Bioinformatics analysis and verification of gene targets for renal clear cell carcinoma

BACKGROUND It is estimated that there are 338,000 new renal-cell carcinoma releases every year in the world. Renal cell carcinoma (RCC) is a heterogeneous tumor, of which more than 70% is clear cell renal cell carcinoma (ccRCC). It is estimated that about 30% of new renal-cell carcinoma patients have metastases at the time of diagnosis. However, the pathogenesis of renal clear cell carcinoma has not been elucidated. Therefore, it is necessary to further study the pathogenesis of ccRCC. METHODS Two expression profiling datasets (GSE68417, GSE71963) were downloaded from the GEO database. Differentially expressed genes (DEGs) between ccRCC and normal tissue samples were identified by GEO2R. Functional enrichment analysis was made by the DAVID tool. Protein-protein interaction (PPI) network was constructed. The hub genes were excavated. The clustering analysis of expression level of hub genes was performed by UCSC (University of California Santa Cruz) Xena database. The hub gene on overall survival rate (OS) in patients with ccRCC was performed by Kaplan-Meier Plotter. Finally, we used the ccRCC renal tissue samples to verify the hub genes. RESULTS 1182 common DEGs between the two datasets were identified. The results of GO and KEGG analysis revealed that variations in were predominantly enriched in intracellular signaling cascade, oxidation reduction, intrinsic to membrane, integral to membrane, nucleoside binding, purine nucleoside binding, pathways in cancer, focal adhesion, cell adhesion molecules. 10 hub genes ITGAX, CD86, LY86, TLR2, TYROBP, FCGR2A, FCGR2B, PTPRC, ITGB2, ITGAM were identified. FCGR2B and TYROBP were negatively correlated with the overall survival rate in patients with ccRCC (P < 0.05). RT-qPCR analysis showed that the relative expression levels of CD86, FCGR2A, FCGR2B, TYROBP, LY86, and TLR2 were significantly higher in ccRCC samples, compared with the adjacent renal tissue groups. CONCLUSIONS In summary, bioinformatics technology could be a useful tool to predict the progression of ccRCC. In addition, there are DEGs between ccRCC tumor tissue and normal renal tissue, and these DEGs might be considered as biomarkers for ccRCC.

[1]  J. Mclaughlin,et al.  The epidemiology of renal cell carcinoma. , 2006, The Journal of urology.

[2]  Yang Liu,et al.  Bioinformatic profiling identifies an immune-related risk signature for glioblastoma , 2016, Neurology.

[3]  S. Meister,et al.  The presence of FCGR2B promoter or transmembrane region variant alleles leads to reduced serum IL-6 levels in rheumatoid arthritis , 2015, Rheumatology International.

[4]  Sean R. Davis,et al.  NCBI GEO: archive for functional genomics data sets—update , 2012, Nucleic Acids Res..

[5]  J. Larkin,et al.  Preliminary results for avelumab plus axitinib as first-line therapy in patients with advanced clear-cell renal-cell carcinoma (JAVELIN Renal 100): an open-label, dose-finding and dose-expansion, phase 1b trial. , 2018, The Lancet. Oncology.

[6]  Mark W. Ball,et al.  Genomic correlates of response to immune checkpoint therapies in clear cell renal cell carcinoma , 2018, Science.

[7]  Christian M. Metallo,et al.  Reductive glutamine metabolism by IDH1 mediates lipogenesis under hypoxia , 2011, Nature.

[8]  J. Dai,et al.  Study on the mechanism behind lncRNA MEG3 affecting clear cell renal cell carcinoma by regulating miR‐7/RASL11B signaling , 2018, Journal of cellular physiology.

[9]  Trey Ideker,et al.  Cytoscape 2.8: new features for data integration and network visualization , 2010, Bioinform..

[10]  Ronald C. Petersen,et al.  TYROBP genetic variants in early-onset Alzheimer's disease , 2016, Neurobiology of Aging.

[11]  Steven J. M. Jones,et al.  Genome-wide discovery of somatic regulatory variants in diffuse large B-cell lymphoma , 2018, Nature Communications.

