Graph theory analysis of a human body metabolic network: A systematic and organ‐specific study

作者：Jingxuan Ruan, Yaping Wu, Haiyan Wang, Zhenxing Huang, Ziwei Liu, Xinlan Yang, Yongfeng Yang, Hairong Zheng, Dong Liang, Meiyun Wang, Zhanli Hu · 发表于：Medical Physics · 年份：2024 · DOI：10.1002/mp.17568 · 被引用次数：7 · 研究领域：Bioinformatics and Genomic Networks、Cancer, Hypoxia, and Metabolism、Microbial Metabolic Engineering and Bioproduction

PURPOSES: Positron emission tomography (PET) imaging is widely used to detect focal lesions or diseases and to study metabolic abnormalities between organs. However, analyzing organ correlations alone does not fully capture the characteristics of the metabolic network. Our work proposes a graph-based analysis method for quantifying the topological properties of the network, both globally and at the nodal level, to detect systemic or single-organ metabolic abnormalities caused by diseases such as lung cancer. METHODS: F-FDG) standardized uptake value (SUV) images from 32 lung cancer patients and 20 healthy controls to construct two-organ glucose metabolism correlation networks at the population level. We calculated five global measures and three nodal centralities for these networks to explore the small-world, rich-club and modular organization in the metabolic network. Additionally, we analyzed the preference for connections significantly affected by lung cancer by dividing organs according to system level and spatial location. RESULTS: , t > 0), indicating more localized and dispersed metabolic activities. At the nodal level, certain organs, such as the pancreas, liver, heart, and right kidney, were no longer hubs in lung cancer patients (decreased nodal centralities, t > 0), whereas the left adrenal gland, left kidney, and left lung showed significantly increased centralities (increased nodal centralities, t < 0). This change suggests compensatory effects between organs. Co...