
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12577-7
10.1016/j.heliyon.2024.e36546
e36546
Research Article
In-silico identification and functional characterization of common genes associated with type 2 diabetes and hypertension
Rabby Md. Golam a1
Suzauddula Md. b1
Hasan Md. Sakib a
Dewan Mahbubur Alam a
Islam Md. Numan numanislam55@gmail.com
ac∗
a Department of Nutrition and Food Technology, Jashore University of Science and Technology, Jashore, 7408, Bangladesh
b College of Agriculture and Natural Resources, National Chung Hsing University, Taichung City, 40227, Taiwan
c Department of Food Science and Technology, University of Nebraska Lincoln, USA
∗ Corresponding Author. Department of Nutrition and Food Technology, Jashore University of Science and Technology, Jashore, 7408, Bangladesh. numanislam55@gmail.com
1 These authors contributed to this work equally.

21 8 2024
30 8 2024
21 8 2024
10 16 e3654617 7 2023
12 8 2024
19 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Type 2 diabetes (T2D) and hypertension are global public health concerns and major metabolic disorders in humans. Experimental evidence indicates considerable hereditary influences on the etiology of T2D and hypertension, but the molecular basis of these diseases is still limited. Thus, the current study analyzed 185 (132 T2D and 53 hypertension) GWAS catalog datasets and identified 83 common genes linked to T2D and hypertension pathogenesis. These genes were further examined using various bioinformatics approaches to elucidate their molecular mechanisms underlying the pathophysiology of T2D and hypertension. Gene ontology (GO) analysis revealed the biological, cellular, and molecular functions of these genes, which were also linked to different T2D and hypertension pathways. Specifically, seven genes were found to be crucial for T2D, and nine were directly associated with hypertension. Protein-protein interaction (PPI) analysis identified 28 candidate genes and seven hub genes through 11 topological methods. Among 231 miRNAs, seven were significant in interacting with the hub genes, and nine transcription factors (TFs) out of 36 were linked to these hub genes. Additionally, two of the seven hub genes were downregulated by 43 FDA-approved drugs. These findings elucidate the molecular processes underlying T2D and hypertension, suggesting that targeting these genes could lead to future drug development and therapeutic strategies to treat T2D and hypertension.

Keywords

Diabetes
Hypertension
Pathophysiology
Genetic marker
GWAS
==== Body
pmc1 Introduction

The emergence of hypertension in diabetic patients complicates treatment, raises healthcare expenditures, and significantly increases the risk of macrovascular and microvascular problems.

[1]. T2D is a multifactorial inherited disorder characterized by dysregulated glucose homeostasis. It is caused by several pathophysiological mechanisms, including impaired insulin secretion, decreased incretin effect, increased glucagon secretion, adiposity, and insulin resistance [2,3]. The development of T2D is influenced by both genetic and environmental factors, with genetic predisposition being exacerbated by poor diet and lack of physical activity [4]. T2D heritability ranges from 35 to 80 % globally, indicating a strong genetic basis for T2D pathogenesis [5].

Not surprisingly, nearly two-thirds of the population with T2D have hypertension. The underlying mechanisms for both T2D and hypertension are similar to those of metabolic syndrome [6]. Hypertension is a significant public health issue worldwide and a major risk factor for cardiovascular diseases such as stroke, coronary heart disease, renal dysfunction, and congestive heart failure. It results from a complex interaction of environmental and genetic factors [7]. Purinergic signaling pathways regulate hypertension through the neurons in the brain stem, the sympathetic nervous system, the renin-angiotensin system, renal autoregulation, and epithelial sodium channel activity. Hypertension ranks first among all cardiovascular diseases regarding global morbidity and mortality [8].

According to the consequences of the existing findings, T2D and hypertension are global public health concerns. They increase the likelihood of developing cardiovascular diseases, which can lead to disability and death [9]. T2D patients with hypertension have reputable risk factors for micro and macrovascular complexities, which have similar underlying pathways [10]. Diabetes patients have a twofold or greater risk of developing cardiovascular disease than non-diabetics. T2D patients with hypertension and obesity in developing countries are more vulnerable than those in high-income countries [11]. Various mechanisms have been proposed to detect the co-existence of T2D and hypertension in the same individuals. The inactive renin-angiotensin system (RAAS) is thought to be the primary cause of T2D and hypertension coexistence. Furthermore, insulin resistance is also considered a critical factor in the development of both entities [6].

Modern high-throughput genotyping technology, such as genome-wide association studies (GWASs), has become a powerful tool for identifying the etiology and characterizing the functions of genetically complex diseases. The GWAS establishes links between DNA sequence variation and a disease or trait of biomedical significance [12]. The GWASs among T2D datasets revealed an unprecedented opportunity to determine T2D pathogenesis heritability. The GWAS identifies population-specific genetic influence and allelic heterogeneity for T2D glycemic identification [13]. Specifically, several GWAS have identified genes and single nucleotide variants (SNV) associated with T2D [5]. GWAS significantly increased our understanding of cardiovascular disease's genetic influence and architecture in the last decade. Hypertension is a quantitative trait generally distributed in the general population, with inherited genetic factors accounting for 30–50 % of the variation in hypertension [14] and 30–70 % heritability in T2D [15]. Several GWAS have identified more than 900 genomic regions associated with hypertension phenotypes.

