Duflo Suzy M, Thibeault Susan L, Li Wenhua, Smith Marshall E, Schade Goetz, Hess Markus M
Division of Otolaryngology-Head and Neck Surgery, Department of Surgery, School of Medicine, The University of Utah, Salt Lake City, Utah, USA.
Ann Otol Rhinol Laryngol. 2006 Sep;115(9):703-14. doi: 10.1177/000348940611500910.
Our purpose was to determine whether complementary DNA (cDNA) microarray analysis (MA) can establish distinct gene expression profiles for 2 phenotypically similar vocal fold lesions: Reinke's edema (RE) and polyps. Established transcript profiles can provide insight into the molecular and cellular processes involved in these diseases.
Eleven RE specimens and 17 polyps were analyzed with MA for 8,745 genes. Further MA profiling was attempted within each lesion group to identify molecular markers for reflux exposure and smoking. Prediction analysis was used to predict lesion classification for 2 unclassified samples. A real-time polymerase chain reaction was performed to corroborate MA transcript levels for selected significant genes.
Sixty-five genes were found to differentiate RE and polyps (p = .0088). For RE, 19 genes were differentiated for reflux exposure (p = .016). No genes were found to differentiate smokers from nonsmokers. For polyps, no genes were found to differentiate for reflux (p = .16) and smoking (p = .565). Categorization of unclassified lesions was possible with a minimum of 13 genes.
We demonstrate the feasibility of benign lesion classification based on MA. Microarray analysis is useful not only for improving diagnosis and classification of such lesions, but also for potentially generating prognostic indicators and targets for therapy.
我们的目的是确定互补DNA(cDNA)微阵列分析(MA)是否能够为两种表型相似的声带病变:任克氏水肿(RE)和息肉,建立不同的基因表达谱。已建立的转录谱能够深入了解这些疾病所涉及的分子和细胞过程。
对11个RE标本和17个息肉进行MA分析,检测8745个基因。在每个病变组内进一步进行MA分析,以确定反流暴露和吸烟的分子标志物。采用预测分析对2个未分类样本的病变类型进行预测。通过实时聚合酶链反应对选定的重要基因的MA转录水平进行验证。
发现65个基因可区分RE和息肉(p = 0.0088)。对于RE,19个基因可区分反流暴露(p = 0.016)。未发现可区分吸烟者和非吸烟者的基因。对于息肉,未发现可区分反流(p = 0.16)和吸烟(p = 0.565)的基因。使用至少13个基因即可对未分类病变进行分类。
我们证明了基于MA进行良性病变分类的可行性。微阵列分析不仅有助于改善此类病变的诊断和分类,还可能生成预后指标和治疗靶点。