Identification of a novel autophagy signature for predicting survival in patients with lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33976960", "kind": "science-record", "revision": 7, "content_hash": "sha256:318bc36060048e45837cd2db4366f70afa00c63b7b88e5210e677eaace9e60e6", "created_at": "2026-11-01T15:41:35Z", "updated_at": "2026-11-01T22:03:43Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a novel autophagy signature for predicting survival in patients with lung adenocarcinoma.", "doi": "10.7717/peerj.11074", "pmid": "33976960", "authors": ["Jin Duan", "Youming Lei", "Guoli Lv", "Yinqiang Liu", "Wei Zhao", "Qingmei Yang", "Xiaona Su", "Zhijian Song", "Leilei Lu", "Yunfei Shi"], "journal": "PeerJ", "year": 2021, "abstract": "BACKGROUND: Lung adenocarcinoma (LUAD) is the most commonhistological lung cancer subtype, with an overall five-year survivalrate of only 17%. In this study, we aimed to identify autophagy-related genes (ARGs) and develop an LUAD prognostic signature.\nMETHODS: In this study, we obtained ARGs from three databases and downloaded gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used TCGA-LUAD (n = 490) for a training and testing dataset, and GSE50081 (n = 127) as the external validation dataset.The least absolute shrinkage and selection operator (LASSO) Cox and multivariate Cox regression models were used to generate an autophagy-related signature. We performed gene set enrichment analysis (GSEA) and immune cell analysis between the high- and low-risk groups. A nomogram was built to guide the individual treatment for LUAD patients.\nRESULTS: We identified a total of 83 differentially expressed ARGs (DEARGs) from the TCGA-LUAD dataset, including 33 upregulated DEARGs and 50 downregulated DEARGs, both with thresholds of adjusted P < 0.05 and |Fold change| > 1.5. Using LASSO and multivariate Cox regression analyses, we identified 10 ARGs that we used to build a prognostic signature with areas under the curve (AUCs) of 0.705, 0.715, and 0.778 at 1, 3, and 5 years, respectively. Using the risk score formula, the LUAD patients were divided into low- or high-risk groups. Our GSEA results suggested that the low-risk group were enriched in metabolism and immune-related pathways, while the high-risk group was involved in tumorigenesis and tumor progression pathways. Immune cell analysis revealed that, when compared to the high-risk group, the low-risk group had a lower cell fraction of M0- and M1- macrophages, and higher CD4 and PD-L1 expression levels.\nCONCLUSION: Our identified robust signature may provide novel insight into underlying autophagy mechanisms as well as therapeutic strategies for LUAD treatment.", "claims": [{"id": "claim-method", "statement": "The least absolute shrinkage and selection operator (LASSO) Cox and multivariate Cox regression models were used to generate an autophagy-related signature.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33976960", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The least absolute shrinkage and selection operator (LASSO) Cox and multivariate Cox regression models were used to generate an autophagy-related signature."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The following information was supplied regarding data availability:\nData are available at the Gene Expression Omnibus (GEO) database: GSE50081. Data is also available at The Cancer Genome Atlas (TCGA): TCGA-LUAD.\nOur retrieval strategy for TCGA dataset is as follows: in the Cases section, we chose bronchus and lung as Primary Site, TCGA as Program, TCGA-LUAD as Project. In the Files section, we chose transcriptome profiling as Data Category, Gene Expression Quantification as Data Type, RNA-Seq as Experimental Strategy, HTSeq - FPKM as Workflow Type.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33976960", "locator": "sections[31] \"Data Availability Statement\" paragraphs 1-4", "quote": "The following information was supplied regarding data availability:\nData are available at the Gene Expression Omnibus (GEO) database: GSE50081. Data is also available at The Cancer Genome Atlas (TCGA): TCGA-LUAD.\nOur retrieval strategy for TCGA dataset is as follows: in the Cases section, we chose bronchus and lung as Primary Site, TCGA as Program, TCGA-LUAD as Project. In the Files section, we chose transcriptome profiling as Data Category, Gene Expression Quantification as Data Type, RNA-Seq as Experimental Strategy, HTSeq - FPKM as Workflow Type."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:33976960"], "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:GSE50081"], "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:GSE50081"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33976960", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.11074", "sha256": "sha256:6279feb857ae9d3756508c80645ac516bab909e8b8b0be1e6cac65ee600dd2bf", "bytes": 46180, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.7717/peerj.11074", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC8067911", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE50081", "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=GSE50081", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE50081", "version": null, "note": "The following information was supplied regarding data availability:\nData are available at the Gene Expression Omnibus (GEO) database: GSE50081. Data is also available at The Cancer Genome Atlas (TCGA): TCGA-LUAD.\nOur retrieval strategy for TCGA dataset is as follows: in the Cases section, we chose bronchus and lung as Primary Site, TCGA as Program, TCGA-LUAD as Project. In the Files section, we chose transcriptome profiling as Data Category, Gene Expression Quantification as Data Type, RNA-Seq as Experimental Strategy, HTSeq - FPKM as Workflow Type.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The following information was supplied regarding data availability:\nData are available at the Gene Expression Omnibus (GEO) database: GSE50081. Data is also available at The Cancer Genome Atlas (TCGA): TCGA-LUAD.\nOur retrieval strategy for TCGA dataset is as follows: in the Cases section, we chose bronchus and lung as Primary Site, TCGA as Program, TCGA-LUAD as Project. 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