Differentially Expressed Genes and Enriched Signaling Pathways in the Adipose Tissue of Obese People.
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ABSTRACT:
SUBMITTER: Lu Z
PROVIDER: S-EPMC8175074 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:OBJECTIVE:To profile the differential gene expression of the KEGG Adipocytokine Signaling pathway in omental compared to subcutaneous tissue in normal pregnancy. STUDY DESIGN:Subjects included 14 nonobese, normal glucose tolerant, healthy pregnant women. Matched omental and subcutaneous tissue were obtained at elective cesarean delivery. Gene expression was evaluated using microarray and validated by RT-PCR. Differential gene expression was defined as ?1.5 fold increase at p < 0.05. RESULTS:Six genes were significantly downregulated with two upregulated genes in omental tissue. Downregulation of Adiponectin and Insulin Receptor substrate, key genes mediating insulin sensitivity, were observed with borderline upregulation of GLUT-1. There were downregulations of CD36 and acyl-CoA Synthetase Long-chain Family Member 1which are genes involved in fatty acid uptake and activation. There was a novel expression of Carnitine palmitoyltransferase 1C. CONCLUSION:Differential gene expression of Adipocytokine Signaling Pathway in omental relative to subcutaneous adipose tissue in normal pregnancy suggests a pattern of insulin resistance, hyperlipidemia, and inflammation.
| S-EPMC4447103 | biostudies-literature
Project description:Lung cancer is the leading cause of cancer?associated mortality worldwide. The aim of the present study was to identify the differentially expressed genes (DEGs) and enriched pathways in lung cancer by bioinformatics analysis, and to provide potential targets for diagnosis and treatment. Valid microarray data of 31 pairs of lung cancer tissues and matched normal samples (GSE19804) were obtained from the Gene Expression Omnibus database. Significance analysis of the gene expression profile was used to identify DEGs between cancer tissues and normal tissues, and a total of 1,970 DEGs, which were significantly enriched in biological processes, were screened. Through the Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, 77 KEGG pathways associated with lung cancer were identified, among which the Toll?like receptor pathway was observed to be important. Protein?protein interaction network analysis extracted 1,770 nodes and 10,667 edges, and identified 10 genes with key roles in lung cancer with highest degrees, hub centrality and betweenness. Additionally, the module analysis of protein?protein interactions revealed that 'chemokine signaling pathway', 'cell cycle' and 'pathways in cancer' had a close association with lung cancer. In conclusion, the identified DEGs, particularly the hub genes, strengthen the understanding of the development and progression of lung cancer, and certain genes (including advanced glycosylation end?product specific receptor and epidermal growth factor receptor) may be used as candidate target molecules to diagnose, monitor and treat lung cancer.
| S-EPMC6390056 | biostudies-literature
Project description:Psoriasis is an immune‑mediated cutaneous disorder with a high incidence and prevalence. Patients with psoriasis may experience irritation, pain and psychological problems. The cause and underlying molecular etiology of psoriasis remains unknown. In an attempt to achieve a more comprehensive understanding of the molecular pathogenesis of psoriasis, the gene expression profiles of 175 pairs of lesional and corresponding non‑lesional skin samples were downloaded from 5 data sets in the Gene Expression Omnibus (GEO) database. Integrated differentially expressed genes (DEGs) were obtained with the use of R software. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were analyzed using the DAVID online analysis tool. The protein‑protein interaction (PPI) network was constructed on the STRING platform and hub genes were calculated with the use of Cytoscape software. Finally, GEO2R was used to determine the expression of the hub genes in scalp psoriasis. A total of 373 genes from the 5 data sets were identified as DEGs, including 277 upregulated and 96 downregulated genes. GO analysis revealed that immune responses and epidermal differentiation/development were the most enriched terms in biological processes, extracellular space/matrix was the most enriched term in cellular components, and endopeptidase inhibitor activity was the most enriched term in molecular functions. In the KEGG pathway enrichment, DEGs were mainly enriched in the metabolic and viral infection‑associated pathways. A total of 17 hub genes were calculated, including CSK2, CDC45, MCM10, SPC25, NDC80, NUF2, AURKA, CENPE, RRM2, DLGP5, HMMR, TTK, IFIT1, RSAD2, IFI6, IFI27 and ISG20, among which interferon‑α‑inducible genes were revealed to display a similar expression pattern as that obtained in scalp psoriasis. This comprehensive bioinformatic re‑analysis of GEO data provides new insights on the molecular pathogenesis of psoriasis and the identification of potential therapeutic targets for the treatment of psoriasis.
