Screening and verification of prognostic lncRNA markers related to immune infiltration in the metastasis of osteosarcoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36237238", "kind": "science-record", "revision": 3, "content_hash": "sha256:2276882f3cfa4e1e8847ab712ddccce98c79609dc53f2ab410ebca8df7d804d4", "created_at": "2026-11-01T18:15:36Z", "updated_at": "2026-11-01T21:29:12Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Screening and verification of prognostic lncRNA markers related to immune infiltration in the metastasis of osteosarcoma.", "doi": "10.21037/tcr-22-1926", "pmid": "36237238", "authors": ["Chuan He", "Xiaolong Wang", "Linying Ni", "Chunyu Song", "Wei Mai"], "journal": "Translational cancer research", "year": 2022, "abstract": "Background: We sought to screen and verify the long non-coding ribonucleic acids (lncRNAs) related to immune infiltration in metastatic osteosarcoma (OS).\nMethods: We downloaded the RNA-sequencing expression data from The Cancer Genome Atlas (TCGA) database as the training data set. We downloaded the GSE39055 data set from the National Center for Biotechnology Information, Gene Expression Omnibus as the validation data set. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used to screen the optimized lncRNA combinations. Kaplan-Meier curves were used to evaluate the associations between the lncRNAs and actual prognosis. The independent survival prognosis clinical factors were obtained by univariate and multivariate Cox analyses. A nomogram was established to explore the correlation between survival rate and risk information. The Tumor IMmune Estimation Resource was applied to estimate the composition of 6 subtypes of immune infiltration cells.\nResults: In total, 1,398 lncRNAs and 14,631 messenger RNAs were screened from TCGA data set, and divided into the low and high immunity groups. The Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data (ESTIMATE) scores differed significantly between the samples in the two groups. Additionally, 5 optimized lncRNA combinations were obtained using the LASSO algorithm. Risk factors including age, metastatic tumor, and risk-score (RS) were related to the prognosis of OS patients. The survival rates predicted by the nomogram model were consistent with the actual survival rates of OS patients. Finally, we found that RS was negatively correlated with the proportion of immune cells.\nConclusions: In total, 5 feature lncRNAs were identified as novel biomarkers for OS. Next, a RS nomogram model was constructed based on the 5 identified lncRNAs. This model predicted the survival rates and prognoses of OS patients well.", "claims": [{"id": "claim-method", "statement": "The least absolute shrinkage and selection operator (LASSO) regression algorithm was used to screen the optimized lncRNA combinations.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36237238", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The least absolute shrinkage and selection operator (LASSO) regression algorithm was used to screen the optimized lncRNA combinations."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We downloaded the RNA-sequencing expression data from TCGA database, including 265 samples from the Illumina HiSeq 2000 RNA Sequencing platform. After the 265 samples were matched with the clinical information of the OS samples, the samples with information on metastasis were retained. In total, 176 OS samples were included in the analysis, and these were used as the training data set.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36237238", "locator": "sections[3] \"Data source and preprocessing\" paragraph 2", "quote": "We downloaded the RNA-sequencing expression data from TCGA database, including 265 samples from the Illumina HiSeq 2000 RNA Sequencing platform. After the 265 samples were matched with the clinical information of the OS samples, the samples with information on metastasis were retained. In total, 176 OS samples were included in the analysis, and these were used as the training data set."}], "state": "complete"}, {"id": "claim-target-result", "statement": "In total, 5 feature lncRNAs were identified as novel biomarkers for OS.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36237238", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "In total, 5 feature lncRNAs were identified as novel biomarkers for OS."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "In total, 5 feature lncRNAs were identified as novel biomarkers for OS.", "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:36237238"], "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:GSE39055"], "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:GSE39055"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36237238", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tcr-22-1926", "sha256": "sha256:2aefd5316b22fde48e725b4d9eac77796f7743b877fd59441c4c24c97c520562", "bytes": 38956, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.21037/tcr-22-1926", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9552095", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE39055", "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=GSE39055", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE39055", "version": null, "note": "We downloaded the RNA-sequencing expression data from TCGA database, including 265 samples from the Illumina HiSeq 2000 RNA Sequencing platform. 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