Shared Differentially Expressed Genes Research Articles

Exploring shared molecular signatures and regulatory mechanisms in nonalcoholic steatohepatitis and inflammatory bowel disease using integrative bioinformatics analysis.

The co-existence of inflammatory bowel disease (IBD) and non-alcoholic steatohepatitis (NASH) has raised interest in identifying shared molecular mechanisms and potential therapeutic targets. However, the relationship between these two diseases remains unclear and effective medical treatments are still lacking. Through the bioinformatics analysis in this study, 116 shared differentially expressed genes (SDEGs) were identified between IBD and NASH datasets. GO and KEGG pathway analyses revealed significant involvement of SDEGs in apoptotic processes, cell death, defense response, cytokine and chemokine activity, and signaling pathways. Furthermore, weighted gene co-expression network analysis (WGCNA) identified five shared signature genes associated specifically with IBD and NASH, they were CXCL9, GIMAP2, ADAMTS5, GRAP, and PRF1. These five genes represented potential diagnostic biomarkers for distinguishing patients with diseases from healthy individuals by using two classifier algorithms and were positively related to autophagy, ferroptosis, angiogenesis, and immune checkpoint factors in the two diseases. Additionally, single-cell analysis of IBD and NASH samples highlighted the expression of regulatory genes in various immune cell subtypes, emphasizing their significance in disease pathogenesis. Our work elucidated the shared signature genes and regulatory mechanisms of IBD and NASH, which could provide new potential therapies for patients with IBD and NASH.

Dysregulated AEBP1 and COLEC12 Genes in Late-Onset Alzheimer's Disease: Insights from Brain Cortex and Peripheral Blood Analysis.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by memory and cognitive impairment, often accompanied by alterations in mood, confusion, and, ultimately, a state of acute mental disturbance. The cerebral cortex is considered a promising area for investigating the underlying causes of AD by analyzing transcriptional patterns, which could be complemented by investigating blood samples obtained from patients. We analyzed the RNA expression profiles of three distinct areas of the brain cortex, including the frontal cortex (FC), temporal cortex (TC), and entorhinal cortex (EC) in patients with AD. Functional enrichment analysis was performed on the differentially expressed genes (DEGs) across the three regions. The two genes with the most significant expression changes in the EC region were selected for assessing mRNA expression levels in the peripheral blood of late-onset AD patients using quantitative PCR (qPCR). We identified eight shared DEGs in these regions, including AEBP1 and COLEC12, which exhibited prominent changes in expression. Functional enrichment analysis uncovered a significant association of these DEGs with the transforming growth factor-β (TGF-β) signaling pathway and processes related to angiogenesis. Importantly, we established a robust connection between the up-regulation of AEBP1 and COLEC12 in both the brain and peripheral blood. Furthermore, we have demonstrated the potential of AEBP1 and COLEC12 genes as effective diagnostic tools for distinguishing between late-onset AD patients and healthy controls. This study unveils the intricate interplay between AEBP1 and COLEC12 in AD and underscores their potential as markers for disease detection and monitoring.

Exploration of the shared gene signatures and biological mechanisms between ischemia-reperfusion injury and antibody-mediated rejection in renal transplantation

BackgroundAntibody-mediated rejection (ABMR) plays a crucial role in graft loss during allogeneic renal transplantation. In renal transplantation, ischemia-reperfusion injury (IRI) is unavoidable, serves as a major contributor to acute rejection, and is linked to graft loss. However, the mechanisms underlying IRI and ABMR are unclear. Therefore, this study aimed to investigate the shared genetic characteristics and biological mechanisms between IRI and ABMR. MethodsGene expressions for IRI (GSE43974) and ABMR (GSE129166 and GSE36059) were retrieved from the Gene Expression Omnibus database. The shared differentially expressed genes (DEGs) of IRI and ABMR were identified, and subsequent functional enrichment analysis was performed. Immune cell infiltration in ABMR and its relationship with the shared DEGs were investigated using the CIBERSORT method. Random forest analysis, a protein–protein interaction network, and Cytoscape were used to screen hub genes, which were subsequently subjected to gene set enrichment analysis, miRNA prediction, and transcription factors analysis. The survival analysis was performed through Kaplan–Meier curves. Finally, drug compound prediction was performed on the shared DEGs using the Drug Signature Database. ResultsOverall, 27 shared DEGs were identified between the renal IRI and ABMR groups. Among these, 24 genes exhibited increased co-expression, whereas none showed decreased co-expression. The shared DEGs were primarily enriched in the inflammation signaling pathways. Notably, CD4 memory T cells were identified as potential critical mediators of IRI, leading to ABMR. Tumor necrosis factor alpha-induced protein 3 (TNFAIP3), interferon regulatory factor 1 (IRF1), and early growth response 2 (EGR2) were identified as key components in the potential mechanism that link IRI and ABMR. Patients undergoing renal transplantation with higher expression levels of TNFAIP3, IRF1, and EGR2 exhibited decreased survival rates compared to those with lower expression levels. ConclusionInflammation is a key mechanism that links IRI and ABMR, with a potential role played by CD4 memory T cells. Furthermore, TNFAIP3, IRF1, and EGR2 are implicated in the underlying mechanism between IRI and ABMR.