| S-EPMC6580009 | biostudies-literature
Project description:BackgroundThe metabolic syndrome is becoming increasingly prevalent in the general population that is at simultaneous risk for both type 2 diabetes and cardiovascular disease. The critical pathogenic mechanisms underlying these diseases are obesity-driven insulin resistance and atherosclerosis, respectively. To obtain a better understanding of molecular mechanisms involved in pathogenesis of the metabolic syndrome as a basis for future treatment strategies, studies considering both inherent risks, namely metabolic and cardiovascular, are needed. Hence, the aim of this study was to identify pathways commonly dysregulated in obese adipose tissue and atherosclerotic plaques.MethodsWe carried out a gene set enrichment analysis utilizing data from two microarray experiments with obese white adipose tissue and atherosclerotic aortae as well as respective controls using a combined insulin resistance-atherosclerosis mouse model.ResultsWe identified 22 dysregulated pathways common to both tissues with p values below 0.05, and selected inflammatory response and oxidative phosphorylation pathways from the Hallmark gene set to conduct a deeper evaluation at the single gene level. This analysis provided evidence of a vast overlap in gene expression alterations in obese adipose tissue and atherosclerosis with Il7r, C3ar1, Tlr1, Rgs1 and Semad4d being the highest ranked genes for the inflammatory response pathway and Maob, Bckdha, Aldh6a1, Echs1 and Cox8a for the oxidative phosphorylation pathway.ConclusionsIn conclusion, this study provides extensive evidence for common pathogenic pathways underlying obesity-driven insulin resistance and atherogenesis which could provide a basis for the development of novel strategies to simultaneously prevent type 2 diabetes and cardiovascular disease in patients with metabolic syndrome.
| S-EPMC5000404 | biostudies-literature
Project description:Background: Gastric cancer (GC) has a high mortality rate in cancer-related deaths worldwide. Currently, the pathogenesis of gastric cancer progression remains unclear. Here, we identified several vital candidate genes related to gastric cancer development and revealed the potential pathogenic mechanisms using integrated bioinformatics analysis. Methods: Two microarray datasets from Gene Expression Omnibus (GEO) database integrated. Limma package was used to analyze differentially expressed genes (DEGs) between GC and matched normal specimens. DAVID was utilized to conduct Gene ontology (GO) and KEGG enrichment analysis. The relative expression of OLFM4, IGF2BP3, CLDN1 and MMP1were analyzed based on TCGA database provided by UALCAN. Western blot and quantitative real time PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines, respectively. Results: We downloaded the expression profiles of GSE103236 and GSE118897 from the Gene Expression Omnibus (GEO) database. Two integrated microarray datasets were used to obtain differentially expressed genes (DEGs), and bioinformatics methods were used for in-depth analysis. After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analysis, we identified 61 DEGs in common, of which the expression of 34 genes were elevated and 27 genes were decreased. GO analysis displayed that the biological functions of DEGs mainly focused on negative regulation of growth, fatty acid binding, cellular response to zinc ion and calcium-independent cell-cell adhesion. KEGG pathway analysis demonstrated that these DEGs mainly related to the Wnt and tumor signaling pathway. Interestingly, we found 4 genes were most significantly upregulated in the DEGs, which were OLFM4, IGF2BP3, CLDN1 and MMP1. Then, we confirmed the upregulation of these genes in STAD based on sample types. In the final, western blot and qRT-PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines. Conclusion: In our study, using integrated bioinformatics to screen DEGs in gastric cancer could benefit us for understanding the pathogenic mechanism underlying gastric cancer progression. Meanwhile, we also identified four significantly upregulated genes in DEGs from both two datasets, which might be used as the biomarkers for early diagnosis and prevention of gastric cancer.
