Staging of colorectal cancer using lipid biomarkers and machine learning

• Published in: Metabolomics Vol. 19; no. 10; p. 84
• Main Authors: Krishnan, Sanduru Thamarai, Winkler, David, Creek, Darren, Anderson, Dovile, Kirana, Chandra, Maddern, Guy J, Fenix, Kevin, Hauben, Ehud, Rudd, David, Voelcker, Nicolas Hans
• Format: Journal Article
• Language: English
• Published: New York Springer US 20.09.2023; Springer Nature B.V
• Subjects: Bayesian analysis; Biochemistry; Biomarkers; Biomedical and Life Sciences; Biomedicine; Cancer; Cell Biology; Chemokines; Colorectal cancer; Colorectal carcinoma; Developmental Biology; Interleukin 8; Learning algorithms; Life Sciences; Lipid metabolism; Lipids; Liquid chromatography; Liver; Machine learning; Mass spectroscopy; Mathematical models; Medical prognosis; Metastases; Metastasis; Molecular Medicine; Original; Original Article; Platelet factor 4; Platelets; Polyps; Prognosis; Lipidomics; Biomarker; Cancer Subtypes; Metastatic colorectal cancer classification; Multi-omics; Machine learning
• ISSN: 1573-3890; 1573-3882; 1573-3890
• DOI: 10.1007/s11306-023-02049-z

Introduction Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide. Alteration in lipid metabolism and chemokine expression are considered hallmark characteristics of malignant progression and metastasis of CRC. Validated diagnostic and prognostic biomarkers are urgently needed to define molecular heterogeneous CRC clinical stages and subtypes, as liver dominant metastasis has poor survival outcomes. Objectives The aim of this study was to integrate lipid changes, concentrations of chemokines, such as platelet factor 4 and interleukin 8, and gene marker status measured in plasma samples, with clinical features from patients at different CRC stages or who had progressed to stage-IV colorectal liver metastasis (CLM). Methods High-resolution liquid chromatography-mass spectrometry (HR-LC-MS) was used to determine the levels of candidate lipid biomarkers in each CRC patient’s preoperative plasma samples and combined with chemokine, gene and clinical data. Machine learning models were then trained using known clinical outcomes to select biomarker combinations that best classify CRC stage and group. Results Bayesian neural net and multilinear regression-machine learning identified candidate biomarkers that classify CRC (stages I-III), CLM patients and control subjects (cancer-free or patients with polyps/diverticulitis), showing that integrating specific lipid signatures and chemokines (platelet factor-4 and interluken-8; IL-8) can improve prognostic accuracy. Gene marker status could contribute to disease prediction, but requires ubiquitous testing in clinical cohorts. Conclusion Our findings demonstrate that correlating multiple disease related features with lipid changes could improve CRC prognosis. The identified signatures could be used as reference biomarkers to predict CRC prognosis and classify stages, and monitor therapeutic intervention.