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.

Inorganic Matrices Assisted Laser Desorption/Ionization Mass Spectrometry for Metabolic Analysis in Biofluids

“…The plasma is an alternate bio‐fluid for in vitro metabolic diagnosis. Li et al [84] . presented MIL‐88 A anchored Pt nanoparticles as an advanced matrix to distinguish colorectal cancer by sparse learning algorithm with an AUC value of 0.947/0.912 for the discovery cohort/validation cohort.…”

Emerging Applications of Mass Spectrometry‐Based Metabolic Fingerprinting in Clinics

“…MS‐based metabolic fingerprinting have been conducted on the following diseases: cancers (including brain, [8b] lung, [ 20 ] pancreatic, [ 21 ] colorectal, [ 22 ] gastric, [8a,23] cervical, [ 10,24 ] and gynecological cancer [ 25 ] ), inflammatory or infectious disease, [9a,26] kidney disease, [2d] and cardiovascular/cerebrovascular diseases. [ 27 ] Compared with conventional diagnostic imaging and biopsy methods, MS‐based metabolic fingerprinting affords desirable accuracy with minimum invasiveness and low costs.…”

Surface-Functionalized Terahertz Metamaterial Biosensor Used for the Detection of Exosomes in Patients

“…Su et al optimized plasmonic alloys to reveal a distinct metabolic phenotype in early gastric cancer . Li et al demonstrated that MOF/Pt hybrid-assisted LDI MS combined with machine learning analysis of the obtained MFs is a powerful method for aiding CRC diagnosis . Imaging tests are also helpful in diagnosing CRC, including computed tomography (CT), nuclear magnetic resonance (NMR), a roentgeno­graphic (Rtg) examination of the thorax, endorectal ultrasonography (USG), and abdominal USG.…”

“…11 Li et al demonstrated that MOF/Pt hybrid-assisted LDI MS combined with machine learning analysis of the obtained MFs is a powerful method for aiding CRC diagnosis. 12 Imaging tests are also helpful in diagnosing CRC, including computed tomography (CT), nuclear magnetic resonance (NMR), a roentgenographic (Rtg) examination of the thorax, endorectal ultrasonography (USG), and abdominal USG. However, these methods are efficient only in cases of severe focal lesions or during the middle−late stage.…”