Expression and prognosis of CDC45 in cervical cancer based on the GEO database

He, Zikang; Wang, Xiaojin; Yang, Zhiming; Jiang, Ying; Li, Luhui; Song, Zheyao; Wan, Jiahui; Shu, Jiangbo; Zhang, Naiwen; Cui, Rongjun

doi:10.7717/peerj.12114

Cited by 13 publications

(10 citation statements)

References 55 publications

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“…76 The previous studies found that CDC45 as a proliferation-associated antigen and is associated with the progression of malignant tumors. 77,78 Furthermore, it is associated with prognosis in CC, which also coincides with our F I G U R E 6 PPI network of key genes with two clusters.…”

Section: Discussionsupporting

confidence: 87%

Identification of key molecular players and associated pathways in cervical squamous cell carcinoma progression through network analysis

et al. 2023

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Cervical cancer is the primary cause of mortality among women in developing countries. Preventing cervical cancer is partially possible through early vaccination against the human papillomavirus, the most common cause of the disease. Nevertheless, it is imperative to understand the genetics of the disease progression to develop new therapeutic strategies. The present study aims to identify potential genes and associated pathways associated with cervical squamous cell carcinoma progression. We used an integrative approach by combining differential expression analysis, network biology, and functional enrichment analysis with survival analysis. In the present study, differential expression analysis of the microarray‐based gene expression profiles of cervical cancer resulted in identifying a total of 544 significantly differentially expressed genes (DEGs). Further, centrality and network vulnerability analysis of the protein–protein interaction network (PPIN) and not well documented in cervical cancer, resulted in seven proteins (FN1, MCM5, TRIP13, KIF11, TTK, CDC45, and BUB1B), in which four proteins were vulnerable. These genes are mostly enriched in biological processes of cell division, mitotic nuclear division, cell cycle checkpoint, and cell proliferation in gene ontology analysis. The KEGG pathway enrichment analysis of the proteins lists them as mainly associated with the cell cycle. In the survival analysis, it was found that the genes MCM5, FN1, KIF11, and CDC45 were statistically significant prognostic factors for cervical cancer. The outcome of the current study identifies and explores the key role of the candidate genes involved in the progression of cervical cancer.

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Section: Discussionsupporting

confidence: 87%

Identification of key molecular players and associated pathways in cervical squamous cell carcinoma progression through network analysis

et al. 2023

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“…On the other hand, we reviewed 52 published articles associated with CC infections that provided transcriptome-guided hub proteins (genomic biomarkers) for cross-validation of the proposed key genes and the candidate drug agents. There were 255 hub genes reported in those 52 articles, with 7 hub proteins (AURKA, PCNA, CCNB1, CDC45, MCM2, TOP2A, CDK1) appearing in at least 5 articles ( Table S2 ) [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 23 , 24 , 25 , 26 , 27 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 ]. Five (AURKA, CCNB1, MCM2, TOP2A, and CDK1) of the seven reported hub proteins were found to be similar to our suggested seven KGs.…”

Section: Resultsmentioning

confidence: 99%

Bioinformatics Screening of Potential Biomarkers from mRNA Expression Profiles to Discover Drug Targets and Agents for Cervical Cancer

