ArticleInternational journal of molecular medicine2017
A support vector machine classifier for the prediction of osteosarcoma metastasis with high accuracy.
Article in International journal of molecular medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- TMO-Net+: An Enhanced Tumor Multi-Omics Pre-Trained Network for Multi-Task Learning in Oncology.Genes · 2026Article
- MOFR: Multi-omics Feature Reconstruction for Cancer Classification and Metastasis Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Development and Validation of Machine Learning Models for Predicting Occult Nodal Metastasis in Early-Stage Oral Cavity Squamous Cell Carcinoma.JAMA network open · 2022Article
- Multiple-model machine learning identifies potential functional genes in dilated cardiomyopathy.Frontiers in cardiovascular medicine · 2022Article
- Aortic Dissection Auxiliary Diagnosis Model and Applied Research Based on Ensemble Learning.Frontiers in cardiovascular medicine · 2021Article
- Machine learning and deep learning methods that use omics data for metastasis prediction.Computational and structural biotechnology journal · 2021Review
- Article
- A novel risk score model based on eight genes and a nomogram for predicting overall survival of patients with osteosarcoma.BMC cancer · 2020Article
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3 authors.
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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.
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