Vuong Huy Gia, Duong Uyen N P, Altibi Ahmed M A, Ngo Hanh T T, Pham Thong Quang, Tran Hung Minh, Gandolfi Greta, Hassell Lewis
Department of PathologyCho Ray Hospital, Ho Chi Minh City, Vietnam
Pham Ngoc Thach University of MedicineHo Chi Minh City, Vietnam.
Endocr Connect. 2017 Apr;6(3):R8-R17. doi: 10.1530/EC-17-0010. Epub 2017 Feb 20.
The prognostic role of molecular markers in papillary thyroid carcinoma (PTC) is a matter of ongoing debate. The aim of our study is to investigate the impact of , , promoter mutations and rearrangements on the prognosis of PTC patients. We performed a search in four electronic databases: PubMed, Scopus, Web of Science and Virtual Health Library (VHL). Data of hazard ratio (HR) and its 95% confidence interval (CI) for disease-specific survival (DSS) and disease-free survival (DFS) were directly obtained from original papers or indirectly estimated from Kaplan-Meier curve (KMC). Pooled HRs were calculated using random-effect model weighted by inverse variance method. Publication bias was assessed by using Egger's regression test and visual inspection of funnel plots. From 2630 studies, we finally included 35 studies with 17,732 patients for meta-analyses. promoter mutation was significantly associated with unfavorable DSS (HR = 7.64; 95% CI = 4.00-14.61) and DFS (HR = 2.98; 95% CI = 2.27-3.92). mutations significantly increased the risk for recurrence (HR = 1.63; 95% CI = 1.27-2.10) but not for cancer mortality (HR = 1.41; 95% CI = 0.90-2.23). In subgroup analyses, mutation only showed its prognostic value in short-/medium-term follow-up. Data regarding mutations and fusions were insufficient for meta-analyses. promoter mutation can be used as an independent and reliable marker for risk stratification and predicting patient's outcomes. The use of mutation to assess patient prognosis should be carefully considered.
分子标志物在甲状腺乳头状癌(PTC)中的预后作用一直是争论的焦点。我们研究的目的是探讨[具体基因1]、[具体基因2]、[具体基因3]启动子突变及[具体基因4]重排对PTC患者预后的影响。我们在四个电子数据库进行了检索:PubMed、Scopus、科学网和虚拟健康图书馆(VHL)。疾病特异性生存(DSS)和无病生存(DFS)的风险比(HR)及其95%置信区间(CI)的数据直接从原始论文中获取或通过Kaplan-Meier曲线(KMC)间接估计。采用随机效应模型加权的逆方差法计算合并HR。通过Egger回归检验和漏斗图的可视化检查评估发表偏倚。从2630项研究中,我们最终纳入了35项研究,共17732例患者进行荟萃分析。[具体基因1]启动子突变与不良DSS(HR = 7.64;95%CI = 4.00 - 14.61)和DFS(HR = 2.98;95%CI = 2.27 - 3.92)显著相关。[具体基因2]突变显著增加复发风险(HR = 1.63;95%CI = 1.27 - 2.10),但不增加癌症死亡率(HR = 1.41;95%CI = 0.90 - 2.23)。在亚组分析中,[具体基因2]突变仅在短期/中期随访中显示其预后价值。关于[具体基因3]突变和[具体基因4]融合的数据不足以进行荟萃分析。[具体基因1]启动子突变可作为风险分层和预测患者预后的独立可靠标志物。使用[具体基因2]突变评估患者预后应谨慎考虑。