Investigation of the shared biological mechanisms and common biomarker APTAF1 of sleep deprivation and mild cognitive impairment using integrated bioinformatics analysis.

Introduction: The relationship between sleep loss and cognitive impairment has long been widely recognized, but there is still a lack of complete understanding of the underlying mechanisms and potential biomarkers. The purpose of this study is to further explore the shared biological mechanisms and common biomarkers between sleep loss and cognitive impairment. Methods: The mitochondria-related genes and gene expression data were downloaded from the MitoCarta3.0 and Gene Expression Omnibus (GEO) databases. We identified the differentially expressed mitochondrial-related genes by combing the differentially expressed genes (DEGs) in sleep deprivation (SD) and mild cognitive impairment (MCI) datasets with mitochondria-related gene lists. Shared DEGs were then further analyzed for enrichment analysis. Next, the common biomarker was identified using two machine learning techniques and further validated using two independent GEO datasets. Then GSEA and GSVA were conducted to analyze the functional categories and pathways enriched for the common biomarker. Finally, immune infiltration analysis was used to investigate the correlation of immune cell infiltration with the common biomarker in SD and MCI. Results: A total of 32 mitochondrial-related differentially expressed genes were identified in SD and MCI. GO analysis indicated that these genes were significantly enriched for mitochondrial transport, and KEGG analysis showed they were mainly involved in pathways of neurodegenerative diseases. In addition, ATPAF1, which was significantly down-regulated in both SD and MCI, was identified through machine learning algorithms as the common biomarker with favorable diagnostic performance. GSEA and GSVA revealed that ATPAF1 was mainly involved in metabolic pathways, such as oxidative phosphorylation, acetylcholine metabolic process, valine, leucine and isoleucine degradation. Immune infiltration analysis showed that the expression of ATPAF1 was correlated with changes in immune cells, especially those key immune cell types associated with SD and MCI. Discussion: This study firstly revealed that mitochondrial dysfunction may be the common pathogenesis of sleep loss and mild cognitive impairment and identified ATPAF1 as a possible biomarker and therapeutic target involved in SD and MCI.

Exploration of common genomic signatures of systemic juvenile rheumatoid arthritis and type 1 diabetes

To explore the genetic characteristics of systemic juvenile rheumatoid arthritis (sJRA) and type 1 diabetes mellitus (T1D). The microarray data of sJRA and T1D from Gene Expression Omnibus (GEO) were analyzed. The shared differentially expressed genes (SDEGs) were identified by the Meta-analysis, and genes of extracellular proteins were identified. Then, transcription factors (TFs) and their target genes in SDEGs were obtained by comparing databases from HumanTFDB, and hTFtarget. After that, functional enrichment analyses of the previously identified gene sets were performed by metascape tool. Finally, immune infiltration was analysed by CIBERSORT. We found 175 up-regulated and 245 down-regulated SDEGs, and by constructing a TFs-targeted SDEGs network, 3 key TFs (ARID3A, NEF2, RUNX3) were screened. Functional enrichment analyses and immune infiltration results suggested not only the adaptive immune system but also the innate immune system, and signaling pathways like JAK-STAT are important in the pathogenesis of sJRA and T1D, involving biological processes such as CD4 T cell functions and neutrophil degranulation. This work suggests that innate immune abnormalities also play important roles in sJRA and T1D, CD4 T cell functions, neutrophil degranulation and the JAK-STAT pathway may be involved. The regulatory roles of ARID3A, NEF2, and RUNX3 in this network need to be further investigated.

Bioinformatics and Raman spectroscopy-based identification of key pathways and genes enabling differentiation between acute myeloid leukemia and T cell acute lymphoblastic leukemia.

