Dataset Information

Bioinformatic identification of key genes and analysis of prognostic values in clear cell renal cell carcinoma.

ABSTRACT: The present study aimed to identify new key genes as potential biomarkers for the diagnosis, prognosis or targeted therapy of clear cell renal cell carcinoma (ccRCC). Three expression profiles (GSE36895, GSE46699 and GSE71963) were collected from Gene Expression Omnibus. GEO2R was used to identify differentially expressed genes (DEGs) in ccRCC tissues and normal samples. The Database for Annotation, Visualization and Integrated Discovery was utilized for functional and pathway enrichment analysis. STRING v10.5 and Molecular Complex Detection were used for protein-protein interaction (PPI) network construction and module analysis, respectively. Regulation network analyses were performed with the WebGestal tool. UALCAN web-portal was used for expression validation and survival analysis of hub genes in ccRCC patients from The Cancer Genome Atlas (TCGA). A total of 65 up- and 164 downregulated genes were identified as DEGs. DEGs were enriched with functional terms and pathways compactly related to ccRCC pathogenesis. Seventeen hub genes and one significant module were filtered out and selected from the PPI network. The differential expression of hub genes was verified in TCGA patients. Kaplan-Meier plot showed that high mRNA expression of enolase 2 (ENO2) was associated with short overall survival in ccRCC patients (P=0.023). High mRNA expression of cyclin D1 (CCND1) (P<0.001), fms related tyrosine kinase 1 (FLT1) (P=0.004), plasminogen (PLG) (P<0.001) and von Willebrand factor (VWF) (P=0.008) appeared to serve as favorable factors in survival. These findings indicate that the DEGs may be key genes in ccRCC pathogenesis and five genes, including ENO2, CCND1, PLT1, PLG and VWF, may serve as potential prognostic biomarkers in ccRCC.

SUBMITTER: Luo T

PROVIDER: S-EPMC6036467 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Bioinformatic identification of key genes and analysis of prognostic values in clear cell renal cell carcinoma.

Luo Ting T Chen Xiaoyi X Zeng Shufei S Guan Baozhang B Hu Bo B Meng Yu Y Liu Fanna F Wong Taksui T Lu Yongpin Y Yun Chen C Hocher Berthold B Yin Lianghong L

Oncology letters 20180530 2

The present study aimed to identify new key genes as potential biomarkers for the diagnosis, prognosis or targeted therapy of clear cell renal cell carcinoma (ccRCC). Three expression profiles (GSE36895, GSE46699 and GSE71963) were collected from Gene Expression Omnibus. GEO2R was used to identify differentially expressed genes (DEGs) in ccRCC tissues and normal samples. The Database for Annotation, Visualization and Integrated Discovery was utilized for functional and pathway enrichment analysi ...[more]

PMID: 30008862

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Project description:The present techniques of clinical and histopathological diagnosis hardly distinguish chromophobe renal cell carcinoma (ChRCC) from renal oncocytoma (RO). To identify differentially expressed genes (DEGs) as effective biomarkers for diagnosis and prognosis of ChRCC and RO, three mRNA microarray datasets (GSE12090, GSE19982, and GSE8271) were downloaded from the GEO database. Functional enrichment analysis of DEGs was performed by DAVID. STRING and Cytoscape were applied to construct the protein-protein interaction (PPI) network and key modules of DEGs. Visualized plots were conducted by the R language. We downloaded clinical data from the TCGA database and the influence of key genes on the overall survival of ChRCC was performed by Kaplan-Meier and Cox analyses. Gene set enrichment analysis (GSEA) was utilized in exploring the function of key genes. A total of 79 DEGs were identified. Enrichment analyses revealed that the DEGs are closely related to tissue invasion and metastasis of cancer. Subsequently, 14 hub genes including ESRP1, AP1M2, CLDN4, and CLDN7 were detected. Kaplan-Meier analysis indicated that the low expression of CLDN7 and GNAS was related to the worse overall survival in patients with ChRCC. Univariate Cox analysis showed that CLDN7 might be a helpful biomarker for ChRCC prognosis. Subgroup analysis revealed that the expression of CLDN7 showed a downtrend with the development of the clinical stage, topography, and distant metastasis of ChRCC. GSEA analysis identified that cell adhesion molecules cams, B cell receptor signaling pathway, T cell receptor signaling pathway, RIG-I like receptor signaling pathway, Toll-like receptor signaling pathway, and apoptosis pathway were associated with the expression of CLDN7. In conclusion, ESRP1, AP1M2, CLDN4, PRSS8, and CLDN7 were found to distinguish ChRCC from RO. Besides, the low expression of CLDN7 was closely related to ChRCC progression and could serve as an independent risk factor for the overall survival in patients with ChRCC.

