Integrative analyzes of biomarkers and pathways for exploration the mechanisms and targets associated with pyroptosis in type 2 diabetes

doi:10.21203/rs.3.rs-1947453/v1

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Integrative analyzes of biomarkers and pathways for exploration the mechanisms and targets associated with pyroptosis in type 2 diabetes

https://doi.org/10.21203/rs.3.rs-1947453/v1

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Purpose: This study aimed to investigate the association between pyroptosis and type 2 diabetes (T2D).

Methods: Gene expression omnibus (GEO) was used to obtain two microarray datasets (GSE7014 and GSE25724), and differentially expressed genes (DEGs) were evaluated. DEGs in type 2 diabetes mellitus pyroptosis-related genes (T2DM-PRGs) were obtained by intersecting the differential genes associated with T2D and the genes associated with pyroptosis. The T2DM-PRGs were verified, and functional enrichment analysis was performed through gene ontology (GO) annotation analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and gene set enrichment analysis (GSEA). The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic predictive value of T2DM-PRG-related genes. The single-sample gene set enrichment analysis (ssGSEA) algorithm was used to analyze immune infiltration in T2DM-PRGs and immune infiltration levels.

Results：A total of 25 T2DM-PRGs were obtained. GSEA comprehensively proved that the majority of genes were enriched in the NF−kappa B signaling pathway and prostate cancer. The top five miRNAs targeting T2DM-PRG-related differentially expressed prognostic genes were PTEN, BRD4, HSP90AB1, VIM, and PKN2. The top five T2DM-PRG-related DEGs of transcription factors (TFs) were HSP90AB1, VIM, PLCG1, and PTEN. ROC analysis showed that in both datasets, PLCG1, PTEN, TP63, CHI3L1, SDHB, DPP8, BCL2, SERPINB1, ACE2, DRD2, DDX58, and BTK have good diagnostic performance. We found that T2DM-PRG-related genes in the GSE7014 dataset had 28 immune cells that were significantly different between T2DM tissues and normal tissues, whereas 28 immune cells in the GSE25724 dataset were substantially different between T2DM tissues and normal tissues.

Conclusions：Exploration of pyroptosis-related mechanisms and biomarkers may contribute to the understanding of T2D pathophysiology and provide a novel therapeutic option for DM.

Diabetes mellitus

pyroptosi

immune infiltration

risk prediction

The metabolic disorder known as type 2 diabetes mellitus (T2DM) is complex and marked by insulin resistance and insulin deficiency due to hyperglycemia [1]. Approximately 285 million people worldwide are thought to have type 1 type 2 diabetes, and this disease has become a severe global problem owing to its high morbidity and rising clinical impact. DM imposes significant health and economic consequences, forcing us to seek effective treatment choices in medical and public health situations to seek out effective treatment choices [2]. However, the underlying processes involving several chemicals and signaling pathways remain unknown. Owing to the multiple processes and variables that contribute to diabetes, there is an imperative need to discover key biological molecules as prospective therapeutic targets and enhance treatment strategies.

pyroptosis, a novel cell-intrinsic cell death process, is linked to an inflammatory response. The pyroptosis mechanism is distinct from that of apoptosis and necrosis. The key characteristics of pyroptosis include caspase-1, 4, 5, or 11 breakage and the production of proinflammatory cytokines interleukin-1 and IL-18, which result in immune cell aggregation [3, 4]. During pyroptosis, a significant number of holes grow on the cell membrane, resulting in a breakdown of membrane stability; this allows ions and water to enter the cell, resulting in swelling and lysis [5]. Thus, pyroptosis poses a dilemma for innate immunity. In addition, it aids in preventing bacterial infiltration in multicellular organisms; however, excessive pyroptosis activation may result in chronic inflammation [6]. This paradox may be explained by variations in the virulence tactics employed and the cell types targeted by various pathogens [3]. The discovery of novel therapies for immunological disorders or other diseases may be aided by further studies on pyroptosis.

At present, bioinformatics analysis is a crucial technique for examining gene expression data and identifying target genes in common diseases. Bioinformatics and gene identification technologies will be fully utilized to investigate the mechanisms of numerous illnesses, including T2D. Studies have shown that pyroptosis plays an important role in the development of diabetes and its associated complications. Other studies have shown that hyperglycemia can lead to an increase in the inflammatory response and cell apoptosis in diabetic models, which mainly manifests in the form of significant muscle cell loss and tissue remodeling [7]. However, the pathophysiology and origin of T2D are complicated and largely unknown. Therefore, understanding the pathophysiology of T2D and identifying important biomarkers and pathways are crucial for tailored and successful therapies. With the development and widespread use of high-throughput system (HTS) technologies, integrated bioinformatics analysis has become a potential method for examining the curative mechanism evident after T2D.

Previous studies [8–10] have used bioinformatics to discover potential genes involved in the mechanism of T2D. In this study, the correlation between the pyroptosis-related pathway and T2D was analyzed using the GEO and PubMed databases. In total, 25 T2DM-PRGs were obtained. T2DM-PRGs were verified, and function enrichment analysis was performed through GO annotation analysis, KEGG pathways, and GSEA. The ROC curve was used to evaluate the diagnostic predictive value of the T2DM-PRG-related genes. The ssGSEA algorithm was used to analyze immune infiltration in T2DM-PRGs and immune infiltration levels. The exploration of pyroptosis may help to understand the pathogenesis of T2D and provide a new approach for the treatment of DM.

