Integrative analysis of circulating biomarkers of colorectal cancer combining mid-infrared spectroscopy and targeted metabolomics

Colorectal cancer (CRC) is a major health burden, the third most common cancer and the second leading cause of cancer-related death worldwide. The development of biomarkers for screening, diagnosis, prognosis and prediction is crucial for early management and treatment of CRC. Despite existing biomarkers, particularly tests targeting alterations in DNA, proteins other than hemoglobin or other molecules in biological samples, there is a need for new, comprehensive and minimally invasive assays that reflect neoplastic biology. This study proposes a novel approach combining Mid InfraRed (MIR) spectroscopy and targeted metabolomics to identify novel biomarkers of CRC. Here we present an original statistical pipeline developed in R, which integrates spectroscopic and metabolomic data to identify biomarkers and their associated biological pathways through automated searches of HMDB and KEGG databases. The pipeline is validated by two studies. The first study aims to extract relevant biomarkers to identify the population subgroup more likely to have CRC or advanced precursor lesions in colorectal screening strategy for average-risk adults starting at age 50. It seeks to distinguish between low-risk and high-risk patients for CRC among those undergoing colonoscopy screening. The second study involved a preclinical model of xenografts of CRC cell lines to demonstrate the portability of the pipeline. Our results indicate that this integrated approach may provide a relevant, inexpensive, and accurate method for screening, diagnosing, and understanding CRC pathology. This innovative tool could improve patient management by enhancing the risk assessment of CRC and providing a better understanding of the underlying biological processes. The application of this methodology could extend beyond CRC to other diseases, providing improved screening, diagnosis and treatment strategies in oncology. • Integrated MIR spectroscopy and targeted metabolomics for low-risk versus high-risk CRC biomarker discovery. • An original R-based statistical pipeline for multi-omics data fusion and analysis. • Automated pathway mapping via HMDB and KEGG for deeper biological insights. • Validated portability across clinical screening cohorts and preclinical xenograft models. • Cost-effective and accurate tool for enhanced CRC risk assessment and diagnosis.