Clear Cell Renal Cell Carcinoma: A Comprehensive in silico Study in Searching for Therapeutic Targets
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Karger Publishers
2023-02-01
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| Series: | Kidney & Blood Pressure Research |
| Subjects: | |
| Online Access: | https://www.karger.com/Article/FullText/529861 |
| _version_ | 1797862071132487680 |
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| author | Mohammadjavad Naghdibadi Maryam Momeni Parvin Yavari Alieh Gholaminejad Amir Roointan |
| author_facet | Mohammadjavad Naghdibadi Maryam Momeni Parvin Yavari Alieh Gholaminejad Amir Roointan |
| author_sort | Mohammadjavad Naghdibadi |
| collection | DOAJ |
| description | 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 <0.049. The intensity values of the identified DEGs were introduced to the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to construct co-expression modules. Functional enrichment analyses were performed using the DEGs in the disease-correlated module, and hub genes were identified among the top genes in a protein-protein interaction network and the disease most correlated module. The expression analysis of hub genes was done by utilizing GEPIA, and the GSCA server was used to compare the expression patterns of hub genes in ccRCC and other cancers. DGIdb database was utilized to identify the hub gene-related drugs. Results: Three datasets, including GSE11151, GSE12606, and GSE36897, were retrieved, merged, normalized, and analyzed. Using WGCNA, the DEGs were clustered into eight different modules. Translocation of ZAP-70 to immunological synapse, endosomal/vacuolar pathway, cell surface interactions at the vascular wall, and immune-related pathways were the topmost enriched terms for the ccRCC-correlated DEGs. Twelve genes including PTPRC, ITGAM, TLR2, CD86, PLEK, TYROBP, ITGB2, RAC2, CSF1R, CCR5, CCL5, and LCP2 were introduced as hub genes. All the 12 hub genes were upregulated in ccRCC samples and showed a positive correlation with the infiltration of different immune cells. According to the DGIdb database, 127 drugs, including tyrosine kinase inhibitors, glucocorticoids, and chemotaxis targeting molecules, were identified to interact with the hub genes. Conclusion: By utilizing an integrative bioinformatics approach, this experiment shed light on the underlying pathways in the pathogenesis of ccRCC and introduced several potential therapeutic targets for repurposing or developing novel drugs for an efficient treatment of this cancer. Our next step would be to assess the gene expression profiles of the identified hubs in different cell populations in the tumor microenvironment. |
| first_indexed | 2024-04-09T22:13:20Z |
| format | Article |
| id | doaj.art-da1727e79f1a43328d2331e82a32e2fc |
| institution | Directory Open Access Journal |
| issn | 1420-4096 1423-0143 |
| language | English |
| last_indexed | 2024-04-09T22:13:20Z |
| publishDate | 2023-02-01 |
| publisher | Karger Publishers |
| record_format | Article |
| series | Kidney & Blood Pressure Research |
| spelling | doaj.art-da1727e79f1a43328d2331e82a32e2fc2023-03-23T06:54:58ZengKarger PublishersKidney & Blood Pressure Research1420-40961423-01432023-02-0148113515010.1159/000529861529861Clear Cell Renal Cell Carcinoma: A Comprehensive in silico Study in Searching for Therapeutic TargetsMohammadjavad Naghdibadi0Maryam Momeni1https://orcid.org/0000-0003-3294-3516Parvin Yavari2https://orcid.org/0000-0001-7735-274XAlieh Gholaminejad3https://orcid.org/0000-0003-0329-2937Amir Roointan4Regenerative Medicine Research Center, Isfahan University of Medical Sciences, Isfahan, IranDepartment of Biotechnology, Faculty of Biological Science and Technology, The University of Isfahan, Isfahan, IranRegenerative Medicine Research Center, Isfahan University of Medical Sciences, Isfahan, IranRegenerative Medicine Research Center, Isfahan University of Medical Sciences, Isfahan, IranRegenerative Medicine Research Center, Isfahan University of Medical Sciences, Isfahan, IranIntroduction: 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 <0.049. The intensity values of the identified DEGs were introduced to the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to construct co-expression modules. Functional enrichment analyses were performed using the DEGs in the disease-correlated module, and hub genes were identified among the top genes in a protein-protein interaction network and the disease most correlated module. The expression analysis of hub genes was done by utilizing GEPIA, and the GSCA server was used to compare the expression patterns of hub genes in ccRCC and other cancers. DGIdb database was utilized to identify the hub gene-related drugs. Results: Three datasets, including GSE11151, GSE12606, and GSE36897, were retrieved, merged, normalized, and analyzed. Using WGCNA, the DEGs were clustered into eight different modules. Translocation of ZAP-70 to immunological synapse, endosomal/vacuolar pathway, cell surface interactions at the vascular wall, and immune-related pathways were the topmost enriched terms for the ccRCC-correlated DEGs. Twelve genes including PTPRC, ITGAM, TLR2, CD86, PLEK, TYROBP, ITGB2, RAC2, CSF1R, CCR5, CCL5, and LCP2 were introduced as hub genes. All the 12 hub genes were upregulated in ccRCC samples and showed a positive correlation with the infiltration of different immune cells. According to the DGIdb database, 127 drugs, including tyrosine kinase inhibitors, glucocorticoids, and chemotaxis targeting molecules, were identified to interact with the hub genes. Conclusion: By utilizing an integrative bioinformatics approach, this experiment shed light on the underlying pathways in the pathogenesis of ccRCC and introduced several potential therapeutic targets for repurposing or developing novel drugs for an efficient treatment of this cancer. Our next step would be to assess the gene expression profiles of the identified hubs in different cell populations in the tumor microenvironment.https://www.karger.com/Article/FullText/529861clear cell renal cell carcinomatranscriptomicssystems biologyweighted gene co-expression networkdrug target |
| spellingShingle | Mohammadjavad Naghdibadi Maryam Momeni Parvin Yavari Alieh Gholaminejad Amir Roointan Clear Cell Renal Cell Carcinoma: A Comprehensive in silico Study in Searching for Therapeutic Targets Kidney & Blood Pressure Research clear cell renal cell carcinoma transcriptomics systems biology weighted gene co-expression network drug target |
| title | Clear Cell Renal Cell Carcinoma: A Comprehensive in silico Study in Searching for Therapeutic Targets |
| title_full | Clear Cell Renal Cell Carcinoma: A Comprehensive in silico Study in Searching for Therapeutic Targets |
| title_fullStr | Clear Cell Renal Cell Carcinoma: A Comprehensive in silico Study in Searching for Therapeutic Targets |
| title_full_unstemmed | Clear Cell Renal Cell Carcinoma: A Comprehensive in silico Study in Searching for Therapeutic Targets |
| title_short | Clear Cell Renal Cell Carcinoma: A Comprehensive in silico Study in Searching for Therapeutic Targets |
| title_sort | clear cell renal cell carcinoma a comprehensive in silico study in searching for therapeutic targets |
| topic | clear cell renal cell carcinoma transcriptomics systems biology weighted gene co-expression network drug target |
| url | https://www.karger.com/Article/FullText/529861 |
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