Moreover, the diverse race and ethnic backgrounds and admixture mapping studies based on GWAS also show that hypertension phenotypes have some ancestry-specific or ancestry-enriched genetic components [16]. Since the pathophysiological mechanisms for T2D and hypertension are closely interrelated, the combined genetic influence on T2D and hypertension needs to be addressed for better understanding and treatment. As far as we know, a few studies have demonstrated the relationship between T2D and hypertension through bioinformatics analysis. However, there has been minimal use of GWAS data to significantly identify and characterize the genes linked to both T2D and hypertension.

This study aims to identify genes and hub genes associated with T2D and hypertension using GWAS data and to evaluate their molecular and biological roles in the pathogenesis of these diseases. We employed various bioinformatics analyses to identify common genes linked to T2D and hypertension, which could elucidate the etiology of both conditions. Furthermore, we explored potential KEGG pathways, conducted disease association analyses, and performed protein-protein interaction network analyses. We also identified hub genes through clustering module construction and examined hub gene regulatory networks, including hub gene-miRNA, hub gene-TF, and hub gene-drug interactions. Our findings contribute to a better understanding of the genetic connections between T2D and hypertension and aid in developing targeted treatments for their pathophysiology.

2 Materials and methods

2.1 Identification of associated genes by GWAS

The GWAS catalog is a publicly accessible database of SNV-trait associations that includes differentially expressed genes (DEGs) and SNVs linked to various diseases. We utilized the GWAS catalog database (https://www.ebi.ac.uk/gwas/) to identify genes implicated in the pathogenesis of T2D and hypertension [17]. We have chosen 204 T2D and 108 hypertension-related GWAS catalog datasets from the total number of published GWAS studies to date. Among them, 132 T2D and 53 hypertension GWAS catalog datasets showed associated genes. After collecting the total number of genes from GWAS datasets, overlapping genes were omitted to find the unique number of genes [18]. Furthermore, the publicly available drawing software Venny 2.1.0 (http://bioinfogp.cnb.csic.es/tools/venny/index.html) was used to identify the common genes associated with the pathogenesis of T2D and hypertension [19].

2.2 Gene ontology (GO) enrichment analysis

To perform GO enrichment analysis, we used the publicly accessible DAVID (Database for Annotation, Visualization, and Integrated Discovery) database (https://david.ncifcrf.gov/) [20]. The identified common genes were submitted to the DAVID database to interpret GO functions. Statistical significance was determined using a p-value and false discovery rate (FDR) cutoff of 0.05. The analysis yielded the top ten most significant biological processes (BPs), cellular components (CCs), and molecular functions (MFs) [21].

2.3 KEGG pathway enrichment analysis

We utilized the publicly available Web-Gestalt (WEB-based Gene SeT AnaLysis Toolkit) database (http://www.webgestalt.org/) to perform KEGG pathway enrichment, applying a false discovery rate (FDR) cutoff of 0.05 [22,23]. The KEGG pathway enrichment analysis was conducted using the Over Representation Analysis (ORA) method, one of the three analysis methods available in WebGestalt. Homo sapiens was selected as the reference genome, and Gene Symbol ID was used for the analysis [23].

2.4 Disease association analysis

The publicly accessible ShinyGO database (http://bioinformatics.sdstate.edu/go/) was used to identify the disease association [24]. During disease association analysis, the identified common genes were compared to the Homo sapiens reference genome, with FDR cutoffs of 0.05. We selected the top ten statistically significant levels from the database [25].

2.5 PPI network analysis and identification of hub genes

The freely accessible STRING (Search Tool for the Retrieval of Interacting Genes and Proteins) database (https://string-db.org/) was used to predict the interactions between relevant proteins [26]. The identified common genes were submitted to the database against the Homo sapiens organism, and a 0.4 confidence score was set as the cutoff. Then, the PPI (Protein-Protein Intersection) networks were visualized using the freely accessible Cytoscape software (https://cytoscape.org/) [27].

Furthermore, the Cyto-Hubba plugin (http://apps.cytoscape.org/apps/cytohubba) was used to identify the hub genes using 11 topological methods, including the Percolated Component (EPC), Maximal Clique Centrality (MCC) algorithms, Density of Maximum Neighborhood Component (DMNC), Edge Maximum Neighborhoods Component (MNC), Betweenness and Stress Paths, Bottleneck (BN), Closeness, Radiality, Eccentricity, Shortest Paths, and Degrees [28]. Finally, the MCODE (Molecular Complex Detection) plugin (http://apps.cytoscape.org/apps/mcode) was used to identify the significant modules for validating hub genes [29,30].

2.6 Construction of hub gene-miRNA regulatory network

To construct the hub gene-miRNA regulatory network, we used the publicly available miRNet database (https://www.mirnet.ca/) [31]. During the construction of the miRNA regulatory network, identified hub genes were submitted to the database against the Homo sapiens organism, and the “Official Gene Symbol” was selected. Finally, the constructed hub gene-miRNA regulatory network was visualized using freely accessible Cytoscape software (https://cytoscape.org/) [27].

2.7 Construction of hub gene-TF regulatory network

We utilized the publicly available JASPAR resources within the miRNet database (https://www.mirnet.ca/) to predict the hub gene-transcription factor regulatory network [31]. Selected hub genes were submitted to the database, using Homo sapiens as the reference organism and “Official Gene Symbol” for regulatory network development. The resulting hub gene-transcription factor regulatory network was then visualized using the freely accessible Cytoscape software (https://cytoscape.org/) [27].

