A novel risk score model based on gamma-aminobutyric acid signature predicts the survival prognosis of patients with breast cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36969015", "kind": "science-record", "revision": 3, "content_hash": "sha256:c0570b260a0f56e6fdfbe31c7db548857747c0ada32b32fd3f6d91049235ed87", "created_at": "2026-11-01T19:01:36Z", "updated_at": "2026-11-01T22:09:07Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A novel risk score model based on gamma-aminobutyric acid signature predicts the survival prognosis of patients with breast cancer.", "doi": "10.3389/fonc.2023.1108823", "pmid": "36969015", "authors": ["Liping Yang", "Jin Zhu", "Lieliang Wang", "Longbo He", "Yi Gong", "Qingfeng Luo"], "journal": "Frontiers in oncology", "year": 2023, "abstract": "Background: Gamma-aminobutyric acid (GABA) participates in the migration, differentiation, and proliferation of tumor cells. However, the GABA-related risk signature has never been investigated. Hence, we aimed to develop a reliable gene signature based on GABA pathways-related genes (GRGs) to predict the survival prognosis of breast cancer patients.\nMethods: GABA-related gene sets were acquired from the MSigDB database, while mRNA gene expression profiles and corresponding clinical data of breast cancer patients were downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Univariate Cox regression analysis was used to identify prognostic-associated GRGs. Subsequently, LASSO analysis was applied to establish a risk score model. We also constructed a clinical nomogram to perform the survival evaluation. Besides, ESTIMATE and ssGSEA algorithms were used to assess the immune cell infiltration among the risk score subgroups.\nResults: A GRGs-related risk score model was constructed in the TCGA cohort, and validated in the GSE21653 cohort. The risk score was significantly related to the overall survival of breast cancer patients, which could predict the survival prognosis of breast cancer patients independently of other clinical features. Breast cancer patients in the low-risk score group exhibited higher immune cell infiltration levels.\nConclusion: A novel prognostic model containing five GRGs could accurately predict the survival prognosis and immune infiltration of breast cancer patients. Our findings provided a novel insight into investigating the immunoregulation roles of GRGs.", "claims": [{"id": "claim-method", "statement": "Subsequently, we used the “survival” and “glmnet” packages of R to carry out the least absolute shrinkage and selection operator (LASSO) Cox regression analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36969015", "locator": "sections[6] \"Establishment of the prognostic risk score model based on DEGRGs\" paragraph 2", "quote": "Subsequently, we used the “survival” and “glmnet” packages of R to carry out the least absolute shrinkage and selection operator (LASSO) Cox regression analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material .", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36969015", "locator": "sections[21] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material ."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A GRGs-related risk score model was constructed in the TCGA cohort, and validated in the GSE21653 cohort.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36969015", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A GRGs-related risk score model was constructed in the TCGA cohort, and validated in the GSE21653 cohort."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A GRGs-related risk score model was constructed in the TCGA cohort, and validated in the GSE21653 cohort.", "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:36969015"], "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:GSE21653"], "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:GSE21653"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36969015", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2023.1108823", "sha256": "sha256:88a33c45d7abf7e60ab0b4e26b0605e93ef99f0daab45bc827e0b3071f22ba7d", "bytes": 36536, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fonc.2023.1108823", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10031029", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE21653", "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=GSE21653", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE21653", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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