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

Yang, Huili; Xue, Jinmin; Li, Jie; Wan, Linghong; Zhu, Yubing

doi:10.1002/mgg3.1200

Cited by 33 publications

(38 citation statements)

References 59 publications

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“…11 ). However, the high expression of CXCL8 [ 35 ], TNFAIP6 , CXCL5 and CDA in cervical cancer tissues was associated with a poor patient prognosis (Fig. 12 ).…”

Section: Resultsmentioning

confidence: 99%

“…Further analysis showed that NUSAP1 [ 36 ] , TOP2A, KIF2C [ 37 ] , NDC80 [ 23 ] , ASPM [ 21 , 38 ] , KIF20A [ 39 ] , CDK1 [ 19 , 38 ] , KIF11 [ 40 , 41 ] , BIRC5 [ 42 ] , MCM2 and CHEK1 [ 40 , 41 , 43 ] were high scoring hub genes and showed significantly upregulated expression in cervical cancer tissues compared with normal tissues ( P < 0.01). By analyzing the prognostic values of the hub genes from the 12 subnetworks, a total of 14 potential molecular targets ( MCM2 [ 23 , 35 , 44 – 46 ], TOP2A [ 19 , 35 , 40 ], BLM, RMI2 , EXO1 , RFC4 , PSCA , KNTC1 , CDC45 , GINS2 , CXCL8 , TNFAIP6 , CXCL5 , and CDA ) were obtained and annotated.…”

Section: Discussionmentioning

confidence: 99%

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Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

2021

BMC Cancer

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Background This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis. Methods Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets. Results A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained. Conclusions These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer.

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“…11 ). However, the high expression of CXCL8 [ 35 ], TNFAIP6 , CXCL5 and CDA in cervical cancer tissues was associated with a poor patient prognosis (Fig. 12 ).…”

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

2021

BMC Cancer

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“…To improve our understanding of the molecular mechanisms underlying CC, bioinformatics analysis has been widely used to identify differentially expressed genes (DEGs), functional pathways and hub genes involved in the carcinogenesis and progression of CC. Although several genes have been identified in predicting the clinical outcome of CC, there have been inconsistencies between previous studies ( 10 , 11 ), which may be due to small sample sizes, heterogeneous histological subtypes, different detection platforms and various data processing methods. In the present study, three mRNA microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database to acquire DEGs between CC and normal tissues.…”

Section: Introductionmentioning

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 DEGs were imported into Cytoscape software to realize the visualization of the PPI network (Yang et al, 2020). Mark the immune‐related DEGs, which were annotated in the STRING database in the PPI.…”

Section: Methodsmentioning

confidence: 99%

Transcriptome analysis reveals immune‐related differentially expressed genes in the hepatopancreas and gills of Penaeus vannamei after Vibrio anguillarum infection

Song

Soomro

et al. 2021

Aquaculture Research

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Penaeus vannamei is one of the commercially important aquaculture species in the world but sensitive to Vibrio anguillarum infection. To understand the immune response of P. vannamei against V. anguillarum infection, the transcriptomes of hepatopancreas and gills of P. vannamei were analysed before and after V. anguillarum infection. Total 6213 differentially expressed genes (DEGs) were obtained from the hepatopancreas, and 3780 were obtained from the gills. The DEGs of interest were screened to construct a heat map. The reliability and accuracy of the transcriptome data were verified using RT‐qPCR. HSP70 and ARF6 were found highly expressed in both hepatopancreas and gills, while MLC2s encoded by two different gene sequences were found to be expressed in hepatopancreas and gills. Six immune‐related DEGs were identified from the protein–protein interaction (PPI) network including the HSP70, ARF6 and MLC2s. The sub‐network of PPI was constructed to explore the mode of interaction of immune‐related DEGs with their neighbouring proteins. Based on the result of PPI network, we infer that phosphorylated MLC2‐1 conducts signal transduction to induce leukocyte transendothelial migration in P. vannamei gills tissue while MLC2‐2 plays a role in immunity of V. anguillarum infection not only through activating signal transduction to mediate immune responses but also through the formation of phagosomes in hepatopancreas tissue. HSP70 is associated with the transporting exogenous cargos through presenting antigenic peptides in the process of V. anguillarum infection in P. vannamei. ARF6 interacts with Rab proteins to regulate phagolysosome formation by regulating GTPase activity in P. vannamei gills tissue. In hepatopancreas tissue, ARF6 plays a role in immunity by interacting with cytohesin. This study will contribute to further exploring the immune mechanism of P. vannamei to V. anguillarum infection.

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

Cited by 33 publications

References 59 publications

Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

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

Transcriptome analysis reveals immune‐related differentially expressed genes in the hepatopancreas and gills of Penaeus vannamei after Vibrio anguillarum infection

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