High-Throughput Metabolic Pattern Screening Strategy for Early Colorectal and Gastric Cancers Based on Covalent Organic Frameworks-Assisted Laser Desorption/Ionization Mass Spectrometry

Abstract

Precise early diagnosis and staging are conducive to improving the prognosis of colorectal cancer (CRC) and gastric cancer (GC) patients. However, due to intrusive inspections and limited sensitivity, the prevailing diagnostic methods impede precisely large-scale screening. In this work, we reported a highthroughput serum metabolic patterns (SMP) screening strategy based on covalent organic frameworks-assisted laser desorption/ ionization mass spectrometry (hf-COFsLDI-MS) for early diagnosis and staging of CRC and GC. Notably, 473 high-quality SMP were extracted without any tedious sample pretreatment and coupled with multiple machine learning algorithms; the area under the curve (AUC) value is 0.938 with 96.9% sensitivity for early CRC diagnosis, and the AUC value is 0.974 with 100% sensitivity for early GC diagnosis. Besides, the discrimination of CRC and GC is accomplished with an AUC value of 0.966 for the validation set. Also, the screened-out features were identified by MS/MS experiments, and 8 metabolites were identified as the biomarkers for CRC and GC. Finally, the corresponding disordered metabolic pathways were revealed, and the staging of CRC and GC was completed. This work provides an alternative high-throughput screening strategy for CRC and GC and highlights the potential of metabolic molecular diagnosis in clinical applications.

Multichannel Nanogenerator-Driven Collaborative Metabolic Fingerprint Diagnostic Strategy for Early Screening and Risk Evaluation of Nonalcoholic Fatty Liver Disease

“…Compared to the hundreds of microliters required by general LC-MS analysis, the nanoliter biofluid without any tedious pretreatments can meet the analysis threshold; (2) ultrafast detection and rapid results output; and (3) high-throughput analysis capability, which can contain hundreds or even thousands of sample analysis arrays . Various nanomaterial matrices, for instance, silicon-based, noble metals, , metal oxide, and metal–organic frameworks (MOFs), − and covalent-organic frameworks (COFs), − have been explored as promising nanogenerators and applied to the analysis of metabolites. Notably, titanium oxide nanomaterials are beneficial for UV laser utilization in the LDI-MS process owing to their excellent optical properties. , In addition, the metal nodes have been elucidated to promote charge transfer and analyte ionization .…”

High-Performance Metabolic Profiling of High-Risk Thyroid Nodules by ZrMOF Hybrids

“…The rational design of nanomaterial for the LDI-MS matrix is particularly crucial for biofluidic chemical extraction, enabling early diagnosis, personalized treatment of diseases, and improved management of postoperative complications. − Nanomaterials offer significant advantages due to their high surface area, distinctive geometries, and unique physicochemical properties. These attributes have led to their widespread application in biomedical, sensing, catalytic, and environmental fields. − Metal–organic frameworks (MOFs), an emerging class of crystalline porous materials, have attracted significant attention in nanomedicine due to their construction from metallic complexes and organic ligands. − Among these, zirconium-based MOFs stand out for their robustness, high stability, low toxicity, large specific area, and excellent photothermal performance, offering flexibility for designing hybrid nanostructures with desired matrix effects.…”