Department of Obstetrics and Gynecology, The Third Hospital of Jilin University, Changchun, Jilin, P.R. China.
J Obstet Gynaecol Res. 2021 Sep;47(9):3310-3321. doi: 10.1111/jog.14827. Epub 2021 Jul 4.
We aimed to screen for the genes related to survival prognosis of cervical squamous cell carcinoma (CSCC) and then constructed a prognosis prediction model.
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.
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.
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.
筛选与宫颈鳞状细胞癌（CSCC）生存预后相关的基因，并构建预后预测模型。
从国家生物技术信息中心（NCBI）基因表达综合数据库（GEO）获取 GSE63514 数据集。从癌症基因组图谱（TCGA）和 NCBI GEO 获取 CSCC 基因数据集和 GSE44001 数据集。采用 Kaplan-Meier（KM）曲线评估高低预后与实际生存预后信息之间的关联。采用 Cox 比例风险模型筛选出优化的预后相关差异表达基因（DEG）组合。基因集富集分析用于对与风险分组相关的 DEGs 进行通路富集注释分析。
共获得 16 399 个 DEG，筛选出 23 个基因本体生物学过程和 8 个京都基因与基因组百科全书通路。选择了 9 个与独立预后相关的优化 DEG 组。病理 N0 和 N1 的 KM 曲线表明，低风险组与更好的总生存期相关（p=1.518e；p=1.704e-01）。与风险分组相关的通路为细胞因子-细胞因子受体相互作用、JAK 信号转导通路和糖酵解-糖异生。
基于本研究，我们建立了一个预后风险模型，为 CSCC 中定位与生存预后相关的生物标志物提供了可靠的预后工具，具有重要意义。
