Dataset Information

Identification and validation of a 44-gene expression signature for the classification of renal cell carcinomas.

ABSTRACT: Renal cancers account for more than 3% of all adult malignancies and cause more than 23,400 deaths per year in China alone. The four most common types of kidney tumours include clear cell, papillary, chromophobe and benign oncocytoma. These histological subtypes vary in their clinical course and prognosis, and different clinical strategies have been developed for their management. Some kidney tumours can be very difficult to distinguish based on the pathological assessment of morphology and immunohistochemistry.Six renal cell carcinoma microarray data sets, including 106 clear cell, 66 papillary, 42 chromophobe, 46 oncocytoma and 35 adjacent normal tissue samples, were subjected to integrative analysis. These data were combined and used as a training set for candidate gene expression signature identification. In addition, two independent cohorts of 1020 RNA-Seq samples from The Cancer Genome Atlas database and 129 qRT-PCR samples from Fudan University Shanghai Cancer Center (FUSCC) were analysed to validate the selected gene expression signature.A 44-gene expression signature derived from microarray analysis was strongly associated with the histological differentiation of renal tumours and could be used for tumour subtype classification. The signature performance was further validated in 1020 RNA-Seq samples and 129 qRT-PCR samples with overall accuracies of 93.4 and 93.0%, respectively.A 44-gene expression signature that could accurately discriminate renal tumour subtypes was identified in this study. Our results may prompt further development of this gene expression signature into a molecular assay amenable to routine clinical practice.

SUBMITTER: Wang Q

PROVIDER: S-EPMC5717815 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification and validation of a 44-gene expression signature for the classification of renal cell carcinomas.

Wang Qifeng Q Gan Hualei H Chen Chengshu C Sun Yifeng Y Chen Jinying J Xu Midie M Weng Weiwei W Cao Liyu L Xu Qinghua Q Wang Jian J

Journal of experimental & clinical cancer research : CR 20171206 1

<h4>Background</h4>Renal cancers account for more than 3% of all adult malignancies and cause more than 23,400 deaths per year in China alone. The four most common types of kidney tumours include clear cell, papillary, chromophobe and benign oncocytoma. These histological subtypes vary in their clinical course and prognosis, and different clinical strategies have been developed for their management. Some kidney tumours can be very difficult to distinguish based on the pathological assessment of ...[more]

PMID: 29208006

Similar Datasets

Project description:Clear cell renal cell carcinoma (ccRCC) is the most prevalent subtype of renal cell carcinoma, which is characterized by metabolic reprogramming. Cuproptosis, a novel form of cell death, is highly linked to mitochondrial metabolism and mediated by protein lipoylation. However, the clinical impacts of cuproptosis-related genes (CRGs) in ccRCC largely remain unclear. In the current study, we systematically evaluated the genetic alterations of cuproptosis-related genes in ccRCC. Our results revealed that CDKN2A, DLAT, DLD, FDX1, GLS, PDHA1 and PDHB exhibited differential expression between ccRCC and normal tissues (|log2(fold change)| > 2/3 and p < 0.05). Utilizing an iterative sure independence screening (SIS) method, we separately constructed the prognostic signature of CRGs for predicting the overall survival (OS) and progression-free survival (PFS) in ccRCC patients. The prognostic score of CRGs yielded an area under the curve (AUC) of 0.658 and 0.682 for the prediction of 5-year OS and PFS, respectively. In the Kaplan-Meier survival analysis of OS, a higher risk score of cuproptosis-related gene signature was significantly correlated with worse overall survival (HR = 2.72 (2.01-3.68), log-rank p = 1.76 × 10-7). Patients with a higher risk had a significantly shorter PFS (HR = 2.83 (2.08-3.85), log-rank p = 3.66 × 10-7). Two independent validation datasets (GSE40435 (N = 101), GSE53757 (N = 72)) were collected for meta-analysis, suggesting that CDKN2A (log2(fold change) = 1.46, 95%CI: 1.75-2.35) showed significantly higher expression in ccRCC tissues while DLAT (log2(fold change) = -0.54, 95%CI: -0.93--0.15) and FDX1 (log2(fold change) = -1.01, 95%CI: -1.61--0.42) were lowly expressed. The expression of CDKN2A and FDX1 in ccRCC was also significantly associated with immune infiltration levels and programmed cell death protein 1 (PD-1) expression (CDKN2A: r = 0.24, p = 2.14 × 10-8; FDX1: r = -0.17, p = 1.37 × 10-4). In conclusion, the cuproptosis-related gene signature could serve as a potential prognostic predictor for ccRCC patients and may offer novel insights into the cancer treatment.

Project description:Purpose: To investigate the potential mechanisms contributing to metastasis of clear cell renal cell carcinoma (ccRCC), screen the hub genes, associated pathways of metastatic ccRCC and identify potential biomarkers. Methods: The ccRCC metastasis gene expression profile GSE47352 was employed to analyze the differentially expressed genes (DEGs). DAVID was performed to assess Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The protein-protein interaction (PPI) network and modules were constructed. The function pathway, prognostic and diagnostic analysis of these hub genes was picked out to estimate their potential effects on metastasis of ccRCC. Results: A total of 873 DEGs were identified (503 upregulated genes and 370 downregulated genes). Meanwhile, top 20 hub genes were displayed. GO analysis showed that the top 20 hub genes were enriched in regulation of phosphatidylinositol 3-kinase signaling, positive regulation of DNA replication, protein autophosphorylation, protein tyrosine kinase activity, etc. KEGG analysis indicated these hub genes were enriched in the Ras signaling pathway, PI3K-Akt signaling pathway, HIF-1 signaling pathway, Pathways in cancer, etc. The GO and KEGG enrichment analyses for the hub genes disclosed important biological features of metastatic ccRCC. PPI network showed the interaction of top 20 hub genes. Gene Set Enrichment Analysis (GSEA) revealed that some of the hub genes was associated with metastasis, epithelial mesenchymal transition (EMT), hypoxia cancer and adipogenesis of ccRCC. Some top hub genes were distinctive and new discoveries compared with that of the existing associated researches. Conclusions: Our analysis uncovered that changes in signal pathways such as Ras signaling pathway, PI3K-Akt signaling pathway, etc. may be the main signatures of metastatic ccRCC. We identified several candidate biomarkers related with overall survival (OS) and disease-free survival (DFS) of ccRCC patients. Accordingly, they might be novel therapeutic targets and used as potential biomarkers for diagnosis, prognosis of ccRCC.

Dataset Information

Identification and validation of a 44-gene expression signature for the classification of renal cell carcinomas.

Publications

Identification and validation of a 44-gene expression signature for the classification of renal cell carcinomas.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets