&lt;p&gt;Identification of candidate biomarkers correlated with the diagnosis and prognosis of cervical cancer via integrated bioinformatics analysis&lt;/p&gt;

Dai, Fangfang; Chen, Gantao; Wang, Yanqing; Zhang, Li; Long, Youmei; Yuan, Mengqin; Yang, Dongyong; Liu, Shiyi; Cheng, Yanxiang; Zhang, Liping

doi:10.2147/ott.s199615

Cited by 31 publications

(22 citation statements)

References 37 publications

(34 reference statements)

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“…A total of three datasets (GSE63514, GSE9750 and GSE7410) were selected for further investigation. 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.…”

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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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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“…For instance, using a comprehensive bioinformatics analysis, Xia et al [7] demonstrated that ANLN was dramatically up-regulated in CC tissues, where it predicts poor prognosis. Dai et al [8] identified that the lower expression of KLF4 and ESR1 is closely related to the poor prognosis of patients with CC. Therefore, analyzing the gene expression profiles and the interaction of differentially expressed genes (DEGs) network of CC tissues is vital for understanding the molecular mechanisms of the causes and pathogenesis of CC and the identification of new prognostic biomarkers that may be exploited therapeutically.…”

Section: Introductionmentioning

confidence: 99%

Identification of Core Prognosis-Related Candidate Genes in Cervical Cancer via Integrated Bioinformatical Analysis

Wei

Shi

Wang

2020

BioMed Research International

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Purposes. Cervical cancer (CC) is one of the highest frequently occurred malignant gynecological tumors with high rates of morbidity and mortality. Here, we aimed to identify significant genes associated with poor outcome. Materials and methods. Differentially expressed genes (DEGs) between CC tissues and normal cervical tissues were picked out by GEO2R tool and Venn diagram software. Database for Annotation, Visualization and Integrated Discovery (DAVID) was performed to analyze gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway. The protein-protein interactions (PPIs) of these DEGs were visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). Afterwards, Kaplan-Meier analysis was applied to analyze the overall survival among these genes. The Gene Expression Profiling Interactive Analysis (GEPIA) was applied for further validation of the expression level of these genes. Results. The mRNA expression profile datasets of GSE63514, GSE27678, and GSE6791 were downloaded from the Gene Expression Omnibus database (GEO). In total, 76 CC tissues and 35 normal tissues were collected in the three profile datasets. There were totally 73 consistently expressed genes in the three datasets, including 65 up-regulated genes and 8 down-regulated genes. Of PPI network analyzed by Molecular Complex Detection (MCODE) plug-in, all 65 up-regulated genes and 4 down-regulated genes were selected. The results of the Kaplan-Meier survival analysis showed that 3 of the 65 up-regulated genes had a significantly worse prognosis, while 3 of the 4 down-regulated genes had a significantly better outcome. For validation in GEPIA, 4 of 6 genes (PLOD2, ANLN, AURKA, and AR) were confirmed to be significantly deregulated in CC tissues compared to normal tissues. Conclusion. We have identified three up-regulated (PLOD2, ANLN, and AURKA) and a down-regulated DEGs (AR) with poor prognosis in CC on the basis of integrated bioinformatical methods, which could be regarded as potential therapeutic targets for CC patients.

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“…MiRNA hsa-miR-107, as the hub in this network, was reported to be overexpressed in gastric carcinoma and promote tumor growth and survival [39]. Many researchers have shown that PPP1R3C, PPKAR2B and AKT3 may play important roles in cervical cancer [40], cardiovascular events [41] and prostate cancer [42]. In addition, lncRNAs TUG1, an important regulator of cancers, could facilitate proliferation and suppressed apoptosis by regulating miR-132-3p in osteosarcoma cells [43].…”

Section: Discussionmentioning

confidence: 99%

Identification of biomarkers and pathways in hypertensive nephropathy based on the ceRNA regulatory network

Wang

Liu

et al. 2020

Preprint

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Abstract Background Hypertensive nephropathy (HTN) is a kind of renal injury caused by chronic hypertension, which seriously affect people’s life. The purpose of this study was to identify the potential biomarkers of HTN and understand its possible mechanisms. Methods The dataset numbered GSE28260 related to hypertensive and normotensive was downloaded from NCBI Gene Expression Omnibus. Then, the differentially expressed RNAs (DERs) were screened using R limma package, and functional analyses of DE-mRNA were performed by DAVID. Afterwards, a ceRNA network was established and KEGG pathway was analyzed based on the Gene Set Enrichment Analysis (GSEA) database. Finally, a ceRNA regulatory network directly associated with HTN was proposed. Results A total of 947 DERs were identified, including 900 DE-mRNAs, 20 DE-lncRNAs and 27 DE-miRNAs. Based on these DE-mRNAs, they were involved in biological processes such as fatty acid beta-oxidation, IRE1-mediated unfolded protein response, and transmembrane transport, and many KEGG pathways like glycine, serine and threonine metabolism, carbon metabolism. Subsequently, lncRNAs KCTD21-AS1 , LINC00470 and SNHG14 were found to be hub nodes in the ceRNA regulatory network. KEGG analysis showed that insulin signaling pathway, glycine, serine and threonine metabolism, pathways in cancer, lysosome, and apoptosis was associated with hypertensive. Finally, insulin signaling pathway was screened to directly associate with HTN and was regulated by mRNAs PPP1R3C , PPKAR2B and AKT3 , miRNA has-miR-107, and lncRNAs SNHG14 , TUG1 , ZNF252P-AS1 and MIR503HG . Conclusions Insulin signaling pathway was directly associated with HTN, and miRNA has-miR-107 and lncRNAs SNHG14 , TUG1 , ZNF252P-AS1 and MIR503HG were the biomarkers of HTN. These results would improve our understanding of the occurrence and development of HTN.

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<p>Identification of candidate biomarkers correlated with the diagnosis and prognosis of cervical cancer via integrated bioinformatics analysis</p>

Cited by 31 publications

References 37 publications

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

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

Identification of Core Prognosis-Related Candidate Genes in Cervical Cancer via Integrated Bioinformatical Analysis

Identification of biomarkers and pathways in hypertensive nephropathy based on the ceRNA regulatory network

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