Project description:Clear cell renal cell carcinoma (ccRCC) is one of the most common and lethal types of cancer within the urinary system. Great efforts have been made to elucidate the pathogeny. However, the molecular mechanism of ccRCC is still not well understood. The aim of this study is to identify key genes in the carcinogenesis and progression of ccRCC. The mRNA microarray dataset GSE53757 was downloaded from the Gene Expression Omnibus database. The GSE53757 dataset contains tumor and matched paracancerous specimens from 72 ccRCC patients with clinical stage I to IV. The linear model of microarray data (limma) package in R language was used to identify differentially expressed genes (DEGs). The protein-protein interaction (PPI) network of the DEGs was constructed using the search tool for the retrieval of interacting genes (STRING). Subsequently, we visualized molecular interaction networks by Cytoscape software and analyzed modules with MCODE. A total of 1,284, 1,416, 1,610 and 1,185 up-regulated genes, and 932, 1,236, 1,006 and 929 down-regulated genes were identified from clinical stage I to IV ccRCC patients, respectively. The overlapping DEGs among the four clinical stages contain 870 up-regulated and 645 down-regulated genes. The enrichment analysis of DEGs in the top module was carried out with DAVID. The results showed the DEGs of the top module were mainly enriched in microtubule-based movement, mitotic cytokinesis and mitotic chromosome condensation. Eleven up-regulated genes and one down-regulated gene were identified as hub genes. Survival analysis showed the high expression of CENPE, KIF20A, KIF4A, MELK, NCAPG, NDC80, NUF2, TOP2A, TPX2 and UBE2C, and low expression of ACADM gene could be involved in the carcinogenesis, invasion or recurrence of ccRCC. Literature retrieval results showed the hub gene NDC80, CENPE and ACADM might be novel targets for the diagnosis, clinical treatment and prognosis of ccRCC. In conclusion, the findings of present study may help us understand the molecular mechanisms underlying the carcinogenesis and progression of ccRCC, and provide potential diagnostic, therapeutic and prognostic biomarkers.

Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is a commonly occurring tumor. Through a deeper understanding of the immune regulatory mechanisms in the tumor microenvironment, immunotherapy may serve as a potential treatment for cancer patients. This study aimed at identifying the survival-related immune cells and hub genes, which could be potential targets for immunotherapy in ccRCC.MethodsThe gene expression profiles and clinical data of ccRCC patients were extracted from UCSC Xena and Gene Expression Omnibus (GEO) databases. Kaplan-Meier (KM) survival and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were utilized to select the survival-related tumor-infiltrating immune cells. Multivariate Cox regression was utilized to develop a signature based on the tumor-infiltrating immune cells (TIICs). Based on the signature, the risk score was calculated, following which the samples were divided into high-risk and low-risk groups. Differentially expressed genes (DEGs) between the two risk groups were identified. Functional enrichment analysis was performed and cytoHubba plug-in of Cytoscape was used to identify the hub genes. Multiple datasets were utilized to validate these hub genes, including the Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, and the Human Protein Atlas (HPA), and the GEO datasets. Finally, a correlation analysis was performed to evaluate the relationship between the hub genes and TIICs.ResultsFour immune survival-related cells, including T cell CD4 memory-activated, T cell regulatory (Tregs), eosinophils, and mast cell resting were identified. Nine immune-specific hub genes were identified, which included APOE, CASR, CTLA4, CXCL8, EGF, F2, KNG1, MMP9, and IL6. Furthermore, these hub genes were significantly correlated with clinical traits and closely associated with some TIICs.ConclusionA total of four survival-related immune cell types and nine hub genes were found to be closely associated with ccRCC. These findings may have implications for the development of novel potential immunotherapeutic targets for ccRCC.

Dataset Information

Bioinformatic identification of key genes and analysis of prognostic values in clear cell renal cell carcinoma.

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Bioinformatic identification of key genes and analysis of prognostic values in clear cell renal cell carcinoma.

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