Year: 2026
Type: article
Study design: Observational Study
Abstract: High Resolution Image Download MS PowerPoint Slide Hepatocellular carcinoma (HCC) typically develops in a cirrhotic background, where elevated serum α-fetoprotein (AFP), a commonly used HCC biomarker, performs inconsistently. We implemented an integrated serum intact glycoproteomics strategy that profiles N-linked and O-linked glycopeptides in parallel. This strategy couples prevalence-aware filtering with multialgorithm feature selection to prioritize glycoform-resolved markers distinguishing HCC from cirrhosis. Serum from HCC ( n = 20) and cirrhosis ( n = 20) underwent high-abundance protein depletion, proteolysis, glycopeptide enrichment, and high-resolution liquid chromatography–mass spectrometry/MS (LC–MS/MS). Intact glycopeptides were filtered to a high-confidence quantitative matrix requiring ≥70% feature presence, with statistically significant low-prevalence features recovered by chi-square testing and incorporated into the final modeling pool. Candidate prioritization integrated SelectKBest, SVM-RFE, Elastic Net, Transformer-RFE, and Random Forest, with discrimination assessed by ROC analysis. This workflow delivered broad N/O glycoproteome coverage and a candidate of 11 intact glycopeptide features spanning both glycosylation classes. Multiple candidates exhibited pronounced group-associated detectability and/or significant abundance differences, and prioritized markers exceeded clinically measured AFP in univariate ROC comparisons within this cohort. Network-level interpretation (PPI and IPA) revealed an immune/complement-centered module and enrichment of pathways encompassing the complement system, neutrophil degranulation, O-linked glycosylation, sphingolipid metabolism, and nuclear receptor signaling (LXR/RXR activation and androgen signaling).
Source: Journal of Proteome Research
Authors: Muhammad Salman Sajid, Rency S. Varghese, Shafaq Saleem, Xinran Zhang, Alexander Kroemer, Habtom W. Ressom
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $4,500
OpenAlex ID: W7165052318