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Prediction and analysis of Hub Genes in Renal Cell Carcinoma based on CFS Gene selection method combined with Adaboost algorithm.

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机构： [1]Department of VIP Medical Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China [2]Huaxia Eye Hospital of Foshan, Huaxia Eye Hospital Group, Foshan, Guangdong, 528000, China [3]Department of Head and Neck Surgery, The Cancer Hospital of Shantou University Medical College, Shantou 515000, China [4]Department of Acupuncture and Moxibustion Foshan Hospital of TCM, Foshan 528000, China [5]Center for Bioinformatics and Computational Biology, Pai Chai University, Daejeon, South Korea

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关键词： Gene expression profiles gene selection renal cell carcinoma correlation-based feature subset (CFS) Adaboost gene ontology

摘要：

Renal cell carcinoma (RCC) is the most common malignant tumor of the adult kidney. The aim of this study was to identify key genes signatures during RCC and uncover their potential mechanisms. Firstly, the gene expression profiles of GSE53757 which contained 144 samples, including 72 kidney cancer samples and 72 controls, was downloaded from GEO database. And then differentially expressed genes (DEGs) between the kidney cancer samples and the controls were identified. After that, GO and KEGG enrichment analyses of DEGs were performed by DAVID. Furthermore, correlation-based feature subset (CFS) method was applied to the selection of key genes of DEGs. In addition, the classification model between the kidney cancer samples and the controls was built by Adaboost based on selected of key genes. 213 DEGs including 80 up-regulated and 133 down-regulated genes were selected as the feature genes to build the classification model between the kidney cancer samples and the controls by CFS method. And accuracy of the classification model by using 5-folds cross-validation test and independent set test is 84.4% and 83.3%, respectively. Besides, TYROBP, CD4163, CAV1, CXCL9, CXCL11 and CXCL13 also can be found in the top 20 hub genes screened by protein-protein interaction (PPI) network. It indicated that CFS is a useful tool to identify key genes in kidney cancer. Besides, we also predicted genes such as TYROBP , CD4163, CAV1, CXCL9, CXCL11 and CXCL13 might be target genes for diagnosing the kidney cancer. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.

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出版当年[2019]版：

大类 | 3 区医学

小类 | 3 区药物化学

最新[2025]版：

大类 | 4 区医学

小类 | 4 区药物化学

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出版当年[2018]版：

Q3 CHEMISTRY, MEDICINAL

最新[2023]版：

Q3 CHEMISTRY, MEDICINAL

影响因子： 1.9 最新[2023版] 2 最新五年平均 2.53 出版当年[2018版] 2.291 出版当年五年平均 2.631 出版前一年[2017版] 2.577 出版后一年[2019版]

第一作者：

第一作者机构： [1]Department of VIP Medical Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China

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通讯作者：

通讯机构： [1]Department of VIP Medical Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China [2]Huaxia Eye Hospital of Foshan, Huaxia Eye Hospital Group, Foshan, Guangdong, 528000, China [*1]Department of VIP Medical Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China [*2]Huaxia Eye Hospital of Foshan, Huaxia Eye Hospital Group, Foshan, Guangdong, 528000, China

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Prediction and analysis of Hub Genes in Renal Cell Carcinoma based on CFS Gene selection method combined with Adaboost algorithm.

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