2.8 Construction of hub gene-drug regulatory network

The publicly available DGIdb v4.2.0 (Drug Gene Interaction Database) (https://dgidb.genome.wustl.edu/) was used to identify the hub gene-drug regulatory network [32]. DGIdb mined drug-gene interactions from DrugBank, PharmGKB, Drug Target Commons, and other databases using a combination of expert curation and text mining. Finally, the hub gene-drug regulatory network was visualized using the freely accessible Cytocsape software (https://cytoscape.org/) [27,33].

3 Results

3.1 Identification of associated genes by GWAS

Searching the GWAS catalog database using the keywords T2D and hypertension, we found 312 (204 T2D and 108 hypertension) GWAS catalog datasets in different populations. Among them, 185 (132 T2D and 53 hypertension) GWAS catalog datasets revealed an association regarding functional gene identification. Following analysis, we identified a total of 2270 genes from 185 T2D and hypertension-related GWAS catalog datasets, including 1718 from 132 T2D and 489 from 53 hypertension datasets (Table 1).Table 1 Identification of genes associated with T2D and Hypertension.

Table 1Gene ID	T2D related datasets	Hypertension related datasets	
GTF2I	GCST010555, GCST010557, GCST90018926	GCST010774	
CDKN2B-AS1	GCST000277, GCST005413, GCST009379, GCST002352, GCST005047, GCST010118, GCST90013693, GCST006801, GCST002128, GCST001070, GCST001666, GCST003400, GCST004894, GCST003619, GCST000383, GCST000028, GCST001397, GCST001759, GCST000024, GCST000167, GCST000712, GCST000025, GCST006867, GCST010557, GCST010553, GCST010555, GCST010436, GCST007847, GCST006484, GCST007077, GCST90093109, GCST90093110, GCST90086072, GCST90132184, GCST90132185, GCST90132186, GCST90132187, GCST90137502, GCST90132183, GCST90161239, GCST90018706, GCST90018926	GCST007707	
MIR5702	GCST009379, GCST002352, GCST005047, GCST004894, GCST006867, GCST010557, GCST010555, GCST007077, GCST90093109, GCST90093110, GCST90132183, GCST90132184, GCST90132186, GCST90018926	GCST006023	
NYAP2	GCST009379, GCST002352, GCST005047, GCST004894, GCST006867, GCST010557, GCST010555, GCST007077, GCST90093109, GCST90093110, GCST90132183, GCST90132184, GCST90132186, GCST90018926	GCST006023	
CDKAL1	GCST000027, GCST000277, GCST005413, GCST004304, GCST009379, GCST002352, GCST005047, GCST010118, GCST90013693, GCST006801, GCST008833, GCST002128, GCST001965, GCST002718, GCST001666, GCST003400, GCST004894, GCST003619, GCST001033, GCST000221, GCST000383, GCST000028, GCST000478, GCST000024, GCST000047, GCST000167, GCST000712, GCST000025, GCST001550, GCST006867, GCST010557, GCST010553, GCST010555, GCST010436, GCST007847, GCST007077, GCST90093109, GCST90093110, GCST90132185, GCST90132183, GCST90132184, GCST90132186, GCST90137502, GCST90161239, GCST90018706, GCST90018926, GCST90093109, GCST90093110, GCST90132185, GCST90132183, GCST90132184, GCST90132186, GCST90137502, GCST90161239, GCST90018706, GCST90018926	GCST90000064, GCST90000060	
INSR	GCST009379, GCST010118, GCST010555, GCST010557	GCST009685	
NUP160	GCST010557	GCST009685	
LCORL	GCST010557, GCST010555, GCST90132183, GCST90132184	GCST90000060	
PDGFC	GCST009379, GCST010557, GCST010555, GCST90132183, GCST90132184	GCST90000064, GCST90000060	
NFATC2	GCST010118, GCST90013693, GCST010553, GCST010557, GCST007847, GCST90018706,	GCST007707	
FTO	GCST000047, GCST000712, GCST000167, GCST000024, GCST001759, GCST000025, GCST001550, GCST000277, GCST004894, GCST002352, GCST003400, GCST006801, GCST009379, GCST005047, GCST010555, GCST007847, GCST010553, GCST90018706, GCST90132183, GCST90132184, GCST90132187, GCST90132185, GCST90132186, GCST90018926, GCST010118, GCST010557, GCST010556, GCST90013693, GCST006867, GCST007077	GCST90013478, GCST004388	
ANK1	GCST001461, GCST004894, GCST002352, GCST003400, GCST009379, GCST005047, GCST010555, GCST90013693, GCST007847, GCST90018706, GCST90132184, GCST90132183, GCST90018926, GCST010118, GCST010557, GCST010556, GCST006867, GCST90132185, GCST007077	GCST004384, GCST004388	
RPL26P19	GCST005047, GCST90013693, GCST006801, GCST003400, GCST004894, GCST006867,	GCST90000060	
PDE3A	GCST009379, GCST010557,	GCST90000060	
EML6	GCST010557, GCST007077, GCST008114	GCST90000060	
RSPO3	GCST009379, GCST006867, GCST007847, GCST010555, GCST010557, GCST90132183, GCST90132184	GCST007707	
