Integrating Multi-Omics Data and AI For Early Detection And Personalized Treatment of Colorectal Cancer
Gowthamm Mandala
Abstract
Colorectal cancer (CRC) is the fourth leading cause of cancer-related morbidity and mortality worldwide. More than half of the CRC cases are due to preventable causes. Aberrant gut microbiota has been found to play an important role in the early phase of CRC by causing chronic inflammation. However, this residual risk combined with the lack of patient-friendly methods can limit early detection, intervention success, and post-intervention monitoring. This creates an urgent need for additional innovative point-of-care tests that could distinguish early preclinical cancers from precancers, both of which could be associated with immune dysbiosis. Artificial intelligence (AI) tools are expected to assist with these challenges, as vehicles for these assays have come to the forefront, with promising results in other epigenetic tests. In this review, we focus on innovative colorectal cancer risk biomaterials to detect these early events, highlighting the potential of artificial intelligence (AI) to aid in data integration and develop better tools, ultimately leading to patient-tailored treatments for better colorectal cancer outcomes. As synergistic material platforms, carbon nanopipettes (CNPs) are then discussed, along with circuit nanosensors, with a focused discussion on early CRC disease detection.