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Integrative Analysis of Key Genes and SignalingPathways By Bioinformatics in Patients with Type 1Diabetes Mellitus Combined with Acute MyocardialInfarctionZhenfei Ou 

A�liated Hospital of Qingdao UniversityXuejuan Zhang  ( dzhangxue@126.com )

A�liated Hospital of Qingdao University

Research Article

Keywords: Type 1 diabetes, acute myocardial infarction (AMI), differentially expressed genes

Posted Date: December 6th, 2021

DOI: https://doi.org/10.21203/rs.3.rs-1056890/v1

License: This work is licensed under a Creative Commons Attribution 4.0 International License.  Read Full License

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AbstractAlthough the pathogenesis of type 1 diabetes mellitus (T1D) and acute myocardial infarction (AMI)remains unclear. We investigated the key genes and signaling pathways common to T1D and AMI. First,we screened differentially expressed genes (DEGs) co-expressed by T1D and AMI through geneexpression synthesis (GEO) database and text mining. David database was used for enrichment andfunctional analysis of selected genes. The interaction between proteins (PPI) was created using STRINGand Cytoscape software. MCODE is used for module analysis of PPI network. A total of 74 human genesthat met the criteria were found in T1D and AMI. The �rst 10 central genes include STAT3, ITGAM, MMP9,ERBB2, MAPK3, FOS, MYD88, MAPK1, TFRC and TNFRSF1A.The establishment of the aforementionedkey genes might serve as novel biomarkers for precision diagnosis and providing medical treatment forthe occurrence of AMI in T1D patients in the future.

IntroductionType 1 diabetes is a chronic disease caused by an autoimmune attack on beta cells in the pancreas [1, 2,

3]. Patients with acute myocardial infarction have an acute onset, and the mortality rate is high if nottreated in time [4, 5]. At present, the mortality rate is greatly reduced through timely percutaneous coronaryintervention (PCI), stent implantation and drug therapy [6]. Poor blood sugar control is associated withmost myocardial infarction [7]. Despite signi�cant advances in the treatment of acute myocardialinfarction, there is still a lack of effective predictive methods for patients at high risk for type 1 diabetes.Therefore, by studying the correlation between T1D and AMI, we provide evidence for the early diagnosisand prevention of high-risk T1D patients.

The birth of high-throughput sequencing technology is a milestone in the �eld of genomics research,making it possible to conduct large-scale whole genome resequencing. The GEO database is a geneexpression database created and maintained by the NATIONAL Center for Biotechnology Information(NCBI), which contains storage chips, second-generation sequencing, and other high-throughputsequencing data [8]. Although the pathogenesis of IMA is currently related to T1D, its molecularmechanism remains unclear. Therefore, we retrieved AMI gene expression chips GSE60993 andGSE34198 from geo database, and screened differentially expressed genes (DEG) for analysis by Rsoftware (version 3.6.1) [9]. T1D related genes were obtained through text mining. DEG and text mininggene sets were analyzed using Venny, and interactions between proteins (PPI) were created using STRINGand Cytoscape software. In this study, we explored the key genes and signaling pathways of AMIassociated with T1D, providing prediction and therapeutic targets for the possibility of ACS in T1Dpatients in the future.

Methods

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Data abstraction. We abstracted the gene expression chip data GSE60993 and GSE34198 from the NCBIGene Expression Comprehensive (GEO) web resource (https://www.ncbi.nlm.nih.gov/geo/)19,23 [10]. TheGSE60993 cohort contains seven normal control and seven ST-elevation myocardial infarction samples,while the GSE34198 dataset includes seven normal control and seven AMI samples.

Identi�cation of DEGs. The core R package was used to process the downloaded matrix �les. Afternormalization, the differences between AMI and the control group were determined by truncation criteria|log2 fold change (FC)|≥2, adjusted P<0.05), and we selected the remarkable DEGs for downstreamanalyses [11].

Text mining. We based on web services GenCLIP3 platform(http://ci.smu.edu.cn/genclip3/analysis.php/) for text mining. After operation, GenCLIP3 retrieves andsearches the names of all genes associated with type 1 diabetes found in the existing literature. The geneset obtained by text mining and the differential gene set obtained before were screened for furtheranalysis [12].

