Colorectal Cancer Detection via Metabolites and Machine Learning.
Publication Title
Current issues in molecular biology
Document Type
Article
Publication Date
4-30-2024
Keywords
california; santa monica; pacific neurosci; machine learning
Abstract
Today, colorectal cancer (CRC) diagnosis is performed using colonoscopy, which is the current, most effective screening method. However, colonoscopy poses risks of harm to the patient and is an invasive process. Recent research has proven metabolomics as a potential, non-invasive detection method, which can use identified biomarkers to detect potential cancer in a patient's body. The aim of this study is to develop a machine-learning (ML) model based on chemical descriptors that will recognize CRC-associated metabolites. We selected a set of metabolites found as the biomarkers of CRC, confirmed that they participate in cancer-related pathways, and used them for training a machine-learning model for the diagnostics of CRC. Using a set of selective metabolites and random compounds, we developed a range of ML models. The best performing ML model trained on Stage 0-2 CRC metabolite data predicted a metabolite class with 89.55% accuracy. The best performing ML model trained on Stage 3-4 CRC metabolite data predicted a metabolite class with 95.21% accuracy. Lastly, the best-performing ML model trained on Stage 0-4 CRC metabolite data predicted a metabolite class with 93.04% accuracy. These models were then tested on independent datasets, including random and unrelated-disease metabolites. In addition, six pathways related to these CRC metabolites were also distinguished: aminoacyl-tRNA biosynthesis; glyoxylate and dicarboxylate metabolism; glycine, serine, and threonine metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; arginine biosynthesis; and alanine, aspartate, and glutamate metabolism. Thus, in this research study, we created machine-learning models based on metabolite-related descriptors that may be helpful in developing a non-invasive diagnosis method for CRC.
Clinical Institute
Neurosciences (Brain & Spine)
Clinical Institute
Digestive Health
Clinical Institute
Cancer
Specialty
Gastroenterology
Specialty
Neurosciences
Specialty
Oncology
Recommended Citation
Yang, Rachel; Tsigelny, Igor F; Kesari, Santosh; and Kouznetsova, Valentina L, "Colorectal Cancer Detection via Metabolites and Machine Learning." (2024). Articles, Abstracts, and Reports. 8903.
https://digitalcommons.providence.org/publications/8903