| Title: | Pan-cancer risk factors identification and prediction model construction based on UK Biobank database. |
| Lead Institution: | The First Hospital of China Medical University |
| Principal investigator: |
Application 197250
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Aims: a. Identification of risk factors associated with pan-cancer in a large, long-term follow-up cohort from the UK Biobank; b. Identifying a set of precise prediction biomarkers applicable for pan-cancer in the genetic context. c. Construction of a systematic strategy applicable to prediction high-risk groups for pan-cancer in large populations; Scientific rationale: a. Cancer is a major chronic disease that endangers the health of the global population. Cancer mortality can be significantly reduced by early diagnosis and treatment. China leads the world in both new cases and deaths. In addition, the cancer spectrum of developing countries and developed countries coexist in China, and the incidence characteristics and screening needs of cancer are different between urban and rural areas, which poses a great challenge to cancer screening. b. Traditional strategy of cancer screening is mostly through molecular marker detection, imaging examination and other means, which makes patients often seek medical treatment after symptoms appear in the advanced stage or advanced stage of cancer, which also leads to the five-year survival rate of most cancer patients is relatively low. c. Through health data, such as basic information, family history, life and diet habits and other information that can be obtained by questioning, or routine biochemical testing, to determine the high-risk group of pan-cancer occurrence, and then the precision screening in high-risk groups can not only avoid cancer patients missing the best treatment window, but also be an efficient and economical screening strategy. Project duration: 36 months expected Public health impact: a. To explore the risk factors associated with pan-cancer in a large, long-term follow-up cohort based on data available from the UKB; b. Using genome-wide data to mine risk factors truly associated with pan-cancer based on the Mendelian randomization method, and constructing a simple prediction model for high-risk populations, with a view to predict high-risk groups for cancer in large populations; c. Using genome-wide data, develop a new set of molecular markers of pan-cancer with diagnostic or prognostic potential, which is expected to be transformed into clinical applications to improve the effectiveness of non-invasive cancer screening and longevity.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 15025 | Global, regional, and national burden of aortic aneurysm attributable to body mass index from 1990 to 2021 and prediction to 2040: a cross-sectional study. | Jingjing Jing (+7) | 2025 | International Journal of Surgery |
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