P48

Renal cell carcinoma (RCC) is the most widespread kidney tumor, which originates mostly from distal kidney tubules. RCC is the major mortality cause in excretory system cancer in adults, and constitutes about 80% of various kidney cancers. The 5-year survival rate is 60–70%, but lowers significantly...

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Main Authors: S. Solodskikh, V. Bashmakov, T. Gorbacheva, A. Panevina, A. Maslov, A. Mikhailov, I. Moshurov, V. Popov
Format: Article
Language:English
Published: Elsevier 2015-11-01
Series:EJC Supplements
Online Access:http://www.sciencedirect.com/science/article/pii/S1359634915001007
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spelling doaj-91e013b8e871437bae47dba085c699962020-11-25T03:58:12ZengElsevierEJC Supplements1359-63492015-11-011315510.1016/j.ejcsup.2015.08.099P48S. Solodskikh0V. Bashmakov1T. Gorbacheva2A. Panevina3A. Maslov4A. Mikhailov5I. Moshurov6V. Popov7Voronezh State University, Voronezh, Russian FederationVoronezh State University, Voronezh, Russian FederationVoronezh State University, Voronezh, Russian FederationVoronezh State University, Voronezh, Russian FederationAlbert Einstein College of Medicine of Yeshiva University, NY, USAVoronezh State University, Voronezh, Russian FederationVoronezh Regional Clinical Oncology Center, Voronezh, Russian FederationVoronezh State University, Voronezh, Russian FederationRenal cell carcinoma (RCC) is the most widespread kidney tumor, which originates mostly from distal kidney tubules. RCC is the major mortality cause in excretory system cancer in adults, and constitutes about 80% of various kidney cancers. The 5-year survival rate is 60–70%, but lowers significantly in case of metastasis formation. The tumor is relatively resistant to chemotherapy and radiation therapy, but responds to immunotherapy. Target drugs (e.g. sunitinib, bevacizumab, α-interferon, sorafenib) are more preferable in RCC therapy. In most cases RCC is caused by obesity, smoking, arterial hypertension and hereditary factors. 4 patients of Voronezh Regional Clinical Oncology Center, aged from 50 to 75 years old were enrolled in this study. All were diagnosed with renal cell carcinoma confirmed by immunohistochemical analysis. Gene expression profiling was performed using Affymetrix GeneAtlas system with Affymetrix Hunam Gene 1.1 ST DNA- microarrays. Data moralization, statistical analysis and differentially expressed genes (DEG) list creation were performed using Partek Genomics Suite v. 6.6. DNA samples were sequenced on Ion Proton sequencer with Comprehensive Cancer Panel primer pool. Subsequent pathway analysis and biological interpretation were conducted using Ingenuity Pathway Analysis system and Ion Reporter Suite. 3528 genes were differentially expressed with expression change more than 2 times, and 351 genes changed their expression more than 5 times (p-value <0.05). Only mutations causing either amino-acid substitution in corresponding protein, open reading frame shift, or truncated transcript formation were taken into account. Targeted DNA sequencing revealed 99 common mutations in normal tissues of all test subjects. 14 mutations were localized in SDHB, TRIM33, PDE4DIP, PBX1, ABL2, MTR, VHL, ROS1, PRKDC, CSMD3, MLLT10, TRIP11, PER1, genes and were tumor-specific in all patients. 33 mutations were localized in promoter regions of EGFR, PDGFRA and HNF1A genes. The most representative metabolic and signaling pathways according to Ingenuity knowledge base were “FXR/RXR Activation”, “Atherosclerosis Signaling”, “LXR/RXR Activation”, “Production of NO and ROS in Macrophages”, “Cell migration and adhesion”. Five most downregulated genes among all patients were CALB1 (−187), HPD (−133), KNG1 (−126), SLC36A2 (−126) and PAH (−122). Five most upregulated genes were TNFAIP6 (+34), ANGPT2 (+23), SERPINE1 (+22), CP (+20), HILPDA (+20). Genes with mutations and differentially expressed genes were simultaneously included in pathways analysis in order to generate gene networks with possible upstream regulators (mutated genes) included. This allowed observing a number of gene interactions not present in existing reports. This method of genomic and transcriptomic data integration allowed us to determine the sources of mRNA level variation. A number of mutations and specific DEGs discovered in this study are not registered in existing databases of annotated mutations, which allows us to propose population heterogeneity of RCC causes. These DNA mutations and mRNA level changes could be used as predictive biomarkers of renal cell carcinoma.http://www.sciencedirect.com/science/article/pii/S1359634915001007
