Characteristic gene prognostic model of type 1 diabetes mellitus via machine learning strategy

Previous studies have shown that asthma is a risk factor for lung cancer, while the mechanisms involved remain unclear. We attempted to further explore the association between asthma and non-small cell lung cancer (NSCLC) via bioinformatics analysis. We obtained GSE143303 and GSE18842 from the GEO database. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) groups were downloaded from the TCGA database. Based on the results of differentially expressed genes (DEGs) between asthma and NSCLC, we determined common DEGs by constructing a Venn diagram. Enrichment analysis was used to explore the common pathways of asthma and NSCLC. A protein-protein interaction (PPI) network was constructed to screen hub genes. KM survival analysis was performed to screen prognostic genes in the LUAD and LUSC groups. A Cox model was constructed based on hub genes and validated internally and externally. Tumor Immune Estimation Resource (TIMER) was used to evaluate the association of prognostic gene models with the tumor microenvironment (TME) and immune cell infiltration. Nomogram model was constructed by combining prognostic genes and clinical features. 114 common DEGs were obtained based on asthma and NSCLC data, and enrichment analysis showed that significant enrichment pathways mainly focused on inflammatory pathways. Screening of 5 hub genes as a key prognostic gene model for asthma progression to LUAD, and internal and external validation led to consistent conclusions. In addition, the risk score of the 5 hub genes could be used as a tool to assess the TME and immune cell infiltration. The nomogram model constructed by combining the 5 hub genes with clinical features was accurate for LUAD. Five-hub genes enrich our understanding of the potential mechanisms by which asthma contributes to the increased risk of lung cancer.

BackgroundPolycystic ovary syndrome (PCOS) and type 2 diabetes mellitus (T2DM) are two prevalent and interrelated disorders that pose an increasingly significant global health burden. Cellular senescence may represent a pivotal process driving the progression of both conditions. Senescent cells, through the senescence-associated secretory phenotype (SASP), can induce chronic inflammation, which is highly likely to exacerbate the pathological progression of PCOS and T2DM. However, the molecular pathways linking cellular senescence to PCOS and T2DM have not yet been systematically elucidated.MethodsThe transcriptome datasets of PCOS (GSE54248) and T2DM (GSE23561) were obtained from the GEO database, and differentially expressed genes (DEGs) were screened using the limma package. Age-related DEGs (ARDEGs) were obtained by intersecting DEGs with age-related genes, and the protein-protein interaction (PPI) network was constructed based on the STRING database. Hub genes with diagnostic value were determined via the Wilcoxon rank sum test and receiver operating characteristic (ROC) curve. CIBERSORT was used to analyze the infiltration characteristics of immune cells, and the functions of the hub gene were analyzed by gene set enrichment analysis (GSEA). Single-cell sequencing was used to locate gene expression patterns, and qRT–PCR was used to verify the expression of candidate genes in clinical samples.Results80 DEGs between PCOS and T2DM samples were obtained, and 15 ARDEGs were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that they were related to inflammatory response and immune response, and were involved in specific functions and pathways. Four hub genes were identified: TUBA4A, RTN1, G6PD, and HP. qRT–PCR experimental results showed that HP, G6PD, TUBA4A, and RTN1 were highly expressed in the peripheral blood of PCOS and T2DM patients, compared to healthy people.DiscussionThis study revealed the potential connections between PCOS, T2DM, and aging-related molecular networks and signaling pathways and discovered multiple potential therapeutic targets. It provides new intervention directions for clinicians, especially based on aging mechanisms.

Type 2 diabetes mellitus (T2DM) and cancer are highly prevalent diseases imposing major health burden globally. Several epidemiological studies indicate increased susceptibility to cancer in T2DM patients. However, genetic factors linking T2DM with cancer have been poorly studied. In this study, we followed computational approaches using the raw gene expression data of peripheral blood mononuclear cells of T2DM and cancer patients available in the gene expression omnibus (GEO) database. Our analysis identified shared differentially expressed genes (DEGs) in T2DM and three common cancer types, namely, pancreatic cancer (PC), liver cancer (LC), and breast cancer (BC). The functional and pathway enrichment analysis of identified common DEGs highlighted the involvement of critical biological pathways, including cell cycle events, immune system processes, cell morphogenesis, gene expression, and metabolism. We retrieved the protein–protein interaction network for the top DEGs to deduce molecular-level interactions. The network analysis found 7, 6, and 5 common hub genes in T2DM vs. PC, T2DM vs. LC, and T2DM vs. BC comparisons, respectively. Overall, our analysis identified important genetic markers potentially able to predict the chances of PC, LC, and BC onset in T2DM patients.

