Identifying novel candidate biomarkers of RCC based on WGCNA analysis

Jin Deng; Wei Kong; Xiaoyang Mou; Shuaiqun Wang; Weiming Zeng

doi:10.2217/pme-2017-0091

Identifying novel candidate biomarkers of RCC based on WGCNA analysis

Jin Deng, Wei Kong, Xiaoyang Mou, Shuaiqun Wang, Weiming Zeng

Chemistry

Research output: Contribution to journal › Article › peer-review

14 Scopus citations

Abstract

Aim: Extracting differential expression genes (DEGs) is an effective approach to improve the accuracy of determining the candidate biomarker genes. However, the previous DEGs analysis methods ignore that the expression levels of genes in different pathology stages of cancers are complex and various. Methods: In our study, staging DEGs analysis and weighted gene co-expression network analysis were applied to gene expression data of renal cell carcinoma (RCC). Results: According to construct gene topology network for exploring hub genes, 12 genes were identified as hub genes. Conclusion: Combining with the effect of hub gene expression level on RCC patient survival and different biological data analysis, three hub genes were found that they might be three novel candidate biomarkers of RCC.

Original language	English (US)
Pages (from-to)	381-394
Number of pages	14
Journal	Personalized Medicine
Volume	15
Issue number	5
DOIs	https://doi.org/10.2217/pme-2017-0091
State	Published - Sep 1 2018

All Science Journal Classification (ASJC) codes

Molecular Medicine
Pharmacology

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title = "Identifying novel candidate biomarkers of RCC based on WGCNA analysis",

abstract = "Aim: Extracting differential expression genes (DEGs) is an effective approach to improve the accuracy of determining the candidate biomarker genes. However, the previous DEGs analysis methods ignore that the expression levels of genes in different pathology stages of cancers are complex and various. Methods: In our study, staging DEGs analysis and weighted gene co-expression network analysis were applied to gene expression data of renal cell carcinoma (RCC). Results: According to construct gene topology network for exploring hub genes, 12 genes were identified as hub genes. Conclusion: Combining with the effect of hub gene expression level on RCC patient survival and different biological data analysis, three hub genes were found that they might be three novel candidate biomarkers of RCC.",

author = "Jin Deng and Wei Kong and Xiaoyang Mou and Shuaiqun Wang and Weiming Zeng",

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year = "2018",

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doi = "10.2217/pme-2017-0091",

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AU - Deng, Jin

AU - Kong, Wei

AU - Mou, Xiaoyang

AU - Wang, Shuaiqun

AU - Zeng, Weiming

PY - 2018/9/1

Y1 - 2018/9/1

N2 - Aim: Extracting differential expression genes (DEGs) is an effective approach to improve the accuracy of determining the candidate biomarker genes. However, the previous DEGs analysis methods ignore that the expression levels of genes in different pathology stages of cancers are complex and various. Methods: In our study, staging DEGs analysis and weighted gene co-expression network analysis were applied to gene expression data of renal cell carcinoma (RCC). Results: According to construct gene topology network for exploring hub genes, 12 genes were identified as hub genes. Conclusion: Combining with the effect of hub gene expression level on RCC patient survival and different biological data analysis, three hub genes were found that they might be three novel candidate biomarkers of RCC.

AB - Aim: Extracting differential expression genes (DEGs) is an effective approach to improve the accuracy of determining the candidate biomarker genes. However, the previous DEGs analysis methods ignore that the expression levels of genes in different pathology stages of cancers are complex and various. Methods: In our study, staging DEGs analysis and weighted gene co-expression network analysis were applied to gene expression data of renal cell carcinoma (RCC). Results: According to construct gene topology network for exploring hub genes, 12 genes were identified as hub genes. Conclusion: Combining with the effect of hub gene expression level on RCC patient survival and different biological data analysis, three hub genes were found that they might be three novel candidate biomarkers of RCC.

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Identifying novel candidate biomarkers of RCC based on WGCNA analysis

Abstract

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