collection DOAJ
language English
format Article
sources DOAJ
author S. Solodskikh
V. Bashmakov
T. Gorbacheva
A. Panevina
A. Maslov
A. Mikhailov
I. Moshurov
V. Popov
spellingShingle S. Solodskikh
V. Bashmakov
T. Gorbacheva
A. Panevina
A. Maslov
A. Mikhailov
I. Moshurov
V. Popov
P48
EJC Supplements
author_facet S. Solodskikh
V. Bashmakov
T. Gorbacheva
A. Panevina
A. Maslov
A. Mikhailov
I. Moshurov
V. Popov
author_sort S. Solodskikh
title P48
title_short P48
title_full P48
title_fullStr P48
title_full_unstemmed P48
title_sort p48
publisher Elsevier
series EJC Supplements
issn 1359-6349
publishDate 2015-11-01
description Renal cell carcinoma (RCC) is the most widespread kidney tumor, which originates mostly from distal kidney tubules. RCC is the major mortality cause in excretory system cancer in adults, and constitutes about 80% of various kidney cancers. The 5-year survival rate is 60–70%, but lowers significantly in case of metastasis formation. The tumor is relatively resistant to chemotherapy and radiation therapy, but responds to immunotherapy. Target drugs (e.g. sunitinib, bevacizumab, α-interferon, sorafenib) are more preferable in RCC therapy. In most cases RCC is caused by obesity, smoking, arterial hypertension and hereditary factors. 4 patients of Voronezh Regional Clinical Oncology Center, aged from 50 to 75 years old were enrolled in this study. All were diagnosed with renal cell carcinoma confirmed by immunohistochemical analysis. Gene expression profiling was performed using Affymetrix GeneAtlas system with Affymetrix Hunam Gene 1.1 ST DNA- microarrays. Data moralization, statistical analysis and differentially expressed genes (DEG) list creation were performed using Partek Genomics Suite v. 6.6. DNA samples were sequenced on Ion Proton sequencer with Comprehensive Cancer Panel primer pool. Subsequent pathway analysis and biological interpretation were conducted using Ingenuity Pathway Analysis system and Ion Reporter Suite. 3528 genes were differentially expressed with expression change more than 2 times, and 351 genes changed their expression more than 5 times (p-value <0.05). Only mutations causing either amino-acid substitution in corresponding protein, open reading frame shift, or truncated transcript formation were taken into account. Targeted DNA sequencing revealed 99 common mutations in normal tissues of all test subjects. 14 mutations were localized in SDHB, TRIM33, PDE4DIP, PBX1, ABL2, MTR, VHL, ROS1, PRKDC, CSMD3, MLLT10, TRIP11, PER1, genes and were tumor-specific in all patients. 33 mutations were localized in promoter regions of EGFR, PDGFRA and HNF1A genes. The most representative metabolic and signaling pathways according to Ingenuity knowledge base were “FXR/RXR Activation”, “Atherosclerosis Signaling”, “LXR/RXR Activation”, “Production of NO and ROS in Macrophages”, “Cell migration and adhesion”. Five most downregulated genes among all patients were CALB1 (−187), HPD (−133), KNG1 (−126), SLC36A2 (−126) and PAH (−122). Five most upregulated genes were TNFAIP6 (+34), ANGPT2 (+23), SERPINE1 (+22), CP (+20), HILPDA (+20). Genes with mutations and differentially expressed genes were simultaneously included in pathways analysis in order to generate gene networks with possible upstream regulators (mutated genes) included. This allowed observing a number of gene interactions not present in existing reports. This method of genomic and transcriptomic data integration allowed us to determine the sources of mRNA level variation. A number of mutations and specific DEGs discovered in this study are not registered in existing databases of annotated mutations, which allows us to propose population heterogeneity of RCC causes. These DNA mutations and mRNA level changes could be used as predictive biomarkers of renal cell carcinoma.
url http://www.sciencedirect.com/science/article/pii/S1359634915001007
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