Identification of potential autophagy-associated lncRNA in prostate cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33971627", "kind": "science-record", "revision": 9, "content_hash": "sha256:c750ea8ceb2232a00dd90687a4b128c4526246d8555807304b3cb94ddf508eaa", "created_at": "2026-11-01T15:41:35Z", "updated_at": "2026-11-01T22:46:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of potential autophagy-associated lncRNA in prostate cancer.", "doi": "10.18632/aging.202997", "pmid": "33971627", "authors": ["Jun Li", "Hong Du", "Wenqiang Chen", "Mingxing Qiu", "Peng He", "Zhiwei Ma"], "journal": "Aging", "year": 2021, "abstract": "BACKGROUND: Long non-coding RNAs (lncRNAs) have been linked to autophagy. It is urgent to identify and assess the hub autophagy-associated lncRNA in prostate cancer.\nMETHODS: Differentially expressed lncRNAs associated with autophagy were identified in prostate cancer based on The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) data. An autophagy-mediated competing endogenous RNA network was constructed to screen for autophagy-associated lncRNA, and the preselected lncRNAs were further validated using Gene Expression Omnibus (GEO) datasets. Furthermore, a prognostic lncRNA signature was established and assessed. Additionally, Gene Set Enrichment Analysis (GSEA) revealed the underlying molecular mechanisms.\nRESULTS: Using a competing endogenous RNA network, 66 differentially expressed lncRNAs associated with autophagy were identified, and the differential expression of 7 lncRNAs were verified using the TCGA-PRAD, GSE21034, and GSE94767 datasets. Additionally, a lncRNA signature associated with autophagy, including MKNK1-AS1 and INE1, was identified as an independent indicator of survival with a C-index of 0.882. The GSEA analysis indicated that several autophagy-related signaling pathways were enriched in different risk groups.\nCONCLUSIONS: The lncRNAs associated with autophagy were identified, and a prediction model was developed that could be used as a prognostic predictor for prostate cancer, indicating the critical role of lncRNA in the regulation of prostate cancer autophagy regulation.", "claims": [{"id": "claim-method", "statement": "The differentially expressed mRNAs (DEmRNAs), differentially expressed miRNAs (DEmiRNAs), and DElncRNAs between PCa tissues and paracancerous tissues were analyzed using the limma package of R 4.0.2 software https://www.r-project.org/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33971627", "locator": "sections[13] \"DEmRNAs, DEmiRNAs, and DElncRNAs in PCa\" paragraph 2", "quote": "The differentially expressed mRNAs (DEmRNAs), differentially expressed miRNAs (DEmiRNAs), and DElncRNAs between PCa tissues and paracancerous tissues were analyzed using the limma package of R 4.0.2 software https://www.r-project.org/)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data on RNA-sequencing (RNA-seq) and corresponding clinical information were derived from The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD, https://portal.gdc.cancer.gov/) [40].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33971627", "locator": "sections[12] \"Patient data extraction\" paragraph 2", "quote": "Data on RNA-sequencing (RNA-seq) and corresponding clinical information were derived from The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD, https://portal.gdc.cancer.gov/) [40]."}], "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:33971627"], "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:GSE21034"], "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:GSE21034"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33971627", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.18632/aging.202997", "sha256": 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