Dataset Information

Identification of Hub Genes Associated With Clear Cell Renal Cell Carcinoma by Integrated Bioinformatics Analysis

ABSTRACT:

Background

Clear cell renal cell carcinoma (ccRCC) is a common genitourinary cancer type with a high mortality rate. Due to a diverse range of biochemical alterations and a high level of tumor heterogeneity, it is crucial to select highly validated prognostic biomarkers to be able to identify subtypes of ccRCC early and apply precision medicine approaches.

Methods

Transcriptome data of ccRCC and clinical traits of patients were obtained from the GSE126964 dataset of Gene Expression Omnibus and The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) database. Weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) screening were applied to detect common differentially co-expressed genes. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analysis, survival analysis, prognostic model establishment, and gene set enrichment analysis were also performed. Immunohistochemical analysis results of the expression levels of prognostic genes were obtained from The Human Protein Atlas. Single-gene RNA sequencing data were obtained from the GSE131685 and GSE171306 datasets.

Results

In the present study, a total of 2,492 DEGs identified between ccRCC and healthy controls were filtered, revealing 1,300 upregulated genes and 1,192 downregulated genes. Using WGCNA, the turquoise module was identified to be closely associated with ccRCC. Hub genes were identified using the maximal clique centrality algorithm. After having intersected the hub genes and the DEGs in GSE126964 and TCGA-KIRC dataset, and after performing univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses, ALDOB, EFHD1, and ESRRG were identified as significant prognostic factors in patients diagnosed with ccRCC. Single-gene RNA sequencing analysis revealed the expression profile of ALDOB, EFHD1, and ESRRG in different cell types of ccRCC.

Conclusions

The present results demonstrated that ALDOB, EFHD1, and ESRRG may act as potential targets for medical therapy and could serve as diagnostic biomarkers for ccRCC.

SUBMITTER: Huang H

PROVIDER: S-EPMC8516333 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Clear cell renal cell carcinoma (ccRCC) is one of the most common tumor types of the urinary system. Bioinformatics tools have been used to identify new biomarkers of ccRCC and to explore the mechanisms underlying development and progression of ccRCC. The present study analyzed the differentially expressed genes (DEGs) associated with RCC using data obtained from Gene Expression Omnibus datasets and GEO2R software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of these DEGs was performed and analyzed using the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes to identify the hub genes, defined as the genes with the highest degree of interrelation. Subsequently, differential expression and survival analyses of hub genes was performed using The Cancer Genome Atlas database and Gene Expression Profiling Interactive Analysis (GEPIA) online tool. Using GEO2R, 1,650 DEGs were identified, including 743 upregulated and 907 downregulated genes. GO and KEGG pathway analyses indicated that the upregulated DEGs were primarily involved in blood vessel and vasculature development, whereas downregulated DEGs were primarily involved in organic acid metabolic processes and carboxylic acid metabolic processes. Subsequently, important modules were identified in the PPI network using Cytoscape's Molecular Complex Detection. The 15 most connected hub genes were identified among DEGs, including glycine decarboxylase (GLDC), enolase 2 (ENO2) and topoisomerase II alpha. GEPIA revealed the association between expression levels of hub genes and survival. Specifically, GLDC and ENO2 were associated with the prognosis of patients with RCC and thus, the effects of GLDC and ENO2 involvement in renal cancer were investigated in vitro. GLDC and ENO2 affected the proliferation and apoptosis of renal cancer cells. These hub genes may reveal a new mechanism underlying development or progression of RCC and identify new markers for its diagnosis and prognosis.

Project description:BackgroundClear cell renal cell carcinoma (ccRCC) represents the most invasive and common adult kidney neoplasm. Mounting evidence suggests that microRNAs (miRNAs) are important regulators of gene expression. But their function in tumourigenesis in this tumour type remains elusive. With the development of high throughput technologies such as microarrays and NGS, aberrant miRNA expression has been widely observed in ccRCC. Systematic and integrative analysis of multiple microRNA expression datasets may reveal potential mechanisms by which microRNAs contribute to ccRCC pathogenesis.MethodsWe collected 5 public microRNA expression datasets in ccRCC versus non-matching normal renal tissues from GEO database and published literatures. We analyzed these data sets with an integrated bioinformatics framework to identify expression signatures. The framework incorporates a novel statistic method for abnormal gene expression detection and an in-house developed predictor to assess the regulatory activity of microRNAs. We then mapped target genes of DE-miRNAs to different databases, such as GO, KEGG, GeneGo etc, for functional enrichment analysis.ResultsUsing this framework we identified a consistent panel of eleven deregulated miRNAs shared by five independent datasets that can distinguish normal kidney tissues from ccRCC. After comparison with 3 RNA-seq based microRNA profiling studies, we found that our data correlated well with the results of next generation sequencing. We also discovered 14 novel molecular pathways that are likely to play a role in the tumourigenesis of ccRCC.ConclusionsThe integrative framework described in this paper greatly improves the inter-dataset consistency of microRNA expression signatures. Consensus expression profile should be identified at pathway or network level to address the heterogeneity of cancer. The DE-miRNA signature and novel pathways identified herein could provide potential biomarkers for ccRCC that await further validation.

Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is the most common malignant tumor of kidney with high mortality. The pathogenesis of ccRCC is complicated and effective prognostic predictors for clinical practice are still limited. This study aimed to identify significant genes with prognostic influence in ccRCC via bioinformatics analysis.MethodsFour gene expression profiles were acquired from the Gene Expression Omnibus (GEO) database, including 168 ccRCC tissues and 143 normal tissues. Common differentially expressed genes (DEGs) between ccRCC tissues and normal kidney tissues were screened out. Then gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were investigated. Protein-protein interaction (PPI) network of the common DEGs was diagrammed and analyzed. Kaplan-Meier analysis was conducted to identify genes with prognostic influence in ccRCC. Gene Expression Profiling Interactive Analysis (GEPIA) was finally applied to validating differential expression of genes.ResultsNinety-nine common DEGs between ccRCC tissues and normal kidney tissues were eventually screened out (P<0.05, |log FC| >2). GO functional analysis showed that the down-regulated genes were enriched in excretion, negative regulation of cell proliferation, heparin binding and cellular response to BMP stimulus, etc. KEGG pathway analysis indicated that the common DEGs were particularly enriched in HIF-1 signaling pathway and aldosterone-regulated sodium reabsorption. Seven core DEGs were distinguished through PPI network analysis, of which 6 core genes ANGPTL4, CA9, CXCR4, LOX, EGF and HRG showed significantly prognostic difference in patients with ccRCC by Kaplan-Meier analysis (P<0.05). And GEPIA confirmed these genes were expressed differentially between tumor and normal tissues (P<0.05). High expression of HRG was correlated with good OS in ccRCC patients. Specifically, HRG was commonly down-regulated in ccRCC tissues compared with normal tissues according to GEPIA.ConclusionsOur study shows that high expression of HRG denotes a better prognosis in ccRCC patients. HRG is down-regulated in ccRCC tissues compared with normal kidney tissues. The selective expression pattern suggests that HRG could be a novel prognostic predictor and potential therapeutic target for ccRCC patients.

Project description:Psoriasis is a chronic, prolonged, and recurrent inflammatory skin disease and the current therapeutics can only alleviate the symptoms rather than cure it completely. Therefore, we aimed to identify the molecular signatures and specific biomarkers of psoriasis to provide novel clues for psoriasis and targeted therapy. In the present study, the Gene Expression Omnibus (GEO) database was used to retrieve three microarray datasets (GSE166388, GSE50790 and GSE42632) and to explore the differentially expressed genes (DEGs) in psoriasis using the Affy package in R software. The gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment were utilized to determine the common DEGs and their capabilities. The STRING database was used to develop DEG-encoded proteins and a protein-protein interaction network (PPI) and the Cytohubba plugin to classify hub genes. Using the NetworkAnalyst platform, we detected transcription factors (TFs), microRNAs and drug candidates interacting with hub genes. In addition, the expression levels of hub genes in HaCaT cells were detected by western blot. We screened the up- and downregulated DEGs from the transcriptome microarrays of corresponding psoriasis patients. Functional enrichment of DEGs in psoriasis was mainly associated with positive regulation of leukocyte cell-cell adhesion and T cell activation, cytokine binding, cytokine activity and the Wnt signaling pathway. Through further data processing, we obtained 57 intersecting genes in the three datasets and probed them in STRING to determine the interaction of their expressed proteins and we obtained the critical 10 hub genes in the Cytohubba plugin, including TOP2A, CDKN3, MCM10, PBK, HMMR, CEP55, ASPM, KIAA0101, ESC02, and IL-1β. Using these hub genes as targets, we obtained 35 TFs and 213 miRNAs that may regulate these genes and 33 potential therapeutic agents for psoriasis. Furthermore, the expression levels of TOP2A, MCM10, PBK, ASPM, KIAA0101 and IL-1β were observably increased in HaCaT cells. In conclusion, we identified potential biomarkers, risk factors and drugs for psoriasis.