Dataset Information

A systematic search strategy identifies cubilin as independent prognostic marker for renal cell carcinoma.

ABSTRACT: There is an unmet clinical need for better prognostic and diagnostic tools for renal cell carcinoma (RCC).Human Protein Atlas data resources, including the transcriptomes and proteomes of normal and malignant human tissues, were searched for RCC-specific proteins and cubilin (CUBN) identified as a candidate. Patient tissue representing various cancer types was constructed into a tissue microarray (n?=?940) and immunohistochemistry used to investigate the specificity of CUBN expression in RCC as compared to other cancers. Two independent RCC cohorts (n?=?181; n?=?114) were analyzed to further establish the sensitivity of CUBN as RCC-specific marker and to explore if the fraction of RCCs lacking CUBN expression could predict differences in patient survival.CUBN was identified as highly RCC-specific protein with 58% of all primary RCCs staining positive for CUBN using immunohistochemistry. In venous tumor thrombi and metastatic lesions, the frequency of CUBN expression was increasingly lost. Clear cell RCC (ccRCC) patients with CUBN positive tumors had a significantly better prognosis compared to patients with CUBN negative tumors, independent of T-stage, Fuhrman grade and nodal status (HR 0.382, CI 0.203-0.719, P?=?0.003).CUBN expression is highly specific to RCC and loss of the protein is significantly and independently associated with poor prognosis. CUBN expression in ccRCC provides a promising positive prognostic indicator for patients with ccRCC. The high specificity of CUBN expression in RCC also suggests a role as a new diagnostic marker in clinical cancer differential diagnostics to confirm or rule out RCC.

SUBMITTER: Gremel G

PROVIDER: S-EPMC5215231 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A systematic search strategy identifies cubilin as independent prognostic marker for renal cell carcinoma.

Gremel Gabriela G Djureinovic Dijana D Niinivirta Marjut M Laird Alexander A Ljungqvist Oscar O Johannesson Henrik H Bergman Julia J Edqvist Per-Henrik PH Navani Sanjay S Khan Naila N Patil Tushar T Sivertsson Åsa Å Uhlén Mathias M Harrison David J DJ Ullenhag Gustav J GJ Stewart Grant D GD Pontén Fredrik F

BMC cancer 20170104 1

<h4>Background</h4>There is an unmet clinical need for better prognostic and diagnostic tools for renal cell carcinoma (RCC).<h4>Methods</h4>Human Protein Atlas data resources, including the transcriptomes and proteomes of normal and malignant human tissues, were searched for RCC-specific proteins and cubilin (CUBN) identified as a candidate. Patient tissue representing various cancer types was constructed into a tissue microarray (n = 940) and immunohistochemistry used to investigate the specif ...[more]

PMID: 28052770

Similar Datasets

Project description:Gasdermin (GSDM) family members are involved in numerous biological processes, including pyroptosis, as well as in the initiation and progression of various types of cancer. However, the specific role of GSDM genes in clear cell renal cell carcinoma (ccRCC) has yet to be fully clarified. The present study investigated the differential expression and genetic alterations GSDM genes, their effects on prognosis and immune modulation, and their functional enrichment in ccRCC. Several bioinformatics databases were used, including UALCAN, The Cancer Genome Atlas, Gene Expression Profiling Interactive Analysis, Metascape, Tumor Immune Estimation Resource, GSCALite and cBioPortal. The results revealed that the expression levels of GSDMA, GSDMB, GSDMC and GSDMD were significantly upregulated in cancer tissues compared with those in paracancerous tissues in patients with ccRCC, whereas the expression of DFNB59 exhibited the opposite trend. The results were experimentally validated in patients with ccRCC, and it was confirmed that the expression levels of GSDMA, GSDMB, GSDMC, GSDMD and GSDME (DFNA5) were significantly enhanced, whereas (PJVK, DFNB59) expression was reduced. In addition, elevated GSDMB, GSDMD and DFNA5 expression levels were clearly associated with worse pathological characteristics of ccRCC, including a high pathological stage and high tumor grade. Furthermore, the high expression levels of GSDMB, GSDMC, GSDMD, DFNA5 and PJVK were shown to be associated with worse overall survival (OS) and progression-free interval in patients with ccRCC. Both univariate and multivariate analyses indicated that the expression of GSDMB was independently associated with the OS of patients with ccRCC. Additionally, a high mutation rate of GSDM genes (33%) was observed in patients with ccRCC, and GSDM gene mutations were also significantly associated with a poor OS in patients with ccRCC. Significant associations between GSDM genes and ccRCC immunoprofiling and drug sensitivity were also determined. In conclusion, the findings of the present study indicated that GSDMB, GSDMD and DFNA5 may be considered promising therapeutic agents and potential biomarkers for patients with ccRCC. Furthermore, GSDMB could act as an independent predictor for the OS of patients with ccRCC.

