scholarly journals Sex Difference of Ribosome in Stroke-Induced Peripheral Immunosuppression by Integrated Bioinformatics Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Jian-Qin Xie ◽  
Ya-Peng Lu ◽  
Hong-Li Sun ◽  
Li-Na Gao ◽  
Pei-Pei Song ◽  
...  
Keyword(s):  
Gene Expression ◽  
Sex Difference ◽  
Bioinformatics Analysis ◽  
Prognostic Biomarkers ◽  
Pathway Enrichment Analysis ◽  
Loss Of Function ◽  
Ppi Network ◽  
Translational Initiation ◽  
Nonalcoholic Fatty Liver ◽  
Female Specific

Ischemic stroke (IS) greatly threatens human health resulting in high mortality and substantial loss of function. Recent studies have shown that the outcome of IS has sex specific, but its mechanism is still unclear. This study is aimed at identifying the sexually dimorphic to peripheral immune response in IS progression, predicting potential prognostic biomarkers that can lead to sex-specific outcome, and revealing potential treatment targets. Gene expression dataset GSE37587, including 68 peripheral whole blood samples which were collected within 24 hours from known onset of symptom and again at 24-48 hours after onset (20 women and 14 men), was downloaded from the Gene Expression Omnibus (GEO) datasets. First, using Bioconductor R package, two kinds of differentially expressed genes (DEGs) (nonsex-specific- and sex-specific-DEGs) were screened by follow-up (24-48 hours) vs. baseline (24 hours). 30 nonsex-specific DEGs (1 upregulated and 29 downregulated), 79 female-specific DEGs (25 upregulated and 54 downregulated), and none of male-specific DEGs were obtained finally. Second, bioinformatics analysis of female-specific DEGs was performed. Gene Ontology (GO) functional annotation analysis shows that DEGs were mainly enriched in translational initiation, cytosolic ribosome, and structural constituent of ribosome. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis shows that the top 6 enrichment pathways are ribosome, nuclear factor-­kappa B (NF-kappa B) signaling pathway, apoptosis, mineral absorption, nonalcoholic fatty liver disease, and pertussis. Three functional modules were clustered in the protein–protein interaction (PPI) network of DEGs. The top 10 key genes of the PPI network constructed were selected, including RPS14, RPS15A, RPS24, FAU, RPL27, RPL31, RPL34, RPL35A, RSL24D1, and EEF1B2. Sex difference of ribosome in stroke-induced peripheral immunosuppression may be the potential mechanism of sex disparities in outcome after IS, and women are more likely to have stroke-induced immunosuppression. RPS14, RPS15A, RPS24, FAU, RPL27, RPL31, RPL34, RPL35A, RSL24D1, and EEF1B2 may be novel prognostic biomarkers and potential therapeutic targets for IS.

scholarly journals Identifying MMP14 and COL12A1 as a potential combination of prognostic biomarkers in pancreatic ductal adenocarcinoma using integrated bioinformatics analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10419
Author(s):  
Jingyi Ding ◽  
Yanxi Liu ◽  
Yu Lai
Keyword(s):  
Gene Expression ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Prognostic Biomarkers ◽  
Ductal Adenocarcinoma ◽  
Receptor Interaction ◽  
Analysis Tool ◽  
Ppi Network ◽  
Hub Genes

