Identification of 10 Hub genes related to the progression of colorectal cancer by co-expression analysis

Background Colorectal cancer (CRC) is the third most common cancer in the world. The present study is aimed at identifying hub genes associated with the progression of CRC. Method The data of the patients with CRC were obtained from the Gene Expression Omnibus (GEO) database and assessed by weighted gene co-expression network analysis (WGCNA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses performed in R by WGCNA, several hub genes that regulate the mechanism of tumorigenesis in CRC were identified. Differentially expressed genes in the data sets GSE28000 and GSE42284 were used to construct a co-expression network for WGCNA. The yellow, black and blue modules associated with CRC level were filtered. Combining the co-expression network and the PPI network, 15 candidate hub genes were screened. Results After validation using the TCGA-COAD dataset, a total of 10 hub genes (MT1X, MT1G, MT2A, CXCL8, IL1B, CXCL5, CXCL11, IL10RA, GZMB, KIT) closely related to the progression of CRC were identified. The expressions of MT1G, CXCL8, IL1B, CXCL5, CXCL11 and GZMB in CRC tissues were higher than normal tissues (p-value < 0.05). The expressions of MT1X, MT2A, IL10RA and KIT in CRC tissues were lower than normal tissues (p-value < 0.05). Conclusions By combinating with a series of methods including GO enrichment analysis, KEGG pathway analysis, PPI network analysis and gene co-expression network analysis, we identified 10 hub genes that were associated with the progression of CRC.

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Identification of potential and novel target genes in pituitary prolactinoma by bioinformatics analysis

10.1101/2020.12.21.423732 ◽

2020 ◽

Author(s):

Vikrant Ghatnatti ◽

Basavaraj Vastrad ◽

Swetha Patil ◽

Chanabasayya Vastrad ◽

Iranna Kotturshetti

Keyword(s):

Gene Ontology ◽

Signaling Pathways ◽

Target Gene ◽

Bioinformatics Analysis ◽

Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Condensed Chromosome ◽

Go Enrichment ◽

Go Enrichment Analysis

AbstractPituitary prolactinoma is one of the most complicated and fatally pathogenic pituitary adenomas. Therefore, there is an urgent need to improve our understanding of the underlying molecular mechanism that drives the initiation, progression, and metastasis of pituitary prolactinoma. The aim of the present study was to identify the key genes and signaling pathways associated with pituitary prolactinoma using bioinformatics analysis. Transcriptome microarray dataset GSE119063 was acquired from Gene Expression Omnibus datasets, which included 5 pituitary prolactinoma samples and 4 normal pituitaries samples. We screened differentially expressed genes (DEGs) with limma and investigated their biological function by pathway and Gene Ontology (GO) enrichment analysis. A protein-protein interaction (PPI) network of the up and down DEGs were constructed and analyzed by HIPPIE and Cytoscape software. Module analyses were performed. In addition, a target gene - miRNA network and target gene - TF network of the up and down DEGs were constructed by NetworkAnalyst and Cytoscape software. The set of DEGs exhibited an intersection consisting of 989 genes (461 up-regulated and 528 down-regulated), which may be associated with pituitary prolactinoma. Pathway enrichment analysis showed that the 989 DEGs were significantly enriched in the retinoate biosynthesis II, signaling pathways regulating pluripotency of stem cells, ALK2 signaling events, vitamin D3 biosynthesis, cell cycle and aurora B signaling. Gene Ontology (GO) enrichment analysis also showed that sensory organ morphogenesis, extracellular matrix, hormone activity, nuclear division, condensed chromosome and microtubule binding. In the PPI network and modules, SOX2, PRSS45, CLTC, PLK1, B4GALT6, RUNX1 and GTSE1 were considered as hub genes. In the target gene miRNA network and target gene - TF network, LINC00598, SOX4, IRX1 and UNC13A were considered as hub genes. Using integrated bioinformatics analysis, we identified candidate genes in pituitary prolactinoma, which may improve our understanding of the mechanisms of the pathogenesis and integration; genes may be therapeutic targets and prognostic markers for pituitary prolactinoma.