Reza

Harun-Or-Roshid

Islam

et al. 2022

IJMS

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Bioinformatics analysis has been playing a vital role in identifying potential genomic biomarkers more accurately from an enormous number of candidates by reducing time and cost compared to the wet-lab-based experimental procedures for disease diagnosis, prognosis, and therapies. Cervical cancer (CC) is one of the most malignant diseases seen in women worldwide. This study aimed at identifying potential key genes (KGs), highlighting their functions, signaling pathways, and candidate drugs for CC diagnosis and targeting therapies. Four publicly available microarray datasets of CC were analyzed for identifying differentially expressed genes (DEGs) by the LIMMA approach through GEO2R online tool. We identified 116 common DEGs (cDEGs) that were utilized to identify seven KGs (AURKA, BRCA1, CCNB1, CDK1, MCM2, NCAPG2, and TOP2A) by the protein–protein interaction (PPI) network analysis. The GO functional and KEGG pathway enrichment analyses of KGs revealed some important functions and signaling pathways that were significantly associated with CC infections. The interaction network analysis identified four TFs proteins and two miRNAs as the key transcriptional and post-transcriptional regulators of KGs. Considering seven KGs-based proteins, four key TFs proteins, and already published top-ranked seven KGs-based proteins (where five KGs were common with our proposed seven KGs) as drug target receptors, we performed their docking analysis with the 80 meta-drug agents that were already published by different reputed journals as CC drugs. We found Paclitaxel, Vinorelbine, Vincristine, Docetaxel, Everolimus, Temsirolimus, and Cabazitaxel as the top-ranked seven candidate drugs. Finally, we investigated the binding stability of the top-ranked three drugs (Paclitaxel, Vincristine, Vinorelbine) by using 100 ns MD-based MM-PBSA simulations with the three top-ranked proposed receptors (AURKA, CDK1, TOP2A) and observed their stable performance. Therefore, the proposed drugs might play a vital role in the treatment against CC.

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“…Several research groups already published different sets of studied or hub differentially expressed genes (studied/hub-DEGs) associated with CC [ 12 , 13 , 20 , 22 – 71 ]. Here DEGs indicates the differentially expressed genes between CC and control samples.…”

Section: Methodsmentioning

confidence: 99%

“…As opposed to developing a new drug, repurposing existing drugs for certain conditions might save time and money. By this time, numerous studies have proposed different sets of hub/key-genes to explore molecular mechanisms and pathogenetic processes in CC [19][20][21][22][23][24]. Some of these studies have also been suggested their hub-genes or studied-gene guided candidate drugs for the treatment of CC [23,24].…”

Section: Introductionmentioning

confidence: 99%

Metadata analysis to explore hub of the hub-genes highlighting their functions, pathways and regulators for cervical cancer diagnosis and therapies

et al. 2022

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Cervical cancer (CC) is considered as the fourth most common women cancer globally.that shows malignant features of local infiltration and invasion into adjacent organs and tissues. There are several individual studies in the literature that explored CC-causing hub-genes (HubGs), however, we observed that their results are not so consistent. Therefore, the main objective of this study was to explore hub of the HubGs (hHubGs) that might be more representative CC-causing HubGs compare to the single study based HubGs. We reviewed 52 published articles and found 255 HubGs/studied-genes in total. Among them, we selected 10 HubGs (CDK1, CDK2, CHEK1, MKI67, TOP2A, BRCA1, PLK1, CCNA2, CCNB1, TYMS) as the hHubGs by the protein–protein interaction (PPI) network analysis. Then, we validated their differential expression patterns between CC and control samples through the GPEA database. The enrichment analysis of HubGs revealed some crucial CC-causing biological processes (BPs), molecular functions (MFs) and cellular components (CCs) by involving hHubGs. The gene regulatory network (GRN) analysis identified four TFs proteins and three miRNAs as the key transcriptional and post-transcriptional regulators of hHubGs. Then, we identified hHubGs-guided top-ranked FDA-approved 10 candidate drugs and validated them against the state-of-the-arts independent receptors by molecular docking analysis. Finally, we investigated the binding stability of the top-ranked three candidate drugs (Docetaxel, Temsirolimus, Paclitaxel) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore the finding of this study might be the useful resources for CC diagnosis and therapies.

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Expression and prognosis of CDC45 in cervical cancer based on the GEO database

Cited by 13 publications

References 55 publications

Identification of key molecular players and associated pathways in cervical squamous cell carcinoma progression through network analysis

Identification of key molecular players and associated pathways in cervical squamous cell carcinoma progression through network analysis

Bioinformatics Screening of Potential Biomarkers from mRNA Expression Profiles to Discover Drug Targets and Agents for Cervical Cancer

Metadata analysis to explore hub of the hub-genes highlighting their functions, pathways and regulators for cervical cancer diagnosis and therapies

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