Identification and validation of anoikis-related lncRNAs for prognostic significance and immune microenvironment characterization in ovarian cancer.

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However, the potential roles of anoikis-related long non-coding RNAs (arlncRNAs) in the tumor microenvironment are not well understood. In this study, five candidate lncRNAs were screened through least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis based on differentially expressed lncRNAs associated with anoikis-related genes (ARGs) from TCGA and GSE40595 datasets. The prognostic accuracy of the risk model was evaluated using Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves. Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis (GSEA) analyses revealed significant differences in immune-related hallmarks and signal transduction pathways between the high-risk and low-risk groups. Additionally, immune infiltrate analysis showed significant differences in the distribution of macrophages M2, follicular T helper cells, plasma cells, and neutrophils between the two risk groups. Lastly, silencing the expression of PRR34_AS1 and SPAG5_AS1 significantly increased anoikis-induced cell death in ovarian cancer cells. In conclusion, our study constructed a risk model that can predict clinicopathological features, tumor microenvironment characteristics, and prognosis of ovarian cancer patients. The immune-related pathways identified in this study may offer new treatment strategies for ovarian cancer.", "claims": [{"id": "claim-method", "statement": "Multivariate Cox regression and LASSO Cox regression were employed using the “glmnet” package (v4.1.6) in R to identify five characteristic arlncRNAs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38226979", "locator": "sections[5] \"Construction of lncRNA signature model\" paragraph 2", "quote": "Multivariate Cox regression and LASSO Cox regression were employed using the “glmnet” package (v4.1.6) in R to identify five characteristic arlncRNAs."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data are available from the corresponding author upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38226979", "locator": "sections[15] \"Availability of data and materials\" paragraph 2", "quote": "Data are available from the corresponding author upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study screened five candidate anoikis-related lncRNAs using LASSO and multivariate Cox regression based on differentially expressed anoikis-related lncRNAs from TCGA and GSE40595 datasets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38226979", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "In this study, five candidate lncRNAs were screened through least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis based on differentially expressed lncRNAs associated with anoikis-related genes (ARGs) from TCGA and GSE40595 datasets."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The study screened five candidate anoikis-related lncRNAs using LASSO and multivariate Cox regression based on differentially expressed anoikis-related lncRNAs from TCGA and GSE40595 datasets.", "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:38226979"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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