Data preprocessing and differentially expressed genes (DEGs)

The gene expression profile datasets GSE7014 [11] and GSE25724 [12] for T2D were collected using the R package GEOquery [13] from the GEO database [14], where GSE7014 used GPL570 [HG-U133_Plus_2] sequenced on the Affymetrix Human Genome U133 Plus 2.0 Array platform; the samples were derived from human (Homo sapiens) skeletal muscle biopsies. GSE25724 was sequenced using the GPL96 [HG-U133A] Affymetrix Human Genome U133A Array platform, and the samples were derived from human (Homo sapiens) islet tissue samples. The GSE7014 dataset was then divided into 20 T2D groups and six normal tissues (non-diabetic), and the GSE25724 dataset was divided into seven T2D groups and six normal tissues (non-diabetic) for gene expression grouping.

To analyze the effect of gene expression values on T2D relative to normal tissues, the R package limma [15] was used to perform differential analysis of the groupings according to the grouping information in the data. First, the normalization of the samples was analyzed. Thereafter, the genes with logFC > 0.5 and adj.P value < 0.05 were set as upregulated genes, and genes with logFC < -0.5 and adj.P value < 0.05 were categorized as down-regulated genes.

We searched PubMed for studies related to pyroptosis [16–30] and combined them with the GeneCards [31], AmiGO2 [32], and MSigDB databases [33]. We retrieved 356 pyroptosis-related genes (PRGs, pyroptosis-related genes), which are shown in Table S1.

We obtained DEGs related to T2D (T2DM-EDGs, diabetes mellitus type 2-DEGs) by intersecting the DEGs between the disease and normal groups in the two datasets. T2DM-PRGs were obtained by intersecting the differential genes related to T2D and the genes related to pyroptosis.

Functional enrichment analysis

GO [34] functional annotation analysis is a common method for large-scale functional enrichment studies of genes, including biological process (BP), molecular function (MF), and cellular components (CC). The KEGG [35] is a widely used database for storing information about genomes, biological pathways, diseases, and drugs. The R software package clusterProfiler [36] was used to perform GO functional annotation analysis and KEGG pathway enrichment analysis of the DEGs related to T2DM-PRGs. In this study, a P value < 0.05 was considered statistically significant.

GSEA

GSEA was used to evaluate the distribution trend of genes in a pre-defined gene set in the gene table ranked by phenotype correlation to judge its contribution to the phenotype [37]. We obtained the “c2.kegg.v7.2. symbols” and “c5.go.v7.2. symbols” gene sets in the MSigDB database [33], aperformed GSEA analysis on the two datasets using the “clusterprofiler” R package A [36], and a P value < 0.05 was considered statistically significant.

Construction of PPI network

The STRING database [38] is used to search for known proteins and PPI, which includes 2,031 species, 1.38 million PPI, and 9.6 million proteins. It includes outcomes derived from experimental data, results gleaned through text mining of PubMed abstracts and other databases, and results forecast using bioinformatic techniques. We constructed a PPI network using the STRING database for DEGs connected to T2DM-PRGs.

Construction of the interaction network between T2DM-PRGs and related miRNAs, transcription factors and related drugs

The NetworkAnalyst database [39] is a visualization platform for gene expression profiling and meta-analysis, which supports data types from 17 species, single or multiple gene or protein lists, single RNA-seq or microarray gene expression data tables, multiple gene expression tables, network files, and other upload formats. The control of gene expression by miRNAs and TFs at the post-transcriptional stage has been analyzed under conditions that define disease through interactions with target genes [40, 41]. We used the TarBase v8.0 database [42] and ENCODE database [43] to identify differentially expressed gene-related miRNAs and transcription factors of T2DM-PRGs. We used the DrugBank database (version 5.0) [44] to predict the correlation between target genes and drugs. We visualized differentially expressed target gene-miRNA networks, target gene-TF networks, and target gene-drug network analyses of T2DM-PRGs using the Cytoscape [45] software.

Expression analysis and ROC validation of PRGs

The gene expression levels of T2DM-PRG-related genes in the disease and normal groups were tested, and box plots were drawn. Receiver operating characteristic curve (ROC) [46]: A graphical analysis tool that can be used to select the best model, discard the next best model, or set the best threshold within the same model. The ROC curve is a comprehensive index reflecting the continuous variables of sensitivity and specificity. The relationship between sensitivity and specificity is reflected by the composition method. The area under the ROC curve is generally between 0.5 and 1. The closer the AUC is to 1, the better the diagnosis. The AUC has low accuracy when it is 0.5 to 0.7, the AUC has a certain accuracy when it is 0.7 to 0.9, and the AUC has high accuracy when it is above 0.9. We used the survivalROC package of R to draw the ROC curves of PRGs-related genes in the datasets (GSE7014 and GSE25724), and calculated the area under the curve (AUC) to evaluate the diagnostic effect of PRGs in T2DM patients. "

Immune infiltration analysis of PRGs

To investigate the relationship between genes related to pyroptosis in T2D and immune cells, we performed Single-sample Gene Set Enrichment Analysis (ssGSEA) analysis.

ssGSEA is an extension of the GSEA method that provides the degree of enrichment of the gene set in each sample in the input data by defining the enrichment score [46]. In this study, the ssGSEA method was used to compare the immune infiltration between the disease and normal population; the differential enrichment of two types of disease and the normal population was determined, and the correlation between T2DM-PRGs and immune cells was calculated. Pearson correlation analysis revealed the correlation between T2DM-PRG-related genes and degree of immune invasion.

Statistical Analysis

All data processing and statistical analyses were performed using R (https://www.r-project.org/version 4.1.2). For comparing two continuous variables, we used an independent student's t-test to estimate the significance of normally distributed variables and the Mann-Whitney U test (Wilcoxon rank sum) to assess the differences between non-normally distributed variables. The ROC curve was drawn using R's pROC software, and the area under the curve (AUC) was calculated to evaluate and predict patient prognosis. P < 0.05 was considered a statistically significant statistical value.