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Exploring the Molecular Mechanism of the Drug-Treated Breast Cancer Based on Gene Expression Microarray

Biomolecules ◽

10.3390/biom9070282 ◽

2019 ◽

Vol 9 (7) ◽

pp. 282

Author(s):

Alshabi ◽

BasavarajVastrad ◽

Shaikh ◽

Vastrad

Keyword(s):

Target Genes ◽

Interaction Network ◽

Enrichment Analysis ◽

Network Module ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Go Enrichment ◽

Human Protein Atlas ◽

Go Enrichment Analysis

: Breast cancer (BRCA) remains the leading cause of cancer morbidity and mortality worldwide. In the present study, we identified novel biomarkers expressed during estradiol and tamoxifen treatment of BRCA. The microarray dataset of E-MTAB-4975 from Array Express database was downloaded, and the differential expressed genes (DEGs) between estradiol-treated BRCA sample and tamoxifen-treated BRCA sample were identified by limma package. The pathway and gene ontology (GO) enrichment analysis, construction of protein-protein interaction (PPI) network, module analysis, construction of target genes—miRNA interaction network and target genes-transcription factor (TF) interaction network were performed using bioinformatics tools. The expression, prognostic values, and mutation of hub genes were validated by SurvExpress database, cBioPortal, and human protein atlas (HPA) database. A total of 856 genes (421 up-regulated genes and 435 down-regulated genes) were identified in T47D (overexpressing Split Ends (SPEN) + estradiol) samples compared to T47D (overexpressing Split Ends (SPEN) + tamoxifen) samples. Pathway and GO enrichment analysis revealed that the DEGs were mainly enriched in response to lysine degradation II (pipecolate pathway), cholesterol biosynthesis pathway, cell cycle pathway, and response to cytokine pathway. DEGs (MCM2, TCF4, OLR1, HSPA5, MAP1LC3B, SQSTM1, NEU1, HIST1H1B, RAD51, RFC3, MCM10, ISG15, TNFRSF10B, GBP2, IGFBP5, SOD2, DHF and MT1H) , which were significantly up- and down-regulated in estradiol and tamoxifen-treated BRCA samples, were selected as hub genes according to the results of protein-protein interaction (PPI) network, module analysis, target genes—miRNA interaction network and target genes-TF interaction network analysis. The SurvExpress database, cBioPortal, and Human Protein Atlas (HPA) database further confirmed that patients with higher expression levels of these hub genes experienced a shorter overall survival. A comprehensive bioinformatics analysis was performed, and potential therapeutic applications of estradiol and tamoxifen were predicted in BRCA samples. The data may unravel the future molecular mechanisms of BRCA.

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Identification of Key Genes Potentially Related to Triple Receptor Negative Breast Cancer by Microarray Analysis

10.1101/2020.12.21.423796 ◽

2020 ◽

Author(s):

