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pmid:30930932
Identification of Triple-Negative Breast Cancer Genes and a Novel High-Risk Breast Cancer Prediction Model Development Based on PPI Data and Support Vector Machines.
Ming Li · Yu Guo · Yuan-Ming Feng · Ning Zhang
Frontiers in genetics · 2019 doi:10.3389/fgene.2019.00180
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Experiment
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Complete file
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It is crucial to identify breast cancer-related genes that could provide new biomarkers for breast cancer diagnosis and potential treatment goals. In the development of our new high-risk breast cancer prediction model, seven raw gene expression datasets from the NCBI gene expression omnibus (GEO) database (GSE31519, GSE9574, GSE20194, GSE20271, GSE32646, GSE45255, and GSE15852) were used. Using the maximum relevance minimum redundancy (mRMR) method, we selected significant genes. Then, we mapped transcripts of the genes on the protein-protein interaction (PPI) network from the Search Tool for the Retrieval of Interacting Genes (STRING) database, as well as traced the shortest path between each pair of proteins. Genes with higher betweenness values were selected from the shortest path proteins. In order to ensure validity and precision, a permutation test was performed. We randomly selected 248 proteins from the PPI network for shortest path tracing and repeated the procedure 100 times. We also removed genes that appeared more frequently in randomized results. As a result, 54 genes were selected as potential TNBC-related genes. Using 14 out the 54 genes, which are potential TNBC associated genes, as input features into a support vector machine (SVM), a novel model was trained to predict high-risk breast cancer. The prediction accuracy of normal tissues and TNBC tissues reached 95.394%, and the predictions of Stage II and Stage III TNBC reached 86.598%, indicating that such genes play important roles in distinguishing breast cancers, and that the method could be promising in practical use. According to reports, some of the 54 genes we identified from the PPI network are associated with breast cancer in the literature. Several other genes have not yet been reported but have functional resemblance with known cancer genes. These may be novel breast cancer-related genes and need further experimental validation. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to appraise the 54 genes. It was indicated that cellular response to organic cyclic compounds has an influence in breast cancer, and most genes may be related with viral carcinogenesis.", "claims": [{"id": "claim-method", "statement": "We employed the mRMR method (Peng et al., 2005; Li et al., 2012a,b; Zhang et al., 2012; Zou et al., 2016b; Su et al., 2018) to rank the importance of all 12,437 genes examined.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30930932", "locator": "sections[4] \"The mRMR Method\" paragraph 2", "quote": "We employed the mRMR method (Peng et al., 2005; Li et al., 2012a,b; Zhang et al., 2012; Zou et al., 2016b; Su et al., 2018) to rank the importance of all 12,437 genes examined."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated for this study can be found in NCBI GEO, GSE31519, GSE9574, GSE20194, GSE20271, GSE32646, GSE45255, and GSE15852.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30930932", "locator": "sections[21] \"Data Availability\" paragraph 2", "quote": "The datasets generated for this study can be found in NCBI GEO, GSE31519, GSE9574, GSE20194, GSE20271, GSE32646, GSE45255, and GSE15852."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:30930932"], "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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"open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[11]/table-wrap[1]#T1", "version": null, "note": "The 54 candidate breast cancer-related genes and betweenness.", "structure": {"pmcid": "PMC6428707", "section_index": 11, "section_title": "The Top 54 Genes on PPI Shortest Paths", "row_count": 55, "column_count": 4}, "table": {"label": "Table 1", "caption": "The 54 candidate breast cancer-related genes and betweenness.", "rows": [["hgnc_symbol", "ensp", "Betweenness", "Reference"], ["MAGOH", "ENSP00000360525", "1777", "Kataoka et al., 2001"], ["CBL", "ENSP00000264033", "1548", "Thien and Langdon, 2001"], ["RPS3", "ENSP00000433821", "1380", "Jang et al., 2004"], ["FGFR1", "ENSP00000393312", "1286", "Pirvola et al., 2002"], ["RHOA", "ENSP00000400175", "1237", "Strutt et al., 1997"], ["EP300", "ENSP00000263253", "1048", "Gayther et al., 2000"], ["RAC1", "ENSP00000348461", "871", "Gu et al., 2003"], ["CDK1", "ENSP00000378699", "852", 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