scholarly journals Bioinformatics analysis of key genes and pathways in Hashimoto thyroiditis tissues

2020 ◽  
Vol 40 (7) ◽  
Author(s):  
Long Zheng ◽  
Xiaojie Dou ◽  
Huijia Song ◽  
Pengwei Wang ◽  
Wei Qu ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Interaction Analysis ◽  
Differentially Expressed Gene ◽  
Enrichment Analysis ◽  
Hashimoto Thyroiditis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Key Genes

Abstract Hashimoto thyroiditis (HT) is one of the most common autoimmune diseases, and the incidence of HT continues to increase. Long-term, uncontrollable HT results in thyroid dysfunction and even increases carcinogenesis risks. Since the origin and development of HT involve many complex immune processes, there is no effective therapy for HT on a pathogenesis level. Although bioinformatics analysis has been utilized to seek key genes and pathways of thyroid cancer, only a few bioinformatics studies that focus on HT pathogenesis and mechanisms have been reported. In the present study, the Gene Expression Omnibus dataset (GSE29315) containing 6 HT and 8 thyroid physiological hyperplasia samples was downloaded, and differentially expressed gene (DEG) analysis, Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, protein–protein interaction analysis, and gene set enrichment analysis were performed. In total, 85 DEGs, containing 76 up-regulated and 9 down-regulated DEGS, were identified. The DEGs were mainly enriched in immune and inflammatory response, and the signaling pathways were involved in cytokine interaction and cytotoxicity. Moreover, ten hub genes were identified, and IFN-γ, IFN-α, IL6/JAK/STAT3, and inflammatory pathways may promote the origin and progression of HT. The present study indicated that exploring DEGs and pathways by bioinformatics analysis has important significance in understanding the molecular mechanisms of HT and providing potential targets for the prevention and treatment of HT.

scholarly journals Identification of Potential Key Genes and Pathways in Enzalutamide-Resistant Prostate Cancer Cell Lines: A Bioinformatics Analysis with Data from the Gene Expression Omnibus (GEO) Database

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Long Zheng ◽  
Xiaojie Dou ◽  
Xiaodong Ma ◽  
Wei Qu ◽  
Xiaoshuang Tang
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Cell Lines ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Key Genes ◽  
Geo Database

Enzalutamide (ENZ) has been approved for the treatment of advanced prostate cancer (PCa), but some patients develop ENZ resistance initially or after long-term administration. Although a few key genes have been discovered by previous efforts, the complete mechanisms of ENZ resistance remain unsolved. To further identify more potential key genes and pathways in the development of ENZ resistance, we employed the GSE104935 dataset, including 5 ENZ-resistant (ENZ-R) and 5 ENZ-sensitive (ENZ-S) PCa cell lines, from the Gene Expression Omnibus (GEO) database. Integrated bioinformatics analyses were conducted, such as analysis of differentially expressed genes (DEGs), Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein-protein interaction (PPI) analysis, gene set enrichment analysis (GSEA), and survival analysis. From these, we identified 201 DEGs (93 upregulated and 108 downregulated) and 12 hub genes (AR, ACKR3, GPER1, CCR7, NMU, NDRG1, FKBP5, NKX3-1, GAL, LPAR3, F2RL1, and PTGFR) that are potentially associated with ENZ resistance. One upregulated pathway (hedgehog pathway) and seven downregulated pathways (pathways related to androgen response, p53, estrogen response, TNF-α, TGF-β, complement, and pancreas β cells) were identified as potential key pathways involved in the occurrence of ENZ resistance. Our findings may contribute to further understanding the molecular mechanisms of ENZ resistance and provide some clues for the prevention and treatment of ENZ resistance.

scholarly journals Gene Expression Profile of Active HE4 Stimulation in Epithelial Ovarian Cancer Cells: Microarray Study and Comprehensive Bioinformatics Analysis

2020 ◽  
Author(s):  
Liancheng Zhu ◽  
Mingzi Tan ◽  
Haoya Xu ◽  
Bei Lin
Keyword(s):  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Clinical Significance ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Coexpression Analysis ◽  
Pathway Enrichment