Background Pancreatic ductal adenocarcinoma (PDAC) is a fatal malignant neoplasm. It is necessary to improve the understanding of the underlying molecular mechanisms and identify the key genes and signaling pathways involved in PDAC. Methods The microarray datasets GSE28735, GSE62165, and GSE91035 were downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified by integrated bioinformatics analysis, including protein–protein interaction (PPI) network, Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The PPI network was established using the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software. GO functional annotation and KEGG pathway analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery. Hub genes were validated via the Gene Expression Profiling Interactive Analysis tool (GEPIA) and the Human Protein Atlas (HPA) website. Results A total of 263 DEGs (167 upregulated and 96 downregulated) were common to the three datasets. We used STRING and Cytoscape software to establish the PPI network and then identified key modules. From the PPI network, 225 nodes and 803 edges were selected. The most significant module, which comprised 11 DEGs, was identified using the Molecular Complex Detection plugin. The top 20 hub genes, which were filtered by the CytoHubba plugin, comprised FN1, COL1A1, COL3A1, BGN, POSTN, FBN1, COL5A2, COL12A1, THBS2, COL6A3, VCAN, CDH11, MMP14, LTBP1, IGFBP5, ALB, CXCL12, FAP, MATN3, and COL8A1. These genes were validated using The Cancer Genome Atlas (TCGA) and Genotype–Tissue Expression (GTEx) databases, and the encoded proteins were subsequently validated using the HPA website. The GO analysis results showed that the most significantly enriched biological process, cellular component, and molecular function terms among the 20 hub genes were cell adhesion, proteinaceous extracellular matrix, and calcium ion binding, respectively. The KEGG pathway analysis showed that the 20 hub genes were mainly enriched in ECM–receptor interaction, focal adhesion, PI3K-Akt signaling pathway, and protein digestion and absorption. These findings indicated that FBN1 and COL8A1 appear to be involved in the progression of PDAC. Moreover, patient survival analysis performed via the GEPIA using TCGA and GTEx databases demonstrated that the expression levels of COL12A1 and MMP14 were correlated with a poor prognosis in PDAC patients (p < 0.05). Conclusions The results demonstrated that upregulation of MMP14 and COL12A1 is associated with poor overall survival, and these might be a combination of prognostic biomarkers in PDAC.

MYLK, CNN1, TAGLN and LMOD1 identified as potential prognostic biomarkers for bladder cancer using bioinformatics analysis

2021 ◽  
Author(s):  
zhiyong tan ◽  
Xuhua Qiao ◽  
Shi Fu ◽  
Xianzhong Duan ◽  
Yigang Zuo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Extracellular Matrix ◽  
Bladder Cancer ◽  
Signaling Pathway ◽  
Kegg Pathway ◽  
Mapk Signaling ◽  
Prognostic Biomarkers ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes

Abstract Background: Bladder cancer (BCa) is a challenge carcinoma that occurs on the bladder mucosa, which is the most common malignant neoplasm of the urinary system. Great efforts have been made to elucidate its pathogenesis. However, the molecular mechanisms involved in BCa remain unclear. Therefore, there is an urgent need to identify effective biomarkers to accurately predict the progression and prognosis of BCa.Material and methods: To investigate potential prognostic biomarkers of BCa, we download the GSE23732 expression profile from Gene Expression Omnibus (GEO) database. The GEO2R analysis tool was performed to identify the DEGs between BCa and normal bladder mucosae tissue. Gene Ontology (GO) functional annotation analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed for the screened DEGs by the Database for Annotation, Visualization, and Integrated Discovery (DAVID) online tool. We employed the Search Tool for the Retrieval of Interacting Genes (STRING) database to construct the protein-protein interaction (PPI) network of DEGs. Subsequently, the PPI network’s information was visualized by Cytoscape software. The Gene Expression Profiling Interactive Analysis (GEPIA) resource was used to describe the OS and DFS outcomes in bladder cancer patients based on the hub genes expression levels.Results: A total of 396 DEGs comprising 344 upregulated genes and 52 downregulated genes were screened. The results of the GO analysis showed that DEG was mainly enriched in proteinaceous extracellular matrix, extracellular matrix, heparin binding and extracellular matrix organization. In addition, KEGG pathway analysis showed that DEGs were mainly enriched in PI3K-Akt signaling pathway, Focal adhesion, MAPK signaling pathway. A PPI network was constructed using the 396 DEGs, 10 hub genes were selected and 4 of them including MYLK, CNN1, TAGLN and LMOD1 were associated with overall survival and disease-free survival.Conclusion: MYLK, CNN1, TAGLN and LMOD1 may represent promising prognostic biomarkers and potential therapeutic option for BCa.

