scholarly journals Bioinformatics Analysis and Identification of Potential Biomarkers in Gastric Cancer

2020 ◽  
Author(s):  
Jingdi Yang ◽  
Bo Peng ◽  
Xianzheng Qin ◽  
Tian Zhou
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Abstract Background: Although the morbidity and mortality of gastric cancer are declining, gastric cancer is still one of the most common causes of death. Early detection of gastric cancer is of great help to improve the survival rate, but the existing biomarkers are not sensitive to diagnose early gastric cancer. The aim of this study is to identify the novel biomarkers for gastric cancer.Methods: Three gene expression profiles (GSE27342, GSE63089, GSE33335) were downloaded from Gene Expression Omnibus database to select differentially expressed genes. Then, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis were performed to explore the biological functions of differentially expressed genes. Cytoscape was utilized to construct protein-protein interaction network and hub genes were analyzed by plugin cytoHubba of Cytoscape. Furthermore, Gene Expression Profiling Interactive Analysis and Kaplan-Meier plotter were used to verify the identified hub genes.Results: 35 overlapping differentially expressed genes were screened from gene expression datasets, which consisted of 11 up-regulated genes and 24 down-regulated genes. Gene Ontology functional enrichment analysis revealed that differentially expressed genes were significantly enriched in digestion, regulation of biological quality, response to hormone and steroid hormone, and homeostatic process. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed differentially expressed genes were enriched in the secretion of gastric acid and collecting duct acid, leukocyte transendothelial migration and ECM-receptor interaction. According to protein-protein interaction network, 10 hub genes were identified by Maximal Clique Centrality method.Conclusion: By using bioinformatics analysis, COL1A1, BGN, THY1, TFF2 and SST were identified as the potential biomarkers for early detection of gastric cancer.

scholarly journals Aberrantly hydroxymethylated differentially expressed genes and the associated protein pathways in osteoarthritis

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6425 ◽  
Author(s):  
Yang Fang ◽  
Pingping Wang ◽  
Lin Xia ◽  
Suwen Bai ◽  
Yonggang Shen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Differentially Expressed Genes ◽  
Kegg Pathway ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Highly Expressed Genes ◽  
Protein Protein Interaction Network

Background The elderly population is at risk of osteoarthritis (OA), a common, multifactorial, degenerative joint disease. Environmental, genetic, and epigenetic (such as DNA hydroxymethylation) factors may be involved in the etiology, development, and pathogenesis of OA. Here, comprehensive bioinformatic analyses were used to identify aberrantly hydroxymethylated differentially expressed genes and pathways in osteoarthritis to determine the underlying molecular mechanisms of osteoarthritis and susceptibility-related genes for osteoarthritis inheritance. Methods Gene expression microarray data, mRNA expression profile data, and a whole genome 5hmC dataset were obtained from the Gene Expression Omnibus repository. Differentially expressed genes with abnormal hydroxymethylation were identified by MATCH function. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the genes differentially expressed in OA were performed using Metascape and the KOBAS online tool, respectively. The protein–protein interaction network was built using STRING and visualized in Cytoscape, and the modular analysis of the network was performed using the Molecular Complex Detection app. Results In total, 104 hyperhydroxymethylated highly expressed genes and 14 hypohydroxymethylated genes with low expression were identified. Gene ontology analyses indicated that the biological functions of hyperhydroxymethylated highly expressed genes included skeletal system development, ossification, and bone development; KEGG pathway analysis showed enrichment in protein digestion and absorption, extracellular matrix–receptor interaction, and focal adhesion. The top 10 hub genes in the protein–protein interaction network were COL1A1, COL1A2, COL2A1, COL3A1, COL5A1, COL5A2, COL6A1, COL8A1, COL11A1, and COL24A1. All the aforementioned results are consistent with changes observed in OA. Conclusion After comprehensive bioinformatics analysis, we found aberrantly hydroxymethylated differentially expressed genes and pathways in OA. The top 10 hub genes may be useful hydroxymethylation analysis biomarkers to provide more accurate OA diagnoses and target genes for treatment of OA.

scholarly journals Bioinformatics-Based Study to Investigate Potential Differentially Expressed Genes and miRNAs in Hashimoto’s Thyroiditis

