Journal of cancer research and therapeutics · 2026
Enhancing non-invasive colorectal cancer screening with stool DNA methylation markers and light gradient-boosting machine learning.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Zhu Gangfeng, Zhu Yunlong, Lu Zenghong, Zheng Liangjian, Feng Xiaofei, Zhu Rui, Wang Xiangcai, Xu Xueming, Huang Li
Аннотация
OBJECTIVE: This study evaluated the effectiveness of stool DNA methylation markers Cannabinoid Receptor Type 1 (CNRIP1), Secreted Frizzled-Related Protein 2 (SFRP2), and Vimentin (VIM), along with Fecal Occult Blood Testing (FOBT), in the non-invasive screening for colorectal cancer (CRC), further integrating these markers with the Light Gradient Boosting Machine (LightGBM) machine learning (ML) algorithm.
METHODS: The study analyzed 100 stool samples (50 CRC, 50 normal) to assess the methylation status of CNRIP1, SFRP2, and VIM gene promoters. FOBT was performed in parallel. Diagnostic performance was assessed using Receiver Operating Characteristic (ROC) curve analysis, and a LightGBM-based ML model was developed, incorporating methylation markers and FOBT results.
RESULTS: ROC analysis demonstrated that SFRP2 had the highest diagnostic accuracy with an area under the curve (AUC) of 0.87 (95% confidence interval [CI]: 0.794-0.946) and a sensitivity of 0.88. CNRIP1 and VIM also showed substantial screening effectiveness, with AUC values of 0.83 and 0.80, respectively. FOBT, in comparison, had a lower predictive value with an AUC of 0.67. The LightGBM-based ML model significantly outperformed individual markers, achieving a high AUC of 0.95 (95% CI: 0.916-0.991). However, the sensitivity of the ML model was 0.78, suggesting a need for improvement in correctly identifying all CRC cases.
CONCLUSION: Stool DNA methylation markers CNRIP1, SFRP2, and VIM exhibited high sensitivity in non-invasive CRC screening. The integration of these biomarkers with the LightGBM ML algorithm enhanced diagnostic accuracy, offering a promising approach for early CRC detection.
METHODS: The study analyzed 100 stool samples (50 CRC, 50 normal) to assess the methylation status of CNRIP1, SFRP2, and VIM gene promoters. FOBT was performed in parallel. Diagnostic performance was assessed using Receiver Operating Characteristic (ROC) curve analysis, and a LightGBM-based ML model was developed, incorporating methylation markers and FOBT results.
RESULTS: ROC analysis demonstrated that SFRP2 had the highest diagnostic accuracy with an area under the curve (AUC) of 0.87 (95% confidence interval [CI]: 0.794-0.946) and a sensitivity of 0.88. CNRIP1 and VIM also showed substantial screening effectiveness, with AUC values of 0.83 and 0.80, respectively. FOBT, in comparison, had a lower predictive value with an AUC of 0.67. The LightGBM-based ML model significantly outperformed individual markers, achieving a high AUC of 0.95 (95% CI: 0.916-0.991). However, the sensitivity of the ML model was 0.78, suggesting a need for improvement in correctly identifying all CRC cases.
CONCLUSION: Stool DNA methylation markers CNRIP1, SFRP2, and VIM exhibited high sensitivity in non-invasive CRC screening. The integration of these biomarkers with the LightGBM ML algorithm enhanced diagnostic accuracy, offering a promising approach for early CRC detection.