Exploring the Molecular Mechanisms Underlying the Comorbidity of Type 2 Diabetes Mellitus and Nonalcoholic Steatohepatitis: A Bioinformatics Analysis

Exploring the Molecular Mechanisms Underlying the Comorbidity of Type 2 Diabetes Mellitus and Nonalcoholic Steatohepatitis: A Bioinformatics Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Exploring the Molecular Mechanisms Underlying the Comorbidity of Type 2 Diabetes Mellitus and Nonalcoholic Steatohepatitis: A Bioinformatics Analysis Yingjiao Sha, Zefeng Yan, Zhenlong Liang, Yu Sha, Peipei Liu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6211779/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Investigating the comorbidity mechanisms of Type 2 Diabetes Mellitus (T2DM) and Non-Alcoholic Steatohepatitis (NASH) using bioinformatics analysis. T2DM and NASH are significant global health challenges, often coexisting and exacerbating each other's pathophysiology. Our study aims to elucidate the underlying mechanisms linking these two conditions by identifying common differentially expressed genes (DEGs) and microRNAs (miRNAs) through the analysis of publicly available gene expression datasets from the Gene Expression Omnibus (GEO). Methods : Gene expression datasets related to T2DM and NASH were retrieved from GEO. Differentially expressed genes were identified using the GEO2R tool, and common DEGs were determined through Venn diagram analysis. Functional enrichment analysis was performed using the R package "clusterProfiler," and protein-protein interaction (PPI) networks were constructed using STRING. Hub genes were identified using Cytohubba in Cytoscape. Transcription factors (TFs) were predicted using the TRRUST database, and common miRNAs were identified using the R package "edgeR" and GEO2R. The miRNAs-mRNAs regulatory network was established by integrating common DEGs and predicted miRNAs. Results : A total of 129 common DEGs were identified, including 20 downregulated and 109 upregulated genes. Enrichment analysis revealed that these DEGs were involved in biological processes such as peptidyl-serine modification, RNA splicing, and cellular response to nutrient levels. Nine hub genes were identified: MAPK1, U2AF1, SNRNP70, ZC3H13, TAF15, SMARCA4, HNRNPD, SRSF3, and SRSF11. These genes were associated with pathways related to RNA splicing, and metabolic regulation. Six common miRNAs (hsa-miR-361-5p, hsa-miR-520e, hsa-miR-320b, hsa-miR-595, hsa-miR-610, and hsa-miR-498) were identified, which were involved in cell cycle regulation, angiogenesis, and inflammation. The miRNAs-mRNAs network showed interactions between these miRNAs and three important genes: SRSF3, HNRNPD, and ZC3H13. Conclusion our study provides insights into the comorbidity mechanisms of T2DM and NASH through bioinformatics analysis. The identified hub genes and miRNAs offer potential therapeutic targets for future research. Biological sciences/Computational biology and bioinformatics Health sciences/Endocrinology Health sciences/Medical research Type 2 Diabetes Mellitus Non-Alcoholic Steatohepatitis Differentially Expressed Genes MicroRNAs Bioinformatics Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1 Introduction Type 2 Diabetes (T2DM) is a condition marked by a non-autoimmune, heterogeneous, and progressive decline in the ability of pancreatic islet β cells to secrete sufficient insulin, often occurring alongside insulin resistance (IR) and metabolic syndrome (MS)[ 1 ]. The disease can occur at any age but is more prevalent in middle-aged and older individuals. However, recent trends indicate a rising incidence among younger populations. T2DM places a significant burden on both patients and society, impacting them deeply.impacting physical health and quality of life. The typical pathological features of T2DM encompass insulin resistance, where the body's cells do not respond adequately to insulin, as well as a relative lack of insulin secretion from the pancreatic beta cells. This leads to elevated blood glucose levels and impaired glucose metabolism.The etiology of T2DM is multifactorial, involving both genetic predisposition and environmental triggers[ 2 ]. Genetic factors play a significant role, as individuals with Studies indicate that individuals with a family history of type 2 diabetes face a significantly elevated risk of developing the condition, with some research suggesting that a positive family history can double the risk of pre-diabetes. Among the environmental factors contributing to T2DM are an unhealthy lifestyle, characterized by a diet rich in calories and low in fiber, lack of physical activity, obesity, smoking, and excessive alcohol consumption. Additionally, certain medical conditions like hypertension and dyslipidemia are also associated with an increased risk of developing T2DM.The pathogenesis of T2DM is primarily driven by the interplay of insulin resistance and impaired insulin secretion. This leads to chronic hyperglycemia, which can cause significant damage to various organs, including the heart, kidneys, eyes, and nerves. Key mechanisms involve the dysregulation of glucose uptake in peripheral tissues and the failure of pancreatic beta cells to compensate adequately. Additionally, patients with T2DM are at an increased risk of developing non-alcoholic fatty liver disease (NAFLD) and other metabolic disorders. Pharmacological treatments include oral hypoglycemic agents (e.g., metformin, SGLT2 inhibitors) and insulin therapy, with newer drugs like GLP-1 receptor agonists offering additional options. Regular monitoring of blood glucose and glycated hemoglobin (HbA1c) levels is crucial for assessing treatment efficacy and making necessary adjustments. Non-alcoholic fatty liver disease (NAFLD) is a prevalent chronic liver condition characterized by the accumulation of fat in the liver, occurring globally with an estimated prevalence of 25% worldwide by2021.[ 3 ]. This condition can progress to non-alcoholic steatohepatitis (NASH), a more severe form associated with liver inflammation and damage, affecting 1.5–6.5% of adults. NAFLD can present at any age but is more prevalent in middle-aged individuals and those with metabolic syndrome. It imNon-alcoholic fatty liver disease (NAFLD) poses a significant burden on both individuals and healthcare systems due to its association with severe complications such as cardiovascular diseases, diabetes, liver cirrhosis, hepatocellular carcinoma (HCC), and metabolic syndrome.The histological features of NAFLD include diffuse hepatic steatosis (fat accumulation in liver cells), while NASH may show additional signs such as hepatocyte ballooning, lobular inflammation, and progressive fibrosis. Untreated NASH can progress to severe conditions such as cirrhosis, liver failure, and hepatocellular carcinoma, as detailed in the comprehensive overview of NAFLD and NASH.Nonalcoholic Steatohepatitis (NASH) was predominantly viewed as a severe condition, primarily affecting obese females and frequently linked to Type 2 Diabetes Mellitus (T2DM). It was also considered to have a relatively benign prognosis. However, it is important to note that these factors—obesity, female sex, and T2DM—are significant predictors of cardiovascular disease, stroke, and diabetes.[ 4 – 5 ] Other risk factors include rapid weight gain, poor dietary habits, a sedentary lifestyle, and certain other factors.tain genetic polymorphisms. Interestingly, The pathogenesis of NAFLD is primaprimarily driven by metabolic dysfunction, resulting in excessive accumulation of fat in the liver. This, in turn, triggers inflammatory responses and oxidative stress, potentially leading to liver injury and fibrosis. The progression to NASH is associated with increased liver enzyme levels and worsening metabolic parameters[ 6 ].NAFLD is also linked to a higher incidence of comorbidities compared to the general population. These include cardiovascular diseases, type 2 diabetes, hypertension, dyslipidemia, and other metabolic disorders. Symptoms of NAFLD are often nonspecific and may include fatigue, discomfort in the upper right abdomen, and unexplained weight changes. In advanced stages, patients may experience signs of liver decompensation such as jaundice, ascites, and hepatic encephalopathy.Management of NAFLD focuses on lifestyle modifications, including weight loss, increased physical activity, and a balanced diet. Pharmacological interventions may be necessary for patients with advanced disease or significant metabolic comorbidities. Regular monitoring of liver function and imaging studies are essential to assess disease progression and guide treatment. Type 2 diabetes mellitus (T2DM) and nonalcoholic steatohepatitis (NASH) are two significant health challenges that have garnered increasing attention in recent years. Increasing evidence indicates that T2DM and NASH often coexist, significantly elevating the health risks for those affected. This coexistence is particularly concerning given the rising global prevalence of both conditions. T2DM is a complex metabolic disorder characterized by insulin resistance and β-cell dysfunction, while NASH is a severe form of nonalcoholic fatty liver disease (NAFLD), marked by inflammation and hepatocyte injury in addition to hepatic fat accumulation.The association between T2DM and NASH is multifaceted. Up to 75% of individuals with insulin resistance and type 2 diabetes mellitus (T2DM) are affected by fatty liver disease, which is often accompanied by a higher incidence of cirrhosis. Furthermore, metabolic syndrome, characterized by obesity, hypertension, and dyslipidemia, exhibits a robust correlation with both type 2 diabetes mellitus (T2DM) and non-alcoholic fatty liver disease (NAFLD), as