Eom Bang Wool, Joo Jungnam, Park Boram, Jo Min Jung, Choi Seung Ho, Cho Soo-Jeong, Ryu Keun Won, Kim Young-Woo, Kook Myeong-Cherl
Department of Medicine, Yonsei University Graduate School of Medicine, Seoul, Republic of Korea.
Gastric Cancer Branch, Research Institute & Hospital, National Cancer Center, Goyang, Gyeonggi-do, Republic of Korea.
PLoS One. 2016 Aug 2;11(8):e0159424. doi: 10.1371/journal.pone.0159424. eCollection 2016.
Treatment strategy for early gastric cancer depends on the probability of lymph node metastasis. The aim of this study is to develop a nomogram predicting lymph node metastasis in early gastric cancer using clinicopathological factors and biomarkers.
A literature review was performed to identify biomarkers related to lymph node metastasis in gastric cancer. Seven markers were selected and immunohistochemistry was performed in 336 early gastric cancer tissues. Based on the multivariable analysis, a prediction model including clinicopatholgical factors and biomarkers was developed, and benefit of adding biomarkers was evaluated using the area under the receiver operating curve and net reclassification improvement. Functional study in gastric cancer cell line was performed to evaluate mechanism of biomarker.
Of the seven biomarkers studied, α1 catenin and CD44v6 were significantly associated with lymph node metastasis. A conventional prediction model, including tumor size, histological type, lymphatic blood vessel invasion, and depth of invasion, was developed. Then, a new prediction model including both clinicopathological factors and CD44v6 was developed. Net reclassification improvement analysis revealed a significant improvement of predictive performance by the addition of CD44v6, and a similar result was shown in the internal validation using bootstrapping. Prediction nomograms were then constructed based on these models. In the functional study, CD44v6 was revealed to affect cell proliferation, migration and invasion.
Overexpression of CD44v6 was a significant predictor of lymph node metastasis in early gastric cancer. The prediction nomograms incorporating CD44v6 can be useful to determine treatment plans in patients with early gastric cancer.
早期胃癌的治疗策略取决于淋巴结转移的可能性。本研究的目的是利用临床病理因素和生物标志物开发一种预测早期胃癌淋巴结转移的列线图。
进行文献综述以确定与胃癌淋巴结转移相关的生物标志物。选择了7种标志物，并在336例早期胃癌组织中进行免疫组织化学检测。基于多变量分析，开发了一个包括临床病理因素和生物标志物的预测模型，并使用受试者操作特征曲线下面积和净重新分类改善来评估添加生物标志物的益处。在胃癌细胞系中进行功能研究以评估生物标志物的机制。
在所研究的7种生物标志物中，α1连环蛋白和CD44v6与淋巴结转移显著相关。开发了一个传统的预测模型，包括肿瘤大小、组织学类型、淋巴管血管侵犯和浸润深度。然后，开发了一个包括临床病理因素和CD44v6的新预测模型。净重新分类改善分析显示，添加CD44v6可显著提高预测性能，在使用自举法的内部验证中也显示了类似结果。然后根据这些模型构建预测列线图。在功能研究中，发现CD44v6影响细胞增殖、迁移和侵袭。
CD44v6的过表达是早期胃癌淋巴结转移的重要预测指标。纳入CD44v6的预测列线图可有助于确定早期胃癌患者的治疗方案。
