Quantitative site-specific N-glycosylation analysis reveals IgG glyco-signatures for pancreatic cancer diagnosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39734184", "kind": "science-record", "revision": 2, "content_hash": "sha256:959c73f034f30097300a7c616a516f166723e01c0193586b54a927bf4ab292e2", "created_at": "2026-11-01T20:30:03Z", "updated_at": "2026-11-01T21:34:37Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Quantitative site-specific N-glycosylation analysis reveals IgG glyco-signatures for pancreatic cancer diagnosis.", "doi": "10.1186/s12014-024-09522-4", "pmid": "39734184", "authors": ["Yi Jin", "Ran Hu", "Yufan Gu", "Ailin Wei", "Ang Li", "Yong Zhang"], "journal": "Clinical proteomics", "year": 2024, "abstract": "BACKGROUND: Pancreatic cancer is a highly aggressive tumor with a poor prognosis due to a low early detection rate and a lack of biomarkers. Most of pancreatic cancer is pancreatic ductal adenocarcinoma (PDAC). Alterations in the N-glycosylation of plasma immunoglobulin G (IgG) have been shown to be closely associated with the onset and development of several cancers and could be used as biomarkers for diagnosis. The study aimed to explore intact N-glycosylation profile of IgG in patients with PDAC and find relation between intact N-glycosylation profile of IgG and clinical information such as diagnosis and prognosis.\nMETHODS: In this study, we employed a well-evaluated approach (termed GlycoQuant) to assess the site-specific N-glycosylation profile of human plasma IgG in both healthy individuals and patients with pancreatic ductal adenocarcinoma (PDAC). The datasets generated and analyzed during the current study are available in the ProteomeXchange Consortium ( http://www.proteomexchange.org/ ) via the iProX partner repository, with the dataset identifier PXD051436.\nRESULTS: The analysis of rapidly purified IgG samples from 100 patients with different stages of PDAC, in addition to 30 healthy controls, revealed that the combination of carbohydrate antigen 19 - 9 (CA19-9), IgG1-GP05 (IgG1-TKPREEQYNSTYR-HexNAc [4]Hex [5]Fuc [1]NeuAc [1]), and IgG4-GP04 (IgG4-EEQFNSTYR- HexNAc [4]Hex [5]Fuc [1]NeuAc [1]) can be used to distinguish between PDAC patients and healthy individuals (AUC = 0.988). In addition, cross validation of the diagnosis model showed satisfactory result.\nCONCLUSIONS: The study demonstrated that the integrated quantitative method can be utilized for large-scale clinical N-glycosylation research to identify novel N-glycosylated biomarkers. This could facilitate the development of clinical glycoproteomics.", "claims": [{"id": "claim-method", "statement": "Binary logistic regression was used to perform univariate and multivariate analyses in SPSS (v.27) to establish a diagnostic model for PDAC patients compared to HC. Besides, K-fold cross validation was used to evaluate the model (k = 10). Subsequently, the multivariate diagnosis of pancreatic cancer was performed using receiver operating characteristic (ROC) analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39734184", "locator": "sections[8] \"Data analysis and bioinformatics\" paragraph 4", "quote": "Binary logistic regression was used to perform univariate and multivariate analyses in SPSS (v.27) to establish a diagnostic model for PDAC patients compared to HC. Besides, K-fold cross validation was used to evaluate the model (k = 10). Subsequently, the multivariate diagnosis of pancreatic cancer was performed using receiver operating characteristic (ROC) analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The RAW data and search results were deposited in the ProteomeXchange Consortium via the iProX partner repository, with the dataset identifier PXD051436.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39734184", "locator": "sections[22] \"Data availability\" paragraph 2", "quote": "The RAW data and search results were deposited in the ProteomeXchange Consortium via the iProX partner repository, with the dataset identifier PXD051436."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The combined CA19-9, IgG1-GP05, and IgG4-GP04 diagnostic model distinguished PDAC patients from healthy individuals with an AUC of 0.988.", "kind": "quantitative", "target": {"value": 0.988, "unit": "auc", "tolerance": null, "source": "The analysis of rapidly purified IgG samples from 100 patients with different stages of PDAC, in addition to 30 healthy controls, revealed that the combination of carbohydrate antigen 19 − 9 (CA19-9), IgG1-GP05 (IgG1-TKPREEQYNSTYR-HexNAc [4]Hex [5]Fuc [1]NeuAc [1]), and IgG4-GP04 (IgG4-EEQFNSTYR- HexNAc [4]Hex [5]Fuc [1]NeuAc [1]) can be used to distinguish between PDAC patients and healthy individuals (AUC = 0.988)."}, "evidence": [{"asset": "source:pmid:39734184", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The analysis of rapidly purified IgG samples from 100 patients with different stages of PDAC, in addition to 30 healthy controls, revealed that the combination of carbohydrate antigen 19 − 9 (CA19-9), IgG1-GP05 (IgG1-TKPREEQYNSTYR-HexNAc [4]Hex [5]Fuc [1]NeuAc [1]), and IgG4-GP04 (IgG4-EEQFNSTYR- HexNAc [4]Hex [5]Fuc [1]NeuAc [1]) can be used to distinguish between PDAC patients and healthy individuals (AUC = 0.988)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The combined CA19-9, IgG1-GP05, and IgG4-GP04 diagnostic model distinguished PDAC patients from healthy individuals with an AUC of 0.988.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:39734184"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_unresolved", "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:PXD051436"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:PXD051436"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_unresolved"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39734184", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12014-024-09522-4", "sha256": "sha256:2b3e47030b24d73c4f1c300a6cde4cdcf8502b907f8b453201be47731106d9e8", "bytes": 42311, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12014-024-09522-4", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11684065", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:PXD051436", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=PXD051436", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "PXD051436", "version": null, "note": "The RAW data and search results were deposited in the ProteomeXchange Consortium via the iProX partner repository, with the dataset identifier PXD051436.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The RAW data and search results were deposited in the ProteomeXchange Consortium via the iProX partner repository, with the dataset identifier PXD051436.", "access_statement_kind": "data_availability_statement", "sample_accessions": [], "sample_count": null, "group_sizes": null, "group_sizes_basis": null, "platform": null, "taxon": null, "title": null, "resolution_checked_at": "2026-11-01T20:16:17Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:PXD051436 not downloaded", "asset:data:geo:PXD051436 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "regression_modelling", "specific": "logistic", "label": "Regression modelling", "dsl_operations": ["glm_logit_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictors_and_outcome"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Binary logistic regression was used to perform univariate and multivariate analyses in SPSS (v.27) to establish a diagnostic model for PDAC patients compared to HC. 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