evidenced by clinical studies. The mechanisms underlying this comorbidity are not fully understood, but several factors are believed to play a role. These include insulin resistance, inflammation[ 7 ], oxidative stress[ 8 ], and gut microbiota dysbiosis[ 9 ]. For instance, insulin resistance is a common feature in both T2DM and NASH, contributing to the development and progression of these diseases[ 10 ]. Additionally, the accumulation of lipotoxic metabolites in hepatocytes can lead to cellular injury and inflammation, further exacerbating the condition. Despite the growing recognition of the comorbidity between T2DM and NASH, with approximately 60–70% of T2DM patients also suffering from fatty liver disease, the precise mechanisms linking these two conditions remain unclear. This gap in knowledge highlights the need for further research to elucidate the underlying pathways and identify potential therapeutic targets. Our research aims to explore the potential comorbidity mechanisms in T2DM and NASH, focusing on the interplay between metabolic pathways, inflammatory responses, and cellular signaling by overlapping DEGs and pathways. Through bioinformatics analysis, we aim to gain a deeper understanding of these mechanisms, thereby contributing data support and innovative perspectives for the prevention and treatment of both T2DM and NASH. 2 Material and Methods 2.1 Date source The Gene Expression Omnibus (GEO) dataset ( https://www.ncbi.nlm.nih.gov/geo/ ) comprises millions of microarray datasets and high-throughput sequences submitted by researchers globally. We conducted a search for gene expression datasets using the keywords "T2DM" or "NASH". The inclusion and exclusion criteria were as follows: (1) the test specimens must be from Homo sapiens; (2) the gene expression profiles must include both cases and controls; (3) the samples for both diseases must be local pathological tissues, specifically liver tissues for NASH; (4) the sequencing platform must be consistent; and (5) patients receiving clinical intervention were excluded. Consequently, the GEO datasets GSE7014 and GSE29231 for T2DM, and GSE17470 and GSE24807 for NASH were selected. These datasets were further analyzed using the GEO2R online tool. Table 1 provides detailed information on these datasets, including disease types, platforms, sample types, quantities, and experiment types. Table 1 Details of the GEO datasets Disease Series Platform Case Control Total Experiment type T2DM GSE7014 GPL570 20 6 26 Expression Profiling by array GSE29231 GPL6947 12 12 24 Expression Profiling by array GSE185845 GPL25243 44 16 60 Non-coding RNA profiling by array NASH GSE17470 GPL2895 7 4 11 Expression Profiling by array GSE24807 GPL2895 12 5 17 Expression Profiling by array GSE33857 GPL10656 12 7 19 Non-coding RNA profiling by array 2.2 Identification of DEGs Differentially expressed genes (DEGs) were identified using the GEO2R online tool. When multiple probes corresponded to a single gene symbol, the first probe was selected. Probes that did not correspond to any gene symbol were removed. DEGs were defined as those with |logFC (fold change)| ≥ 1 and an adjusted p-value < 0.05. Common DEGs were identified by constructing a Venn diagram using http://bioinformatics.psb.ugent.be/webtools/Venn/ . 2.3 Enrichment analysis of common DEGs The R package "clusterProfiler" was utilized to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The GO analysis encompassed three categories: biological process (BP), cellular component (CC), and molecular function (MF). Visualization was performed using the R package "ggplot2". The R version used was 4.4.1. 2.4 PPI network construction and hub genes selection The complex regulatory relationships among proteins of interest were explored using STRING ( https://cn.string-db.org/ ). A combined score over 0.4 was considered statistically significant. Hub genes were identified using the Degree algorithm of Cytohubba in Cytoscape. The HUGO Gene Nomenclature Committee (HGNC) database ( https://www.genenames.org/ ) was used to obtain detailed information on each hub gene. 2.5 Enrichment analysis and expression in immune cells of hub genes GeneMANIA ( http://www.genemania.org ) was employed to establish the co-expression network of hub genes. The Human Protein Atlas Database ( https://www.proteinatlas.org/ ) was used to explore the expression of each hub gene in various immune cells. 2.6 Prediction of transcription factors (TFs) The TRRUST database ( https://www.grnpedia.org/trrust ) was used to identify target genes of common TFs and the regulatory relationships between TFs. TFs regulating the hub genes were identified with a p-value < 0.05. 2.7 Identification of common miRNAs in T2DM and NASH MicroRNAs (miRNAs) regulate gene expression by degrading mRNAs or inhibiting their function. The R package "edgeR" and GEO2R online were used to identify miRNAs associated with T2DM and NASH using the GEO datasets (GSE185845 for T2DM and GSE33857 for NASH). The information of these datasets is presented in Table 1 . Common miRNAs associated with both T2DM and NASH were identified and intersected. The miRDB database ( http://mirdb.org ) was used to search for mature miRNAs for further analysis. TAM 2.0 ( http://www.lirmed.com/tam2 ) was used to perform miRNA function analysis. Terms with p-values < 0.05 were considered significant. 2.8 The common miRNAs-mRNAs network MiRTarbase ( https://mirtarbase.cuhk.edu.cn ) is a database of experimentally validated miRNA target interactions. The miRNAs-mRNAs regulatory network was established by intersecting common DEGs and predicted consensus miRNAs in T2DM and NASH. 2.9 Ethics exemption statement In accordance with the latest governmental legal ethical regulation titled "Ethical Review Measures for Life Science and Medical Research Involving Human Beings," issued and approved by the National Science and Technology Ethics Committee and State Council of PR China on February 18th, 2023, studies utilizing public database data are exempt from ethical review. 3 Results 3.1 Identification of common DEGs The data analysis process is illustrated in the flowchart (Fig. 1 ). A total of 10,575 DEGs were identified in GSE7014, 4,863 in GSE29231, 3,370 in GSE17470, and 7,794 in GSE24807 (Fig. 2 A-D). A Venn diagram intersection revealed 20 common downregulated DEGs and 109 common upregulated DEGs (Fig. 2 E,F). 3.2 Enrichment analysis of common DEGs GO and KEGG pathway enrichment analyses were performed to explore the functions and pathways of the 129 common DEGs. The GO analysis revealed major biological processes (BPs), cellular components (CCs), and molecular functions (MFs) as shown in Fig. 3 A, including each function and their p-values. The KEGG pathway analysis identified major enrichment pathways, also shown in Fig. 3 A, including each pathway and their p-values. Bar charts (Fig. 3 B) and network plots (Fig. 3 C) were used to visualize the enrichment results. These results strongly suggest that peptidyl-serine modification, peptidyl-serine phosphorylation, RNA splicing, peptidyl-threonine modification, positive regulation of TOR signaling, positive regulation of brown fat cell differentiation, cellular response to nutrient levels, and cellular response to extracellular stimulus are primarily responsible for the comorbidity of T2DM and NASH. 3.3 PPI network construction and identification of hub genes Cytoscape was used to construct a protein-protein interaction (PPI) network containing 20 nodes and 46 interactions for the common DEGs. Nine hub genes were identified using the Degree algorithm of Cytohubba in Cytoscape: MAPK1, U2AF1, SNRNP70, ZC3H13, TAF15, SMARCA4, HNRNPD, SRSF3, and SRSF11 (Fig. 3 D). These genes were identified at the intersection of the Upset diagram. 3.4 Co-expression and enrichment analysis of hub genes The co-expression network of hub genes was established using GeneMANIA, revealing related functions and their interaction weights: physical interactions (49.64%), co-expression (18.28%), shared protein domains (11.62%), pathway (9.49%), co-localization (7.31%), predicted functional relationships (3.08%), and genetic interactions (0.58%) (Fig. 4 A). GO and KEGG pathway enrichment analyses were performed to explore the functions and pathways of the eight hub genes. The GO analysis revealed major BPs, CCs, and MFs as shown in Fig. 4 B, including each function and their p-values. The KEGG pathway analysis identified major enrichment pathways, also shown in Fig. 4 C, including each pathway and their p-values. Network plots (Fig. 4 D) were used to visualize the enrichment results. Additionally, the expression of hub genes in various immune cells was shown in bar charts, with neutrophils and monocytes expressing the most hub genes (Fig. 5 A-I). Furthermore, the network of nine hub genes and their nearest neighbor genes based on immune cell RNA expression was shown (Fig. 6 A). 3.5 TFs prediction of hub genes On the basis of the TTRUST database, 35 TFs were obtained that regulate the hub genes, The TFs–Hub genes network was constructed, including 44nodes and 68 edges (Fig. 6 ). 3.6 Exploration of common miRNAs in T2DM and NASH A total of 193 miRNAs related to T2DM and 88 miRNAs related to NASH were identified using the GEO datasets (GSE185145, GSE33857) and the Linux operating system. Six common miRNAs between T2DM and NASH were identified using a Venn diagram: hsa-miR-361-5p, hsa-miR-520e, hsa-miR-320b, hsa-miR-595, hsa-miR-610, and hsa-miR-498 (Fig. 7 A). These miRNAs are cell-specific to chorioic membrane cells, hepatocytes, GP to induce calcification, hepatic sinusoidal endothelial cells, and natural killer cells, as shown in a bar chart (Fig. 7 B). Additionally, these miRNAs were involved in the top two functions: cell cycle, angiogenesis, and inflammation, presented in a heat map (Fig. 7 C) and bar chart (Fig. 7 D). 