Identification of CFTR as a novel key gene in chromophobe renal cell carcinoma through bioinformatics analysis

Bioinformatics Study Identified EGF as a Crucial Gene in Papillary Renal Cell Cancer

Background

Due to a lack of knowledge of the disease process, papillary renal cell carcinoma (PRCC) has a dismal outlook. This research was aimed at uncovering the possible biomarkers and the underlying principles in PRCC using a bioinformatics method.

Methods

We searched the Gene Expression Omnibus (GEO) datasets to obtain the GSE11151 and GSE15641 gene expression profiles of PRCC. We used the R package limma to identify the differentially expressed genes (DEGs). The online tool DAVID and ClusterProfiler package in R software were used to analyze Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway dominance, respectively. The STRING database was utilized to construct the PPI network of DEGs. Using the Cytoscape technology, a protein-protein interaction (PPI) network that associated with DEGs was created, and the hub genes were identified using the Cytoscape plug-in CytoHubba. The hub genes were subjected to a Kaplan-Meier analysis to identify their correlations with survival rates.

Results

From the selected datasets, a total of 240 common DEGs were identified in the PRCC, including 50 upregulated genes and 190 downregulated regulated genes. Renal growth, external exosome, binding of heparin, and metabolic processes were all substantially associated with DEGs. The CytoHubba plug-in-based analysis identified the 10 hub genes (ALB, KNG1, C3, CXCL12, EGF, TIMP1, VCAN, PLG, LAMC1, and CASR) from the original PPI network. The higher expression group of EGF was associated with poor outcome in patients with PRCC.

Conclusions

We revealed important genes and proposed biological pathways that may be implicated in the formation of PRCC. EGF might be a predictive biomarker for PRCC and therefore should be investigated as a novel treatment strategy.

Modeling Neoplastic Growth in Renal Cell Carcinoma and Polycystic Kidney Disease

Renal cell carcinoma (RCC) and autosomal dominant polycystic kidney disease (ADPKD) share several characteristics, including neoplastic cell growth, kidney cysts, and limited therapeutics. As well, both exhibit impaired vasculature and compensatory VEGF activation of angiogenesis. The PI3K/AKT/mTOR and Ras/Raf/ERK pathways play important roles in regulating cystic and tumor cell proliferation and growth. Both RCC and ADPKD result in hypoxia, where HIF-α signaling is activated in response to oxygen deprivation. Primary cilia and altered cell metabolism may play a role in disease progression. Non-coding RNAs may regulate RCC carcinogenesis and ADPKD through their varied effects. Drosophila exhibits remarkable conservation of the pathways involved in RCC and ADPKD. Here, we review the progress towards understanding disease mechanisms, partially overlapping cellular and molecular dysfunctions in RCC and ADPKD and reflect on the potential for the agile Drosophila genetic model to accelerate discovery science, address unresolved mechanistic aspects of these diseases, and perform rapid pharmacological screens.

Identification of novel prognostic biomarkers in renal cell carcinoma

Objective: To identify novel prognostic biomarkers in renal cell carcinoma (RCC).

Results: 12 coding genes and one miRNA were finally identified as prognostic biomarkers. All of them were related to a poor prognosis. Lower expression levels of the coding genes were observed in higher clinical stages. Prognostic signatures including 7 biomarkers were identified. Patients in the high-risk group had worse survival than those in the low-risk group. The areas under the curves in different years indicated that it was a valuable signature in prognosis. It was found that elevated WDR72 inhibited the survival and invasion of 786-O and 769P cells in vitro.

Conclusions: Thirteen prognostic biomarkers of RCC were identified. Among them, 7 biomarkers comprised a signature to evaluate the RCC prognosis. WDR72 was a cancer suppressor and a potential therapeutic target in RCC.

Methods: Differentially expressed genes/miRNAs (DEGs/DEMs) and prognosis-related genes/miRNAs were acquired from public database. Prognostic biomarkers were identified by overlapping the significant DEGs/DEMs and prognosis-related genes/miRNAs. The associations between these biomarkers and the clinical stages were analyzed. All of these prognostic biomarkers were further investigated with multi-variable Cox regression. Finally, the inhibitory effect of WDR72 on the growth and invasion of RCC cells was studied.

Identification and Analysis of Novel Biomarkers Involved in Chromophobe Renal Cell Carcinoma by Integrated Bioinformatics Analyses

In renal cell carcinoma, chromophobe renal cell carcinoma (ChRCC) is a distinct subtype, whose clinical manifestations often lack specificity, and the molecular mechanisms of ChRCC tumorigenesis remain generally vague. The target of this study was to discover novel biomarkers involved in ChRCC by integrated bioinformatics analyses. We found 2608 differentially expressed genes (DEGs), of which 1518 were upregulated and 1090 were downregulated. Gene ontology (GO) analysis of DEGs uncovered significant functional enrichment in three aspects: biological process (BP), molecular function (MF), and cellular component (CC). The results of Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated DEGs were largely enriched in retinol metabolism, arachidonic acid metabolism, and pentose and glucuronate interconversions. Then, the protein–protein interactions (PPI) network was constructed and top three hub genes were identified by the Cytoscape plugin cytoHubba. Through calculating the degree, betweenness centrality, and Stress of mRNAs, CENPA was upregulated and KNG1 and AGT were downregulated. A survival assay performed according to Oncomine data showed only CENPA high expression exhibited a worse prognosis. This study identified crucial genes and pathways for the progress of ChRCC, and CENPA might be a novel biomarker for diagnosis, treatment, and prognosis of ChRCC.