Li M, Guo Y, Feng YM, Zhang N – 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. – Front Genet. 2019 Mar 15;10:180. doi: 10.3389/fgene.2019.00180
Triple-negative breast cancer (TNBC) is a special subtype of breast cancer that is difficult to treat. 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
s of recommended treatment in breast cancer care in Norway and to improve the quality of epidemiological data, the Cancer Registry of Norway (CRN) in cooperation with the Norwegian Breast Cancer Group (NBCG) developed the Norwegian Breast Cancer Registry (NBCR). The objective of this study is to assess the feasibility of using the NBCR for estimating the EUSOMA QI individually for all hospitals diagnosing and treating breast cancer in Norway.ù
Methods: To provide researchers with high quality cancer data as well as for the purpose of national cancer statistics, the CRN employs a cancer registry system to 1) longitudinal capture data from all patients from all medical entities that diagnose and/or treat cancer patients (e.g., pathology, radiology and clinical departments) in Norway; 2) curate data, i.e. validate the correctness of collected data, and assemble the validated cancer data as cancer cases; 3) provide data for analytics and presentation. Estimates for 10 EUSOMA QI were calculated at national and hospital level. To compare hospitals, a summary score of QIs was defined for each hospital.
Results: All hospitals currently treating breast cancer patients have the technical ability to submit data to the NBCR for estimation of QIs defined by EUSOMA. Data from pathology and surgery are of high quality. However, data from oncological and radiological departments are incomplete, but improving. This currently hinders three QIs from being calculated. QI on benign to malign diagnosis needs to be calculated at the individual Breast Centre. Over time the adherence to guidelines have improved and the hospital variation for the respective QI have decreased. Two hospitals met all minimum standard on ten QIs in year 2016 and one hospital did not meet one minimum standard, but met all other targets.
Conclusion: The NBCR has since 2012 published annual reports on breast cancer care and for the year 2016 measured 10 of 14 QI defined by EUSOMA. Increased compliance of recommended treatment in Norway has been observed during the years the registry has been active.