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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. [ABSTRACT FROM AUTHOR]

Cervical cancer (CC) is one kind of female cancer. With the development of bioinformatics, targeted specific biomarkers therapy has become much more valuable. GSE26511 was obtained from gene expression omnibus (GEO). We utilized a package called "WGCNA" to build co-expression network and choose the hub module. Search Tool for the Retrieval of Interacting Genes Database (STRING) was used to analyze protein-protein interaction (PPI) information of those genes in the hub module. A Plug-in called MCODE was utilized to choose hub clusters of PPI network, which was visualized in Cytoscape. Clusterprofiler was used to do functional analysis. Univariate and multivariate cox proportional hazards regression analysis were both conducted to predict the risk score of CC patients. Kaplan-Meier curve analysis was done to show the overall survival. Receiver operating characteristic (ROC) curve analysis was utilized to evaluate the predictive value of the patient outcome. Validation of the hub gene in databases, Gene set enrichment analysis (GSEA) and GEPIA were completed. We built co-expression network based on GSE26511 and one CC-related module was identified. Functional analysis of this module showed that extracellular space and Signaling pathways regulating pluripotency of stem cells were most related pathways. PPI network screened GNG11 as the most valuable protein. Cox analysis showed that ACKR1 was negatively correlated with CC progression, which was validated in Gene Expression Profiling Interactive Analysis (GEPIA) and datasets. Survival analysis was performed and showed the consistent result. GSEA set enrichment analysis was also completed. This study showed hub functional terms and gene participated in CC and then speculated that ACKR1 might be tumor suppressor for CC. [ABSTRACT FROM AUTHOR]

Cervical cancer is the fourth most common malignancy in women worldwide and cervical squamous cell carcinoma (CESC) is the most common histological type of cervical cancer. The dysregulation of genes plays a significant role in cancer. In the present study, we screened out differentially expressed genes (DEGs) of CESC in the GSE63514 data set from the Gene Expression Omnibus database. An integrated bioinformatics analysis was used to select hub genes, as well as to investigate their related prognostic signature, functional annotation, methylation mechanism, and candidate molecular drugs. As a result, a total of 1907 DEGs were identified (944 were upregulated and 963 were downregulated). In the protein–protein interaction network, three hub modules and 30 hub genes were identified. And two hub modules and 116 hub genes were screened out from four CESC‐related modules by the weighted gene coexpression network analysis. The gene ontology term enrichment analysis and Kyoto encyclopedia of genes and genomes pathway analysis were performed to better understand functions and pathways. Genes with a significant prognostic value were found by prognostic signature analysis. And there were five genes (EPHX2, CHAF1B, KIAA1524, CDC45, and RMI2) identified as significant CESC‐associated genes after expression validation and survival analysis. Among them, EPHX2 and RMI2 were noted as two novel key genes for the CESC‐associated methylation and expression. In addition, four candidate small molecule drugs for CESC (camptothecin, resveratrol, vorinostat, and trichostatin A) were defined. Further studies are required to explore these significant CESC‐associated genes for their potentiality in diagnosis, prognosis, and targeted therapy. [ABSTRACT FROM AUTHOR]