Bioinformatics analysis of the genes involved in the extension of prostate cancer to adjacent lymph nodes by supervised and unsupervised machine learning methods: The role of SPAG1 and PLEKHF2
| Autor: | Elham Shamsara, Jamal Shamsara |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0106 biological sciences
Male Candidate gene Bioinformatics analysis Vesicular Transport Proteins Datasets as Topic Computational biology Biology 01 natural sciences Metastasis 03 medical and health sciences Prostate cancer GTP-Binding Proteins Genetics medicine Diagnostic biomarker Cluster Analysis Humans Gene 030304 developmental biology 0303 health sciences Computational Biology Prostatic Neoplasms medicine.disease Lymphatic Metastasis Antigens Surface Unsupervised learning Lymph Supervised Machine Learning 010606 plant biology & botany Unsupervised Machine Learning |
| Zdroj: | Genomics. 112(6) |
| ISSN: | 1089-8646 |
| Popis: | The present study aimed to identify the genes associated with the involvement of adjunct lymph nodes of patients with prostate cancer (PCa) and to provide valuable information for the identification of potential diagnostic biomarkers and pathological genes in PCa metastasis. The most important candidate genes were identified through several machine learning approaches including K-means clustering, neural network, Naive Bayesian classifications and PCA with or without downsampling. In total, 21 genes associated with lymph nodes involvement were identified. Among them, nine genes have been identified in metastatic prostate cancer, six have been found in the other metastatic cancers and four in other local cancers. The amplification of the candidate genes was evaluated in the other PCa datasets. Besides, we identified a validated set of genes involved in the PCa metastasis. The amplification of SPAG1 and PLEKHF2 genes were associated with decreased survival in patients with PCa. |
| Databáze: | OpenAIRE |
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