Prognostic gene expression profiling in esophageal cancer: a systematic review
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Els Visser1, Ingrid A. Franken1, Lodewijk A.A. Brosens2, Jelle P. Ruurda1 and Richard van Hillegersberg1
1 Department of Surgery, University Medical Center Utrecht, The Netherlands
2 Department of Pathology, University Medical Center Utrecht, The Netherlands
Richard van Hillegersberg, email:
Keywords: esophageal cancer, gene expression profiling, response to chemo(radio)therapy, lymph node metastasis, survival, prognosis
Received: July 06, 2016 Accepted: October 13, 2016 Published: November 12, 2016
Background: Individual variability in prognosis of esophageal cancer highlights the need for advances in personalized therapy. This systematic review aimed at elucidating the prognostic role of gene expression profiles and at identifying gene signatures to predict clinical outcome.
Methods: A systematic search of the Medline, Embase and the Cochrane library databases (2000-2015) was performed. Articles associating gene expression profiles in patients with esophageal adenocarcinoma or squamous cell carcinoma to survival, response to chemo(radio)therapy and/or lymph node metastasis were identified. Differentially expressed genes and gene signatures were extracted from each study and combined to construct a list of prognostic genes per outcome and histological tumor type.
Results: This review includes a total of 22 studies. Gene expression profiles were related to survival in 9 studies, to response to chemo(radio)therapy in 7 studies, and to lymph node metastasis in 9 studies. The studies proposed many differentially expressed genes. However, the findings were heterogeneous and only 12 (ALDH1A3, ATR, BIN1, CSPG2, DOK1, IFIT1, IFIT3, MAL, PCP4, PHB, SPP1) of the 1.112 reported genes were identified in more than 1 study. Overall, 16 studies reported a prognostic gene signature, which was externally validated in 10 studies.
Conclusion: This systematic review shows heterogeneous findings in associating gene expression with clinical outcome in esophageal cancer. Larger validated studies employing RNA next-generation sequencing are required to establish gene expression profiles to predict clinical outcome and to select optimal personalized therapy.
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