Correlation of MCM2 Detection with Stage and Virology of Cervical Cancer

Amaro-Filho, Sergio M.; Nuovo, Gerard J.; Cunha, Cynthia Braga da; Pereira, Luiza de Oliveira Ramos; Oliveira-Silva, Michelle; Russomano, Fábio Bastos; Pires, Andrea; Nicol, Alcina F.

doi:10.5301/jbm.5000081

Cited by 30 publications

(19 citation statements)

References 32 publications

(33 reference statements)

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“…Our analysis results showed that the expression levels of TYMS , MCM2 , HELLS , and TOP2A in cervical cancer increased and positively correlated with the prognosis of cervical cancer. Previous studies have shown that MCM2 and TOP2A may be markers for screening of precancerous lesions and cervical cancer, whereas others believe that the expression level of MCM2 cannot predict tumor stage and posttreatment response (Amaro Filho et al, 2014; Kuku et al, 2015). And few studies have followed patients, and analyzed their survival and prognosis.…”

Section: Discussionmentioning

confidence: 99%

Identification of key genes and pathways of diagnosis and prognosis in cervical cancer by bioinformatics analysis

Yang

Xue

et al. 2020

Molec Gen & Gen Med

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Background Cervical cancer as one of the most common malignant tumors lead to bad prognosis among women. Some researches already focus on the carcinogenesis and pathogenesis of cervical cancer, but it is still necessary to identify more key genes and pathways. Methods Differentially expressed genes were identified by GEO2R from the gene expression omnibus (GEO) website, then gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyzed by DAVID. Meanwhile, protein–protein interaction network was constructed by STRING, and both key genes and modules were found in visualizing network through Cytoscape. Besides, GEPIA did the differential expression of key genes and survival analysis. Finally, the expression of genes related to prognosis was further explored by UNLCAN, oncomine, and the human protein atlas. Results Totally 57 differentially expressed genes were founded, not only enriched in G1/S transition of mitotic cell cycle, mitotic nuclear division, and cell division but also participated in cytokine–cytokine receptor interaction, toll‐like receptor signaling pathway, and amoebiasis. Additionally, 12 hub genes and 3 key modules were screened in the Cytoscape visualization network. Further survival analysis showed that TYMS (OMIM accession number ), MCM2 (OMIM accession number ), HELLS (OMIM accession number ), TOP2A (OMIM accession number ), and CXCL8 (OMIM accession number ) were associated with the prognosis of cervical cancer. Conclusion This study aim to better understand the characteristics of some genes and signaling pathways about cervical cancer by bioinformatics, and could provide further research ideas to find new mechanism, more prognostic factors, and potential therapeutic targets for cervical cancer.

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Section: Discussionmentioning

confidence: 99%

Identification of key genes and pathways of diagnosis and prognosis in cervical cancer by bioinformatics analysis

Yang

Xue

et al. 2020

Molec Gen & Gen Med

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“…26 Amaro Filho et al reported an increasing expression of MCM2 in invasive cervical cancers compared to controls, which was due to the correlation between the MCM2 -positive cells and the presence of HPV DNA detected by in situ hybridization. 27 Another study indicates that cervical cancer cells may use excess MCMs as a backup for replicative stress. 28 The level of MCM4 and PCNA was reported to be upregulated by mutant p53, a primary determinant in variety cancers.…”

Section: Discussionmentioning

confidence: 99%

Identification of Significant Gene Signatures and Prognostic Biomarkers for Patients With Cervical Cancer by Integrated Bioinformatic Methods

