Deep N-glycoproteomic profiling of FFPE tissues enables precise diagnosis of primary liver cancer from metastatic liver cancer.
8 affiliations
- 1Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, Jiangsu, 214122, China.
- 2Key Laboratory of Biopharmaceutical Preparation and Delivery, Chinese Academy of Sciences, Beijing, 100190, China.
- 3Department of Pathology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, 214122, China.
- 4Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), The School of IOT Engineering, Jiangnan University, Wuxi, Jiangsu, China.
- 5Department of Medical Oncology, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, 214122, China. [email protected].
- 6Key Laboratory of Biopharmaceutical Preparation and Delivery, Chinese Academy of Sciences, Beijing, 100190, China. [email protected].
- 7Key Laboratory of Carbohydrate Chemistry and Biotechnology, Ministry of Education, School of Biotechnology, Jiangnan University, Wuxi, Jiangsu, 214122, China. [email protected].
- 8Key Laboratory of Biopharmaceutical Preparation and Delivery, Chinese Academy of Sciences, Beijing, 100190, China. [email protected].
Abstract
Background
Reliable tumor origin assessment from routinely archived specimens remains challenging, particularly for diagnostically ambiguous liver malignancies. Although formalin-fixed, paraffin-embedded (FFPE) tissues are widely available, deep and quantitative site-specific N-glycoproteomics of these specimens is limited by formaldehyde-induced crosslinking and incomplete peptide recovery.Methods
We developed GlyFFPE, a quantitative platform for site-specific N-glycosylation profiling from FFPE archival tissues, and applied it to discovery and independent verification datasets from colorectal cancer liver metastasis (CRLM) and liver hepatocellular carcinoma (LIHC) patients. We then integrated glyco-signatures with clinicopathological variables using an ensemble machine-learning classifier and evaluated performance in a temporal validation cohort. Model robustness and incremental value were assessed using a pathology-only comparator, repeated nested cross-validation, feature-selection stability analysis, and a same-institution temporal cohort.Results
GlyFFPE enabled deep site-specific N-glycoproteome profiling from FFPE tissues and revealed metastasis-associated glyco-signatures characterized by elevated levels of high-mannose glycans on cell-adhesion molecules, prominently CEACAM1 and CEACAM5. The two-glycopeptide panel from CEACAM high-mannose signature showed modest discrimination between CRLM and LIHC in an independent TMT-PRM verification cohort (AUC = 0.73), indicating the need of expanding the targeted panel. The model integrating glyco-signatures with clinicopathological variables substantially improved diagnostic performance, achieving glycoproteomic-assisted discrimination in a same-institution temporal cohort. Repeated nested cross-validation provided a more conservative performance estimate with a mean AUC of 0.92 and a mean AUPRC of 0.96. Feature-selection analysis identified repeatedly selected molecular features, although moderate feature-set variability remained because of the limited cohort size. In addition to classification, GlyFFPE revealed metastasis-associated glycosylation remodeling, including increased high-mannose occupancy on alpha-1-acid glycoprotein 1 (ORM1) and coordinated alterations in extracellular matrix-associated glycoproteins.Conclusion
GlyFFPE establishes an FFPE-compatible workflow for site-specific N-glycosylation profiling and identifies candidate molecular signatures that complement routine pathological assessment for tumor origin evaluation in challenging liver malignancies. Given the modest cohort size and the same-institution temporal evaluation, multicenter, blinded external validation are required before clinical implementation.
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BibTeX
@article{li2026deep,
title = {{Deep N-glycoproteomic profiling of FFPE tissues enables precise diagnosis of primary liver cancer from metastatic liver cancer.}},
author = {Hanjie Li and Ruizhen Bai and Chenyu Huang and Xiuyuan Wang and Zeyang Yu and Biao Zhou and Ye Yuan and Quan Liu and Ganglong Yang and Xiao-Dong Gao},
journal = {Journal of translational medicine},
year = {2026},
month = aug,
doi = {10.1186/s12967-026-08747-1},
url = {https://europepmc.org/article/MED/42958405},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Deep N-glycoproteomic profiling of FFPE tissues enables precise diagnosis of primary liver cancer from metastatic liver cancer.
AU - Hanjie Li
AU - Ruizhen Bai
AU - Chenyu Huang
AU - Xiuyuan Wang
AU - Zeyang Yu
AU - Biao Zhou
AU - Ye Yuan
AU - Quan Liu
AU - Ganglong Yang
AU - Xiao-Dong Gao
PY - 2026
DA - 2026/08/03
JO - Journal of translational medicine
DO - 10.1186/s12967-026-08747-1
UR - https://europepmc.org/article/MED/42958405
AB - Background Reliable tumor origin assessment from routinely archived specimens remains challenging, particularly for diagnostically ambiguous liver malignancies. Although formalin-fixed, paraffin-embedded (FFPE) tissues are widely available, deep and quantitative site-specific N-glycoproteomics of these specimens is limited by formaldehyde-induced crosslinking and incomplete peptide recovery.Methods We developed GlyFFPE, a quantitative platform for site-specific N-glycosylation profiling from FFPE archival tissues, and applied it to discovery and independent verification datasets from colorectal cancer liver metastasis (CRLM) and liver hepatocellular carcinoma (LIHC) patients. We then integrated glyco-signatures with clinicopathological variables using an ensemble machine-learning classifier and evaluated performance in a temporal validation cohort. Model robustness and incremental value were assessed using a pathology-only comparator, repeated nested cross-validation, feature-selection stability analysis, and a same-institution temporal cohort.Results GlyFFPE enabled deep site-specific N-glycoproteome profiling from FFPE tissues and revealed metastasis-associated glyco-signatures characterized by elevated levels of high-mannose glycans on cell-adhesion molecules, prominently CEACAM1 and CEACAM5. The two-glycopeptide panel from CEACAM high-mannose signature showed modest discrimination between CRLM and LIHC in an independent TMT-PRM verification cohort (AUC = 0.73), indicating the need of expanding the targeted panel. The model integrating glyco-signatures with clinicopathological variables substantially improved diagnostic performance, achieving glycoproteomic-assisted discrimination in a same-institution temporal cohort. Repeated nested cross-validation provided a more conservative performance estimate with a mean AUC of 0.92 and a mean AUPRC of 0.96. Feature-selection analysis identified repeatedly selected molecular features, although moderate feature-set variability remained because of the limited cohort size. In addition to classification, GlyFFPE revealed metastasis-associated glycosylation remodeling, including increased high-mannose occupancy on alpha-1-acid glycoprotein 1 (ORM1) and coordinated alterations in extracellular matrix-associated glycoproteins.Conclusion GlyFFPE establishes an FFPE-compatible workflow for site-specific N-glycosylation profiling and identifies candidate molecular signatures that complement routine pathological assessment for tumor origin evaluation in challenging liver malignancies. Given the modest cohort size and the same-institution temporal evaluation, multicenter, blinded external validation are required before clinical implementation.
SN - 1479-5876
ER -