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. 2022 Jul 30:2022:2818777.
doi: 10.1155/2022/2818777. eCollection 2022.

Identification of Pathologic Grading-Related Genes Associated with Kidney Renal Clear Cell Carcinoma

Weijian Xiong  1 , Jin Zhong  1 , Ying Li  1 , Xunjia Li  1 , Lili Wu  1 , Ling Zhang  1
Affiliations

Identification of Pathologic Grading-Related Genes Associated with Kidney Renal Clear Cell Carcinoma

Weijian Xiong et al. J Immunol Res. .

Abstract

Background: Renal epithelium lesions can cause renal cell carcinoma. This kind of tumor is common among all renal cancers with poor prognosis, of which more than 70% belong to kidney renal clear cell carcinoma. As the pathogenesis of KIRC has not been elucidated, it is necessary to be further explored.

Methods: The Genomic Spatial Event database was used to obtain the analysis dataset (GSE126964) based on the GEO database, and The Cancer Genome Atlas was applied for KIRC data collection. edgeR and limma analyses were subsequently conducted to identify differentially expressed genes. Based on the systems biology approach of WGCNA, potential biomarkers and therapeutic targets of this disease were screened after the establishment of a gene coexpression network. GO and KEGG enrichment used cluster Profiler, enrichplot, and ggplot2 in the R software package. Protein-protein interaction network diagrams were plotted for hub gene collection via the STRING platform and Cytoscape software. Hub genes associated with overall survival time of KIRC patients were ultimately identified using the Kaplan-Meier plotter.

Results: There were 1863 DEGs identified in total and ten coexpressed gene modules discovered using a WGCNA method. GO and KEGG analysis findings revealed that the most enrichment pathways included Notch binding, cell migration, cell cycle, cell senescence, apoptosis, focal adhesions, and autophagosomes. Twenty-seven hub genes were identified, among which FLT1, HNRNPU, ATP6V0D2, ATP6V1A, and ATP6V1H were positively correlated with OS rates of KIRC patients (p < 0.05).

Conclusions: In conclusion, bioinformatic techniques can be useful tools for predicting the progression of KIRC. DEGs are present in both KIRC and normal kidney tissues, which can be considered the KIRC biomarkers.

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Conflict of interest statement

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Figures

Figure 1
Figure 1
DEGs screening. DEGs of cancer patients and healthy group are screened from GEO database using (a) edgeR and (b) limma. DEGs of cancer patients and normal groups are screened from TCGA database using (c) edgeR and (d) limma. Overlapped DEGs of all (e), upregulated (f), and downregulated (g) are obtained from GEO and TCGA databases.
Figure 2
Figure 2
DEGs were analyzed using the WGCNA and gene clustering tree analyses of module eigengenes. (a) Scale-free exponential analysis of various soft threshold powers (β). (b) Clustered module dendrogram (top) and colored bands (bottom) of DEGs, and each dendrogram represents module color. (c) Gene cluster dendrogram based on dissimilarity of topological overlap and module color assignment. (d) Correlation analysis of modules. ME: module eigengenes.
Figure 3
Figure 3
Common module eigengenes and module-clinical signature relationships at different stages of KIRC. The chart rows correspond to different ME, and the columns correspond to clinical parameters. The cell numbers indicate the corresponding correlation and p values. Colors of each cell were filled based on correlation and the color legend. The strength and direction of the correlation are shown in the heatmap on the right panel. ME: module eigengenes.
Figure 4
Figure 4
GO and KEGG analyses of module genes. GO terms (a) and KEGG enrichment analysis (b) of MEblack module. GO terms (c) and KEGG enrichment analysis (d) of MEyellow module. GO terms (e) and KEGG enrichment analysis (f) of MEgreen modules. GO terms (g) and KEGG enrichment analysis (h) of MEmagenta module.
Figure 5
Figure 5
Construction of a PPI network diagram and key gene screening. (a and e) PPI network of DEGs in the MEblack module. (c and f) PPI network of DEGs in the MEyellow module. (b and g) PPI network of DEGs in the MEgreen modules. (d and h) PPI network of DEGs in the MEmagenta module.
Figure 6
Figure 6
Key gene expression analysis in MEblack, MEyellow, MEgreen, and MEmagenta modules. (a) KIF20A, (b) UBE2C, (c) CCNA2, (d) TOP2A, (e) PLK1, (f) RRM2, (g) CDC20, (h) AURKB, (i) TPX2, and (j) CCNB2. Red: KIRC group; gray: normal group.
Figure 7
Figure 7
OS analysis of ten hub genes in KIRC (TCGA data in GEPIA). The expression of (a) KIF20A, (b) UBE2C, (c) CCNA2, (d) TOP2A, (e) PLK1, (f) RRM2, (g) CDC20, (h) AURKB, (i) TPX2, and (j) CCNB2 was highly related to OS rates of KIRC patients.
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