Introduction:
Distant metastasis (M1) is the principal determinant of outcome in colorectal cancer (CRC). Identifying robust molecular and clinical predictors at diagnosis may improve risk stratification.

Aim of the research:
To develop and compare complementary statistical and machine‑learning models for predicting metastatic status and to synthesize convergent predictors.

Material and methods:
In a single-centre cohort (N = 54, M1 = 26, M0 = 28), we modelled a binary outcome – any type of CRC metastasis (M_any) using multivariable logistic regression (LR), Random Forest (RF), LASSO-penalized logistic regression, and Support Vector Machine (SVM). In a secondary analysis, we fitted a multinomial logistic regression with three categories Early_No_Mets (pT1-2M0), Advanced_No_Mets (pT3-4M0), and Metastatic (M1). Discrimination was summarized as AUC; classification metrics used native validation schemes (LR in-sample; RF out-of-bag [OOB]; SVM 10-fold cross-validation [CV]). Reporting follows TRIPOD guidance.

Results:
LR identified TP53 mutation as the strongest predictor (OR = 3.47; 95% CI: 0.92–14.5; p = 0.073; AUC = 0.713). RF achieved OOB error of 40.74% (accuracy = 59.3%); top features were NRAS, TP53_pathway, TP53, WNT_pathway, and n_genes. LASSO (10‑fold CV) retained NRAS, TP53, and age (coefficients +1.15, +0.85, −0.02). SVM yielded AUC = 0.678, accuracy = 55.6%. The multinomial model revealed complete separation for NRAS in the Advanced_No_Mets group, precluding standard MLE inference.

Conclusions:
TP53 (gene/pathway) is a consistent risk signal across methods; NRAS carries high importance in ensemble/regularized models. Overall discrimination is modest, consistent with a small sample size; findings are hypothesis‑generating and warrant validation.