Abstract Background.Human Epididymis Protein 4 (HE4) is a novel serum biomarker for diagnosis of epithelial ovarian cancer (EOC) with high specificity and sensitivity compared with CA125, and the increasing researches have been carried out on its roles in promoting carcinogenesis and chemoresistance in EOC in recent years, however, its underlying molecular mechanisms remain poorly understood. The aim of this study was to elucidate the molecular mechanisms of HE4 stimulation and to identify the key genes and pathways mediating carcinogenesis in EOC using microarray and bioinformatics analysis.Methods. We established a stable HE4-silence ES-2 ovarian cancer cell line labeled as “S”, and its active HE4 protein stimulated cells labeled as “S4”. Human whole genome microarray analysis was used to identify deferentially expressed genes (DEGs) from triplicate samples of S4 and S cells. “clusterProfiler” package in R, DAVID, Metascape, and Gene Set Enrichment Analysis (GSEA) were used to perform gene ontology (GO) and pathway enrichment analysis, and cBioPortal for WFDC2 coexpression analysis. GEO dataset (GSE51088) and quantitative real-time polymerase chain reaction (qRT-PCR) was applied for validation. The protein–protein interaction (PPI) network and modular analyses were performed using Metascape and Cytoscape. Results.In total, 713 DEGs were found (164 up regulated and 549 down regulated) and further analyzed by GO, pathway enrichment and PPI analyses. We found that MAPK pathway accounted for a significant portion of the enriched terms. WFDC2 coexpression analysis revealed ten WFDC2 coexpressed genes (TMEM220A, SEC23A, FRMD6, PMP22, APBB2, DNAJB4, ERLIN1, ZEB1, RAB6B, and PLEKHF1) that were also dramatically changed in S4 cells and validated by dataset GSE51088. Kaplan–Meier survival statistics revealed clinical significance for all of the 10 target genes. Finally, PPI was constructed, sixteen hub genes and eight molecular complex detections (MCODEs) were identified, the seeds of five most significant MCODEs were subjected to GO and KEGG enrichment analysis and their clinical significance was evaluated.Conclusions.By applying microarray and bioinformatics analyses, we identified DEGs and determined a comprehensive gene network of active HE4 stimulation in EOC cells. We offered several possible mechanisms and identified therapeutic and prognostic targets of HE4 in EOC.

Gene expression profiles following active HE4 stimulation in epithelial ovarian cancer cells: microarray study and comprehensive bioinformatics analysis

2020 ◽  
Author(s):  
Liancheng Zhu ◽  
Mingzi Tan ◽  
Haoya Xu ◽  
Bei Lin
Keyword(s):  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Coexpression Analysis ◽  
Pathway Enrichment

Abstract Background: Human epididymis protein 4 (HE4) is a novel serum biomarker for diagnosing epithelial ovarian cancer (EOC) with high specificity and sensitivity, compared with CA125. Recent studies have focused on the roles of HE4 in promoting carcinogenesis and chemoresistance in EOC; however, the molecular mechanisms underlying its action remain poorly understood. This study was conducted to determine the molecular mechanisms underlying HE4 stimulation and identifying key genes and pathways mediating carcinogenesis in EOC by microarray and bioinformatics analysis.Methods: We established a stable HE4-silenced ES-2 ovarian cancer cell line labeled as “S”; the S cells were stimulated with the active HE4 protein, yielding cells labeled as “S4”. Human whole-genome microarray analysis was used to identify differentially expressed genes (DEGs) in S4 and S cells. The “clusterProfiler” package in R, DAVID, Metascape, and Gene Set Enrichment Analysis were used to perform gene ontology (GO) and pathway enrichment analysis, and cBioPortal was used for WFDC2 coexpression analysis. The GEO dataset (GSE51088) and quantitative real-time polymerase chain reaction were used to validate the results. Protein–protein interaction (PPI) network and modular analyses were performed using Metascape and Cytoscape, respectively. Results: In total, 713 DEGs were identified (164 upregulated and 549 downregulated) and further analyzed by GO, pathway enrichment, and PPI analyses. We found that the MAPK pathway accounted for a significant large number of the enriched terms. WFDC2 coexpression analysis revealed ten WFDC2-coexpressed genes (TMEM220A, SEC23A, FRMD6, PMP22, APBB2, DNAJB4, ERLIN1, ZEB1, RAB6B, and PLEKHF1) whose expression levels were dramatically altered in S4 cells; this was validated using the GSE51088 dataset. Kaplan–Meier survival statistics revealed that all 10 target genes were clinically significant. Finally, in the PPI network, 16 hub genes and 8 molecular complex detections (MCODEs) were identified; the seeds of the five most significant MCODEs were subjected to GO and KEGG enrichment analyses and their clinical relevance was evaluated.Conclusions: Through microarray and bioinformatics analyses, we identified DEGs and determined a comprehensive gene network following active HE4 stimulation in EOC cells. We proposed several possible mechanisms underlying the action of HE4 and identified the therapeutic and prognostic targets of HE4 in EOC.

