Identification of key gene modules and genes in colorectal cancer by weighted gene co-expression network analysis (WGCNA)
- Published
- Accepted
- Subject Areas
- Bioinformatics, Oncology, Pathology
- Keywords
- WGCNA, Modules, CYTH1, Co-expression, Gene Expression
- Copyright
- © 2019 Zhang et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2019. Identification of key gene modules and genes in colorectal cancer by weighted gene co-expression network analysis (WGCNA) PeerJ Preprints 7:e27667v1 https://doi.org/10.7287/peerj.preprints.27667v1
Abstract
Background: Colorectal cancer (CRC) is a malignant tumor particularly common in developing countries. In this study, we used Weighted Gene Co-Expression Network Analysis (WGCNA) of chip data and screened hub genes in CRC to find the gene modules specifically correlated with clinical traits. Methods: WGCNA was used to identify the gene modules specifically associated with metastasis in colorectal cancer. Cytoscape software was used to construct a co-expression network. The expression of CYTH1 was determined by qRT-PCR. Results: Based on the predicted co-expression network, we identified that the turquoise module was associated with CRC clinical metastasis traits. Turquoise module genes were analyzed, and we identified the hub gene CYTH1 using Cytoscape software. Additionally, we found CYTH1’s expression was lower in CRC tissue and cells when compared with normal counterparts.
Author Comment
This is a submission to PeerJ for review.
Supplemental Information
Data for qRT-PCR results
qRT-PCR results were normalized to the expression of GAPDH. The 2-ΔΔCt method was used to calculate relative expression levels of CYTH1 in tissues. ΔCt = CtCYTH1 – CtGAPDH, ΔΔCt = ΔCtTumor –ΔCtControl. GraphPad Prism 6 software was used for graph construction. The Data is the last results for graph.