Random Forests algoritm-based bioinformatic screening of functional genes involved in lymph metastasis of cervical cancer
10.3969/j.issn.1005-1678.2016.04.02
- VernacularTitle:基于随机森林算法的宫颈癌淋巴结转移相关基因的生物信息学筛选
- Author:
Shuying FAN
;
Chunxiao LI
;
Ting WANG
;
Chunxia ZHOU
;
Haili QIAN
;
Haijuan WANG
;
Qimin ZHAN
- Publication Type:Journal Article
- Keywords:
random forests algoritm;
cervical cancer;
lymph node metastasis;
bioinformatics
- From:
Chinese Journal of Biochemical Pharmaceutics
2016;36(4):5-8
- CountryChina
- Language:Chinese
-
Abstract:
Objective To screen the genes most relevant to lymph node metastasis of cervical cancer and identify the genes at the key knots of the regulatory network to provide the potential targets for cervical cancer intervention.Methods The transcriptional profiling database of TCGA was used, and random forests algorithm was adopted to rank the genes related to lymph node metastasis extracted from GeneCards database.STRING and Cytospace tolls were used to build the interactive regulatory network and identify the most weighted genes localized in the central of the network.DAVID platform was used to perform a functional annotation for the whole geneset.Results We ranked 2784 genes in respect to their potential contributions to lymph node metastasis of cervical cancer and identified the genes at the key knob.The genes related to cancer metastasis were enriched to cytokines pathway, MAPK pathway, wnt pathway, intercellular interaction, adhesive conjunction, cellular skeleton regulation, etc.Some of the identified key genes, like EGFR, NOTCH1, RHOA, etc. have been verified to be closely related cervical cancer metastasis in the basic and clinical research. Conclusion Random forests algorithm is useful, taking advantages of TCGA database, in enriching the genes playing significant role in cervical cancer metastasis.A majority of the genes in the analyzed geneset were indicated to be significantly correlated with lymph node metastasis.
