Identification of 4 immune cells and a 5-lncRNA risk signature with prognosis for early-stage lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33771173", "kind": "science-record", "revision": 6, "content_hash": "sha256:f3ddb8869a875b8f5cec4ef36bf375d789c633e02cd15d3a156ab0c9aeb50c0c", "created_at": "2026-11-01T15:41:30Z", "updated_at": "2026-11-01T21:24:59Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of 4 immune cells and a 5-lncRNA risk signature with prognosis for early-stage lung adenocarcinoma.", "doi": "10.1186/s12967-021-02800-x", "pmid": "33771173", "authors": ["Lan Mu", "Ke Ding", "Ranran Tu", "Wei Yang"], "journal": "Journal of translational medicine", "year": 2021, "abstract": "BACKGROUND: Lung cancer is the most common cancer and cause of cancer-related mortality worldwide, increasing evidence indicated that there was a significant correlation between tumors and the long non-coding RNAs (lncRNAs), as well as tumor immune infiltration, but their role in early lung adenocarcinoma (LUAD) are still unclear.\nMETHODS: Gene expression data and corresponding clinical data of early-stage LUAD patients were downloaded from GEO and TCGA databases. 24 kinds of tumor-infiltrating immune cells were analyzed by quantity analysis and univariate cox regression analysis, we divided patients into two subgroups using consensus clustering, recognized the differentially expressed genes (DEGs) in the subgroups, then, established lncRNA risk signature by least absolute shrinkage and selection operator (LASSO) regression.\nRESULTS: A total of 718 patients were enrolled in this study, including 246 from GSE31210 dataset, 127 from GSE50081 dataset and 345 from TCGA-LUAD. We identified that Th2 cells, TFH, NK CD56dim cells and Mast cells were prognosis-related(p < 0.05), then established a 5-lncRNA risk signature (risk score = 0.374600616* LINC00857 + 0.173825706* LINC01116 + (- 0.021398903)* DRAIC + (- 0.113658256)* LINC01140 + (- 0.008403702)* XIST), and draw a nomogram showed that the signature had a well prediction accuracy and discrimination.\nCONCLUSIONS: We identified 4 immune infiltrating cells related to the prognosis of early-stage LUAD, and established a novel 5 immune-related lncRNA signature for predicting patients' prognosis.", "claims": [{"id": "claim-method", "statement": "we used the ssGSEA (single-sample geneset enrichment analysis) as it described to quantify infiltrating immune cells, one of the biggest advantages of this method is that we can customize and quantify the types of immune infiltrating cells by using the \"GSVA\" package (R package).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33771173", "locator": "sections[5] \"Quantification of infiltrating immune cells\" paragraph 2", "quote": "we used the ssGSEA (single-sample geneset enrichment analysis) as it described to quantify infiltrating immune cells, one of the biggest advantages of this method is that we can customize and quantify the types of immune infiltrating cells by using the \"GSVA\" package (R package)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets supporting the conclusions of this article are available in the Gene Expression Omnibus (GEO) database and the cancer genome atlas (TCGA) database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33771173", "locator": "sections[24] \"notes\" paragraph 1", "quote": "The datasets supporting the conclusions of this article are available in the Gene Expression Omnibus (GEO) database and the cancer genome atlas (TCGA) database."}], "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:33771173"], "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:GSE31210"], "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:GSE31210"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33771173", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12967-021-02800-x", "sha256": 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