Crosstalk Genes Research Articles

Integrated machine learning to predict the prognosis of lung adenocarcinoma patients based on SARS-COV-2 and lung adenocarcinoma crosstalk genes.

Viruses are widely recognized to be intricately associated with both solid and hematological malignancies in humans. The primary goal of this research is to elucidate the interplay of genes between SARS-CoV-2 infection and lung adenocarcinoma (LUAD), with a preliminary investigation into their clinical significance and underlying molecular mechanisms. Transcriptome data for SARS-CoV-2 infection and LUAD were sourced from public databases. Differentially expressed genes (DEGs) associated with SARS-CoV-2 infection were identified and subsequently overlapped with TCGA-LUAD DEGs to discern the crosstalk genes (CGs). In addition, CGs pertaining to both diseases were further refined using LUAD TCGA and GEO datasets. Univariate Cox regression was conducted to identify genes associated with LUAD prognosis, and these genes were subsequently incorporated into the construction of a prognosis signature using 10 different machine learning algorithms. Additional investigations, including tumor mutation burden assessment, TME landscape, immunotherapy response assessment, as well as analysis of sensitivity to antitumor drugs, were also undertaken. We discovered the risk stratification based on the prognostic signature revealed that the low-risk group demonstrated superior clinical outcomes (p < 0.001). Gene set enrichment analysis results predominantly exhibited enrichment in pathways related to cell cycle. Our analyses also indicated that the low-risk group displayed elevated levels of infiltration by immunocytes (p < 0.001) and superior immunotherapy response (p < 0.001). In our study, we reveal a close association between CGs and the immune microenvironment of LUAD. This provides preliminary insight for further exploring the mechanism and interaction between the two diseases.

Unveiling the genetic link and pathogenesis between psoriasis and IgA nephropathy based on Mendelian randomization and transcriptome data analyses.

It has been reported that many people with psoriasis have been diagnosed with secondary IgA nephropathy (IgAN). However, the mechanisms behind the association between psoriasis and IgAN have not been well clarified. The connection between psoriasis and IgAN deserves deeper exploration. Mendelian randomization (MR) analysis would be employed to explore the link of causality between IgAN and psoriasis, psoriasis vulgaris, other and unspecified psoriasis, guttate psoriasis, and arthropathic psoriasis. Transcriptomic analyses were carried out against the Gene Expression Omnibus databases. We identified crosstalk genes through the analysis of Differentially expressed genes and weight gene co-expression network analysis. Functional annotations were enriched for these crosstalk genes. Subsequently, we established a protein-protein interaction network, and candidate genes would be discovered through the utilization of the MCODE and CytoHubba plug-in applications. Lastly, the predictive efficacy of these genes was examined via creating receiver operating characteristic curves. The MR analysis suggested that psoriasis vulgaris patients were at a higher risk for IgAN. [OR = 1.040, 95%CI (1.005,1.076), p = 0.026 < 0.05]. Additionally, arthropathic psoriasis may augment the incidence of IgAN [OR = 1.081, 95%CI (1.040-1.124), p < 0.01] in the European population. Through the analysis of DEGs and WGCNA, we identified 12 significant genes (NETO2, RRM2, SLAMF7, GBP1, KIF20A, CCL4, MMP1, IL1β, NDC80, CXCL9, C15orf48, GSTA3), which may be potential crosstalk genes between the two diseases. Then, the functional annotation results indicated that the crosstalk genes seemed primarily involved in immune and inflammatory responses. By establishing the PPI network, we further discovered that CXCL9, IL1β, CCL4, and MMP1 play a vital part in psoriasis and IgAN, and all have good diagnostic values. Our MR analysis provided evidence that genetic vulnerability to IgAN may be associated with an elevated risk of psoriasis vulgaris and arthropathic psoriasis respectively among Europeans. Doctors should be aware of these associations when patients with psoriasis present with renal dysfunction, especially those with psoriasis vulgaris and arthropathic psoriasis. Chronic inflammation, drug effects, and immunity may contribute to the generation and development of both diseases. IL1β, CXCL9, CCL4, and MMP1 may be core biomarkers for psoriasis and IgAN.

Exploring the transcriptomic landscape of moyamoya disease and systemic lupus erythematosus: insights into crosstalk genes and immune relationships.

