Comprehensive serum glycopeptide spectrum analysis with machine learning for non-invasive early detection of gastrointestinal cancers.

ABSTRACT:

Purpose

Gastrointestinal cancers, including colorectal cancer (CRC), gastric cancer (GC), and esophageal cancer (EC), are among the most common and lethal malignancies worldwide. Early detection is critical for improving patient outcomes, but the current diagnostic methods, such as endoscopy, are burdensome, costly, and inaccessible for widespread screening. Here, we have identified the transformative potential of non-invasive blood-based diagnostics by integrating advanced glycan biomarkers and machine learning.

Experimental design

This study analyzed serum samples from 296 CRC, 180 GC, and 42 EC patients, alongside 590 healthy controls. Nine conventional tumor markers were quantified and 1688 enriched glycopeptides (EGPs) were identified via liquid chromatography-mass spec

SUBMITTER: Hisamatsu Y 

PROVIDER: S-EPMC12636384 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature