Open Access

ARTICLE

Alteration of Ornithine Metabolic Pathway in Colon Cancer and Multivariate Data Modelling for Cancer Diagnosis

by Xin Hu^1,2,#, Fangyu Jing^3,#, Qingjun Wang^1,4, Linyang Shi¹, Yunfeng Cao^4,5, Zhitu Zhu^1,4,*

1 The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, 121000, China
2 General Hospital of Benxi Iron and Steel, Liaoning Healthcare Group, Benxi, 117000, China
3 Benxi Central Hospital, Benxi, 117000, China
4 Key Laboratory of Liaoning Tumor Clinical Metabolomics, Jinzhou, 121000, China
5 Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, 116023, China

* Corresponding Author: Zhitu Zhu. Email:
# These authors contributed equally to this work

Oncologie 2021, 23(2), 203-217. https://doi.org/10.32604/Oncologie.2021.016155

Received 11 February 2021; Accepted 19 April 2021; Issue published 22 June 2021

Abstract

It is noteworthy that colon cancer is the fourth place in new cases and the fifth in mortalities according to global cancer statistics 2018. Tumorigenesis displays specific correlation with metabolic alterations. A variety of metabolites, including ornithine (Orn), are related to colon cancer according to sources of disease metabolic information retrieval in human metabolome database. The metabolic regulation of Orn pathway is a key link in the survival of cancer cells. In this study, the plasma Orn levels in colon cancer patients and healthy participants were measured by liquid chromatography tandem mass spectrometry, and the metabolic disturbances of Orn in colon cancer were identified. Based on exploring the pathway structure of Orn metabolism via MetaboAnalyst and Kyoto Encyclopedia of Genes and Genomes database, we found that the upstream and downstream metabolites included arginine (Arg) and N1, N12-diacetylspermine (DiAcSpm). We observed that the Arg-Orn-DiAcSpm metabolic pathway was up-regulated in colon cancer through pathway analysis. We used multivariate data modelling to build a regression diagnosis model based on the three metabolites for colon cancer, and the diagnosis capability of this model was analyzed via receiver operating characteristic curve. This study provides a theoretical basis for further feature description of tumor metabolic pathway, which may lead to discover new therapeutic targets and drugs against colon cancer. Multivariate data modelling is expected to be a novel technology for developing noninvasive screening tool of cancer.

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