Type 2 diabetes mellitus (T2DM) and cancer are highly prevalent diseases imposing major health burden globally. Several epidemiological studies indicate increased susceptibility to cancer in T2DM patients. However, genetic factors linking T2DM with cancer have been poorly studied. In this study, we followed computational approaches using the raw gene expression data of peripheral blood mononuclear cells of T2DM and cancer patients available in the gene expression omnibus (GEO) database. Our analysis identified shared differentially expressed genes (DEGs) in T2DM and three common cancer types, namely, pancreatic cancer (PC), liver cancer (LC), and breast cancer (BC). The functional and pathway enrichment analysis of identified common DEGs highlighted the involvement of critical biological pathways, including cell cycle events, immune system processes, cell morphogenesis, gene expression, and metabolism. We retrieved the protein–protein interaction network for the top DEGs to deduce molecular-level interactions. The network analysis found 7, 6, and 5 common hub genes in T2DM vs. PC, T2DM vs. LC, and T2DM vs. BC comparisons, respectively. Overall, our analysis identified important genetic markers potentially able to predict the chances of PC, LC, and BC onset in T2DM patients.

Identification and verification of key genes related to oxidative stress in type 2 diabetes and screening of candidate drugs from Traditional Chinese Medicine.

To clarify the expression of N6-methyladenosine (m6 A) modulators involved in the pathogenesis of type 2 diabetes mellitus (T2DM). We further explored the association of serum insulin-like growth factor 2 mRNA-binding proteins 3 (IGF2BP3) levels and odds of T2DM in a high-risk population. The gene expression data set GSE25724 was obtained from the Gene Expression Omnibus, and a cluster heatmap was generated by using the R package ComplexHeatmap. Differential expression analysis for 13 m6 A RNA methylation regulators between nondiabetic controls and T2DM subjects was performed using an unpaired t test. A cross-sectional design, including 393 subjects (131 patients with newly diagnosed T2DM, 131 age- and sex-matched subjects with prediabetes, and 131 healthy controls), was carried out. The associations between serum IGF2BP3 concentrations and T2DM were modeled by restricted cubic spline and logistic regression models. Two upregulated (IGF2BP2 and IGF2BP3) and 5 downregulated (methyltransferase-like 3 [METTL3], alkylation repair homolog protein 1 [ALKBH1], YTH domain family 2 [YTHDF2], YTHDF3, and heterogeneous nuclear ribonucleoprotein [HNRNPC]) m6 A-related genes were found in islet samples of T2DM patients. A U-shaped association existed between serum IGF2BP3 levels and odds of T2DM according to cubic natural spline analysis models, after adjustment for body mass index, waist circumference, diastolic blood pressure, total cholesterol, and triglyeride. Multivariate logistic regression showed that progressively higher odds of T2DM were observed when serum IGF2BP3 levels were below 0.62 ng/mL (odds ratio 3.03 [95% confidence interval 1.23-7.47]) in model 4. Seven significantly altered m6 A RNA methylation genes were identified in T2DM. There was a U-shaped association between serum IGF2BP3 levels and odds of T2DM in the general Chinese adult population. This study provides important evidence for further examination of the role of m6 A RNA methylation, especially serum IGF2BP3 in T2DM risk assessment.

Investigating the molecular mechanisms between type 1 diabetes and mild cognitive impairment using bioinformatics analysis, with a focus on immune response

To study the expression of CD14 + CD16 + monocytes and VEGF and the levels of serum adiponectin and MCP-1 in peripheral blood of patients with type 2 diabetes mellitus (T2DM) and diabetic macroangiopathy to understand the possible mechanism of inflammatory immune response in T2DM and diabetic macroangiopathy. Peripheral blood CD14 + CD16 + monocytes were detected by flow cytometry in 50 T2DM patients, 50 patients with diabetic macroangiopathy, and 20 healthy controls or normal controls who participated in outpatient physical examination, and used the Ficoll-Hypaque density gradient centrifugation isolated PBMC and quantitative PCR technology comparison between groups research object in the peripheral blood PBMC VEGF mRNA expression level. Serum levels of adiponectin and MCP-1 were measured by ELISA. Compared with normal control group (NGT), the fluorescence intensity of proinflammatory CD14 + CD16 + monocytes in simple T2DM group and T2DM combined with macroangiopathy group were significantly increased (p T2DM patients> healthy volunteers, the differences were significant (p < 0.05). Compared with NGT group, the levels of serum adiponectin in T2DM group and T2DM combined with macroangiopathy group were significantly lower than those in NGT group (p < 0.01), and the levels of serum adiponectin, simple T2DM Group were lower than T2DM combined with macroangiopathy group (p < 0.05). The level of MCP-1 in serum compared with simple T2DM group and NGT group, T2DM combined with macroangiopathy group had statistically significant difference (p < 0.05). The serum level of MCP-1 in T2DM group was also higher than that in NGT group (p < 0.05). At the same time, we also found that the increase of CD14 + CD16 + monocytes was positively correlated with serum MCP-1 levels. T2DM patients and T2DM combined with macroangiopathy patients have increased expression of VEGF and MCP-1 concentration in peripheral blood mononuclear cells. The increase of MCP-1 may increase the number of CD14 + CD16 + monocytes, which is involved in the chronic inflammation in patients with T2DM and T2DM combined with macroangiopathy, resulting in the occurrence and development of T2DM and its complications.

The etiology of type 1 diabetes mellitus (T1DM) in pediatric populations remains poorly understood. The key to precise prevention and treatment of T1DM in identifying crucial pathogenic genes. These key pathogenic genes can serve as biological markers for early diagnosis and classification, as well as therapeutic targets. However, there is currently a lack of relevant research on screening key pathogenic genes based on sequencing data and efficient algorithms. The transcriptome sequencing results of peripheral blood mononuclear cells (PBMCs) of children with T1DM (GSE156035) were downloaded from the Gene Expression Omnibus (GEO) database. The data set contained 20 T1DM samples and 20 control samples. Differentially expressed genes (DEGs) in children with T1DM were selected based on fold change (FC) >1.5 times and adjusted P value <0.05. The weighted gene co-expression network was constructed. Hub genes were screened as modular membership (MM) >0.8 and gene significance (GS) >0.5. Intersection genes of DEGs and hub genes were defined as key pathogenic genes. The diagnostic efficacy of key pathogenic genes was evaluated using receiver operator characteristic (ROC) curves. A total of 293 DEGs were selected. Compared with the control group, 94 genes were down-regulated and 199 genes were up-regulated in the treatment group. Black modules (Cor =0.52, P=2e-12) were positively correlated with diabetic traits, whereas brown modules (Cor =-0.51, P=5e-12) and pink modules (Cor =-0.53, P=5e-13) were negatively correlated with diabetic traits. The black module contained 15 hub genes, the pink gene module contained 9 hub genes, and the brown module contained 52 hub genes. The intersection of hub genes and DEGs contained 2 genes, CCL25 and EGFR. The expression of CCL25 and EGFR was low in control samples and high in the test group (P<0.001). The areas under ROC curves (AUCs) of CCL25 and EGFR were 0.852 and 0.867, respectively (P<0.05). Weighted correlation network analysis (WGCNA) was used to identify the key pathogenic genes of T1DM in children, including CCL25 and EGFR, which have good diagnostic efficacy for T1DM in children.

Immune cell-mediated chronic inflammation is one of the causes of type 2 diabetes mellitus (T2DM). Therefore, identifying inflammatory markers in circulating immune cells is highly important for predicting insulin resistance (IR) and the occurrence of T2DM. In this study, we discovered that differentially expressed genes (DEGs) in peripheral blood mononuclear cells (PBMCs) from T2DM patients were associated with innate immunity and chronic inflammatory responses through bulk transcriptome sequencing (bulk RNA-seq). Gene integration analysis revealed that nine DEGs were upregulated, and receiver operating characteristic (ROC) curve analysis revealed that V-maf musculoaponeurotic fibrosarcoma oncogene homolog B (MAFB), a candidate biomarker, has a certain predictive value for T2DM. In population-based cohort studies, we found that MAFB expression was significantly upregulated in the PBMCs of T2DM patients and was significantly correlated with homeostasis model assessment of IR (HOMA-IR), tumor necrosis factor-α (TNF-α), adiponectin (Adipoq), etc. We further evaluated the sensitivity and specificity of MAFB and other clinical parameters for predicting and diagnosing T2DM and found that MAFB expression in PBMCs had a positive effect on the prediction and diagnosis of T2DM. Finally, single-cell RNA sequencing (scRNA-seq) analysis revealed that the increase in MAFB expression was mainly in nonclassical monocytes. Our results suggest that increased MAFB expression in circulating monocytes may mediate chronic inflammatory status in patients with T2DM. Therefore, MAFB gene expression in circulating monocytes has certain clinical significance for predicting and assisting in the diagnosis of T2DM.

