Sparse feature selection for classification and prediction of metastasis in endometrial cancer
| Autor: | Nitin Kumar Singh, Jayanthi S. Lea, David Miller, Todd Boren, Michael A. White, Mehmet Eren Ahsen, Mathukumalli Vidyasagar, Kathleen N. Moore, C.K. McCourt, David G. Mutch, Floor J. Backes, Burook Misganaw |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Oncology medicine.medical_specialty lcsh:QH426-470 lcsh:Biotechnology Feature selection Biology Bioinformatics Metastasis 03 medical and health sciences 0302 clinical medicine Endometrial cancer Internal medicine lcsh:TP248.13-248.65 Machine learning medicine Genetics Humans Neoplasm Metastasis Lymph node Neoplasm Staging Lymph node metastasis Research Gene Expression Profiling Computational Biology Genomics medicine.disease Prognosis 3. Good health Endometrial Neoplasms Clinical trial lcsh:Genetics MicroRNAs 030104 developmental biology medicine.anatomical_structure Lymphatic system 030220 oncology & carcinogenesis Cohort Female Lymph Sparse classification Algorithms Biotechnology |
| Zdroj: | BMC Genomics BMC Genomics, Vol 18, Iss S3, Pp 1-12 (2017) |
| ISSN: | 1471-2164 |
| Popis: | Background Metastasis via pelvic and/or para-aortic lymph nodes is a major risk factor for endometrial cancer. Lymph-node resection ameliorates risk but is associated with significant co-morbidities. Incidence in patients with stage I disease is 4–22% but no mechanism exists to accurately predict it. Therefore, national guidelines for primary staging surgery include pelvic and para-aortic lymph node dissection for all patients whose tumor exceeds 2cm in diameter. We sought to identify a robust molecular signature that can accurately classify risk of lymph node metastasis in endometrial cancer patients. 86 tumors matched for age and race, and evenly distributed between lymph node-positive and lymph node-negative cases, were selected as a training cohort. Genomic micro-RNA expression was profiled for each sample to serve as the predictive feature matrix. An independent set of 28 tumor samples was collected and similarly characterized to serve as a test cohort. Results A feature selection algorithm was designed for applications where the number of samples is far smaller than the number of measured features per sample. A predictive miRNA expression signature was developed using this algorithm, which was then used to predict the metastatic status of the independent test cohort. A weighted classifier, using 18 micro-RNAs, achieved 100% accuracy on the training cohort. When applied to the testing cohort, the classifier correctly predicted 90% of node-positive cases, and 80% of node-negative cases (FDR = 6.25%). Conclusion Results indicate that the evaluation of the quantitative sparse-feature classifier proposed here in clinical trials may lead to significant improvement in the prediction of lymphatic metastases in endometrial cancer patients. Electronic supplementary material The online version of this article (doi:10.1186/s12864-017-3604-y) contains supplementary material, which is available to authorized users. |
| Databáze: | OpenAIRE |
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