scholarly journals Bioinformatics analysis reveals key genes and pathways that promote melanoma metastasis

2020 ◽  
Author(s):  
wenxing su ◽  
yi guan ◽  
biao huang ◽  
juanjuan wang ◽  
yuqian wei ◽  
...  
Keyword(s):  
Differentially Expressed Gene ◽  
Primary Melanoma ◽  
Enrichment Analysis ◽  
Gene Interaction ◽  
Underlying Mechanism ◽  
Gene Expression Omnibus ◽  
Pathway Enrichment Analysis ◽  
Melanoma Metastasis ◽  
Ppi Network ◽  
Key Genes

Abstract Melanoma has the highest mortality rate of all skin tumors, and metastases are the major cause of death from it. The molecular mechanism leading to melanoma metastasis is currently unclear. With the goal of revealing the underlying mechanism, three data sets with accession numbers GSE8401, GSE46517 and GSE7956 were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the differentially expressed gene (DEG) of primary melanoma and metastatic melanoma, three kinds of analyses were performed, namely functional annotation, protein-protein interaction (PPI) network and module construction, and co-expression and drug-gene interaction prediction analysis. 41 up-regulated genes and 79 down-regulated genes were selected for subsequent analyses. Results of pathway enrichment analysis showed that extracellular matrix organization and proteoglycans in cancer are closely related to melanoma metastasis. In addition, seven pivotal genes were identified from PPI network, including CXCL8, THBS1, COL3A1, TIMP3, KIT, DCN, and IGFBP5, which have all been verified in the TCGA database, but only CXCL8, THBS1 and KIT had significant differences in expression. To conclude, CXCL8, THBS1 and KIT may be the key genes in the metastasis of melanoma and thus may be regarded as therapeutic targets in the future.

scholarly journals Identification of Vital Hub Genes and Potential Molecular Pathways of Dermatomyositis by Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xueren Ouyang ◽  
Yuning Zeng ◽  
Xiaotao Jiang ◽  
Hua Xu ◽  
Yile Ning
Keyword(s):  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Pathway Enrichment Analysis ◽  
Molecular Pathways ◽  
Potential Candidate ◽  
Molecular Signaling ◽  
Hub Genes ◽  
High Association

Dermatomyositis is an autoimmune disease characterized by severe symmetrical muscle dysfunction and pain. This study was aimed at discovering vital hub genes and potential molecular pathways of DM through bioinformatics analysis, which contributes to identifying potential diagnostic or therapeutic biomarkers and targets. In this study, a total of 915 DEGs in DM samples including 167 upregulated genes and 748 downregulated genes were screened out by the limma package based on the GSE142807 dataset from the Gene Expression Omnibus (GEO) database. Furthermore, the results of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis indicated that these downregulated genes were highly associated with the immune-related biological processes and pathways. Therefore, 41 genes closely related to DM were extracted for further study based on the subcluster analysis through the Molecular Complex Detection (MCODE) software plugin in Cytoscape. Ultimately, 10 hub genes (including ISG15, DDX58, IFIT3, CXCL10, and STAT1) were identified as the potential candidate biomarkers and targets. Besides, we found that the identified hub genes directly or indirectly communicated with each other via molecular signaling pathways on the protein and transcription level. In general, under the guidance of bioinformatics analysis, 10 vital hub genes and molecular mechanisms in DM were identified and the expression of proinflammatory factors and interferon family proteins and genes showed high association with DM, which might help provide a theoretical foundation for the development of point-to-point targeted therapy in the future treatment of DM.

scholarly journals Identification of Ferroptotic Genes in Spinal Cord Injury at Different Time Points: Bioinformatics and Experimental Validation

2021 ◽  
Author(s):  
Yu Kang ◽  
Qiangwei Li ◽  
Rui Zhu ◽  
Shuang Li ◽  
Xin Xu ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Signaling Pathway ◽  
Molecular Mechanisms ◽  
Interaction Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Protein Protein Interaction ◽  
Cord Injury ◽  
Key Genes