ZC3H11B	GCST90132184, GCST90132183, GCST010557, GCST010555, GCST004773, GCST002352, GCST009379, GCST005414,	GCST90000064	
HSD17B12	GCST009379, GCST004773, GCST010555, GCST010557, GCST010557, GCST90132183, GCST90132184, GCST90132183, GCST90134620, GCST90132184	GCST010774	
H4P1	GCST90018926, GCST90132184, GCST90132183, GCST007077, GCST010555, GCST010557, GCST006867, GCST000712, GCST005047, GCST009379	GCST90000060	
MSRA	GCST010555, GCST010557, GCST009379	GCST000398	
XKR6	GCST009379, GCST010557, GCST010555, GCST90132183, GCST90132184	GCST011952, GCST011953	
TCF4	GCST009379, GCST010555, GCST010557, GCST90132183, GCST90132184	GCST90058972	
ITPR2	GCST009379, GCST004894, GCST006867, GCST010555, GCST010557, GCST90132183, GCST90132184	GCST90013475	
FBRSL1	GCST009379, GCST010555, GCST010557, GCST90132183, GCST90132184, GCST90018926,	GCST90000064	
EBF1	GCST010555, GCST010557	GCST007707	
PURG	GCST009379, GCST006867, GCST010555, GCST010557, GCST90018926	GCST010774	
WBP1L	GCST010555, GCST010557	GCST007707	
SBF2	GCST010555, GCST010557	GCST009685, GCST90086092	
ADRB1	GCST010557	GCST007707, GCST90239614	
RPTOR	GCST010557, GCST90134620	GCST007707	
ZNF746	GCST010557	GCST004388	
ALDH1A2	GCST010557	GCST000447	
PTPRD	GCST009379, GCST010118, GCST001092, GCST000601, GCST010557	GCST007136	
SLC39A8	GCST010557	GCST009685, GCST001238	
LINC02523	GCST010555, GCST010557	GCST011953	
GUCY1B1	GCST010557	GCST007707	
SGCZ	GCST010555, GCST010557, GCST90026413	GCST004384	
GACAT3	GCST009379, GCST010555, GCST010557, GCST90018926	GCST009685	
Y_RNA	GCST000012, GCST009379, GCST009379, GCST009379, GCST009379	GCST007707, GCST90128469, GCST90225533	
UNC79	GCST010557	GCST010774	
ZNF767P	GCST010557	GCST004388	
ZFAT	GCST001351, GCST000712, GCST010557	GCST001085	
KLF14	GCST009379, GCST005047, GCST000712, GCST006867, GCST010557	GCST90000060	
LINC00529	GCST001070, GCST010557, GCST010555, GCST90132184, GCST90132187, GCST90132183	GCST011952	
C5orf67	GCST004894, GCST003400, GCST005413, GCST003619, GCST006801, GCST009379, GCST005047, GCST010555, GCST007847, GCST010553, GCST90018706, GCST90132184, GCST90132188, GCST90132187, GCST90018926, GCST90132183, GCST90132183, GCST010118, GCST010118, GCST007077, GCST010556, GCST90013693, GCST006867, GCST90132185,	GCST90000060	
WWTR1	GCST010557	GCST001949	
HLA-DQB1	GCST003619, GCST001550, GCST006867, GCST010553, GCST010555, GCST90026412, GCST90086072, GCST90018706	GCST010774	
CBLN2	GCST90026412	GCST001908	
TMEM212	GCST010557, GCST010555, GCST90026412	GCST011952, GCST011953	
MSH2	GCST90026413	GCST90086157	
POLR2KP2	GCST90026413	GCST90058972	
ITM2B	GCST001351, GCST90026413	GCST90058972	
RBM47	GCST90026414	GCST006023	
DLGAP1	GCST90134620, GCST90026416	GCST90058972	
CHST1	GCST90026416	GCST90058972	
ATXN2	GCST009875	GCST009685	
LINC02571	GCST010555	GCST009685	
UMOD1	GCST90026413	GCST006023, GCST000849	
LINC02398	GCST010551	GCST007707	
CCHCR1	GCST007847	GCST90013478	
LINC02227	GCST009380	GCST001238, GCST90225533	
PLCB3	GCST007518, GCST007516, GCST010555	GCST90000060,	
LYPLAL1-AS1	GCST006867, GCST009379, GCST90132184, GCST90132183, GCST010557, GCST010555, GCST002352, GCST009379, GCST005414	GCST90000064	
MCC	GCST004773	GCST90000060	
OPRM1	GCST004125	GCST000973	
MACROD2	GCST006495	GCST001085	
VPS33B	GCST002352, GCST005047, GCST005047	GCST010477	
LINC01405	GCST001965	GCST011141	
HECTD4	GCST001965, GCST010118	GCST002627, GCST011141, GCST011141, GCST011141	
OAS1	GCST001965	GCST011141	
CUX2	GCST001965	GCST009685, GCST011141, GCST007707, GCST90225533	
MAP3K1	GCST004773, GCST009379, GCST001759	GCST011953	
MARCHF1	GCST001033	GCST010477	
GPR45	GCST000049	GCST001949	
RPS4XP9	GCST009379, GCST006867, GCST007847, GCST90132183, GCST90132184	GCST007707	
LINC00461	GCST90013693, GCST90013693, GCST90018926,	GCST90225533, GCST010774	
HCG22	GCST010118	GCST010774	
ENPP3	GCST010118, GCST010555, GCST90132185, GCST90132183	GCST009100	
KCNB2	GCST010118	GCST005688	
LINC02356	GCST010118	GCST90013478	
PLCB2	GCST010118	GCST010477	
RGMA	GCST90018926, GCST90132183, GCST90132185	GCST005688	
ESRP1	GCST009379	GCST90058972	

Furthermore, we discovered 83 common genes linked to the pathogenesis of both T2D and hypertension (Fig. 1).Fig. 1 Common genes between T2D and Hypertension.