3.7 The common miRNAs-mRNAs network A total of 7,785 target genes were identified using miRTarbase. Three important genes were found in both the 7,785 target genes and the nine common DEGs: SRSF3, HNRNPD, and ZC3H13 (Fig. 8 A). The miRNAs-mRNAs network was established, showing the relationship between two miRNAs and three mRNAs (Fig. 8 B, 8 C). 4 Discussion A co-occurrence of T2DM and NASH, has been extensively documented[ 11 ]. Cross-sectional analysis of 2 million Chinese individuals revealed the prevalence of NASH was 29.2% in the general population[ 12 ]. T2DM is closely associated with NASH, as evidenced by studies indicating that 49–62% of T2DM patients concurrently suffer from NASH[ 13 ]. A recent cross-sectional study of 2 420 participants in 6 provinces in China revealed that the prevalence of NASH in normal population, pre-diabetes and T2DM was 23.3%, 44.0% and 55.3%, respectively[ 14 ]. Furthermore, longitudinal studies have demonstrated that NASH patients exhibit a more than two-fold increased risk of developing diabetes compared to the general population, highlighting the intertwined pathophysiology of these metabolic disorders[ 15 ]. Based on our research findings, MAPK1, U2AF1, SNRNP70, ZC3H13, TAF15, SMARCA4, HNRNPD, SRSF3, and SRSF11 were identified as nine hub genes shared between T2DM and NASH. MAPK1 (mitogen-activated protein kinase 1) is a key protein in the MAPK signaling pathway, which plays a crucial role in cell proliferation, stress response, inflammation, and apoptosis. Studies have demonstrated that inhibiting the MAPK signaling pathway can reduce the level of oxidative stress and inflammation in the body, thus delaying the occurrence and development of NASH[ 16 ]. MAPK1 is closely associated with glucose homeostasis[ 17 ]. In T2DM, the abnormal activation of MAPK1 may lead to insulin resistance. It has been shown that downregulating the expression of MAPK1 could enzymatically inhibit the activation of proteins related to IR, such as STAT3, and had been proposed as a valuable candidate target for diabetes treatment[ 18 ]. From our data, it appears that RNA splicing draws close ties with T2DM and NASH. As the members of the SR splicing factor family, U2 small nuclear RNA auxiliary factor 1 (U2AF1), SRSF3 (previously known as SRp20), and SRSF11 play essential roles in regulating RNA splicing during eukaryotic gene expression. These genes are specifically involved in the removal of introns from pre-mRNA and the precise joining of exons. Diseases linked to U2AF1 mutations include myelodysplastic syndrome, primary myelofibrosis, chronic myelomonocytic leukemia, hairy cell leukemia, and various solid tumors, particularly lung, pancreatic, and ovarian carcinomas[ 19 ]. However, the role of U2AF1 mutations in T2DM and NASH has not yet been reported. We speculate that U2AF1 mutation may be related to the splicing regulation of insulin signaling components and transcripts involved in glycolipid homeostasis. The stabilization of hepatic SRSF3 has been proved to reduce liver fibrosis and inflammation[ 20 ]. Conversely, the genetic loss of SRSF3 in hepatocytes impaired hepatocyte maturation and disrupted glycolipid metabolism, potentially contributing to the progression of NASH, cirrhosis, and ultimately hepatocellular carcinoma[ 21 ]. Zhu et al. profiled gene expression in healthy controls (n = 7) and patients with NAFLD (n = 40) by microarray. They identified that the expression of 92 splicing factor genes, including SRSF11, was altered[ 22 ]. Small nuclear ribonucleoprotein U1 subunit 70 (SNRNP70) is one of the components of the U1 small nuclear ribonucleoprotein (snRNP), which is essential for recognition of the pre-mRNA 5' splice-site and the subsequent assembly of the spliceosome. Aberrant expression of SNRNP70 has been observed in various cancers and is closely associated with tumor progression. The study revealed down-regulation of SNRNP70 expression significantly inhibited the proliferation and migration of HCC cells[ 23 ]. ZC3H13 is a regulator of m6A methylation. Downregulation of ZC3H13 was associated with a poor prognosis and adverse outcomes in hepatocellular carcinoma[ 24 ]. The mean m6A levels were significantly lower in T2D patients compared to normal controls (SMD = -1.35, 95% CI: -2.58 to -0.11), indicating that ZC3H13 may play a crucial role in the pathogenesis of T2D[ 25 ]. The study by Yang demonstrated that TAF15 exacerbates NASH progression by regulating lipid metabolism and inflammation through the transcriptional activation of FASN and interaction with p65 to activate the NF-κB signaling pathway[ 26 ]. SMARCA4, the core subunit of the SWI/SNF chromatin remodeling complex, has been proved to associated with NASH by promoting liver fibrosis[ 27 ]. In addition, some findings demonstrate that SMARCA4 alleviated oxidative stress and apoptosis, which played a potential role in the pathogenesis of diabetic retinopathy[ 28 – 29 ]. HNRNPD, also known as AUF1, is a multifunctional RNA-binding protein involved in mRNA regulation and protein stability during inflammatory responses[ 30 ]. Studies have shown that knockdown of HNRNPD reduces apoptosis induced by glucose deprivation, while its overexpression has the opposite effect[ 31 ]. Furthermore, HNRNPD plays a role in liver dedifferentiation, development, and the progression of human hepatocellular carcinoma (HCC)[ 32 ]. Among our enrichment results, multiple types of RNA splicing were found to be predominantly associated with these two diseases. Alternative RNA splicing is a process by which introns are removed and exons are assembled to generate different mRNA isoforms from a single pre-mRNA[ 33 ]. Recent studies have highlighted the close relationship between the pathogenesis of NASH and dysregulation of the RNA splicing machinery[ 34 ]. Notably, most of the altered mRNAs or splicing factors are associated with lipid metabolism, gluconeogenesis, and fibrosis, which may contribute to disease progression, inflammation, and fibrosis in NASH[ 35 ]. Additionally, several genes linked to obesity and insulin resistance, such as ANO1, HNF-1α, IPF-1, GCK, SUR1, TCF7L2, VEGF, and NOVA1, are regulated by RNA splicing[ 36 – 37 ]. Our findings identified six common miRNAs for both T2DM and NASH, including hsa-miR-361-5p, hsa-miR-520e, hsa-miR-320b, hsa-miR-595, hsa-miR-610 and hsa-miR-498. miRNAs are small non-coding RNAs that regulate gene expression at the post-transcriptional level and are involved in various cellular processes such as cell proliferation, inflammation, and apoptosis. Several studies have reported that various miRNAs are associated with the occurrence and development of NASH and T2DM. Alterations in miRNA activity can lead to hepatocyte injury, apoptosis, and fibrosis in the liver[ 38 ]. In NAFLD, the antagonism of specific miRNAs can decrease fatty acid synthesis and increase fatty acid oxidation in the liver. miR-361-5p was overexpressed in the livers of obese mouse models and NAFLD patients. Overexpression of miR-361-5p resulted in hepatosteatosis, while inhibition of miR-361-5p expression alleviated triglyceride (TG) accumulation and improved insulin sensitivity[ 39 ]. A study by Katherine Johnson et al. found that miR-320b was significantly upregulated in NAFLD patients compared to healthy controls[ 40 ]. Moreover, Wang Y reported that placental miR-320b levels were positively correlated with 1-hour and 2-hour glucose levels during a 75 g oral glucose tolerance test (OGTT) in human pregnancies. Furthermore, overexpression of miR-320 impaired insulin secretion and increased apoptosis in MIN6 cells and islets from mice with normal insulin sensitivity[ 41 ] miR-595 was proved to downregulate in HCC. Using both in vitro and in vivo experiments, Wang H et al. demonstrated that miR-595 significantly suppressed the proliferation and metastasis of HCC cells by inhibiting the NF-κB signaling pathway[ 42 ]. Hsa-miR-520e, miR-610 and miR-498 have been confirmed to be involved in multiple cancers[ 43 – 45 ], however, there are no relevant studies on its relationship with T2DM and NASH, which is worthy of further studies. MAPK1, U2AF1, SNRNP70, ZC3H13, TAF15, SMARCA4, HNRNPD, SRSF3, and SRSF11 are nine hub genes identified for both T2DM and NASH. These findings provide new approaches and potential targets for the treatment of the co-occurrence of these two diseases in the future. Moreover, a potential relationship between the common miRNAs (hsa-miR-320b and hsa-miR-595) and differentially expressed genes (DEGs) such as SRSF3, HNRNPD, and ZC3H13 was demonstrated. Further experiments will be conducted to verify how hsa-miR-320b and hsa-miR-595 play significant roles in the pathogenesis of both T2DM and NASH by regulating these DEGs. Conclusion In summary, we investigated the potential comorbidity mechanisms of T2DM and NASH through bioinformatics analysis, aiming to elucidate the relationship between these two diseases using a population-based case-control study. However, this study has certain limitations, and further experiments as well as an increased sample size would be necessary to validate our findings. Declarations This research has no funding support. Author Contribution YJ.Sha and ZL.Liang wrote the main manuscript textZF.Yan participated in the design of the study and performed the bioinformatics analysisY.Sha conceived of the study and helped to draft the manuscript PP.Liu , WB.Wei and PJ.Liu participated in the discussion and revised the manuscriptMY.Li was responsible for the overall planning and design of the research, oversaw its supervision and coordination, revised the manuscript, and provided technical guidance.All authors reviewed the manuscript. Data availability The gene expression profiles of GSE7014, GSE29231, GSE17470 and GSE24807 were downloaded from the 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=GSE29231 , https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17470 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE24807 ). The non-coding RNA profiles of GSE185845 and GSE33857 were downloaded from GEO( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE185845 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33857 ) References Lu, X. et al. Type 2 diabetes mellitus in adults: pathogenesis, prevention and therapy. Signal. Transduct. Target. Ther. 9 (1), 262. 