Department of Bioscience and Bioinformatics, School of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan; Bioinformatics Institute (BII), Agency for Science, Technology and Research (A(∗)STAR), Singapore 138671, Singapore.
Department of Bioscience and Bioinformatics, School of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan.
Biosystems. 2024 Feb;236:105122. doi: 10.1016/j.biosystems.2024.105122. Epub 2024 Jan 8.
The integration of multiple omics data promises to reveal new insights into the pathogenic mechanisms of complex human diseases, with the potential to identify avenues for the development of targeted therapies for disease subtypes. However, the extraction of diagnostic/disease-specific biomarkers from multiple omics data with biological pathway knowledge is a challenging issue in precision medicine. In this paper, we present a novel computational method to identify diagnosis-specific trans-omic biomarkers from multiple omics data. In the algorithm, we integrated multi-class sparse canonical correlation analysis (MSCCA) and molecular pathway analysis in order to derive discriminative molecular features that are correlated across different omics layers. We applied our proposed method to analyzing proteome and metabolome data of heart failure (HF), and extracted trans-omic biomarkers for HF subtypes; specifically, ischemic cardiomyopathy (ICM) and dilated cardiomyopathy (DCM). We were able to detect not only individual proteins that were previously reported from single-omics studies but also correlated protein-metabolite pairs characteristic of HF disease subtypes. For example, we identified hexokinase1(HK1)-d-fructose-6-phosphate as a paired trans-omic biomarker for DCM, which could significantly perturb amino-sugar metabolism. Our proposed method is expected to be useful for various applications in precision medicine.
多组学数据的整合有望揭示复杂人类疾病发病机制的新见解，并有可能为疾病亚型的靶向治疗开辟途径。然而，从具有生物学途径知识的多组学数据中提取诊断/疾病特异性生物标志物是精准医学中的一个具有挑战性的问题。在本文中，我们提出了一种从多组学数据中识别诊断特异性跨组学生物标志物的新计算方法。在该算法中，我们整合了多类稀疏典型相关分析（MSCCA）和分子途径分析，以推导出跨不同组学层相关的有区别的分子特征。我们将我们提出的方法应用于心力衰竭（HF）的蛋白质组和代谢组学数据分析，并提取了 HF 亚型（即缺血性心肌病[ICM]和扩张型心肌病[DCM]）的跨组学生物标志物。我们不仅能够检测到以前从单组学研究中报道的单个蛋白质，还能够检测到与 HF 疾病亚型相关的蛋白质-代谢物对。例如，我们确定己糖激酶 1（HK1）-d-果糖-6-磷酸是 DCM 的配对跨组学生物标志物，它可以显著扰乱氨基酸糖代谢。我们提出的方法有望在精准医学的各种应用中发挥作用。
