Differential expression patterns of housekeeping genes increase diagnostic and prognostic value in lung cancer.
Differential expression patterns of housekeeping genes increase diagnostic and prognostic value in lung cancer.
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作者：
Chang YC;Ding Y;Dong L;Zhu LJ;Jensen RV;Hsiao LL
使用DNA微阵列，我们以前确定了451个基因在19个不同的人体组织中表达。虽然这些“管家基因”（HKG）的表达普遍存在，但其表达模式的差异可以将一种正常的人体组织类型与另一种组织类型区分开来。目前对鉴定“特定疾病标志物”的关注是有问题的，因为给定样品中的单个基因表达代表了样品在收集时的特定细胞状态。在这项研究中，我们研究了HKG在肺癌中的可变表达的诊断和预后潜力。从在线数据库收集正常肺、肺腺癌（AD）、肺鳞状细胞癌（SQCLC）和肺小细胞癌（SCLC）的微阵列和RNA-seq数据。使用451个HKG中的374个，通过双侧同方差t检验确定样品类型对之间的差异表达基因。主成分分析和层次聚类根据相对基因表达差异对正常肺和肺癌亚型进行分类。我们使用单变量和多变量cox回归来确定AD患者总生存率的显著预测因素。使用一组训练样本选择分类基因，然后使用独立的测试集进行验证。基因本体论是由PANTHER检验的。这项研究表明，242、245和99个HKG的差异表达模式能够分别将正常肺与AD、SCLC和SQCLC区分开。其中，70个HKG在三种肺癌亚型中很常见。与目前的肺癌标志物（例如，EGFR，KRAS），并参与最常见的生物学过程（例如，代谢、应激反应）。此外，单独的106个HKG的表达模式是AD与SQCLC的显著分类器。我们进一步强调，一组13个HKG是AD患者总生存期和累积风险的独立预测因子。在这里，我们报告HKG表达模式可能是一个有效的工具，用于评估肺癌状态。例如，仅70个HKG的差异表达模式就可以将正常肺组织与各种肺癌区分开来，而一组106个HKG是非小细胞癌亚型的有效类别预测因子。我们还报告了HKG在样本中的方差显著低于传统癌症标志物，突出了一组基因对任何一种特定生物标志物的稳健性。使用RNA-seq数据，我们发现13种HKG的表达模式是AD患者总生存率的重要独立预测因素。这增强了HKG面板在不同基因表达测量平台上的预测能力。因此，我们建议HKG单独的表达模式可能足以用于肺癌个体的诊断和预后。
Using DNA microarrays, we previously identified 451 genes expressed in 19 different human tissues. Although ubiquitously expressed, the variable expression patterns of these “housekeeping genes” (HKGs) could separate one normal human tissue type from another. Current focus on identifying “specific disease markers” is problematic as single gene expression in a given sample represents the specific cellular states of the sample at the time of collection. In this study, we examine the diagnostic and prognostic potential of the variable expressions of HKGs in lung cancers. Microarray and RNA-seq data for normal lungs, lung adenocarcinomas (AD), squamous cell carcinomas of the lung (SQCLC), and small cell carcinomas of the lung (SCLC) were collected from online databases. Using 374 of 451 HKGs, differentially expressed genes between pairs of sample types were determined via two-sided, homoscedastic t-test. Principal component analysis and hierarchical clustering classified normal lung and lung cancers subtypes according to relative gene expression variations. We used uni- and multi-variate cox-regressions to identify significant predictors of overall survival in AD patients. Classifying genes were selected using a set of training samples and then validated using an independent test set. Gene Ontology was examined by PANTHER. This study showed that the differential expression patterns of 242, 245, and 99 HKGs were able to distinguish normal lung from AD, SCLC, and SQCLC, respectively. From these, 70 HKGs were common across the three lung cancer subtypes. These HKGs have low expression variation compared to current lung cancer markers (e.g., EGFR, KRAS) and were involved in the most common biological processes (e.g., metabolism, stress response). In addition, the expression pattern of 106 HKGs alone was a significant classifier of AD versus SQCLC. We further highlighted that a panel of 13 HKGs was an independent predictor of overall survival and cumulative risk in AD patients. Here we report HKG expression patterns may be an effective tool for evaluation of lung cancer states. For example, the differential expression pattern of 70 HKGs alone can separate normal lung tissue from various lung cancers while a panel of 106 HKGs was a capable class predictor of subtypes of non-small cell carcinomas. We also reported that HKGs have significantly lower variance compared to traditional cancer markers across samples, highlighting the robustness of a panel of genes over any one specific biomarker. Using RNA-seq data, we showed that the expression pattern of 13 HKGs is a significant, independent predictor of overall survival for AD patients. This reinforces the predictive power of a HKG panel across different gene expression measurement platforms. Thus, we propose the expression patterns of HKGs alone may be sufficient for the diagnosis and prognosis of individuals with lung cancer.
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影响因子：
56.9
作者：
Golub, TR;Slonim, DK;Lander, ES
通讯作者：
Lander, ES
DOI：
10.2165/00066982-200408020-00005
发表时间：
2004-01-01
期刊：
Molecular diagnosis : a journal devoted to the understanding of human disease through the clinical application of molecular biology
影响因子：
--
作者：
Janssens, Nico;Janicot, Michel;Bakker, Annette
通讯作者：
Bakker, Annette
影响因子：
3.4
作者：
Caracausi M;Piovesan A;Antonaros F;Strippoli P;Vitale L;Pelleri MC
通讯作者：
Pelleri MC
影响因子：
82.9
作者：
Beer, DG;Kardia, SLR;Hanash, S
通讯作者：
Hanash, S
影响因子：
3.3
作者：
Rubie, C;Kempf, K;Schilling, M
通讯作者：
Schilling, M