Systematic analysis to identify a key role of CDK1 in mediating gene interaction networks in cervical cancer development

Luo, Yijing; Wu, Yan; Peng, Yi; Liu, X.; Bie, Jun; Li, S.

doi:10.1007/s11845-015-1283-8

Cited by 37 publications

(30 citation statements)

References 32 publications

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“…Abnormal cell proliferation and genomic instability were caused by dysregulation of CDKs . According to our integrated analysis, up‐regulated CDK1 was modulated by hsa‐miR‐205 and hsa‐miR‐24 in the tissues of CIN versus N and by hsa‐miR‐195 and hsa‐miR‐497 in CIN versus CSCC, which have the same pattern in a previous study . In the PPI analysis, CDK1 has the highest degree among the hub proteins.…”

Section: Discussionsupporting

confidence: 74%

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Prediction of a miRNA‐mRNA functional synergistic network for cervical squamous cell carcinoma

et al. 2019

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Cervical squamous cell carcinoma (CSCC) accounts for a significant proportion of cervical cancer; thus, there is a need for novel and noninvasive diagnostic biomarkers for this malignancy. In this study, we performed integrated analysis of a dataset from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRNAs) between CSCC, cervical intraepithelial neoplasia (CIN) and healthy control subjects. We further established protein‐protein interaction and DEmiRNA‐target gene interaction networks, and performed functional annotation of the target genes of DEmiRNAs. In total, we identified 1375 DEGs and 19 DEmiRNAs in CIN versus normal control, and 2235 DEGs and 33 DEmiRNAs in CSCC versus CIN by integrated analysis. Our protein‐protein interaction network indicates that the common DEGs, Cyclin B/cyclin‐dependent kinase 1 (CDK1), CCND1, ESR1 and Aurora kinase A (AURKA), are the top four hub genes. P53 and prostate cancer were identified as significantly enriched signaling pathways of common DEGs and DEmiRNA targets, respectively. We validated that expression levels of three DEGs (TYMS, SASH1 and CDK1) and one DEmiRNA of hsa‐miR‐99a were altered in blood samples of patients with CSCC. In conclusion, a total of four DEGs (TYMS, SASH1, CDK1 and AURKA) and two DEmiRNAs (hsa‐miR‐21 and hsa‐miR‐99a) may be involved in the pathogenesis of CIN and the progression of CIN into CSCC. Of these, TYMS is predicted to be regulated by hsa‐miR‐99a and SASH1 to be regulated by hsa‐miR‐21.

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Section: Discussionsupporting

confidence: 74%

“…Also, CDK1 was enriched in the p53 signaling pathway in the KEGG analysis. Luo et al [16] reported that CDK1 played a complicated role in regulating genetic networks involved in the progression of cervical cancer. Prognosis of advanced stage cervical cancer may be enhanced by new therapeutics targeting CDK1 or its related pathways.…”

Section: Discussionmentioning

confidence: 99%

Prediction of a miRNA‐mRNA functional synergistic network for cervical squamous cell carcinoma

et al. 2019

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“…CDK1 is one of the serine/threonine kinases that regulate the cell cycle by interacting with specific cell-cycle-regulatory cyclins (22). It has been reported that CDK1 regulated CC (23).It is also involved in the proliferation of many tumors. Y. Zeng et al found that Cyclin-dependent kinase 1 (CDK1)-mediated mitotic phosphorylation of the transcriptional co-repressor Vgll4 inhibits its tumorsuppressing activity.…”

Section: Discussionmentioning

confidence: 99%

Prognostic value of two novel genes SNX10 and PTGDS for cervical squamous cell carcinoma: an integrated bioinformatic analysis

