Huo Xiao, Zhou Xiaoshuang, Peng Peng, Yu Mei, Zhang Ying, Yang Jiaxin, Cao Dongyan, Sun Hengzi, Shen Keng
Medical Research Center, Peking University Third Hospital, Beijing,, People's Republic of China.
Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China.
Onco Targets Ther. 2021 Feb 5;14:809-822. doi: 10.2147/OTT.S276553. eCollection 2021.
Although the incidence of cervical cancer has decreased in recent decades with the development of human papillomavirus vaccines and cancer screening, cervical cancer remains one of the leading causes of cancer-related death worldwide. Identifying potential biomarkers for cervical cancer treatment and prognosis prediction is necessary.
Samples with mRNA sequencing, copy number variant, single nucleotide polymorphism and clinical follow-up data were downloaded from The Cancer Genome Atlas database and randomly divided into a training dataset (N=146) and a test dataset (N=147). We selected and identified a prognostic gene set and mutated gene set and then integrated the two gene sets with the random survival forest algorithm and constructed a prognostic signature. External validation and immunohistochemical staining were also performed.
We obtained 1416 differentially expressed prognosis-related genes, 624 genes with copy number amplification, 1038 genes with copy number deletion, and 163 significantly mutated genes. A total of 75 candidate genes were obtained after overlapping the differentially expressed genes and the genes with genomic variations. Subsequently, we obtained six characteristic genes through the random survival forest algorithm. The results showed that high expression of , and and low expression of and were associated with a poor prognosis in cervical cancer patients. We constructed a six-gene signature that can separate cervical cancer patients according to their different overall survival rates, and it showed robust performance for predicting survival (training set: ˂ 0.001, AUC = 0.82; testing set: ˂ 0.01, AUC = 0.59).
Our study identified a novel six-gene signature and nomogram for predicting the overall survival of cervical cancer patients, which may be beneficial for clinical decision-making for individualized treatment.
尽管近几十年来随着人乳头瘤病毒疫苗的研发和癌症筛查的开展，宫颈癌的发病率有所下降，但宫颈癌仍然是全球癌症相关死亡的主要原因之一。因此，有必要确定宫颈癌治疗和预后预测的潜在生物标志物。
从癌症基因组图谱数据库下载具有mRNA测序、拷贝数变异、单核苷酸多态性和临床随访数据的样本，并随机分为训练数据集（N=146）和测试数据集（N=147）。我们选择并鉴定了一个预后基因集和突变基因集，然后用随机生存森林算法将这两个基因集整合起来，构建了一个预后特征。同时进行了外部验证和免疫组化染色。
我们获得了1416个差异表达的预后相关基因、624个拷贝数增加的基因、1038个拷贝数缺失的基因以及163个显著突变的基因。将差异表达基因与基因组变异基因进行重叠后，共获得75个候选基因。随后，通过随机生存森林算法获得了6个特征基因。结果表明，[此处原文缺失基因名称]的高表达以及[此处原文缺失基因名称]的低表达与宫颈癌患者的不良预后相关。我们构建了一个六基因特征，可根据宫颈癌患者不同的总生存率将其区分开来，并且在预测生存方面表现出强大的性能（训练集：P<0.001，AUC=0.82；测试集：P<0.01，AUC=0.59）。
我们研究中鉴定出的一种新型六基因特征和列线图可用于预测宫颈癌患者的总生存期，这可能有助于临床个体化治疗决策。