Background The onset and progression of type 1 diabetes mellitus (T1DM) is closely related to autoimmunity. Effective monitoring of the immune system and developing targeted therapies are frontier fields in T1DM treatment. Currently, the most available tissue that reflects the immune system is peripheral blood mononuclear cells (PBMCs). Thus, the aim of this study was to identify key PBMC biomarkers of T1DM.Methods Common differentially expressed genes (DEGs) were screened from the Gene Expression Omnibus (GEO) datasets GSE9006, GSE72377, and GSE55098, and PBMC mRNA expression in T1DM patients was compared with that in healthy participants by GEO2R. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interaction (PPI) network analyses of DEGs were performed using the Cytoscape, DAVID, and STRING databases. The vital hub genes were validated by reverse transcription-polymerase chain reaction using clinical samples. The disease-gene-drug interaction network was built using the Comparative Toxicogenomics Database (CTD) and Drug Gene Interaction Database (DGIdb).Results We found that various biological functions or pathways related to the immune system and glucose metabolism changed in PBMCs from T1DM patients. In the PPI network, the DEGs of module 1 were significantly enriched in processes including inflammatory and immune responses and in pathways of proteoglycans in cancer. Moreover, we focused on four vital hub genes, namely, chitinase-3-like protein 1 (CHI3L1), C-X-C motif chemokine ligand 1 (CXCL1), matrix metallopeptidase 9 (MMP9), and granzyme B (GZMB), and confirmed them in clinical PBMC samples. Furthermore, the disease-gene-drug interaction network revealed the potential of key genes as reference markers in T1DM.Conclusion These results provide new insight into T1DM pathogenesis and novel biomarkers that could be widely representative reference indicators or potential therapeutic targets for clinical applications.

BackgroundType 1 diabetes mellitus (T1DM) is a chronic autoimmune disease caused by severe loss of pancreatic β cells. Immune cells are key mediators of β cell destruction. This study attempted to investigate the role of immune cells and immune-related genes in the occurrence and development of T1DM.MethodsThe raw gene expression profile of the samples from 12 T1DM patients and 10 normal controls was obtained from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by Limma package in R. The least absolute shrinkage and selection operator (LASSO)—support vector machines (SVM) were used to screen the hub genes. CIBERSORT algorithm was used to identify the different immune cells in distribution between T1DM and normal samples. Correlation of the hub genes and immune cells was analyzed by Spearman, and gene-GO-BP and gene-pathway interaction networks were constructed by Cytoscape plug-in ClueGO. Receiver operating characteristic (ROC) curves were used to assess diagnostic value of genes in T1DM.ResultsThe 50 immune-related DEGs were obtained between the T1DM and normal samples. Then, the 50 immune-related DEGs were further screened to obtain the 5 hub genes. CIBERSORT analysis revealed that the distribution of plasma cells, resting mast cells, resting NK cells and neutrophils had significant difference between T1DM and normal samples. Natural cytotoxicity triggering receptor 3 (NCR3) was significantly related to the activated NK cells, M0 macrophages, monocytes, resting NK cells, and resting memory CD4+ T cells. Moreover, tumor necrosis factor (TNF) was significantly associated with naive B cell and naive CD4+ T cell. NCR3 [Area under curve (AUC) = 0.918] possessed a higher accuracy than TNF (AUC = 0.763) in diagnosis of T1DM.ConclusionsThe immune-related genes (NCR3 and TNF) and immune cells (NK cells) may play a vital regulatory role in the occurrence and development of T1DM, which possibly provide new ideas and potential targets for the immunotherapy of diabetes mellitus (DM).

Type 2 diabetes mellitus (T2DM) is closely related to hepatocellular carcinoma (HCC). The pathophysiological mechanism of coexistence of T2DM and HCC is unclear. The study aimed to investigate the core genes and pathways involved in the development and progression of T2DM and HCC. Datasets for T2DM and HCC were downloaded from the GEO to screen differentially expressed genes (DEGs). Protein-protein interaction (PPI) network analysis was performed on these DEGs to explore their functions and verify hub genes. These genes were validated by quantitative real-time polymerase chain reaction (qRT-PCR) and UALCAN analysis based on The Cancer Genome Atlas (TCGA). Finally, the transcription factor (TF)-miRNA-target gene network was constructed with hub genes, and visualized using Cytoscape software 3.6.1. A total of 77 common DEGs were identified. KEGG enrichment revealed that pathways of metabolic processes are enriched in T2DM and HCC. Combining the results of MCODE and CytoHubba showed that AASS, SDS, HAL, KYNU and TDO2 were hub genes. Then, we verified the above results by UALCAN analysis and qRT-PCR. Compared with normal liver tissues, the expression levels of 5 hub genes based on tumor grade were lower in liver hepatocellular carcinoma (LIHC) tissues. mRNA levels of these genes were significantly down-regulated in HepG2 and SNU-449 compared with LO2 cells. Furthermore, we depicted the TF-miRNA-gene interaction network. This study proposed a strategy for exploring pathogenic mechanisms of T2DM and HCC. Network hub genes hold promise as disease status biomarkers and treatment targets for alleviating both T2DM and HCC.