Fig. 1

3.2 Gene ontology enrichment analysis

A GO analysis was performed on 83 potential genes associated with T2D and hypertension, identifying the top ten biological processes, cellular components, and molecular functions (Supplementary Fig. 1). Functional enrichment analysis revealed that ribonuclease activity and synapse assembly regulation were significantly overexpressed BP. Other significant BPs were a response to xenobiotic stimuli, phosphatidylinositol and phosphate-containing metabolic processes, glucose homeostasis, cardiac muscle tissue development, cytosolic calcium ion concentration regulation, cAMP-mediated signaling, and dendrite morphogenesis (Table 2, Supplementary Fig. 1).Table 2 The top 10 most significant gene ontology (GO) terms.

Table 2Category (GO)	ID	Name	p-value	Genes from Input	Fold Enrichment	
Biological Processes	GO:0060700	regulation of ribonuclease activity	1.50E-02	2	126.3	
GO:0051965	positive regulation of synapse assembly	1.60E-02	3	15.3	
GO:0009410	response to xenobiotic stimulus	3.90E-02	4	5.2	
GO:0046488	phosphatidylinositol metabolic process	4.30E-02	2	45.1	
GO:0051481	negative regulation of cytosolic calcium ion concentration	4.30E-02	2	45.1	
GO:0042593	glucose homeostasis	4.90E-02	3	8.3	
GO:0043951	negative regulation of cAMP-mediated signaling	5.20E-02	2	37.1	
GO:0006796	phosphate-containing compound metabolic process	5.50E-02	2	35.1	
GO:0048738	cardiac muscle tissue development	5.50E-02	2	35.1	
GO:0050775	positive regulation of dendrite morphogenesis	6.30E-02	2	30.1	
Cellular Component	GO:0005765	lysosomal membrane	2.50E-02	5	4.4	
GO:0016020	integral component of membrane	3.50E-02	24	1.5	
GO:0016020	membrane	3.50E-02	14	1.8	
GO:0010008	endosome membrane	4.00E-02	4	5.2	
GO:0042383	sarcolemma	4.00E-02	3	9.3	
GO:0005783	endoplasmic reticulum	5.20E-02	8	2.3	
GO:0005886	plasma membrane	5.80E-02	22	1.4	
GO:0005764	lysosome	6.50E-02	4	4.3	
GO:1990904	ribonucleoprotein complex	8.50E-02	3	6.1	
GO:0032809	neuronal cell body membrane	8.60E-02	2	22	
Molecular Functions	GO:0008081	phosphoric diester hydrolase activity	3.20E-03	4	13.3	
GO:0008022	protein C-terminus binding	2.60E-02	4	6.1	
GO:0004629	phospholipase C activity	3.60E-02	2	53.1	
GO:0044877	macromolecular complex binding	3.90E-02	5	3.9	
GO:0042578	phosphoric ester hydrolase activity	3.90E-02	5	3.8	
GO:0004871	signal transducer activity	7.10E-02	11	1.8	
GO:0004435	phosphatidylinositol phospholipase C activity	7.70E-02	2	24.5	
GO:1990837	sequence-specific double-stranded DNA binding	8.50E-02	10	1.8	
GO:0003700	transcription factor activity, sequence-specific DNA binding	9.10E-02	5	2.9	
	GO:0000981	RNA polymerase II transcription factor activity, sequence-specific DNA binding	9.70E-02	8	2	

The cellular component of GO indicated that the lysosomal membrane was the most important cellular component of the identified candidate genes. Several cellular components were also implicated in T2D and hypertension pathogenesis, including an integral component of the membrane, the endosome membrane, the sarcolemma, the endoplasmic reticulum, the plasma membrane, the lysosome, the ribonucleoprotein complex, the neuronal cell body membrane (Table 2, Supplementary Fig. 1).

The most significant molecular function was phosphoric diester hydrolase activity. Other significant molecular functions were protein C-terminus binding, phospholipase C activity, macromolecular complex binding, phosphoric ester hydrolase activity, signal transducer activity, phosphatidylinositol phospholipase C activity, sequence-specific double-stranded DNA binding, and RNA polymerase II transcription factor activity (Table 2, Supplementary Fig. 1).

3.3 KEGG pathway enrichment analysis

Pathway enrichment analysis was used to identify significant pathways enriched with genes found in T2D and hypertension pathogenesis. The specific pathways were enriched based on the FDR and highest enrichment ratio. Our analysis revealed several pathways enriched with the target genes. The top-most significant pathways are summarized in Table 3, Supplementary Fig. 2.Table 3 The KEGG pathway analysis of T2D and Hypertension.

Table 3Pathway ID	Pathway Name	Ratio of enrichment	P-Value	FDR	
hsa04924	Renin secretion	26.847	6.26E-08	0.000010196	
hsa04540	Gap junction	19.83	3.90E-07	0.000042341	
hsa04730	Long-term depression	19.389	0.000046565	0.0030361	
hsa04970	Salivary secretion	16.158	0.000011354	0.00092534	
hsa04022	cGMP-PKG signaling pathway	16.059	1.72E-09	5.62E-07	
hsa04912	GnRH signaling pathway	12.509	0.00025829	0.012029	
hsa04724	Glutamatergic synapse	10.205	0.00056226	0.022912	
hsa04270	Vascular smooth muscle contraction	9.6145	0.0007043	0.024423	
hsa04921	Oxytocin signaling pathway	9.5671	0.00014164	0.0076957	
hsa04611	Platelet activation	9.4582	0.00074917	0.024423	

The identified genes regulate the cGMP-PKG (cGMP-dependent protein kinase or protein kinase G), GnRH (Gonadotropin hormone-releasing hormone), and oxytocin signaling pathways. They also contribute to renin secretion, gap junctions, salivary secretion, neural and glutamatergic synapse, vascular muscle contraction, and platelet activation (Table 2, Supplementary Fig. 2).

