Research Question
Gliomas, particularly glioblastoma, remain one of the deadliest and most prevalent forms of brain cancer, causing significant global morbidity and mortality. Despite advances in treatment, outcomes remain poor, and effective prevention strategies are still lacking. There is an urgent need to identify modifiable factors that could reduce glioma risk or slow its progression.
Objective
This research seeks to fill critical gaps in our understanding of glioma etiology by employing a multi-omics approach using blood proteomics, metabolomics, and clinical data. The study will leverage the UK Biobank dataset to:
Identify and validate novel blood-based biomarkers for glioma risk using proteomic and metabolomic profiles.
Assess the relationship between modifiable clinical factors (e.g., diet, exercise, medication) and glioma risk through epidemiological data.
Employ machine learning techniques to build predictive models that integrate these multi-omics and clinical factors, aiming to uncover new causal relationships between exposures and glioma development.
Scientific Rationale
While previous studies have provided insight into genetic and environmental factors contributing to glioma risk, they have often been limited by small sample sizes or narrow datasets. Proteomics and metabolomics offer unique opportunities to uncover blood-based biomarkers that reflect underlying molecular mechanisms of glioma. By integrating these data with clinical factors and genetic information, we aim to identify new modifiable risk factors and develop predictive models. Machine learning algorithms will help navigate the complexity of these data and uncover patterns that traditional statistical methods might miss.