A support vector machine classifier for the prediction of osteosarcoma metastasis with high accuracy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:28901446", "kind": "science-record", "revision": 6, "content_hash": "sha256:0c69bcbe824053980009d879fcdbf5cc3bf42906587493a55c922bda586a5ab2", "created_at": "2026-11-01T15:40:28Z", "updated_at": "2026-11-01T21:59:30Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A support vector machine classifier for the prediction of osteosarcoma metastasis with high accuracy.", "doi": "10.3892/ijmm.2017.3126", "pmid": "28901446", "authors": ["Yunfei He", "Jun Ma", "Xiaojian Ye"], "journal": "International journal of molecular medicine", "year": 2017, "abstract": "In this study, gene expression profiles of osteosarcoma (OS) were analyzed to identify critical genes associated with metastasis. Five gene expression datasets were screened and downloaded from Gene Expression Omnibus (GEO). Following assessment by MetaQC, the dataset GSE9508 was excluded for poor quality. Subsequently, differentially expressed genes (DEGs) between metastatic and non-metastatic OS were identified using meta‑analysis. A protein-protein interaction (PPI) network was constructed with information from Human Protein Reference Database (HPRD) for the DEGs. Betweenness centrality (BC) was calculated for each node in the network and top featured genes ranked by BC were selected out to construct support vector machine (SVM) classifier using the training set GSE21257, which was then validated using the other three independent datasets. Pathway enrichment analysis was performed for the featured genes using Fisher's exact test. A total of 353 DEGs were identified and a PPI network including 164 nodes and 272 edges was then constructed. The top 64 featured genes ranked by BC were included in the SVM classifier. The SVM classifier exhibited high prediction accuracies in all of the 4 datasets, with accuracies of 100, 100, 92.6 and 100%, respectively. Further analysis of the featured genes revealed that 11 Gene Ontology (GO) biological pathways and 5 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were significantly over-represented, including the regulation of cell proliferation, regulation of apoptosis, pathways in cancer, regulation of actin cytoskeleton and the TGF-β signaling pathway. On the whole, an SVM classifier with high prediction accuracy was constructed and validated, in which key genes associated with metastasis in OS were also revealed. These findings may promote the development of genetic diagnostic methods and may enhance our understanding of the molecular mechanisms underlying the metastasis of OS.", "claims": [{"id": "claim-method", "statement": "In order to identify novel and reliable genes associated with metastasis, a meta-analysis of gene expression profiles was performed in the present study.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:28901446", "locator": "sections[1] \"Introduction\" paragraph 5", "quote": "In order to identify novel and reliable genes associated with metastasis, a meta-analysis of gene expression profiles was performed in the present study."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The gene expression datasets GSE14359 and GSE14827 were acquired with Affymetrix platforms.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:28901446", "locator": "sections[3] \"Gene expression profiles and pre-treament\" paragraph 4", "quote": "The gene expression datasets GSE14359 and GSE14827 were acquired with Affymetrix platforms."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The paper used meta-analysis of gene-expression datasets downloaded from Gene Expression Omnibus (GEO) to identify differentially expressed genes associated with osteosarcoma metastasis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:28901446", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Five gene expression datasets were screened and downloaded from Gene Expression Omnibus (GEO)."}, {"asset": "source:pmid:28901446", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Subsequently, differentially expressed genes (DEGs) between metastatic and non-metastatic OS were identified using meta-analysis."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The paper used meta-analysis of gene-expression datasets downloaded from Gene Expression Omnibus (GEO) to identify differentially expressed genes associated with osteosarcoma metastasis.", "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:28901446"], "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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