Identification and Prognostic Analysis of Hub Genes in Bladder Cancer

Abstract Background:Bladder cancer(BC) is one of the most common tumors worldwide. Its incidence and mortality rate rank first in urological malignancies. Due to the lack of credible predictors, most patients are not timely diagnosed and treated. Moreover, in the past 30 years, the clinical treatment of BC had seen little progress, and the 5-year survival rates of patients were flat.Therefore,identifying novel potential markers or therapeutic targets are urgently required for the diagnosis and prognosis of BC.Methods: The BC gene expression chip data （GSE121711）were downloaded from the GEO database and the BLCA RNA-seq data were downloaded from the TCGA database. The differentially expressed genes (DEGs) were identified by R software using limma package and the edgeR package, and obtained the overlapped DEGs from two databases. Then, the Gene Ontology(GO) function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of overlapped DEGs were performed through DAVID database, and the protein–protein interaction(PPI) network was constructed to screen Hub genes for regulatory protein expression in BC. Expression and prognostic analysis of the hub genes were performed by UALCAN and Kaplan-Meier plotter.Results: A total of 372 overlap DEGs were obtained, of which 93 were up-regulated and 279 were down-regulated. These genes were mainly associated with the function and pathway enrichment such as glycosaminoglycan binding, vasculature development, Cell cycle, Proteoglycans in cancer. The protein-protein interaction network analysis obtained 12 hub genes. Among these hub genes，HMMR，NCAPG2，SMC4, TROAP were closely related to the survival rate of bladder cancer patients revealed that these genes might be the key genes play an important role in the occurrence and progression.Conclusion:Therefore, our current studies demonstrated thatHMMR, NCAPG2, SMC4, TROAP are potential prognostic biomarkers for BC.In the future, these may also become clinical therapeutic targets.

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Module-based functional pathway enrichment analysis of a protein-protein interaction network to study the effects of intestinal microbiota depletion in mice

Molecular Medicine Reports ◽

10.3892/mmr.2014.2137 ◽

2014 ◽

Vol 9 (6) ◽

pp. 2205-2212 ◽

Cited By ~ 5

Author(s):

ZHEN-YI JIA ◽

YANG XIA ◽

DANIAN TONG ◽

JING YAO ◽

HONG-QI CHEN ◽

...

Keyword(s):

Intestinal Microbiota ◽

Protein Interaction ◽

Protein Interaction Network ◽

Interaction Network ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Pathway Enrichment ◽

Protein Protein Interaction ◽

Functional Pathway ◽

Protein Protein Interaction Network

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Immunological analysis and differential genes screening of venous thromboembolism

Hereditas ◽

10.1186/s41065-020-00166-6 ◽

2021 ◽

Vol 158 (1) ◽

Author(s):

Li-Na Gao ◽

Qiang Li ◽

Jian-Qin Xie ◽

Wan-Xia Yang ◽

Chong-Ge You

Keyword(s):

Venous Thromboembolism ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

R Software ◽

Pathway Enrichment ◽

Protein Protein Interaction ◽

New Ideas ◽

Gene Expression Profile Data ◽

Differential Genes

Abstract Purpose To explore the pathogenesis of venous thromboembolism (VTE) and provide bioinformatics basis for the prevention and treatment of VTE. Methods The R software was used to obtain the gene expression profile data of GSE19151, combining with the CIBERSORT database, obtain immune cells and differentially expressed genes (DEGs) of blood samples of VTE patients and normal control, and analyze DEGs for GO analysis and KEGG pathway enrichment analysis. Then, the protein-protein interaction (PPI) network was constructed by using the STRING database, the key genes (hub genes) and immune differential genes were screened by Cytoscape software, and the transcription factors (TFs) regulating hub genes and immune differential genes were analyzed by the NetworkAnalyst database. Results Compared with the normal group, monocytes and resting mast cells were significantly expressed in the VTE group, while regulatory T cells were significantly lower. Ribosomes were closely related to the occurrence of VTE. 10 hub genes and immune differential genes were highly expressed in VTE. MYC, SOX2, XRN2, E2F1, SPI1, CREM and CREB1 can regulate the expressions of hub genes and immune differential genes. Conclusions Ribosomal protein family genes are most relevant to the occurrence and development of VTE, and the immune differential genes may be the key molecules of VTE, which provides new ideas for further explore the pathogenesis of VTE.

