Lung cancer remains one of the leading causes of cancer-related deaths globally, with long-term smoking identified as its most significant risk factor. However, some long-term smokers never develop lung cancer, hinting at underlying protective biological mechanisms.
This study aims to investigate these protective factors by analyzing biochemical, proteomic, and genetic differences among three groups: long-term smokers without lung cancer, long-term smokers with lung cancer, and non-smokers with lung cancer. Using data from UKB participants, we will examine multiple biochemical markers, such as glucose, lipids, inflammation, liver, and kidney functions, while leveraging machine learning on proteomic data from a subset of participants to identify key biomarkers linked to lung cancer resistance.
Beyond identifying protective factors, the study will assess how these biomarkers interact with medical interventions like surgery, radiotherapy, and immunotherapy to influence treatment outcomes. By integrating multi-omics data and clinical records, we aim to determine the differential responses across patient subgroups, providing insights into personalized treatment strategies.
Lastly, this research will explore the impact of healthcare quality and accessibility on lung cancer incidence. Controlling for genetic predisposition through polygenic risk scores and stratifying by smoking exposure, we will analyze the role of healthcare services, including screening and early diagnosis, in shaping disease outcomes. Advanced statistical methods, such as Mendelian randomization, will establish causal links between healthcare factors and lung cancer risk. Findings will inform public health policies aimed at improving equity in healthcare delivery and reducing lung cancer burden across diverse populations.
