Frontiers in oncology · 2025
Identifying novel biomarkers for biliary tract cancer based on volatile organic compounds analysis and machine learning.
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Qian Jingrong, Liu Qi, Wang Jue, Zhuang Xuewei, Fang Jun
Аннотация
BACKGROUND: The current diagnostic methods for biliary tract cancer (BTC) have limitations in sensitivity and specificity. This study aims to explore the use of volatile organic compounds (VOCs) in serum to distinguish BTC and benign biliary diseases (BBD).
METHOD: We collected 158 serum samples from BTC and BBD patients, and used gas chromatography ion mobility spectrometry (GC-IMS) for VOCs detection. Six machine learning methods (RF, SVM, LDA, KNN, LASSO, and XGBoost) were used to construct and evaluate diagnostic prediction models.
RESULT: We detected a total of 40 VOCs in patients, of which 14 VOCs were statistically significant (p < 0.05), including 11 up-regulated and 3 down-regulated VOCs. In BTC and BBD patients, the diagnostic model was constructed based on six machine learning method. Among them, RF had the highest diagnostic performance (AUC = 0.935, p < 0.001), with a sensitivity of 76.2% and a specificity of 96.3%. Based on the importance score, we selected the top 4 VOCs, and constructed an optimized diagnostic model through five fold cross validation. The model's AUC was 0.982, sensitivity was 87.9%, and specificity was 96.7%, which improved the diagnostic sensitivity and reduced FNR. In addition, in patients with cholangiocarcinoma and BBD, we further screened for 4-VOCs and constructed diagnostic model, with an AUC of 0.977, accuracy of 92.4%, specificity of 98.9%, sensitivity of 77.5%.
CONCLUSION: The diagnostic model based on 4-VOCs may be a feasible method for distinguishing the diagnosis of BTC and BBD patients.
METHOD: We collected 158 serum samples from BTC and BBD patients, and used gas chromatography ion mobility spectrometry (GC-IMS) for VOCs detection. Six machine learning methods (RF, SVM, LDA, KNN, LASSO, and XGBoost) were used to construct and evaluate diagnostic prediction models.
RESULT: We detected a total of 40 VOCs in patients, of which 14 VOCs were statistically significant (p < 0.05), including 11 up-regulated and 3 down-regulated VOCs. In BTC and BBD patients, the diagnostic model was constructed based on six machine learning method. Among them, RF had the highest diagnostic performance (AUC = 0.935, p < 0.001), with a sensitivity of 76.2% and a specificity of 96.3%. Based on the importance score, we selected the top 4 VOCs, and constructed an optimized diagnostic model through five fold cross validation. The model's AUC was 0.982, sensitivity was 87.9%, and specificity was 96.7%, which improved the diagnostic sensitivity and reduced FNR. In addition, in patients with cholangiocarcinoma and BBD, we further screened for 4-VOCs and constructed diagnostic model, with an AUC of 0.977, accuracy of 92.4%, specificity of 98.9%, sensitivity of 77.5%.
CONCLUSION: The diagnostic model based on 4-VOCs may be a feasible method for distinguishing the diagnosis of BTC and BBD patients.