Comprehensive gene and pathway analysis of cervical cancer progression

Abstract

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.

Markers of Angiogenesis, Lymphangiogenesis, and Epithelial–Mesenchymal Transition (Plasticity) in CIN and Early Invasive Carcinoma of the Cervix: Exploring Putative Molecular Mechanisms Involved in Early Tumor Invasion

“…Selectivity of gene expression changes of mediators of inflammation involved in TLR-pathway is of noteworthy mention: for example, while the expression of CXCL10, a proinflammatory chemokine, was increased in invasive CR samples, the expression of IL-18 ("IFNγ-inducing factor") was, conversely, decreased. The coupling of TLR signaling pathway to the CIN3-to-cancer transition was also noted by Yi et al [60]. Another finding that the genes significantly up/downregulated in the early-stage invasive carcinoma were significantly enriched for components of the TNFα-and IL1-signaling pathways suggests the inducing role of the inflamed microenvironment.…”

STAT1 expression and HPV16 viral load predict cervical lesion progression

“…These findings suggest that STAT1 may have dual effects on cervical tumorigenesis. In early stages, STAT1 has a protective effect, while it is carcinogenic in aggressive tumors (30,31). STAT1 activation and enhanced expression in tumor cells are induced by different stimuli, such as IFN-α, IFN-β, IFN-γ, the action of oncogenes or loss of tumor suppressors, interaction with the tumor stroma, or genotoxic agents (32).…”

“…The amplification protocol was as follows: 9 min of denaturation at 95˚C and 40 cycles of 20 sec of denaturation at 95˚C, 30 sec of annealing at 55˚C, and 30 sec of elongation at 72˚C, followed by a final extension for 5 min at 72˚C. This method was based on a flow-through hybridization principle and is able to classify 21 HPV genotypes, including 13 high-risk subtypes (16,18,31,33,35,39,45,51,52,56,58, 59 and 68), 5 low-risk subtypes (6, 11, 42, 43 and 44) and 3 subtypes that are common in China (53, 66 and CP8304) (24).…”

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

“…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.…”