Differentially expressed analysis

The workflow diagram is shown in Fig. 1 (Fig. 1) First, the normalization of the samples in the two datasets was tested, and box plots were drawn (Fig. 2A-B). To analyze the effect of gene expression values on T2D tissues relative to normal tissues, we used the differential analysis package limma to obtain the DEGs of the two datasets and drew volcano plots for the DEGs (Fig. 3A-B). In the GSE7014 dataset, 5684 DEGs were obtained, including 1807 upregulated genes and 3877 downregulated genes. DEGs and data grouping were used to generate a classification heatmap (Fig. 3C). DEGs can be differentiated between the two types. Diabetic and Normal Tissues. In the GSE25724 dataset, 4560 DEGs were identified, including 2373 upregulated genes and 2187 downregulated genes. DEGs and data grouping were used to generate a classification heat map (Fig. 3D). DEGs can be used to distinguish diseased tissues and normal tissue. We intersected the DEGs in the two datasets to obtain 1561 DEGs associated with T2D (Fig. 4A). We used the intersection of pyroptosis-related genes and the above two datasets to obtain 25 differentially expressed type 2 diabetes genes related to pyroptosis-related genes (Fig. 4B, Table S1).

Functional enrichment analysis of DEGs in T2DM-PRGs

To analyze the relationship between the DEGs of T2DM-PRGs and biological processes, molecular functions, cellular components, biological pathways and diseases, we first performed functional enrichment analysis of the DEGs of T2DM-PRGs (Fig. 5A, Table 1). The DEGs of T2DM-PRGs are mainly enriched in cytokine secretion, cell junction assembly, regulation of innate immune response, positive regulation of establishment of protein localization, cell junction organization, stem cell population maintenance, calcium ion transport into cytosol, maintenance of cell number, viral life cycle, cytosolic calcium ion transport, and other biological processes (Fig. 5B). Meanwhile, they are enriched in cellular components such as cell projection membrane, COP9 signalosome, dendritic spine, neuron spine, cell leading edge, myelin sheath, sperm part, brush border membrane, sperm flagellum, and 9 + 2 motile cilium (Fig. 5C); they are also enriched in double-stranded RNA binding, glutamate receptor binding, ubiquitin protein ligase binding, ubiquitin-like protein ligase binding, and ionotropic glutamate molecular functions of receptor binding, protein phosphatase 2A binding, and p53 binding (Fig. 5D). Next, the pathway enrichment analysis of T2DM-PRGs was performed, and the results showed that T2DM-PRGs were enriched in prostate cancer, NF-kappa B signaling pathway, microRNAs in cancer, NOD-like receptor signaling pathway, EGFR tyrosine kinase inhibitor resistance, Epstein- Barr virus infection, AGE-RAGE signaling pathway in diabetic complications, Parkinson disease, thyroid hormone signaling pathway, autophagy-animal and other biological pathways (Fig. 5E, Table 2). We also analyzed the most significantly enriched pathway of prostate cancer: hsa05215 (Fig. 5F).

Table 1

GO enrichment analysis of differentially expressed genes in T2DM-PRGs
Ontology	ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue
BP	GO:0050663	cytokine secretion	5/25	240/18670	1.45e-05	0.012	0.006
BP	GO:0034329	cell junction assembly	5/25	241/18670	1.48e-05	0.012	0.006
BP	GO:0045088	regulation of innate immune response	6/25	452/18670	2.33e-05	0.012	0.006
BP	GO:1904951	positive regulation of establishment of protein localization	6/25	456/18670	2.45e-05	0.012	0.006
BP	GO:0034330	cell junction organization	5/25	290/18670	3.60e-05	0.012	0.006
BP	GO:0019827	stem cell population maintenance	4/25	157/18670	5.30e-05	0.012	0.006
BP	GO:0060402	calcium ion transport into cytosol	4/25	158/18670	5.44e-05	0.012	0.006
BP	GO:0098727	maintenance of cell number	4/25	159/18670	5.57e-05	0.012	0.006
BP	GO:0019058	viral life cycle	5/25	328/18670	6.46e-05	0.012	0.006
BP	GO:0060401	cytosolic calcium ion transport	4/25	171/18670	7.40e-05	0.012	0.006
CC	GO:0031253	cell projection membrane	4/25	345/19717	8.71e-04	0.037	0.028
CC	GO:0008180	COP9 signalosome	2/25	36/19717	9.47e-04	0.037	0.028
CC	GO:0043197	dendritic spine	3/25	169/19717	0.001	0.037	0.028
CC	GO:0044309	neuron spine	3/25	171/19717	0.001	0.037	0.028
CC	GO:0031252	cell leading edge	4/25	403/19717	0.002	0.037	0.028
CC	GO:0043209	myelin sheath	2/25	49/19717	0.002	0.037	0.028
CC	GO:0097223	sperm part	3/25	191/19717	0.002	0.037	0.028
CC	GO:0031526	brush border membrane	2/25	53/19717	0.002	0.038	0.028
CC	GO:0036126	sperm flagellum	2/25	93/19717	0.006	0.071	0.054
CC	GO:0097729	9 + 2 motile cilium	2/25	98/19717	0.007	0.071	0.054
MF	GO:0003725	double-stranded RNA binding	4/25	75/17697	3.52e-06	5.84e-04	3.96e-04
MF	GO:0035254	glutamate receptor binding	3/25	47/17697	3.88e-05	0.003	0.002
MF	GO:0031625	ubiquitin protein ligase binding	5/25	290/17697	4.63e-05	0.003	0.002
MF	GO:0044389	ubiquitin-like protein ligase binding	5/25	308/17697	6.17e-05	0.003	0.002
MF	GO:0035255	ionotropic glutamate receptor binding	2/25	32/17697	9.26e-04	0.026	0.017
MF	GO:0051721	protein phosphatase 2A binding	2/25	32/17697	9.26e-04	0.026	0.017
MF	GO:0002039	p53 binding	2/25	66/17697	0.004	0.092	0.063