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Immunological Analysis and Differential Genes Screening of Venous Thromboembolism

10.21203/rs.3.rs-33006/v1 ◽

2020 ◽

Author(s):

Li-Na Gao ◽

Qiang Li ◽

Jian-Qin Xie ◽

Wan-Xia Yang ◽

Chong-Ge You

Keyword(s):

Venous Thromboembolism ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

R Software ◽

Pathway Enrichment ◽

Protein Protein Interaction ◽

New Ideas ◽

Gene Expression Profile Data ◽

Differential Genes

Abstract Purpose: To explore the pathogenesis of venous thromboembolism (VTE) and provide bioinformatics basis for the prevention and treatment of VTE. Methods: The R software was used to obtain the gene expression profile data of GSE19151, combining with the CIBERSORT database, obtain immune cells and differentially expressed genes (DEGs) of blood samples of VTE patients and normal control, and analyze DEGs for GO analysis and KEGG pathway enrichment analysis. Then, the protein-protein interaction (PPI) network was constructed by using the STRING database, the key genes (hub genes) and immune differential genes were screened by Cytoscape software, and the transcription factors (TFs) regulating hub genes and immune differential genes were analyzed by the NetworkAnalyst database. Results: Compared with the normal group, monocytes and resting mast cells were significantly expressed in the VTE group, while regulatory T cells were significantly lower. Ribosomes were closely related to the occurrence of VTE. 10 hub genes and immune differential genes were highly expressed in VTE. MYC, SOX2, XRN2, E2F1, SPI1, CREM and CREB1 can regulate the expressions of hub genes and immune differential genes. Conclusions: Ribosomal protein family genes are most relevant to the occurrence and development of VTE, and the immune differential genes may be the key molecules of VTE, which provides new ideas for further explore the pathogenesis of VTE.

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Immunological analysis and differential genes screening of venous thromboembolism

10.21203/rs.3.rs-33006/v2 ◽

2020 ◽

Author(s):

Lina Gao ◽

Qiang Li ◽

Jianqin Xie ◽

Wanxia Yang ◽

Chongge You

Keyword(s):

Venous Thromboembolism ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

R Software ◽

Pathway Enrichment ◽

Protein Protein Interaction ◽

New Ideas ◽

Gene Expression Profile Data ◽

Differential Genes

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CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 May Be Potential Therapeutic Targets for Hepatocellular Carcinoma Using Integrated Bioinformatic Analysis

BioMed Research International ◽

10.1155/2019/1245072 ◽

2019 ◽

Vol 2019 ◽

pp. 1-16 ◽

Cited By ~ 2

Author(s):

Wan-Xia Yang ◽

Yun-Yan Pan ◽

Chong-Ge You

Keyword(s):

Hepatocellular Carcinoma ◽

Age Of Onset ◽

Function Analysis ◽

Therapeutic Targets ◽

Enrichment Analysis ◽

Bioinformatic Analysis ◽

Pathway Enrichment Analysis ◽

Poor Survival ◽

Hub Genes ◽

Normal Tissues

Hepatocellular carcinoma (HCC) is a malignant tumor with high mortality. The abnormal expression of genes is significantly related to the occurrence of HCC. The aim of this study was to explore the differentially expressed genes (DEGs) of HCC and to provide bioinformatics basis for the occurrence, prevention and treatment of HCC. The DEGs of HCC and normal tissues in GSE102079, GSE121248, GSE84402 and GSE60502 were obtained using R language. The GO function analysis and KEGG pathway enrichment analysis of DEGs were carried out using the DAVID database. Then, the protein–protein interaction (PPI) network was constructed using the STRING database. Hub genes were screened using Cytoscape software and verified using the GEPIA, UALCAN, and Oncomine database. We used HPA database to exhibit the differences in protein level of hub genes and used LinkedOmics to reveal the relationship between candidate genes and tumor clinical features. Finally, we obtained transcription factor (TF) of hub genes using NetworkAnalyst online tool. A total of 591 overlapping up-regulated genes were identified. These genes were related to cell cycle, DNA replication, pyrimidine metabolism, and p53 signaling pathway. Additionally, the GEPIA database showed that the CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 were associated with the poor survival of HCC patients. UALCAN, Oncomine, and HPA databases and qRT-PCR confirmed that these genes were highly expressed in HCC tissues. LinkedOmics database indicated these genes were correlated with overall survival, pathologic stage, pathology T stage, race, and the age of onset. TF analysis showed that MYBL2, KDM5B, MYC, SOX2, and E2F4 were regulators to these nine hub genes. Overexpression of CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 in tumor tissues predicted poor survival in HCC. They may be potential therapeutic targets for HCC.