Systemic Lupus Erythematosus (SLE) is acknowledged for its significant influence on systemic health. This study sought to explore potential crosstalk genes, pathways, and immune cells in the relationship between SLE and moyamoya disease (MMD). We obtained data on SLE and MMD from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were conducted to identify common genes. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on these shared genes. Hub genes were further selected through the least absolute shrinkage and selection operator (LASSO) regression, and a receiver operating characteristic (ROC) curve was generated based on the results of this selection. Finally, single-sample Gene Set Enrichment Analysis (ssGSEA) was utilized to assess the infiltration levels of 28 immune cells in the expression profile and their association with the identified hub genes. By intersecting the important module genes from WGCNA with the DEGs, the study highlighted CAMP, CFD, MYO1F, CTSS, DEFA3, NLRP12, MAN2B1, NMI, QPCT, KCNJ2, JAML, MPZL3, NDC80, FRAT2, THEMIS2, CCL4, FCER1A, EVI2B, CD74, HLA-DRB5, TOR4A, GAPT, CXCR1, LAG3, CD68, NCKAP1L, TMEM33, and S100P as key crosstalk genes linking SLE and MMD. GO analysis indicated that these shared genes were predominantly enriched in immune system process and immune response. LASSO analysis identified MPZL3 as the optimal shared diagnostic biomarkers for both SLE and MMD. Additionally, the analysis of immune cell infiltration revealed the significant involvement of activation of T and monocytes cells in the pathogenesis of SLE and MMD. This study is pioneering in its use of bioinformatics tools to explore the close genetic relationship between MMD and SLE. The genes CAMP, CFD, MYO1F, CTSS, DEFA3, NLRP12, MAN2B1, NMI, QPCT, KCNJ2, JAML, MPZL3, NDC80, FRAT2, THEMIS2, CCL4, FCER1A, EVI2B, CD74, HLA-DRB5, TOR4A, GAPT, CXCR1, LAG3, CD68, NCKAP1L, TMEM33, and S100P have been identified as key crosstalk genes that connect MMD and SLE. Activation of T and monocytes cells-mediated immune responses are proposed to play a significant role in the association between MMD and SLE.

Identification of cross-talk pathways and PANoptosis-related genes in periodontitis and Alzheimer's disease by bioinformatics analysis and machine learning.

Periodontitis (PD), a chronic inflammatory disease, is a serious threat to oral health and is one of the risk factors for Alzheimer's disease (AD). A growing body of evidence suggests that the two diseases are closely related. However, current studies have not provided a comprehensive understanding of the common genes and common mechanisms between PD and AD. This study aimed to screen the crosstalk genes of PD and AD and the potential relationship between cross-talk and PANoptosis-related genes. The relationship between core genes and immune cells will be analyzed to provide new targets for clinical treatment. The PD and AD datasets were downloaded from the GEO database and differential expression analysis was performed to obtain DEGs. Overlapping DEGs had cross-talk genes linking PD and OP, and PANoptosis-related genes were obtained from a literature review. Pearson coefficients were used to compute cross-talk and PANoptosis-related gene correlations in the PD and AD datasets. Cross-talk genes were obtained from the intersection of PD and AD-related genes, protein-protein interaction(PPI) networks were constructed and cross-talk genes were identified using the STRING database. The intersection of cross-talk and PANoptosis-related genes was defined as cross-talk-PANoptosis genes. Core genes were screened using ROC analysis and XGBoost. PPI subnetwork, gene-biological process, and gene-pathway networks were constructed based on the core genes. In addition, immune infiltration on the PD and AD datasets was analyzed using the CIBERSORT algorithm. 366 cross-talk genes were overlapping between PD DEGs and AD DEGs. The intersection of cross-talk genes with 109 PANoptosis-related genes was defined as cross-talk-PANoptosis genes. ROC and XGBoost showed that MLKL, DCN, IL1B, and IL18 were more accurate than the other cross-talk-PANoptosis genes in predicting the disease, as well as better in overall characterization. GO and KEGG analyses showed that the four core genes were involved in immunity and inflammation in the organism. Immune infiltration analysis showed that B cells naive, Plasma cells, and T cells gamma delta were significantly differentially expressed in patients with PD and AD compared with the normal group. Finally, 10 drugs associated with core genes were retrieved from the DGIDB database. This study reveals the joint mechanism between PD and AD associated with PANoptosis. Analyzing the four core genes and immune cells may provide new therapeutic directions for the pathogenesis of PD combined with AD.

Identification and functional analysis of genes selected using different chemometric techniques on multiarray expression data of liver from high-fat diet treated mice

