Profiling plasma protease activity with charge-changing peptides enables detection and classification of gastrointestinal cancers

Abstract

Early detection of gastrointestinal (GI) cancers—including colorectal cancer (CRC), gastric cancer (GC), and esophagogastric junction cancer (EGJC)—is essential for improving patient outcomes. However, current diagnostic methods such as endoscopy and colonoscopy are invasive, costly, and not widely accessible. Proteases are elevated in many cancers and are detectable in peripheral blood, making them promising candidates for noninvasive diagnostic strategies. We employed a six-probe charge-changing peptide (CCP) panel to profile cancer-associated protease activity in human plasma. Each CCP undergoes a charge shift upon cleavage by a specific protease, enabling detection via gel electrophoresis. Plasma samples from GI cancer patients (CRC, GC, EGJC; N = 68) and healthy controls (HC; N = 31) were analyzed. Protease activity profiles were analyzed using statistical tests, principal component analysis, and binary logistic regression (LR) models trained on the most informative probes. Model performance was evaluated through repeated cross-validation. Distinct protease activity profiles were observed among CRC, upper GI cancers (UGIC; GC + EGJC), and HC groups. Probe designed to be cleaved by cathepsin B showed the strongest discrimination between cancer and control samples, while probes designed to be cleaved by ubiquitin-specific peptidase 15 and plasmin were identified as the most informative subtype-specific markers for UGIC and CRC, respectively. LR models built on these single probes demonstrated excellent diagnostic performance, with AUCs exceeding 0.95, and both sensitivity and specificity greater than 90%. Our findings highlight CCP-based protease profiling as a minimally invasive, accurate, and scalable method for GI cancer detection and classification. This platform holds strong potential for clinical application in cancer screening, pending further validation in larger, independent cohorts.