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Identification of Key Biomarkers Associated with Survival and Prognosis in Colon Adenocarcinoma

10.21203/rs.3.rs-444592/v1 ◽

2021 ◽

Author(s):

Yanjie Zhou ◽

Lu Jiang ◽

Jiang Lin ◽

Wendong Tang ◽

Wenqian Jiang ◽

...

Keyword(s):

Gene Expression ◽

Survival Analysis ◽

Protein Interaction ◽

Colon Adenocarcinoma ◽

High Rate ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Core Genes

Abstract Background: Colorectal cancer (CRC) has a high rate of relapse and recurrence that result in poor prognosis and unsatisfactory outcomes. Colon adenocarcinoma (COAD) is the most prevalent type of CRC. It is crucial to identify novel molecular biomarkers for the early diagnosis, prognosis evaluation and disease monitoring of COAD.Methods: Three gene expression profile data were downloaded from the Gene Expression Omnibus(GEO), and the differential expression genes(DEGs) were identified by GEO2R. Gene Ontology (GO) and KEGG pathway enrichment analysis were conducted by WebGestalt online tool. String database and Cytoscape software were used for protein–protein interaction (PPI) network construction and module analysis. The top 20 Hub Genes were screened from the PPI network using MCC algorithm on CytoHubba app of Cytoscape software, and were verified by ONCOMINE database then. The core genes affecting CRC prognosis were screened by GEPIA2 survival analysis web tool. Finally, the expression level and clinical indicators including core genes was analyzed by TCGA-COAD dataset.Results: In total, 413 differentially expressed genes (DEGs) were identified, and the GO and KEGG enrichment analyses of DEGs were processed. After, the protein–protein interaction (PPI) network was constructed and 20 hub genes were identified. Furthermore, three core genes were selected via survival analysis . Finally, the diagnostic and prognostic value of these core genes was verified by clinical analysis of TCGA-COAD dataset.Conclusion: SPP1, GRP and GNGT1 were all over-expressed in COAD, and may be regarded as novel diagnostic and prognostic biomarkers for COAD.

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A2M is a potential core gene in intrahepatic cholangiocarcinoma

BMC Cancer ◽

10.1186/s12885-021-09070-2 ◽

2022 ◽

Vol 22 (1) ◽

Author(s):

Guanran Zhang ◽

Xuyue Liu ◽

Zhengyang Sun ◽

Xiaoning Feng ◽

Haiyan Wang ◽

...

Keyword(s):

Gene Expression ◽

Survival Analysis ◽

Protein Interaction ◽

Intrahepatic Cholangiocarcinoma ◽

Therapeutic Targets ◽

Pathway Enrichment Analysis ◽

Analysis Tool ◽

Potential Core ◽

Protein Protein Interaction ◽

Microarray Datasets

Abstract Background Intrahepatic cholangiocarcinoma (ICC) is a type of malignant tumor ranking the second in the incidence of primary liver cancer following hepatocellular carcinoma. Both the morbidity and mortality have been increasing in recent years. Small duct type of ICC has potential therapeutic targets. But overall, the prognosis of patients with ICC is usually very poor. Methods To search latent therapeutic targets for ICC, we programmatically selected the five most suitable microarray datasets. Then, we made an analysis of these microarray datasets (GSE26566, GSE31370, GSE32958, GSE45001 and GSE76311) collected from the Gene Expression Omnibus (GEO) database. The GEO2R tool was effective to find out differentially expressed genes (DEGs) between ICC and normal tissue. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were executed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) v 6.8. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to analyze protein–protein interaction of these DEGs and protein–protein interaction of these DEGs was modified by Cytoscape3.8.2. Survival analysis was performed using Gene Expression Profiling Interactive Analysis (GEPIA) online analysis tool. Results A total of 28 upregulated DEGs and 118 downregulated DEGs were screened out. Then twenty hub genes were selected according to the connectivity degree. The survival analysis results showed that A2M was closely related to the pathogenesis and prognosis of ICC and was a potential therapeutic target for ICC. Conclusions According to our study, low A2M expression in ICC compared to normal bile duct tissue was an adverse prognostic factor in ICC patients. The value of A2M in the treatment of ICC needs to be further studied.