The prevalence of obesity is increasing, and this lifestyle disease is related to a high-fat diet, a surplus in caloric intake, and increased inflammation. This study aimed to use a publicly available dataset of microarray gene expression data from the liver of high-fat diet fed mice (GSE39549) to determine the functional importance of small subsets of the overall genes. The regulatory aspects of the chosen mice genes were extrapolated to human genes for the determination of potential diagnostic and therapeutic targets. The chemometric approaches of principal component analysis (PCA), random forest (RF), and genetic algorithm (GA) were used as data reduction techniques to select 50 genes from a total of 15,000 genes to differentiate liver samples from high-fat diet and normal diet-fed mice. A subset of 30 genes from each of the techniques were processed with classification techniques of k-nearest neighbor and support vector machines. The results showed that random forest was best at differentiating the samples and GA was the least accurate. The results of functional annotation and protein-protein interactions showed that genes selected by PCA and RF were more associated with obesity as they identified functions related to inflammatory processes, as well as lipid and cholesterol metabolic processes. The genes selected by GA identified processes related to cilium and cell projection. The proteins identified by RF, such as Msmo and Sqle, had roles in cholesterol metabolic and biosynthetic processes. The results showed that combining the genes selected by PCA and RF allowed a better understanding of the overall functional protein modules. The crosstalk genes such as Abcg5 as well as Abcg8 that relate cholesterol metabolic and biosynthetic process to glutathione metabolic process were identified. Various miRNAs-gene interactions are present in humans for most of the genes identified by PCA, RF, or GA. Some genes that showed fewer interactions with human miRNAs are CIDEA, PLIN4, and NME8. The results suggest the use of different chemometric analyses in combination with functional genomics to identify different sets of targets for diagnostic, therapeutic, and future research.

Transcriptome analysis to explore the mechanism of downregulated TNIK influencing the effect of risperidone.

Risperidone is one of the most reliable and effective antipsychotics for schizophrenia treatment. However, the mechanism of action of risperidone is not yet fully understood. Traf2 and Nck-interacting protein kinase (TNIK), a schizophrenia susceptibility gene, is associated with risperidone treatment response. Our previous in vitro experiments confirmed that downregulated TNIK affected the effect of risperidone on downstream targets. However, the effect of downregulated TNIK on risperidone-induced molecular expression remains to be further explored. Transcriptome analysis was performed on U251 cells subjected to risperidone, TNIK siRNA, and no treatment, respectively. Compared to the no-treatment group, two groups of DEGs were screened out and then intersected with the schizophrenia-related genes to screen the cross-talk genes. Those DEGs were analyzed using GO and KEGG. STRING and Cytoscape were used to construct a protein-protein interaction (PPI) network for the cross-talk gene. The results showed that the parathyroid hormone synthesis, secretion, and action were significantly enriched after risperidone treatment. Downregulated TNIK could have an impact on the collagen-containing extracellular matrix, signaling receptor activator activity, and PI3K-Akt signaling pathway. Interestingly, bone mineralization function and calcium signaling pathway were enriched in the cross-talk genes. Additionally, FGFR2, FGF1, and FGFR might be the potential targets for TNIK affecting the effects of risperidone. The study indicated that risperidone primarily influences functions and/or pathways associated with bone metabolism, potentially contributing to the adverse effect of osteoporosis. Our study may offer a novel perspective on investigating the mechanisms underlying the adverse effects of risperidone.

Exploring the crosstalk molecular mechanisms between IgA nephropathy and Sjögren’s syndrome based on comprehensive bioinformatics and immunohistochemical analyses

IgA nephropathy (IgAN) and Sjogren's syndrome (SS) are two autoimmune diseases with undetermined etiology and related to abnormal activation of lymphocytes. This study aims to explore the crucial genes, pathways and immune cells between IgAN and SS. Gene expression profiles of IgAN and SS were obtained from the Gene Expression Omnibus and Nephroseq data. Differentially expressed gene (DEG) and weighted gene co-expression network analyses (WGCNA) were done to identify common genes. Enrichment analysis and protein–protein interaction network were used to explore potential molecular pathways and crosstalk genes between IgAN and SS. The results were further verified by external validation and immunohistochemistry (IHC) analysis. Additionally, immune cell analysis and transcription factor prediction were also conducted. The DEG analysis revealed 28 commonly up-regulated genes, while WGCNA identified 98 interactively positive-correlated module genes between IgAN and SS. The enrichment analysis suggested that these genes were mainly involved in the biological processes of response to virus and antigen processing and presentation. The external validation and IHC analysis identified 5 hub genes (PSMB8, PSMB9, IFI44, ISG15, and CD53). In the immune cell analysis, the effector memory CD8 T and T follicular helper cells were significantly activated, and the corresponding proportions showed positively correlations with the expressions of the 5 hub genes in the two autoimmune diseases. Together, our data identified the crosstalk genes, molecular pathways, and immune cells underlying the IgAN and SS, which provides valuable insights into the intricate mechanisms of these diseases and offers potential intervention targets.

Identification of crosstalk genes and immune characteristics between Alzheimer's disease and atherosclerosis.