3.4 Disease association analysis

The disease association analysis was conducted to identify the diseases linked to 83 candidate genes. The predicted outcomes revealed that the identified genes are associated with several diseases, including hypertension, T2D, cardiovascular disease, obesity, celiac disease, kidney disease, croup, retinal disease, and Werner syndrome (Fig. 2, Supplementary Table S1). Among 83 candidate genes, nine genes (ADRB1, ALDH1A2, SLC39A8, EBF1, UMOD, HECTD4, RGMA, TMEM212, ATXN2) are significantly associated with hypertension, and seven genes (ANK1, OAS1, MAP3K1, FTO, CDKAL1, MACROD2, HECTD4) are significantly associated with T2D (Fig. 2, Supplementary Table S1).Fig. 2 Disease association analysis of T2D and Hypertension.

Fig. 2

3.5 PPI network analysis and identification of hub genes

Based on 83 candidate genes, a PPI network was constructed where 28 genes showed interaction with the combination of 64 nodes (Fig. 3 a).Fig. 3 Protein-protein interaction network and Hub genes of T2D and Hypertension

*Circles and rectangles represent genes, and lines represent interactions among genes (Green lines represent gene neighborhood, red lines represent gene fusions, blue lines represent gene co-occurrence, yellow color represent textmining, black color represent the co-expression and purple color represent the protein homology).

(a) Protein-protein interaction network (b) The identified hub genes cluster 1 (c) The identified hub genes cluster 2 (d) The identified hub genes cluster 3.

Fig. 3

Moreover, we identified hub genes using 11 topological analysis methods. For each method, the top ten genes were chosen. Among these, seven (ITPR2, PLCB2, INSR, PLCB3, FTO, CDKAL1, and KLF14) genes were found in at least nine methods and selected as T2D and hypertension-related hub genes. For the strong basis of hub genes, we also used the MCODE algorithm to obtain a clustering module based on the highest score of the PPI network. Then, we found three clusters with nine genes containing all seven hub genes (Fig. 3 b, c, d).

3.6 Construction of hub gene-miRNA regulatory network

The regulatory analysis of target genes and miRNAs revealed that multiple miRNAs can regulate a single gene. In the hub gene-miRNA regulatory network, we identified 231 miRNAs targeting seven hub genes (Fig. 4, Supplementary Table S2). Specifically, the CDKAL1 gene was targeted by 94 miRNAs (e.g., hsa-mir-98-5p); the INSR gene by 76 miRNAs (e.g., hsa-let-7a-5p); the ITPR2 gene by 53 miRNAs (e.g., hsa-let-7f-5p); the FTO gene by 52 miRNAs (e.g., hsa-let-7b-5p); the PLCB3 gene by 20 miRNAs (e.g., hsa-let-7i-5p); the KLF14 gene and PLCB2 gene by 3 miRNAs each (e.g., hsa-mir-16-5p and hsa-mir-4762-5p, respectively). Based on high interaction scores, seven miRNAs were selected. Among them, hsa-let-7b-5p regulated 5 hub genes (CDKAL1, INSR, ITPR2, FTO, PLCB3), while hsa-let-7a-5p, hsa-let-7f-5p, hsa-mir-16-5p, hsa-mir-98-5p, hsa-let-7g-5p, and hsa-let-7i-5p regulated 4 hub genes each (Fig. 4, Supplementary Table S2).Fig. 4 Hub gene-miRNA regulatory network of T2D and Hypertension.

Fig. 4

3.7 Construction of hub gene-TF regulatory network

The hub gene-TF regulatory network showed that the hub genes were regulated by 36 TFs (Fig. 5, Supplementary Table S3). Specifically, the INSR gene was regulated by 10 TFs (USF2), the KLF14 gene was regulated by 10 TFs (FOXC1), the CDKAL1 gene was regulated by 9 TFs (NR3C1), the PLCB3 gene was regulated by 6 TFs (HOXA5), the PLCB2 gene was regulated by 6 TFs (YY1) that regulates, 5 TFs (ARID3A) that regulates FTO and 3 TFs (PPARG) that regulates ITPR2 hub genes (Fig. 5, Supplementary Table S3).Fig. 5 Hub gene-TF regulatory network of T2D and Hypertension.

Fig. 5

Based on a high level of interaction, nine TFs were selected from the hub gene-TF regulatory network. Among them, FOXC1 TF regulates 5 hub genes (INSR, KLF14, CDKAL1, PLCB3, and FTO), PPARG TF regulates 3 hub genes (INSR, CDKAL1, and ITPR2), and ARID3A, YY1, HOXA5, NFKB1, TP53, USF2, and NR3C1 TFs regulate 2 hub genes (Fig. 5, Supplementary Table S3).

3.8 Construction of hub gene-drug regulatory network

Drug-gene interaction studies have great importance in drug design and discovery. According to the DGIdb database results, two of the seven hub genes were targeted by 43 potential FDA-approved drugs. Among the drugs targeting seven hub genes, the INSR and FTO genes were targeted by 38 and five FDA-approved drugs, respectively (Fig. 6, Supplementary Table S4). The remaining five hub genes (ITPR2, PLCB2, PLCB3, CDKAL1, and KLF14) did not interact with any drugs.Fig. 6 Hub gene-drug interaction networks T2D and Hypertension.

