Identification of EPHX2 and RMI2 as two novel key genes in cervical squamous cell carcinoma by an integrated bioinformatic analysis

Liu, Jinhui; Nie, Song; Gao, Mei; Jiang, Yi; Wan, Yicong; Ma, Xiaoling; Zhou, Shulin; Cheng, Wenjun

doi:10.1002/jcp.28731

Cited by 43 publications

(31 citation statements)

References 48 publications

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“…25 The bioinformatics studies showed that CDC45 was up-regulated in the cervical cancer tissues and a high expression of CDC45 was correlated with longer overall survival of patients with cervical cancer. 28,29 Our findings were consistent with the previous studies, suggesting the robust bioinformatics analysis in this study. However, the expression of CDC45 may be investigated in the cervical cancer tissues with different clinical stages, which can further reveal the prognostic role of CDC45 in the cervical cancer.…”

Section: Discussionsupporting

confidence: 93%

Identification of Hub Genes as Potential Prognostic Biomarkers in Cervical Cancer Using Comprehensive Bioinformatics Analysis and Validation Studies

Han

Sun

et al. 2021

CMAR

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Background: Cervical cancer belongs to one of the most common female cancers; yet, the exact underlying mechanisms are still elusive. Recently, microarray and sequencing technologies have been widely used for screening biomarkers and molecular mechanism discovery in cancer studies. In this study, we aimed to analyse the microarray datasets using comprehensive bioinformatics tools and identified novel biomarkers associated with the prognosis of patients with cervical cancer. Methods: The differentially expressed genes (DEGs) from Gene Expression Omnibus (GEO) datasets including GSE138080, GSE113942 and GSE63514 were analysed using GEO2R tool. The functional enrichment analysis was performed using g:Profiler tool. The protein-protein interaction (PPI) network construction and hub genes identification were performed using the STRING database and Cytoscape software, respectively. The hub genes were subjected to expression and survival analysis in the cervical cancer. The EdU incorporation and Cell Counting Kit-8 assays were performed to evaluate the effects of hub gene knockdown on the proliferation of cervical cancer cells. Results: A total of 89 overlapping DEGs (63 up-regulated and 26 down-regulated genes) were identified in the microarray datasets. The functional enrichment analysis indicated that the overlapping DEGs were mainly associated with "DNA replication" and "cell cycle". Furthermore, the PPI network analysis revealed that the network contains 87 nodes and 309 edges. Sub-module analysis using the Molecular Complex Detection tool identified 21 hub genes from the PPI network. The expression levels of the 21 hub genes were all up-regulated in the cervical cancer tissues when compared to normal cervical tissues as analysed by GEPIA tool. The survival analysis showed that the low expression of cell division cycle 45 (CDC45), GINS complex subunit 2 (GINS2), minichromosome maintenance complex component 2 (MCM2) and proliferating cell nuclear antigen (PCNA) was significantly correlated with the shorter overall survival of patients with cervical cancer. Moreover, the protein expression levels of GINS2, MCM2 and PCNA, but not CDC45, were significantly upregulated in the cervical cancer tissues when compared to normal cervical tissues. Finally, knockdown of MCM2 significantly suppressed the proliferation of HeLa and SiHa cells. Conclusion:In conclusion, we screened a total of 89 overlapping DEGs from the GEO datasets, and further analysis identified four hub genes (CDC45, GINS2, MCM2 and PCNA) that were likely associated with the prognosis of patients with cervical cancer. MCM2 knockdown repressed the cervical cancer cell proliferation. The current findings may provide novel insights into understanding the pathophysiology of cervical cancer and develop therapeutic targets for patients with cervical cancer.

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Section: Discussionsupporting

confidence: 93%

Identification of Hub Genes as Potential Prognostic Biomarkers in Cervical Cancer Using Comprehensive Bioinformatics Analysis and Validation Studies

Han

Sun

et al. 2021

CMAR

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“…Among these, GSE63514 and GSE9750 have been analyzed together (26,27). Other studies have also mined the GSE63514 (28) and GSE9750 (29) datasets; only the GSE7410 dataset had not yet been thoroughly examined previously, to the best of our knowledge. Therefore, the GEO2R online tool was used to perform the analyses of the three datasets to determine any potentially relevant factors.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis shows that TOP2A functions as a key candidate gene in the progression of cervical cancer