Abstract Programmed cell death (PCD) is an important pathologic process after spinal cord injury (SCI), and as a newly type of PCD, ferroptosis is also involved in the secondary SCI, however, the underlying molecular mechanisms remain unclear. Integrating animal experiment and bioinformatics, we validated the ferroptotic phenotype in SCI first, and then bioinformatic analyses, including Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, gene set enrichment analysis and protein-protein interaction analysis were performed to investigate the ferroptotic genes at 1 day, 3 days, 7 days, 14 days and 56 days post-SCI, finally, the ferroptotic genes in SCI were identified and expression of 5 key genes were validated by western blot. The ferroptotic symbols including iron overload, lipid peroxidation, shrunken mitochondria and ROS accumulation were detected in the acute and sub-acute phase of SCI. The outcomes of bioinformatics suggested that mTOR signaling pathway, HIF-1 signaling pathway, VEGF signaling pathway, Protein processing in endoplasmic reticulum were involved in ferroptotic regulation and ATF-3, XBP-1, HO-1, DDIT-3 and CHAC-1 were selected as the ferroptotic key genes in SCI. Besides, response to oxidative stress, amide metabolic process, cation transport and cytokine production were showed as the essential biological process in ferroptosis after SCI. The ferroptotic phenotype following SCI was validated and the ferroptotic genes and signaling pathways were identified. The results contribute to exploring the ferroptotic mechanism underlying secondary SCI and to providing potential target for clinical treatment.

scholarly journals Bioinformatics analysis reveals key genes and pathways that promote melanoma metastasis

2020 ◽  
Author(s):  
wenxing su ◽  
yi guan ◽  
biao huang ◽  
juanjuan wang ◽  
yuqian wei ◽  
...  
Keyword(s):  
Differentially Expressed Gene ◽  
Primary Melanoma ◽  
Enrichment Analysis ◽  
Gene Interaction ◽  
Underlying Mechanism ◽  
Gene Expression Omnibus ◽  
Pathway Enrichment Analysis ◽  
Melanoma Metastasis ◽  
Ppi Network ◽  
Key Genes

Abstract Melanoma has the highest mortality rate of all skin tumors, and metastases are the major cause of death from it. The molecular mechanism leading to melanoma metastasis is currently unclear. With the goal of revealing the underlying mechanism, three data sets with accession numbers GSE8401, GSE46517 and GSE7956 were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the differentially expressed gene (DEG) of primary melanoma and metastatic melanoma, three kinds of analyses were performed, namely functional annotation, protein‐protein interaction (PPI) network and module construction, and co-expression and drug-gene interaction prediction analysis. 41 up-regulated genes and 79 down-regulated genes were selected for subsequent analyses. Results of pathway enrichment analysis showed that extracellular matrix organization and proteoglycans in cancer are closely related to melanoma metastasis. In addition, seven pivotal genes were identified from PPI network, including CXCL8, THBS1, COL3A1, TIMP3, KIT, DCN, and IGFBP5, which have all been verified in the TCGA database, but only CXCL8, THBS1 and KIT had significant differences in expression. To conclude, CXCL8, THBS1 and KIT may be the key genes in the metastasis of melanoma and thus may be regarded as therapeutic targets in the future.

scholarly journals Screen Key Genes Associated with Distraction-Induced Osteogenesis of Stem Cells Using Bioinformatics Methods

2021 ◽  
Vol 22 (12) ◽  
pp. 6505
Author(s):  
Jishizhan Chen ◽  
Jia Hua ◽  
Wenhui Song
Keyword(s):  
Stem Cells ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Negative Control ◽  
Gene Set Enrichment Analysis ◽  
Venn Diagram ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Two Samples ◽  
Key Genes

Applying mesenchymal stem cells (MSCs), together with the distraction osteogenesis (DO) process, displayed enhanced bone quality and shorter treatment periods. The DO guides the differentiation of MSCs by providing mechanical clues. However, the underlying key genes and pathways are largely unknown. The aim of this study was to screen and identify hub genes involved in distraction-induced osteogenesis of MSCs and potential molecular mechanisms. Material and Methods: The datasets were downloaded from the ArrayExpress database. Three samples of negative control and two samples subjected to 5% cyclic sinusoidal distraction at 0.25 Hz for 6 h were selected for screening differentially expressed genes (DEGs) and then analysed via bioinformatics methods. The Gene Ontology (GO) terms and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment were investigated. The protein–protein interaction (PPI) network was visualised through the Cytoscape software. Gene set enrichment analysis (GSEA) was conducted to verify the enrichment of a self-defined osteogenic gene sets collection and identify osteogenic hub genes. Results: Three hub genes (IL6, MMP2, and EP300) that were highly associated with distraction-induced osteogenesis of MSCs were identified via the Venn diagram. These hub genes could provide a new understanding of distraction-induced osteogenic differentiation of MSCs and serve as potential gene targets for optimising DO via targeted therapies.