Fig. 6

4 Discussion

T2D and hypertension are the leading causes of the global disease burden, and having both conditions increases the risk of cardiovascular disease by two to four times [34]. They are consequently interlinked in light of comparable risk factors, including vascular inflammation, endothelial dysfunction, obesity, atherosclerosis, arterial remodeling, and dyslipidemia. Additionally, they share similar mechanisms like the RAAS, immune system activation, inflammation, and oxidative stress [35]. Although multiple prior investigations have identified particular prospective molecular biomarkers and their mechanisms linked with developing T2D-associated diverse disorders like obesity, the potential molecular biomarkers and their mechanisms of T2D-associated hypertension have not yet been explored [36]. This study explored a comprehensive bioinformatics investigation to identify and characterize common genes implicated in molecular mechanisms and the etiology of T2D and hypertension.

In the beginning, 185 GWAS catalog datasets were used to identify 2270 genes linked to T2D and hypertension. In addition, we identified 83 common genes in the Venn diagram that contribute to the pathogenesis of T2D and hypertension (Fig. 1). Subsequently, the common genes (83) were used to enrich the GO, KEGG pathway, and disease association analyses to reveal their molecular mechanisms. Additionally, 83 genes were used to construct PPI networks and select hub genes. Hub genes were used to construct gene-miRNA, gene-TF, and gene-drug regulatory networks [37]. Hence, targeted marker genes are crucial in the pathogenesis of T2D and hypertension, and exploring their regulatory functions may be key to developing treatments for these conditions [38,39]. This study represents the first comprehensive in silico analysis of GWAS catalog datasets focused on T2D and hypertension.

GO is a biological exploration for annotating genes and gene products to identify biological characteristics based on biological processes, cellular components, and molecular functions [40]. The biological processes of GO analysis revealed that our targeted genes are mostly linked with ribonuclease activities and synapse formation, among other functions (Table 2, Supplementary Fig. 1). Interestingly, previous research showed that ribonuclease activities trigger beta-cell degeneration, apoptosis, and diabetes [41]; synapse and dendrite regulation are closely related to insulin signaling [42]; and xenobiotics are involved in the pathogenesis of diabetes through different mechanisms, including the insulin response and the regulation of glucose and lipid metabolism [43]. The cyclic adenosine monophosphate (cAMP) signaling pathway is the regulator of insulin and glucagon secretion from the pancreatic beta cells and is crucial for metabolism and glucose homeostasis [44]. The activation of the cAMP pathways promotes phosphoenolpyruvate carboxy kinase and glucose-6-phosphatase expression, which are related to gluconeogenesis [45]. The findings from the biological processes of GO analysis are consistent with those of previous research. Regarding the cellular component of GO analysis, our targeted genes are mostly linked with the integral components of the lysosome, endosome, and plasma membrane, among other functions. The molecular function of GO analysis revealed that our targeted genes are mostly linked with phosphoric diester hydrolase activities, among other functions (Table 2, Supplementary Fig. 1). Not surprisingly, the hydrolytic lysosomal enzymes play a key impact in glycoconjugate metabolism [46]. Phosphoric diester hydrolase plays a role in cAMP and cyclic guanosine monophosphate (cGMP) signaling pathways [47] that trigger T2D and hypertension pathogenesis. Indeed, the results from cellular components and molecular functions of GO analysis are reliable with previous findings. Thus, the essential functions of the identified common genes elucidate how the 83 genes contribute to the pathogenesis of T2D and hypertension by modulating biological, cellular, and molecular functions.

Pathway enrichment analysis is a crucial phase in interpreting critical gene functions and biological processes using high-throughput data [48]. The cGMP-PKG signaling pathways contribute to vascular complications in diabetes pathogenesis [49]. The GnRH is an important regulator for insulin secretion from the pancreatic beta cells [50]. The oxytocin signaling pathways have a positive metabolic role in glucose metabolism. It modifies insulin sensitivity and glucose uptake [51]. Our study's KEGG pathway enrichment analysis revealed that the cGMP-PKG, GnRH, and oxytocin signaling pathways are most closely related to our targeted genes (Table 3, Supplementary Fig. 2). From now on, the regulatory functions of identified pathways revealed the pathogenicity of our targeted genes in T2D and hypertension.

Network-based disease association analysis has appeared as a novel and influential approach to identifying probable disease-disease associations, redefining disease categories, and explaining different disease phenotypes [52]. Previous research revealed that ADRB-1 increased the release of ghrelin and renin hormones, which are linked to T2D and insulin resistance [53]. Higher ADRB-1 expression is also linked to pregnancy-induced diabetes and hypertension [54]. The mutated ALDH1A2 and SLC39A8 genes are identified as risk factors for hypertension [55,56]. The higher expression of EBF1 regulates blood glucose fluctuations, lipid metabolism, and hypertension [57,58]. The alteration of the UMOD gene sequence is associated with T2D, hypertension, and kidney function [59,60]. Our findings from disease association analysis showed that ADRB1, ALDH1A2, SLC39A8, EBF1, and UMOD are closely related to T2D and hypertension pathogenesis (Fig. 2, Supplementary Table S1). Consequently, our targeted genes are significantly associated with T2D and hypertension pathogenesis.

