The ordering of expression among a few genes can provide simple cancer biomarkers and signal BRCA1 mutations.
The ordering of expression among a few genes can provide simple cancer biomarkers and signal BRCA1 mutations.
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DOI：
10.1186/1471-2105-10-256
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发表时间：
2009-08-20
影响因子：
3
通讯作者：
Geman D
中科院分区：
文献类型：
--
作者：
Lin X;Afsari B;Marchionni L;Cope L;Parmigiani G;Naiman D;Geman D
计算生物学中的一个主要挑战是从高通量数据中提取关于疾病遗传本质的知识。然而，生物学理解和临床应用的一个重要障碍是大多数机器学习方法提供的决策规则的黑箱性质，通常涉及许多基因以高度复杂的方式组合。获得与生物学相关的结果需要一种不同的策略。一种有希望的替代方案是完全基于少数基因的相对表达顺序进行预测。我们提出了一个三基因版本的“相对表达分析”(RXA)，与各种癌症研究中早期的方法进行了严格和系统的比较，并在临床上应用于预测乳腺癌种系BRCA1突变，以及用于预测ER状态的交叉研究验证。在BRCA1的研究中，RXA以一个简单的判定规则产生了高准确度：在携带突变的肿瘤中，“参考基因”的表达介于两个差异表达基因PPP1CB和RNF14的表达之间。对三组基因和BRCA1之间的蛋白质-蛋白质相互作用的分析表明，该分类器具有生物学基础。RXA有可能通过合理的生物学解释和直接的临床适用性来识别基因组“标记相互作用”。它为理解决策规则中涉及的基因的作用提供了一个一般框架，如识别BRCA1突变携带者的困难和临床相关问题所示。
A major challenge in computational biology is to extract knowledge about the genetic nature of disease from high-throughput data. However, an important obstacle to both biological understanding and clinical applications is the "black box" nature of the decision rules provided by most machine learning approaches, which usually involve many genes combined in a highly complex fashion. Achieving biologically relevant results argues for a different strategy. A promising alternative is to base prediction entirely upon the relative expression ordering of a small number of genes. We present a three-gene version of "relative expression analysis" (RXA), a rigorous and systematic comparison with earlier approaches in a variety of cancer studies, a clinically relevant application to predicting germline BRCA1 mutations in breast cancer and a cross-study validation for predicting ER status. In the BRCA1 study, RXA yields high accuracy with a simple decision rule: in tumors carrying mutations, the expression of a "reference gene" falls between the expression of two differentially expressed genes, PPP1CB and RNF14. An analysis of the protein-protein interactions among the triplet of genes and BRCA1 suggests that the classifier has a biological foundation. RXA has the potential to identify genomic "marker interactions" with plausible biological interpretation and direct clinical applicability. It provides a general framework for understanding the roles of the genes involved in decision rules, as illustrated for the difficult and clinically relevant problem of identifying BRCA1 mutation carriers.
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影响因子：
56.9
作者：
Golub, TR;Slonim, DK;Lander, ES
通讯作者：
Lander, ES
影响因子：
5.8
作者：
Dobbin, KK;Kawasaki, ES;Simon, RM
通讯作者：
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影响因子：
3.4
作者：
Kuvbachieva, A;Bestel, AM;Simonneau, M
通讯作者：
Simonneau, M
影响因子：
3.3
作者：
Rakha, Emad A.;El-Sheikh, Sornaia E.;Ellis, Ian O.
通讯作者：
Ellis, Ian O.
DOI：
10.1073/pnas.252466999
发表时间：
2002-12-24
影响因子：
11.1
作者：
Li, KC
通讯作者：
Li, KC