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. 2018 Feb 12;14(3):266-279.
doi: 10.7150/ijbs.23574. eCollection 2018.

Identification of key genes and pathways in human clear cell renal cell carcinoma (ccRCC) by co-expression analysis

Lushun Yuan  1   2 , Guang Zeng  1 , Liang Chen  1 , Gang Wang  1   2   3 , Xiaolong Wang  4 , Xinyue Cao  3 , Mengxin Lu  1 , Xuefeng Liu  5 , Guofeng Qian  6 , Yu Xiao  1   3   7 , Xinghuan Wang  1   2
Affiliations

Identification of key genes and pathways in human clear cell renal cell carcinoma (ccRCC) by co-expression analysis

Lushun Yuan et al. Int J Biol Sci. .

Abstract

Human clear cell renal cell carcinoma (ccRCC) is the most common solid lesion within kidney, and its prognostic is influenced by the progression covering a complex network of gene interactions. In our study, we screened differential expressed genes, and constructed protein-protein interaction (PPI) network and a weighted gene co-expression network to identify key genes and pathways associated with the progression of ccRCC (n = 56). Functional and pathway enrichment analysis demonstrated that upregulated differentially expressed genes (DEGs) were significantly enriched in response to wounding, positive regulation of immune system process, leukocyte activation, immune response and cell activation. Downregulated DEGs were significantly enriched in oxidation reduction, monovalent inorganic cation transport, ion transport, excretion and anion transport. In the PPI network, top 10 hub genes were identified (TOP2A, MYC, ALB, CDK1, VEGFA, MMP9, PTPRC, CASR, EGFR and PTGS2). In co-expression network, 6 ccRCC-related modules were identified. They were associated with immune response, metabolic process, cell cycle regulation, angiogenesis and ion transport. In conclusion, our study illustrated the hub genes and pathways involved in the progress of ccRCC, and further molecular biological experiments are needed to confirm the function of the candidate biomarkers in human ccRCC.

Keywords: biomarker; clear cell renal cell carcinoma (ccRCC); differentially expressed genes (DEGs); protein-protein interaction (PPI); weighted gene co-expression network analysis (WGCNA).

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interest exists.

Figures

Figure 1
Figure 1
Flow diagram of study. Data preparing, processing and analysis was shown in the flow diagram.
Figure 2
Figure 2
Samples clustering and identification of differentially expressed genes (DEGs) in ccRCC tissues. (A) Samples clustering of GSE53000 to detect outliers. (B) The volcano plot of all DEGs.
Figure 3
Figure 3
GO analysis of DEGs. (A) upregulated DEGs with fold change > 2. (B) downregulated DEGs with fold change < -2. BP: biological process, CC: cellular component, MF: molecular function.
Figure 4
Figure 4
KEGG enrichment analysis of all DEGs with |fold change| > 2. All differentially expressed genes (DEGs) were analysed by KEGG enrichment. Fold change > 2 was set as cut-off value.
Figure 5
Figure 5
Module analysis of PPI network. (A) Module rank 1. (B) GO enrichment analysis of module rank 1. (C) Module rank 2. (D) GO enrichment analysis of module rank 2. (E) Module rank 3. (F) GO enrichment analysis of module rank 3.
Figure 6
Figure 6
Determination of soft-thresholding power in the weighted gene co-expression network analysis (WGCNA). (A) Analysis of the scale-free fit index for various soft-thresholding powers (β). (B) Analysis of the mean connectivity for various soft-thresholding powers. (C) Histogram of connectivity distribution when β = 6. (D) Checking the scale free topology when β = 6. (E) Dendrogram of all differentially expressed genes clustered based on a dissimilarity measure (1-TOM).
Figure 7
Figure 7
GO enrichment analysis of 6 genes modules. (A) blue module, (B) brown module, (C) green module, (D) red module, (E) turquoise module and (F) yellow module.
Figure 8
Figure 8
KEGG pathway enrichment analysis of 6 genes modules. (A) blue module, (B) brown module, (C) green module, (D) red module, (E) turquoise module and (F) yellow module.
Figure 9
Figure 9
Pathway validation using qRT-PCR analysis. (A) PIK3CB, (B) PIK3CD, (C) PIK3CG, (D) AKT1, (E) AKT2, (F) AKT3, (G) NFKB2, (H) RAP1A and (I) RAP1B.
Figure 10
Figure 10
Pathway validation using TCGA KIRC data. (A) PIK3CB, (B) PIK3CD, (C) PIK3CG, (D) AKT1, (E) AKT2, (F) AKT3, (G) NFKB2, (H) RAP1A and (I) RAP1B (* p < 0.05; ** p < 0.01; *** p <0.001).
Figure 11
Figure 11
Venn plot of common deregulated genes. Blue: downregulated genes; red: upregulated genes.
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