Basavaraj Vastrad ◽

Chanabasayya Vastrad ◽

Iranna Kotturshetti

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Cell Cycle ◽

Immune Response ◽

Target Gene ◽

Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Go Enrichment ◽

Go Enrichment Analysis

AbstractTriple receptor negative breast cancer (TNBC) is the type of gynecological cancer in the elderly women. This study is aimed to explore molecular mechanism of TNBC via bioinformatics analysis. The gene expression profiles of GSE88715 (including 38 TNBC and 38 normal control) was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened using the limma package in R software. Pathway and gene ontology (GO) enrichment analysis were performed based on various pathway dabases and GO database. Then, InnateDb interactome database, Cytoscape and PEWCC1 were applied to construct the protein-protein interaction (PPI) network and screen hub genes. Similarly, miRNet database, NetworkAnalyst database and Cytoscape were applied to construct the target gene - miRNA network and target gene - TF network, and screen targate genes. Pathway and GO enrichment analysis was further performed for hub genes, gene clusters identified via module analysis and targate genes. The expression of hub genes with prognostic values was validated on the UALCAN, cBio Portal, The Human Protein Atlas, receiver operator characteristic (ROC) curve analysis, RT-PCR analysis and immune infiltration analysis. A total of 949 DEGs were identified in TNBC (469 up regulated genes, and 480 down regulated genes), and they were mainly enriched in the terms of phospholipases, toxoplasmosis, immune response, cell surface, glycolysis, biosynthesis of amino acids, carboxylic acid metabolic process and organic substance catabolic process extracellular space. Hub genes including UBD, HLA-B, MYC and HSP90AB1 were identified via PPI network and modules, which were mainly enriched in immune response, antigen processing and presentation, cell cycle and pathways in cancer. Targate genes including CCDC80, PEG10, HOPX and CCNA2 were identified via target gene - miRNA network and target gene - TF network, which were mainly enriched in extracellular structure organization, validated targets of C-MYC transcriptional activation, ensemble of genes encoding core extracellular matrix including ECM glycoproteins and cell cycle. The top five significantly overexpressed mRNA (ADAM15, BATF, NOTCH3, ITGAX and SDC1) and the top five significantly underexpressed mRNA (RPL4, EEF1G, RPL3, RBMX and ABCC2) were selected for further validation in TNBCpatients and healthy controls. Analysis of the expression of genes in the various databases showed that ADAM15, BATF, NOTCH3, ITGAX, SDC1, RPL4, EEF1G, RPL3, RBMX and ABCC2 expressions have a cancer specific pattern in TNBC. Collectively, ADAM15, BATF, NOTCH3, ITGAX, SDC1, RPL4, EEF1G, RPL3, RBMX and ABCC2 may be useful candidate biomarkers for TNBC diagnosis, prognosis and theraputic targates.

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Identification of Issue Inhibitor of Metalloproteinase 1 (TIMP1) as a Potential Biomarker for the Diagnosis, Pathogenesis and Prognosis of Colorectal Cancer via Integrated Bioinformatic Analysis

10.21203/rs.3.rs-136189/v1 ◽

2020 ◽

Author(s):

Guona Li ◽

Mengmeng Kang ◽

Siyuan Sheng ◽

Ziyi Chen ◽

Kunshan Li ◽

...

Keyword(s):

Colorectal Cancer ◽

Network Analysis ◽

Enrichment Analysis ◽

Bioinformatic Analysis ◽

Molecular Therapy ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Pathway Enrichment ◽

Potential Biomarker

Abstract Background: Colorectal cancer (CRC) is a common malignant tumor of the digestive system. It is crucial to screen potential biomarkers for the diagnosis, pathogenesis, and prognosis of CRC because there are limited clinical symptoms associated with this cancer. Therefore, we attempted to identify biomarkers associated with the occurrence and progression of CRC by utilizing bioinformatic analysis and to elucidate a molecular mechanism for the diagnosis and treatment of CRC. Methods: Two independent gene expression profile datasets of colonic neoplasms (GSE44076 and GSE37182) were collected from public GEO datasets, which included 182 tumor tissues and 236 normal tissues. Next, differentially expressed genes (DEGs) between CRC colonic samples and non-CRC colonic samples were obtained via GEO2R online tools. Subsequently, hub genes were selected by several analyses of DEGs, including GO pathway enrichment analysis, KEGG pathway enrichment analysis, and PPI network analysis. Finally, the correlation between the hub genes and the occurrence of CRC was tested by harnessing survival analysis and ROC curve analysis. Results: Sixty-one shared DEGs were screened, including 44 high-expression genes and 17 low-expression genes, in CRC samples. Four genes (MYC, TIMP1, MMP7, and COL1A1) were considered to be hub genes because they exhibited higher connectivity degree scores through PPI network analysis. More importantly, there was a significant correlation between increased expression of TIMP1 and reduced survival time in patients with colorectal cancer. Conclusion: By using bioinformatic analysis, this study suggested that Timp-1 may represent a potential biomarker for the diagnosis and prognosis of targeted molecular therapy for CRC.

