Staging of colorectal cancer using lipid biomarkers and machine learning

Thamarai Krishnan, S., Winkler, D., Creek, D., Anderson, D., Kirana, C., Maddern, G. J., Fenix, K., Hauben, E., Rudd, D. and Voelcker, N. H. (2023) Staging of colorectal cancer using lipid biomarkers and machine learning. Metabolomics, 19 (10). 84. ISSN 1573-3890 doi: 10.1007/s11306-023-02049-z

Abstract/Summary

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. 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). 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. 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. 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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/113528
Identification Number/DOI	10.1007/s11306-023-02049-z
Refereed	Yes
Divisions	Life Sciences > School of Chemistry, Food and Pharmacy > Department of Chemistry
Uncontrolled Keywords	Multi-omics, Machine learning, Cancer Subtypes, Metastatic colorectal cancer classification, Lipidomics, Biomarkers, Colorectal Neoplasms - diagnosis - genetics, Lipids, Liver Neoplasms - diagnosis, Biomarker, Humans, Metabolomics, Machine Learning, Bayes Theorem
Publisher	Springer
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