Bennani-Baiti Nabila, Bennani-Baiti Idriss M
Division of Hematology, Mayo Clinic, Rochester, MN 55905, USA.
The B Scientific Group (B SG), 1010 Vienna, Austria.
Cancer Inform. 2015 Nov 2;14:131-9. doi: 10.4137/CIN.S32696. eCollection 2015.
Whole-genome analyses have uncovered that most cancer-relevant genes cluster into 12 signaling pathways. Knowledge of the signaling pathways and associated gene signatures not only allows us to understand the mechanisms of oncogenesis inherent to specific cancers but also provides us with drug targets, molecular diagnostic and prognosis factors, as well as biomarkers for patient risk stratification and treatment. Publicly available genomic data sets constitute a wealth of gene mining opportunities for hypothesis generation and testing. However, the increasingly recognized genetic and epigenetic inter- and intratumor heterogeneity, combined with the preponderance of small-size cohorts, hamper reliable analysis and discovery. Here, we review two methods that are used to infer meaningful biological events from small-size data sets and discuss some of their applications and limitations.
全基因组分析发现，大多数与癌症相关的基因聚集在12条信号通路中。对信号通路及相关基因特征的了解不仅使我们能够理解特定癌症发生的内在机制，还为我们提供了药物靶点、分子诊断和预后因素，以及用于患者风险分层和治疗的生物标志物。公开可用的基因组数据集为假设的产生和检验提供了丰富的基因挖掘机会。然而，越来越被认识到的肿瘤间和肿瘤内的遗传及表观遗传异质性，再加上小样本队列占主导地位，阻碍了可靠的分析和发现。在此，我们综述了两种用于从小样本数据集中推断有意义生物学事件的方法，并讨论了它们的一些应用和局限性。