Jiang¹,

Cao²,

Gao³

et al. 2020

Preprint

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KEYWORDSCervical squamous cell carcinoma, methylation prognosis risk model SNX10, PTGDS in females (1). Despite pre-cancerous screening and emerging treatments, CC remains the primary cause of death in women in developing countries (2). When CC becomes metastasizes and recurs, the prognosis gets even worse. Therefore, it is of great significance to establish target treatments of CC based on its to-be-clarified molecular mechanism.Gene expression microarray, as an efficient means of acquiring large-scale genetic data, has been mostly used to study gene expression profiling in many human cancers. New methods and good foreground are provided by microarrays for studying tumor-associated genes, molecular targeting, molecular prediction and therapy. The integration of databases containing gene expression chips allows in-depth study of molecular mechanisms(3, 4).Up to now, the expression profiles of thousands of differentially expressed genes (DEGs) in CC have been researched (5-7). However, the results on some mRNAs are inconsistent. Here we use an unbiased approach to solve this problem.In our study, we screened DEGs from four profiles downloaded from GEO. PPI network was built by STRING Database and hub modules selected via plug-in MCODE. CMap was used to find potential molecules associated with CC. We also validated hub genes with GEO datasets, GEPIA, immunohistochemistry and ONCOMINE. ROC curve analysis and GESA were also done to tease out the significance of hub genes. The flow chart of this research was displayed in Fig. 1. Methods Screening DEGsKeywords "cervical cancer geo accession" were put in the GEO database (https://www.ncbi.nlm.nih.gov/geo/) and the gene expression profiles of GSE6791, GSE63514, GSE39001 and GSE9750 were downloaded. The dataset information is shown in Table 1. We processed unqualified data by R package. The data is calibrated, standardized and log2-transformed.Gene expression analysis was performed using the limma(8) package in the Bioconductor package.Relevant codes were placed into R. We selected four microarray datasets and analyzed them with limma. The |log2fold change (FC)| > 2 and adjusted p < 0.05 were set as cutoffs. RRA package was download (http://cran.r-project.org/)(9) and R was used for running the instruction code.

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“…is a member of the Serine/Threonine protein kinase family. Previous studies showed that CDK1 was related to many types of tumors growth and the patients survival [17][18][19][20][21] .…”

Section: Discussionmentioning

confidence: 99%

Identification of key genes related to dexamethasone-resistance in acute lymphoblastic leukemia

Chen

Shí

et al. 2018

Preprint

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Drug resistance is the main cause of poor chemotherapy response in acute leukemia.Despite the extensive use of dexamethasone(DEX) in the treatment of acute lymphoblastic leukemia for many years, the mechanisms of dexamethasone -resistance has not been fully understood. We choose GSE94302 from GEO database aiming to identify key genes that contribute to the DEX resistance in acute lymphoblastic leukemia. Differentially expressed gene(DEGs) are selected by using GEO2R tools. A total of 837 DEGs were picked out, including 472 up-regulated and 365 down-regulated DEGs. All the DEGs were underwent gene ontology(GO) analysis and Kyoto Encyclopedia of Gene and Genome(KEGG) pathway analysis. In addition, the DEGsencoded protein-protein interaction (PPI) was screened by using Cytoscape and Search Tool for the Retrieval of Interacting Genes(STRING). Total 20 genes were found as key genes related to DEX resistance with high degree of connectivity, including CDK1,

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Systematic analysis to identify a key role of CDK1 in mediating gene interaction networks in cervical cancer development

Abstract: CDK1 plays a comprehensive role in mediating genetic networks implicated in the progression of cervical cancer. Novel therapeutics targeting CDK1 or its related pathways might help improve prognosis of advanced stage cervical cancer.

Cited by 37 publications

References 32 publications

Prediction of a miRNA‐mRNA functional synergistic network for cervical squamous cell carcinoma

Prediction of a miRNA‐mRNA functional synergistic network for cervical squamous cell carcinoma

Prognostic value of two novel genes SNX10 and PTGDS for cervical squamous cell carcinoma: an integrated bioinformatic analysis

Identification of key genes related to dexamethasone-resistance in acute lymphoblastic leukemia

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