Project description:Purpose: In this study, we have undertaken the whole proteomic analysis and got a better understanding of biological processes involved in the development and progression of ccRCC. We hope promising biomarkers can be uncovered to facilitate early diagnosis, predict the prognosis and progression, more importantly, to be applied as potential therapeutic targets. Experimental design: Fresh frozen tissue samples were surgically resected from patients with local or locally advanced ccRCC. Trypsin digested proteins were analyzed using TMT-based LC-MS/MS proteomic approach, followed by bioinformatic analysis. A potential prognostic marker FMNL1 was chosen to be validated in TCGA_KIRC datasets (n=525 and 72), further validation sets (n=10 and 10) and expanded validation sets (n=81 and 16). The effects of FMNL1 on proliferation, migration and invasion were determined by colony formation, wound healing, and transwell assays in 786-O and Caki-1 cells in vitro study. Results: A total of 657 differentially expressed proteins were identified and quantified between ccRCC and adjacent normal tissues (p-value<0.05, FC>2 or<1/2), of which 186 proteins were up-regulated and 471 proteins were down-regulated. Bioinformatic analysis showed enriched metabolic biological processes and pathways. Univariate and multivariate analysis defined FMNL1 as an independent negative prognostic marker in the TCGA datasets. High expression of FMNL1 correlated significantly with tumor stage and distant metastasis (P<0.05) both in the TCGA-KIRC datasets and expanded validation sets. Kaplan-Meier survival curve illustrated that the patients with high FMNL1 protein level had shorter OS time in the expanded validation sets (p=0.0273). In vitro experiments presented the functional effects of FMNL1 knockdown on the inhibition of proliferation, migration and invasion in cancer cell lines. Conclusion and clinical relevance: The proteomic results uncovered sophisticated metabolic reprogramming of ccRCC and indicated that the upregulation of rate-limiting enzymes in glycolysis and mitochondrial impairment may be the cause of metabolic reprogramming in ccRCC. Moreover, FMNL1 has been identified as a promising prognostic marker, and knockdown of FMNL1 could inhibit ccRCC cell proliferation, migration and invasion, which might be used as a new effective therapeutic strategy to inhibit the progression of ccRCC.

Project description:Background:Lung squamous cell carcinoma (LUSC) is the second most common histological subtype of lung cancer (LC), and the prognoses of most LUSC patients are so far still very poor. The present study aimed at integrating lncRNA, miRNA and mRNA expression data to identify lncRNA signature in competitive endogenous RNA (ceRNA) network as a potentially prognostic biomarker for LUSC patients. Methods:Gene expression data and clinical characteristics of LUSC patients were retrieved from The Cancer Genome Atlas (TCGA) database, and were integratedly analyzed using bioinformatics methods including Differentially Expressed Gene Analysis (DEGA), Weighted Gene Co-expression Network Analysis (WGCNA), Protein and Protein Interaction (PPI) network analysis and ceRNA network construction. Subsequently, univariate and multivariate Cox regression analyses of differentially expressed lncRNAs (DElncRNAs) in ceRNA network were performed to predict the overall survival (OS) in LUSC patients. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of multivariate Cox regression model. Gene expression profiling interactive analysis (GEPIA) was used to validate key genes. Results:WGCNA showed that turquoise module including 1,694 DElncRNAs, 2,654 DEmRNAs as well as 113 DEmiRNAs was identified as the most significant modules (cor=0.99, P<1e-200), and differentially expressed RNAs in the module were used to subsequently analyze. PPI network analysis identified FPR2, GNG11 and ADCY4 as critical genes in LUSC, and survival analysis revealed that low mRNA expression of FPR2 and GNG11 resulted in a higher OS rate of LUSC patients. A lncRNA-miRNA-mRNA ceRNA network including 121 DElncRNAs, 18 DEmiRNAs and 3 DEmRNAs was established, and univariate and multivariate Cox regression analysis of those 121 DElncRNAs showed a group of 3 DElncRNAs (TTTY16, POU6F2-AS2 and CACNA2D3-AS1) had significantly prognostic value in OS of LUSC patients. ROC analysis showed that the area under the curve (AUC) of the 3-lncRNA signature associated with 3-year survival was 0.629. Conclusions:The current study provides novel insights into the lncRNA-related regulatory mechanisms underlying LUSC, and identifying 3-lncRNA signature may serve as a potentially prognostic biomarker in predicting the OS of LUSC patients.

Dataset Information

A systematic search strategy identifies cubilin as independent prognostic marker for renal cell carcinoma.

Publications

A systematic search strategy identifies cubilin as independent prognostic marker for renal cell carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets