Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, Jiangsu, P.R. China.
Zhongda Hospital, Nanjing, Jiangsu, P.R. China.
DNA Cell Biol. 2020 Apr;39(4):645-653. doi: 10.1089/dna.2019.5167. Epub 2020 Feb 11.
Cervical cancer (CC) is a malignant tumor that could seriously endanger women's life and health, of which cervical squamous cell carcinoma (CESC) accounts for more than 80%. High-risk human papillomavirus (HR-HPV) infection is the primary cause of CC. The 5-year survival rate is low due to poor prognosis. We need to explore the pathogenesis of CC and seek effective biomarkers to improve prognosis. The purpose of this research is to construct an HR-HPV-related long non-coding RNA (lncRNA) signature for predicting the survival and finding the biomarkers related to CC prognosis. First, we downloaded the CESC data from The Cancer Genome Atlas (TCGA) database to find HR-HPV-related lncRNAs in CC. Then, the differentially expressed lncRNAs were analyzed by univariate and multivariate Cox regression. Six lncRNAs were found to be associated with the prognosis and can be used as independent prognostic factors. Next, based on these prognostic genes, we established a risk score model, which showed that patients with higher score had poorer prognosis and higher mortality. Moreover, the Kaplan-Meier curve of the model indicated that the model was statistically significant ( < 0.05). The survival-receiver operating characteristic curve showed that the model could also predict the survival of CC patients (the area under the curve, AUC = 0.65). More importantly, nomogram was drawn with clinical features and risk score, which verified the above conclusion, and its calibration curve and c-index index fully demonstrated that the prediction model could predict the progress of CC. We also validated the risk score model in head and neck cancer, and the results indicated that the model had obvious prognostic ability. Finally, we analyzed the correlation between clinical features and survival, and found that neoplasm cancer ( < 0.000) and risk score ( < 0.000) were independent prognostic factors for CC. In conclusion, the study established HR-HPV-related lncRNA signature, which provided a reliable prognostic tool, and was of great significance for finding the biomarkers related to HR-HPV infection in CC.
宫颈癌(CC)是一种严重威胁女性生命健康的恶性肿瘤,其中宫颈鳞状细胞癌(CESC)占比超过 80%。高危型人乳头瘤病毒(HR-HPV)感染是 CC 的主要病因。由于预后较差,5 年生存率较低。我们需要探索 CC 的发病机制,寻求有效的生物标志物以改善预后。本研究旨在构建用于预测生存和寻找与 CC 预后相关的生物标志物的 HR-HPV 相关长非编码 RNA(lncRNA)特征。首先,我们从癌症基因组图谱(TCGA)数据库中下载 CESC 数据,以在 CC 中寻找 HR-HPV 相关的 lncRNA。然后,通过单变量和多变量 Cox 回归分析差异表达的 lncRNA。发现 6 个 lncRNA 与预后相关,可以作为独立的预后因素。接下来,我们基于这些预后基因建立了风险评分模型,该模型显示评分较高的患者预后较差,死亡率较高。此外,该模型的 Kaplan-Meier 曲线表明该模型具有统计学意义( < 0.05)。模型的生存-接收者操作特征曲线表明该模型也可以预测 CC 患者的生存情况(曲线下面积,AUC=0.65)。更重要的是,绘制了包含临床特征和风险评分的列线图,验证了上述结论,且其校准曲线和 C 指数充分证明了该预测模型可以预测 CC 的进展。我们还对头颈部癌症验证了风险评分模型,结果表明该模型具有明显的预后能力。最后,我们分析了临床特征与生存的相关性,发现肿瘤癌症( < 0.000)和风险评分( < 0.000)是 CC 的独立预后因素。总之,本研究建立了 HR-HPV 相关的 lncRNA 特征,为 CC 提供了可靠的预后工具,对于寻找与 HR-HPV 感染相关的 CC 生物标志物具有重要意义。