10.1038/s41392-024-01951-9 (2024). Tinajero, M. G. & Malik, V. S. An Update on the Epidemiology of Type 2 Diabetes: A Global Perspective. Endocrinol. Metab. Clin. North. Am. 50 (3), 337–355. 10.1016/j.ecl.2021.05.013 (2021). Powell, E. E., Wong, V. 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Pract. 92 (1), e23–e26. 10.1016/j.diabres.2011.01.014 (2011). Schmid, D. et al. An abundant, truncated human sulfonylurea receptor 1 splice variant has prodiabetic properties and impairs sulfonylurea action. Cell. Mol. Life Sci. 69 (1), 129–148. 10.1007/s00018-011-0739-x (2012). Bala, S. et al. Emerging role of microRNAs in liver diseases. World J Gastroenterol. ;15(45):5633-40. 10.3748/wjg.15.5633. PMID: 19960558;PMC2789214. Hand NJ, Hepatic function is preserved in the absence of mature microRNAs. Hepatology. 2009;49(2):618 – 26. doi: 10.1002/hep.22656. (2009). Zhang, Z. et al. Obesity-induced upregulation of miR-361-5p promotes hepatosteatosis through targeting Sirt1. Metabolism 88 , 31–39. 10.1016/j.metabol.2018.08.007 (2018). Johnson, K. et al. Consortium Investigators§; LITMUS Consortium Investigators. Increased serum miR-193a-5p during non-alcoholic fatty liver disease progression: Diagnostic and mechanistic relevance. JHEP Rep. 4 (2), 100409. 10.1016/j.jhepr.2021.100409 (2021). Wang, Y. et al. Placenta-derived exosomes exacerbate beta cell dysfunction in gestational diabetes mellitus through delivery of miR-320b. Front. Endocrinol. (Lausanne) . 14 , 1282075. 10.3389/fendo.2023.1282075 (2024). Wang, H., Jiang, F., Liu, W. & Tian, W. miR-595 suppresses cell proliferation and metastasis in hepatocellular carcinoma by inhibiting NF-κB signalling pathway. Pathol. Res. Pract. 216 (4), 152899. 10.1016/j.prp.2020.152899 (2020). Kucuksayan, H. et al. TGF-β-SMAD-miR-520e axis regulates NSCLC metastasis through a TGFBR2-mediated negative-feedback loop. Carcinogenesis 40 (5), 695–705. 10.1093/carcin/bgy166 (2019). Wu, D., Liu, J., Yu, L., Wu, S. & Qiu, X. Circular RNA hsa_circ_0000144 aggravates ovarian Cancer progression by regulating ELK3 via sponging miR-610. J. Ovarian Res. 15 (1), 113. 10.1186/s13048-022-01048-3 (2022). Li, Z., Feng, Y., Zhang, Z., Cao, X. & Lu, X. TMPO-AS1 promotes cell proliferation of thyroid cancer via sponging miR-498 to modulate TMPO. Cancer Cell. Int. 20 , 294. 10.1186/s12935-020-01334-4 (2020). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6211779","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":435688386,"identity":"12dd83cb-949e-4827-a1bd-3c0b31ac29d1","order_by":0,"name":"Yingjiao Sha","email":"","orcid":"","institution":"Department of Laboratory Medicine, the First Medical Centre, Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yingjiao","middleName":"","lastName":"Sha","suffix":""},{"id":435688387,"identity":"12769e78-9a13-4bc6-9b4b-dfe8f3a2ba2e","order_by":1,"name":"Zefeng Yan","email":"","orcid":"","institution":"Department of Laboratory Medicine, the First Medical Centre, Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zefeng","middleName":"","lastName":"Yan","suffix":""},{"id":435688388,"identity":"552be87e-13d4-469c-969b-00378d3b9b7e","order_by":2,"name":"Zhenlong Liang","email":"","orcid":"","institution":"Department of Laboratory Medicine, the First Medical Centre, Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhenlong","middleName":"","lastName":"Liang","suffix":""},{"id":435688389,"identity":"7c391907-e3c3-4b11-ab96-39120d9b0292","order_by":3,"name":"Yu Sha","email":"","orcid":"","institution":"Weihai Municipal Hospital, Cheeloo College of Medicine, Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Sha","suffix":""},{"id":435688390,"identity":"0e5cd58d-0ee2-4c26-ad77-96255f98998e","order_by":4,"name":"Peipei Liu","email":"","orcid":"","institution":"Department of Laboratory Medicine, the First Medical Centre, Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Peipei","middleName":"","lastName":"Liu","suffix":""},{"id":435688391,"identity":"c61357a9-74af-476f-8878-c13fbdae445f","order_by":5,"name":"Wenbin Wei","email":"","orcid":"","institution":"Department of Laboratory Medicine, the First Medical Centre, Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenbin","middleName":"","lastName":"Wei","suffix":""},{"id":435688392,"identity":"3b921fac-b46c-4149-9c2e-49bbfa338ce2","order_by":6,"name":"Peijiang Liu","email":"","orcid":"","institution":"Department of Laboratory Medicine, the First Medical Centre, Chinese PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Peijiang","middleName":"","lastName":"Liu","suffix":""},{"id":435688393,"identity":"01e7df22-4019-49be-9906-b6e619d13cc9","order_by":7,"name":"Mianyang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYBACPmYGhgMQJvuBAwkVEnLyhLSwIbTwJD54cMbC2LCBkBYktrHhw7aKRJgJuLWwcyce+LnjsLw5/4I0icR5EgmMDcwPH93A6zDeDQd7zxw23Dnj4TGJxG0SeewMbMbGOQS0HOBtO8y44caBNJCWYsYGHjZpQloO/m07bA/UYiaROEciseEAEVoOA21J3HC+wdggsYFYLbJt6ckbbgADOeGYhLFhMwG/8POf3fzxbZu17Ybzxw8c/FFTJyfP3vzwMT4tUNDMwCCRAGUzE1YOAnVA+w4Qp3QUjIJRMApGHgAAFrxSVsphM8AAAAAASUVORK5CYII=","orcid":"","institution":"Department of Laboratory Medicine, the First Medical Centre, Chinese PLA General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Mianyang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-03-12 11:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6211779/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6211779/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79778957,"identity":"a59ab00a-3265-4f2d-961a-d49afd2897f8","added_by":"auto","created_at":"2025-04-02 14:39:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":41523,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of this study.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6211779/v1/a85f42fb4c494ed4027d0149.png"},{"id":79778958,"identity":"968eba8b-7192-4217-8ec5-5ec2a6a9b844","added_by":"auto","created_at":"2025-04-02 14:39:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":404174,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano map of GSE7014, GSE29231, GSE17470, and GSE24807 and Venn diagram of common DEGs. (A) The volcano map of GSE7014. (B) The volcano map of GSE29231. (C) The volcano map of GSE17470. (D) The volcano map of GSE24807. Red represents upregulated genes, and blue represents downregulated genes. (E) The four datasets have an overlap of 20 common downregulated genes. (F) The four datasets have an overlap of 109 common upregulated genes.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6211779/v1/f11bcce710f9483316be23bc.png"},{"id":79779739,"identity":"17890183-e2e4-474c-801f-944b5830cdff","added_by":"auto","created_at":"2025-04-02 14:47:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":383421,"visible":true,"origin":"","legend":"\u003cp\u003eEnrichment analysis and PPI network of common DEGs. (A) Enrichment analysis of common DEGs is shown with bar chart in BP, CC, and MF. (B) Enrichment analysis of common DEGs is shown with bar chart in KEGG. (C) Enrichment analysis of common DEGs is shown with network planning. (D) 9 hub genes are selected from PPI network.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6211779/v1/256f196e538b6c4d6684efb1.png"},{"id":79778959,"identity":"1b22114a-5c75-4622-88f7-354a77d217ea","added_by":"auto","created_at":"2025-04-02 14:39:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":365718,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and analysis of hub genes. (A) Hub genes and their co-expression genes network. (B) Enrichment analysis of hub genes is shown with bar chart in BP, CC, MF. (C) Enrichment analysis of hub genes is shown with bar chart in KEGG. (D) Enrichment analysis of hub genes is shown with network planning.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6211779/v1/76897e5bc484b93e4685fd2c.png"},{"id":79779740,"identity":"772f80a9-d484-4ca2-b1ac-aa7d99794082","added_by":"auto","created_at":"2025-04-02 14:47:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":896448,"visible":true,"origin":"","legend":"\u003cp\u003eExploration of hub genes in immune cells. (A–I) Nine bar charts showing expression of each hub gene in immune cells in decreasing order.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6211779/v1/cb8508e652e472b30bb137d3.png"},{"id":79779742,"identity":"33fe631e-f6a9-4534-8c2c-3d86f3939918","added_by":"auto","created_at":"2025-04-02 14:47:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":113509,"visible":true,"origin":"","legend":"\u003cp\u003eExploration of hub genes with their transcription factors. (A) Regulatory network of TFs. Red represents related hub genes, and blue represents TFs\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6211779/v1/5e13fafa74fb80a79e8442fc.png"},{"id":79778965,"identity":"811674a9-fd48-408a-83c7-ed273b395b79","added_by":"auto","created_at":"2025-04-02 14:39:24","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":126308,"visible":true,"origin":"","legend":"\u003cp\u003eExploration of miRNAs. (A) Five overlapping miRNA in T2DM and NASH. (B) Cell specificity of common miRNAs, top five are shown. (C) Function of common miRNAs via heat map. (D) Function of common miRNAs via bar plot, top three are shown.