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 Gene Expression Profiling of Type 2 Diabetes Mellitus by Bioinformatics Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Huijing Zhu ◽  
Xin Zhu ◽  
Yuhong Liu ◽  
Fusong Jiang ◽  
Miao Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Candidate Genes ◽  
Signaling Pathway ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Ppi Network ◽  
Kegg Pathway Analysis

Objective. The aim of this study was to identify the candidate genes in type 2 diabetes mellitus (T2DM) and explore their potential mechanisms. Methods. The gene expression profile GSE26168 was downloaded from the Gene Expression Omnibus (GEO) database. The online tool GEO2R was used to obtain differentially expressed genes (DEGs). Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by using Metascape for annotation, visualization, and comprehensive discovery. The protein-protein interaction (PPI) network of DEGs was constructed by using Cytoscape software to find the candidate genes and key pathways. Results. A total of 981 DEGs were found in T2DM, including 301 upregulated genes and 680 downregulated genes. GO analyses from Metascape revealed that DEGs were significantly enriched in cell differentiation, cell adhesion, intracellular signal transduction, and regulation of protein kinase activity. KEGG pathway analysis revealed that DEGs were mainly enriched in the cAMP signaling pathway, Rap1 signaling pathway, regulation of lipolysis in adipocytes, PI3K-Akt signaling pathway, MAPK signaling pathway, and so on. On the basis of the PPI network of the DEGs, the following 6 candidate genes were identified: PIK3R1, RAC1, GNG3, GNAI1, CDC42, and ITGB1. Conclusion. Our data provide a comprehensive bioinformatics analysis of genes, functions, and pathways, which may be related to the pathogenesis of T2DM.

scholarly journals Identification of Biomarkers Related To The Diagnosis And Prognosis of Thyroid Cancer Through Bioinformatics Analysis

2021 ◽  
Author(s):  
Xiao-Li Xie ◽  
Hua-Li Yin ◽  
Yu-Lin Pan ◽  
Guo-Xia Li ◽  
Chun-Yan Yuan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Thyroid Cancer ◽  
Bioinformatics Analysis ◽  
Prognostic Biomarkers ◽  
Cancer Tissue ◽  
Data Sets ◽  
Hub Genes ◽  
Normal Thyroid ◽  
Key Genes ◽  
Cancer Tissues

Abstract Background: Thyroid cancer is the most common malignant tumor of the head and neck. In recent years, the incidence of thyroid cancer (THCA) worldwide has rapidly increased and shows a trend in the younger generation. This study attempted to screen key genes and potential prognostic biomarkers for thyroid cancer using bioinformatics analysis.Methods: This study attempted to screen key genes and potential prognostic biomarkers for thyroid cancer using bioinformatics analysis. 101 cases of thyroid cancer and 78 cases of normal thyroid tissue were collected from three Gene Expression Omnibus (GEO) databases, then we identified the differentially expressed genes (DEGs) and conducted downstream analyses. Moreover, we screened hub genes by constructing a protein‐protein interaction (PPI) network. Finally, we assessed the expression level of hub genes in thyroid cancer tissue and its normal tissue using GEPIA and qRT-PCR respectively. Results: 159 upregulated and 251 downregulated genes were determined after gene integration of these three GEO data sets. Through PPI analysis, we consider the top 20 DEGs with high connectivity as the hub genes of THCA. After that, this study verified 20 central genes through the GEPIA database and found that only four hub genes (TOP2A, FN1, TIMP1, and MMP9) had significantly higher expression levels in thyroid cancer tissues than in normal thyroid tissues. We further analyzed the correlation between these four hub genes and the prognosis of patients with thyroid cancer, which suggests that FN1, MMP9, TIMP1 help assess the prognosis of patients with thyroid cancer. We performed GSEA analysis on these 4 hub genes simultaneously, found that the high expression of these 4 hub genes enriched the "cell cycle." Subsequently, we collected thyroid cancer tissue specimens, verified these four hub gene expression levels by RT-PCR, and found that only FN1 and TIMP1 genes in thyroid cancer tissues had significantly higher mRNA levels than normal tissues. Conclusions: Our research has identified 20 hub genes that may be related to the occurrence and development of thyroid cancer through multiple gene expression profile data sets and a series of comprehensive bioinformatics analyses. Further database and tissue validation analysis revealed that only 2 hub genes may be considered as potential prognostic biomarkers, including FN1 and TIMP1. In addition, these two hub genes are involved in the cell cycle, suggesting that they may play a role in the occurrence and development of thyroid cancer.

