A Novel Signature Integrated of Immunoglobulin, Glycosylation and Anti-Viral Genes to Predict Prognosis for Breast Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35432482", "kind": "science-record", "revision": 4, "content_hash": "sha256:68cdb91e834569884145e55c6657fbba0165344aa39460922d655c2af57434cd", "created_at": "2026-11-01T18:14:16Z", "updated_at": "2026-11-01T22:06:19Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A Novel Signature Integrated of Immunoglobulin, Glycosylation and Anti-Viral Genes to Predict Prognosis for Breast Cancer.", "doi": "10.3389/fgene.2022.834731", "pmid": "35432482", "authors": ["Shengshan Xu", "Yuchen Liu", "Hansu Ma", "Shuo Fang", "Shoupeng Wei", "Xiaoping Li", "Zhuming Lu", "Youbin Zheng", "Tong Liu", "Xiaojian Zhu", "Dongming Xu", "Yihang Pan"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Background: Aberrant glycosylation is significantly related to the occurrence, progression, metastasis, and drug resistance of tumors. It is essential to identify glycosylation and related genes with prognostic value for breast cancer. Objective: We aimed to construct and validate a prognostic model based on glycosylation and related genes, and further investigate its prognosis values in validation set and external independent cohorts. Materials and Methods: The transcriptome and clinical data of breast cancer patients were downloaded from The Cancer Genome Atlas (TCGA, n = 1072), Molecular Taxonomy of Breast Cancer International Consortium (METABRIC, n = 1451), and GSE2741 (n = 120). Glycosylation-related genes were downloaded from the Genecards website. Differentially expressed glycosylation-related geneswere identified by comparing the tumor tissues with the adjacent tissues. The TCGA data were randomly divided into training set and validation set in a 1:1 ratio for further analysis. The glycosylation risk-scoring prognosis model was constructed by univariate and multivariate Cox regression analysis, followed by confirmation in TCGA validation, METABRIC, and GEO datasets. Gene set enrichment analysis (GSEA) and Gene ontology analysis for identifying the affected pathways in the high- and low-risk groups were performed. Results: We attained 1072 breast cancer samples from the TCGA database and 786 glycosylation genes from the Genecards website. A signature contains immunoglobulin, glycosylation and anti-viral related genes was constructed to separate BRCA patients into two risk groups. Low-risk patients had better overall survival than high-risk patients (p < 0.001). A nomogram was constructed with risk scores and clinical characteristics. The area under time-dependent ROC curve reached 0.764 at 1 year, 0.744 at 3 years, and 0.765 at 5 years in the training set. Subgroup analysis showed differences in OS between the high- and low-risk patients in different subgroups. Moreover, the risk score was confirmed as an independent prognostic indicator of BRCA patients and was potentially correlated with immunotherapy response and drug sensitivity. Conclusion: We identified a novel signature integrated of immunoglobulin (IGHA2), glycosylation-related (SLC35A2) and anti-viral gene (BST2) that was an independent prognostic indicator for BRCA patients. The risk-scoring model could be used for predicting prognosis and immunotherapy in BRCA, thus providing a powerful instrument for combating BRCA.", "claims": [{"id": "claim-method", "statement": "To evaluate the relationship between clinicopathological factors and risk scores on survival time, we used the “Survival” R package to perform univariate and multivariate Cox regression.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35432482", "locator": "sections[5] \"Independent Prognostic Analysis\" paragraph 2", "quote": "To evaluate the relationship between clinicopathological factors and risk scores on survival time, we used the “Survival” R package to perform univariate and multivariate Cox regression."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: The data could be downloaded at (https://www.cancer.gov; http://www.ncbi.nlm.nih.gov/geo; http://www.cbioportal.org; https://www.genecards.org)and the code used during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35432482", "locator": "sections[25] \"Data Availability Statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: The data could be downloaded at (https://www.cancer.gov; http://www.ncbi.nlm.nih.gov/geo; http://www.cbioportal.org; https://www.genecards.org)and the code used during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "In summary, it can be concluded that the aberrant glycosylation-related risk scoring model is a significant independent prognostic factor for BRCA patients.", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "The HR of the risk scores and 95% CI were 2.059 and 1.458–2.906 (p < 0.001), respectively, in univariate Cox regression analysis (Figure 4A), which were 2.049 and 1.389–3.022 (p < 0.001) in multivariate Cox regression analysis, respectively (Figure 4B)."}, "evidence": [{"asset": "source:pmid:35432482", "locator": "sections[16] \"Explore the Independent Prognostic Factors of Breast Cancer\" paragraph 2", "quote": "The HR of the risk scores and 95% CI were 2.059 and 1.458–2.906 (p < 0.001), respectively, in univariate Cox regression analysis (Figure 4A), which were 2.049 and 1.389–3.022 (p < 0.001) in multivariate Cox regression analysis, respectively (Figure 4B)."}, {"asset": "source:pmid:35432482", "locator": "sections[16] \"Explore the Independent Prognostic Factors of Breast Cancer\" paragraph 2", "quote": "Additionally, compared with other clinicopathological factors, the AUC of the risk score for 1-year OS shown by ROC analysis reached 0.759, which was superior to other clinicopathological variables (Figure 4C)."}, {"asset": "source:pmid:35432482", "locator": "sections[16] \"Explore the Independent Prognostic Factors of Breast Cancer\" paragraph 2", "quote": "In summary, it can be concluded that the aberrant glycosylation-related risk scoring model is a significant independent prognostic factor for BRCA patients."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "In summary, it can be concluded that the aberrant glycosylation-related risk scoring model is a significant independent prognostic factor for BRCA patients.", "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:35432482"], "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_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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