2021 ◽  
Author(s):  
Li Guoquan ◽  
Du Junwei ◽  
He Qi ◽  
Fu Xinghao ◽  
Ji Feihong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Inflammatory Responses ◽  
Expression Profiles ◽  
Treatment Options ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Chronic Lymphocytic Thyroiditis ◽  
Hub Genes

Abstract BackgroundHashimoto's thyroiditis (HT), also known as chronic lymphocytic thyroiditis, is a common autoimmune disease, which mainly occurs in women. The early manifestation was hyperthyroidism, however, hypothyroidism may occur if HT was not controlled for a long time. Numerous studies have shown that multiple factors, including genetic, environmental, and autoimmune factors, were involved in the pathogenesis of the disease, but the exact mechanisms were not yet clear. The aim of this study was to identify differentially expressed genes (DEGs) by comprehensive analysis and to provide specific insights into HT. MethodsTwo gene expression profiles (GSE6339, GSE138198) about HT were downloaded from the Gene Expression Omnibus (GEO) database. The DEGs were assessed between the HT and normal groups using the GEO2R. The DEGs were then sent to the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The hub genes were discovered using Cytoscape and CytoHubba. Finally, NetworkAnalyst was utilized to create the hub genes' targeted microRNAs (miRNAs). ResultsA total of 62 DEGs were discovered, including 60 up-regulated and 2 down-regulated DEGs. The signaling pathways were mainly engaged in cytokine interaction and cytotoxicity, and the DEGs were mostly enriched in immunological and inflammatory responses. IL2RA, CXCL9, IL10RA, CCL3, CCL4, CCL2, STAT1, CD4, CSF1R, and ITGAX were chosen as hub genes based on the results of the protein-protein interaction (PPI) network and CytoHubba. Five miRNAs, including mir-24-3p, mir-223-3p, mir-155-5p, mir-34a-5p, mir-26b-5p, and mir-6499-3p, were suggested as likely important miRNAs in HT. ConclusionsThese hub genes, pathways and miRNAs contribute to a better understanding of the pathophysiology of HT and offer potential treatment options for HT.

Uncovering potential lncRNAs and nearby mRNAs in systemic lupus erythematosus from the Gene Expression Omnibus dataset

Epigenomics ◽  
2019 ◽  
Vol 11 (16) ◽  
pp. 1795-1809 ◽  
Author(s):  
Haiyu Cao ◽  
Dong Li ◽  
Huixiu Lu ◽  
Jing Sun ◽  
Haibin Li
Keyword(s):  
Gene Expression ◽  
Systemic Lupus Erythematosus ◽  
Lupus Erythematosus ◽  
Noncoding Rnas ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Systemic Lupus ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Aim: The aim of this study was to find potential differentially expressed long noncoding RNAs (lncRNAs) and mRNAs in systemic lupus erythematosus. Materials & methods: Differentially expressed lncRNAs and mRNAs were obtained in the Gene Expression Omnibus dataset. Functional annotation of differentially expressed mRNAs was performed, followed by protein–protein interaction network analysis. Then, the interaction network of lncRNA-nearby targeted mRNA was built. Results: Several interaction pairs of lncRNA-nearby targeted mRNA including NRIR-RSAD2, RP11-153M7.5-TLR2, RP4-758J18.2-CCNL2, RP11-69E11.4-PABPC4 and RP11-496I9.1-IRF7/ HRAS/ PHRF1 were identified. Measles and MAPK were significantly enriched signaling pathways of differentially expressed mRNAs. Conclusion: Our study identified several differentially expressed lncRNAs and mRNAs. And their interactions may play a crucial role in the process of systemic lupus erythematosus.

scholarly journals Hub Genes Related to the Pathogenesis and Progression of Colorectal Cancer and Adenoma by Integrative Bioinformatics Approaches

2021 ◽  
Author(s):  
Yuxuan HUANG ◽  
Ge CUI
Keyword(s):  
Colorectal Cancer ◽  
Cell Cycle ◽  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Biological Networks ◽  
Interaction Network ◽  
Differentially Expressed ◽  
Venn Diagram ◽  
Hub Genes ◽  
Protein Protein Interaction

