Gene-Expression Profile Changes Correlated with Tumor Progression and Lymph Node Metastasis in Esophageal Cancer

• Published in: Clinical cancer research Vol. 10; no. 11; pp. 3629 - 3638
• Main Authors: TAMOTO, Eiji, TADA, Mitsuhiro, FUJIWARA, Yoshie, KOBAYASHI, Nozomi, SHIRATA, Katsutoshi, NISHIMURA, Norihiro, OKUSHIBA, Shun-Ichi, KONDO, Satoshi, HAMADA, Jun-Ichi, YOSHIKI, Takashi, MORIUCHI, Tetsuya, KATOH, Hiroyuki, MURAKAWA, Katsuhiko, TAKADA, Minoru, SHINDO, Gaku, TERAMOTO, Ken-Ichi, MATSUNAGA, Akihiro, KOMURO, Kazuteru, KANAI, Motoshi, KAWAKAMI, Akiko
• Format: Journal Article
• Language: English
• Published: Philadelphia, PA American Association for Cancer Research 01.06.2004
• Subjects: Algorithms; Antineoplastic agents; Biological and medical sciences; Biomarkers, Tumor; Carcinoma, Squamous Cell - pathology; Cell Differentiation; Cell Proliferation; Disease Progression; DNA, Complementary - metabolism; Esophageal Neoplasms - genetics; Esophageal Neoplasms - pathology; Female; Gene Expression Regulation, Neoplastic; Humans; Ligands; Linear Models; Lymph Nodes - pathology; Lymphatic Metastasis; Male; Medical sciences; Neoplasm Metastasis; Oligonucleotide Array Sequence Analysis; Pharmacology. Drug treatments; Tumors; Esophageal disease; Tumor progression; Digestive diseases; Malignant lymphadenopathy; Malignant tumor; Esophagus cancer; Gene expression profile
• ISSN: 1078-0432; 1557-3265
• DOI: 10.1158/1078-0432.CCR-04-0048

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.