Autophagy-related long non-coding RNA prognostic model predicts prognosis and survival of melanoma patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35611195", "kind": "science-record", "revision": 3, "content_hash": "sha256:7b90e4576d218e87bd23222900581a38531aff27198373fc5ca9a0052defd3ae", "created_at": "2026-11-01T18:14:31Z", "updated_at": "2026-11-01T21:27:56Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Autophagy-related long non-coding RNA prognostic model predicts prognosis and survival of melanoma patients.", "doi": "10.12998/wjcc.v10.i11.3334", "pmid": "35611195", "authors": ["Yue Qiu", "Hong-Tao Wang", "Xi-Fan Zheng", "Xing Huang", "Jin-Zhi Meng", "Jun-Pu Huang", "Zhen-Pei Wen", "Jun Yao"], "journal": "World journal of clinical cases", "year": 2022, "abstract": "BACKGROUND: Melanomas are malignant tumors that can occur in different body parts or tissues such as the skin, mucous membrane, uvea, and pia mater. Long non-coding RNAs (lncRNAs) are key factors in the occurrence and development of many malignant tumors, and are involved in the prognosis of some patients.\nAIM: To identify autophagy-related lncRNAs in melanoma that are crucial for the diagnosis, treatment, and prognosis of melanoma patients.\nMETHODS: We retrieved transcriptome expression profiles and clinical information of 470 melanoma patients from The Cancer Genome Atlas (TCGA) database. Then, we identified autophagy-related genes in the Human Autophagy Database. Using R, coexpression analysis of lncRNAs and autophagy-related genes was conducted to obtain autophagy-related lncRNAs and their expression levels. We also performed univariate and multivariate Cox proportional risk analyses on the obtained datasets, to systematically evaluate the prognostic value of autophagy-related lncRNAs in melanoma. Fifteen autophagy-related lncRNAs were identified and an autophagy-related prognostic signature for melanoma was established. The Kaplan-Meier and univariate and multivariate Cox regression analyses were used to calculate risk scores. Based on the risk scores, melanoma patients were randomly divided into high- and low-risk groups. Receiver operating characteristic curve analysis, dependent on time, was performed to assess the accuracy of the prognostic model. At the same time, we also downloaded the melanoma data sets GSE65904, GSE19234, and GSE78220 from the GENE EXPRESSION OMNIBUS database for model verification. Finally, we performed Gene Set Enrichment Analysis functional annotation, which showed that the low and the high-risk groups had different enriched pathways.\nRESULTS: The co-expression network for autophagy-related genes was constructed using R, and 936 lncRNAs related to autophagy were identified. Then, 52 autophagy-related lncRNAs were significantly associated with TCGA melanoma patients' survival by univariate Cox proportional risk analysis (P < 0.01). Further, the 52 autophagy-related lncRNAs mentioned above were analyzed by multivariate Cox analysis with R. Fifteen lncRNAs were selected: LINC01943, AC090948.3, USP30-AS1, AC068282.1, AC004687.1, AL133371.2, AC242842.1, PCED1B-AS1, HLA-DQB1-AS1, AC011374.2, LINC00324, AC018553.1, LINC00520, DBH-AS1, and ITGB2-AS1. The P values in all survival analyses using these 15 lncRNAs were < 0.05. These lncRNAs were used to build a risk model based on the risk score. Negative correlations were observed between risk scores and overall survival rate in melanoma patients over time. Additionally, the melanoma risk curve and scatter plot analyses showed that the death number increased along with the increase in the risk score. Overall, we identified and established a new prognostic risk model for melanoma using 15 autophagy-related lncRNAs. The risk model constructed with these lncRNAs can help and guide melanoma patient prognosis predictions and individualized treatments in the future.\nCONCLUSION: Overall, the risk model developed based on the 15 autophagy-related lncRNAs can have important prognostic value and may provide autophagy-related clinical targets for melanoma treatment.", "claims": [{"id": "claim-method", "statement": "We also performed univariate and multivariate Cox proportional risk analyses on the obtained datasets, to systematically evaluate the prognostic value of autophagy-related lncRNAs in melanoma.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35611195", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We also performed univariate and multivariate Cox proportional risk analyses on the obtained datasets, to systematically evaluate the prognostic value of autophagy-related lncRNAs in melanoma."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data sharing statement: No additional data are available.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35611195", "locator": "sections[23] \"Footnotes\" paragraph 2", "quote": "Data sharing statement: No additional data are available."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Overall, we identified and established a new prognostic risk model for melanoma using 15 autophagy-related lncRNAs.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35611195", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Overall, we identified and established a new prognostic risk model for melanoma using 15 autophagy-related lncRNAs."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Overall, we identified and established a new prognostic risk model for melanoma using 15 autophagy-related lncRNAs.", "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:35611195"], "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:GSE19234"], "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:GSE19234"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35611195", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.12998/wjcc.v10.i11.3334", "sha256": 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