Autophagy-related prognostic signature for survival prediction of triple negative breast cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35186475", "kind": "science-record", "revision": 4, "content_hash": "sha256:8e8eceb83e9fdaf66dc91784ee7cdabb20a583446ecb625096d5b709817f5437", "created_at": "2026-11-01T17:36:57Z", "updated_at": "2026-11-01T22:05:56Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Autophagy-related prognostic signature for survival prediction of triple negative breast cancer.", "doi": "10.7717/peerj.12878", "pmid": "35186475", "authors": ["Qiong Yang", "Kewang Sun", "Wenjie Xia", "Ying Li", "Miaochun Zhong", "Kefeng Lei"], "journal": "PeerJ", "year": 2022, "abstract": "BACKGROUND: Triple-negative breast cancer (TNBC) is a highly aggressive type of cancer with few available treatment methods. The aim of the current study was to provide a prognostic autophagy-related gene (ARG) model to predict the outcomes for TNBC patients using bioinformatic analysis.\nMETHODS: mRNA expression data and its clinical information for TNBC samples obtained from The Cancer Genome Atlas (TCGA) and Metabric databases were extracted for bioinformatic analysis. Differentially expressed autophagy genes were identified using the Wilcoxon rank sum test in R software. ARGs were downloaded from the Human Autophagy Database. The Kaplan-Meier plotter was employed to determine the prognostic significance of the ARGs. The sample splitting method and Cox regression analysis were employed to establish the risk model and to demonstrate the association between the ARGs and the survival duration. The corresponding ARG-transcription factor interaction network was visualized using the Cytoscape software.\nRESULTS: A signature-based risk score model was established for eight genes (ITGA3, HSPA8, CTSD, ATG12, CLN3, ATG7, MAP1LC3C, and WIPI1) using the TCGA data and the model was validated with the GSE38959 and Metabric datasets, respectively. Patients with high risk scores had worse survival outcomes than those with low risk scores. Of note, amplification of ATG12 and reduction of WIPI were confirmed to be significantly correlated with the clinical stage of TNBC.\nCONCLUSION: An eight-gene autophagic signature model was developed in this study to predict the survival risk for TNBC. The genes identified in the study may favor the design of target agents for autophagy control in advanced TNBC.", "claims": [{"id": "claim-method", "statement": "To determine the genes that could be used for survival prediction, the correlations between the survival information for TNBC and the expression of autophagy-related genes were analyzed using the univariate Cox proportional regression model (O’Quigley & Moreau, 1986) with P < 0.05 set as the significance threshold. To prevent over-fitting, LASSO regression analysis was conducted to screen out autophagy genes significantly related to the prognosis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35186475", "locator": "sections[5] \"Construction of risk score model for survival\" paragraph 2", "quote": "To determine the genes that could be used for survival prediction, the correlations between the survival information for TNBC and the expression of autophagy-related genes were analyzed using the univariate Cox proportional regression model (O’Quigley & Moreau, 1986) with P < 0.05 set as the significance threshold. To prevent over-fitting, LASSO regression analysis was conducted to screen out autophagy genes significantly related to the prognosis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data is available in the Supplementary File.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35186475", "locator": "sections[21] \"Additional Information and Declarations\" paragraph 5", "quote": "The raw data is available in the Supplementary File."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Patients with high risk scores had worse survival outcomes than those with low risk scores.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35186475", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Patients with high risk scores had worse survival outcomes than those with low risk scores."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Patients with high risk scores had worse survival outcomes than those with low risk scores.", "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:35186475"], "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:GSE38959"], "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:GSE38959"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35186475", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.12878", "sha256": "sha256:7f6e46f3083eb616e9777f46188255c794aaf8fbcc114d68c2d1139af0510b6b", "bytes": 28303, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.7717/peerj.12878", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8840057", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE38959", "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=GSE38959", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE38959", "version": null, "note": "The raw data is available in the Supplementary File.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The raw data is available in the Supplementary File.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM952871", "GSM952872", "GSM952873", "GSM952874", "GSM952875", "GSM952876", "GSM952877", "GSM952878", "GSM952879", "GSM952880", "GSM952881", "GSM952882", "GSM952883", "GSM952884", "GSM952885", "GSM952886", "GSM952887", "GSM952888", "GSM952889", "GSM952890", "GSM952891", "GSM952892", "GSM952893", "GSM952894", "GSM952895", "GSM952896", "GSM952897", "GSM952898", "GSM952899", "GSM952900", "GSM952901", "GSM952902", "GSM952903", "GSM952904", "GSM952905", "GSM952906", "GSM952907", "GSM952908", "GSM952909", "GSM952910", "GSM952911", "GSM952912", "GSM952913", "GSM952914", "GSM952915", "GSM952916", "GSM952917"], "sample_count": 47, "group_sizes": null, "group_sizes_basis": null, "platform": "4133", "taxon": "Homo sapiens", "title": "Gene expression profiling of triple negative breast cancer, normal ductal cells, and normal tissues", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE38959 not downloaded", "asset:data:geo:GSE38959 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "To determine the genes that could be used for survival prediction, the correlations between the survival information for TNBC and the expression of autophagy-related genes were analyzed using the univariate Cox proportional regression model (O’Quigley & Moreau, 1986) with P < 0.05 set as the significance threshold. 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