Metabolic network-based identification of plasma markers for non-small cell lung cancer

代谢网络 小桶 代谢组学 计算生物学 代谢途径 生物 生物信息学 生物化学 基因 转录组 基因表达
作者
Linling Guo,Linrui Li,Zhiyun Xu,Fanchen Meng,Huimin Guo,Peijia Liu,Peifang Liu,Yuan Tian,Fengguo Xu,Zunjian Zhang,Shuai Zhang,Yin Huang
出处
期刊:Analytical and Bioanalytical Chemistry [Springer Science+Business Media]
卷期号:413 (30): 7421-7430 被引量:9
标识
DOI:10.1007/s00216-021-03699-5
摘要

Metabolic markers, offering sensitive information on biological dysfunction, play important roles in diagnosing and treating cancers. However, the discovery of effective markers is limited by the lack of well-established metabolite selection approaches. Here, we propose a network-based strategy to uncover the metabolic markers with potential clinical availability for non-small cell lung cancer (NSCLC). First, an integrated mass spectrometry-based untargeted metabolomics was used to profile the plasma samples from 43 NSCLC patients and 43 healthy controls. We found that a series of 39 metabolites were altered significantly. Relying on the human metabolic network assembled from Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we mapped these differential metabolites to the network and constructed an NSCLC-related disease module containing 23 putative metabolic markers. By measuring the PageRank centrality of molecules in this module, we computationally evaluated the network-based importance of the 23 metabolites and demonstrated that the metabolism pathways of aromatic amino acids and long-chain fatty acids provided potential molecular targets of NSCLC (i.e., IL4l1 and ACOT2). Combining network-based ranking and support-vector machine modeling, we further found a panel of eight metabolites (i.e., pyruvate, tryptophan, and palmitic acid) that showed a high capability to differentiate patients from controls (accuracy > 97.7%). In summary, we present a meaningful network method for metabolic marker discovery and have identified eight strong candidate metabolites for NSCLC diagnosis.
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