Abstract Aims: To utilize the bioinformatics to analyze the differentially expressed genes (DEGs), interaction proteins, perform gene enrichment analysis, protein-protein interaction network (PPI) and map the hub genes between colorectal cancer(CRC) and colorectal adenocarcinomas(CA).Methods: We analyzed a microarray dataset (GSE32323 and GSE4183) from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in tumor tissues and non-cancerous tissues were identified using the dplyr and Venn diagram packages of the R Studio software. Functional annotation of the DEGs was performed using the Gene Ontology (GO) website. Pathway enrichment (KEGG) used the WebGestalt to analyze the data and R Studio to generate the graph. We constructed a protein–protein interaction (PPI) network of DEGs using STRING and Cytoscape software was used for visualization. Survival analysis of the hub genes and was performed using the online platform GEPIA to determine the prognostic value of the expression of hub genes in cell lines from CRC patients. The expression of molecules with prognostic values was validated on the UALCAN database. The expression of hub genes was examined using the Human Protein Atlas. Results: Applying the GEO2R analysis and R studio, we identified a total of 471 upregulated and 278 downregulated DEGs. By using the online database WebGestalt, we identified the most relevant biological networks involving DEGs with statistically significant differences in expression were mainly associated with biological processes involved in the cell proliferation, cell cycle transition, cell homeostasis and indicated the role of each DEGs in cell cycle regulation pathways. We found 10 hub genes with prognostic values were overexpressed in the CRC and CA samples.Conclusion: we found out ten hub genes and three core genes closely associated with the pathogenesis and prognosis of CRC and CA, which is of great significance for colorectal tumor early detection and prognosis evaluation.

scholarly journals A Network Pharmacology Approach to Investigating the Mechanism of Sophorae Flavescentis Radix for the Treatment of Lung Cancer

2020 ◽  
Author(s):  
Le Yu ◽  
Kangyao Yuan ◽  
Jian Zhang ◽  
Jingya Zhao ◽  
Shuchen Pei
Keyword(s):  
Lung Cancer ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Bioactive Components ◽  
Akt Signaling Pathway ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Human Cytomegalovirus Infection ◽  
Protein Protein Interaction Network

Abstract In this study, the bioactive components and predictive targets of Sophorae Flavescentis Radix were investigated by network pharmacology analysis, so as to further elucidate its potential biological mechanism in treating lung cancer. The targets corresponding to lung cancer were obtained by OMIM and Genecards. By intersecting with the targets of Sophorae Flavescentis Radix and lung cancer, the Sophorae Flavescentis Radix-lung cancer targets were obtained. Protein-protein interaction network was constructed by an online database STRING and hub genes were screened by Cytoscape 3.7.0 software. ClusterProfiler package was used to analyze Gene ontology (GO) and KEGG enrichment of the targets in R. A total of 45 bioactive components were screened from Sophorae Flavescentis Radix, corresponding to 482 Sophorae Flavescentis Radix targets and 25019 lung cancer targets. According to the GO and KEGG enrichment analysis, Sophorae Flavescentis Radix played a therapeutic role in treating lung cancer via proteoglycans lung cancer, human cytomegalovirus infection, microRNAs in cancer, PI3K-Akt signaling pathway, etc. Seven hub genes (IL6, CASP3, EGFR, VEGFA, MYC, CCND1 and ESR1) were screened by degree algorithm. In a word, the results of this study may provide novel insights into the mechanisms of Sophorae Flavescentis Radix in treatment of lung cancer.

scholarly journals Exploration and validation of related hub gene expression during SARS-CoV-2 infection of human bronchial organoids

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Ke-Ying Fang ◽  
Wen-Chao Cao ◽  
Tian-Ao Xie ◽  
Jie Lv ◽  
Jia-Xin Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Social Order ◽  
Expression System ◽  
Differentially Expressed ◽  
Dimensional Structure ◽  
Hub Genes ◽  
R Software ◽  
Protein Protein Interaction