scholarly journals Bioinformatics Analysis Reveals the Potential Diagnostic Biomarkers for Abdominal Aortic Aneurysm

2021 ◽  
Vol 8 ◽  
Author(s):  
Xinsheng Xie ◽  
En ci Wang ◽  
Dandan Xu ◽  
Xiaolong Shu ◽  
Yu fei Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Inflammatory Responses ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Aortic Aneurysms ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Abdominal Aortic

Objectives: Abdominal aortic aneurysms (AAAs) are associated with high mortality rates. The genes and pathways linked with AAA remain poorly understood. This study aimed to identify key differentially expressed genes (DEGs) linked to the progression of AAA using bioinformatics analysis.Methods: Gene expression profiles of the GSE47472 and GSE57691 datasets were acquired from the Gene Expression Omnibus (GEO) database. These datasets were merged and normalized using the “sva” R package, and DEGs were identified using the limma package in R. The functions of these DEGs were assessed using Cytoscape software. We analyzed the DEGs using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Protein–protein interaction networks were assembled using Cytoscape, and crucial genes were identified using the Cytoscape plugin, molecular complex detection. Data from GSE15729 and GSE24342 were also extracted to verify our findings.Results: We found that 120 genes were differentially expressed in AAA. Genes associated with inflammatory responses and nuclear-transcribed mRNA catabolic process were clustered in two gene modules in AAA. The hub genes of the two modules were IL6, RPL21, and RPL7A. The expression levels of IL6 correlated positively with RPL7A and negatively with RPL21. The expression of RPL21 and RPL7A was downregulated, whereas that of IL6 was upregulated in AAA.Conclusions: The expression of RPL21 or RPL7A combined with IL6 has a diagnostic value for AAA. The novel DEGs and pathways identified herein might provide new insights into the underlying molecular mechanisms of AAA.

scholarly journals Network Pharmacology-Based Study on the Mechanism of Gegen Qinlian Decoction against Colorectal Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Qiaowei Fan ◽  
Lin Guo ◽  
Jingming Guan ◽  
Jing Chen ◽  
Yujing Fan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Target Genes ◽  
Enrichment Analysis ◽  
R Package ◽  
Gastrointestinal Diseases ◽  
Gene Expression Omnibus ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Ppi Network

