Dataset Information

Key pathways and genes controlling the development and progression of clear cell renal cell carcinoma (ccRCC) based on gene set enrichment analysis.

ABSTRACT:

Background

Clear-cell renal cell carcinoma (ccRCC) is one of the most common types of kidney cancer in adults; however, its causes are not completely understood. The study was designed to filter the key pathways and genes associated with the occurrence or development of ccRCC, acquaint its pathogenesis at gene and pathway level, to provide more theory evidence and targeted therapy for ccRCC.

Methods

Gene set enrichment analysis (GSEA) and meta-analysis (Meta) were used to screen the critical pathways and genes which may affect the occurrence and progression of ccRCC on the transcription level. Corresponding pathways of significant genes were obtained with the online website DAVID ( http://david.abcc.ncifcrf.gov/ ).

Results

Thirty seven consistent pathways and key genes in these pathways related to ccRCC were obtained with combined GSEA and meta-analysis. These pathways were mainly involved in metabolism, organismal systems, cellular processes and environmental information processing.

Conclusion

The gene pathways that we identified could provide insight concerning the development of ccRCC. Further studies are needed to determine the biological function for the positive genes.

SUBMITTER: Huang H

PROVIDER: S-EPMC3955485 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Kidney cancer ranks as one of the top 10 causes of cancer death; this cancer is difficult to detect, difficult to treat, and poorly understood. The most common subtype of kidney cancer is clear cell renal cell carcinoma (ccRCC) and its progression is influenced by complex gene interactions. Few clinical studies have investigated the molecular markers associated with the progression of ccRCC. In this study, we collected microarray profiles of 72 ccRCCs and matched normal samples to identify differentially expressed genes (DEGs). Then a weighted gene co-expression network analysis (WGCNA) was conducted to identify co-expressed gene modules. By relating all co-expressed modules to clinical features, we found that the brown module and Fuhrman grade had the highest correlation (r = -0.8, p = 1e-09). Thus, the brown module was regarded as a clinically significant module and subsequently analyzed. Functional annotation showed that the brown module focused on metabolism-related biological processes and pathways, such as fatty acid oxidation and amino acid metabolism. We then performed a protein-protein interaction (PPI) network to identify the hub nodes in the brown module. It is worth noting that only one candidate, acetyl-CoA acetyltransferase (ACAT1), was considered to be the final target most relevant to the Fuhrman grade of ccRCC, by applying the intersection of hub genes in the co-expressed network and the PPI network. ACAT1 was subsequently validated using another two external microarray datasets and the TCGA dataset. Intriguingly, validation results indicated that ACAT1 was negatively correlated with four grades of ccRCC, which was also consistent with our results from qRT-PCR analysis and immunohistochemistry staining of clinical samples. Overexpression of ACAT1 inhibited the proliferation and migration of human ccRCC cells in vitro. In addition, the Kaplan-Meier survival curve showed that patients with a lower expression of ACAT1 showed a significantly lower overall survival rate and disease-free survival rate, indicating that ACAT1 could act as a prognostic and recurrence/progression biomarker of ccRCC. In summary, we found and confirmed that ACAT1 might help to identify the progression of ccRCC, which might have important clinical implications for enhancing risk stratification, therapeutic decision, and prognosis prediction in ccRCC patients.