Identification of primary genes in glomeruli compartment of immunoglobulin A nephropathy by bioinformatic analysis

The current study is aimed to explore the specific genes which are responsible for the manifestation of Immunoglobulin A nephropathy (IgAN). Gene expression profiles GSE37460, GSE93798 and GSE104948 were analyzed using biological informatics methods to identify differentially expressed genes (DEGs) in IgAN glomeruli samples which were then compared to normal control samples. Subsequently, the DEGs were overlapped to explore genes with significant expression in at least two profiles. Finally, the enrichment analysis was conducted and the protein-protein interaction (PPI) network was constructed for the overlapping DEGs. A total of 28 genes were up-regulated and 10 genes were down-regulated. The up-regulated genes including CD44 and FN1 were chiefly involved in extracellular matrix receptors interaction pathway. In addition, CX3CR1 and CCL4 were associated with chemokine signaling pathway. ITGB2, PTPRC, FN1, and FCER1G were hub genes with a high degree of interaction in the PPI network. Therefore, this study identified many significant genes associated with extracellular matrix expansion and inflammatory mechanism which may be the novel biomarker and target candidates in IgAN.

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Analysis of Potential Genes and Pathways Involved in the Pathogenesis of Acne by Bioinformatics

BioMed Research International ◽

10.1155/2019/3739086 ◽

2019 ◽

Vol 2019 ◽

pp. 1-8 ◽

Cited By ~ 2

Author(s):

Biao Chen ◽

Yan Zheng ◽

Yanhua Liang

Keyword(s):

Signaling Pathway ◽

Normal Skin ◽

Enrichment Analysis ◽

Bioinformatic Analysis ◽

Cytokine Receptor ◽

Receptor Interaction ◽

Ppi Network ◽

Pathway Enrichment ◽

Chemokine Signaling ◽

Chemokine Signaling Pathway

Acne is the eighth most frequent disease worldwide. Inflammatory response runs through all stages of acne. It is complicated and is involved in innate and adaptive immunity. This study aimed to explore the candidate genes and their relative signaling pathways in inflammatory acne using data mining analysis. Microarray data GSE6475 and GSE53795, including 18 acne lesion tissues and 18 matched normal skin tissues, were obtained. Differentially expressed genes (DEGs) were filtered and subjected to functional and pathway enrichment analyses. Protein–protein interaction (PPI) network and module analyses were also performed based on the DEGs. In this work, 154 common DEGs, including 145 upregulated and 9 downregulated, were obtained from two microarray profiles. Gene Ontology and pathway enrichment of DEGs were clustered using significant enrichment analysis. A PPI network containing 110 nodes/DEGs was constructed, and 31 hub genes were obtained. Four modules in the PPI network, which mainly participated in chemokine signaling pathway, cytokine–cytokine receptor interaction, and Fc gamma R-mediated phagocytosis, were extracted. In conclusion, aberrant DEGs and pathways involved in acne pathogenesis were identified using bioinformatic analysis. The DEGs included FPR2, ITGB2, CXCL8, C3AR1, CXCL1, FCER1G, LILRB2, PTPRC, SAA1, CCR2, ICAM1, and FPR1, and the pathways included chemokine signaling pathway, cytokine–cytokine receptor interaction, and Fc gamma R-mediated phagocytosis. This study could serve as a basis for further understanding the pathogenesis and potential therapeutic targets of inflammatory acne.

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Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

BMC Cancer ◽

10.1186/s12885-021-08412-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Baojie Wu ◽

Shuyi Xi

Keyword(s):

Gene Expression ◽

Cervical Cancer ◽

Differentially Expressed Genes ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Molecular Targets ◽

Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Kegg Pathways

Abstract Background This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis. Methods Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets. Results A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained. Conclusions These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer.