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Identification of Hub Genes and Pathways Associated With Idiopathic Pulmonary Fibrosis via Bioinformatics Analysis

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.711239 ◽

2021 ◽

Vol 8 ◽

Author(s):

Hanxi Wan ◽

Xinwei Huang ◽

Peilin Cong ◽

Mengfan He ◽

Aiwen Chen ◽

...

Keyword(s):

Gene Expression ◽

Network Analysis ◽

Idiopathic Pulmonary Fibrosis ◽

Pulmonary Fibrosis ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Ppi Network ◽

Hub Genes ◽

Expression Levels ◽

Protein Protein Interaction

Idiopathic pulmonary fibrosis (IPF) is a progressive disease whose etiology remains unknown. The purpose of this study was to explore hub genes and pathways related to IPF development and prognosis. Multiple gene expression datasets were downloaded from the Gene Expression Omnibus database. Weighted correlation network analysis (WGCNA) was performed and differentially expressed genes (DEGs) identified to investigate Hub modules and genes correlated with IPF. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and protein-protein interaction (PPI) network analysis were performed on selected key genes. In the PPI network and cytoHubba plugin, 11 hub genes were identified, including ASPN, CDH2, COL1A1, COL1A2, COL3A1, COL14A1, CTSK, MMP1, MMP7, POSTN, and SPP1. Correlation between hub genes was displayed and validated. Expression levels of hub genes were verified using quantitative real-time PCR (qRT-PCR). Dysregulated expression of these genes and their crosstalk might impact the development of IPF through modulating IPF-related biological processes and signaling pathways. Among these genes, expression levels of COL1A1, COL3A1, CTSK, MMP1, MMP7, POSTN, and SPP1 were positively correlated with IPF prognosis. The present study provides further insights into individualized treatment and prognosis for IPF.

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High Expression of PDE8B and DUOX2 Associated with Ability of Metastasis in Thyroid Carcinoma

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/2362195 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Zhenguo Sun ◽

Xiaoshuai Yuan ◽

Peng Du ◽

Peng Chen

Keyword(s):

Thyroid Carcinoma ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Thyroid Stimulating Hormone ◽

Hub Genes ◽

Gene Expressions ◽

Go Enrichment ◽

Poorly Differentiated ◽

Microarray Datasets ◽

Go Enrichment Analysis

Background. Hormone is an independent factor that induces differentiation of thyroid cancer (TC) cells. The thyroid-stimulating hormone (TSH) could promote the progression and invasion in TC cells. However, few genes related to hormone changes are studied in poorly differentiated metastatic TC. This study is aimed at constructing a gene set’s coexpression correlation network and verifying the changes of some hub genes involved in regulating hormone levels. Methods. Microarray datasets of TC samples were obtained from public Gene Expression Omnibus (GEO) databases. R software and bioinformatics packages were utilized to identify the differentially expressed genes (DEGs), important gene module eigengenes, and hub genes. Subsequently, the Gene Ontology (GO) enrichment analysis was constructed to explore important biological processes that are associated with the mechanism of poorly differentiated TC. Finally, some hub gene expressions were validated through real-time PCR and immunoblotting. Results. Gene chip with category number GSE76039 was analyzed, and 1190 DEGs were screened with criteria of P < 0.05 and ∣ log 2 foldchange ∣ > 2 . Our analysis showed that human dual oxidase 2 (DUOX2) and phosphodiesterase 8B (PDE8B) are the two important hub genes in a coexpression network. In addition, the validated experimental results showed that the expression levels of both DUOX2 and PDE8B were elevated in poorly differentiated metastatic TC tissues. Conclusion. This study identified and validated that DUOX2 and PDE8B were significantly associated with the metastasis ability of thyroid carcinoma.

