This study aims to explore the relationship between various biomarkers, diseases, and cancer subtypes using UK Biobank data to develop predictive models, ultimately enhancing early cancer diagnosis and personalized treatment strategies. The key research questions include: 1) the associations between different cancer subtypes and clinical, genetic, metabolomic, and proteomic biomarkers; 2) the longitudinal relationship between various diseases and cancer incidence/progression; 3) the development and evaluation of cancer subtype predictive models using machine learning techniques.
The specific objectives of this research are:
To investigate the longitudinal relationship between baseline biomarker levels and cancer incidence/progression using logistic regression models;
To build a predictive model for cancer subtypes based on multi-omics data (clinical, genetic, metabolomic, and proteomic data) using LightGBM;
To utilize pathway enrichment analysis and other methodologies to uncover the underlying biological mechanisms of cancer subtypes;
To identify potentially repurposable drugs based on the findings.
The scientific rationale for this study is that the heterogeneity of cancer subtypes and the complexity of biomarkers present significant challenges for early diagnosis and therapeutic decision-making. By integrating multi-omics data and applying advanced statistical and machine learning techniques, this research will provide new scientific insights into the precise classification of cancer subtypes and the discovery of relevant biomarkers, thus contributing to the advancement of precision oncology.