Purpose. Gegen Qinlian decoction (GQD) has been used to treat gastrointestinal diseases, such as diarrhea and ulcerative colitis (UC). A recent study demonstrated that GQD enhanced the effect of PD-1 blockade in colorectal cancer (CRC). This study used network pharmacology analysis to investigate the mechanisms of GQD as a potential therapeutic approach against CRC. Materials and Methods. Bioactive chemical ingredients (BCIs) of GQD were collected from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. CRC-specific genes were obtained using the gene expression profile GSE110224 from the Gene Expression Omnibus (GEO) database. Target genes related to BCIs of GQD were then screened out. The GQD-CRC ingredient-target pharmacology network was constructed and visualized using Cytoscape software. A protein-protein interaction (PPI) network was subsequently constructed and analyzed with BisoGenet and CytoNCA plug-in in Cytoscape. Gene Ontology (GO) functional and the Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis for target genes were then performed using the R package of clusterProfiler. Results. One hundred and eighteen BCIs were determined to be effective on CRC, including quercetin, wogonin, and baicalein. Twenty corresponding target genes were screened out including PTGS2, CCNB1, and SPP1. Among these genes, CCNB1 and SPP1 were identified as crucial to the PPI network. A total of 212 GO terms and 6 KEGG pathways were enriched for target genes. Functional analysis indicated that these targets were closely related to pathophysiological processes and pathways such as biosynthetic and metabolic processes of prostaglandins and prostanoids, cytokine and chemokine activities, and the IL-17, TNF, Toll-like receptor, and nuclear factor-kappa B (NF-κB) signaling pathways. Conclusion. The study elucidated the “multiingredient, multitarget, and multipathway” mechanisms of GQD against CRC from a systemic perspective, indicating GQD to be a candidate therapy for CRC treatment.

scholarly journals Identification of Genes and Pathways of Nonsteroidal Anti-inflammatory Drugs acting on Synovia from Women with Knee Osteoarthritis by Bioinformatics Analysis

2021 ◽  
Author(s):  
Chao Zhao ◽  
Han Wang ◽  
Conglei Dong ◽  
Huijun Kang ◽  
Fei Wang
Keyword(s):  
Gene Expression ◽  
Knee Osteoarthritis ◽  
Bioinformatics Analysis ◽  
Gene Expression Omnibus ◽  
Control Group ◽  
Ppi Network ◽  
Kegg Pathways ◽  
Inflammatory Drugs ◽  
Anti Inflammatory ◽  
R Programming

Abstract Objective: Through the bioinformatics analysis, to identify the genes and pathways of nonsteroidal anti-inflammatory drugs(NSARDs) acting on synovia from women with knee osteoarthritis (KOA), and to provide reference for clinical application. Methods: We downloaded the gene microarray datasets with the accession number of GSE55457 and GSE55584 from the Gene Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) database, including 5 untreated KOA patients, 9 NSARDs treated KOA patients and 2 patients without KOA The samples in the untreated KOA group and the NSARDs treated KOA group were used for main analysis. The samples in the untreated KOA group and the normal control group were used for cooperative analysis. Then we performed robust multi-array (RMA) normalization with affy R programming package. After that, differential expression genes (DEGs) in main analysis and cooperative analysis were identified based on limma package separately. Screening the common DEGs from main analysis and cooperative analysis. Enriched gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of DEGs were obtained through the Database for Annotation, Visualization and Integrated Discovery (DAVID). What's more, protein-protein interaction (PPI) network was constructed, and we identified modules of PPI network through Cytoscape to screen valuable targets. The value of gene expression fold change (FC) ≥1.4 or ≤1/1.4, and P <0.05 were used as the screening conditions. P <0.05 and Associated genes count>5 were used as the screening conditions.Results: There were 338 DEGs in main analysis. Among them, 211 genes were up-regulated and 127 genes were down-regulated. There were 7005 DEGs in cooperative analysis. Among them, 6952 genes were up-regulated and 53 genes were down-regulated. A total of 129 common DEGs were identified between main analysis and cooperative analysis. There are 2 biological processes, 3 cell components and 2 molecular functions for the enrichment of differentially expressed genes.Conclusion: NSARDs may play a certain role in synovia from women with KOA by regulating the mRNA expressions of il-6, TNFRSF11A and CSF1R, which may become one of the indicators for monitoring the efficacy of NSAIDs.

scholarly journals Complement C3 as therapeutic target in diabetic nephropathy by bioinformatics analysis