In the PPI network, we identified 28 candidate genes significantly associated with the pathogenesis of T2D and hypertension. Among these, seven hub genes were highlighted from the 83 candidate genes. The integrated results from module selection (Fig. 3 b, c, d) and disease association analysis (Fig. 2) revealed that the FTO and CDKAL1 genes play crucial roles in T2D pathogenesis and related diseases.

The gene-microRNA analysis identified these miRNAs as key regulators in tissue remodeling and disease identification [61]. miRNAs are crucial in disease progression through mechanisms such as epigenetic modification, histone modification, and DNA methylation, and they are also linked to disease diagnosis and treatment response [62]. Additionally, miRNAs play a role in glucose homeostasis and regulate genes involved in diabetes-related signaling pathways, including the insulin signaling pathway [[63], [64], [65]]. Previous studies showed that the hsa-let-7b-5p microRNA is involved in the advanced glycation end products (RAGE) signaling pathway that is associated with T2D and endocrine resistance [66]. The hsa-let-7a-5p microRNA shows good sensitivity and specificity for T2D prediction [67]. Furthermore, the hsa-miR-16-5p microRNA is used to treat hypertension and modulates the PI3K-AKT signaling pathway, which is linked to T2D and hypertension [68,69]. The hub gene-miRNA regulatory network of our study showed that hsa-let-7a-5p, hsa-let-7b-5p, and hsa-mir-16-5p microRNAs closely linked our identified hub genes (Fig. 4, Supplementary Table S2). As a result, our identified hub genes are involved in the pathophysiology of T2D and hypertension.

The transcription factor regulatory networks modulate gene expression in response to various genetic and environmental circumstances [70]. FOXC1 overexpression increases glucose absorption and induces FGF19 production, leading to the activation of the AMPK signaling system [71]. The PPARG is involved in both the maintenance and pathophysiology of hypertension. It may also be connected to T2D, metabolic syndrome, and coronary artery disease [72]. ARID3A is a potential hypertension diagnostic biomarker, while NFKB1 is an autonomous risk factor for evolving T2D [73,74]. The hub gene-TF regulatory network in our investigation revealed that FOXC1, PPARG, and ARID3A transcription factors were predominantly connected with our targeted hub genes (Fig. 5, Supplementary Table S3). This finding is consistent with prior study findings and indicates that our identified hub genes are involved in the pathophysiology of T2D and hypertension.

Furthermore, the drug-gene association analysis identified 43 potential FDA-approved drugs for T2D and hypertension that significantly downregulated two of the seven hub genes (Fig. 6, Supplementary Table S4).

A previous study revealed that methazolamide could be used as a viable pharmacological therapy for T2D patients [75]. Topiramate is another antidiabetic mediator or prospective chemotype therapy for the management of T2D [76]. It also appears to be useful in the treatment of hypertension and weight loss [77]. In our study, methazolamide and topiramate significantly downregulated the expression of the INSR and FTO genes (Fig. 6, Supplementary Table S4). This effect may extend to other drugs as well. Therefore, beyond these two drugs, other drugs identified in the gene-drug interaction network could potentially be used to downregulate the expression of hub genes for treating T2D and hypertension. Given that different drugs interact with specific genes, it may be possible to prescribe the appropriate drug to individual T2D and hypertension patients based on the detection of genetic mutations and expression levels of relevant genes. Although our findings suggest that understanding the molecular mechanisms and gene expression levels could guide the recommendation of precise medication for T2D and hypertension patients, there is still a lack of experimental validation.

5 Conclusion

Since genetic markers are crucial in the development of metabolic diseases, 185 GWAS catalog datasets were analyzed using a variety of bioinformatic tools to identify the common genes linked to T2D and hypertension. In the GO analysis, most target genes significantly contributed to T2D and hypertension-related biological processes, cellular components, and molecular activities. The KEGG pathway analysis revealed significant enrichment of the cGMP-PKG, GnRH, and oxytocin signaling pathways by the common genes. Among the 83 identified genes, nine were significantly associated with T2D and hypertension. Critical PPI analysis identified 28 genes, including seven hub genes linked to T2D and hypertension pathogenesis. Additionally, 231 miRNAs were associated with these seven hub genes, with several miRNAs regulating a single gene or vice versa (e.g., hsa-mir-98-5p, hsa-let-7a-5p, and hsa-let-7f-5p). Two of the seven hub genes interacted with 43 FDA-approved drugs, with some drugs significantly downregulating the expression of target genes. These findings provide insights into the mechanisms by which target genes contribute to T2D and hypertension pathogenesis through interactions with various biological functions, pathways, TFs, and miRNAs. Consequently, precise medication could be recommended by diagnosing the molecular mechanisms and assessing the expression levels of marker genes in T2D and hypertension patients.

Funding

This research received no external funding.

Ethics approval and consent to participate

Not applicable.

Human and animal rights and informed consent

This article contains no studies with human or animal subjects performed by any authors.

Data availability

The data supporting this study's results are available in databases described in the manuscript and from the corresponding authors upon request.

CRediT authorship contribution statement

Md. Golam Rabby: Formal analysis, Conceptualization. Md. Suzauddula: Writing – review & editing. Md. Sakib Hasan: Formal analysis. Mahbubur Alam Dewan: Writing – original draft. Md. Numan Islam: Writing – review & editing, Writing – original draft, Supervision, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A Supplementary data

The following are the Supplementary data to this article:Multimedia component 1

Multimedia component 1

Multimedia component 2

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Multimedia component 3

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Acknowledgments

Not applicable.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e36546.

Fig. 5. Construction of hub gene-TF regulatory network.
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