Metal‐Organic Framework Hybrids Aid Metabolic Profiling for Colorectal Cancer

Abstract

Colorectal cancer (CRC) is the third most common fatal cancer worldwide, accounting for ≈10% of cancer‐related mortality. Metabolic shift occurs from the very early stage during the development of CRC, which is of significant etiological and diagnostic importance toward precision medicine. Here, an advanced molecular tool to characterize the metabolic alterations in CRC, based on metal‐organic framework (MOF) hybrids is reported. Consuming only 500 nL of plasma without any sample pretreatment, MOF hybrids yield direct metabolic fingerprints by laser desorption/ionization mass spectrometry in seconds. A diagnostic prediction model by a machine learning algorithm is constructed, to discriminate CRC patients from normal controls with an average area under the curve of 0.947 for the discovery cohort and 0.912 for the independent validation cohort. In addition, CRC‐specific metabolic signature consisting of 34 potential biomarkers, based on the aforementioned diagnostic model is identified. The results advance the design of nanomaterial‐based platforms for metabolic analysis and establish a new liquid biopsy tool for CRC screening compatible with the current clinical workflow in practice.