Zhao

Zhu

et al. 2020

Biomed Rep

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Cervical cancer (CC) is one of the most prevalent types of cancer affecting females worldwide. However, the molecular mechanisms underlying the development and progression of CC remains to be elucidated. Taking the high incidence and mortality rates amongst women into consideration, the identification of novel biomarkers to prevent CC is of great significance and required to improve diagnosis. Using three raw microarray datasets from the Gene Expression Omnibus database, 188 differentially expressed genes (DEGs) were identified. Gene Ontology and pathway analyses were performed on the DEGs. Through protein-protein interaction network construction and module analysis, eight hub genes [cell division cycle 6, cyclin-dependent kinase 1 (CDK1), cell division control protein 45, budding uninhibited by benzimidazoles 1 (BUB1), DNA topoisomerase II α (TOP2A) and minichromosome maintenance complex component 4, CCNB2 and CCNB1] were identified, but only TOP2A was considered a prognostic factor in survival analysis. There were strong positive correlations between TOP2A and BUB1 (P<0.0001, rs= 0.635), CDK1 (P<0.0001, rs= 0.511), centromere protein F (CENPF) (P<0.0001, rs= 0.677), Rac GTPase activating protein 1 (RACGAP1) (P<0.0001, rs= 0.612), F-box protein 5 (FBXO5) (P<0.0001, rs=0.585) and BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) (P<0.0001, rs= 0.584). Additionally, BUB1, CDK1, CENPF, RACGAP1, FBXO5 and BUB1B are all potentially suitable candidate targets for the diagnosis and treatment of CC. In conclusion, the present study identified TOP2A as a potential tumor oncogene and a biomarker for the prognosis of CC.

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“…The ID corresponding to the probe name was converted into an international standard name for genes (gene symbol) and saved in a TXT file. If several probes were mapped to one gene symbol, the mean value was set as the final expression value of this gene (Irizarry et al, 2003;Liu et al, 2019) (Supplementary Table S1). Meanwhile, the expression profiles of messenger RNAs in 309 tissues (306 CESC tissues and 3 normal tissues) and the corresponding clinical information were downloaded from TCGA database.…”

Section: Figmentioning

confidence: 99%

Identification of Key Genes in Association with Progression and Prognosis in Cervical Squamous Cell Carcinoma

Meng

Liu

Qiu

et al. 2020

DNA and Cell Biology

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Cervical cancer remains a primary cause of female death in developing countries, but its prognosis can be greatly improved if patients are diagnosed earlier. In the present study, we screened the common differentially expressed genes (DEGs) of cervical squamous cell carcinoma (CESC) from dataset GSE7803, Gene Expression Omnibus, and The Cancer Genome Atlas databases. An integrated bioinformatics analysis was performed based on these DEGs for their enrichment in functions and pathways, interaction network, prognostic signature, and candidate molecular drugs. As a result, 164 (114 upregulated and 47 downregulated) DEGs of CESC were identified for further investigation. We then conducted the gene ontology term enrichment and Kyoto Encyclopedia of Genes and Genomes Pathway analyses to reveal the underlying functions and pathways of these DEGs. In the protein-protein interaction network, hub module and hub genes were identified. Five genes of significant prognostic value-DSG2, ITM2A, CENPM, RIBC2, and MEIS2-were identified by prognostic signature analysis and used to construct a risk linear model. Further validation and investigation suggested DSG2 might be a key gene in CESC prognosis. We then identified two candidate small molecules (trichostatin A and tanespimycin) against CESC. Further validation and exploration of these hub genes are warranted for future prospect in clinical applications.

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Identification of EPHX2 and RMI2 as two novel key genes in cervical squamous cell carcinoma by an integrated bioinformatic analysis

Cited by 43 publications

References 48 publications

Identification of Hub Genes as Potential Prognostic Biomarkers in Cervical Cancer Using Comprehensive Bioinformatics Analysis and Validation Studies

Identification of Hub Genes as Potential Prognostic Biomarkers in Cervical Cancer Using Comprehensive Bioinformatics Analysis and Validation Studies

Bioinformatics analysis shows that TOP2A functions as a key candidate gene in the progression of cervical cancer

Identification of Key Genes in Association with Progression and Prognosis in Cervical Squamous Cell Carcinoma

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