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This study explores the machine learning-based assessment of predisposition to colorectal cancer based on single nucleotide polymorphisms (SNP). Such a computational approach may be used as a risk indicator and an auxiliary diagnosis method that complements the traditional methods such as biopsy and CT scan. Moreover, it may be used to develop a low-cost screening test for the early detection of colorectal cancers to improve public health. We employ several supervised classification algorithms. Besides, we apply data imputation to fill in the missing genotype values. The employed dataset includes SNPs observed in particular colorectal cancer-associated genomic loci that are located within DNA regions of 11 selected genes obtained from 115 individuals. We make the following observations: (i) random forest-based classifier using one-hot encoding and K-nearest neighbor (KNN)-based imputation performs the best among the studied classifiers with an F1 score of 89% and area under the curve (AUC) score of 0.96. (ii) One-hot encoding together with K-nearest neighbor-based data imputation increases the F1 scores by around 26% in comparison to the baseline approach which does not employ them. (iii) The proposed model outperforms a commonly employed state-of-the-art approach, ColonFlag, under all evaluated settings by up to 24% in terms of the AUC score. Based on the high accuracy of the constructed predictive models, the studied 11 genes may be considered a gene panel candidate for colon cancer risk screening.

Tryptophan metabolism in the tumor microenvironment exerts immunosuppressive effects by affecting the anti-tumor functions of immune cells. The immunosuppressive roles of tryptophan and tryptophan metabolites and their effects on the FOXP3 gene, highly expressed in regulatory T cells (Tregs), are remarkable. Our study aimed to investigate the relation between tryptophan metabolism and the transcription factor FOXP3 gene in colorectal cancer (CRC). Patients with CRC (n = 159) and controls (n = 112) were included in the study. The FOXP3 rs3761548 variant genotyping from the isolated genomic DNA was performed by PCR-RFLP. FOXP3 gene expression was determined by Q-PCR in RNAs isolated from resected tissues at the same time. Serum tryptophan, kynurenine, kynurenic acid levels of the cases were determined by HPLC. In serum samples with CRC, tryptophan level was 14.32 ± 1.09 μmol/L, kynurenine level was 1.33 ± 0.02 μmol/L, and the kynurenic acid level was 0.01 ± 0.001 μmol/L. The level of tryptophan was found to be low in CRC compared to control (p