Advancements in modern medicine have extended human lifespan, but they have also led to an increase in age-related diseases such as Alzheimer's disease (AD) and atherosclerosis (AS). Growing research evidence indicates a close connection between these two conditions. We downloaded four gene expression datasets related to AD and AS from the Gene Expression Omnibus (GEO) database (GSE33000, GSE100927, GSE44770, and GSE43292) and performed differential gene expression (DEGs) analysis using the R package "limma". Through Weighted gene correlation network analysis (WGCNA), we selected the gene modules most relevant to the diseases and intersected them with the DEGs to identify crosstalk genes (CGs) between AD and AS. Subsequently, we conducted functional enrichment analysis of the CGs using DAVID. To screen for potential diagnostic genes, we applied the least absolute shrinkage and selection operator (LASSO) regression and constructed a logistic regression model for disease prediction. We established a protein-protein interaction (PPI) network using STRING (https://cn.string-db.org/) and Cytoscape and analyzed immune cell infiltration using the CIBERSORT algorithm. Additionally, NetworkAnalyst (http://www.networkanalyst.ca) was utilized for gene regulation and interaction analysis, and consensus clustering was employed to determine disease subtypes. All statistical analyses and visualizations were performed using various R packages, with a significance level set at p<0.05. Through intersection analysis of disease-associated gene modules identified by DEGs and WGCNA, we identified a total of 31 CGs co-existing between AD and AS, with their biological functions primarily associated with immune pathways. LASSO analysis helped us identify three genes (C1QA, MT1M, and RAMP1) as optimal diagnostic CGs for AD and AS. Based on this, we constructed predictive models for both diseases, whose accuracy was validated by external databases. By establishing a PPI network and employing four topological algorithms, we identified four hub genes (C1QB, CSF1R, TYROBP, and FCER1G) within the CGs, closely related to immune cell infiltration. NetworkAnalyst further revealed the regulatory networks of these hub genes. Finally, defining C1 and C2 subtypes for AD and AS respectively based on the expression profiles of CGs, we found the C2 subtype exhibited immune overactivation. This study utilized gene expression matrices and various algorithms to explore the potential links between AD and AS. The identification of CGs revealed interactions between these two diseases, with immune and inflammatory imbalances playing crucial roles in their onset and progression. We hope these findings will provide valuable insights for future research on AD and AS.

Transcriptomic analysis reveals cross-talk genes between type 2 diabetes and recurrent benign paroxysmal positional vertigo

BackgroundBenign paroxysmal positional vertigo (BPPV) is a common neurological disorder with a high recurrence rate. Type 2 diabetes mellitus (T2DM) is recognized as a risk factor for BPPV recurrence. However, the genomic association between T2DM and BPPV recurrence remains understudied. MethodsDifferential gene expression analysis and weighted gene co-expression network analysis were used to identify shared genes between BPPV recurrence and T2DM. The MCC algorithm was employed to select hub genes from the protein-protein interaction network of the shared genes. The predictive efficacy of hub genes for BPPV recurrence and T2DM was assessed using ROC curve analysis. Genemania database was used to identify downstream targets of hub genes. The immune infiltration landscape of BPPV and T2DM was characterized using the CIBERSORT algorithm. Correlation analysis was performed to explore the relationship between hub genes and immune cells. The expression levels of hub genes in patient blood samples were validated using qPCR. ResultsThirteen shared genes were identified and a protein-protein interaction network was constructed for BPPV recurrence and T2DM. Subsequently, four hub genes were selected, and their expression levels effectively predicted the occurrence of BPPV recurrence and T2DM. These hub genes were highly correlated with immune cell infiltration, indicating a common mechanism underlying recurrent BPPV and T2DM. Finally, the upregulation of hub genes in patients with T2DM comorbid with BPPV recurrence was confirmed in blood samples. These hub genes may serve as predictive biomarkers for assessing the recurrence rate in BPPV patients with comorbid T2DM. ConclusionWe proposed shared gene characteristics between BPPV recurrence and T2DM, revealing an immune-mediated inflammatory regulation as a common pathway and identifying four immune-related biomarkers and potential therapeutic targets for T2DM comorbid with recurrent BPPV.

An analysis of the potential association between obstructive sleep apnea and osteoporosis from the perspective of transcriptomics and NHANES

BackgroundObstructive sleep apnea (OSA) and osteoporosis (OP) are prevalent diseases in the elderly. This study aims to reveal the clinical association between OSA and OP and explore potential crosstalk gene targets.MethodsParticipants diagnosed with OSA in the National Health and Nutrition Examination Survey (NHANES) database (2015–2020) were included, and OP was diagnosed based on bone mineral density (BMD). We explored the association between OSA and OP, and utilized multivariate logistic regression analysis and machine learning algorithms to explore the risk factors for OP in OSA patients. Overlapping genes of comorbidity were explored using differential expression analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Random Forest (RF) methods.ResultsIn the OSA population, the weighted prevalence of OP was 7.0%. The OP group had more females, lower body mass index (BMI), and more low/middle-income individuals compared to the non-OP group. Female gender and lower BMI were identified as independent risk factors for OP in OSA patients. Gene expression profiling revealed 8 overlapping differentially expressed genes in OP and OSA patients. KCNJ1, NPR3 and WT1-AS were identified as shared diagnostic biomarkers or OSA and OP, all of which are associated with immune cell infiltration.ConclusionThis study pinpointed female gender and lower BMI as OP risk factors in OSA patients, and uncovered three pivotal genes linked to OSA and OP comorbidity, offering fresh perspectives and research targets.

