Bioinformatics prediction and analysis of hub genes and pathways of three types of gynecological cancer

Liu, Yanyan; Yi, Yuexiong; Wu, Wanrong; Wu, Kejia; Zhang, Wei

doi:10.3892/ol.2019.10371

Cited by 23 publications

(23 citation statements)

References 60 publications

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“…This further reinforces the fundamental involvement of TTAGPs in cellular division. Our findings are further validated by the identification of hub genes CCNA2, CDK1, AURKA and CCNB1 in the network of DEGs between EC and normal tissue, which are consistent with previous studies [ 118 , 119 , 120 , 121 , 122 ]. Therefore, it is not surprising that many of the identified hub genes have already been implicated in EC.…”

Section: Discussionsupporting

confidence: 92%

Investigating the Role of Telomere and Telomerase Associated Genes and Proteins in Endometrial Cancer

Bradfield

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Hill

et al. 2020

MPs

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Endometrial cancer (EC) is the commonest gynaecological malignancy. Current prognostic markers are inadequate to accurately predict patient survival, necessitating novel prognostic markers, to improve treatment strategies. Telomerase has a unique role within the endometrium, whilst aberrant telomerase activity is a hallmark of many cancers. The aim of the current in silico study is to investigate the role of telomere and telomerase associated genes and proteins (TTAGPs) in EC to identify potential prognostic markers and therapeutic targets. Analysis of RNA-seq data from The Cancer Genome Atlas identified differentially expressed genes (DEGs) in EC (568 TTAGPs out of 3467) and ascertained DEGs associated with histological subtypes, higher grade endometrioid tumours and late stage EC. Functional analysis demonstrated that DEGs were predominantly involved in cell cycle regulation, while the survival analysis identified 69 DEGs associated with prognosis. The protein-protein interaction network constructed facilitated the identification of hub genes, enriched transcription factor binding sites and drugs that may target the network. Thus, our in silico methods distinguished many critical genes associated with telomere maintenance that were previously unknown to contribute to EC carcinogenesis and prognosis, including NOP56, WFS1, ANAPC4 and TUBB4A. Probing the prognostic and therapeutic utility of these novel TTAGP markers will form an exciting basis for future research.

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Section: Discussionsupporting

confidence: 92%

Investigating the Role of Telomere and Telomerase Associated Genes and Proteins in Endometrial Cancer

Bradfield

Button

Hill

et al. 2020

MPs

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“…Thus, we have reason to speculate that AURKA has potential as a biomarker in ovarian cancer. CCNA2, which is also an important cell cycle regulatory gene, has been revealed as a hub gene in other gynecological cancers, such as cervical, endometrial, and vulvar cancers (16,17). In ovarian cancer, our results suggest that CCNA2 is overexpressed and associated with cancer prognosis, tumor grade, and KRAS mutation, indicating the critical role of CCNA2 in ovarian cancer.…”

Section: Discussionmentioning

confidence: 58%

Screening and identification of key biomarkers and related transcription factors in ovarian cancer by integrated bioinformatics analysis

