Dataset Information

Clear Cell Renal Cell Carcinoma is linked to Epithelial-to-Mesenchymal Transition and to Fibrosis.

ABSTRACT: Clear cell renal cell carcinoma (ccRCC) represents the most common type of kidney cancer with high mortality in its advanced stages. Our study aim was to explore the correlation between tumor epithelial-to-mesenchymal transition (EMT) and patient survival. Renal biopsies of tumorous and adjacent nontumorous tissue were taken with a 16 g needle from our patients (n = 26) undergoing partial or radical nephrectomy due to ccRCC RNA sequencing libraries were generated using Illumina TruSeq® Access library preparation protocol and TruSeq Small RNA library preparation kit. Next generation sequencing (NGS) was performed on Illumina HiSeq2500. Comparative analysis of matched sample pairs was done using the Bioconductor Limma/voom R-package. Liquid chromatography-tandem mass spectrometry and immunohistochemistry were applied to measure and visualize protein abundance. We detected an increased generic EMT transcript score in ccRCC Gene expression analysis showed augmented abundance of AXL and MMP14, as well as down-regulated expression of KL (klotho). Moreover, microRNA analyses demonstrated a positive expression correlation of miR-34a and its targets MMP14 and AXL Survival analysis based on a subset of genes from our list EMT-related genes in a publicly available dataset showed that the EMT genes correlated with ccRCC patient survival. Several of these genes also play a known role in fibrosis. Accordingly, recently published classifiers of solid organ fibrosis correctly identified EMT-affected tumor samples and were correlated with patient survival. EMT in ccRCC linked to fibrosis is associated with worse survival and may represent a target for novel therapeutic interventions.

SUBMITTER: Landolt L

PROVIDER: S-EPMC5471444 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Clear Cell Renal Cell Carcinoma is linked to Epithelial-to-Mesenchymal Transition and to Fibrosis.

Landolt Lea L Eikrem Øystein Ø Strauss Philipp P Scherer Andreas A Lovett David H DH Beisland Christian C Finne Kenneth K Osman Tarig T Ibrahim Mohammad M MM Gausdal Gro G Ahmed Lavina L Lorens James B JB Thiery Jean Paul JP Tan Tuan Zea TZ Sekulic Miroslav M Marti Hans-Peter HP

Physiological reports 20170601 11

Clear cell renal cell carcinoma (ccRCC) represents the most common type of kidney cancer with high mortality in its advanced stages. Our study aim was to explore the correlation between tumor epithelial-to-mesenchymal transition (EMT) and patient survival. Renal biopsies of tumorous and adjacent nontumorous tissue were taken with a 16 g needle from our patients (<i>n</i> = 26) undergoing partial or radical nephrectomy due to ccRCC RNA sequencing libraries were generated using Illumina TruSeq<sup ...[more]

PMID: 28596300

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Project description:BackgroundA rising amount of data demonstrates that the epithelial-mesenchymal transition (EMT) in clear cell renal cell carcinomas (ccRCC) is connected with the advancement of the cancer. In order to understand the role of EMT in ccRCC, it is critical to integrate molecules involved in EMT into prognosis prediction. The objective of this project was to establish a prognosis prediction model using genes associated with EMT in ccRCC.MethodsWe acquired the mRNA expression profiles and clinical information about ccRCC from TCGA database. In this study, we measured differentially expressed EMT-related genes (DEEGs) by two comparison groups (tumor versus normal tissues; "stages I-II" versus "stages III-IV" tumor tissues). Based on classification and regression random forest models, we identified the most important DEEGs in predicting prognosis. Afterwards, a risk-score model was created using the identified important DEEGs. The prediction ability of the risk-score model was calculated by the area under the curve (AUC). A nomogram for prognosis prediction was built using the risk-score in combination with clinical factors.ResultsAmong the 72 DEEGs, the classification and regression random forest models identified six hub genes (DKK1, DLX4, IL6, KCNN4, RPL22L1, and SPDEF), which exhibited the highest importance values in both models. Through the expression of these six hub genes, a novel risk-score was developed for the prognosis prediction of ccRCC. ROC curves showed the risk-score performed well in both the training (0.749) and testing (0.777) datasets. According to the survival analysis, individuals who were separated into high/low-risk groups had statistically different outcomes in terms of prognosis. Besides, the risk-score model also showed outstanding ability in assessing the progression of ccRCC after treatment. In terms of nomogram, the concordance index (C-index) was 0.79. Additionally, we predicted the differences in response to chemotherapy drugs among patients from low- and high-risk groups.ConclusionGene signatures related to EMT could be useful in predicting ccRCC prognosis.