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Decoding the molecular mechanism of parthenocarpy in Musa spp. through protein–protein interaction network

Scientific Reports ◽

10.1038/s41598-021-93661-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Suthanthiram Backiyarani ◽

Rajendran Sasikala ◽

Simeon Sharmiladevi ◽

Subbaraya Uma

Keyword(s):

Candidate Genes ◽

Protein Interaction ◽

Target Genes ◽

Interaction Network ◽

Enrichment Analysis ◽

Auxin Signaling ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Protein Protein Interaction ◽

The Difference

AbstractBanana, one of the most important staple fruit among global consumers is highly sterile owing to natural parthenocarpy. Identification of genetic factors responsible for parthenocarpy would facilitate the conventional breeders to improve the seeded accessions. We have constructed Protein–protein interaction (PPI) network through mining differentially expressed genes and the genes used for transgenic studies with respect to parthenocarpy. Based on the topological and pathway enrichment analysis of proteins in PPI network, 12 candidate genes were shortlisted. By further validating these candidate genes in seeded and seedless accession of Musa spp. we put forward MaAGL8, MaMADS16, MaGH3.8, MaMADS29, MaRGA1, MaEXPA1, MaGID1C, MaHK2 and MaBAM1 as possible target genes in the study of natural parthenocarpy. In contrary, expression profile of MaACLB-2 and MaZEP is anticipated to highlight the difference in artificially induced and natural parthenocarpy. By exploring the PPI of validated genes from the network, we postulated a putative pathway that bring insights into the significance of cytokinin mediated CLAVATA(CLV)–WUSHEL(WUS) signaling pathway in addition to gibberellin mediated auxin signaling in parthenocarpy. Our analysis is the first attempt to identify candidate genes and to hypothesize a putative mechanism that bridges the gaps in understanding natural parthenocarpy through PPI network.

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The Screening and Identification of Key Biomarkers in Adrenocortical Carcinoma: Evidence from a Bioinformatics Analysis

Journal of Biomaterials and Tissue Engineering ◽

10.1166/jbt.2022.2834 ◽

2022 ◽

Vol 12 (3) ◽

pp. 523-532

Author(s):

Xin Yan ◽

Chunfeng Liang ◽

Xinghuan Liang ◽

Li Li ◽

Zhenxing Huang ◽

...

Keyword(s):

Cell Cycle ◽

Gene Ontology ◽

Protein Interaction ◽

Adrenocortical Carcinoma ◽

Gene Expression Omnibus ◽

Heparin Binding ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Upregulated Genes

<sec> <title>Objective:</title> This study aimed to identify the potential key genes associated with the progression and prognosis of adrenocortical carcinoma (ACC). </sec> <sec> <title>Methods:</title> Differentially expressed genes (DEGs) in ACC cells and normal adrenocortical cells were assessed by microarray from the Gene Expression Omnibus database. The biological functions of the classified DEGs were examined by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses and a protein–protein interaction (PPI) network was mapped using Cytoscape software. MCODE software was also used for the module analysis and then 4 algorithms of cytohubba software were used to screen hub genes. The overall survival (OS) examination of the hub genes was then performed by the ualcan online tool. </sec> <sec> <title>Results:</title> Two GSEs (GSE12368, GSE33371) were downloaded from GEO including 18 and 43 cases, respectively. One hundred and sixty-nine DEGs were identified, including 57 upregulated genes and 112 downregulated genes. The Gene Ontology (GO) analyses showed that the upregulated genes were significantly enriched in the mitotic cytokines is, nucleus and ATP binding, while the downregulated genes were involved in the positive regulation of cardiac muscle contraction, extracellular space, and heparin-binding (P < 0.05). The Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) pathway examination showed significant pathways including the cell cycle and the complement and coagulation cascades. The protein– protein interaction (PPI) network consisted of 162 nodes and 847 edges, including mitotic nuclear division, cytoplasmic, protein kinase binding, and cell cycle. All 4 identified hub genes (FOXM1, UBE2C, KIF11, and NDC80) were associated with the prognosis of adrenocortical carcinoma (ACC) by survival analysis. </sec> <sec> <title>Conclusions:</title> The present study offered insights into the molecular mechanism of adrenocortical carcinoma (ACC) that may be beneficial in further analyses. </sec>

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