Qin¹,

Yuan²,

Liu³

et al. 2020

Preprint

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Background Ovarian tumors are the most malignant tumors of all gynecological tumors, and although multiple efforts have been made to elucidate the pathogenesis, the molecular mechanisms of ovarian cancer remain unclear. Methods In this study, we used bioinformatics to identify genes involved in the carcinogenesis and progression of ovarian cancer. Three microarray datasets (GSE14407, GSE29450, and GSE54388) were downloaded from Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified. For a more in-depth understanding of the DEGs, functional and pathway enrichment analyses were performed and a protein-protein interaction (PPI) network was constructed. The associated transcriptional factor (TFs) regulation network of the DEGs was also constructed. Kaplan Meier-plotter, Gene Expression Profiling Interactive Analysis (GEPIA), the Human Protein Atlas (HPA) database and the Oncomine database were implemented to validated hub genes. Results A total of 514 DEGs were detected after the analysis of the three gene expression profiles, including 171 upregulated and 343 downregulated genes. Nine hub genes ( CCNB1, CDK1, BUB1, CDC20, CCNA2, BUB1B, AURKA, RRM2, TTK) were obtained from the PPI network. Survival analysis showed that high expression levels of seven hub genes ( CCNB1, BUB1, BUB1B, CCNA2, AURKA, CDK1, and RRM2) were associated with worse overall survival (OS). All of seven hub genes were discovered highly expressed in ovarian cancer samples compared to normal ovary samples in GEPIA. Immunostaining results from the HPA database suggested that the expressions of CCNB1, CCNA2, AURKA, and CDK1 proteins were increased in ovarian cancer tissues, and Oncomine analysis indicated that the expression patterns of BUB1B, CCNA2, AURKA, CCNB1, CDK1, and BUB1 have associated with patient clinicopathological information. From the gene-transcriptional factor network, key transcriptional factors, such as POLR2A, ZBTB11, KLF9, and ELF1, were identified with close interactions with these hub genes. Conclusion We identified six significant DEGs with poor prognosis in ovarian cancer, which could be of potential biomarkers for ovarian cancer patients.

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“…The networks of human proteins during viral mono-infections were constructed using Cytoscape and observed to be densely interconnected. The construction of proteome network and its analysis using Cytoscape have also been utilized to study protein–protein interactions for other viruses including the Hepatitis C virus (HCV) [ 17 ], Hepatitis B virus (HBV) [ 54 ], and gynaecological cancer [ 31 ]. The human interactors during the co-infection of DENV, CHIKV, and SARS-CoV-2 were elucidated by the merged constructed networks.…”

Section: Resultsmentioning

confidence: 99%

Computational approach to decipher cellular interactors and drug targets during co-infection of SARS-CoV-2, Dengue, and Chikungunya virus

Ghildiyal

Gabrani

2021

VirusDis.

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The world is reeling under severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, and it will be frightening if compounded by other co-existing infections. The co-occurrence of the Dengue virus (DENV) and Chikungunya virus (CHIKV) has been into existence, but recently the co-infection of DENV and SARS-CoV-2 has been reported. Thus, the possibility of DENV, CHIKV, and SARS-CoV-2 co-infection could be predicted in the future with enhanced vulnerability. It is essential to elucidate the host interactors and the connected pathways to understand the biological insights. The in silico approach using Cytoscape was exploited to elucidate the common human proteins interacting with DENV, CHIKV, and SARS-CoV-2 during their probable co-infection. In total, 17 interacting host proteins were identified showing association with envelope, structural, non-structural, and accessory proteins. Investigating the functional and biological behaviour using PANTHER, UniProtKB, and KEGG databases uncovered their association with several cellular pathways including, signaling pathways, RNA processing and transport, cell cycle, ubiquitination, and protein trafficking. Withal, exploring the DrugBank and Therapeutic Target Database, total seven druggable host proteins were predicted. Among all integrin beta-1, histone deacetylase-2 (HDAC2) and microtubule affinity-regulating kinase-3 were targeted by FDA approved molecules/ drugs. Furthermore, HDAC2 was predicted to be the most significant target, and some approved drugs are available against it. The predicted druggable targets and approved drugs could be investigated to obliterate the identified interactions that could assist in inhibiting viral infection.

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Bioinformatics prediction and analysis of hub genes and pathways of three types of gynecological cancer

Cited by 23 publications

References 60 publications

Investigating the Role of Telomere and Telomerase Associated Genes and Proteins in Endometrial Cancer

Investigating the Role of Telomere and Telomerase Associated Genes and Proteins in Endometrial Cancer

Screening and identification of key biomarkers and related transcription factors in ovarian cancer by integrated bioinformatics analysis

Computational approach to decipher cellular interactors and drug targets during co-infection of SARS-CoV-2, Dengue, and Chikungunya virus

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