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6211779/v1/c18218cd710fcdebdfe20990.png"},{"id":79779750,"identity":"f14f6d15-9da7-4130-ac96-610a71988044","added_by":"auto","created_at":"2025-04-02 14:47:24","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":84417,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Two overlapping genes between target genes of miRNAs and common DEGs. (B) The miRNAs-mRNAs network. Blue represents miRNAs and yellow represents common DEGs\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6211779/v1/8024a3a8961dc29e206c36a4.png"},{"id":92958481,"identity":"2ee76376-5b3f-43f5-8f02-695cddd06e73","added_by":"auto","created_at":"2025-10-07 14:32:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3052958,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6211779/v1/68e34702-9cbc-4aa9-9019-1ac7463dd243.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the Molecular Mechanisms Underlying the Comorbidity of Type 2 Diabetes Mellitus and Nonalcoholic Steatohepatitis: A Bioinformatics Analysis","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eType 2 Diabetes (T2DM) is a condition marked by a non-autoimmune, heterogeneous, and progressive decline in the ability of pancreatic islet β cells to secrete sufficient insulin, often occurring alongside insulin resistance (IR) and metabolic syndrome (MS)[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The disease can occur at any age but is more prevalent in middle-aged and older individuals. However, recent trends indicate a rising incidence among younger populations. T2DM places a significant burden on both patients and society, impacting them deeply.impacting physical health and quality of life. The typical pathological features of T2DM encompass insulin resistance, where the body's cells do not respond adequately to insulin, as well as a relative lack of insulin secretion from the pancreatic beta cells. This leads to elevated blood glucose levels and impaired glucose metabolism.The etiology of T2DM is multifactorial, involving both genetic predisposition and environmental triggers[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Genetic factors play a significant role, as individuals with Studies indicate that individuals with a family history of type 2 diabetes face a significantly elevated risk of developing the condition, with some research suggesting that a positive family history can double the risk of pre-diabetes. Among the environmental factors contributing to T2DM are an unhealthy lifestyle, characterized by a diet rich in calories and low in fiber, lack of physical activity, obesity, smoking, and excessive alcohol consumption. Additionally, certain medical conditions like hypertension and dyslipidemia are also associated with an increased risk of developing T2DM.The pathogenesis of T2DM is primarily driven by the interplay of insulin resistance and impaired insulin secretion. This leads to chronic hyperglycemia, which can cause significant damage to various organs, including the heart, kidneys, eyes, and nerves. Key mechanisms involve the dysregulation of glucose uptake in peripheral tissues and the failure of pancreatic beta cells to compensate adequately. Additionally, patients with T2DM are at an increased risk of developing non-alcoholic fatty liver disease (NAFLD) and other metabolic disorders. Pharmacological treatments include oral hypoglycemic agents (e.g., metformin, SGLT2 inhibitors) and insulin therapy, with newer drugs like GLP-1 receptor agonists offering additional options. Regular monitoring of blood glucose and glycated hemoglobin (HbA1c) levels is crucial for assessing treatment efficacy and making necessary adjustments.\u003c/p\u003e \u003cp\u003eNon-alcoholic fatty liver disease (NAFLD) is a prevalent chronic liver condition characterized by the accumulation of fat in the liver, occurring globally with an estimated prevalence of 25% worldwide by2021.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This condition can progress to non-alcoholic steatohepatitis (NASH), a more severe form associated with liver inflammation and damage, affecting 1.5\u0026ndash;6.5% of adults. NAFLD can present at any age but is more prevalent in middle-aged individuals and those with metabolic syndrome. It imNon-alcoholic fatty liver disease (NAFLD) poses a significant burden on both individuals and healthcare systems due to its association with severe complications such as cardiovascular diseases, diabetes, liver cirrhosis, hepatocellular carcinoma (HCC), and metabolic syndrome.The histological features of NAFLD include diffuse hepatic steatosis (fat accumulation in liver cells), while NASH may show additional signs such as hepatocyte ballooning, lobular inflammation, and progressive fibrosis. Untreated NASH can progress to severe conditions such as cirrhosis, liver failure, and hepatocellular carcinoma, as detailed in the comprehensive overview of NAFLD and NASH.Nonalcoholic Steatohepatitis (NASH) was predominantly viewed as a severe condition, primarily affecting obese females and frequently linked to Type 2 Diabetes Mellitus (T2DM). It was also considered to have a relatively benign prognosis. However, it is important to note that these factors\u0026mdash;obesity, female sex, and T2DM\u0026mdash;are significant predictors of cardiovascular disease, stroke, and diabetes.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] Other risk factors include rapid weight gain, poor dietary habits, a sedentary lifestyle, and certain other factors.tain genetic polymorphisms. Interestingly, The pathogenesis of NAFLD is primaprimarily driven by metabolic dysfunction, resulting in excessive accumulation of fat in the liver. This, in turn, triggers inflammatory responses and oxidative stress, potentially leading to liver injury and fibrosis. The progression to NASH is associated with increased liver enzyme levels and worsening metabolic parameters[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].NAFLD is also linked to a higher incidence of comorbidities compared to the general population. These include cardiovascular diseases, type 2 diabetes, hypertension, dyslipidemia, and other metabolic disorders. Symptoms of NAFLD are often nonspecific and may include fatigue, discomfort in the upper right abdomen, and unexplained weight changes. In advanced stages, patients may experience signs of liver decompensation such as jaundice, ascites, and hepatic encephalopathy.Management of NAFLD focuses on lifestyle modifications, including weight loss, increased physical activity, and a balanced diet. Pharmacological interventions may be necessary for patients with advanced disease or significant metabolic comorbidities. Regular monitoring of liver function and imaging studies are essential to assess disease progression and guide treatment.\u003c/p\u003e \u003cp\u003eType 2 diabetes mellitus (T2DM) and nonalcoholic steatohepatitis (NASH) are two significant health challenges that have garnered increasing attention in recent years. Increasing evidence indicates that T2DM and NASH often coexist, significantly elevating the health risks for those affected. This coexistence is particularly concerning given the rising global prevalence of both conditions. T2DM is a complex metabolic disorder characterized by insulin resistance and β-cell dysfunction, while NASH is a severe form of nonalcoholic fatty liver disease (NAFLD), marked by inflammation and hepatocyte injury in addition to hepatic fat accumulation.The association between T2DM and NASH is multifaceted. Up to 75% of individuals with insulin resistance and type 2 diabetes mellitus (T2DM) are affected by fatty liver disease, which is often accompanied by a higher incidence of cirrhosis. Furthermore, metabolic syndrome, characterized by obesity, hypertension, and dyslipidemia, exhibits a robust correlation with both type 2 diabetes mellitus (T2DM) and non-alcoholic fatty liver disease (NAFLD), as evidenced by clinical studies. The mechanisms underlying this comorbidity are not fully understood, but several factors are believed to play a role. These include insulin resistance, inflammation[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], oxidative stress[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and gut microbiota dysbiosis[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. For instance, insulin resistance is a common feature in both T2DM and NASH, contributing to the development and progression of these diseases[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Additionally, the accumulation of lipotoxic metabolites in hepatocytes can lead to cellular injury and inflammation, further exacerbating the condition.\u003c/p\u003e \u003cp\u003eDespite the growing recognition of the comorbidity between T2DM and NASH, with approximately 60\u0026ndash;70% of T2DM patients also suffering from fatty liver disease, the precise mechanisms linking these two conditions remain unclear. This gap in knowledge highlights the need for further research to elucidate the underlying pathways and identify potential therapeutic targets.\u003c/p\u003e \u003cp\u003eOur research aims to explore the potential comorbidity mechanisms in T2DM and NASH, focusing on the interplay between metabolic pathways, inflammatory responses, and cellular signaling by overlapping DEGs and pathways. Through bioinformatics analysis, we aim to gain a deeper understanding of these mechanisms, thereby contributing data support and innovative perspectives for the prevention and treatment of both T2DM and NASH.