| S-EPMC7797537 | biostudies-literature
Project description:Background:The mortality rate associated with ovarian cancer ranks the highest among gynecological malignancies. However, the cause and underlying molecular events of ovarian cancer are not clear. Here, we applied integrated bioinformatics to identify key pathogenic genes involved in ovarian cancer and reveal potential molecular mechanisms. Results:The expression profiles of GDS3592, GSE54388, and GSE66957 were downloaded from the Gene Expression Omnibus (GEO) database, which contained 115 samples, including 85 cases of ovarian cancer samples and 30 cases of normal ovarian samples. The three microarray datasets were integrated to obtain differentially expressed genes (DEGs) and were deeply analyzed by bioinformatics methods. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were performed by DAVID and KOBAS online analyses, respectively. The protein-protein interaction (PPI) networks of the DEGs were constructed from the STRING database. A total of 190 DEGs were identified in the three GEO datasets, of which 99 genes were upregulated and 91 genes were downregulated. GO analysis showed that the biological functions of DEGs focused primarily on regulating cell proliferation, adhesion, and differentiation and intracellular signal cascades. The main cellular components include cell membranes, exosomes, the cytoskeleton, and the extracellular matrix. The molecular functions include growth factor activity, protein kinase regulation, DNA binding, and oxygen transport activity. KEGG pathway analysis showed that these DEGs were mainly involved in the Wnt signaling pathway, amino acid metabolism, and the tumor signaling pathway. The 17 most closely related genes among DEGs were identified from the PPI network. Conclusion:This study indicates that screening for DEGs and pathways in ovarian cancer using integrated bioinformatics analyses could help us understand the molecular mechanism underlying the development of ovarian cancer, be of clinical significance for the early diagnosis and prevention of ovarian cancer, and provide effective targets for the treatment of ovarian cancer.
| S-EPMC5858852 | biostudies-literature
Project description:Coronavirus disease 2019 (COVID-19) is acutely infectious pneumonia. Currently, the specific causes and treatment targets of COVID-19 are still unclear. Herein, comprehensive bioinformatics methods were employed to analyze the hub genes in COVID-19 and tried to reveal its potential mechanisms. First of all, 34 groups of COVID-19 lung tissues and 17 other diseases' lung tissues were selected from the GSE151764 gene expression profile for research. According to the analysis of the DEGs (differentially expressed genes) in the samples using the limma software package, 84 upregulated DEGs and 46 downregulated DEGs were obtained. Later, by the Database for Annotation, Visualization, and Integrated Discovery (DAVID), they were enriched in the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. It was found that the upregulated DEGs were enriched in the type I interferon signaling pathway, AGE-RAGE signaling pathway in diabetic complications, coronavirus disease, etc. Downregulated DEGs were in cellular response to cytokine stimulus, IL-17 signaling pathway, FoxO signaling pathway, etc. Then, based on GSEA, the enrichment of the gene set in the sample was analyzed in the GO terms, and the gene set was enriched in the positive regulation of myeloid leukocyte cytokine production involved in immune response, programmed necrotic cell death, translesion synthesis, necroptotic process, and condensed nuclear chromosome. Finally, with the help of STRING tools, the PPI (protein-protein interaction) network diagrams of DEGs were constructed. With degree ≥13 as the cutoff degree, 3 upregulated hub genes (ISG15, FN1, and HLA-G) and 4 downregulated hub genes (FOXP3, CXCR4, MMP9, and CD69) were screened out for high degree. All these findings will help us to understand the potential molecular mechanisms of COVID-19, which is also of great significance for its diagnosis and prevention.
