The Role of High-Risk Human Papillomavirus-Related Long Non-Coding RNAs in the Prognosis of Cervical Squamous Cell Carcinoma
The Role of High-Risk Human Papillomavirus-Related Long Non-Coding RNAs in the Prognosis of Cervical Squamous Cell Carcinoma
复制标题
高危人乳头瘤病毒相关长非编码RNA在宫颈鳞状细胞癌预后中的作用
DOI：
10.1089/dna.2019.5167
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发表时间：
2020-02-11
影响因子：
3.1
通讯作者：
Liang, Geyu
中科院分区：
文献类型：
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作者：
Cheng, Yanping;Yang, Sheng;Liang, Geyu
宫颈癌（Cervical cancer， CC）是一种严重危害妇女生命健康的恶性肿瘤，其中宫颈鳞状细胞癌（Cervical squamous cell carcinoma， CESC）占80%以上。高危人乳头瘤病毒（HR-HPV）感染是CC的主要病因，由于预后差，5年生存率低。我们需要探索CC的发病机制，寻找有效的生物标志物来改善预后。本研究的目的是构建hr - hpv相关的长链非编码RNA （lncRNA）标记，用于预测生存和寻找与CC预后相关的生物标志物。首先，我们从the Cancer Genome Atlas （TCGA）数据库中下载CESC数据，寻找CC中hr - hpv相关的lncrna，然后通过单因素和多因素Cox回归分析差异表达的lncrna。发现6个lncrna与预后相关，可作为独立的预后因素。接下来，基于这些预后基因，我们建立了风险评分模型，得分越高的患者预后越差，死亡率越高。此外，模型的Kaplan-Meier曲线表明模型具有统计学意义（p < 0.05）。生存-受者工作特征曲线显示，该模型还能预测CC患者的生存（曲线下面积，AUC = 0.65）。更重要的是，我们绘制了临床特征和风险评分的nomogram，验证了上述结论，其校准曲线和c-index指数充分证明了该预测模型可以预测CC的进展，我们还对头颈癌的风险评分模型进行了验证，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，发现肿瘤肿瘤（p < 0.000）和危险评分（p < 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标记，提供了可靠的预后工具，对于寻找CC中HR-HPV感染相关的生物标志物具有重要意义。
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 (p < 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 (p < 0.000) and risk score (p < 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.