Identification and verification of a BMPs-related gene signature for osteosarcoma prognosis prediction.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36814224", "kind": "science-record", "revision": 2, "content_hash": "sha256:715b2f6e1db29c6c1b2afbb83e02702c590676090e426856a10370c38efda56f", "created_at": "2026-11-01T19:01:23Z", "updated_at": "2026-11-01T21:30:09Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and verification of a BMPs-related gene signature for osteosarcoma prognosis prediction.", "doi": "10.1186/s12885-023-10660-5", "pmid": "36814224", "authors": ["Long Xie", "Jiaxing Zeng", "Maolin He"], "journal": "BMC cancer", "year": 2023, "abstract": "BACKGROUND: This study aimed to get a deeper insight into new osteosarcoma (OS) signature based on bone morphogenetic proteins (BMPs)-related genes and to confirm the prognostic pattern to speculate on the overall survival among OS patients.\nMETHODS: Firstly, pathway analyses using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were managed to search for possible prognostic mechanisms attached to the OS-specific differentially expressed BMPs-related genes (DEBRGs). Secondly, univariate and multivariate Cox analysis was executed to filter the prognostic DEBRGs and establish the polygenic model for risk prediction in OS patients with the least absolute shrinkage and selection operator (LASSO) regression analysis. The receiver operating characteristic (ROC) curve weighed the model's accuracy. Thirdly, the GEO database (GSE21257) was operated for independent validation. The nomogram was initiated using multivariable Cox regression. Immune infiltration of the OS sample was calculated. Finally, the three discovered hallmark genes' mRNA and protein expressions were verified.\nRESULTS: A total of 46 DEBRGs were found in the OS and control samples, and three prognostic DEBRGs (DLX2, TERT, and EVX1) were screened under the LASSO regression analyses. Multivariate and univariate Cox regression analysis were devised to forge the OS risk model. Both the TARGET training and validation sets indicated that the prognostic biomarker-based risk score model performed well based on ROC curves. In high- and low-risk groups, immune cells, including memory B, activated mast, resting mast, plasma, and activated memory CD4 + T cells, and the immune, stromal, and ESTIMATE scores showed significant differences. The nomogram that predicts survival was established with good performance according to clinical features of OS patients and risk scores. Finally, the expression of three crucial BMP-related genes in OS cell lines was investigated using quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting (WB).\nCONCLUSION: The new BMP-related prognostic signature linked to OS can be a new tool to identify biomarkers to detect the disease early and a potential candidate to better treat OS in the future.", "claims": [{"id": "claim-method", "statement": "Secondly, univariate and multivariate Cox analysis was executed to filter the prognostic DEBRGs and establish the polygenic model for risk prediction in OS patients with the least absolute shrinkage and selection operator (LASSO) regression analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36814224", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Secondly, univariate and multivariate Cox analysis was executed to filter the prognostic DEBRGs and establish the polygenic model for risk prediction in OS patients with the least absolute shrinkage and selection operator (LASSO) regression analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated from The TARGET database https://ocg.cancer.gov/programs/target) and GEO database (https://www.ncbi.nlm.nih.gov/geo/) during and/or analyzed during the current study are publicly available. All raw datas including gels/blots can be obtained from https://www.jianguoyun.com/p/DWHuGG8Ql-GaCxjRlOsEIAA.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36814224", "locator": "sections[26] \"notes\" paragraph 1", "quote": "The datasets generated from The TARGET database https://ocg.cancer.gov/programs/target) and GEO database (https://www.ncbi.nlm.nih.gov/geo/) during and/or analyzed during the current study are publicly available. All raw datas including gels/blots can be obtained from https://www.jianguoyun.com/p/DWHuGG8Ql-GaCxjRlOsEIAA."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A total of 46 DEBRGs were found in the OS and control samples, and three prognostic DEBRGs (DLX2, TERT, and EVX1) were screened under the LASSO regression analyses.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36814224", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A total of 46 DEBRGs were found in the OS and control samples, and three prognostic DEBRGs (DLX2, TERT, and EVX1) were screened under the LASSO regression analyses."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A total of 46 DEBRGs were found in the OS and control samples, and three prognostic DEBRGs (DLX2, TERT, and EVX1) were screened under the LASSO regression analyses.", "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:36814224"], "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:GSE21257"], "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:GSE21257"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36814224", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12885-023-10660-5", "sha256": "sha256:85ae39f6aaea04dee5ce5a252aea709c2bf15870e6c311889b7c5a73d751545e", "bytes": 33143, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12885-023-10660-5", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9945650", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE21257", "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=GSE21257", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE21257", "version": null, "note": "The datasets generated from The TARGET database https://ocg.cancer.gov/programs/target) and GEO database (https://www.ncbi.nlm.nih.gov/geo/) during and/or analyzed during the current study are publicly available. 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