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.
展开▼
机译:随着个性化疗法在侵袭性甲状腺乳头状癌(PTC)中的重要性不断提高,需要准确的风险分层。为了开发针对PTC(n = 455)患者的新型预后评分系统,我们使用了The Cancer Genome Atlas的mRNA表达和临床数据。我们使用通路数据库中基因网络的网络正则化高维Cox回归进行变量选择。使用回归系数和mRNA表达的线性组合计算风险评分。通过几项生存分析评估风险评分和临床变量。风险评分对无事件生存以及转移的预测具有很高的区分力。在多变量分析中,风险得分和转移的存在是一起检查的临床变量中的重要风险因素。在当前的研究中,我们开发了一种风险评分系统,该系统将有助于为PTC确定合适的治疗选择。
展开▼