Acute myeloid leukemia (AML) and T cell acute lymphoblastic leukemia (T-ALL) are two of the most prevalent hematological malignancies diagnosed among adult leukemia patients, with both being difficult to treat and associated with high rates of recurrence and mortality. In the present study, bioinformatics approaches were used to analyze both of these types of leukemia in an effort to identify characteristic gene expression patterns that were subsequently validated via Raman spectroscopy. For these analyses, four Gene Expression Omnibus datasets (GSE13204, GSE51082, GSE89565, and GSE131184) pertaining to acute leukemia were downloaded, and differentially expressed genes (DEGs) were then identified through comparisons of AML and T-ALL patient samples using the R Bioconductor package. Shared DEGs were then subjected to Gene Ontology (GO) enrichment analyses and were used to establish a protein-protein interaction (PPI) network analysis. In total, 43 and 129 upregulated and downregulated DEGs were respectively identified. Enrichment analyses indicated that these DEGs were closely tied to immune function, collagen synthesis and decomposition, inflammation, the synthesis and decomposition of lipopolysaccharide, and antigen presentation. PPI network module clustering analyses further led to the identification of the top 10 significantly upregulated and downregulated genes associated with disease incidence. These key genes were then validated in patient samples via Raman spectroscopy, ultimately confirming the value of these genes as tools that may aid the differential diagnosis and treatment of AML and T-ALL. Overall, these results thus highlight a range of novel pathways and genes that are linked to the incidence and progression of AML and T-ALL, providing a list of important diagnostic and prognostic molecular markers that have the potential to aid in the clinical diagnosis and treatment of these devastating malignancies.

Identification of Candidate Genes for Drought Resistance during Soybean Seed Development

Drought is the main abiotic stress limiting soybean production worldwide and seriously limits the reproductive growth of soybean. To elucidate the molecular mechanisms of drought stress response in soybean seed developmental stages, transcriptome analysis was performed on five seed developmental stages under drought stress and control conditions. Thousands of genes were found to change in expression level under drought stress, GO and KEGG enrichment analysis showed that differentially expressed genes (DEGs) were involved in drought resistance at every stage or tissue, but there were very few DEGs at the primary stage of seed development, which indicated that the seeds at the primary developmental stage were influenced little by drought stress. There was no shared DEG among all five stages or tissues, this indicated that they have different mechanisms to resist drought stress. Furthermore, we investigated DEGs associated with drought response at different stages to identify candidate regulators of drought stress response during soybean seed development. These genes are associated with metabolic and regulatory networks, among others. Taken together, the results will expand our understanding of the adaptation of soybean to drought stress and provide new insights into the molecular mechanisms of soybean drought tolerance development.

Transcriptomic analysis of the chorioallantois in equine premature placental separation.

Equine premature placental separation (PPS) is poorly understood and represents an important risk factor for fetal/neonatal hypoxia. To examine transcriptomic changes in the chorioallantois (CA) from mares with clinical PPS compared with the CA from normal foaling mares. Differential gene expression was determined and gene ontology as well as molecular pathways related to PPS were characterised. Retrospective case: control study. CA were collected from Thoroughbred mares with a clinical history of PPS (n=33) and from control Thoroughbred mares (n=4) with normal parturition for examination of transcriptional changes in the placenta associated with PPS. Transcriptomic changes in the villous CA near the cervical star were determined by Illumina® sequencing and subsequent bioinformatic analysis. PPS samples were divided by k-means clustering, and differentially expressed genes (DEGs) in each PPS cluster were identified by comparing to controls. Shared DEGs between PPS clusters were used for gene ontology analysis and pathway analysis. A total of 1204 DEGs were identified between PPS and control. Gene ontology revealed extracellular matrix (ECM) and cell adhesion, and pathway analysis revealed fatty acid, p-53, hypoxia and inflammation. Eleven key regulator genes of PPS including growth factors (IGF1, TGFB2, TGFB3), transcription factors (HIF1A, JUNB, SMAD3), and transmembrane receptors (FGFR1, TNFRSF1A, TYROBP) were also identified. The use of clinical history of PPS, in the absence of other criteria, may have led to misidentification of some cases as PPS. Transcriptomic analysis indicated that changes in ECM and cell adhesion were important factors in equine PPS. Key predicted upstream events include genes associated with hypoxia, inflammation and growth factors related to the pathogenesis of equine PPS.

Differentiation of T Helper 17 Cells May Mediate the Abnormal Humoral Immunity in IgA Nephropathy and Inflammatory Bowel Disease Based on Shared Genetic Effects.

