Upregulation of Associated with Cervical Cancer Incidence and Development.

BACKGROUND

Cervical cancer is a common malignant tumor of women. Using integrated bioinformatics, this study identified key disease-causing genes in cervical cancer that may provide effective biomarkers or therapeutic targets for early diagnosis and treatment.

RESULTS

We used high-throughput sequencing data from the Gene Expression Omnibus (GEO) to identify new cervical cancer biomarkers. The GSE63678 dataset was downloaded. The data was analyzed via bioinformatics methods, and 61 differentially expressed genes were obtained. These differential genes were analyzed by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analyses. GO analysis demonstrated that the basic biological functions of differential genes were mostly regulating cell division, mitotic nuclear division, and immune response. Analysis of the KEGG pathway showed the primary involved in the cell cycle, p53 signaling pathway, and cytokine-cytokine receptor interactions. Using TCGA database to query differential expression of differential genes in cervical cancer, the gene was found to be highly expressed. analysis of protein interactions using the STRING database revealed that interacts with many proteins. These findings were then validated with immunohistochemistry and qRt-PCR to confirm that is highly expressed in cervical cancer tissues. Cell function tests demonstrated that inhibition of expression could inhibit the proliferation and migration of cervical cancer HeLa and SiHa cells and promote apoptosis.

CONCLUSION

With comprehensive bioinformatics combined with clinical and cellular function analysis, is important to the development of cervical cancer. Targeting of this biomarker may improve the early diagnosis and treatment of cervical cancer.