Gene ontology analysis of DEGs and KEGG pathway analysis. Use David v. 6.8(https://david.ncifcrf.gov/) to obtain the DEGs were analyzed. GO annotation and KEGG pathwayenrichment were performed. GO terms include biological processes (BP), Cellular composition (CC) andmolecular function (MF). The function description of gene products obtained from various databases isconsistent [13]. The Kyoto Encyclopedia of Genes and Genomes (KEGG) is a bioinformatics resource forunderstanding biological function from a genomic perspective. It is a multi-species, comprehensiveresource composed of genomic, chemical and network information, cross-referencing numerous externaldatabases, and containing a complete set of building blocks (genes and molecules) and wiring diagrams(biological pathways) to represent cellular function [14]. The above genes were expressed as P 0.05 wasthe signi�cant threshold for analysis.

PPI network and module analysis. We used the STRING online search tool for protein-protein interaction(PPI) analysis of screened DEGs [15]. When the PPIs comprehensive score was > 0.6, the gene wasconsidered to be selected. The PPI network was visualized by Cytoscape and the key genes wereclassi�ed by MCODE [16]. In order to P 0.05 was used as the standard to analyze the functionalenrichment of each DEGs.

ResultsDEGs screening

Firstly, 3842 DEGs were screened from AMI samples and normal controls in GSE6099 dataset by limmapackage built-in R software. 1962 upregulated genes and 1880 downregulated genes were selected.Meanwhile, 1263 differentially expressed genes, including 569 up-regulated genes and 594 down-regulated genes, were obtained by analyzing ACS samples from GSE34198 dataset and the normal

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control group. Then, the two data sets and the overall distribution of the top 100 differential genes wererepresented by volcano map and heatmap respectively (Figure 1A-D). Sample using |log2 fold change(FC)|≥2 criteria and adjusted P<0.05. Through text mining, 2115 human genes associated with T1D werescreened out. DEGs in microcolumn data were hybridized to obtain the intersection of three selectedgenes, resulting in 74 genes (Figure. 2A).

Function and signal pathway enrichment analysis

DEGs obtained above were introduced into DAVID for GO and KEGG enrichment analysis to study thebiological functions of DEGs in AMI with T1D. In the GO analysis results, 10 biological process items(BP), 15 cell component items (CC) and 10 molecular function items (MF) were found. P<0.05 signi�edthreshold signi�cance. (Figure 3A) The BP item "in�ammatory response" is mainly rich in 18 genes, theCC item "plasma membrane" is mainly rich in 32 genes, and the MF item "protein binding" is mainly rich in54 genes. KEGG enrichment evaluation showed that integrated DEGs were signi�cantly enriched inProlactin signaling Pathway, Hepatitis B, and Chemokine signaling Pathway (Figure 3B).Module screening from the PPI network.

Based on 74 DEGs, the PPI network was developed using Cytoscape public platform and STRINGresources for module analysis and visualization. Therefore, we developed a PPI network with 158crosstalk based on 60 integrated DEGs associated with AMI (Figure 4A). Based on the degree, The mainhub genes extracted from AMI group include STAT3 (signal and Activator of transcription 3), ITGAM(Integrin Subunit alpha M) and MMP9 (matrix Metallopeptidase 9), ERBB2 (ErB-B2 receptor Tyrosinekinase 2) and MAPK3 (Mitogen-activated protein kinase 3). On the other hand, in the AMI group, we usethe MCODE algorithm to identify highly interconnected subnets, which are usually protein complexes, andby calculation, we �nd 2 highly clustered modules. (Figure 4B ,C)

DiscussionHigh blood sugar in type 1 diabetes can lead to microvascular complications [17]. Hyperglycemia in bloodaccelerates oxidative stress in blood vessels [18], of which cardiovascular disease is an important part [19].Previous studies suggest that intensive hypoglycemia can reduce complications and improve prognosisin patients with type I diabetes [20]. A recent study of type 1 diabetes followed for 30 years found thatintensive glucose reduction was associated with a 30% reduction in the incidence of cardiovasculardisease compared with a control group [21]. Timely screening of high-risk type I diabetes patients toprevent the occurrence of myocardial infarction is very important to the health of patients. When weperformed PPI analysis on DEGs, we also found many strongly associated genes, such as STAT3, ITGAM,MMP9, ERBB2, MAPK3, FOS13, MYD88, MAPK1, TFRC and TNFRSF1A.

