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An expression meta-analysis of predicted microRNA targets identifies a diagnostic signature for lung cancer

机译：预测的microRNA靶标的表达荟萃分析确定了肺癌的诊断特征

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Background Patients diagnosed with lung adenocarcinoma (AD) and squamous cell carcinoma (SCC), two major histologic subtypes of lung cancer, currently receive similar standard treatments, but resistance to adjuvant chemotherapy is prevalent. Identification of differentially expressed genes marking AD and SCC may prove to be of diagnostic value and help unravel molecular basis of their histogenesis and biologies, and deliver more effective and specific systemic therapy. Methods MiRNA target genes were predicted by union of miRanda, TargetScan, and PicTar, followed by screening for matched gene symbols in NCBI human sequences and Gene Ontology (GO) terms using the PANTHER database that was also used for analyzing the significance of biological processes and pathways within each ontology term. Microarray data were extracted from Gene Expression Omnibus repository, and tumor subtype prediction by gene expression used Prediction Analysis of Microarrays. Results Computationally predicted target genes of three microRNAs, miR-34b/34c/449, that were detected in human lung, testis, and fallopian tubes but not in other normal tissues, were filtered by representation of GO terms and their ability to classify lung cancer subtypes, followed by a meta-analysis of microarray data to classify AD and SCC. Expression of a minimal set of 17 predicted miR-34b/34c/449 target genes derived from the developmental process GO category was identified from a training set to classify 41 AD and 17 SCC, and correctly predicted in average 87% of 354 AD and 82% of 282 SCC specimens from total 9 independent published datasets. The accuracy of prediction still remains comparable when classifying 103 AD and 79 SCC samples from another 4 published datasets that have only 14 to 16 of the 17 genes available for prediction (84% and 85% for AD and SCC, respectively). Expression of this signature in two published datasets of epithelial cells obtained at bronchoscopy from cigarette smokers, if combined with cytopathology of the cells, yielded 89–90% sensitivity of lung cancer detection and 87–90% negative predictive value to non-cancer patients. Conclusion This study focuses on predicted targets of three lung-enriched miRNAs, compares their expression patterns in lung cancer by their GO terms, and identifies a minimal set of genes differentially expressed in AD and SCC, followed by validating this gene signature in multiple published datasets. Expression of this gene signature in bronchial epithelial cells of cigarette smokers also has a great sensitivity to predict the patients having lung cancer if combined with cytopathology of the cells.
机译：背景被诊断为肺癌的两种主要组织学亚型的肺腺癌（AD）和鳞状细胞癌（SCC）的患者目前接受相似的标准治疗，但对辅助化学疗法的耐药性普遍存在。鉴定AD和SCC的差异表达基因的鉴定可能具有诊断价值，并有助于弄清其组织发生和生物学的分子基础，并提供更有效和特异性的全身疗法。方法通过miRanda，TargetScan和PicTar的结合来预测MiRNA靶基因，然后使用PANTHER数据库筛选NCBI人序列和Gene Ontology（GO）术语中匹配的基因符号，该数据库还用于分析生物学过程和基因的重要性。每个本体术语内的通路。从基因表达综合库中提取微阵列数据，并使用微阵列预测分析通过基因表达预测肿瘤亚型。结果通过GO项及其对肺癌分类的能力，过滤了在人肺，睾丸和输卵管中但未在其他正常组织中检测到的三种microRNA miR-34b / 34c / 449的计算预测靶基因。亚型，然后进行微阵列数据的荟萃分析，以对AD和SCC进行分类。从用于分类41 AD和17 SCC的训练集中鉴定出至少17种源自发育过程GO类别的预测miR-34b / 34c / 449目标基因的表达，并正确预测354 AD和82中平均87％的表达总共9个独立出版的数据集中的282个SCC标本的百分比。当对来自另外4个已发布数据集的103个AD和79个SCC样本进行分类时，预测的准确性仍保持可比性，这些数据集中只有17至14个基因可用于预测（AD和SCC分别为84％和85％）。如果结合支气管镜从吸烟者的上皮细胞的两个已公开数据集中表达该特征，再结合细胞的细胞病理学，则对肺癌检测的敏感性为89–90％，对非癌症患者的阴性预测值为87–90％。结论本研究着眼于三种富含肺的miRNA的预测靶标，通过其GO术语比较它们在肺癌中的表达模式，并鉴定出在AD和SCC中差异表达的最小基因集，然后在多个已公开的数据集中验证该基因签名。如果与细胞的细胞病理学结合，在吸烟者的支气管上皮细胞中该基因标记的表达也具有很大的敏感性来预测患有肺癌的患者。

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