\u003c/p\u003e"},{"header":"2 Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Date source\u003c/h2\u003e \u003cp\u003eThe Gene Expression Omnibus (GEO) dataset (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) comprises millions of microarray datasets and high-throughput sequences submitted by researchers globally. We conducted a search for gene expression datasets using the keywords \"T2DM\" or \"NASH\". The inclusion and exclusion criteria were as follows: (1) the test specimens must be from Homo sapiens; (2) the gene expression profiles must include both cases and controls; (3) the samples for both diseases must be local pathological tissues, specifically liver tissues for NASH; (4) the sequencing platform must be consistent; and (5) patients receiving clinical intervention were excluded. Consequently, the GEO datasets GSE7014 and GSE29231 for T2DM, and GSE17470 and GSE24807 for NASH were selected. These datasets were further analyzed using the GEO2R online tool. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides detailed information on these datasets, including disease types, platforms, sample types, quantities, and experiment types.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDetails of the GEO datasets\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeries\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlatform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eExperiment type\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE7014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eExpression Profiling by array\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE29231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL6947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eExpression Profiling by array\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE185845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL25243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNon-coding RNA profiling by array\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNASH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE17470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL2895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eExpression Profiling by array\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE24807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL2895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eExpression Profiling by array\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGSE33857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPL10656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNon-coding RNA profiling by array\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Identification of DEGs\u003c/h2\u003e \u003cp\u003eDifferentially expressed genes (DEGs) were identified using the GEO2R online tool. When multiple probes corresponded to a single gene symbol, the first probe was selected. Probes that did not correspond to any gene symbol were removed. DEGs were defined as those with |logFC (fold change)| \u0026ge; 1 and an adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Common DEGs were identified by constructing a Venn diagram using \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinformatics.psb.ugent.be/webtools/Venn/\u003c/span\u003e\u003cspan address=\"http://bioinformatics.psb.ugent.be/webtools/Venn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Enrichment analysis of common DEGs\u003c/h2\u003e \u003cp\u003eThe R package \"clusterProfiler\" was utilized to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The GO analysis encompassed three categories: biological process (BP), cellular component (CC), and molecular function (MF). Visualization was performed using the R package \"ggplot2\". The R version used was 4.4.1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 PPI network construction and hub genes selection\u003c/h2\u003e \u003cp\u003eThe complex regulatory relationships among proteins of interest were explored using STRING (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A combined score over 0.4 was considered statistically significant. Hub genes were identified using the Degree algorithm of Cytohubba in Cytoscape. The HUGO Gene Nomenclature Committee (HGNC) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genenames.org/\u003c/span\u003e\u003cspan address=\"https://www.genenames.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to obtain detailed information on each hub gene.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Enrichment analysis and expression in immune cells of hub genes\u003c/h2\u003e \u003cp\u003eGeneMANIA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genemania.org\u003c/span\u003e\u003cspan address=\"http://www.genemania.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was employed to establish the co-expression network of hub genes. The Human Protein Atlas Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org/\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to explore the expression of each hub gene in various immune cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Prediction of transcription factors (TFs)\u003c/h2\u003e \u003cp\u003eThe TRRUST database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.grnpedia.org/trrust\u003c/span\u003e\u003cspan address=\"https://www.grnpedia.org/trrust\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to identify target genes of common TFs and the regulatory relationships between TFs. TFs regulating the hub genes were identified with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Identification of common miRNAs in T2DM and NASH\u003c/h2\u003e \u003cp\u003eMicroRNAs (miRNAs) regulate gene expression by degrading mRNAs or inhibiting their function. The R package \"edgeR\" and GEO2R online were used to identify miRNAs associated with T2DM and NASH using the GEO datasets (GSE185845 for T2DM and GSE33857 for NASH). The information of these datasets is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Common miRNAs associated with both T2DM and NASH were identified and intersected. The miRDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mirdb.org\u003c/span\u003e\u003cspan address=\"http://mirdb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to search for mature miRNAs for further analysis. TAM 2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.lirmed.com/tam2\u003c/span\u003e\u003cspan address=\"http://www.lirmed.com/tam2\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to perform miRNA function analysis. Terms with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 The common miRNAs-mRNAs network\u003c/h2\u003e \u003cp\u003eMiRTarbase (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mirtarbase.cuhk.edu.cn\u003c/span\u003e\u003cspan address=\"https://mirtarbase.cuhk.edu.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a database of experimentally validated miRNA target interactions. The miRNAs-mRNAs regulatory network was established by intersecting common DEGs and predicted consensus miRNAs in T2DM and NASH.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Ethics exemption statement\u003c/h2\u003e \u003cp\u003eIn accordance with the latest governmental legal ethical regulation titled \"Ethical Review Measures for Life Science and Medical Research Involving Human Beings,\" issued and approved by the National Science and Technology Ethics Committee and State Council of PR China on February 18th, 2023, studies utilizing public database data are exempt from ethical review.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Identification of common DEGs\u003c/h2\u003e\n \u003cp\u003eThe data analysis process is illustrated in the flowchart (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). A total of 10,575 DEGs were identified in GSE7014, 4,863 in GSE29231, 3,370 in GSE17470, and 7,794 in GSE24807 (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA-D). A Venn diagram intersection revealed 20 common downregulated DEGs and 109 common upregulated DEGs (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE,F).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Enrichment analysis of common DEGs\u003c/h2\u003e\n \u003cp\u003eGO and KEGG pathway enrichment analyses were performed to explore the functions and pathways of the 129 common DEGs. The GO analysis revealed major biological processes (BPs), cellular components (CCs), and molecular functions (MFs) as shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA, including each function and their p-values. The KEGG pathway analysis identified major enrichment pathways, also shown in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA, including each pathway and their p-values. Bar charts (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB) and network plots (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC) were used to visualize the enrichment results. These results strongly suggest that peptidyl-serine modification, peptidyl-serine phosphorylation, RNA splicing, peptidyl-threonine modification, positive regulation of TOR signaling, positive regulation of brown fat cell differentiation, cellular response to nutrient levels, and cellular response to extracellular stimulus are primarily responsible for the comorbidity of T2DM and NASH.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 PPI network construction and identification of hub genes\u003c/h2\u003e\n \u003cp\u003eCytoscape was used to construct a protein-protein interaction (PPI) network containing 20 nodes and 46 interactions for the common DEGs. Nine hub genes were identified using the Degree algorithm of Cytohubba in Cytoscape: MAPK1, U2AF1, SNRNP70, ZC3H13, TAF15, SMARCA4, HNRNPD, SRSF3, and SRSF11 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD). These genes were identified at the intersection of the Upset diagram.