Plasma lipid-based machine learning models provides a potential diagnostic tool for colorectal cancer patients

Abstract

Colorectal cancer is the second leading cause of cancer-related death across the world. So far, screening method for colorectal cancer are limited to blood test, imaging test, and digital rectal examination, that are either invasive or ineffective. So, this study aims to explore novel, more convenient and effective diagnostic method for colorectal cancer. First, the experiment cohort was randomly split to train set and test set, and LC-MS-based plasma lipidomics was applied to identify lipid features in colorectal cancer. Second, univariate and multivariate analyses were performed to screen for significantly differentially expressed lipids. Third, single-lipid-based ROC analysis and multiple-lipid-based machine learning modeling were conducted to assess differential lipids' diagnostic performance. Lastly, survival analyses were used to evaluate lipids' prognostic values. In total, 41 differential lipids were screened out, 10 were upregulated and 31 were downregulated in CRC. Only CerP(d15:0_22:0 + O) showed fine predictive accuracy in single-lipid-based ROC analysis. Among the four machine learning models, SVM showed best predictive performance with accuracy (in predicting test set) of 1.0000 (95 %CI: 0.8806, 1.0000), that can be reached by modeling with only 14 lipids. Four lipids had significant prognostic values, that were TG(11:0_18:0_18:0) (HR: 0.34), TG(18:0_18:0_18:1) (HR: 0.34), PC(22:1_12:3) (HR: 2.22), LPC(17:0) (HR: 3.16). In conclusion, this study discovered novel lipid features that have potential diagnostic and prognostic values, and showed combination of plasma lipidomics and machine learning modeling could have outstanding diagnostic performance and may serve as a convenient and more accessible way to aid in clinical diagnosis of colorectal cancer.

Keywords: Diagnosis; Lipidomics; Machine learning; Prognosis; ROC.