Activation of NFE2L2 Alleviates Endoplasmic Reticulum Stress-Mediated Pyroptosis in Murine Hippocampus: A Bioinformatics Analysis and Experimental Validation.

Pyroptosis has been implicated in many pathologic processes, including endoplasmic reticulum stress (ERS). However, the underlying mechanisms and molecular targets of ERS affecting pyroptosis still need further exploration. We obtained gene sets associated with ERS and pyroptosis, and the common genes were regarded as crosstalk genes linking ERS and pyroptosis. Protein-protein interaction (PPI) network was constructed, and the hub genes were obtained via Cytoscape. Moreover, to validate the efficacy of the therapeutic target, neurological tests, brain water content measurements, Nissl staining, Western blot, ELISA, TUNEL analyses, and transmission electron microscopy were performed in a mouse model. A total of 13 crosstalk genes were acquired, and enrichment analysis revealed that these genes were mainly enriched in stress-associated cellular processes and pathways, including KEAP1-NFE2L2 pathway. The hub gene, NFE2L2, was identified by Cytoscape, and tert-butylhydroquinone (tBHQ) was screened as candidate drug to activate NFE2L2. Western blot and ELISA results showed that activation of NFE2L2 could attenuate the expression of ERS and pyroptosis-related proteins by promoting nuclear translocation of Nrf2 (encoded by NFE2L2). Pathological evaluation by Nissl staining and TUNEL assay reflected a similar trend. Furthermore, activation of NFE2L2 ameliorated neurological deficits and reduced brain edema. In conclusion, our bioinformatic analysis results established the theoretical foundation of NFE2L2 as a promising therapeutic target. Moreover, in the mouse model, tBHQ pretreatment further confirmed the effectiveness of this target. We hypothesized NFE2L2 may play a key role in the progression of ERS-mediated pyroptosis. These findings may inspire new ideas to treat neurological disorders.

CXCL9, IL2RB, and SPP1, potential diagnostic biomarkers in the co-morbidity pattern of atherosclerosis and non-alcoholic steatohepatitis

Non-alcoholic steatohepatitis (NASH) is a hepatocyte inflammation based on hepatocellular steatosis, yet there is no effective drug treatment. Atherosclerosis (AS) is caused by lipid deposition in the endothelium, which can lead to various cardiovascular diseases. NASH and AS share common risk factors, and NASH can also elevate the risk of AS, causing a higher morbidity and mortality rate for atherosclerotic heart disease. Therefore, timely detection and diagnosis of NASH and AS are particularly important. In this study, differential gene expression analysis and weighted gene co-expression network analysis were performed on the AS (GSE100927) and NASH (GSE89632) datasets to obtain common crosstalk genes, respectively. Then, candidate Hub genes were screened using four topological algorithms and externally validated in the GSE43292 and GSE63067 datasets to obtain Hub genes. Furthermore, immune infiltration analysis and gene set variation analysis were performed on the Hub genes to explore the underlying mechanisms. The DGIbd database was used to screen candidate drugs for AS and NASH. Finally, a NASH model was constructed using free fatty acid-induced human L02 cells, an AS model was constructed using lipopolysaccharide-induced HUVECs, and a co-morbidity model was constructed using L02 cells and HUVECs to verify Hub gene expression. The result showed that a total of 113 genes common to both AS and NASH were identified as crosstalk genes, and enrichment analysis indicated that these genes were mainly involved in the regulation of immune and metabolism-related pathways. 28 candidate Hub genes were screened according to four topological algorithms, and CXCL9, IL2RB, and SPP1 were identified as Hub genes after in vitro experiments and external dataset validation. The ROC curves and SVM modeling demonstrated the good diagnostic efficacy of these three Hub genes. In addition, the Hub genes are strongly associated with immune cell infiltration, especially macrophages and γ–δ T cell infiltration. Finally, five potential therapeutic drugs were identified. has-miR-185 and hsa-miR-335 were closely related to AS and NASH. This study demonstrates that CXCL9, IL2RB, and SPP1 may serve as potential biomarkers for the diagnosis of the co-morbidity patterns of AS and NASH and as potential targets for drug therapy.

Identification of potential shared gene signatures between gastric cancer and type 2 diabetes: a data-driven analysis.