2020 ◽  
Author(s):  
ShuMei Tang ◽  
XiuFen Wang ◽  
TianCi Deng ◽  
HuiPeng Ge ◽  
XiangCheng Xiao
Keyword(s):  
Diabetic Nephropathy ◽  
Differential Expression ◽  
Expression Analysis ◽  
Therapeutic Target ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Differential Expression Analysis ◽  
Complement C3 ◽  
Pathway Enrichment Analysis ◽  
Ppi Network

Abstract Background: Various public platforms have contained extensive data for deeper bioinformatics analysis. The pathogenesis of diabetic nephropathy is always a hot topic and the underlying molecular events are not completely clear. Methods: Differential expression analysis and weighted correlation network analysis (WGCNA) were applied to screen meaningful gene in GSE30529. The combined result was used for gene ontology (GO) annotation and KEGG pathway enrichment analysis. STRING was used for protein-protein interaction (PPI) network construction and Cytoscape software was used for hub gene identification. Gene methylation profile GSE121820 and miRNA profile GSE51674 were also acquired to perform multi-omics analysis. Clinical features were obtained form Nephroseq and potential drugs were identified by CMap. Results: 345 genes were obtained form GSE30529 after differential expression analysis and WGCNA. GO analysis mainly included neutrophil activation, regulation of immune effector process, positive regulation of cytokine production and neutrophil mediated immunity. KEGG pathway analysis mostly included phagosome, complement and coagulation cascades, cell adhesion molecules and AGE-RAGE signaling pathway in diabetic complications. 16 down regulated miRNAs were obtained from GSE57674 to construct miRNA-mRNA network. Top 20 genes were discerned from PPI network and there were DNA methylation differences in 15 genes among them. Correlation analysis showed SYK, CXCL1, LYN, VWF, ANXA1, C3, HLA-E, RHOA, SERPING1, EGF and KNG1 may involved in DN. 10 small molecule compounds have been identified as potential therapeutic drugs. Conclusion: We screened 11 target genes and suggested C3 may serve as a therapeutic target in diabetic nephropathy.

scholarly journals Screening of Osteoarthritis-Related Genes and Function Determination Based on Bioinformatics Tools

2021 ◽  
Author(s):  
Zheng Fu ◽  
Weiqian Jiang ◽  
Wenlong Yan ◽  
Fei Xie ◽  
Yu Chen ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Experimental Studies ◽  
Therapeutic Targets ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Pathway Enrichment ◽  
Early Diagnostic

Abstract Background:Osteoarthritis(OA), commonly seen in the middle-aged and elderly population, imposes a heavy burden on patients from the clinical, humanistic and economic aspects. Our work aims at the discovery of early diagnostic and therapeutic targets for OA and new candidate biomarkers for experimental studies on OA via bioinformatics analysis.Methods:The dataset GSE114007 was downloaded from GEO to identify differentially expressed genes(DEGs) in R using 3 different algorithms. Overlapping DEGs were subject to GO and KEGG pathway enrichment analysis and functional annotation. Following the identification of DEGs, a protein-protein interaction(PPI) network was established and imported into Cytoscape to screen for hubgenes. The expression of each hubgene was verified in two other datasets and create miRNA-mRNA regulatory networks.Results:174 upregulated genes and 117 downregulated genes were identified among the overlapping DEGs. According to the results of GO enrichment analysis,MF enrichment was basically found in ECM degradation and collagen breakdown; enrichment was also present in the development, ossification, and differentiation of cells. The KEGG pathway enrichment analysis suggested significant enrichment in such pathways as PI3K-AKT, P53, TNF, and FoxO. 23 hubgenes were obtained from the PPI network, and 11 genes were identified as DEGs through verification. 8 genes were used for the establishment of miRNA-mRNA regulatory networks.Conclusion:OA-related genes, proteins, pathways and miRNAs that were identified through bioinformatics analysis may provide a reference for the discovery of early diagnostic and therapeutic targets for OA, as well as candidate biomarkers for experimental studies on OA.

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