A glycolysis-related signature to improve the current treatment and prognostic evaluation for breast cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39119106", "kind": "science-record", "revision": 3, "content_hash": "sha256:59d5ed0da1a7791b1792cdc889d1e05ebb168baa4ae44c922561e047ff1481fa", "created_at": "2026-11-01T20:29:12Z", "updated_at": "2026-11-01T22:12:25Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A glycolysis-related signature to improve the current treatment and prognostic evaluation for breast cancer.", "doi": "10.7717/peerj.17861", "pmid": "39119106", "authors": ["Sijie Feng", "Linwei Ning", "Huizhen Zhang", "Zhenhui Wang", "Yunkun Lu"], "journal": "PeerJ", "year": 2024, "abstract": "Background: As a heterogeneous malignancy, breast cancer (BRCA) shows high incidence and mortality. Discovering novel molecular markers and developing reliable prognostic models may improve the survival of BCRA.\nMethods: The RNA-seq data of BRCA patients were collected from the training set The Cancer Genome Atlas (TCGA)-BRCA and validation set GSE20685 in the Gene Expression Omnibus (GEO) databases. The \"GSVA\" R package was used to calculate the glycolysis score for each patient, based on which all the patients were divided into different glycolysis groups. The \"limma\" package was employed to perform differentially expression genes (DEGs) analysis. Key signature genes were selected by performing un/multivariate and least absolute shrinkage and selection operator (LASSO) C regression and used to develop a RiskScore model. The ESTIMATE and MCP-Counter algorithms were used for quantifying immune infiltration level. The functions of the genes were validated using Western blot, colony formation, transwell and wound-healing assay.\nResults: The glycolysis score and prognostic analysis showed that high glycolysis score was related to tumorigenesis pathway and a poor prognosis in BRCA as overactive glycolysis inhibited the normal functions of immune cells. Subsequently, we screened five key prognostic genes using the LASSO Cox regression analysis and used them to establish a RiskScore with a high classification efficiency. Based on the results of the RiskScore, it was found that patients in the high-risk group had significantly unfavorable immune infiltration and prognostic outcomes. A nomogram integrating the RiskScore could well predict the prognosis for BRCA patients. Knockdown of PSCA suppressed cell proliferation, invasion and migration of BRCA cells.\nConclusion: This study developed a glycolysis-related signature with five genes to distinguish between high-risk and low-risk BRCA patients. A nomogram developed on the basis of the RiskScore was reliable to predict BRCA survival. Our model provided clinical guidance for the treatment of BRCA patients.", "claims": [{"id": "claim-method", "statement": "LASSO Cox regression with 10-fold cross-validation to progressively reduce the number of key genes in “glmnet” package (Wang et al., 2020). Subsequently, multivariate Cox regression analysis (Liu et al., 2021) was conducted to formulate a glycolysis-related RiskScore", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39119106", "locator": "sections[7] \"Development of a glycolysis-related prognostic model\" paragraph 2", "quote": "LASSO Cox regression with 10-fold cross-validation to progressively reduce the number of key genes in “glmnet” package (Wang et al., 2020). Subsequently, multivariate Cox regression analysis (Liu et al., 2021) was conducted to formulate a glycolysis-related RiskScore"}], "state": "complete"}, {"id": "claim-data-access", "statement": "The following information was supplied regarding data availability:The dataset generated and/or analyzed during the current study are available at GEO: GSE20685.The raw data is available at GitHub and Zenodo:- https://github.com/KevenLoo/My-New-Raw-Data.git.- KevenLoo. (2024). KevenLoo/My-New-Raw-Data: Raw data (v1.1.0). Zenodo. https://doi.org/10.5281/zenodo.11078791.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39119106", "locator": "sections[25] \"Additional Information and Declarations\" paragraph 4", "quote": "The following information was supplied regarding data availability:The dataset generated and/or analyzed during the current study are available at GEO: GSE20685.The raw data is available at GitHub and Zenodo:- https://github.com/KevenLoo/My-New-Raw-Data.git.- KevenLoo. (2024). KevenLoo/My-New-Raw-Data: Raw data (v1.1.0). Zenodo. https://doi.org/10.5281/zenodo.11078791."}], "state": "complete"}, {"id": "claim-target-result", "statement": "ROC analysis demonstrated that the AUC value of 1-, 3- and 5-year survival was 0.75, 0.71 and 0.65, respectively (Fig. 3D), showing a strong classification performance of the RiskScore.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39119106", "locator": "sections[16] \"Construction of a glycolysis-related risk model\" paragraph 2", "quote": "ROC analysis demonstrated that the AUC value of 1-, 3- and 5-year survival was 0.75, 0.71 and 0.65, respectively (Fig. 3D), showing a strong classification performance of the RiskScore."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "ROC analysis demonstrated that the AUC value of 1-, 3- and 5-year survival was 0.75, 0.71 and 0.65, respectively (Fig. 3D), showing a strong classification performance of the RiskScore.", "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:39119106"], "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": "complete", "blocked_on": null, "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:GSE20685"], "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:GSE20685"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39119106", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.17861", "sha256": "sha256:df28ecbcaf368cd699ffe07155807a1b075cb6580b3cd70a41c01ffe57f5fbf7", "bytes": 36607, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.7717/peerj.17861", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11308995", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE20685", "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=GSE20685", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE20685", "version": null, "note": "The following information was supplied regarding data availability:The dataset generated and/or analyzed during the current study are available at GEO: GSE20685.The raw data is available at GitHub and Zenodo:- https://github.com/KevenLoo/My-New-Raw-Data.git.- KevenLoo. 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