Gastric cancer (GC) and type 2 diabetes (T2D) contribute to each other, but the interaction mechanisms remain undiscovered. The goal of this research was to explore shared genes as well as crosstalk mechanisms between GC and T2D. The Gene Expression Omnibus (GEO) database served as the source of the GC and T2D datasets. The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were utilized to identify representative genes. In addition, overlapping genes between the representative genes of the two diseases were used for functional enrichment analysis and protein-protein interaction (PPI) network. Next, hub genes were filtered through two machine learning algorithms. Finally, external validation was undertaken with data from the Cancer Genome Atlas (TCGA) database. A total of 292 and 541 DEGs were obtained from the GC (GSE29272) and T2D (GSE164416) datasets, respectively. In addition, 2,704 and 336 module genes were identified in GC and T2D. Following their intersection, 104 crosstalk genes were identified. Enrichment analysis indicated that "ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were mutual pathways. Through the PPI network, 10 genes were identified as candidate hub genes. Machine learning further selected BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 as hub genes. "ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were revealed as possible crosstalk mechanisms. BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 were identified as shared genes and potential therapeutic targets for people suffering from GC and T2D.

Identification of common mechanisms and biomarkers for dermatomyositis and atherosclerosis based on bioinformatics analysis.

Dermatomyositis (DM) manifests as an autoimmune and inflammatory condition, clinically characterized by subacute progressive proximal muscle weakness, rashes or both along with extramuscular manifestations. Literature indicates that DM shares common risk factors with atherosclerosis (AS), and they often co-occur, yet the etiology and pathogenesis remain to be fully elucidated. This investigation aims to utilize bioinformatics methods to clarify the crucial genes and pathways that influence the pathophysiology of both DM and AS. Microarray datasets for DM (GSE128470, GSE1551, GSE143323) and AS (GSE100927, GSE28829, GSE43292) were retrieved from the Gene Expression Omnibus (GEO) database. The weighted gene co-expression network analysis (WGCNA) was used to reveal their co-expressed modules. Differentially expression genes (DEGs) were identified using the "limma" package in R software, and the functions of common DEGs were determined by functional enrichment analysis. A protein-protein interaction (PPI) network was established using the STRING database, with central genes evaluated by the cytoHubba plugin, and validated through external datasets. Immune infiltration analysis of the hub genes was conducted using the CIBERSORT method, along with Gene Set Enrichment Analysis (GSEA). Finally, the NetworkAnalyst platform was employed to examine the transcription factors (TFs) responsible for regulating pivotal crosstalk genes. Utilizing WGCNA analysis, a total of 271 overlapping genes were pinpointed. Subsequent DEG analysis revealed 34 genes that are commonly found in both DM and AS, including 31 upregulated genes and 3 downregulated genes. The Degree Centrality algorithm was applied separately to the WGCNA and DEG collections to select the 15 genes with the highest connectivity, and crossing the two gene sets yielded 3 hub genes (PTPRC, TYROBP, CXCR4). Validation with external datasets showed their diagnostic value for DM and AS. Analysis of immune infiltration indicates that lymphocytes and macrophages are significantly associated with the pathogenesis of DM and AS. Moreover, GSEA analysis suggested that the shared genes are enriched in various receptor interactions and multiple cytokines and receptor signaling pathways. We coupled the 3 hub genes with their respective predicted genes, identifying a potential key TF, CBFB, which interacts with all 3 hub genes. This research utilized comprehensive bioinformatics techniques to explore the shared pathogenesis of DM and AS. The three key genes, including PTPRC, TYROBP, and CXCR4, are related to the pathogenesis of DM and AS. The central genes and their correlations with immune cells may serve as potential diagnostic and therapeutic targets.

Transcriptomic analysis of epicardial adipose tissue reveals the potential crosstalk genes and immune relationship between type 2 diabetes mellitus and atrial fibrillation

