The present study was performed with the aim of understanding the mechanisms of pathogenesis and providing novel biomarkers for cervical cancer by constructing a regulatory circular (circ)RNA-micro (mi)RNA-mRNA network. Using an adjusted P-value of <0.05 and an absolute log value of fold-change >1, 16 and 156 miRNAs from GSE30656 and The Cancer Genome Atlas (TCGA), 5,321 mRNAs from GSE63514, 4,076 mRNAs from cervical squamous cell carcinoma and endocervical adenocarcinoma (from TCGA) and 75 circRNAs from GSE102686 were obtained. Using RNAhybrid, Venn and UpSetR plot, 12 circRNA-miRNA pairs and 266 miRNA-mRNA pairs were obtained. Once these pairs were combined, a circRNA-miRNA-mRNA network with 11 circRNA nodes, 4 miRNA nodes, 153 mRNA nodes and 203 edges was constructed. By constructing the protein-protein interaction network using Molecular Complex Detection scores >5 and >5 nodes, 7 hubgenes (RRM2, CEP55, CHEK1, KIF23, RACGAP1, ATAD2 and KIF11) were identified. By mapping the 7 hubgenes into the preliminary circRNA-miRNA-mRNA network, a circRNA-miRNA-hubgenes network consisting of 5 circRNAs (hsa_circRNA_000596, hsa_circRNA_104315, hsa_circRNA_400068, hsa_circRNA_101958 and hsa_circRNA_103519), 2 mRNAs (hsa-miR-15b and hsa-miR-106b) and 7 mRNAs (RRM2, CEP55, CHEK1, KIF23, RACGAP1, ATAD2 and KIF11) was constructed. There were 22 circRNA-miRNA-mRNA regulatory axes identified in the subnetwork. By analyzing the overall survival for the 7 hubgenes using the Gene Expression Profiling Interactive Analysis tool, higher expression of RRM2 was demonstrated to be associated with a significantly poorer overall survival. PharmGkb analysis identified single nucleotide polymorphisms (SNPs) of rs5030743 and rs1130609 of RRM2, which can be treated with cladribine and cytarabine. RRM2 was also indicated to be involved in the gemcitabine pathway. The 5 circRNAs (hsa_circRNA_000596, hsa_circRNA_104315, hsa_circRNA_400068, hsa_circRNA_101958 and hsa_circRNA_103519) may function as competing endogenous RNAs and serve critical roles in cervical cancer. In addition, cytarabine may produce similar effects to gemcitabine and may be an optional chemotherapeutic drug for treating cervical cancer by targeting rs5030743 and rs1130609 or other similar SNPs. However, the specific mechanism of action should be confirmed by further study.

Cervical Cancer is one of the leading causes of cancer-associated mortality in women. The present study aimed to identify key genes and pathways involved in cervical cancer (CC) progression, via a comprehensive bioinformatics analysis. The GSE63514 dataset from the Gene Expression Omnibus database was analyzed for hub genes and cancer progression was divided into four phases (phases I-IV). Pathway enrichment, protein-protein interaction (PPI) and pathway crosstalk analyses were performed, to identify key genes and pathways using a criterion nodal degree ≥5. Gene pathway analysis was determined by mapping the key genes into the key pathways. Co-expression between key genes and their effect on overall survival (OS) time was assessed using The Cancer Genome Atlas database. A total of 3,446 differentially expressed genes with 107 hub genes were identified within the four phases. A total of 14 key genes with 11 key pathways were obtained, following extraction of ≥5 degree nodes from the PPI and pathway crosstalk networks. Gene pathway analysis revealed that CDK1 and CCNB1 regulated the cell cycle and were activated in phase I. Notably, the following terms, 'pathways in cancer', 'focal adhesion' and the 'PI3K-Akt signaling pathway' ranked the highest in phases II-IV. Furthermore, FN1, ITGB1 and MMP9 may be associated with metastasis of tumor cells. STAT1 was indicated to predominantly function at the phase IV via cancer-associated signaling pathways, including 'pathways in cancer' and 'Toll-like receptor signaling pathway'. Survival analysis revealed that high ITGB1 and FN1 expression levels resulted in significantly worse OS. CDK1 and CCNB1 were revealed to regulate proliferation and differentiation through the cell cycle and viral tumorigenesis, while FN1 and ITGB1, which may be developed as novel prognostic factors, were co-expressed to induce metastasis via cancer-associated signaling pathways, including PI3K-Art signaling pathway, and focal adhesion in CC; however, the underlying molecular mechanisms require further research.