Table 2

KEGG enrichment analysis of differentially expressed genes in T2DM-PRGs
Ontology	ID	Description	GeneRatio	BgRatio	pvalue	p.adjust	qvalue
KEGG	hsa05215	Prostate cancer	4/17	97/8076	4.13e-05	0.004	0.003
KEGG	hsa04064	NF-kappa B signaling pathway	4/17	104/8076	5.43e-05	0.004	0.003
KEGG	hsa05206	MicroRNAs in cancer	5/17	310/8076	3.41e-04	0.017	0.012
KEGG	hsa04621	NOD-like receptor signaling pathway	4/17	181/8076	4.62e-04	0.017	0.012
KEGG	hsa01521	EGFR tyrosine kinase inhibitor resistance	3/17	79/8076	5.55e-04	0.017	0.012
KEGG	hsa05169	Epstein-Barr virus infection	4/17	202/8076	7.00e-04	0.018	0.012
KEGG	hsa04933	AGE-RAGE signaling pathway in diabetic complications	3/17	100/8076	0.001	0.025	0.016
KEGG	hsa05012	Parkinson disease	4/17	249/8076	0.002	0.030	0.020
KEGG	hsa04919	Thyroid hormone signaling pathway	3/17	121/8076	0.002	0.033	0.022
KEGG	hsa04140	Autophagy - animal	3/17	137/8076	0.003	0.039	0.026
KEGG	hsa04915	Estrogen signaling pathway	3/17	138/8076	0.003	0.039	0.026
KEGG	hsa05030	Cocaine addiction	2/17	49/8076	0.005	0.054	0.036
KEGG	hsa04340	Hedgehog signaling pathway	2/17	50/8076	0.005	0.054	0.036
KEGG	hsa05110	Vibrio cholerae infection	2/17	50/8076	0.005	0.054	0.036
KEGG	hsa04151	PI3K-Akt signaling pathway	4/17	354/8076	0.005	0.057	0.038
KEGG	hsa04213	Longevity regulating pathway - multiple species	2/17	62/8076	0.007	0.071	0.048
KEGG	hsa04664	Fc epsilon RI signaling pathway	2/17	68/8076	0.009	0.080	0.054
KEGG	hsa00562	Inositol phosphate metabolism	2/17	73/8076	0.010	0.082	0.055
KEGG	hsa04115	p53 signaling pathway	2/17	73/8076	0.010	0.082	0.055
KEGG	hsa05214	Glioma	2/17	75/8076	0.011	0.082	0.055
KEGG	hsa05131	Shigellosis	3/17	246/8076	0.014	0.095	0.064
KEGG	hsa04540	Gap junction	2/17	88/8076	0.014	0.095	0.064
KEGG	hsa04211	Longevity regulating pathway	2/17	89/8076	0.015	0.095	0.064
KEGG	hsa05235	PD-L1 expression and PD-1 checkpoint pathway in cancer	2/17	89/8076	0.015	0.095	0.064
KEGG	hsa05222	Small cell lung cancer	2/17	92/8076	0.016	0.095	0.064
KEGG	hsa04657	IL-17 signaling pathway	2/17	94/8076	0.016	0.095	0.064
KEGG	hsa04070	Phosphatidylinositol signaling system	2/17	97/8076	0.017	0.095	0.064
KEGG	hsa01522	Endocrine resistance	2/17	98/8076	0.018	0.095	0.064
KEGG	hsa04750	Inflammatory mediator regulation of TRP channels	2/17	100/8076	0.018	0.095	0.064
KEGG	hsa04914	Progesterone-mediated oocyte maturation	2/17	100/8076	0.018	0.095	0.064
KEGG	hsa04928	Parathyroid hormone synthesis, secretion and action	2/17	106/8076	0.020	0.096	0.064
KEGG	hsa04659	Th17 cell differentiation	2/17	107/8076	0.021	0.096	0.064
KEGG	hsa04922	Glucagon signaling pathway	2/17	107/8076	0.021	0.096	0.064
KEGG	hsa04931	Insulin resistance	2/17	108/8076	0.021	0.096	0.064
KEGG	hsa04066	HIF-1 signaling pathway	2/17	109/8076	0.022	0.096	0.064
KEGG	hsa04725	Cholinergic synapse	2/17	113/8076	0.023	0.100	0.067

GSEA

To determine the effect of gene expression levels on type 2 diabetes, we analyzed the associations between gene expression and the biological processes involved, the cellular components affected, and the molecular functions exerted in the two sets of data, respectively. (Table 3) The results show that in the GSE7014 data, genes mainly affect adhesion molecule binding, actin cytoskeleton, muscle system process, actin binding, atpase activity, regulation of actin filament based process, muscle tissue development, muscle organ development, muscle contraction, muscle cell differentiation and other biological related functions. (Fig. 6A).

