Combined Amplification and Molecular Classification for Gene Expression Diagnostics
Combined Amplification and Molecular Classification for Gene Expression Diagnostics
复制标题
用于基因表达诊断的组合扩增和分子分类
DOI：
10.1007/978-3-030-26807-7_9
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Seelig, Georg
中科院分区：
文献类型：
--
作者：
Gowri, Gokul;Lopez, Randolph;Seelig, Georg
RNA 表达谱包含有关细胞状态的信息，并且特定的基因表达变化通常与疾病相关。因此，基于 RNA 表达对血液或类似样本进行分类可以成为疾病诊断的有力方法。然而，基于 RNA 表达的诊断决策对于大多数临床应用来说仍然不切实际，因为它需要基于微阵列或下一代测序的昂贵且缓慢的基因表达谱分析，然后通常进行复杂的硅分析。基于 DNA 的分子分类器可以对 RNA 输入进行计算并在原位总结诊断结果，已被开发出来解决这个问题，但缺乏实际生物样本使用所需的灵敏度。为了解决这一限制，我们在此提出了一种基于 DNA 的分类系统，该系统利用基于 PCR 的扩增来提高灵敏度。在我们最初的方案中，转录本对于诊断决策的重要性与与该转录本结合的分子探针的数量成正比。尽管探针浓度与 RNA 输入浓度相似，但随后通过 PCR 扩增探针可以显着提高测定的灵敏度。然而，即使 PCR 效率存在轻微偏差，也会扭曲原始探针组编码的权重信息。为了解决这个问题，我们开发并数学分析了多种策略来减轻与基于 PCR 的扩增相关的偏差。我们使用两个不同的基因表达数据集和相关疾病类别作为输入，通过模拟来评估这些放大的分子分类策略。通过这种分析，我们得出了一种新颖的分子分类器框架，该框架自然地适应 PCR 偏差，并且使用的分子探针数量比最初的简单实施中所需的数量要少。
RNA expression profiles contain information about the state of a cell and specific gene expression changes are often associated with disease. Classification of blood or similar samples based on RNA expression can thus be a powerful method for disease diagnosis. However, basing diagnostic decisions on RNA expression remains impractical for most clinical applications because it requires costly and slow gene expression profiling based on microarrays or next generation sequencing followed by often complexin silicoanalysis. DNA-based molecular classifiers that perform a computation over RNA inputs and summarize a diagnostic resultin situhave been developed to address this issue, but lack the sensitivity required for use with actual biological samples. To address this limitation, we here propose a DNA-based classification system that takes advantage of PCR-based amplification for increased sensitivity. In our initial scheme, the importance of a transcript for a diagnostic decision is proportional to the number of molecular probes bound to that transcript. Although probe concentration is similar to that of the RNA input, subsequent amplification of the probes with PCR can dramatically increase the sensitivity of the assay. However, even slight biases in PCR efficiency can distort weight information encoded by the original probe set. To address this concern, we developed and mathematically analyzed multiple strategies for mitigating the bias associated with PCR-based amplification. We evaluate these amplified molecular classification strategies through simulation using two distinct gene expression data sets and associated disease categories as inputs. Through this analysis, we arrive at a novel molecular classifier framework that naturally accommodates PCR bias and also uses a smaller number of molecular probes than required in the initial, naive implementation.
影响因子：
8.8
作者：
Burchill, S A;Perebolte, L;Johnston, C;Top, B;Selby, P
通讯作者：
Selby, P
影响因子：
21.8
作者：
Lopez, Randolph;Wang, Ruofan;Seelig, Georg
通讯作者：
Seelig, Georg