Machine Learning-Based Integrated Multiomics Characterization of Colorectal Cancer Reveals Distinctive Metabolic Signatures.

Journal: Analytical chemistry
PMID: 38743842

Abstract

The metabolic signature identification of colorectal cancer is critical for its early diagnosis and therapeutic approaches that will significantly block cancer progression and improve patient survival. Here, we combined an untargeted metabolic analysis strategy based on internal extractive electrospray ionization mass spectrometry and the machine learning approach to analyze metabolites in 173 pairs of cancer samples and matched normal tissue samples to build robust metabolic signature models for diagnostic purposes. Screening and independent validation of metabolic signatures from colorectal cancers via machine learning methods (Logistic Regression_L1 for feature selection and eXtreme Gradient Boosting for classification) was performed to generate a panel of seven signatures with good diagnostic performance (the accuracy of 87.74%, sensitivity of 85.82%, and specificity of 89.66%). Moreover, seven signatures were evaluated according to their ability to distinguish between cancer and normal tissues, with the metabolic molecule PC (30:0) showing good diagnostic performance. In addition, genes associated with PC (30:0) were identified by multiomics analysis (combining metabolic data with transcriptomic data analysis) and our results showed that PC (30:0) could promote the proliferation of colorectal cancer cell SW480, revealing the correlation between genetic changes and metabolic dysregulation in cancer. Overall, our results reveal potential determinants affecting metabolite dysregulation, paving the way for a mechanistic understanding of altered tissue metabolites in colorectal cancer and design interventions for manipulating the levels of circulating metabolites.

Authors

  • Ran Zheng
    State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130021, China.
  • Rui Su
    Huami (Beijing) Information Technology Co. Ltd, Beijing, China.
  • Yusi Fan
    College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China.
  • Fan Xing
    State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130021, China.
  • Keke Huang
  • Fei Yan
    Department of Infectious Diseases, Affiliated Taizhou Hospital of Wenzhou Medical University, No.50 Ximeng Road, Taizhou, 317000, China.
  • Huanwen Chen
    School of Pharmacy, Jiangxi University of Chinese Medicine, Nanchang 330004, China.
  • Botong Liu
    State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130021, China.
  • Laiping Fang
    State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130021, China.
  • Yechao Du
    Department of General Surgery Center, First Hospital of Jilin University, 1 Xinmin Street Changchun, Jilin 130012, China.
  • Fengfeng Zhou
  • Daguang Wang
    Department of Gastric Colorectal and Anal Surgery, First Hospital of Jilin University, 1 Xinmin Street Changchun, Jilin 130012, China.
  • Shouhua Feng
    State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun 130021, China.

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