Proliferating cell nuclear antigen (PCNA) is reported as a famous marker in various tumors. A couple of articles have been published about the clinical function of PCNA on cancer progression; however, these results are conflicting in some degree. Thus, it is crucial to perform a systematic review and meta-analysis to identify their real actions. Here, we took cervical cancer and glioma as example and then pooled hazard ratios (HRs) or odds ratios (ORs) with 95 % confidence intervals (95 % CIs). In the present study, the PCNA expression in cervical cancer and gliomas patients was both correlated with 5-year-overall survival (OS) (HR = 4.41, 95 % CI 2.71-7.17, p = 0.000; HR = 4.40, 95 % CI 3.00-6.47, p = 0.000; respectively). In addition, a fixed effect model revealed a significant association between PCNA and FIGO stage (OR = 4.48, 95 % CI 3.48-5.77, p = 0.000) or WHO grade (OR = 5.64, 95 % CI 4.15-7.68, p = 0.000), rather than age (OR = 1.01, 95 % CI 0.71-1.43, p = 0.957; OR = 1.00, 95 % CI 0.80-1.24, p = 0.989; respectively). No heterogeneity was observed across all studies. According to funnel plot, no publication bias was reported. In conclusion, our systematic review suggests that PCNA expression is significantly associated with poor 5-year survival, advanced stage or higher WHO grade, which might be suggested as a useful prognostic and diagnostic biomarker, or an effective therapy target in cervical cancer, gliomas, or even more cancers.

The aim of the present study was to investigate the key pathways and genes in the progression of cervical cancer. The gene expression profiles GSE7803 and GSE63514 were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified using GEO2R and the limma package, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted using the Database for Annotation, Visualization and Integrated Discovery. The hub genes were identified using Cytoscape and protein-protein interaction (PPI) networks were constructed using the STRING database. A total of 127 and 99 DEGs were identified in the pre-invasive and invasive stages of cervical cancer, respectively. GO enrichment analysis indicated that the DEGs in pre-invasive cervical cancer were primarily associated with the ‘protein binding’, ‘single-stranded DNA-dependent ATPase activity’, ‘DNA replication origin binding’ and ‘microtubule binding’ terms, whereas the DEGs in invasive cervical cancer were associated with the ‘extracellular matrix (ECM) structural constituent’, ‘heparin binding’ and ‘integrin binding’. KEGG enrichment analysis revealed that the pre-invasive DEGs were significantly enriched in the ‘cell cycle’, ‘DNA replication’ and ‘p53 signaling pathway’ terms, while the invasive DEGs were enriched in the ‘amoebiasis’, ‘focal adhesion’, ‘ECM-receptor interaction’ and ‘platelet activation’ terms. The PPI network identified 4 key genes (PCNA, CDK2, VEGFA and PIK3CA), which were hub genes for pre-invasive and invasive cervical cancer. In conclusion, bioinformatics analysis identified 4 key genes in cervical cancer progression (PCNA, CDK2, VEGFA and PIK3CA), which may be potential biomarkers for differentiating normal cervical epithelial tissue from cervical cancer.