BackgroundIgA nephropathy (IgAN) is the most frequent glomerulonephritis in inflammatory bowel disease (IBD). However, the inter-relational mechanisms between them are still unclear. This study aimed to explore the shared gene effects and potential immune mechanisms in IgAN and IBD.MethodsThe microarray data of IgAN and IBD in the Gene Expression Omnibus (GEO) database were downloaded. The differential expression analysis was used to identify the shared differentially expressed genes (SDEGs). Besides, the shared transcription factors (TFs) and microRNAs (miRNAs) in IgAN and IBD were screened using humanTFDB, HMDD, ENCODE, JASPAR, and ChEA databases. Moreover, weighted gene co-expression network analysis (WGCNA) was used to identify the shared immune-related genes (SIRGs) related to IgAN and IBD, and R software package org.hs.eg.db (Version3.1.0) were used to identify common immune pathways in IgAN and IBD.ResultsIn this study, 64 SDEGs and 28 SIRGs were identified, and the area under the receiver operating characteristic curve (ROC) of 64 SDEGs was calculated and two genes (MVP, PDXK) with high area under the curve (AUC) in both IgAN and IBD were screened out as potential diagnostic biomarkers. We then screened 3 shared TFs (SRY, MEF2D and SREBF1) and 3 miRNAs (hsa-miR-146, hsa-miR-21 and hsa-miR-320), and further found that the immune pathways of 64SDEGs, 28SIRGs and 3miRNAs were mainly including B cell receptor signaling pathway, FcγR-mediated phagocytosis, IL-17 signaling pathway, toll-like receptor signaling pathway, TNF signaling pathway, TRP channels, T cell receptor signaling pathway, Th17 cell differentiation, and cytokine-cytokine receptor interaction.ConclusionOur work revealed the differentiation of Th17 cells may mediate the abnormal humoral immunity in IgAN and IBD patients and identified novel gene candidates that could be used as biomarkers or potential therapeutic targets.

Identification of critical genes and pathways associated with hepatocellular carcinoma and type 2 diabetes mellitus using integrated bioinformatics analysis

BackgroundHepatocellular carcinoma (HCC) is the fourth main cause of cancer-related death globally. Based on the evidence, type 2 diabetes mellitus (T2DM) is associated with the onset of HCC. The current work was conducted to discover molecular mechanisms and hub genes of both diseases concurrently by carrying out integrated bioinformatics analysis. Material and methodsIn this study, the microarray datasets of HCC and T2DM diseases were retrieved from the Gene Expression Omnibus (GEO) database. In order to identify differentially expressed genes (DEGs), datasets of both diseases were analyzed by employing the GEO2R online tool separately. Shared DEGs between both diseases were used for subsequent analyses. The Enrichr database was applied to evaluate the functional enrichment analysis of DEGs. The STRING database was used to establish protein-protein interaction (PPI) networks visualized using Cytoscape. The hub genes were defined using the cytoHubba plugin of Cytoscape based upon the degree method. The diagnostic accuracy of hub genes was evaluated using the area under the curve (AUC) values obtained from receiver operating characteristic (ROC) curve analysis. Eventually, the interactions of the transcription factors (TFs) and microRNAs (miRNAs) with the hub genes were constructed by utilizing the Networkanalyst database. ResultsA total of 98 common DEGs were found between HCC and T2DM datasets. PPI network acquired from the STRING database was imported to the cytoHubba plugin of Cytoscape, and 10 hub genes were screened based on the degree method using the above plugin (which included BUB1, CDCA8, DLGAP5, ASPM, POLQ, CENPE, WDHD1, HELLS, TRIP13, and DEPDC1). According to the ROC curve, all hub genes have a high diagnostic value, with high specificity and sensitivity. Among miRNAs, miR-192-5p was most correlated with the hub genes, and regarding the TFs, E2F4 was identified as a transcription factor responsible for regulating most of the hub genes. ConclusionsThis research provides a novel vision about the progression and pathogenesis of HCC in T2DM sufferers through bioinformatics tools. However, to verify key genes and biological pathways obtained from this study, additional experiments are required.

Screening for genes, transcription factors and miRNAs associated with the myogenic and osteogenic differentiation of human adipose tissue-derived stem cells.

