Van Dyck Eric, Nazarov Petr V, Muller Arnaud, Nicot Nathalie, Bosseler Manon, Pierson Sandrine, Van Moer Kris, Palissot Valérie, Mascaux Céline, Knolle Ulrich, Ninane Vincent, Nati Romain, Bremnes Roy M, Vallar Laurent, Berchem Guy, Schlesser Marc
Département d'Oncologie, CRP-Santé du Luxembourg, Luxembourg.
Cancer Med. 2014 Apr;3(2):322-36. doi: 10.1002/cam4.190. Epub 2014 Feb 4.
Cigarette smoking is the major cause of cancers of the respiratory tract, including non-small cell lung cancer (NSCLC) and head and neck cancer (HNC). In order to better understand carcinogenesis of the lung and upper airways, we have compared the gene expression profiles of tumor-distant, histologically normal bronchial biopsy specimens obtained from current smokers with NSCLC or HNC (SC, considered as a single group), as well as nonsmokers (NS) and smokers without cancer (SNC). RNA from a total of 97 biopsies was used for gene expression profiling (Affymetrix HG-U133 Plus 2.0 array). Differentially expressed genes were used to compare NS, SNC, and SC, and functional analysis was carried out using Ingenuity Pathway Analysis (IPA). Smoking-related cancer of the respiratory tract was found to affect the expression of genes encoding xenobiotic biotransformation proteins, as well as proteins associated with crucial inflammation/immunity pathways and other processes that protect the airway from the chemicals in cigarette smoke or contribute to carcinogenesis. Finally, we used the prediction analysis for microarray (PAM) method to identify gene signatures of cigarette smoking and cancer, and uncovered a 15-gene signature that distinguished between SNC and SC with an accuracy of 83%. Thus, gene profiling of histologically normal bronchial biopsy specimens provided insight into cigarette-induced carcinogenesis of the respiratory tract and gene signatures of cancer in smokers.
吸烟是呼吸道癌症的主要原因,包括非小细胞肺癌(NSCLC)和头颈癌(HNC)。为了更好地了解肺癌和上呼吸道的致癌机制,我们比较了从患有NSCLC或HNC的现吸烟者(SC,视为一个单一组)以及非吸烟者(NS)和无癌吸烟者(SNC)获得的肿瘤远处、组织学正常的支气管活检标本的基因表达谱。来自总共97份活检标本的RNA用于基因表达谱分析(Affymetrix HG-U133 Plus 2.0芯片)。使用差异表达基因来比较NS、SNC和SC,并使用 Ingenuity Pathway Analysis(IPA)进行功能分析。发现吸烟相关的呼吸道癌症会影响编码外源性生物转化蛋白的基因表达,以及与关键炎症/免疫途径和其他保护气道免受香烟烟雾中化学物质影响或促进致癌作用的过程相关的蛋白表达。最后,我们使用微阵列预测分析(PAM)方法来识别吸烟和癌症的基因特征,并发现了一个15基因特征,其区分SNC和SC的准确率为83%。因此,组织学正常的支气管活检标本的基因谱分析为香烟诱导的呼吸道致癌机制和吸烟者癌症的基因特征提供了深入了解。