Abstract Background In the novel coronavirus pandemic, the high infection rate and high mortality have seriously affected people’s health and social order. To better explore the infection mechanism and treatment, the three-dimensional structure of human bronchus has been employed in a better in-depth study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Methods We downloaded a separate microarray from the Integrated Gene Expression System (GEO) on a human bronchial organoids sample to identify differentially expressed genes (DEGS) and analyzed it with R software. After processing with R software, Gene Ontology (GO) and Kyoto PBMCs of Genes and Genomes (KEGG) were analyzed, while a protein–protein interaction (PPI) network was constructed to show the interactions and influence relationships between these differential genes. Finally, the selected highly connected genes, which are called hub genes, were verified in CytoHubba plug-in. Results In this study, a total of 966 differentially expressed genes, including 490 upregulated genes and 476 downregulated genes were used. Analysis of GO and KEGG revealed that these differentially expressed genes were significantly enriched in pathways related to immune response and cytokines. We construct protein-protein interaction network and identify 10 hub genes, including IL6, MMP9, IL1B, CXCL8, ICAM1, FGF2, EGF, CXCL10, CCL2, CCL5, CXCL1, and FN1. Finally, with the help of GSE150728, we verified that CXCl1, CXCL8, CXCL10, CCL5, EGF differently expressed before and after SARS-CoV-2 infection in clinical patients. Conclusions In this study, we used mRNA expression data from GSE150819 to preliminarily confirm the feasibility of hBO as an in vitro model to further study the pathogenesis and potential treatment of COVID-19. Moreover, based on the mRNA differentiated expression of this model, we found that CXCL8, CXCL10, and EGF are hub genes in the process of SARS-COV-2 infection, and we emphasized their key roles in SARS-CoV-2 infection. And we also suggested that further study of these hub genes may be beneficial to treatment, prognostic prediction of COVID-19.

scholarly journals Identification of Differentially Expressed Genes and Signaling Pathways in Acute Myocardial Infarction Based on Integrated Bioinformatics Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Da-Qiu Chen ◽  
Xiang-Sheng Kong ◽  
Xue-Bin Shen ◽  
Mao-Zhi Huang ◽  
Jian-Ping Zheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Signaling Pathway ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Differentially Expressed ◽  
High Morbidity ◽  
Hub Genes ◽  
Module Analysis

Background. Acute myocardial infarction (AMI) is a common disease with high morbidity and mortality around the world. The aim of this research was to determine the differentially expressed genes (DEGs), which may serve as potential therapeutic targets or new biomarkers in AMI. Methods. From the Gene Expression Omnibus (GEO) database, three gene expression profiles (GSE775, GSE19322, and GSE97494) were downloaded. To identify the DEGs, integrated bioinformatics analysis and robust rank aggregation (RRA) method were applied. These DEGs were performed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses by using Clusterprofiler package. In order to explore the correlation between these DEGs, the interaction network of protein-protein internet (PPI) was constructed using the STRING database. Utilizing the MCODE plug-in of Cytoscape, the module analysis was performed. Utilizing the cytoHubba plug-in, the hub genes were screened out. Results. 57 DEGs in total were identified, including 2 down- and 55 upregulated genes. These DEGs were mainly enriched in cytokine-cytokine receptor interaction, chemokine signaling pathway, TNF signaling pathway, and so on. The module analysis filtered out 18 key genes, including Cxcl5, Arg1, Cxcl1, Spp1, Selp, Ptx3, Tnfaip6, Mmp8, Serpine1, Ptgs2, Il6, Il1r2, Il1b, Ccl3, Ccr1, Hmox1, Cxcl2, and Ccl2. Ccr1 was the most fundamental gene in PPI network. 4 hub genes in total were identified, including Cxcl1, Cxcl2, Cxcl5, and Mmp8. Conclusion. This study may provide credible molecular biomarkers in terms of screening, diagnosis, and prognosis for AMI. Meanwhile, it also serves as a basis for exploring new therapeutic target for AMI.

