Literature DB >> 12941848

Profiling gene expression ratios of paired cancerous and normal tissue predicts relapse of esophageal squamous cell carcinoma.

Yoshio Ishibashi1, Nobuyoshi Hanyu, Koji Nakada, Yutaka Suzuki, Takashi Yamamoto, Katsuhiko Yanaga, Kiyoshi Ohkawa, Noriko Hashimoto, Toshiharu Nakajima, Hirohisa Saito, Masato Matsushima, Mitsuyoshi Urashima.   

Abstract

Esophageal squamous cell carcinoma has heterogeneous clinical outcomes that cannot be predicted well using any existing clinical or molecular prognostic factors. Gene expression profiling may enable more precise prediction of the clinical outcome of these patients. We developed a new approach using gene expression ratios of paired cancerous and normal tissue specimens from the same patient to reduce the effects of variation among individuals. Using oligonucleotide microarrays, we analyzed total RNA expression levels corresponding to 12,600 transcript sequences in 24 paired cancerous and normal tissue operative specimens from 12 patients with esophageal squamous cell carcinoma. Hierarchical clustering analysis using gene expression ratios (cancer:normal) divided the 12 patients into two groups; all 7 patients in the first cluster survived without relapse (median follow-up, 483 days), whereas all 5 patients in the second cluster relapsed (median relapse-free survival time, 280 days; log-rank test, P = 0.006). In contrast, clustering either with cancerous tissue alone or with normal tissue alone did not show significant differences in the outcomes. The expressions of a variety of genes related with cell cycle, gene-repair, apoptosis and chemoradiotherpay resistance were up-regulated in the poor prognostic cluster. These results suggest that ratios of paired gene expression profiles may more efficiently predict relapse-free survival of esophageal squamous cell carcinoma than existing prognostic factors or than gene expression profiling with cancerous tissue alone.

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Year:  2003        PMID: 12941848

Source DB:  PubMed          Journal:  Cancer Res        ISSN: 0008-5472            Impact factor:   12.701


  15 in total

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5.  Integrative genomics analysis of genes with biallelic loss and its relation to the expression of mRNA and micro-RNA in esophageal squamous cell carcinoma.

Authors:  Nan Hu; Chaoyu Wang; Robert J Clifford; Howard H Yang; Hua Su; Lemin Wang; Yuan Wang; Yi Xu; Ze-Zhong Tang; Ti Ding; Tongwu Zhang; Alisa M Goldstein; Carol Giffen; Maxwell P Lee; Philip R Taylor
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9.  Inferring Gene Regulatory Networks of Metabolic Enzymes Using Gradient Boosted Trees.

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10.  Computational purification of individual tumor gene expression profiles leads to significant improvements in prognostic prediction.

Authors:  Gerald Quon; Syed Haider; Amit G Deshwar; Ang Cui; Paul C Boutros; Quaid Morris
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