Subclinical left ventricular (LV) dysfunction is prevalent in type 2 diabetes (T2DM). As obesity has been proposed as one causal factor in the disease process, this could bias the reported prevalences. We wanted to characterize echocardiographic LV dysfunction in obese T2DM subjects as compared to non-diabetic obese controls. One hundred patients with T2DM without clinical signs of heart failure (29% females, mean ± SD age 58.4 ± 10.5 years, body mass index (BMI) 30.1 ± 5.5 kg/m(2), blood pressure (BP) 141 ± 18/83 ± 9 mmHg) and 100 non-diabetic controls (29% females) matched for age (58.6 ± 10.5 years), BMI (29.8 ± 4.0 kg/m(2) and systolic BP (140 ± 14 mmHg) underwent echocardiography and color tissue Doppler imaging (TDI). Diastolic function was evaluated with conventional Doppler recordings and early (e') and late (a') myocardial velocities. The ratio between early transmitral filling (E) and the corresponding myocardial tissue velocity (e') served as an index of LV filling pressure. T2DM patients had more concentric hypertrophy with a relative wall thickness of 0.42 ± 0.07 vs controls 0.38 ± 0.07, P < 0.001. The T2DM group had signs of diastolic dysfunction with lower E/A ratio (0.91 ± 0.27 vs. 1.12 ± 0.38, P < 0.001), deceleration time (195 ± 49 vs 242 ± 72 ms, P < 0.001), e' (5.7 ± 2.0 vs. 6.6 ± 1.8 cm/s, P = 0.001), and a' (6.5 ± 2.0 vs. 7.6 ± 1.5 cm/s, P < 0.001) compared to the controls, and higher E/e' (13.3 ± 4.7 vs. 11.1 ± 3.5, P < 0.001). Thus, there were indications of pseudo normalization and increased filling pressure in the T2DM group, whereas the controls had evidence for relaxation abnormalities without elevated filling pressure. Compared to a non-diabetic obese group, more advanced subclinical impairment of diastolic function was seen in T2DM.

The goal of our study was to identify and assess the functionally significant SNPs with potentially important roles in the development of type 2 diabetes mellitus (T2DM) and/or their effect on individual response to antihyperglycemic medication with metformin. We applied a bioinformatics approach to identify the regulatory SNPs (rSNPs) associated with allele-asymmetric binding and expression events in our paired ChIP-seq and RNA-seq data for peripheral blood mononuclear cells (PBMCs) of nine healthy individuals. The rSNP outcomes were analyzed using public data from the GWAS (Genome-Wide Association Studies) and Genotype-Tissue Expression (GTEx). The differentially expressed genes (DEGs) between healthy and T2DM individuals (GSE221521), including metformin responders and non-responders (GSE153315), were searched for in GEO RNA-seq data. The DEGs harboring rSNPs were analyzed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). We identified 14,796 rSNPs in the promoters of 5132 genes of human PBMCs. We found 4280 rSNPs to associate with both phenotypic traits (GWAS) and expression quantitative trait loci (eQTLs) from GTEx. Between T2DM patients and controls, 3810 rSNPs were detected in the promoters of 1284 DEGs. Based on the protein-protein interaction (PPI) network, we identified 31 upregulated hub genes, including the genes involved in inflammation, obesity, and insulin resistance. The top-ranked 10 enriched KEGG pathways for these hubs included insulin, AMPK, and FoxO signaling pathways. Between metformin responders and non-responders, 367 rSNPs were found in the promoters of 131 DEGs. Genes encoding transcription factors and transcription regulators were the most widely represented group and many were shown to be involved in the T2DM pathogenesis. We have formed a list of human rSNPs that add functional interpretation to the T2DM-association signals identified in GWAS. The results suggest candidate causal regulatory variants for T2DM, with strong enrichment in the pathways related to glucose metabolism, inflammation, and the effects of metformin.