Chan Simon K, Griffith Obi L, Tai Isabella T, Jones Steven J M
Canada's Michael Smith Genome Sciences Centre, British Columbia Cancer Research Centre, Suite 100, 570 West 10th Avenue, Vancouver, British Columbia, Canada V5Z 4S6.
Cancer Epidemiol Biomarkers Prev. 2008 Mar;17(3):543-52. doi: 10.1158/1055-9965.EPI-07-2615.
Elucidation of candidate colorectal cancer biomarkers often begins by comparing the expression profiles of cancerous and normal tissue by performing gene expression profiling. Although many such studies have been done, the resulting lists of differentially expressed genes tend to be inconsistent with each other, suggesting that there are some false positives and false negatives. One solution is to take the intersection of the lists from independent studies. However, often times, the statistical significance of the observed intersection are not assessed.
Recently, we developed a meta-analysis method that ranked differentially expressed genes in thyroid cancer based on the intersection among studies, total sample sizes, average fold change, and direction of differential expression. We applied an improved version of the method to 25 independent colorectal cancer profiling studies that compared cancer versus normal, adenoma versus normal, and cancer versus adenoma to highlight genes that were consistently reported as differentially expressed at a statistically significant frequency.
We observed that some genes were consistently reported as differentially expressed with a statistically significant frequency (P < 0.05) in cancer versus normal and adenoma versus normal comparisons but not in the cancer versus adenoma comparison.
Our meta-analysis method identified genes that were consistently reported as differentially expressed. A review of some of the candidates revealed genes described previously as having diagnostic and/or prognostic value as well as novel candidate biomarkers. The genes presented here will aid in the identification of highly sensitive and specific biomarkers in colorectal cancer.
阐明结直肠癌候选生物标志物通常始于通过进行基因表达谱分析来比较癌组织和正常组织的表达谱。尽管已经进行了许多此类研究，但差异表达基因的结果列表往往相互不一致，这表明存在一些假阳性和假阴性。一种解决方案是取独立研究列表的交集。然而，通常情况下，并未评估观察到的交集的统计学意义。
最近，我们开发了一种荟萃分析方法，该方法基于研究之间的交集、总样本量、平均倍数变化和差异表达方向对甲状腺癌中的差异表达基因进行排名。我们将该方法的改进版本应用于25项独立的结直肠癌分析研究，这些研究比较了癌组织与正常组织、腺瘤与正常组织以及癌组织与腺瘤组织，以突出那些在统计学上具有显著频率且一直被报道为差异表达的基因。
我们观察到，在癌组织与正常组织以及腺瘤与正常组织的比较中，一些基因一直被报道为差异表达且具有统计学显著性（P < 0.05），但在癌组织与腺瘤组织的比较中并非如此。
我们的荟萃分析方法鉴定出了一直被报道为差异表达的基因。对一些候选基因的综述揭示了先前被描述为具有诊断和/或预后价值的基因以及新的候选生物标志物。本文介绍的基因将有助于鉴定结直肠癌中高度敏感和特异的生物标志物。
