The gut microbiota has been increasingly recognized as a promising non-invasive biomarker source for Colorectal Cancer (CRC) detection. In this study, we develop a multi-layer stacking ensemble learning framework that integrates multiple machine learning models to improve the classification accuracy of CRC based on gut microbiota profiles. Our framework is trained using publicly available microbiome datasets comprising 1129 samples from diverse geographical regions (Europe, America, and Asia), and independently evaluated on an external validation cohort collected from Peking Union Medical College Hospital (PUMCH) in China. Based on our experiments, feature extraction of gut microbiota at the genus and species levels is found to achieve the best performance. The framework integrates multiple base classifiers—including Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), Random Forests (RF), and Support Vector Machines (SVM)—combined through a weighting layer to optimize final classifications. Analysis of the feature importance in the trained model reveals several microbial populations previously reported to be associated with CRC, such as Gemella morbillorum and Fusobacterium nucleatum. These findings support the microbiological interpretability of our proposed framework. Experimental results show that our ensemble model achieves an Area Under the Receiver Operating Characteristic Curve (namely ROC_AUC) of 77.04% when validated on a real-world clinical dataset from Peking Union Medical College Hospital, surpassing existing microbiome-based CRC classification approaches.
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To evaluate and project the disease burden of early-onset colorectal cancer (EOCRC), and to identify its major risk factors.
Data from the Global Burden of Disease Study 2021 were used. EOCRC was defined as colorectal cancer diagnosed at ages 15-49 years. We extracted the absolute numbers and crude rates of incidence, mortality, and disability-adjusted life years (DALYs) of EOCRC in the world, China, and the United States from 1990 to 2021. Age-standardized rates were then calculated accordingly, and the associated risk factors were summarized. Joinpoint regression was applied to assess temporal trends in global EOCRC age-standardized incidence rate (ASIR), mortality rate (ASMR), and DALY rate. The Bayesian age-period-cohort (BAPC) model was used to project the EOCRC burden from 2022 to 2040.
In 2021, an estimated 211 900 new EOCRC cases were reported globally, corresponding to an ASIR of 5.37 per 100 000, along with 79 500 deaths and a total of 4.00 million DALYs lost. China experienced a substantially higher burden than the global average, with an ASIR of 10.02 per 100 000, an ASMR of 3.11 per 100 000, and an age-standardized DALYs rate(ASDR) of 160.93 per 100 000, disproportionately affecting males. The United States had a higher ASIR(10.73 per 100 000) than China, but exhibited lower ASMR(2.53 per 100 000) and ASDR(127.81 per 100 000). From 1990 to 2021, ASIRs increased globally (AAPC=0.374) and more markedly in China (AAPC=1.423), whereas ASMR (AAPC=-0.855) and DALY rates (AAPC=-0.834) declined. In China, ASMR and DALY rates declined overall but rebounded in recent years, whereas in the United States, ASIR began to decrease after 2017, while ASMR and DALY rates remained relatively stable. By sex, China's male ASIR increased by 89.7% between 1990 and 2021, with rises also observed in ASMR and DALY rates in recent years. Regarding risk factors, insufficient whole-grain intake and excessive red meat consumption emerged as the leading modifiable contributors to EOCRC mortality and DALYs both globally and in the two countries, jointly accounting for 31.20% and 30.82% of the global burden, respectively. Heavy alcohol consumption and tobacco use were particularly significant contributors among Chinese men. Projections indicated that by 2040, China's ASIR is projected to rise to 17.04 per 100 000, indicating a continued increase in disease burden, whereas the United States is expected to maintain a downward trend.
Although China's EOCRC mortality and DALY burden have declined over the past three decades, the incidence remains markedly higher and is rising more rapidly compared with the global level and the U.S. More effective public health strategies are urgently needed, including promoting earlier and wider implementation of colorectal cancer screening, strengthening follow-up and referral systems, improving dietary structure, and intensifying tobacco and alcohol control, to mitigate the growing burden of EOCRC.
Cancer has become one of the most significant diseases threatening human life and health. In 2022, there were 19.97 million new cancer cases and 9.74 million cancer-related deaths worldwide, imposing a heavy burden on society. A systematic and comprehensive understanding of the cancer burden is the cornerstone for formulating effective prevention and control strategies. Open-source databases, represented by the GLOBOCAN database and the Global Burden of Disease (GBD) database, provide critical support for obtaining the latest cancer burden data, identifying key areas for prevention and control, guiding screening and early diagnosis and treatment, and evaluating the effectiveness of intervention measures. However, the field of cancer big data also faces challenges, such as insufficient data standardization and inadequate privacy protection mechanisms. In the future, while ensuring patient privacy and security, efforts should be made to further improve data quality, promote data sharing, enhance the equity of cancer prevention and treatment resources, and strengthen international collaboration. These measures will reasonably advance the precision and scientific development of cancer prevention and control strategies, with the aim of reducing the cancer burden and contributing to global health and well-being.
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