Two predicted models based on ceRNAs and immune cells in lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33828913", "kind": "science-record", "revision": 8, "content_hash": "sha256:50041ce2383b4c34eba76c36ba51c0af97ab201eba054fd77fb378a979b8dd3e", "created_at": "2026-11-01T15:41:31Z", "updated_at": "2026-11-01T22:46:28Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Two predicted models based on ceRNAs and immune cells in lung adenocarcinoma.", "doi": "10.7717/peerj.11029", "pmid": "33828913", "authors": ["Miaomiao Zhang", "Peiyan Zheng", "Yuan Wang", "Baoqing Sun"], "journal": "PeerJ", "year": 2021, "abstract": "BACKGROUND: It is well accepted that both competitive endogenous RNAs (ceRNAs) and immune microenvironment exert crucial roles in the tumor prognosis. The present study aimed to find prognostic ceRNAs and immune cells in lung adenocarcinoma (LUAD).\nMATERIALS AND METHODS: More specifically, we explored the associations of crucial ceRNAs with the immune microenvironment. The Cancer Genome Atlas (TCGA) database was employed to obtain expression profiles of ceRNAs and clinical data. CIBERSORT was utilized to quantify the proportion of 22 immune cells in LUAD.\nRESULTS: We constructed two cox regression models based on crucial ceRNAs and immune cells to predict prognosis in LUAD. Subsequently, seven ceRNAs and seven immune cells were involved in prognostic models. We validated both predicted models via an independent cohort GSE72094. Interestingly, both predicted models proved that the longer patients were smoking, the higher risk scores would be obtained. We further investigated the relationships between seven genes and immune/stromal scores via the ESTIMATE algorithm. The results indicated that CDC14A and H1F0 expression were significantly related to stromal scores/immune scores in LUAD. Moreover, based on the result of the ceRNA model, single-sample gene set enrichment analysis (ssGSEA) suggested that differences in immune status were evident between high- and low-risk groups.", "claims": [{"id": "claim-method", "statement": "The R package “DEseq2” was used to screen the differentially expressed mRNAs,miRNAs and lncRNAs (DEmRNA, DEmiRNA and DElncRNA) between normal and tumor samples (Love, Huber & Anders, 2014).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33828913", "locator": "sections[3] \"Identification of differentially expressed ceRNAs\" paragraph 2", "quote": "The R package “DEseq2” was used to screen the differentially expressed mRNAs,miRNAs and lncRNAs (DEmRNA, DEmiRNA and DElncRNA) between normal and tumor samples (Love, Huber & Anders, 2014)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All raw data are available at TCGA and GEO. TCGA IDs and sample GEO accession numbers are available in the Supplemental Files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33828913", "locator": "sections[21] \"Additional Information and Declarations\" paragraph 4", "quote": "All raw data are available at TCGA and GEO. 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This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE72094"], "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:GSE72094"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33828913", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.11029", "sha256": "sha256:bf91acb5ee689d8a376d635ce7b57d6ada346846eacf79b7dc50f5307169dfc9", "bytes": 30508, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.7717/peerj.11029", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7996073", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE72094", "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=GSE72094", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE72094", "version": null, "note": "All raw data are available at TCGA and GEO. 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