Project description:Epithelial-mesenchymal transition (EMT), a reversible cellular program, is critically important in tumor progression and is regulated by a family of transcription factors, induction factors, and an array of signaling pathway genes. The prognostic role and biological functions of EMT-related lncRNAs in ccRCC are largely unknown. In the present study, we analyzed the gene expression data and clinical information retrieved from The Cancer Genome Atlas (TCGA) database (N=512) and International Cancer Genome Consortium (ICGC) database (N=90) which served as training and external validation dataset, respectively. Then, we constructed an EMT-related lncRNA risk signature based on the comprehensive analysis of the EMT-related lncRNA expression data and clinical information. The Kaplan-Meier curve analysis revealed that patients in the low-risk and high-risk groups exhibited significant divergence in the overall survival (OS) and disease-free survival (DFS) of ccRCC, as was confirmed in the validation dataset. The Cox regression analysis of the clinical factors and risk signature in the OS and DFS demonstrated that the risk signature can be utilized as an independent prognostic predictor. Moreover, we developed an individualized prognosis prediction model relying on the nomogram and receive operator curve (ROC) analysis based on the independent factors. The Gene Set Enrichment Analysis (GSEA) indicated that patients in the low-risk group were associated with adherens junction, focal adhesion, MAPK signaling pathway, pathways in cancer, and renal cell carcinoma pathway. In addition, we identified three robust subtypes (named C1, C2 and C3) of ccRCC with distinct clinical characteristics and prognostic role in the TCGA dataset and ICGC dataset. Among them, C1 was associated with a better survival outcome, whereas C2 and C3 was associated with a worse survival outcome and have more advanced-stage patients. Moreover, C2 was more likely to respond to immunotherapy and was sensitive to chemo drugs, this may provide insights to clinicians to develop an individualized treatment. Collectively, this work developed a reliable EMT-related lncRNA risk signature that can independently predict the OS and DFS of ccRCC. Besides, we identified three stable molecular subtypes based on the EMT-related lncRNA expression, which may comprehensively be vital in elucidating the underlying molecular mechanism of ccRCC.

Project description:BACKGROUND:Metastasis is the principal cause of renal cell carcinoma-associated mortality. Metadherin (MTDH) was identified as a vital metastasis driver involved in the metastatic progression of various types of tumors, suggesting that MTDH is a prognostic metastatic biomarker and potential therapeutic target. The role and mechanism of MTDH in the metastatic progression of ccRCC have not yet been adequately explored. RESULTS:MTDH was remarkably elevated in ccRCC tissues, especially in metastatic ccRCC tissues, compared with normal kidney tissues and correlated with advanced clinicopathological features and poor prognosis. MTDH activated ERK signaling and EMT, thus promoting the migration and invasion of ccRCC cells. The interaction between MTDH and SND1 at the protein level was confirmed using immunoprecipitation and immunofluorescence. Based on the analysis of datasets from GEO and TCGA, SND1 was remarkably increased in ccRCC, especially in metastatic ccRCC, and associated with advanced clinicopathological features and poor prognosis. Knockdown of SND1 mainly abolished the migration and invasion of ccRCC cells by blocking MTDH-mediated ERK and EMT signaling activation. CONCLUSION:These results revealed that MTDH may be a prognostic metastatic biomarker of ccRCC that promotes ccRCC metastasis by activating SND1-mediated the ERK and EMT signaling pathways. MTDH may serve as an anti-tumor therapeutic target that can be applied for the clinical treatment of metastatic ccRCC. METHODS:MTDH/SND1 mRNA expression in clear cell renal cell carcinoma (ccRCC) was comprehensively estimated by analysis of GEO-ccRCC and TCGA-KIRC datasets with R software and packages. MTDH protein expression was assessed in a total of 111 ccRCC patients from Peking University First Hospital by immunohistochemistry (IHC). In vitro migration and invasion assays were carried out, and an in vivo metastatic mouse model was developed to investigate the biological functions of MTDH in ccRCC cells. Correlation analysis, immunoprecipitation, western blotting and immunofluorescence were applied to explore the molecular mechanisms of MTDH in ccRCC.

Dataset Information

Clear Cell Renal Cell Carcinoma is linked to Epithelial-to-Mesenchymal Transition and to Fibrosis.

Publications

Clear Cell Renal Cell Carcinoma is linked to Epithelial-to-Mesenchymal Transition and to Fibrosis.

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