Department of Medical Imaging, Hebei Medical University, 361 Zhongshan East Road, Shijiazhuang, China; The Fifth Department of Oncology, Hebei General Hospital, 348 Heping West Road, Shijiazhuang, China.
Department of Urology, Hebei General Hospital, 348 Heping West Road, Shijiazhuang, China.
Taiwan J Obstet Gynecol. 2022 Jul;61(4):663-671. doi: 10.1016/j.tjog.2022.04.005.
Cervical squamous cell carcinoma (CESC) is a cancer with high mortality caused by human papillomavirus. The aim of this study was to uncover potential CESC biomarkers to help early diagnosis and treatment.
The mRNA transcriptome data and DNA methylation data were downloaded from database for the identification of differentially expressed mRNAs (DEmRNAs) and DNA methylation analysis. Functional analysis was used to reveal the molecular functions. Then, the association between differential methylation and DEmRNA was analyzed. Protein-protein interaction (PPI) network analysis was performed on the selected differentially methylated genes (DEGs). Subsequently, we analyzed the prognosis and constructed a prognostic risk model. We also performed diagnostic analyses of risk model genes. In addition, we verified the protein expression level of identified DEGs.
1438 DEmRNAs, 1669 differentially methylated sites (DMSs), 46 differentially methylated CpG islands and 53 differential methylation genes (DMGs) were obtained. In PPI, the highest interaction scores were MX2 and IRF8, and their interaction score was 0.928. Interestingly, 5 DMGs were found to be associated with CESC prognosis. In addition, our results demonstrated that high risk score was associated with poor prognosis of CESC. Furthermore, it was found that ZIK1, ZNRF2, HHEX, VCAM1 could be diagnostic molecular markers for CESC.
Analysis of methylated-differentially expressed genes may contribute to the identification of early diagnosis and therapeutic targets of CESC. In addition, a prognostic model based on 5 DMGs can be used to predict the prognosis of CESC.
宫颈鳞状细胞癌（CESC）是一种由人乳头瘤病毒引起的高死亡率癌症。本研究旨在发现潜在的 CESC 生物标志物，以帮助早期诊断和治疗。
从数据库中下载 mRNA 转录组数据和 DNA 甲基化数据，以识别差异表达的 mRNAs（DEmRNAs）和 DNA 甲基化分析。功能分析用于揭示分子功能。然后，分析差异甲基化与 DEmRNA 之间的关联。对选定的差异甲基化基因（DEGs）进行蛋白质-蛋白质相互作用（PPI）网络分析。随后，我们分析了预后并构建了预后风险模型。我们还对风险模型基因进行了诊断分析。此外，我们验证了鉴定的 DEmRNAs 的蛋白表达水平。
获得了 1438 个 DEmRNAs、1669 个差异甲基化位点（DMSs）、46 个差异甲基化 CpG 岛和 53 个差异甲基化基因（DMGs）。在 PPI 中，最高相互作用得分是 MX2 和 IRF8，它们的相互作用得分是 0.928。有趣的是，发现 5 个 DMGs 与 CESC 预后相关。此外，我们的结果表明，高风险评分与 CESC 的不良预后相关。此外，发现 ZIK1、ZNRF2、HHEX、VCAM1 可以作为 CESC 的诊断分子标志物。
分析甲基化差异表达基因可能有助于识别 CESC 的早期诊断和治疗靶点。此外，基于 5 个 DMGs 的预后模型可用于预测 CESC 的预后。
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