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Transcriptome Signatures Reveal Candidate Key Genes in the Peripheral Blood Mononuclear Cells of Patients With Coronary Artery Disease

10.21203/rs.3.rs-122558/v1 ◽

2020 ◽

Author(s):

Vijayakrishna Kolur ◽

Basavaraj Vastrad ◽

Chanabasayya Vastrad ◽

Iranna Kotturshetti ◽

Anandkumar Tengli

Keyword(s):

Coronary Artery Disease ◽

Gene Ontology ◽

Coronary Artery ◽

Regulatory Network ◽

Target Gene ◽

Enrichment Analysis ◽

Hub Genes ◽

Go Enrichment ◽

Artery Disease ◽

Go Enrichment Analysis

Abstract BackgroundCoronary artery disease (CAD) is one of the most common disorders in the cardiovascular system. This study aims to explore potential signaling pathways and important biomarkers that drive CAD development. MethodsThe CAD GEO Dataset GSE113079 was featured to screen differentially expressed genes (DEGs). The pathway and Gene Ontology (GO) enrichment analysis of DEGs were analyzed using the ToppGene. We screened hub and target genes from protein-protein interaction (PPI) networks, target gene - miRNA regulatory network and target gene - TF regulatory network, and Cytoscape software. Validations of hub genes were performed to evaluate their potential prognostic and diagnostic value for CAD. Results1,036 DEGs were captured according to screening criteria (525upregulated genes and 511downregulated genes). Pathway and Gene Ontology (GO) enrichment analysis of DEGs revealed that these up and down regulated genes are mainly enriched in thyronamine and iodothyronamine metabolism, cytokine-cytokine receptor interaction, nervous system process, cell cycle and nuclear membrane. Hub genes were validated to find out potential prognostic biomarkers, diagnostic biomarkers and novel therapeutic target for CAD. ConclusionsIn summary, our findings discovered pivotal gene expression signatures and signaling pathways in the progression of CAD. CAPN13, ACTBL2, ERBB3, GATA4, GNB4, NOTCH2, EXOSC10, RNF2, PSMA1 and PRKAA1 might contribute to the progression of CAD, which could have potential as biomarkers or therapeutic targets for CAD.

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Prediction and Validation of Hub Genes Associated with Colorectal Cancer by Integrating PPI Network and Gene Expression Data

BioMed Research International ◽

10.1155/2017/2421459 ◽

2017 ◽

Vol 2017 ◽

pp. 1-12 ◽

Cited By ~ 4

Author(s):

Yongfu Xiong ◽

Wenxian You ◽

Rong Wang ◽

Linglong Peng ◽

Zhongxue Fu

Keyword(s):

Colorectal Cancer ◽

Mrna Expression ◽

Interaction Network ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Advanced Tumor Stage ◽

Ppi Network ◽

Hub Genes ◽

Clinical Value ◽

Advanced Tumor

Although hundreds of colorectal cancer- (CRC-) related genes have been screened, the significant hub genes still need to be further identified. The aim of this study was to identify the hub genes based on protein-protein interaction network and uncover their clinical value. Firstly, 645 CRC patients’ data from the Tumor Cancer Genome Atlas were downloaded and analyzed to screen the differential expression genes (DEGs). And then, the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed, and PPI network of the DEGs was constructed by Cytoscape software. Finally, four hub genes (CXCL3, ELF5, TIMP1, and PHLPP2) were obtained from four subnets and further validated in our clinical setting and TCGA dataset. The results showed that mRNA expression of CXCL3, ELF5, and TIMP1 was increased in CRC tissues, whereas PHLPP2 mRNA expression was decreased. More importantly, high expression of CXCL3, ELF5, and TIMP1 was significantly associated with lymphatic invasion, distance metastasis, and advanced tumor stage. In addition, a shorter overall survival was observed in patients with increased CXCL3, TIMP1, and ELF5 expression and decreased PHLPP2 expression. In conclusion, the four hub genes screened by our strategy could serve as novel biomarkers for prognosis prediction of CRC patients.

