Identification of the Transcription Co-Factor-Related Gene Signature and Risk Score Model for Osteosarcoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35734428", "kind": "science-record", "revision": 3, "content_hash": "sha256:95484e3be83d9fcdae48f4ec4fe21984d775d1b2b15d7260066633032ff7304c", "created_at": "2026-11-01T18:14:45Z", "updated_at": "2026-11-01T21:28:12Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of the Transcription Co-Factor-Related Gene Signature and Risk Score Model for Osteosarcoma.", "doi": "10.3389/fgene.2022.862803", "pmid": "35734428", "authors": ["Zhijian Jin", "Jintao Wu", "Jianwei Lin", "Jun Wang", "Yuhui Shen"], "journal": "Frontiers in genetics", "year": 2022, "abstract": "Osteosarcoma is a malignant tumor with a poor prognosis. Nowadays, there is a lack of good methods to assess the prognosis of osteosarcoma patients. Transcription co-factors (TcoFs) play crucial roles in transcriptional regulation through the interaction with transcription factors (TFs). Many studies have revealed that TcoFs are related to many diseases, especially cancer. However, few studies have been reported about prognostic prediction models of osteosarcoma by using TcoF-related genes. In order to construct a prognostic risk model with TcoF-related genes, the mRNA expression data and matched clinical information of osteosarcoma were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database and the Gene Expression Omnibus (GEO) database. TARGET was used as a training set and GSE21257 from GEO was used as a validation set. Univariate Cox regression was performed to select 13 TcoF-related candidate genes, of which five genes (LMO2, MAML3, MTF2, RBPMS, and SIRT1) were finally used to construct the prognostic risk model by LASSO Cox regression analysis. The Kaplan-Meier (K-M) survival curves showed an obvious difference between high- and low-risk groups. The receiver operating characteristic (ROC) curves based on TARGET demonstrated that this risk model was credible (1-year AUC: 0.607; 3-years AUC: 0.713; 5-years AUC: 0.736). Meanwhile, the risk model was associated with immune cells and immune-related functions. By combining the risk score and clinical factors, the nomogram of osteosarcoma was assessed with a C-index of 0.738 to further support the reliability of this 5-gene prognostic risk model. Finally, the expression of TcoF-related genes was validated in different cell lines by quantitative real-time PCR (qRT-PCR) and also in different tissue samples by immunohistochemistry (IHC). In conclusion, the model can predict the prognosis of osteosarcoma patients and may provide novel targets for the treatment of osteosarcoma patients.", "claims": [{"id": "claim-method", "statement": "After that, in order to screen candidate genes with an optimal number for this risk score model, the least absolute shrinkage and selection operator (LASSO) Cox regression with the “glmnet” R package was utilized for variable filters, and 10-fold cross-validation analysis was used for validation analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35734428", "locator": "sections[5] \"Construction and Validation of the Risk Score Model by Univariate Cox and LASSO Cox Regression Analyses\" paragraph 2", "quote": "After that, in order to screen candidate genes with an optimal number for this risk score model, the least absolute shrinkage and selection operator (LASSO) Cox regression with the “glmnet” R package was utilized for variable filters, and 10-fold cross-validation analysis was used for validation analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35734428", "locator": "sections[29] \"Data Availability Statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The five-gene risk model can predict the prognosis of osteosarcoma patients and may provide novel therapeutic targets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35734428", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "In conclusion, the model can predict the prognosis of osteosarcoma patients and may provide novel targets for the treatment of osteosarcoma patients."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The five-gene risk model can predict the prognosis of osteosarcoma patients and may provide novel therapeutic targets.", "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:35734428"], "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. 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In conclusion, the model can predict", "rent tissue samples by immunohistochemistry (IHC). In conclusion, the model can predict the p"], "order_basis": "paper_text_order:1902", "id": "assay:2"}, {"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["018). Four osteosarcoma cell lines were both cultured in Dulbecco modified Eagle medium (Gibco, 11965", "eptomycin at 37°C with 5% CO2. hFOB 1.19 was cultured in DMEM/F12 medium (Gibco, 11320033) containing"], "order_basis": "paper_text_order:3485", "id": "assay:3"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["After that, in order to screen candidate genes with an optimal number for this risk score model, the least absolute shrinkage and selection operator (LASSO) Cox regression with the “glmnet” R package was utilized for variable filters, and 10-fold cross-validation analysis was used for validation analysis."], "order_basis": "consumes_earlier_output", "id": "assay:4"}]}