Identification of Candidate Biomarkers for Salt Sensitivity of Blood Pressure by Integrated Bioinformatics Analysis

Abstract

In the current study, we aimed to identify potential biomarkers for salt sensitivity of blood pressure (SSBP), which may provide a novel insight into the pathogenic mechanisms of salt-sensitive hypertension. Firstly, we conducted weighted gene coexpression network analysis (WGCNA) and selected a gene module and 60 hub genes significantly correlated to SSBP. Then, GO function and KEGG signaling pathway enrichment analysis and protein–protein interaction (PPI) network analysis were performed. Furthermore, we identified a five-gene signature with high connectivity degree in the PPI network and high AUC of ROC curves, which may have high diagnosis value for SSBP. Moreover, through combining two gene screening methods, we identified 23 differentially expressed circRNAs and selected the top 5% circRNAs (1 circRNA) with the highest connectivity degree in the coexpression network as hub circRNA highly associated with SSBP. Finally, we carried out RT-qPCR to validate the expression of five hub genes, and our results showed that the expression of HECTD1 ( P = 0.017), SRSF5 ( P = 0.003), SRSF1 ( P = 0.006), HERC2 ( P = 0.004), and TNPO1 ( P = 0.002) was significantly upregulated in the renal tissue in salt-sensitive rats compared to salt-resistant rats, indicating that these five hub genes can serve as potential biomarkers for SSBP.

Comprehensive bioinformatics analysis identifies LAPTM5 as a potential blood biomarker for hypertensive patients with left ventricular hypertrophy

“…Therefore, identification and investigation of the underlying biomarkers for early-stage screening and diagnosis of cardiac hypertrophy are urgently required. Recently, data mining strategies on public access databases and integrative bioinformatics analysis have been demonstrated to be valid methods to identify potential biomarkers or even new therapeutic targets in complex diseases [ 13 – 15 ]. To the best of our knowledge, the present study was the first to identify candidate diagnostic biomarkers for hypertensive patients with LVH by using the publicly available GEO dataset and comprehensive bioinformatics approaches, which could provide novel insight into the molecular mechanism associated with the pathogenesis of cardiac hypertrophy.…”

Regulation of MAPK Signaling Pathways by the Large HERC Ubiquitin Ligases

“…HERC1: several cancers [18]; Macrocephaly, Dysmorphic Facies and PsychoMotor Retardation (MDFPMR) syndrome [19][20][21][22][23]; autism spectrum disorder [24,25]; Parkinson's disease [26]; schizophrenia [27]; febrile seizures [28]; neuropathic periphery disease [29]; COVID-19 combined with major depression disorder (COVID-19-MDD) [30]; acquired immunodeficiency syndrome (AIDS) [31]; diabetes [32]; cardiovascular disease [33]; osteopenia [34]. HERC2: several cancers [18]; HERC2 Angelman-like syndrome [35][36][37][38][39][40][41][42][43][44]; autism spectrum disorder [17]; Parkinson's disease [45]; agenesis of the corpus callosum (ACC) [46]; brain arteriovenous malformation (BAVM) [47]; diabetic cerebral ischemia-reperfusion (I/R) injury [48]; central precocious puberty [49]; refractive astigmatism [50]; inflammatory diseases [51][52][53][54][55]; asthma [56]; hypertension [57,58]; skin conditions …”

“…HERC2 has also been linked with other types of pathology such as refractive astigmatism [50], asthma [56], hypertension [57,58], several skin conditions such as vitiligo and rosacea [59][60][61] and some inflammatory diseases. Among these inflammatory diseases there are some related to the digestive system and, recently, HERC2 has also been described to promote inflammation-driven cancer stemness and immune evasion in hepatocellular carcinoma [51][52][53][54][55] (Figure 1, HERC2 section).…”

A nine-hub-gene signature of metabolic syndrome identified using machine learning algorithms and integrated bioinformatics

“…In high-throughput transcriptomics, microarrays and next-generation sequencing (NGS) have been widely used to measure RNA expression levels [13][14][15]. In addition, advanced bioinformatics approaches, such as weighted gene coexpression network analysis (WGCNA), can play an important role in the identification of disease biomarkers, as they have high sensitivity, specificity, and efficiency, based on high-throughput transcriptomic data [16,17]. Compared to traditional bioinformatics methods, such as differentially expressed gene (DEG) analysis, networkfocused algorithm WGCNA can establish a weighted scale-free co-expression network, and then identify key gene modules and hub genes [18].…”