| S-EPMC8710042 | biostudies-literature
Project description:Endometriosis is a common gynecological disease characterized by the presence and growth of endometrial tissue outside the uterus, including the pelvis and abdominal cavity. This condition causes various clinical symptoms, such as non-menstrual pelvic pain, dysmenorrhea and infertility, seriously affecting the health and quality of life of women. To date, the specific mechanism and the key molecules of endometriosis remain uncertain. The purpose of the present study was to elucidate the mechanisms involved in the development and persistence of the disease. A number of mRNA expression profile datasets (namely GSE11691, GSE23339, GSE25628 and GSE78851) were downloaded from the Gene Expression Omnibus (GEO) database. These gene expression profiles were normalized, and the differentially expressed genes (DEGs) were identified by integrated bioinformatics analysis. A total of 103 DEGs were screened upon excluding the genes that exhibited inconsistency of expression (P<0.05). Furthermore, the Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and construction of protein-protein interaction networks of DEGs were performed using online software. The results revealed that the DEGs were closely associated with cell migration, adherens junction and hypoxia-inducible factor signaling. In addition, immunohistochemical assay results were found to be consistent with the bioinformatics results. The present study may help us understand underlying molecular mechanisms and the development of endometriosis, which has a great clinical significance for early diagnosis of the disease.
| S-EPMC6909483 | biostudies-literature
Project description:Background: Rabbit is a good model for genetic and medical studies in other livestock species. The rabbit shows low adipose tissue deposition, and the phenomena indicates that there is some specificity of adipose deposition during the rabbit growth. However, little is known about genes that regulate the growth of adipose tissue in rabbits.Materials and methods: Deep RNA-seq and comprehensive bioinformatics analyses were used to characterize the genes of rabbit visceral adipose tissue (VAT) at 35, 85 and 120 days after birth. Differentially expressed genes (DEGs) were identified at the three growth stages by DESeq. To explore the function of the candidate genes, Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed. Six DEGs were randomly selected, and their expression profiles were validated by q-PCR.Results: A total of 20,303 known transcripts and 99,199 new transcripts from 8 RNA sequencing libraries were identified, and 34 differentially expressed genes (DEGs) were screened. GO enrichment and KEGG pathway analyses revealed that the DEGs were mainly involved in lipid metabolism regulation including acylglycerol metabolic process and mobilization, and decomposition of lipids to generate ATP in adipocytes and fatty acid metabolism, included LOC100342322 and LOC100342572. In addition, 133 protein-coding genes that play a role in adipose growth and development were screened, including acyl-CoA synthetase long-chain family member 5 (ACSL5) and fatty acid-binding protein 2 (FABP2). The validation results of six DEGs by q-PCR showed similar trends with the results of RNA-seq.Conclusion: In summary, this study provides the first report of the coding genes profiles of rabbit adipose tissue during different growth stages. These data allow for the identification of candidate genes for subsequent studies on rabbit genetics and regulation of adipose cells, and provide an animal model for studying obesity in humans.
| S-EPMC7719053 | biostudies-literature
Project description:IntroductionGaoyou duck is famous in China and abroad for its good production of double-yolk eggs. However, there has been no systematic research on the egg-laying characteristics of the Gaoyou duck, which limits the development and utilization of breed resource.MethodsTo identify the essential genes related to ovarian development, the transcriptome profiles of the ovaries of Gaoyou ducks at different physiological stages were analyzed. The transcriptome profiles of the ovaries of Gaoyou ducks at 150 d (before laying), 240 d (egg laying) and 500 d (nesting) were constructed, and the differentially expressed genes (DEGs) underwent GO (gene ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses.ResultsThe 6 randomly selected DEGs were verified by real-time fluorescent quantitative PCR that their relative expression was consistent with the transcriptional expression profile. Furthermore, KEGG analysis found that 8 candidate signaling pathways were essential for ovarian development, including the MAPK signaling pathway, Progesterone-mediated oocyte maturation, Cell adhesion molecules (CAMs), NOD-like receptor signaling pathway, ECM-receptor interaction, Focal adhesion, TGF-beta signaling path-way and Phagosome. Finally, 5 key DEGs were identified to participate in ovarian development, including TGIF1, TGFBR2, RAF1, PTK2 and FGF10.DiscussionOur findings reveal the mechanisms under-lying the molecular regulation of related genes in Gaoyou duck ovarian development.
| S-EPMC10189055 | biostudies-literature