Table 3

GSEA analysis of GSE7014
Description	enrichmentScore	P.adjust
KEGG_FOCAL_ADHESION	0.542565034	5.84E-02
KEGG_CALCIUM_SIGNALING_PATHWAY	0.503782846	5.84E-02
KEGG_DILATED_CARDIOMYOPATHY	0.577282817	5.84E-02
KEGG_ECM_RECEPTOR_INTERACTION	0.549924013	5.84E-02
KEGG_HYPERTROPHIC_CARDIOMYOPATHY_HCM	0.579742517	5.84E-02
KEGG_VIRAL_MYOCARDITIS	0.637502528	5.84E-02
KEGG_VALINE_LEUCINE_AND_ISOLEUCINE_DEGRADATION	0.668462605	5.84E-02
KEGG_FATTY_ACID_METABOLISM	0.647055496	5.84E-02
KEGG_CITRATE_CYCLE_TCA_CYCLE	0.723941462	5.84E-02
KEGG_TIGHT_JUNCTION	0.479568466	7.45E-02
GO_CELL_ADHESION_MOLECULE_BINDING	0.404008961	2.53E-02
GO_ACTIN_CYTOSKELETON	0.475168994	2.53E-02
GO_MUSCLE_SYSTEM_PROCESS	0.499445629	2.53E-02
GO_ACTIN_BINDING	0.506747846	2.53E-02
GO_ATPASE_ACTIVITY	0.484529473	2.53E-02
GO_REGULATION_OF_ACTIN_FILAMENT_BASED_PROCESS	0.420510488	2.53E-02
GO_MUSCLE_TISSUE_DEVELOPMENT	0.483165199	2.53E-02
GO_MUSCLE_ORGAN_DEVELOPMENT	0.47448586	2.53E-02
GO_MUSCLE_CONTRACTION	0.537150195	2.53E-02
GO_MUSCLE_CELL_DIFFERENTIATION	0.449548026	2.53E-02
GO_REGULATION_OF_MRNA_METABOLIC_PROCESS	0.428700325	2.53E-02
GO_REGULATION_OF_SMALL_GTPASE_MEDIATED_SIGNAL_TRANSDUCTION	0.429140294	2.53E-02
GO_REGULATION_OF_CELL_MORPHOGENESIS_INVOLVED_IN_DIFFERENTIATION	0.437501091	2.53E-02
GO_REGULATION_OF_BLOOD_CIRCULATION	0.465333288	2.53E-02
GO_MRNA_BINDING	0.479819779	2.53E-02
GO_HEART_PROCESS	0.480356542	2.53E-02
GO_STRIATED_MUSCLE_CELL_DIFFERENTIATION	0.477470523	2.53E-02
GO_ENERGY_DERIVATION_BY_OXIDATION_OF_ORGANIC_COMPOUNDS	0.482607599	2.53E-02
GO_CONTRACTILE_FIBER	0.64641458	2.53E-02
GO_CARDIAC_MUSCLE_TISSUE_DEVELOPMENT	0.514558056	2.53E-02
GSEA analysis of GSE25724

The genes in the GSE25724 data mainly affect biologically relevant functions such as cycle phase transition, division, cellular nitrogen compound catabolic process, establishment of protein localization to organelle, mitochondrial envelope, modification dependent macromolecule catabolic process, negative regulation of cell cycle, nucleobase containing small molecule metabolic process, organic cyclic compound catabolic process, organophosphate biosynthetic process. (Fig. 6B).

At the same time, we analyzed the biological pathways affected by gene expression in the two sets of data. The results show that genes in the GSE7014 data mainly affect biologically relevant pathways such as focal adhesion, calcium signaling pathway, dilated cardiomyopathy, ecm receptor interaction, hypertrophic cardiomyopathy hcm, viral myocarditis, valine leucine and isoleucine degradation, fatty acid metabolism, citrate cycle tca cycle, tight junction. (Fig. 6C-E). The genes in the GSE25724 data mainly affected biologically relevant pathways such as humtingtons disease, alzheimers disease, cell cycle, spliceosome, ubiquitin mediated proteolysis, oxidative phosphorylation, parkinsons disease, proteasome, citrate cycle tca cycle, protein export. (Fig. 6F-H).

Construction of PPI network

In this study, the differentially expressed T2DM-PRG genes were constructed using the protein-protein interaction network of the STRING T2DM-PRG genes, igraph package (R software), and ggraph package (R software) (Fig. 7A-B). Visualization of Cytoscape. There were 30 T2DM-PRG-related DEGs and 29 protein-protein interaction pairs in the protein-protein interaction network related to T2DM-PRG-related DEGs, among which the DEGs related to other T2DM-PRGs interacted with each other. The top five genes with the strongest cooperative relationships were PTEN, PLCG1, STRT1, HSP90AB1, and TP63.

Network analysis of T2DM-PRGs and related miRNAs, transcription factors and related drugs

We constructed a T2DM-PRG-miRNA interaction network, which contained 25 genes and 512 miRNAs (Fig. 8A). The top 5 miRNAs targeting T2DM-PRG-related differentially expressed prognostic genes were PTEN (128 miRNAs), BRD4 (118 miRNAs), HSP90AB1 (103 miRNAs), VIM (96 miRNAs), and PKN2 (91 miRNAs).

We constructed a T2DM-PRG-TF network containing 21 mRNAs and 274 TFs (Fig. 8B). The top five T2DM-PRG-related DEGs were HSP90AB1 regulated by 151 TFs, VIM regulated by 75 TFs, PLCG1 and SCAF11 regulated by 54 TFs, and PTEN regulated by 44 TFs. We constructed a T2DM-PRGs-drug interaction network that included seven networks and seven genes, among which the first three networks had 92, 44, and 15 drug effects, respectively (Fig. 9A-C).

Expression analysis and ROC validation of T2DM-PRGs-related genes

Box plots were constructed for the expression levels of T2DM-PRG genes in the GSE7014 and GSE25724 datasets (Fig. 10A-B). In the GSE7014 dataset, SCAF11, PKN2, UBR2, PRF1, PLCG1, PTEN, TP63, CHI3L1, SDHB, DP8, BCL2, SERPINB1, DDX58, and BTK were statistically significant (P < 0.05). The expression of ELAVL1, BRD4, PRF1, PLCG1, PTEN, TP63, CHI3L1, SDHB, DPP8, METTL3, SEPRINB1, ACE2, DRD2, DDX58, VIM, BTK, HSP90AB1, NLRP1, and PRKACA was statistically significant in GSE25724 (P < 0.05).

