Gene-expression profile changes correlated with tumor progression and lymph node metastasis in Esophageal cancer
Gene-expression profile changes correlated with tumor progression and lymph node metastasis in Esophageal cancer
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DOI：
10.1158/1078-0432.ccr-04-0048
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发表时间：
2004-06-01
影响因子：
11.5
通讯作者：
Katoh, H
中科院分区：
文献类型：
--
作者：
Tamoto, E;Tada, M;Katoh, H
目的：本研究的目的是确定肿瘤进展和食管癌淋巴结转移的分子线索，并测试其价值作为预测marker.Experimental Design：我们探讨了基因表达谱的cDNA阵列数据的36个组织训练集食管鳞状细胞癌（ESCC）通过使用广义线性模型为基础的回归分析和特征子集选择算法。通过应用确定的最佳特征集（预测基因集），我们训练和开发了由多个概率神经网络结合AdaBoosting组成的集成分类器，以预测肿瘤分期和淋巴结转移。我们用18例独立的ESCC病例验证了该分类器的分类能力。结果：在1289个肿瘤相关基因中，我们鉴定出71个基因的表达与肿瘤分期相关。在71个基因中，47个在肿瘤-淋巴结-转移pT 1/2和pT 3/4阶段之间显著不同。细胞周期调节因子和转录因子可能促进肿瘤细胞的生长在ESCC的早期阶段的高表达，而粘附分子和细胞外基质相关分子可能促进侵袭在后期阶段增加。对于淋巴结转移，我们确定了44个具有预测价值的基因，其中包括在淋巴结阳性病例中表达较高的细胞粘附分子和细胞膜受体，以及在淋巴结阴性病例中表达较高的细胞周期调节因子和细胞内信号分子。用所选特征训练的集成分类器在18个验证病例中预测肿瘤分期和淋巴结转移，准确率分别为94.4%和88.9%。这证明了可重复性和预测值的识别feature.Conclusion：我们认为，这些特征基因将提供有用的信息，了解恶性本质的食管鳞癌以及个性化的治疗有用的信息。
Purpose: The purpose of this research was to identify molecular clues to tumor progression and lymph node metastasis in esophageal cancer and to test their value as predictive markers.Experimental Design: We explored the gene expression profiles in cDNA array data of a 36-tissue training set of esophageal squamous cell carcinoma (ESCC) by using generalized linear model-based regression analysis and a feature subset selection algorithm. By applying the identified optimal feature sets (predictive gene sets), we trained and developed ensemble classifiers consisting of multiple probabilistic neural networks combined with AdaBoosting to predict tumor stages and lymph node metastasis. We validated the classifier abilities with 18 independent cases of ESCC.Results: We identified 71 genes of 1289 cancer-related genes of which the expression correlated with tumor stages. Of the 71 genes, 47 significantly differed between the Tumor-Node-Metastasis pT1/2 and pT3/4 stages. Cell cycle regulators and transcriptional factors possibly promoting the growth of tumor cells were highly expressed in the early stages of ESCC, whereas adhesion molecules and extracellular matrix-related molecules possibly promoting invasiveness increased in the later stages. For lymph node metastasis, we identified 44 genes with predictive values, which included cell adhesion molecules and cell membrane receptors showing higher expression in node-positive cases and cell cycle regulators and intracellular signaling molecules showing higher expression in node-negative cases. The ensemble classifiers trained with the selected features predicted tumor stage and lymph node metastasis in the 18 validation cases with respective accuracies of 94.4% and 88.9%. This demonstrated the reproducibility and predictive value of the identified features.Conclusion: We suggest that these characteristic genes will provide useful information for understanding the malignant nature of ESCC as well as information useful for personalizing the treatments.