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Screening of Hub Genes Associated with Lung Adenocarcinoma by Integrated Bioinformatic Analysis

10.21203/rs.3.rs-580657/v1 ◽

2021 ◽

Author(s):

Zimeng Wei ◽

Min Zhao ◽

Linnan Zang

Keyword(s):

Gene Expression ◽

Lung Adenocarcinoma ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Bioinformatic Analysis ◽

Analysis Tool ◽

Hub Genes ◽

Kegg Analysis ◽

Ppi Networks

Abstract Background Lung adenocarcinoma (LUAD) is the main histological subtype of lung cancer. However, the molecular mechanism underlying LUAD is not yet clearly defined, but elucidating this process in detail would be of great significance for clinical diagnosis and treatment. Methods Gene expression profiles were retrieved from Gene Expression Omnibus database (GEO), and the common differentially expressed genes (DEGs) were identified by online GEO2R analysis tool. Subsequently, the enrichment analysis of function and signaling pathways of DEGs in LUAD were performed by gene ontology (GO) and The Kyoto Encyclopedia of Genes and Genomics (KEGG) analysis. The protein-protein interaction (PPI) networks of the DEGs were established through the Search Tool for the Retrieval of Interacting Genes (STRING) database and hub genes were screened by plug-in CytoHubba in Cytoscape. Afterwards, we detected the expression of hub genes in LUAD and other cancers via GEPIA, Oncomine and HPA databases. Finally, Kaplan-Meier plotter were performed to analyze the prognosis efficacy of hub genes. Results 74 up-regulated and 238 down-regulated DEGs were identified. As for the up-regulated DEGs, KEGG analysis results revealed they were mainly enrolled in protein digestion and absorption. However, the down-regulated DEGs were primarily enriched in cell adhesion molecules. Subsequently, 9 hub genes: KIAA0101, CDCA7, TOP2A, CDC20, ASPM, TPX2, CENPF, UBE2T and ECT2, were identified and showed higher expression in both LUAD and other cancers. Finally, all these hub genes were found significantly related to the prognosis of LUAD (p < 0.05). Conclusions Our results screened out the hub genes and pathways that were related to the development and prognosis of LUAD, which could provide new insight for the future molecularly targeted therapy and prognosis evaluation of LUAD.

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Meta-Analyses of Multiple Gene Expression Profiles to Screen Hub Genes Related to Osteoarthritis

Public Health Genomics ◽

10.1159/000517308 ◽

2021 ◽

Vol 24 (5-6) ◽

pp. 267-279

Author(s):

Xianyang Zhu ◽

Wen Guo

Keyword(s):

Gene Expression ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Meta Analysis ◽

Wnt Signaling Pathway ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Receptor Interaction ◽

Ppi Network ◽

Hub Genes

Background: This study aimed to screen and validate the crucial genes involved in osteoarthritis (OA) and explore its potential molecular mechanisms. Methods: Four expression profile datasets related to OA were downloaded from the Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) from 4 microarray patterns were identified by the meta-analysis method. The weighted gene co-expression network analysis (WGCNA) method was used to investigate stable modules most related to OA. In addition, a protein-protein interaction (PPI) network was built to explore hub genes in OA. Moreover, OA-related genes and pathways were retrieved from Comparative Toxicogenomics Database (CTD). Results: A total of 1,136 DEGs were identified from 4 datasets. Based on these DEGs, WGCNA further explored 370 genes included in the 3 OA-related stable modules. A total of 10 hub genes were identified in the PPI network, including AKT1, CDC42, HLA-DQA2, TUBB, TWISTNB, GSK3B, FZD2, KLC1, GUSB, and RHOG. Besides, 5 pathways including “Lysosome,” “Pathways in cancer,” “Wnt signaling pathway,” “ECM-receptor interaction” and “Focal adhesion” in CTD and enrichment analysis and 5 OA-related hub genes (including GSK3B, CDC42, AKT1, FZD2, and GUSB) were identified. Conclusion: In this study, the meta-analysis was used to screen the central genes associated with OA in a variety of gene expression profiles. Three OA-related modules (green, turquoise, and yellow) containing 370 genes were identified through WGCNA. It was discovered through the gene-pathway network that GSK3B, CDC42, AKT1, FZD2, and GUSB may be key genes related to the progress of OA and may become promising therapeutic targets.