BackgroundThe complex relationship between atrial fibrillation (AF) and type 2 diabetes mellitus (T2DM) suggests a potential role for epicardial adipose tissue (EAT) that requires further investigation. This study employs bioinformatics and experimental approaches to clarify EAT's role in linking T2DM and AF, aiming to unravel the biological mechanisms involved. MethodBioinformatics analysis initially identified common differentially expressed genes (DEGs) in EAT from T2DM and AF datasets. Pathway enrichment and network analyses were then performed to determine the biological significance and network connections of these DEGs. Hub genes were identified through six CytoHubba algorithms and subsequently validated biologically, with further in-depth analyses confirming their roles and interactions. Experimentally, db/db mice were utilized to establish a T2DM model. AF induction was executed via programmed transesophageal electrical stimulation and burst pacing, focusing on comparing the incidence and duration of AF. Frozen sections and Hematoxylin and Eosin (H&E) staining illuminated the structures of the heart and EAT. Moreover, quantitative PCR (qPCR) measured the expression of hub genes. ResultsThe study identified 106 DEGs in EAT from T2DM and AF datasets, underscoring significant pathways in energy metabolism and immune regulation. Three hub genes, CEBPZ, PAK1IP1, and BCCIP, emerged as pivotal in this context. In db/db mice, a marked predisposition towards AF induction and extended duration was observed, with HE staining verifying the presence of EAT. Additionally, qPCR validated significant changes in hub genes expression in db/db mice EAT. In-depth analysis identified 299 miRNAs and 33 TFs as potential regulators, notably GRHL1 and MYC. GeneMANIA analysis highlighted the hub genes' critical roles in stress responses and leukocyte differentiation, while immune profile correlations highlighted their impact on mast cells and neutrophils, emphasizing the genes' significant influence on immune regulation within the context of T2DM and AF. ConclusionThis investigation reveals the molecular links between T2DM and AF with a focus on EAT. Targeting these pathways, especially EAT-related ones, may enable personalized treatments and improved outcomes.

Bioinformatics analysis revealed the potential crosstalk genes and molecular mechanisms between intracranial aneurysms and periodontitis

ObjectivesThe risk of intracranial aneurysms (IAs) development and rupture is significantly higher in patients with periodontitis (PD), suggesting an association between the two. However, the specific mechanisms of association between these two diseases have not been fully investigated.Materials and methodsIn this study, we downloaded IAs and PD data from the Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified, and functional enrichment analysis was performed. The protein-protein interaction (PPI) network and weighted gene co-expression network analysis (WGCNA) was performed to identified key modules and key crosstalk genes. In addition, the immune cell landscape was assessed and the correlation of key crosstalk genes with each immune cell was calculated. Finally, transcription factors (TFs) regulating key crosstalk genes were explored.Results127 overlapping DEGs were identified and functional enrichment analysis highlighted the important role of immune reflection in the pathogenesis of IAs and PD. We identified ITGAX and COL4A2 as key crosstalk genes. In addition, the expression of multiple immune cells was significantly elevated in PDs and IAs compared to controls, and both key crosstalk genes were significantly negatively associated with Macrophages M2. Finally, GATA2 was identified as a potential key transcription factor (TF), which regulates two key crosstalk gene.ConclusionsThe present study identifies key crosstalk genes and TF in PD and IAs, providing new insights for further study of the co-pathogenesis of PD and IAs from an immune and inflammatory perspective. Also, this is the first study to report the above findings.

Transcriptomic analysis reveals the crosstalk between type 2 diabetes and chronic pancreatitis.

Mounting evidence highlights a strong association between chronic pancreatitis (CP) and type 2 diabetes (T2D), although the exact mechanism of interaction remains unclear. This study aimed to investigate the crosstalk genes and pathogenesis between CP and T2D. Transcriptomic gene expression profiles of CP and T2D were extracted from Gene Expression Omnibus, respectively, and the common differentially expressed genes (DEGs) were subsequently identified. Further analysis, such as Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction, transcription factors (TFs), microRNA (miRNAs), and candidate chemicals identification, was performed to explore the possible common signatures between the two diseases. In total, we acquired 281 common DEGs by interacting CP and T2D datasets, and identified 10 hub genes using CytoHubba. GO and KEGG analyses revealed that endoplasmic reticulum stress and mitochondrial dysfunction were closely related to these common DEGs. Among the shared genes, EEF2, DLD, RAB5A, and SLC30A9 showed promising diagnostic value for both diseases based on receiver operating characteristic curve and precision-recall curves. Additionally, we identified 16 key TFs and 16 miRNAs that were strongly correlated with the hub genes, which may serve as new molecular targets for CP and T2D. Finally, candidate chemicals that might become potential drugs for treating CP and T2D were screened out. This study provides evidence that there are shared genes and pathological signatures between CP and T2D. The genes EEF2, DLD, RAB5A, and SLC30A9 have been identified as having the highest diagnostic efficiency and could be served as biomarkers for these diseases, providing new insights into precise diagnosis and treatment for CP and T2D.

