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Approved Research

Identifying exogenous, endogenous, and clinical determinants and their interactions for the development of gastrointestinal cancer, and developing risk prediction models for incidence and survival

Principal Investigator: Dr Kefeng Ding
Approved Research ID: 92606
Approval date: October 11th 2022

Lay summary

Globally, main gastrointestinal (GI) cancer, including colorectal cancer, stomach cancer, liver cancer, esophagus cancer and pancreas cancer, accounted for 26.3% of all newly diagnosed cancers. It has been well recognized that complex mechanisms driven by a combination of exogenous factors (including diet, nutrition, lifestyle, the environment, etc.), endogenous factors (including genetic variations, and circulating biomarkers) contribute to the etiology and development of GI cancer. However, there are few studies investigating the interactions and joint effects of exogenous and endogenous factors in the etiology of GI cancer. Furthermore, due to advances in cancer screening and treatment, the number of GI cancer survivors has been steadily growing. Exogenous factors, endogenous factors and clinical determinants (including tumor location, stage, grade, and treatment choice, etc.) collectively affect the prognosis and survival of GI cancer survivors. However, few studies have paid attention to the interactions between exogenous, endogenous, clinical determinants, and prognosis or survival of GI cancer patients. Additionally, prediction models for incidence and prognosis of GI cancer by including the aforementioned factors will provide evidence-based support in individualized prevention and treatment for GI cancer survivors. Although previous studies established a series of risk prediction models incorporating well-established risk factors, most of them paid little attention in genetic variation, gene-exposure interaction, and changes in circulating biomarkers.

By using the large-scale, high-quality data from the UK biobank, we will address a series of research questions concerning GI cancer etiology, prognosis, and survival. The specific aims include: (1) to identify the factors related to GI cancer etiology, and survival, including genetic predisposition, endogenous exposure such as smoking, diet, physical activity, etc. and other clinical determinants, such as tumor location, stage, common complication, etc. (2) to conduct molecular epidemiology of GI cancer by analyzing circulating biomarkers, genetic factors and gene-environmental interactions, and tumor heterogeneity; (3) to develop risk prediction models for incidence and survival for GI cancer under the help of artificial intelligence and machine learning methods.

We will conduct analyses as soon as data are available and take 36 months to finish this project.

Our study will provide evidence for individualized prevention and treatment for GI cancer. Furthermore, expected findings can provide decision-making tools to develop risk-tailored population screening strategy and precise treatment choice. Thus, this study will be useful in improving efficiency in screening and treatment, and saving health resources.