A novel approach for predicting upstream regulators (PURE) that affect gene expression.
A novel approach for predicting upstream regulators (PURE) that affect gene expression.
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DOI：
10.1038/s41598-023-41374-0
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发表时间：
2023-10-30
影响因子：
4.6
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中科院分区：
文献类型：
--
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外部因素，如接触化学物质、药物或毒物(CDT)，或相反，缺乏某些化学物质可导致许多疾病。在许多研究中，根据基因表达谱的变化来识别这种原因CDT的能力是极其重要的。此外，正确推断CDT能够逆转由特定疾病表型引起的基因表达变化的能力是药物再利用的关键一步。我们提出了一种预测上游监管机构(PURE)的方法，旨在应对这一挑战。Pure可以从测量的特定表型的表达变化中正确地推断CDT，以及正确地识别可以逆转疾病诱导的基因表达变化的药物。我们在涉及8种化学物质或药物的16个数据集(1只大鼠、5只小鼠和10个人类数据集)上，将所提出的方法与四种经典方法以及在独创性路径分析(IPA)中使用的因果分析进行了比较。我们基于正确识别CDT的能力来评估结果，这一能力由其排名指示。我们还考虑了每种方法报告的假阳性的数量，即除了正确的CDT之外的CDT的数量。提出的方法在16个实验中的11个实验中表现最好，报告正确的CDT的次数排在最前面的7次。IPA排名第二，5次报告正确的CDT，但在16次实验中，有5次根本无法识别正确的CDT。验证结果表明，我们的方法，PURE，优于该领域中一些最流行的方法。PURE可以有效地推断导致观察到的基因表达变化的真实CDT，并且在药物再利用方面也可能是有用的。
External factors such as exposure to a chemical, drug, or toxicant (CDT), or conversely, the lack of certain chemicals can cause many diseases. The ability to identify such causal CDTs based on changes in the gene expression profile is extremely important in many studies. Furthermore, the ability to correctly infer CDTs that can revert the gene expression changes induced by a given disease phenotype is a crucial step in drug repurposing. We present an approach for Predicting Upstream REgulators (PURE) designed to tackle this challenge. PURE can correctly infer a CDT from the measured expression changes in a given phenotype, as well as correctly identify drugs that could revert disease-induced gene expression changes. We compared the proposed approach with four classical approaches as well as with the causal analysis used in Ingenuity Pathway Analysis (IPA) on 16 data sets (1 rat, 5 mouse, and 10 human data sets), involving 8 chemicals or drugs. We assessed the results based on the ability to correctly identify the CDT as indicated by its rank. We also considered the number of false positives, i.e. CDTs other than the correct CDT that were reported to be significant by each method. The proposed approach performed best in 11 out of the 16 experiments, reporting the correct CDT at the very top 7 times. IPA was the second best, reporting the correct CDT at the top 5 times, but was unable to identify the correct CDT at all in 5 out of the 16 experiments. The validation results showed that our approach, PURE, outperformed some of the most popular methods in the field. PURE could effectively infer the true CDTs responsible for the observed gene expression changes and could also be useful in drug repurposing applications.
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影响因子：
4.7
作者：
Hanifi-Moghaddam, P.;Boers-Sijmons, B.;Blok, L. J.
通讯作者：
Blok, L. J.
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Liu ET
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作者：
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通讯作者：
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作者：
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通讯作者：
Huang, Enoch S.
影响因子：
3.7
作者：
Himes, Blanca E.;Koziol-White, Cynthia;Weiss, Scott T.
通讯作者：
Weiss, Scott T.