scholarly journals Analysis of MCM Proteins’ Role as a Potential Target of Statins in Patients with Acute Type A Aortic Dissection through Bioinformatics

Genes ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 387
Author(s):  
Zheyong Liang ◽  
Yongjian Zhang ◽  
Qiang Chen ◽  
Junjun Hao ◽  
Haichen Wang ◽  
...  
Keyword(s):  
Aortic Dissection ◽  
Molecular Mechanisms ◽  
Vascular Diseases ◽  
Enrichment Analysis ◽  
Type A ◽  
Protective Role ◽  
Gene Expression Omnibus ◽  
Pathway Enrichment Analysis ◽  
Acute Type ◽  
Type A Aortic Dissection

Acute aortic dissection is one of the most severe vascular diseases. The molecular mechanisms of aortic expansion and dissection are unclear. Clinical studies have found that statins play a protective role in aortic dissection development and therapy; however, the mechanism of statins’ effects on the aorta is unknown. The Gene Expression Omnibus (GEO) dataset GSE52093, GSE2450and GSE8686 were analyzed, and genes expressed differentially between aortic dissection samples and normal samples were determined using the Networkanalyst and iDEP tools. Weight gene correlation network analysis (WGCNA), functional annotation, pathway enrichment analysis, and the analysis of the regional variations of genomic features were then performed. We found that the minichromosome maintenance proteins (MCMs), a family of proteins targeted by statins, were upregulated in dissected aortic wall tissues and play a central role in cell-cycle and mitosis regulation in aortic dissection patients. Our results indicate a potential molecular target and mechanism for statins’ effects in patients with acute type A aortic dissection.

scholarly journals Integrative analyses of biomarkers and pathways for heart failure

2021 ◽  
Author(s):  
Shaowei Fan ◽  
Yuanhui Hu
Keyword(s):  
Heart Failure ◽  
Extracellular Matrix ◽  
Microarray Data ◽  
Enrichment Analysis ◽  
Signal Pathway ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Pathway Enrichment Analysis ◽  
Ppi Networks ◽  
Key Genes

Abstract Background: Heart failure (HF) is the most common potential cause of death, causing a huge health and economic burden all over the world. So far, some impressive progress has been made in the study of pathogenesis. However, the underlying molecular mechanisms leading to this disease remain to be fully elucidated. Methods: The microarray data sets of GSE76701, GSE21610 and GSE8331 were retrieved from the gene expression comprehensive database (GEO). After merging all microarray data and adjusting batch effects, differentially expressed genes (DEG) were determined. Functional enrichment analysis was performed based on Gene Ontology (GO) resources, Kyoto Encyclopedia of Genes and Genomes (KEGG) resources, gene set enrichment analysis (GSEA), response pathway database and Disease Ontology (DO). Protein protein interaction (PPI) network was constructed using string database. Combined with the above important bioinformatics information, the potential key genes were selected. The comparative toxicological genomics database (CTD) is used to explore the interaction between potential key genes and HF. Results: We identified 38 patients with heart failure and 16 normal controls. There were 315 DEGs among HF samples, including 278 up-regulated genes and 37 down-regulated genes. Pathway enrichment analysis showed that most DEGs were significantly enriched in BMP signal pathway, transmembrane receptor protein serine / threonine kinase signal pathway, extracellular matrix, basement membrane, glycosaminoglycan binding, sulfur compound binding and so on. Similarly, GSEA enrichment analysis showed that DEGs were mainly enriched in extracellular matrix and extracellular matrix related proteins. BBS9, CHRD, BMP4, MYH6, NPPA and CCL5 are central genes in PPI networks and modules. Conclusions: the enrichment pathway of DEGs and go ontology may reveal the molecular mechanism of HF. Among them, target genes EIF1AY, RPS4Y1, USP9Y, KDM5D, DDX3Y, NPPA, HBB, TSIX, LOC28556 and XIST are expected to become new targets for heart failure. Our findings provide potential biomarkers or therapeutic targets for the further study of heart failure and contribute to the development of advanced prediction, diagnosis and treatment strategies.

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