Joint Covariate Detection on Expression Profiles for Identifying MicroRNAs Related to Venous Metastasis in Hepatocellular Carcinoma
Abstract Expression profiles of cancer are generally composed of three dimensions including gene probes, patients (e.g., metastasis or non-metastasis) and tissues (i.e., cancer or normal cells of a patient). In order to combine these three dimensions, we proposed a joint covariate detection that not...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2017
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/bd189dd5a16a46719c8bfa13c22774fd |
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| Sumario: | Abstract Expression profiles of cancer are generally composed of three dimensions including gene probes, patients (e.g., metastasis or non-metastasis) and tissues (i.e., cancer or normal cells of a patient). In order to combine these three dimensions, we proposed a joint covariate detection that not only considered projections on gene probes and tissues simultaneously, but also concentrated on distinguishing patients into different groups. Due to highly lethal malignancy of hepatocellular carcinoma, we chose data GSE6857 to testify the effectiveness of our method. A bootstrap and accumulation strategy was introduced in, which could select candidate microRNAs to distinguish metastasis from non-metastasis patient group. Two pairs of microRNAs were further selected. Each component of either significant microRNA pair was derived from different cliques. Targets were sought and pathway analysis were made, which might reveal the mechanism of venous metastasis in primary hepatocellular carcinoma. |
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