In the present study, we aimed to reveal the molecular mechanisms responsible for the differentiation of human adipose tissue-derived stem cells(hASCs) into myocytes and osteoblasts. Microarray data GSE37329 were obtained from the Gene Expression Omnibus database, including three hASC cell lines from healthy donors, two osteogenic lineages and two myogenic lineages from the invitro‑induction of hASCs. Differentially expressed genes(DEGs) in the two lineages were firstly screened. Subsequently, the underlying functions of the two sets of DEGs were investigated by Gene Ontology function and Kyoto Encyclopedia of Genes and Genomespathway enrichment analysis, followed by protein-protein interaction(PPI) network construction. Regulatory relationships between transcription factors(TFs) and microRNAs (miRNAs or miRs) with target genes were finally explored using different algorithms. A total of 665 and 485DEGs were identified from the hASC‑derived myogenic and osteogenic lineages, respectively. The shared upregulated genes (n=205) in the two sets of DEGs were mainly involved in metabolism-related pathways, whereas the shared downregulated genes (n=128) were significantly enriched in the transforming growth factor-β(TGF-β) signaling pathway. Four genes, vascular endothelial growth factorA(VEGFA), fibroblast growth factor2(FGF2), nerve growth factor(NGF) and interleukin1B(IL1B), presented with relatively higher degrees in both PPI networks. The transcription factor RAD21 was predicted to target shared upregulated and downregulated genes as well as specific downregulated genes in the myogenic and the osteogenic lineages. In addition, miRNA-DEG interaction analysis revealed that hsa-miR-1 regulated the most shared DEGs in the two lineages. There may be a correlation between the four genes, VEGFA, FGF2, IL1B and NGF, and the differentiation of hASCs into myocytes and osteoblasts. The TF RAD21 and hsa-miR-1 may play important roles in regulating the expression of differentiation-associated genes.

Microarray Meta-Analysis Focused on the Response of Genes Involved in Redox Homeostasis to Diverse Abiotic Stresses in Rice.

Plants are exposed to a wide range of abiotic stresses (AS), which often occur in combination. Because physiological investigations typically focus on one stress, our understanding of unspecific stress responses remains limited. The plant redox homeostasis, i.e., the production and removal of reactive oxygen species (ROS), may be involved in many environmental stress conditions. Therefore, this study intended to identify genes, which are activated in diverse AS, focusing on ROS-related pathways. We conducted a meta-analysis (MA) of microarray experiments, focusing on rice. Transcriptome data were mined from public databases and fellow researchers, which represented 36 different experiments and investigated diverse AS, including ozone stress, drought, heat, cold, salinity, and mineral deficiencies/toxicities. To overcome the inherent artifacts of different MA methods, data were processed using Fisher, rOP, REM, and product of rank (GeneSelector), and genes identified by most approaches were considered as shared differentially expressed genes (DEGs). Two MA strategies were adopted: first, datasets were separated into shoot, root, and seedling experiments, and these tissues were analyzed separately to identify shared DEGs. Second, shoot and seedling experiments were classed into oxidative stress (OS), i.e., ozone and hydrogen peroxide treatments directly producing ROS in plant tissue, and other AS, in which ROS production is indirect. In all tissues and stress conditions, genes a priori considered as ROS-related were overrepresented among the DEGs, as they represented 4% of all expressed genes but 7–10% of the DEGs. The combined MA approach was substantially more conservative than individual MA methods and identified 1001 shared DEGs in shoots, 837 shared DEGs in root, and 1172 shared DEGs in seedlings. Within the OS and AS groups, 990 and 1727 shared DEGs were identified, respectively. In total, 311 genes were shared between OS and AS, including many regulatory genes. Combined co-expression analysis identified among those a cluster of 42 genes, many involved in the photosynthetic apparatus and responsive to drought, iron deficiency, arsenic toxicity, and ozone. Our data demonstrate the importance of redox homeostasis in plant stress responses and the power of MA to identify candidate genes underlying unspecific signaling pathways.

Transcriptome meta-analysis reveals common differential and global gene expression profiles in cystic fibrosis and other respiratory disorders and identifies CFTR regulators

A meta-analysis of 13 independent microarray data sets was performed and gene expression profiles from cystic fibrosis (CF), similar disorders (COPD: chronic obstructive pulmonary disease, IPF: idiopathic pulmonary fibrosis, asthma), environmental conditions (smoking, epithelial injury), related cellular processes (epithelial differentiation/regeneration), and non-respiratory “control” conditions (schizophrenia, dieting), were compared. Similarity among differentially expressed (DE) gene lists was assessed using a permutation test, and a clustergram was constructed, identifying common gene markers. Global gene expression values were standardized using a novel approach, revealing that similarities between independent data sets run deeper than shared DE genes. Correlation of gene expression values identified putative gene regulators of the CF transmembrane conductance regulator (CFTR) gene, of potential therapeutic significance. Our study provides a novel perspective on CF epithelial gene expression in the context of other lung disorders and conditions, and highlights the contribution of differentiation/EMT and injury to gene signatures of respiratory disease.