Identification of ferroptosis-related key genes associated with immune infiltration in sepsis by bioinformatics analysis and in vivo validation

ObjectivesSepsis is a life-threatening infectious disease in which an immune inflammatory response is triggered. The potential effect of ferroptosis-related genes (FRGs) in inflammation of sepsis remained unclear. We focused on identifying and validating core FRGs and their association with immune infiltration in blood from currently all patients with sepsis. MethodsAll current raw data of septic blood were obtained from Gene Expression Omnibus. After removing the batch effect merging into a complete dataset and obtaining Diferentially expressed genes (DEGs). Common cross-talk genes were identified from DEGs and FRGs. WGCNA, GO, KEGG, PPI, GESA, ROC curves, and LASSO regression analysis were performed to indentify and validate key genes based on external septic datasets. Infiltrated immune cells in 2 hub genes (MAPK14 and ACSL4) were conducted using CIBERSORT algorithm and Spearman correlation analysis. Further, the expressions of 2 core FRGs were verified in the LPS-induced ALI and cardiac injury sepsis mice. ResultsMAPK14 and ACSL4 were identified, mostly enriched in T cell infiltration through NOD-like receptor signaling pathway according to the high or low 2 hub genes expression. The upregulated 2 ferroptosis-related genes were validated in LPS-induced ALI and cardiac injury mice, accompanied by upregulation of the NLRP3 pathway. ConclusionMAPK14 and ACSL4 could become robustly reliable and promising biomarkers for sepsis by regulating ferroptosis through the NLRP3 pathway, which is mainly associated with T-cell infiltration.

Novel immune cross-talk between inflammatory bowel disease and IgA nephropathy

The mechanisms underlying the complex correlation between immunoglobulin A nephropathy (IgAN) and inflammatory bowel disease (IBD) remain unclear. This study aimed to identify the optimal cross-talk genes, potential pathways, and mutual immune-infiltrating microenvironments between IBD and IgAN to elucidate the linkage between patients with IBD and IgAN. The IgAN and IBD datasets were obtained from the Gene Expression Omnibus (GEO). Three algorithms, CIBERSORTx, ssGSEA, and xCell, were used to evaluate the similarities in the infiltrating microenvironment between the two diseases. Weighted gene co-expression network analysis (WGCNA) was implemented in the IBD dataset to identify the major immune infiltration modules, and the Boruta algorithm, RFE algorithm, and LASSO regression were applied to filter the cross-talk genes. Next, multiple machine learning models were applied to confirm the optimal cross-talk genes. Finally, the relevant findings were validated using histology and immunohistochemistry analysis of IBD mice. Immune infiltration analysis showed no significant differences between IBD and IgAN samples in most immune cells. The three algorithms identified 10 diagnostic genes, MAPK3, NFKB1, FDX1, EPHX2, SYNPO, KDF1, METTL7A, RIDA, HSDL2, and RIPK2; FDX1 and NFKB1 were enhanced in the kidney of IBD mice. Kyoto Encyclopedia of Genes and Genomes analysis showed 15 mutual pathways between the two diseases, with lipid metabolism playing a vital role in the cross-talk. Our findings offer insights into the shared immune mechanisms of IgAN and IBD. These common pathways, diagnostic cross-talk genes, and cell-mediated abnormal immunity may inform further experimental studies.

Transcriptomic analysis identifies the shared diagnostic biomarkers and immune relationship between Atherosclerosis and abdominal aortic aneurysm based on fatty acid metabolism gene set.

Epidemiological research has demonstrated that there is a connection between lipid metabolism disorder and an increased risk of developing arteriosclerosis (AS) and abdominal aortic aneurysm (AAA). However, the precise relationship between lipid metabolism, AS, and AAA is still not fully understood. The objective of this study was to examine the pathways and potential fatty acid metabolism-related genes (FRGs) that are shared between AS and AAA. AS- and AAA-associated datasets were retrieved from the Gene Expression Omnibus (GEO) database, and the limma package was utilized to identify differentially expressed FRGs (DFRGs) common to both AS and AAA patients. Functional enrichment analysis was conducted on the (DFRGs), and a protein-protein interaction (PPI) network was established. The selection of signature genes was performed through the utilization of least absolute shrinkage and selection operator (LASSO) regression and random forest (RF). Subsequently, a nomogram was developed using the results of the screening process, and the crucial genes were validated in two separate external datasets (GSE28829 and GSE17901) as well as clinical samples. In the end, single-sample gene set enrichment analysis (ssGSEA) was utilized to assess the immune cell patterns in both AS and AAA. Additionally, the correlation between key crosstalk genes and immune cell was evaluated. In comparison to control group, both AS and AAA patients exhibited a decrease in fatty acid metabolism score. We found 40 DFRGs overlapping in AS and AAA, with lipid and amino acid metabolism critical in their pathogenesis. PCBD1, ACADL, MGLL, BCKDHB, and IDH3G were identified as signature genes connecting AS and AAA. Their expression levels were confirmed in validation datasets and clinical samples. The analysis of immune infiltration showed that neutrophils, NK CD56dim cells, and Tem cells are important in AS and AAA development. Correlation analysis suggested that these signature genes may be involved in immune cell infiltration. The fatty acid metabolism pathway appears to be linked to the development of both AS and AAA. Furthermore, PCBD1, ACADL, MGLL, BCKDHB, and IDH3G have the potential to serve as diagnostic markers for patients with AS complicated by AAA.