ABSTRACT

Cervical cancer and endometrial cancer remain serious threats to women's health. Even though some patients can be treated with surgery plus chemoradiotherapy as a conventional option, the overall efficacy is deemed unsatisfactory. As such, the development for new treatment approaches is truly necessary. In recent years, immunotherapy has been widely used in clinical practice and it is an area of great interest that researchers are keeping attention on. However, a thorough immunerelated genes (IRGs) study for cervical cancer and endometrial cancer is still lacking. We therefore aim to make a comprehensive evaluation of IRGs through bioinformatics and large databases, and also investigate the relationship between the two types of cancer. We reviewed the transcriptome RNAs of IRGs and clinical data based on the TCGA database. Survival-associated IRGs in cervical/endometrial cancer were identified using univariable and multivariable Cox proportional-hazard regression analysis for developing an IRG signature model to evaluate the risk of patients. In the end, this model was validated based on the enrichment analyses through GO, KEGG, and GSEA pathways, Kaplan-Meier survival curve, ROC curves, and immune cell infiltration. Our results showed that out of 25/23 survival-associated IRGs for cervical/endometrial cancer, 13/12 warranted further examination by multivariate Cox proportional-hazard regression analysis and were selected to develop an IRGs signature model. As a result, enrichment analyses for high-risk groups indicated main enriched pathways were associated with tumor development and progression, and statistical differences were found between high-risk and low-risk groups as shown by Kaplan-Meier survival curve. This model could be used as an independent measure for risk assessment and was considered relevant to immune cell infiltration, but it had nothing to do with clinicopathological characteristics. In summary, based on comprehensive analysis, we obtained the IRGs signature model in cervical cancer (

Cervical, endometrial and vulvar cancer are three common types of gynecological tumor that threaten the health of females worldwide. Since their underlying mechanisms and associations remain unclear, a comprehensive and systematic bioinformatics analysis is required. The present study downloaded GSE63678 from the GEO database and then performed functional enrichment analyses, including gene ontology and pathway analysis. To further investigate the molecular mechanisms underlying the three types of gynecological cancer, protein-protein interaction (PPI) analysis was performed. A biological network was generated with the guidance of the Kyoto Encyclopedia of Genes and Genomes database and was presented in Cytoscape. A total of 1,219 DEGs were identified for the three types of cancer, and 25 hub genes were revealed. Pathway analysis and the PPI network indicated that four main types of pathway participate in the mechanism of gynecological cancer, including viral infections and cancer formation, tumorigenesis and development, signal transduction, and endocrinology and metabolism. A preliminary gynecological cancer biological network was constructed. Notably, following all analysis, the phosphoinositide 3-kinase (PI3K)/Akt pathway was identified as a potential biomarker pathway. Seven pivotal hub genes (CCNA2, CDK1, CCND1, FGF2, IGF1, BCL2 and VEGFA) of the three gynecological cancer types were proposed. The seven hub genes may serve as targets in gynecological cancer for prevention and early intervention. The PI3K/Akt pathway was identified as a critical biomarker of the three types of gynecological cancer, which may serve a role in the pathogenesis. In summary, the present study provided evidence that could support the treatment of gynecologic tumors in the future.

We conducted a case-control study to estimate association between six common single nucleotide polymorphisms (SNPs) and risk of cervical cancer and evaluate the interaction between IL-17 gene polymorphisms and environmental factors in cervical cancer patients. This study included 264 consecutive primary cervical cancer patients and 264 age-matched controls. The genotypes of IL-17A rs2275913, rs3748067, and rs3819025 and IL-17A rs763780, rs9382084, and rs1266828 were analyzed using polymerase chain reaction-restriction fragment length of polymorphism (PCR-RFLP) assay. By logistic regression analysis, we found that individuals with AA genotype of rs2275913 were correlated with increased risk of cervical cancer when compared with GG genotype, and the odds ratio (OR) (95 % confidence interval (CI)) for AA genotype was 2.34 (1.24-4.49). By stratified analysis, individuals with AA genotype of rs2275913 were significantly associated with increased risk of cervical cancer in HPV-16- or HPV-18-infected patients when compared with GG genotype, and the OR (95 % CI) was 4.11 (1.14-22.33). In this case-control study, we suggest that rs2275913 may play an important role in the development of cervical cancer, especially in HPV-16- or HPV-18-infected patients.