In the GSE7014 dataset, SCAF11, PKN2, PLCG1, PTEN, TP63, CHI3L1, SDHB, DPP8, BCL2, SERPINB1, ACE2, DRD2, DDX58, and BTK had diagnostic values (AUC > 0.7) (Fig. 11A-C).

. In the GSE25724 dataset, TP63, CHI3L1, SDHB, DPP8, BCL2, TRIM31, METTL3, SEPRINB1, ACE2, DRD2, ELAVL1, UBR2, PRF1, PLCG1, PTEN, DDX58, VIM, BTK, HSP90AB1, NLRP1, and PRKACA exhibit diagnostic values (AUC > 0.7) (Fig. 11D-G).

Immune infiltration analysis

To compare immune permeability between T2DM and normal tissues, we used the ssGSEA method to perform different assays in the two groups. We found that T2DM-PRG-related genes in the GSE7014 dataset have 28 types of immune cells that are significantly different between T2DM tissue and normal tissue, including SCAF11 and natural killer cells, PKN2 and Central memory CD4 T cell, Effector memeory CD4 T cell, ELAVL1 and Central memory CD8 T cell, Effector memeory CD8 T cell, Memory B cell, BRD4 and Activated CD4 T cell, UBR2 and Activated CD4 T cell, Effector memeory CD4 T cell, Plasmacytoid dendritic cell, Type 2 T helper cell, PRF1 and Effector memeory CD4 T cell, PLCG1 and Activated dendritic cell, CD56bright natural killer cell, Immature B cell, Monocyte, Neutrophil, Type 1 T helper cell, PTEN and Central memory CD4 T cell, Central memory CD8 T cell, Plasmacytoid dendritic cell, Type 2 T helper cell, TP63 and Effector memeory CD4 T cell, Type 2 T helper cell, CHI3L1 and Activated dendritic cell, CD56bright natural killer cell, Memory B cell, Neutrophil, SDHB With Central memory CD4 T cell, Effector memeory CD4 T cell, Immature dendritic cell, Plasmacytoid dendritic cell, Type 2 T helper cell, DPP8 with Central memory CD4 T cell, Effector memeory CD4 T cell, Immature dendritic cell, Plasmacytoid dendritic cell, Type 2 T helper cell, BCL2 and Effector memeory CD4 T cell, Type 2 T helper cell, TRIM31 and CD56 bright natural killer cell, METTL3 and Central memory CD4 T cell, Effect memeory CD4 T cell, Immature dendritic cell, Plasmacytoid dendritic cell, SERPINB1 and Neutrophil, Type 1 T helper cell, ACE2 and Gamma delta T cell, Mast cell, Natural killer cell, FOXO1 and Central memory CD8 T cell, Effector memeory CD8 T cell, Neutrophil, DDX58 and Effector memeory CD4 T cell, Type 2 T helper cell, VIM and Activated CD4 T cell, Central memory CD4 T cell, Effector memeory CD4 T cell, Effector Memeory CD8 T cell, Plasmacytoid dendritic cell, Regulatory T cell, HSP90AB1 and Central memory CD4 T cell, Effector memeory CD4 T cell, Effector memeory CD8 T cell, Immature dendritic cell, Plasmacytoid dendritic cell, and Type 2 T helper cell (r > 0.5, P < 0.01) (Fig. 12A).

In the GSE25724 dataset, 26 types of immune cells are significantly different between T2DM tissues and normal tissues, among which PKN2 and Activated CD4 T cell, Central memory CD4 T cell, ELAVL1 and Immature dendritic cell, BRD4 and Activated B cell, Activated dendritic cell, Effector memeory CD8 T cell, Eosinophil, Macrophage, MDSC, T follicular helper cell, Type 1 T helper cell, Type 2 T helper cell, UBR2 and Effector memeory CD4 T cell, Gamma delta T cell, Immature dendritic cell, PRF1 and Activated CD8 T cell, CD56dim natural killer cell, Eosinophil, MDSC, PTEN and Activated CD4 T cell, CHI3L1 and Effector memeory CD4 T cell, Immature dendritic cell, Plasmacytoid dendritic cell, Regulatory T cell, DPP8 and Effector memeory CD4 T cell, Immature dendritic cell, TRIM31 and Activated B cell, Activated dendritic cell, CD56dim natural killer cell, Eosinophil, Mast cell, MDSC, Natural killer T cell, T follicular helper cell, Type 1 T helper cell, SERPINB1 and Activated CD4 T cell, ACE2 and Eosinophil, DRD2 and Activated B cell, CD56dim natural killer cell, Eosinophil, Macrophage, MDSC, T follicular helper cell, DDX58 and Activated B cell, Activated dendritic cell, Macroph age, MDSC, Neutrophil, BTK and Activated B cell, Eosinophil, Neutrophil, HSP90AB1 and Plasmacytoid dendritic cell, NLRP1 and Effector memeory CD4 T cell, Gamma delta T cell, PRKACA and Activated B cell were significantly positively correlated (r > 0.5, P < 0.01) (Fig. 12B).