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Dysfunctional network and mutation genes of hypertrophic cardiomyopathy

10.21203/rs.2.22781/v1 ◽

2020 ◽

Author(s):

Yunwen Cui ◽

Cheng Liu ◽

Jian Luo ◽

Jie Liang

Keyword(s):

Hypertrophic Cardiomyopathy ◽

Differentially Expressed Genes ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Differential Analysis ◽

Ppi Network ◽

Roc Curve Analysis ◽

Study Gene Expression

Abstract Background Hypertrophic cardiomyopathy (HCM) is a group of heterogeneous diseases that affect the myocardium. It is also a common familial disease. The symptoms are not common and easy to find. Methods In this study, gene expression profiles of 37 samples (GSE130036) were downloaded from GEO database. Differential analysis was used to identify the related dysregulated genes in patients with HCM. Enrichment analysis identified the biological function and signal pathway of these differentially expressed genes. Then, we build PPI network and verify it in GSE36961 dataset. Finally, the gene of single nuclear variants (SNVs) in HCM samples was screened by means of maftools. Results Herein, we obtained 920 differentially expressed genes, and found that these genes are mainly related to metabolic related signaling pathways. 187 interacting genes were identified by PPI network analysis, and the expression trends of C1QB, F13A1, CD163, FCN3, PLA2G2A and CHRDL2 were verified by another dataset. ROC curve analysis showed that they had certain clinical diagnostic ability, and they were the potential key dysfunctional genes of HCM. In addition, we found that PRMT5 mutation was the most frequent in HCM samples, which may affect the pathogenesis of HCM. Conclusions Therefore, the key genes and enrichment results identified by our analysis may provide a reference for the occurrence and development mechanism of HCM. In addition, mutations in PRMT5 may be a useful therapeutic and diagnostic target for HCM.

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Bioinformatics Analysis for Identifying Pertinent Pathways and Genes in Sepsis

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/2085173 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Yiran Li ◽

Hongyan Zhang ◽

Jinyan Shao ◽

Jindong Chen ◽

Tiancheng Zhang ◽

...

Keyword(s):

Prion Diseases ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Signal Pathways ◽

Ppi Network ◽

Protein Protein Interaction ◽

Search Tool ◽

Rising Incidence ◽

Medical Burden

Purpose. Sepsis becomes the main death reason in hospitals with rising incidence, causing a growing economic and medical burden. However, the genes related to the pathogenesis and prognosis of sepsis are still unclear, which is a problem that needs to be solved urgently. Materials and Methods. Gene expression profiles of GSE69528 were obtained from the National Center for Biotechnology Information. Limma software package got employed to search for differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were used for enrichment analysis. Protein-protein interaction (PPI) network was built by the Search Tool for the Retrieval of Interacting Genes (STRING) database. Results. We screened 101 DEGs, containing 81 upregulated DEGs and 20 downregulated DEGs. GO analysis demonstrated that the upregulated DEGs were chiefly concentrated in negative regulation of response to interferon-gamma and regulation of granulocyte differentiation. KEGG analysis revealed that the pathways of upregulated DEGs were concentrated in prion diseases, complement and coagulation cascades, and Staphylococcus aureus infection. The PPI network constructed by upregulated DEGs contained 67 nodes (proteins) and 110 edges (interactions). Analysis of bioinformatics results showed that CEACAM8, MPO, and RETN were hub genes of sepsis. Conclusion. Our analysis reveals a series of signal pathways and key genes related to the mechanism of sepsis, which are promising biotargets and biomarkers of sepsis.

