Department of General Surgery, School of Clinical Medicine, Tsinghua University, Beijing Tsinghua Changgung Hospital, Beijing 102218, China.
Exp Biol Med (Maywood). 2021 Jul;246(14):1617-1625. doi: 10.1177/15353702211008809. Epub 2021 Apr 25.
Thyroid cancer is a frequently diagnosed malignancy and the incidence has been increased rapidly in recent years. Despite the favorable prognosis of most thyroid cancer patients, advanced patients with metastasis and recurrence still have poor prognosis. Therefore, the molecular mechanisms of progression and targeted biomarkers were investigated for developing effective targets for treating thyroid cancer. Eight chip datasets from the gene expression omnibus database were selected and the inSilicoDb and inSilicoMerging R/Bioconductor packages were used to integrate and normalize them across platforms. After merging the eight gene expression omnibus datasets, we obtained one dataset that contained the expression profiles of 319 samples (188 tumor samples plus 131 normal thyroid tissue samples). After screening, we identified 594 significantly differentially expressed genes (277 up-regulated genes plus 317 down-regulated genes) between the tumor and normal tissue samples. The differentially expressed genes exhibited enrichment in multiple signaling pathways, such as p53 signaling. By building a protein-protein interaction network and module analysis, we confirmed seven hub genes, and they were all differentially expressed at all the clinical stages of thyroid cancer. A diagnostic seven-gene signature was established using a logistic regression model with the area under the receiver operating characteristic curve (AUC) of 0.967. Seven robust candidate biomarkers predictive of thyroid cancer were identified, and the obtained seven-gene signature may serve as a useful marker for thyroid cancer diagnosis and prognosis.
甲状腺癌是一种常见的恶性肿瘤，近年来发病率迅速上升。尽管大多数甲状腺癌患者的预后良好，但有转移和复发的晚期患者预后仍然较差。因此，研究了进展的分子机制和靶向生物标志物，以开发治疗甲状腺癌的有效靶点。从基因表达综合数据库中选择了 8 个芯片数据集，并使用 InSilicoDb 和 InSilicoMerging R/Bioconductor 软件包对其进行整合和跨平台归一化。在合并了 8 个基因表达综合数据集后，我们获得了一个包含 319 个样本（188 个肿瘤样本加 131 个正常甲状腺组织样本）表达谱的数据集。经过筛选，我们在肿瘤和正常组织样本之间鉴定出 594 个显著差异表达基因（277 个上调基因加 317 个下调基因）。差异表达基因在多个信号通路中富集，如 p53 信号通路。通过构建蛋白质-蛋白质相互作用网络和模块分析，我们验证了 7 个枢纽基因，它们在甲状腺癌的所有临床阶段均有差异表达。使用具有 0.967 接收者操作特征曲线 (AUC) 的逻辑回归模型建立了一个诊断 7 基因特征。鉴定出了 7 个稳健的候选甲状腺癌预测生物标志物，所获得的 7 基因特征可能成为甲状腺癌诊断和预后的有用标志物。