Exploring Shared Pathogenesis of Alzheimer’s Disease and Type 2 Diabetes Mellitus via Co-expression Networks Analysis

2020 ◽  
Vol 17 (6) ◽  
pp. 566-575 ◽  
Author(s):  
Yukun Zhu ◽  
Xuelu Ding ◽  
Zhaoyuan She ◽  
Xue Bai ◽  
Ziyang Nie ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Background: Alzheimer’s Disease (AD) and Type 2 Diabetes Mellitus (T2DM) have an increased incidence in modern society. Although increasing evidence has supported the close linkage between these two disorders, the inter-relational mechanisms remain to be fully elucidated. Objective: The primary purpose of this study is to explore the shared pathophysiological mechanisms of AD and T2DM. Methods: We downloaded the microarray data of AD and T2DM from the Gene Expression Omnibus (GEO) database and constructed co-expression networks by Weighted Gene Co-Expression Network Analysis (WGCNA) to identify gene network modules related to AD and T2DM. Then, Gene Ontology (GO) and pathway enrichment analysis were performed on the common genes existing in the AD and T2DM related modules by clusterProfiler and DOSE package. Finally, we utilized the STRING database to construct the protein-protein interaction network and found out the hub genes in the network. Results: Our findings indicated that seven and four modules were the most significant with AD and T2DM, respectively. Functional enrichment analysis showed that AD and T2DM common genes were mainly enriched in signaling pathways such as circadian entrainment, phagosome, glutathione metabolism and synaptic vesicle cycle. Protein-protein interaction network construction identified 10 hub genes (CALM1, LRRK2, RBX1, SLC6A1, TXN, SNRPF, GJA1, VWF, LPL, AGT) in AD and T2DM shared genes. Conclusions: Our work identified common pathogenesis of AD and T2DM. These shared pathways might provide a novel idea for further mechanistic studies and hub genes that may serve as novel therapeutic targets for diagnosis and treatment of AD and T2DM.

Identification of aberrantly methylated differentially expressed genes in melanoma

2020 ◽  
Author(s):  
Wei Han ◽  
Guo-liang Shen
Keyword(s):  
Gene Expression ◽  
Cell Proliferation ◽  
Differentially Expressed Genes ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Regulation Of Transcription ◽  
Biological Processes ◽  
Hub Genes ◽  
Positive Regulation ◽  
Upregulated Genes

Abstract Background: Skin Cutaneous Melanoma (SKCM) is known as an aggressive malignant cancer, which could be directly derived from melanocytic nevi. However, the molecular mechanisms underlying malignant transformation of melanocytes and melanoma tumor progression still remain unclear. Increasing researches showed significant roles of epigenetic modifications, especially DNA methylation, in melanoma. This study focused on identification and analysis of methylation-regulated differentially expressed genes (MeDEGs) between melanocytic nevus and malignant melanoma in genome-wide profiles. Methods: The gene expression profiling datasets (GSE3189 and GSE114445) and gene methylation profiling datasets (GSE86355 and GSE120878) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) were identified via GEO2R. MeDEGs were obtained by integrating the DEGs and DMGs. Then, functional enrichment analysis of MeDEGs were performed. STRING and Cytoscape were used to describe protein-protein interaction(PPI) network. Furthermore, survival analysis was implemented to select the prognostic hub genes. Finally, we conducted gene set enrichment analysis (GSEA) of hub genes. Results: We identified 237 hypomethylated, upregulated genes and 182 hypermethylated, downregulated genes. Hypomethylation-upregulated genes were enriched in biological processes of the oxidation-reduction process, cell proliferation, cell division, phosphorylation, extracellular matrix disassembly and protein sumoylation. Pathway enrichment showed selenocompound metabolism, small cell lung cancer and lysosome. Hypermethylation-downregulated genes were enriched in biological processes of positive regulation of transcription from RNA polymerase II promoter, cell adhesion, cell proliferation, positive regulation of transcription, DNA-templated and angiogenesis. The most significantly enriched pathways involved the transcriptional misregulation in cancer, circadian rhythm, tight junction, protein digestion and absorption and Hippo signaling pathway. After PPI establishment and survival analysis, seven prognostic hub genes were CKS2, DTL, KIF2C, KPNA2, MYBL2, TPX2 and FBL. Moreover, the most involved hallmarks obtained by GSEA were E2F targets, G2M checkpoint and mitotic spindle. Conclusions: Our study identified potential aberrantly methylated-differentially expressed genes participating in the process of malignant transformation from nevus to melanoma tissues based on comprehensive genomic profiles. Transcription profiles of CKS2, DTL, KIF2C, KPNA2, MYBL2, TPX2 and FBL provided clues of aberrantly methylation-based biomarkers, which might improve the development of precise medicine.

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