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Integrated WGCNA and PPI Network to Screen Hub Genes Signatures for Infantile Hemangioma

Frontiers in Genetics ◽

10.3389/fgene.2020.614195 ◽

2021 ◽

Vol 11 ◽

Author(s):

Miao Xu ◽

Tianxiang Ouyang ◽

Kaiyang Lv ◽

Xiaorong Ma

Keyword(s):

Differential Expression ◽

Enrichment Analysis ◽

Infantile Hemangioma ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Normal Tissues ◽

Network Analyses ◽

Cycle Arrest ◽

Auc Value

BackgroundInfantile hemangioma (IH) is characterized by proliferation and regression.MethodsBased on the GSE127487 dataset, the differentially expressed genes (DEGs) between 6, 12, or 24 months and normal samples were screened, respectively. STEM software was used to screen the continued up-regulated or down-regulated in common genes. The modules were assessed by weighted gene co-expression network analysis (WGCNA). The enrichment analysis was performed to identified the biological function of important module genes. The area under curve (AUC) value and protein-protein interaction (PPI) network were used to identify hub genes. The differential expression of hub genes in IH and normal tissues was detected by qPCR.ResultsThere were 5,785, 4,712, and 2,149 DEGs between 6, 12, and 24 months and normal tissues. We found 1,218 DEGs were up-regulated or down-regulated expression simultaneously in common genes. They were identified as 10 co-expression modules. Module 3 and module 4 were positively or negatively correlated with the development of IH, respectively. These two module genes were significantly involved in immunity, cell cycle arrest and mTOR signaling pathway. The two module genes with AUC greater than 0.8 at different stages of IH were put into PPI network, and five genes with the highest degree were identified as hub genes. The differential expression of these genes was also verified by qRTPCR.ConclusionFive hub genes may distinguish for proliferative and regressive IH lesions. The WGCNA and PPI network analyses may help to clarify the molecular mechanism of IH at different stages.

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Gene Differential Expression and Interaction Networks Illustrate the Biomarkers and Molecular Mechanisms of Atherosclerotic Cerebral Infarction

Journal of Healthcare Engineering ◽

10.1155/2022/3912697 ◽

2022 ◽

Vol 2022 ◽

pp. 1-8

Author(s):

Benzhuo Zhang ◽

Wei Huang ◽

Mingquan Yi ◽

Chunxu Xing

Keyword(s):

Network Analysis ◽

Cerebral Infarction ◽

Antigen Processing ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Neutrophil Activation ◽

Receptor Interaction ◽

Ppi Network ◽

Geo Database

Atherosclerotic cerebral infarction (ACI) seriously threatens the health of the senile patients, and the strategies are urgent for the diagnosis and treatment of ACI. This study investigated the mRNA profiling of the patients with ischemic stroke and atherosclerosis via excavating the datasets in the GEO database and attempted to reveal the biomarkers and molecular mechanism of ACI. In this study, GES16561 and GES100927 were obtained from Gene Expression Omnibus (GEO) database, and the related differentially expressed genes (DEGs) were analyzed with R language. Furthermore, the DEGs were analyzed with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Besides, the protein-protein interaction (PPI) network of DEGs was analyzed by STRING database and Cytoscape. The results showed that 133 downregulated DEGs and 234 upregulated DEGs were found in GES16561, 25 downregulated DEGs and 104 upregulated DEGs were found in GSE100927, and 6 common genes were found in GES16561 and GES100927. GO enrichment analysis showed that the functional models of the common genes were involved in neutrophil activation, neutrophil degranulation, neutrophil activation, and immune response. KEGG enrichment analysis showed that the DEGs in both GSE100927 and GSE16561 were connected with the pathways including Cell adhesion molecules (CAMs), Cytokine-cytokine receptor interaction, Phagosome, Antigen processing and presentation, and Staphylococcus aureus infection. The PPI network analysis showed that 9 common DEGs were found in GSE100927 and GSE16561, and a cluster with 6 nodes and 12 edges was also identified by PPI network analysis. In conclusion, this study suggested that FCGR3A and MAPK pathways were connected with ACI.

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