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January 22, 2026Genes0 citationsOpen Access

Gene Expression-Based Colorectal Cancer Prediction Using Machine Learning and SHAP Analysis

YYYulai YinNankai University ZYZhen YangNankai University XLXiaoyao LiShandong University of Technology

Key Points

This research aims to develop and validate a genetic diagnostic model for colorectal cancer using machine learning techniques.
Identified differential expression genes (DEGs) from TCGA database between cancer and normal groups.
Performed Mendelian randomization analysis using eQTL data to identify associated genes.
Developed a diagnostic model using nine machine learning algorithms including XGBoost and Random Forest.
Selected ten key genes based on predictive performance and SHAP analysis for further evaluation.
Identified 3716 DEGs and 121 associated genes through different analyses.
XGBoost model achieved a high AUC of 0.990, indicating strong prediction ability.
Final diagnostic model indicated an AUC of 0.9875 for training and 0.9601 for validation sets.
Highlighted IFITM1 and DBNDD1 as key influential genes in the model.

Abstract

Objective: To develop and validate a genetic diagnostic model for colorectal cancer (CRC). Methods: First, differential expression genes (DEGs) between colorectal cancer and normal groups were screened using the TCGA database. Subsequently, a two-sample Mendelian randomization analysis was performed using the eQTL genomic data from the IEU OpenGWAS database and colorectal cancer outcomes from the R12 Finnish database to identify associated genes. The intersecting genes from both methods were selected for the development and validation of the CRC genetic diagnostic model using nine machine learning algorithms: Lasso Regression, XGBoost, Gradient Boosting Machine (GBM), Generalized Linear Model (GLM), Neural Network (NN), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Random Forest (RF), and Decision Tree (DT). Results: A total of 3716 DEGs were identified from the TCGA database, while 121 genes were associated with CRC based on the eQTL Mendelian randomization analysis. The intersection of these two methods yielded 27 genes. Among the nine machine learning methods, XGBoost achieved the highest AUC value of 0.990. The top five genes predicted by the XGBoost method—RIF1, GDPD5, DBNDD1, RCCD1, and CLDN5—along with the five most significantly differentially expressed genes (ASCL2, IFITM3, IFITM1, SMPDL3A, and SUCLG2) in the GSE87211 dataset, were selected for the construction of the final colorectal cancer (CRC) genetic diagnostic model. The ROC curve analysis revealed an AUC (95% CI) of 0.9875 (0.9737–0.9875) for the training set, and 0.9601 (0.9145–0.9601) for the validation set, indicating strong predictive performance of the model. SHAP model interpretation further identified IFITM1 and DBNDD1 as the most influential genes in the XGBoost model, with both making positive contributions to the model’s predictions. Conclusions: The gene expression profile in colorectal cancer is characterized by enhanced cell proliferation, elevated metabolic activity, and immune evasion. A genetic diagnostic model constructed based on ten genes (RIF1, GDPD5, DBNDD1, RCCD1, CLDN5, ASCL2, IFITM3, IFITM1, SMPDL3A, and SUCLG2) demonstrates strong predictive performance. This model holds significant potential for the early diagnosis and intervention of colorectal cancer, contributing to the implementation of third-tier prevention strategies.

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