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. 2025 May 15;25(Suppl 2):188.
doi: 10.1186/s12911-025-03012-9.

Developing a multiomics data-based mathematical model to predict colorectal cancer recurrence and metastasis

Bing Li  1 , Ming Xiao  1 , Rong Zeng  2   3   4 , Le Zhang  5
Affiliations

Developing a multiomics data-based mathematical model to predict colorectal cancer recurrence and metastasis

Bing Li et al. BMC Med Inform Decis Mak. .

Abstract

Background: Colorectal cancer is the fourth most deadly cancer, with a high mortality rate and a high probability of recurrence and metastasis. Since continuous examinations and disease monitoring for patients after surgery are currently difficult to perform, it is necessary for us to develop a predictive model for colorectal cancer metastasis and recurrence to improve the survival rate of patients.

Results: Previous studies mostly used only clinical or radiological data, which are not sufficient to explain the in-depth mechanism of colorectal cancer recurrence and metastasis. Therefore, this study proposes such a multiomics data-based predictive model for the recurrence and metastasis of colorectal cancer. LR, SVM, Naïve-bayes and ensemble learning models are used to build this predictive model.

Conclusions: The experimental results indicate that our proposed multiomics data-based ensemble learning model effectively predicts the recurrence and metastasis of colorectal cancer.

Keywords: Colorectal cancer; Data augmentation; Ensemble learning; Multiomics; Recurrence and metastasis.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Fig. 1
Fig. 1
Workflow of the study. The P values in Step 3 were calculated using the T test [61]
Fig. 2
Fig. 2
Feature selection methods for (A) discrete and (B) continuous datasets
Fig. 3
Fig. 3
Illustration of the first two principal components. Here, red points represent patients without recurrence and metastasis, and blue points represent patients with recurrence and metastasis. (A) Proteomics data and (B) phosphoproteomics data
Fig. 4
Fig. 4
Illustration of the dataset mapped to two dimensions and the F1 value. Here, red points represent patients without recurrence and metastasis, and blue points represent patients with recurrence and metastasis. (A) Original dataset and (B) generated dataset
Fig. 5
Fig. 5
The workflow of ensemble learning model development
Fig. 6
Fig. 6
Model performance. (A) Comparison of the classification performance of LR, SVM, Naive-Bayes, and ensemble learning models; (B) ROC curves plotted for LR, SVM, Naive-Bayes, and ensemble learning models
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