Diabetes mellitus (DM), a serious global epidemic, is characterized by hyperglycemia. As of 2019, 463 million individuals had diabetes. Its population is expected to reach 578 million by 2030 and 700 million by 2045 [47]. Chronic hyperglycemia, which is caused by a shortage of insulin or inadequate insulin synthesis in the pancreas, leads to several life-threatening diabetic complications, making diabetes a leading cause of cardiovascular morbidity and death, renal failure, amputations, and blindness [29]. It has been reported that pyroptosis plays an important role in diabetes and other complications. In recent years, targeting pyroptosis and inflammatory factors in patients with diabetes has attracted increasing attention from researchers and clinicians. However, pyroptosis activation and its mechanism of action remain unclear. Our findings suggest that pyroptosis and inflammasome signal transduction pathways have potential roles in the treatment of diabetes and other complications in the future. These molecules may play an important role in future diabetes treatments by inhibiting cell apoptosis and inflammasome signal transduction pathways. Most pathophysiologically significant cell death is necrotic, according to findings from research on basic cell death; meanwhile, apoptosis programs maintain a regular metabolism. Programmed cell death significantly contributes to host survival and growth in the body’s defense mechanisms against various microbial diseases [48–51]. Necrosis is described as cell death via rupture of the plasma membrane, which enables the release of damage-related molecular patterns that trigger an immune response known as necroinflammation. Different types of regulated necrosis exist, including necroptosis, pyroptosis, and ferroptosis. Based on this perspective, we focused on T2DM and the pyroptosis-related pathway.

Pyroptosis is a type of controlled cell death (RCD) that is inflammatory and may be classified as either classical or non-classical signaling pathways [52]. I inflammasomes are mainly composed of NLRP3, NLRP1, NLRC4, AIM2, and other proteins, which can be activated by pathogen-associated molecular patterns (PAMPs) or damage-associated molecular patterns (DAMPs). The gasdermin-N domain of GSDMD creates membrane holes to trigger pyroptosis, as the inflammasome controls caspase-1 activation, which encourages the cleavage of GSDMD. The primary Caspase-4/5/11 enzyme responsible for non-classical signaling pathway cleavage of the GSDMD protein causes pyroptosis [53]. Previously, T cell-mediated adaptive immunity was thought to play a role in the etiology of type 1 diabetes (T1DM). The innate immune system, which is mediated by the Toll-like receptor (TLR) and plays a significant role in the pathogenesis of T1DM, has been demonstrated to be a significant contributor to this disease in an increasing number of studies [54]. However, there is a lack of research on whether pyroptosis plays a role in the etiology and progression of diabetes and its accompanying problems, despite data obtained from mouse models demonstrating the significance of pyroptosis in innate immunity. The tight ties of some of these compounds with T2D have been reported in earlier investigations.

T2DM-PRGs identified by the PPI network have similar features in T2D function and exhibit a high diagnostic value in the clinic. The first five were PTEN, PLCG1, STRT1 HSP90AB1, and TP63. They might be considered essential target genes and are linked to the pathogenic process of T2D. Future research should focus on these genes and examine their function in T2D. The top five miRNAs targeting T2DM-PRG-related differentially expressed prognostic genes were PTEN, BRD4, HSP90AB1, VIM, and PKN2. The top five T2DM-PRG-related DEGs in TF were HSP90AB1, VIM, PLCG1, SCAF11, and PTEN. PTEN activates autophagy through the AKT/mTOR pathway to regulate cellular energy homeostasis. Previous studies have revealed that downregulated circulating miRNA targets indicate increased expression of transcripts such as PTEN, which in turn can inhibit cell growth pathways and activate cell survival pathways and may promote healthy aging [55]. A previous study that may help develop diagnostic and immunotherapeutic targets for patients with lung cancer showed that HSP90AB1 mRNA expression levels were significantly different between squamous cell carcinomas and normal controls [56]. We established a T2DM-PRGs-drug interaction network, and the results showed that treatments targeting dopamine receptor D2 (DRD2) could inhibit various tumors; however, the effect of targeting DRD2 on T2D was unclear. Cushing's syndrome is caused by mutations in the PRKACA gene, which encodes the catalytic subunit of cAMP-dependent protein kinase A (PKA-C) in adrenocortical adenomas

[57]. Few studies have reported on PRKACA-related target drugs, which have not been reported to be related to hypoglycemic drugs.

Analysis results revealed that KEGG, prostate cancer, and NF − kappa B signaling pathways were significant pathways. Insulin resistance is a major factor in the development of T2DM. In this regard, we mapped the KEGG controlled proteins inside the KEGG prostate cancer (entry hsa05215), in which we found drugs that might act over PI3K (a protein implicated in insulin sensitivity), GSK3 (a protein engaged in glycogenesis), and Akt tend to increase insulin sensitivity. The emergence of DM caused by insulin resistance has been directly addressed by these arguments. Earlier research has also shown that drugs that act on PI3K, GSK3, and Akt increase insulin sensitivity (KEGG insulin resistance pathway; entry hsa04931) [58]. In long-term diabetes, the insulin-like growth factor-1 receptor (IGF-1R) is depleted, and high plasma levels of IGF-1 are linked to an increased risk of prostate cancer [59–61]. A wide variety of human cancers have been linked to the overexpression of insulin receptors (IR) and IGF-1R, which is a result of transcriptional misregulation. [62–65]. Previous literature reports on mellitus-induced erectile dysfunction (DMED) have shown that the enrichment of the KEGG signaling pathway is related to the NF-kappa B signaling pathway, PI3K-Akt, and mTOR signaling pathways [66]. In addition, we used GSEA to study the role of T2D-related genes. We found some active pathways in T2D, such as focal adhesion, calcium signaling pathway, citrate cycle, axon guidance, and fatty acid metabolism. Previous studies have shown that DM and DPN may be significantly affected by the immune, inflammatory, and focal adhesion pathways, whereas DM may be significantly affected by cancer, ECM-receptor interaction, and immune-related pathways [67]. Based on the enrichment in ECM-receptor interaction and focal adhesion pathways, the various circRNAs in the PBMCs of patients with DR also played a significant role in cell migration [68]. This is in line with previous studies showing that mitochondrial oxidative phosphorylation is associated with diabetic peripheral nerve lesions.

