Screening of genes related to survival prognosis of cervical squamous cell carcinoma and construction of prognosis prediction model. Issue 9 (4th July 2021)

• Record Type: Journal Article
• Authors: Qin, Rui; Cao, Lu; Ye, Cong; Wang, Junrong; Sun, Ziqian
• Is Part Of: Journal of obstetrics and gynaecology research. Volume 47:Issue 9(2021)
• Page Start: 3310
• Page End: 3321
• Publication Date: 2021-07-04
• Subjects: cervical squamous cell carcinoma -- gene set enrichment analysis -- prognosis prediction model; Gynecology -- Periodicals; Obstetrics -- Periodicals; 618.1005
• DOI: 10.1111/jog.14827
• Languages: English
• ISSNs: 1341-8076

Abstract

Abstract: Aim: We aimed to screen for the genes related to survival prognosis of cervical squamous cell carcinoma (CSCC) and then constructed a prognosis prediction model. Methods: The GSE63514 dataset was obtained from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO). The CSCC gene dataset and the GSE44001 dataset were obtained from The Cancer Genome Atlas and NCBI GEO, respectively. The Kaplan–Meier (KM) curve was used to evaluate the association between high and low prognosis that was with the actual survival prognosis information. The Cox proportional hazards model was used to screen out the optimized prognostic‐related signature differentially expressed gene (DEG) combinations. Gene set enrichment analysis was used to perform pathway enrichment annotation analysis for DEGs that were related to risk grouping. Results: In total, 16 399 DEGs were obtained and 23 gene ontology biological processes and 8 Kyoto Encyclopedia of Genes and Genomes pathways were screened. Nine optimized DEG groups related to independent prognosis were selected. The KM curves of pathologic N0 and N1 showed that low‐risk group were associated with a better overall survival ( p = 1.518e; p = 1.704e−01). The pathways related to risk grouping were cytokine–cytokine receptor interaction, JAK stat signaling pathway, and glycolysis–gluconeogenesis. Conclusion: On the basis of this study, we established a prognostic risk model, which provided a reliable prognostic tool and was of great significance for locating the biomarkers related to survival prognosis in CSCC.