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Identification of Genes and Pathways Associated with Acne Using Integrated Bioinformatics Methods

Dermatology ◽

10.1159/000502203 ◽

2019 ◽

Vol 235 (6) ◽

pp. 445-455 ◽

Cited By ~ 6

Author(s):

Xianglan Li ◽

Yuxi Jia ◽

Shiyi Wang ◽

Tianqi Meng ◽

Mingji Zhu

Keyword(s):

Expression Profiles ◽

Killer Cell ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Cytokine Receptor ◽

Gene Set Enrichment Analysis ◽

Receptor Interaction ◽

Ppi Network ◽

Ncbi Gene Expression Omnibus ◽

Chemokine Signaling

Background: Acne is the most common skin inflammatory condition. The pathogenesis of acne is not fully understood. Aims: We performed weighted gene co-expression network analysis (WGCNA) to select acne-associated genes and pathways. Methods: GSE53795 and GSE6475 datasets including data from lesional and nonlesional skin of acne patients were downloaded from the NCBI Gene Expression Omnibus. Differentially expressed genes (DEGs) in lesions were identified following a false discovery rate <0.05 and | log2 fold change | ≥0.5. DEG-associated biological processes and pathways were identified. WGCNA analysis was performed to identify acne-associated modules. DEGs in the acne-associated modules were used for protein-protein interaction (PPI) network construction and Gene Set Enrichment Analysis (GSEA). Acne-associated candidate DEGs and pathways were identified together with items in the Comparative Toxicogenomics Database (CTD). Results: A total of 2,140 and 1,190 DEGs were identified in GSE53795 and GSE6475 datasets, respectively, including 716 overlapping DEGs with similar expression profiles in the two datasets, which were clustered into 10 consensus modules. Two modules (brown and turquoise, 359 genes) were associated with acne phenotype. Of these 359 DEGs, 254 were enrolled in the PPI network. GSEA showed that these DEGs were associated with chemokine signaling pathway, cytokine-cytokine receptor interaction, and natural killer cell-mediated cytotoxicity. After identification in CTD, one pathway Cytokine-cytokine receptor interaction and 24 acne-associated DEGs, including IL1R1, CXCL1, CXCR4, CCR1, CXCL2 and IL1β, were identified as candidates associated with acne. Conclusion: Our results highlight the important roles of the proinflammatory cytokines including IL1β, CXCL1, CXCL2, CXCR4, and CCR1 in acne pathogenesis or therapeutic management.

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Network-Based Association Study of Obesity and Type 2 Diabetes with Gene Expression Profiles

BioMed Research International ◽

10.1155/2015/619730 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Siyi Zhang ◽

Bo Wang ◽

Jingsong Shi ◽

Jing Li

Keyword(s):

Type 2 Diabetes ◽

Signaling Pathway ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Cell Receptor ◽

Mapk Signaling ◽

Chemokine Signaling ◽

Chemokine Signaling Pathway

The increased prevalence of obesity and type 2 diabetes (T2D) has become an important factor affecting the health of the human. Obesity is commonly considered as a major risk factor for the development of T2D. However, the molecular mechanisms of the disease relations are not well discovered yet. In this study, the combination of multiple differential expression profiles and a comprehensive biological network of obesity and T2D allowed us to identify and compare the disease-responsive active modules and subclusters. The results demonstrated that the connection between obesity and T2D mainly relied on several pathways involved in the digestive metabolism, immunization, and signal transduction, such as adipocytokine, chemokine signaling pathway, T cell receptor signaling pathway, and MAPK signaling pathways. The relationships of almost all of these pathways with obesity and T2D have been verified by the previous reports individually. We also found that the different parts in the same pathway are activated in obesity and T2D. The association of cancer, obesity, and T2D was identified too here. As a conclusion, our network-based method not only gives better support for the close connection between obesity and T2D, but also provides a systemic view in understanding the molecular functions underneath the links. It should be helpful in the development of new therapies for obesity, T2D, and the associated diseases.