In addition, it has been demonstrated that the pyroptosis phenomenon is related to TME immunological activation [69]. In this study, we found that immature dendritic cells (imDCs) were significantly different from those in diabetic and normal mice. This may be due to the induction and maintenance of allogeneic tolerance by dendritic cells (DCs), which are important immune system regulators. According to previous research, imDCs can be modified to prevent the rejection of islet xenografts [70]. In addition, it has been reported that in stable DCs, there is a response that suppresses CD4 + memory T cells [71]. or by acombination of blocking co-stimulatory molecules[72]. Effector memory CD4 T cells were also discovered in this investigation to be considerably different from both diabetic and healthy cells. A higher percentage of effector memory T cells was observed in T2D trial participants. Compared to T2D participants without cardiovascular disease, the fraction of effector memory T cells in these subjects was higher [73]. Furthermore, NLRP1 and PRKACA showed statistically significant differences in gene expression levels and diagnostic value. In particular, PLCG1 and DPP8 showed excellent discriminative ability in the prediction of diabetes. However, the relationship between PLCG1 and DPP8 and their mechanisms of action in T2D have not been investigated. In our model, low PLCG1 expression and high DPP8 expression correlated with poor survival outcomes, which may result from the high activity of pyroptosis. Phospholipase C gamma 1 (PLCG1) is a member of the phosphatidylinositol-specific PLC family, a group of membrane-associated enzymes that cleave PIP2 into DAG and IP3 and can cause cell death and inflammatory responses by releasing intracellular calcium reserves [74]. PLCG1 participates in receptor tyrosine kinase- (RTK-) mediated signal transduction pathway. It regulates GSDMD activity and promotes cell apoptosis. However, previous studies have shown that reducing PLCG1 can disrupt GSDMD-induced pyroptosis [75]. Elevated PLCG1 levels were found in DM rats and high glucose-treated human retinal endothelial cells [76]. This differs from the findings of this study. Dipeptidyl peptidases 8 (DPP8) was a cytosolic protease associated with pathophysiological involvement in cancer biology and intracellular N-terminal dipeptidyl peptidases (preferentially post-proline). Although experimental 3D structures and ligand-/substrate-binding mechanisms of DPP8 have not been described, the DPP family member DPP4 has been thoroughly characterized in molecular terms as a validated therapeutic target for T2D [77]. We speculate that these genes may be involved in the intricate cellular stress process of T2D, although the physiological role of these genes is not fully understood. Considering the aforementioned factors, it is possible that these genes have a significant impact on diabetes and that they will one day serve as diagnostic biomarkers. Further analyses are necessary to assess the effects of these genes.

This study has some limitations. First, microarray samples collected from various stages of T2D are needed to fully understand the molecular mechanisms underlying the occurrence and development of T2D. Second, the biomarkers and metabolic pathways identified by bioinformatics methods need to be confirmed by further experimental studies. Further bioinformatics and experimental verification are needed to further understand the function of the T2D. Third, because the T2D datasets in the GEO database are very strict, we only performed the analysis on two datasets, including skeletal muscle tissue sections and pancreatic islet tissue specimens. Whether this finding can be applied to other tissues requires further investigation. In addition, the relationship between apoptosis and genetic characteristics should be explored further. Therefore, many independent studies are needed to confirm and strengthen the therapeutic adaptation of our signature.

In summary, comprehensive bioinformatics analysis of T2D datasets from GEO (GSE7014 and GSE25724) revealed 25 T2DM-PRG-DEGs and the associations between pyroptosis-related pathways and T2D. Exploration of pyroptosis-related mechanisms and biomarkers may contribute to a better understanding of the etiology of T2D and open up new therapeutic options for DM. However, larger prospective studies are required to confirm this finding. To ascertain the predictive value of T2DM-PRG-DEGs, further experimental validation should be performed to demonstrate the biological role of T2DM-PRG-DEGs in T2D.

ETHICS STATEMENT

This study does not contain any studies with human participants or animals performed by any of the authors.

Consent for publication

Not applicable.

Availability of supporting data

Publicly available datasets were analyzed in this study. These data were available from the National Center for Biotechnology (NCBI) Gene Expression Omnibus (GEO): https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE7014; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE25724

Competing interests

No potential conflict of interest was reported by the authors.

Funding

This research received no external funding.

Authors' contributions

WW performed the literature search and data analysis. WY conceived and designed the project. WW wrote the paper. WY reviewed and amended the manuscript. The manuscript has been read and approved by all authors.

Acknowledgements

We acknowledge GEO databases for their platforms and contributors for uploading their meaningful datasets.

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No competing interests reported.

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Integrative analyzes of biomarkers and pathways for exploration the mechanisms and targets associated with pyroptosis in type 2 diabetes

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Abstract

Figures

Introduction

Materials And Methods

Data preprocessing and differentially expressed genes (DEGs)

Functional enrichment analysis

GSEA

Construction of PPI network

Construction of the interaction network between T2DM-PRGs and related miRNAs, transcription factors and related drugs

Expression analysis and ROC validation of PRGs

Immune infiltration analysis of PRGs

Statistical Analysis

Results

Differentially expressed analysis

Functional enrichment analysis of DEGs in T2DM-PRGs

GSEA

Construction of PPI network

Network analysis of T2DM-PRGs and related miRNAs, transcription factors and related drugs

Expression analysis and ROC validation of T2DM-PRGs-related genes

Immune infiltration analysis

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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