Clear Cell Renal Cell Carcinoma: A Comprehensive in silico Study in Searching for Therapeutic Targets

Abstract Introduction: Clear cell renal cell carcinoma (ccRCC) is recognized as one of the leading causes of illness and death worldwide. Understanding the molecular mechanisms in ccRCC pathogenesis is crucial for discovering novel therapeutic targets and developing efficient drugs. With the applica...

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Veröffentlicht in:	Kidney & blood pressure research 2023-01, Vol.48 (1), p.135-150
Hauptverfasser:	Naghdibadi, Mohammadjavad, Momeni, Maryam, Yavari, Parvin, Gholaminejad, Alieh, Roointan, Amir
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Analysis Bioinformatics Cancer Cancer therapies Carcinoma, Renal cell Care and treatment CCR5 protein CD86 antigen Cell surface Chemotaxis clear cell renal cell carcinoma Clear cell-type renal cell carcinoma Connectivity Datasets Development and progression Diagnosis DNA microarrays Drug development Drug interactions drug target Drug targeting Drugs Gene expression Genes Glucocorticoids Immune system Immunological synapses Immunology Immunosuppressive agents Kidney cancer Kinases Metastases Modules Molecular modelling Mutation Network analysis Pathogenesis Principal components analysis Protein interaction Protein-tyrosine kinase Proteins Quorum sensing Rac2 protein Research Article systems biology Therapeutic applications TLR2 protein Toll-like receptors transcriptomics Translocation Tyrosine weighted gene co-expression network ZAP-70 protein
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Zusammenfassung:	Abstract Introduction: Clear cell renal cell carcinoma (ccRCC) is recognized as one of the leading causes of illness and death worldwide. Understanding the molecular mechanisms in ccRCC pathogenesis is crucial for discovering novel therapeutic targets and developing efficient drugs. With the application of a comprehensive in silico analysis of the ccRCC-related array sets, the main objective of this study was to discover the top molecules and pathways in the pathogenesis of this cancer. Methods: ccRCC microarray datasets were downloaded from the Gene Expression Omnibus database, and after quality checking, normalization, and analysis using the Limma algorithm, differentially expressed genes (DEGs) were identified, considering the adjusted p value
ISSN:	1420-4096 1423-0143
DOI:	10.1159/000529861