Characterization of Multiple Omics Signatures in Relation to Dietary Pattern for in Silico Personalised Colon Cancer Risk Stratification: Study Protocol for a Case-control Study and the Challenges Faced During the COVID-19 Pandemic

Abstract

Background: Personalised nutrition and medicine are the future of healthcare. In relation to cancer, public and healthcare professionals often seek dietary recommendations for cancer prevention. Among the important cancers that can be prevented by diet and lifestyle is colorectal cancer (CRC). CRC is one of the commonest cancers globally, and is a major health concern in Malaysia as it presents with high mortality and morbidity rates, causing a significant socioeconomic burden to the country. While extensive research has been conducted on the treatment and mechanisms of cancer, there have been no reports on the associations between metabolites, novel biomarkers of cancer, and dietary patterns in the context of cancer prevention in the Malaysian multiethnic population. Methods: A case control study will be conducted in Malaysia, involving patients diagnosed with CRC, colorectal adenoma and a group of healthy participants. Multiple endpoints will be analyzed, namely metabolomic signatures, epigenetic marks, inflammatory markers and relationship with dietary patterns will be established. Multiple machine learning models will then be used to develop personalised risk stratification algorithms. Recruitment began in July 2019 and is ongoing due to COVID-19 pandemic. Discussion: This study will be the first to identify alterations in metabolites, inflammatory markers and epigenetic marks associated with dietary patterns and CRC risk in Malaysia. Understanding on how dietary patterns influence CRC risk in the multi-ethnic Malaysian population and identification of novel oncometabolites for CRC risk, will allow for development of personalised evidence-based recommendations in reducing individual risks of CRC.