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. 2019 May;7(5):e607.
doi: 10.1002/mgg3.607. Epub 2019 Feb 21.

The promising novel biomarkers and candidate small molecule drugs in kidney renal clear cell carcinoma: Evidence from bioinformatics analysis of high-throughput data

Bo Zhang  1   2 , Qiong Wu  1   2 , Ziheng Wang  2   3 , Ran Xu  1 , Xinyi Hu  3 , Yidan Sun  4 , Qiuhong Wang  2 , Fei Ju  2 , Shiqi Ren  3 , Chenlin Zhang  5 , Lin Qin  6 , Qianqian Ma  7 , You Lang Zhou  2
Affiliations

The promising novel biomarkers and candidate small molecule drugs in kidney renal clear cell carcinoma: Evidence from bioinformatics analysis of high-throughput data

Bo Zhang et al. Mol Genet Genomic Med. 2019 May.

Abstract

Background: Kidney renal clear cell carcinoma (KIRC) is the most common subtype of renal tumor. However, the molecular mechanisms of KIRC pathogenesis remain little known. The purpose of our study was to identify potential key genes related to the occurrence and prognosis of KIRC, which could serve as novel diagnostic and prognostic biomarkers for KIRC.

Methods: Three gene expression profiles from gene expression omnibus database were integrated to identify differential expressed genes (DEGs) using limma package. Enrichment analysis and PPI construction for these DEGs were performed by bioinformatics tools. We used Gene Expression Profiling Interactive Analysis (GEPIA) database to further analyze the expression and prognostic values of hub genes. The GEPIA database was used to further validate the bioinformatics results. The Connectivity Map was used to identify candidate small molecules that could reverse the gene expression of KIRC.

Results: A total of 503 DEGs were obtained. The PPI network with 417 nodes and 1912 interactions was constructed. Go and KEGG pathway analysis revealed that these DEGs were most significantly enriched in excretion and valine, leucine, and isoleucine degradation, respectively. Six DEGs with high degree of connectivity (ACAA1, ACADSB, ALDH6A1, AUH, HADH, and PCCA) were selected as hub genes, which significantly associated with worse survival of patients. Finally, we identified the top 20 most significant small molecules and pipemidic acid was the most promising small molecule to reverse the KIRC gene expression.

Conclusions: This study first uncovered six key genes in KIRC which contributed to improving our understanding of the molecular mechanisms of KIRC pathogenesis. ACAA1, ACADSB, ALDH6A1, AUH, HADH, and PCCA could serve as the promising novel biomarkers for KIRC diagnosis, prognosis, and treatment.

Keywords: bioinformatics analysis; candidate small molecules; kidney renal clear cell carcinoma; novel biomarkers.

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Conflict of interest statement

None.

Figures

Figure 1
Figure 1
The workflow of this study for identifying key genes and pathways in KIRC
Figure 2
Figure 2
(A) Volcano plot of gene expression profile data between KIRC and normal tissues in each dataset. Red dots: significantly upregulated genes in KIRC; Green dots: significantly downregulated genes in KIRC; Black dots: nondifferentially expressed genes. p < 0.05 and |log2 FC|>1 were considered as significant. (B) a. Venn diagram of 503 overlap DEGs from GSE781, GSE6344, and GSE100666 datasets. b. Upregulated overlap DEGs; c. Downregulated overlap DEGs
Figure 3
Figure 3
Functional and signaling pathway analysis of the overlapped DEGs in KIRC. (a) Biological processes (b) Cellular components (c) Molecular function (d) KEGG pathway
Figure 4
Figure 4
Protein–protein interaction networks construction and module analysis
Figure 5
Figure 5
(a) The heatmap of module genes between KIRC and normal samples. (b) The biological process of module genes analyzed by BiNGO. The color depth of nodes represents the corrected p‐value. The size of nodes represents the number of genes involved
Figure 6
Figure 6
The expression level of hub genes between KIRC and normal tissues in three datasets
Figure 7
Figure 7
(a) The expression of hub genes between KIRC tissues and normal tissues. (b) The prognostic value of hub genes
Figure 8
Figure 8
Representative immunohistochemistry staining results reveal the protein level expression of hub genes in KIRC and normal tissues
Figure 9
Figure 9
(a) The network of module genes and their coexpression genes constructed by cBioPortal. Nodes with thick outline: hub genes; Nodes with thin outline: coexpression genes. (b) Pop plot of top 20 identified small molecules that could reverse the gene expression of KIRC
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