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Co-expression and enrichment analysis of hub genes\u003c/h2\u003e\n \u003cp\u003eThe co-expression network of hub genes was established using GeneMANIA, revealing related functions and their interaction weights: physical interactions (49.64%), co-expression (18.28%), shared protein domains (11.62%), pathway (9.49%), co-localization (7.31%), predicted functional relationships (3.08%), and genetic interactions (0.58%) (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). GO and KEGG pathway enrichment analyses were performed to explore the functions and pathways of the eight hub genes. The GO analysis revealed major BPs, CCs, and MFs as shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB, including each function and their p-values. The KEGG pathway analysis identified major enrichment pathways, also shown in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC, including each pathway and their p-values. Network plots (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD) were used to visualize the enrichment results. Additionally, the expression of hub genes in various immune cells was shown in bar charts, with neutrophils and monocytes expressing the most hub genes (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA-I). Furthermore, the network of nine hub genes and their nearest neighbor genes based on immune cell RNA expression was shown (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 TFs prediction of hub genes\u003c/h2\u003e\n \u003cp\u003eOn the basis of the TTRUST database, 35 TFs were obtained that regulate the hub genes, The TFs\u0026ndash;Hub genes network was constructed, including 44nodes and 68 edges (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Exploration of common miRNAs in T2DM and NASH\u003c/h2\u003e\n \u003cp\u003eA total of 193 miRNAs related to T2DM and 88 miRNAs related to NASH were identified using the GEO datasets (GSE185145, GSE33857) and the Linux operating system. Six common miRNAs between T2DM and NASH were identified using a Venn diagram: hsa-miR-361-5p, hsa-miR-520e, hsa-miR-320b, hsa-miR-595, hsa-miR-610, and hsa-miR-498 (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA). These miRNAs are cell-specific to chorioic membrane cells, hepatocytes, GP to induce calcification, hepatic sinusoidal endothelial cells, and natural killer cells, as shown in a bar chart (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB). Additionally, these miRNAs were involved in the top two functions: cell cycle, angiogenesis, and inflammation, presented in a heat map (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC) and bar chart (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eD).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7 The common miRNAs-mRNAs network\u003c/h2\u003e\n \u003cp\u003eA total of 7,785 target genes were identified using miRTarbase. Three important genes were found in both the 7,785 target genes and the nine common DEGs: SRSF3, HNRNPD, and ZC3H13 (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eA). The miRNAs-mRNAs network was established, showing the relationship between two miRNAs and three mRNAs (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eB,\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eC).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eA co-occurrence of T2DM and NASH, has been extensively documented[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Cross-sectional analysis of 2\u0026nbsp;million Chinese individuals revealed the prevalence of NASH was 29.2% in the general population[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. T2DM is closely associated with NASH, as evidenced by studies indicating that 49–62% of T2DM patients concurrently suffer from NASH[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. A recent cross-sectional study of 2 420 participants in 6 provinces in China revealed that the prevalence of NASH in normal population, pre-diabetes and T2DM was 23.3%, 44.0% and 55.3%, respectively[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furthermore, longitudinal studies have demonstrated that NASH patients exhibit a more than two-fold increased risk of developing diabetes compared to the general population, highlighting the intertwined pathophysiology of these metabolic disorders[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on our research findings, MAPK1, U2AF1, SNRNP70, ZC3H13, TAF15, SMARCA4, HNRNPD, SRSF3, and SRSF11 were identified as nine hub genes shared between T2DM and NASH.\u003c/p\u003e \u003cp\u003eMAPK1 (mitogen-activated protein kinase 1) is a key protein in the MAPK signaling pathway, which plays a crucial role in cell proliferation, stress response, inflammation, and apoptosis. Studies have demonstrated that inhibiting the MAPK signaling pathway can reduce the level of oxidative stress and inflammation in the body, thus delaying the occurrence and development of NASH[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. MAPK1 is closely associated with glucose homeostasis[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In T2DM, the abnormal activation of MAPK1 may lead to insulin resistance. It has been shown that downregulating the expression of MAPK1 could enzymatically inhibit the activation of proteins related to IR, such as STAT3, and had been proposed as a valuable candidate target for diabetes treatment[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. From our data, it appears that RNA splicing draws close ties with T2DM and NASH. As the members of the SR splicing factor family, U2 small nuclear RNA auxiliary factor 1 (U2AF1), SRSF3 (previously known as SRp20), and SRSF11 play essential roles in regulating RNA splicing during eukaryotic gene expression. These genes are specifically involved in the removal of introns from pre-mRNA and the precise joining of exons. Diseases linked to U2AF1 mutations include myelodysplastic syndrome, primary myelofibrosis, chronic myelomonocytic leukemia, hairy cell leukemia, and various solid tumors, particularly lung, pancreatic, and ovarian carcinomas[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, the role of U2AF1 mutations in T2DM and NASH has not yet been reported. We speculate that U2AF1 mutation may be related to the splicing regulation of insulin signaling components and transcripts involved in glycolipid homeostasis. The stabilization of hepatic SRSF3 has been proved to reduce liver fibrosis and inflammation[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Conversely, the genetic loss of SRSF3 in hepatocytes impaired hepatocyte maturation and disrupted glycolipid metabolism, potentially contributing to the progression of NASH, cirrhosis, and ultimately hepatocellular carcinoma[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Zhu et al. profiled gene expression in healthy controls (n = 7) and patients with NAFLD (n = 40) by microarray. They identified that the expression of 92 splicing factor genes, including SRSF11, was altered[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Small nuclear ribonucleoprotein U1 subunit 70 (SNRNP70) is one of the components of the U1 small nuclear ribonucleoprotein (snRNP), which is essential for recognition of the pre-mRNA 5' splice-site and the subsequent assembly of the spliceosome. Aberrant expression of SNRNP70 has been observed in various cancers and is closely associated with tumor progression. The study revealed down-regulation of SNRNP70 expression significantly inhibited the proliferation and migration of HCC cells[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. ZC3H13 is a regulator of m6A methylation. Downregulation of ZC3H13 was associated with a poor prognosis and adverse outcomes in hepatocellular carcinoma[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The mean m6A levels were significantly lower in T2D patients compared to normal controls (SMD = -1.35, 95% CI: -2.58 to -0.11), indicating that ZC3H13 may play a crucial role in the pathogenesis of T2D[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The study by Yang demonstrated that TAF15 exacerbates NASH progression by regulating lipid metabolism and inflammation through the transcriptional activation of FASN and interaction with p65 to activate the NF-κB signaling pathway[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. SMARCA4, the core subunit of the SWI/SNF chromatin remodeling complex, has been proved to associated with NASH by promoting liver fibrosis[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In addition, some findings demonstrate that SMARCA4 alleviated oxidative stress and apoptosis, which played a potential role in the pathogenesis of diabetic retinopathy[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e–\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. HNRNPD, also known as AUF1, is a multifunctional RNA-binding protein involved in mRNA regulation and protein stability during inflammatory responses[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Studies have shown that knockdown of HNRNPD reduces apoptosis induced by glucose deprivation, while its overexpression has the opposite effect[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Furthermore, HNRNPD plays a role in liver dedifferentiation, development, and the progression of human hepatocellular carcinoma (HCC)[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong our enrichment results, multiple types of RNA splicing were found to be predominantly associated with these two diseases. Alternative RNA splicing is a process by which