Tian

Gao

et al. 2018

Technol Cancer Res Treat

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Cervical cancer is the leading cause of death with gynecological malignancies. We aimed to explore the molecular mechanism of carcinogenesis and biomarkers for cervical cancer by integrated bioinformatic analysis. We employed RNA-sequencing details of 254 cervical squamous cell carcinomas and 3 normal samples from The Cancer Genome Atlas. To explore the distinct pathways, messenger RNA expression was submitted to a Gene Set Enrichment Analysis. Kyoto Encyclopedia of Genes and Genomes and protein–protein interaction network analysis of differentially expressed genes were performed. Then, we conducted pathway enrichment analysis for modules acquired in protein–protein interaction analysis and obtained a list of pathways in every module. After intersecting the results from the 3 approaches, we evaluated the survival rates of both mutual pathways and genes in the pathway, and 5 survival-related genes were obtained. Finally, Cox hazards ratio analysis of these 5 genes was performed. DNA replication pathway (P < .001; 12 genes included) was suggested to have the strongest association with the prognosis of cervical squamous cancer. In total, 5 of the 12 genes, namely, minichromosome maintenance 2, minichromosome maintenance 4, minichromosome maintenance 5, proliferating cell nuclear antigen, and ribonuclease H2 subunit A were significantly correlated with survival. Minichromosome maintenance 5 was shown as an independent prognostic biomarker for patients with cervical cancer. This study identified a distinct pathway (DNA replication). Five genes which may be prognostic biomarkers and minichromosome maintenance 5 were identified as independent prognostic biomarkers for patients with cervical cancer.

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“…35 Amaro Filho et al also reported an increasing expression of MCM2 in invasive CC compared to control, but they suggested that MCM2 is not a good biomarker when comparing the different clinical stages of CC. 36 PCNA acts as a central coordinator of DNA transactions by providing a multivalent interaction surface for factors involved in DNA replication and cell cycle regulation. Owing to its function, PCNA has been widely used as a tumor marker for cancer cell progression and patient prognosis.…”

Section: Discussionmentioning

confidence: 99%

Identification of key candidate genes and small molecule drugs in cervical cancer by bioinformatics strategy

Tang¹,

Xu²,

Lü³

et al. 2018

CMAR

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PurposeCervical cancer (CC) is one of the most common malignant tumors among women. The present study aimed at integrating two expression profile datasets to identify critical genes and potential drugs in CC.Materials and methodsExpression profiles, GSE7803 and GSE9750, were integrated using bioinformatics methods, including differentially expressed genes analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and protein–protein interaction (PPI) network construction. Subsequently, survival analysis was performed among the key genes using Gene Expression Profiling Interactive Analysis websites. Connectivity Map (CMap) was used to query potential drugs for CC.ResultsA total of 145 upregulated genes and 135 downregulated genes in CC were identified. The functional changes of these differentially expressed genes related to CC were mainly associated with cell cycle, DNA replication, p53 signaling pathway, and oocyte meiosis. A PPI network was identified by STRING with 220 nodes and 2,111 edges. Thirteen key genes were identified as the intersecting genes of the enrichment pathways and the top 20 nodes in PPI network. Survival analysis revealed that high mRNA expression of MCM2, PCNA, and RFC4 was significantly associated with longer overall survival, and the survival was significantly better in the low-expression RRM2 group. Moreover, CMap predicted nine small molecules as possible adjuvant drugs to treat CC.ConclusionOur study found key dysregulated genes involved in CC and potential drugs to combat it, which might provide insights into CC pathogenesis and might shed light on potential CC treatments.

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Correlation of MCM2 Detection with Stage and Virology of Cervical Cancer

Abstract: The present study shows that HPV-infected cells strongly express MCM2; nevertheless, our data suggests that MCM2 is not a good biomarker when comparing the different clinical stages of cervical cancer.

Cited by 30 publications

References 32 publications

Identification of key genes and pathways of diagnosis and prognosis in cervical cancer by bioinformatics analysis

Identification of key genes and pathways of diagnosis and prognosis in cervical cancer by bioinformatics analysis

Identification of Significant Gene Signatures and Prognostic Biomarkers for Patients With Cervical Cancer by Integrated Bioinformatic Methods

Identification of key candidate genes and small molecule drugs in cervical cancer by bioinformatics strategy

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