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Monozygotic Twins Discordant for Immunoglobulin A Nephropathy Display Differences in DNA Methylation and Gene Expression

Kidney Diseases ◽

10.1159/000512169 ◽

2020 ◽

pp. 1-10

Author(s):

Min Wei ◽

Sijun Meng ◽

Sufang Shi ◽

Lijun Liu ◽

Xujie Zhou ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Expression Profiles ◽

Immunoglobulin A ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Genome Wide ◽

Mz Twins

Introduction: Immunoglobulin A nephropathy (IgAN) is the most common primary glomerulonephritis. It involves both genetic and environmental factors, among which DNA methylation, the most studied epigenetic modification, was shown to play a role. Here, we assessed genome-wide DNA methylation and gene expression profiles in 2 pairs of IgAN-discordant monozygotic (MZ) twins, in order to characterize methylation changes and their potential influences on gene expression in IgAN. Methods: Genome-wide DNA methylation and gene expression profiles were evaluated in peripheral blood mononuclear cells obtained from 2 IgAN-discordant MZ twins. Differentially methylated regions (DMRs) and differentially expressed genes (DEGs) were detected, and an integrated analysis was performed. Finally, functional enrichment analysis was done for DMR-associated genes and DEGs. Results: Totally 521 DMRs were detected for 2 IgAN-discordant MZ twins. Among them, 9 DMRs were found to be mapped to genes that differentially expressed in 2 MZ twins, indicating the potential regulatory mechanisms of expression for these 9 genes (MNDA, DYSF, IL1R2, TLR6, TREML2, TREM1, IL32, S1PR5, and ADGRE3) in IgAN. Biological process analysis of them showed that they were mostly involved in the immune system process. Functional enrichment analysis of DEGs and DMR-associated genes both identified multiple pathways relevant to inflammatory and immune responses. And DMR-associated genes were significantly enriched in terms related to T-cell function. Conclusions: Our findings indicate that changes in DNA methylation patterns were involved in the pathogenesis of IgAN. Nine target genes detected in our study may provide new ideas for the exploration of molecular mechanisms of IgAN.

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Identification of Significant Genes And Pathways In ARDS Via Bioinformatical Analysis

10.21203/rs.3.rs-457902/v1 ◽

2021 ◽

Author(s):

Weina Lu ◽

Ran Ji

Keyword(s):

Cellular Response ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Receptor Interaction ◽

Integrated Analysis ◽

Pathway Enrichment Analysis ◽

Signal Pathways ◽

Ppi Network ◽

Hub Genes

Abstract Background and Aims: Acute respiratory distress syndrome (ARDS) is one of the most common acute thoracopathy with complicated pathogenesis in ICU. The study is to explore the differentially expressed genes (DEGs) in the lung tissue and underlying altering mechanisms in ARDS.Methods: Gene expression profiles of GSE2411 and GSE130936 were available from GEO database, both of them included in GPL 339. Then, an integrated analysis of these genes was performed, including gene ontology (GO) and KEGG pathway enrichment analysis, protein-protein interaction (PPI) network construction, Transcription Factors (TFs) forecasting, and their expression in varied organs.Results: A total of 39 differential expressed genes were screened from the datasets, including 39 up-regulated genes and 0 down-regulated genes. The up-regulated genes were mainly enriched in the biological process, such as immune system process, innate immune response, inflammatory response, cellular response to interferon-beta and also involved in some signal pathways, including cytokine-cytokine receptor interaction, salmonella infection, legionellosis, chemokine, and Toll-like receptor signal pathway. GBP2, IFIT2 and IFIT3 were identified as hub genes in the lung by PPI network analysis with MCODE plug-in, as well as GO and KEGG re-enrichment. All of the three hub genes were regulated by the predictive common TFs, including STAT1, E2F1, IRF1, IRF2, and IRF9. Conclusions: This study implied that hub gene GBP2, IFIT2 and IFIT3, which might be regulated by STAT1, E2F1, IRF1, IRF2, or IRF9, played significant roles in ARDS. They could be potential diagnostic or therapeutic targets for ARDS patients.

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