introns are removed and exons are assembled to generate different mRNA isoforms from a single pre-mRNA[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Recent studies have highlighted the close relationship between the pathogenesis of NASH and dysregulation of the RNA splicing machinery[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Notably, most of the altered mRNAs or splicing factors are associated with lipid metabolism, gluconeogenesis, and fibrosis, which may contribute to disease progression, inflammation, and fibrosis in NASH[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Additionally, several genes linked to obesity and insulin resistance, such as ANO1, HNF-1α, IPF-1, GCK, SUR1, TCF7L2, VEGF, and NOVA1, are regulated by RNA splicing[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e–\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur findings identified six common miRNAs for both T2DM and NASH, including hsa-miR-361-5p, hsa-miR-520e, hsa-miR-320b, hsa-miR-595, hsa-miR-610 and hsa-miR-498. miRNAs are small non-coding RNAs that regulate gene expression at the post-transcriptional level and are involved in various cellular processes such as cell proliferation, inflammation, and apoptosis. Several studies have reported that various miRNAs are associated with the occurrence and development of NASH and T2DM. Alterations in miRNA activity can lead to hepatocyte injury, apoptosis, and fibrosis in the liver[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In NAFLD, the antagonism of specific miRNAs can decrease fatty acid synthesis and increase fatty acid oxidation in the liver. miR-361-5p was overexpressed in the livers of obese mouse models and NAFLD patients. Overexpression of miR-361-5p resulted in hepatosteatosis, while inhibition of miR-361-5p expression alleviated triglyceride (TG) accumulation and improved insulin sensitivity[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. A study by Katherine Johnson et al. found that miR-320b was significantly upregulated in NAFLD patients compared to healthy controls[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Moreover, Wang Y reported that placental miR-320b levels were positively correlated with 1-hour and 2-hour glucose levels during a 75 g oral glucose tolerance test (OGTT) in human pregnancies. Furthermore, overexpression of miR-320 impaired insulin secretion and increased apoptosis in MIN6 cells and islets from mice with normal insulin sensitivity[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] miR-595 was proved to downregulate in HCC. Using both in vitro and in vivo experiments, Wang H et al. demonstrated that miR-595 significantly suppressed the proliferation and metastasis of HCC cells by inhibiting the NF-κB signaling pathway[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Hsa-miR-520e, miR-610 and miR-498 have been confirmed to be involved in multiple cancers[\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e–\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], however, there are no relevant studies on its relationship with T2DM and NASH, which is worthy of further studies.\u003c/p\u003e \u003cp\u003eMAPK1, U2AF1, SNRNP70, ZC3H13, TAF15, SMARCA4, HNRNPD, SRSF3, and SRSF11 are nine hub genes identified for both T2DM and NASH. These findings provide new approaches and potential targets for the treatment of the co-occurrence of these two diseases in the future. Moreover, a potential relationship between the common miRNAs (hsa-miR-320b and hsa-miR-595) and differentially expressed genes (DEGs) such as SRSF3, HNRNPD, and ZC3H13 was demonstrated. Further experiments will be conducted to verify how hsa-miR-320b and hsa-miR-595 play significant roles in the pathogenesis of both T2DM and NASH by regulating these DEGs.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eIn summary, we investigated the potential comorbidity mechanisms of T2DM and NASH through bioinformatics analysis, aiming to elucidate the relationship between these two diseases using a population-based case-control study. However, this study has certain limitations, and further experiments as well as an increased sample size would be necessary to validate our findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis research has no funding support.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYJ.Sha and ZL.Liang wrote the main manuscript textZF.Yan participated in the design of the study and performed the bioinformatics analysisY.Sha conceived of the study and helped to draft the manuscript PP.Liu , WB.Wei and PJ.Liu participated in the discussion and revised the manuscriptMY.Li was responsible for the overall planning and design of the research, oversaw its supervision and coordination, revised the manuscript, and provided technical guidance.All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe gene expression profiles of GSE7014, GSE29231, GSE17470 and GSE24807 were downloaded from the Gene Expression Omnibus (GEO) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE7014\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE7014\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29231\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29231\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17470\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17470\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE24807\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE24807\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe non-coding RNA profiles of GSE185845 and GSE33857 were downloaded from GEO(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE185845\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE185845\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33857\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33857\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLu, X. et al. 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Int.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, 294. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12935-020-01334-4\u003c/span\u003e\u003cspan address=\"10.1186/s12935-020-01334-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Type 2 Diabetes Mellitus Non-Alcoholic Steatohepatitis Differentially Expressed Genes MicroRNAs Bioinformatics Analysis","lastPublishedDoi":"10.21203/rs.3.rs-6211779/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6211779/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInvestigating the comorbidity mechanisms of Type 2 Diabetes Mellitus (T2DM) and Non-Alcoholic Steatohepatitis (NASH) using bioinformatics analysis. T2DM and NASH are significant global health challenges, often coexisting and exacerbating each other's pathophysiology. Our study aims to elucidate the underlying mechanisms linking these two conditions by identifying common differentially expressed genes (DEGs) and microRNAs (miRNAs) through the analysis of publicly available gene expression datasets from the Gene Expression Omnibus (GEO).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Gene expression datasets related to T2DM and NASH were retrieved from GEO. Differentially expressed genes were identified using the GEO2R tool, and common DEGs were determined through Venn diagram analysis. Functional enrichment analysis was performed using the R package \"clusterProfiler,\" and protein-protein interaction (PPI) networks were constructed using STRING. Hub genes were identified using Cytohubba in Cytoscape. Transcription factors (TFs) were predicted using the TRRUST database, and common miRNAs were identified using the R package \"edgeR\" and GEO2R. The miRNAs-mRNAs regulatory network was established by integrating common DEGs and predicted miRNAs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A total of 129 common DEGs were identified, including 20 downregulated and 109 upregulated genes. Enrichment analysis revealed that these DEGs were involved in biological processes such as peptidyl-serine modification, RNA splicing, and cellular response to nutrient levels. Nine hub genes were identified: MAPK1, U2AF1, SNRNP70, ZC3H13, TAF15, SMARCA4, HNRNPD, SRSF3, and SRSF11. These genes were associated with pathways related to RNA splicing, and metabolic regulation. Six common miRNAs (hsa-miR-361-5p, hsa-miR-520e, hsa-miR-320b, hsa-miR-595, hsa-miR-610, and hsa-miR-498) were identified, which were involved in cell cycle regulation, angiogenesis, and inflammation. The miRNAs-mRNAs network showed interactions between these miRNAs and three important genes: SRSF3, HNRNPD, and ZC3H13.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eour study provides insights into the comorbidity mechanisms of T2DM and NASH through bioinformatics analysis. The identified hub genes and miRNAs offer potential therapeutic targets for future research.\u003c/p\u003e","manuscriptTitle":"Exploring the Molecular Mechanisms Underlying the Comorbidity of Type 2 Diabetes Mellitus and Nonalcoholic Steatohepatitis: A Bioinformatics Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-02 14:39:19","doi":"10.21203/rs.3.rs-6211779/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b48578f5-fdbe-4fea-b1f3-337c0b6e6487","owner":[],"postedDate":"April 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46394030,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":46394031,"name":"Health sciences/Endocrinology"},{"id":46394032,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2025-10-07T14:24:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-02 14:39:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6211779","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6211779","identity":"rs-6211779","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}