Hasanzad Mandana, Sarhangi Negar, Ehsani Chimeh Sima, Ayati Nayereh, Afzali Monireh, Khatami Fatemeh, Nikfar Shekoufeh, Aghaei Meybodi Hamid Reza
Medical Genomics Research Center, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran.
Personalized Medicine Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
J Diabetes Metab Disord. 2021 Nov 24;21(1):881-888. doi: 10.1007/s40200-021-00913-0. eCollection 2022 Jun.
It has been well established that understanding the underlying heterogeneity of numerous complex disease process needs new strategies that present in precision medicine for prediction, prevention and personalized treatment strategies. This approach must be tailored for each individual's unique omics that lead to personalized management of disease. The correlation between different omics data should be considered in precision medicine approach. The interaction provides a hypothesis which is called domino effect in the present minireview. Here we review the various potentials of omics data including genomics, transcriptomics, proteomics, metabolomics, pharmacogenomics. We comprehensively summarize the impact of omics data and its major role in precision medicine and provide a description about the domino effect on the pathophysiology of diseases. Each constituent of the omics data typically provides different information in associated with disease. Current research, although inadequate, clearly indicate that the information of omics data can be applicable in the concept of precision medicine. Integration of different omics data type in domino effect hypothesis can explain the causative changes of disease as it is discussed in the system biology too. While most existing studies investigate the omics data separately, data integration is needed on the horizon of precision medicine by using machine learning.
众所周知，理解众多复杂疾病过程的潜在异质性需要新的策略，这些策略体现在精准医学的预测、预防和个性化治疗策略中。这种方法必须针对每个人独特的组学进行定制，从而实现疾病的个性化管理。在精准医学方法中，应考虑不同组学数据之间的相关性。这种相互作用提供了一个假设，在本综述中被称为多米诺效应。在这里，我们回顾了组学数据的各种潜力，包括基因组学、转录组学、蛋白质组学、代谢组学、药物基因组学。我们全面总结了组学数据的影响及其在精准医学中的主要作用，并描述了多米诺效应在疾病病理生理学中的作用。组学数据的每个组成部分通常会提供与疾病相关的不同信息。目前的研究虽然尚不充分，但清楚地表明，组学数据的信息可应用于精准医学概念。正如系统生物学中所讨论的，在多米诺效应假设中整合不同的组学数据类型可以解释疾病的致病变化。虽然大多数现有研究分别调查组学数据，但在精准医学领域，需要通过机器学习进行数据整合。
