Department of Nuclear Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea.
Department of Anatomy and Department of Biomedical Informatics, School of Medicine, Pusan National University, Yangsan, Republic of Korea.
J Cell Mol Med. 2019 Apr;23(4):3010-3015. doi: 10.1111/jcmm.14208. Epub 2019 Feb 7.
As the importance of personalized therapeutics in aggressive papillary thyroid cancer (PTC) increases, accurate risk stratification is required. To develop a novel prognostic scoring system for patients with PTC (n = 455), we used mRNA expression and clinical data from The Cancer Genome Atlas. We performed variable selection using Network-Regularized high-dimensional Cox-regression with gene network from pathway databases. The risk score was calculated using a linear combination of regression coefficients and mRNA expressions. The risk score and clinical variables were assessed by several survival analyses. The risk score showed high discriminatory power for the prediction of event-free survival as well as the presence of metastasis. In multivariate analysis, the risk score and presence of metastasis were significant risk factors among the clinical variables that were examined together. In the current study, we developed a risk scoring system that will help to identify suitable therapeutic options for PTC.
随着个性化治疗在侵袭性甲状腺乳头状癌（PTC）中的重要性不断增加，准确的风险分层至关重要。为了为 PTC 患者（n=455）开发一种新的预后评分系统，我们使用了来自癌症基因组图谱的 mRNA 表达和临床数据。我们使用来自途径数据库的基因网络进行了基于网络正则化的高维 Cox 回归的变量选择。风险评分是通过回归系数和 mRNA 表达的线性组合计算得出的。风险评分和临床变量通过几种生存分析进行评估。风险评分在预测无事件生存和转移的存在方面具有很高的区分能力。在多变量分析中，风险评分和转移的存在是在同时检查的临床变量中具有显著意义的危险因素。在本研究中，我们开发了一种风险评分系统，有助于为 PTC 患者确定合适的治疗选择。