Transcriptional regulator STAT3 plays a key role in in�ammation and immune regulation [22]. Moreover, itplays an important role in the �eld of tumor growth and immunity [23], which can be observed throughseveral important KEGG signaling pathways. Xilan Yang et al. 's study suggested that STAT3 pathway

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also plays a role in atherosclerosis [24], which may play a role in myocardial infarction. When bloodperfusion to the heart is reduced, transcriptional activator 3 (STAT3) may be activated to improve bloodsupply by promoting angiogenesis [25], indicating its important role in cardiac blood supply. Meijing Wanget al. found that STAT3-de�cient mice had signi�cant effects on myocardial function and in�ammation[26].

Studies have suggested that TICAM2 plays a key role in promoting neutrophil depletion [27], which may berelated to the in�ammatory stimulation of blood vessels by hyperglycemia.

Matrix metalloprotease 9 (MMP9) can be activated after myocardial infarction, which intensi�es cardiacischemia and eventually leads to chronic heart failure (CHF) [28]. Timely interruption of MMP9 appears toreduce infarct size after acute myocardial infarction [29]. Cardiac protection by blocking MMP9 isespecially true in diabetics [30] Yadav SK et al. 's study suggested that MMP9 might accelerate theapoptosis of hCSCs cells, reduce the vitality of hCSCs, and accelerate cell death through hyperglycemia[31]. In the future, it may be possible to improve patient outcomes by inhibiting this pathway.

ErbB2 (Her2/ NEU) has been extensively studied in the development of breast cancer. ErbB2 canaccelerate mitochondrial apoptosis through the molecular mechanism of Bcl-XL and -XS. The occurrenceof myocardial infarction in patients with type I diabetes may be related to mitochondrial dysfunction, soerbB2 is also a highly correlated gene [32].

The MAPK3 pathway is involved in the repair of myocardial ischemia [33, 34, 35]. This may be related to thebody's self-repair after acute myocardial infarction, which may provide a target for future treatment.

Inhibition of the MyD88 cardiac in�ammatory pathway reduces obesity-induced cardiac damage [36], Butothers have suggested that MyD88 promotes heart repair after myocardial infarction [37]. In conclusion, inour study, MyD88 pathway plays an important role in myocardial infarction in patients with type 1diabetes.

ConclusionsIn summary, we analyzed two datasets, GSE60993 and GSE34198, and conducted gene expressionmining to establish Pathways in cancer and Hepatitis B molecular modulation networks of core functionsand enriched signal cells. It provides a prediction and clinical treatment direction for ACS in T1M patientsin the future. However, further mechanism needs to be veri�ed by more experimental data.

DeclarationsETHICAL APPROVAL

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This article does not contain any studies with human participants or animals performed by any of theauthors.

CONSENT FOR PUBLICATION    

All authors consent to the publication of this study.

ACKNOWLEDGEMENTS

The authors gratefully acknowledge Hai ning for helpful revised the manuscript.

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Figures

Figure 1

Differentially expressed genes between Type 1 diabetes/myocardial infarction and control groups. (A, C)Volcano plot and cluster heat map of the top 50 differentially expressed genes from GSE60993. (B, D)Volcano plot and cluster heat map of the top 20 differentially expressed genes fromGSE34198. Redrepresents the upregulated genes based on |log2FC|>1 and P value<0.05 and blue represents thedownregulated genes based on the same statistical requirements

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Figure 2

Venn diagram of DEGs from microarray data and genes list from text mining. DEGs, differentiallyexpressed genes.

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Figure 3

GO term and KEGG pathway analysis for DEGs signi�cantly associated with Type 1 diabetes and acutemyocardial infarction. (A) GO terms. Number of gene of GO analysis was acquired from DAVID functionalannotation tool. p<0.05. (B) KEGG pathway.

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Figure 4

(A) Based on the STRING online database, 74 genes/node were �ltered into the DEG PPI network. (B) Themost signi�cant module 1 from the PPI network. (C) The second signi�cant module 2 from the PPInetwork. The size of node indicates the number of interacting proteins with the designated protein.