[12]  K. Matsuura,et al.  Downregulation of WDR20 due to loss of 14q is involved in the malignant transformation of clear cell renal cell carcinoma , 2016, Cancer science.

[13]  M. Berger,et al.  Phase II Trial and Correlative Genomic Analysis of Everolimus Plus Bevacizumab in Advanced Non-Clear Cell Renal Cell Carcinoma. , 2016, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[14]  T. A. Al Hussain,et al.  Molecular and Metabolic Basis of Clear Cell Carcinoma of the Kidney. , 2018, Advances in anatomic pathology.

[15]  J. Fries.  MicroRNAs as markers to monitor endothelin‐1 signalling and potential treatment in renal disease: Carcinoma − proteinuric damage − toxicity , 2019, Biology of the cell.

[16]  J. Larkin,et al.  Current and future systemic treatments for renal cell carcinoma. , 2013, Seminars in cancer biology.

[17]  C. Fan,et al.  VHL substrate transcription factor ZHX2 as an oncogenic driver in clear cell renal cell carcinoma , 2018, Science.

[18]  R. Motzer,et al.  Prognostic Factors for Survival of Patients with Stage IV Renal Cell Carcinoma , 2004, Clinical Cancer Research.

[19]  J. Hafron,et al.  Characterization of clear cell renal cell carcinoma by gene expression profiling. , 2016, Urologic oncology.

[20]  Brad T. Sherman,et al.  The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists , 2007, Genome Biology.

[21]  A. Hakimi,et al.  Tumor Microenvironment Dynamics in Clear-Cell Renal Cell Carcinoma. , 2019, Cancer discovery.

[22]  C. Mathers,et al.  Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012 , 2015, International journal of cancer.

[23]  Lei Yu,et al.  FCGR2B gene polymorphism rather than FCGR2A, FCGR3A and FCGR3B is associated with anti-GBM disease in Chinese. , 2010, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association.

[24]  Alex E. Lash,et al.  Gene Expression Omnibus: NCBI gene expression and hybridization array data repository , 2002, Nucleic Acids Res..

[25]  S. Strittmatter,et al.  Opposing effects of progranulin deficiency on amyloid and tau pathologies via microglial TYROBP network , 2017, Acta Neuropathologica.

[26]  M. Sorokin,et al.  High FREM2 Gene and Protein Expression Are Associated with Favorable Prognosis of IDH-WT Glioblastomas , 2019, Cancers.

[27]  Tim F. Rayner,et al.  A defunctioning polymorphism in FCGR2B is associated with protection against malaria but susceptibility to systemic lupus erythematosus , 2010, Proceedings of the National Academy of Sciences.

[28]  Bin Zhang,et al.  Integrative approach to sporadic Alzheimer’s disease: deficiency of TYROBP in a tauopathy mouse model reduces C1q and normalizes clinical phenotype while increasing spread and state of phosphorylation of tau , 2018, Molecular Psychiatry.

[29]  Michalis Zervakis,et al.  The prognostic value of JUNB-positive CTCs in metastatic breast cancer: from bioinformatics to phenotypic characterization , 2019, Breast Cancer Research.

[30]  Y. Rustum,et al.  Non-Coding Micro RNAs and Hypoxia-Inducible Factors Are Selenium Targets for Development of a Mechanism-Based Combination Strategy in Clear-Cell Renal Cell Carcinoma—Bench-to-Bedside Therapy , 2018, International journal of molecular sciences.

[31]  N. Bowles,et al.  Functional FCGR2B gene variants influence intravenous immunoglobulin response in patients with Kawasaki disease. , 2011, The Journal of allergy and clinical immunology.

[32]  Yong Zhong,et al.  Identification of potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma , 2018, Hereditas.

[33]  M. Dimopoulos,et al.  Current Clinical Practice Guidelines for the Treatment of Renal Cell Carcinoma: A Systematic Review and Critical Evaluation. , 2017, The oncologist.

[34]  Yingnan Lv,et al.  Exploring the mechanism of clear cell renal cell carcinoma metastasis and key genes based on multi-tool joint analysis. , 2019, Gene.