Application of Mendelian randomization in the discovery of risk factors for cancer from 2014 to 2024: a bibliometric review.

introductionCancer represents a significant threat to human health, resulting in considerable suffering and placing strain on the medical system. Mendelian randomization (MR) is a cutting-edge technique widely utilized in epidemiological studies, demonstrating distinct advantages in identifying risk factors associated with various diseases. In recent times, there has been considerable interest in MR studies within cancer research. Researchers are increasingly utilizing MR to explore potential risk factors and mechanisms related to cancer, which enables researchers to evaluate the causal relationship between genetic variation and cancer risk more accurately, serving as a crucial foundation for cancer prevention and treatment.

methodsThis study conducted a bibliometric analysis of MR research in cancer, documented in the Science Network (WOS) Index from 2014 to 2024. By systematically retrieving and summarizing relevant literature, the analysis covered data points such as countries, institutions, authors, journals, publications, keywords, exposures and outcomes.

resultsThe study included 1211 articles written by 5810 scholars affiliated with 1254 organizations in 66 nations, published in 302 journals. China, UK, USA and Australia emerged as the leading countries involved. The key affiliations included the University of Bristol, Karolinska Institute, Sichuan University and Zhejiang University. Prominent journals include ‘Frontiers in Oncology’, ‘Frontiers in Genetics’, ‘International Journal of Cancer’ and ‘Scientific Reports’. The keyword analysis indicated that obesity was the most frequently cited exposure factor in cancer-related MR literature, followed by body mass index, inflammation and smoking. Breast cancer was the most common outcome factor, followed by colorectal cancer, prostate cancer and lung cancer.

conclusionMore and more attention has been paid to the research of MR in tumors, revealing a variety of potential risk factors. However, some risk factors found by MR analysis (such as obesity on lung cancer subtypes) also have contradictory effects, highlighting the complexity of the specific causal relationship between exposure factors and cancer. Future research needs to focus on three directions: integrating cross-omics data (such as epigenetics, single cell analysis) to analyze the mechanism of tumor microenvironment; we use longitudinal MR and Bayesian model to optimize the robustness of causal inference. Strengthen cross-ethnic verification and GWAS data construction of rare cancers, and promote the transformation of MR findings into precise prevention strategies.