigengenes for module-specific genes calculated from principal component analysis and disease status as the dependent and independent variables, respectively. The DcoE criterion is a paired t-test for intramodular connectivity between disease and matched control statuses. A total of 21 and 23 modules were established from CAD status- and ACS-related datasets, respectively, of which six modules per disease status (i.e., obstructive CAD and ACS) were selected based on the DE and DcoE criteria. For each module, gene–gene interactions with extremely high correlation coefficients were individually selected under the two conditions. Genes displaying a significant change in the number of edges (gene–gene interaction) were selected. A total of 6, 10, and 7 genes in each of the three modules were identified as potential CAD status-related genes, and 14 and 8 genes in each of the two modules were selected as ACS-related genes. Our study identified gene sets and genes that were dysregulated in CVD blood samples. These findings may contribute to the understanding of CVD pathophysiology.

Comparative Analysis of Biological Signatures between Freshly Preserved and Cryo-Preserved Bone Marrow Mesenchymal Stem Cells

“…Kwon et al [30] reported a subset of paracrine factors, such as VEGF, MCP1, and IL6, as stem-cell-therapy-related markers. MSCs encompass a novel secretome network that cannot be substituted by single chemokine agents [45]; therefore, co-expression network analysis or deep learning is required to discover MSC-prognosis-related biomarkers [47,48].…”

“…Gupta et al [52] recently analyzed the mRNAs of all genes isolated from BM-MSCs and identified several therapy-response-related genes. Moreover, we previously established an analytical platform for transcriptome data based on a co-expression network [47], which can be used in future studies to determine molecular signatures related to the identity and therapeutic effects of MSCs.…”

“…The current study has a retrospective design and involves database-based research analyzing accumulated data by using machine learning and statistical methods, rather than experiment-based functional studies. Initiated by the present study, a future experimental study is planned to be conducted, and our team is in the process of establishing an analysis platform for biomarker discovery, which will include differential and co-differential patterns [47,59]. Future research utilizing big data, deep phenotypes (e.g., experimental indices and genetic/transcriptomic profiles), and the aforementioned analytical platforms is required to identify in-depth and generalized MSC-related biosignatures.…”

Urinary albumin creatinine ratio is associated with lipid profile

“…In addition, several studies have shown that moderately increased albuminuria (UACR < 30 mg/g) within the accepted normal range is associated with higher cardiovascular morbidity and mortality, even in the general population 3 , 4 . Cardiovascular disease (CVD) is a broad-spectrum disease associated with numerous pathophysiological mechanisms such as inflammation 5 , fatty acid metabolic processes, and cholesterol metabolic process 6 .…”