A cross-study comparison of gene expression studies for the molecular classification of lung cancer
A cross-study comparison of gene expression studies for the molecular classification of lung cancer
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DOI：
10.1158/1078-0432.ccr-03-0490
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发表时间：
2004-05-01
影响因子：
11.5
通讯作者：
Gabrielson, E
中科院分区：
文献类型：
--
作者：
Parmigiani, G;Garrett-Mayer, ES;Gabrielson, E
目的：最近的研究试图利用基因表达微阵列来完善肺癌分类。我们评估这些研究的一致性程度以及结果是否可以整合。实验设计：我们开发了一个实用的分析计划，用于使用公共数据进行癌症分子分类研究的交叉研究比较、验证和整合。我们使用整合相关性来评估基因表达模式的跨平台一致性，这种相关性量化了交叉研究的重复性，而不依赖于跨平台表达测量的直接同化。然后，我们通过基因特异性t统计量的重复性来比较基因表达水平与鳞癌和腺癌的鉴别诊断的相关性，以及通过COX系数的重复性来比较基因表达水平与生存率的相关性。结果：综合相关分析显示，在所有研究中，有很大比例的基因模式与随机预期的模式一致。对于鳞癌和腺癌的诊断，t统计量的相关性很高(0.85%)，当只使用综合相关性确定的最一致的基因时，相关性增加(0.925)。COX系数的相关系数范围为0.13-0.31(与选择的一致性基因的相关系数为0.33-0.49)。虽然我们在多项研究中发现了有意义但效果不一致的基因，但它们的数量大约是偶然预期的。我们报告了通过综合分析可重现的、在所有研究中显著且效果一致的基因。结论：交叉研究比较揭示了与肺癌生物学相关的基因表达模式的显著一致，尽管不完全一致，并确定了可重复性预测结果的基因。这种分析方法广泛适用于基因表达谱项目的交叉研究比较。
Purpose: Recent studies sought to refine lung cancer classification using gene expression microarrays. We evaluate the extent to which these studies agree and whether results can be integrated.Experimental Design: We developed a practical analysis plan for cross-study comparison, validation, and integration of cancer molecular classification studies using public data. We evaluated genes for cross-platform consistency of expression patterns, using integrative correlations, which quantify cross-study reproducibility without relying on direct assimilation of expression measurements across platforms. We then compared associations of gene expression levels to differential diagnosis of squamous cell carcinoma versus adenocarcinoma via reproducibility of the gene-specific t statistics and to survival via reproducibility of Cox coefficients.Results: Integrative correlation analysis revealed a large proportion of genes in which the patterns agreed across studies more than would be expected by chance. Correlation of t statistics for diagnosis of squamous cell carcinoma versus adenocarcinoma is high (0.85) and increases (0.925) when using only the most consistent genes identified by integrative correlation. Correlations of Cox coefficients ranged from 0.13 to 0.31 (0.33-0.49 with genes selected for consistency). Although we find genes that are significant in multiple studies but show discordant effects, their number is approximately that expected by chance. We report genes that are reproducible by integrative analysis, significant in all studies, and concordant in effect.Conclusions: Cross-study comparison revealed significant, albeit incomplete, agreement of gene expression patterns related to lung cancer biology and identified genes that reproducibly predict outcomes. This analysis approach is broadly applicable to cross-study comparisons of gene expression profiling projects.