Identification of key genes CCL5, PLG, LOX and C3 in clear cell renal cell carcinoma through integrated bioinformatics analysis
BackgroundClear Cell Renal Cell Carcinoma (ccRCC) is a malignant tumor with high mortality and recurrence rates and the molecular mechanism of ccRCC genesis remains unclear. In this study, we identified several key genes associated with the prognosis of ccRCC by using integrated bioinformatics.Metho...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Molecular Biosciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2025.1587196/full |
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| author | Zhenwei Xie Cheng Feng Yude Hong Libo Chen Mingyong Li Weiming Deng |
| author_facet | Zhenwei Xie Cheng Feng Yude Hong Libo Chen Mingyong Li Weiming Deng |
| author_sort | Zhenwei Xie |
| collection | DOAJ |
| description | BackgroundClear Cell Renal Cell Carcinoma (ccRCC) is a malignant tumor with high mortality and recurrence rates and the molecular mechanism of ccRCC genesis remains unclear. In this study, we identified several key genes associated with the prognosis of ccRCC by using integrated bioinformatics.MethodsTwo ccRCC expression profiles were downloaded from Gene Expression Omnibus and one dataset was gained from The Cancer Genome Atlas The Robust Rank Aggregation method was used to analyze the three datasets to gain integrated differentially expressed genes The Gene Ontology and KEGG analysis were performed to explore the potential functions of DEGs. The Search Tool for the Retreival of Interacting Genes/Proteins (STRING) and Cytoscape software were used to construct protein-protein interaction network and module analyses to screen the hub genes. Spearman’s correlation analysis was conducted to evaluate the interrelationships among the hub genes. The prognostic value was evaluated through K-M survival analysis, Cox regression analysis, and receiver operating characteristic curve analysis to determine their potential as prognostic biomarkers in ccRCC. The expression of hub genes between ccRCC and adjacent normal tissues was analyzed by RT-qPCR, Western blotting, and immunohistochemical (IHC).Result125 DEGs were identified using the limma package and RRA method, including 62 up-expressed genes and 63 down-expressed genes. GO and KEGG analysis showed some associated pathways. Spearman’s correlation analysis revealed that the hub genes are not only interrelated but also closely associated with immune cell infiltration. Gene expression analysis of the hub genes based on the TCGA-KIRC cohort, along with K-M survival analysis, Cox regression, and ROC curve analysis, consistently demonstrated that CCL5, LOX, and C3 are significantly upregulated in ccRCC and are associated with poor clinical outcomes. In contrast, PLG showed opposite result. These results were further validated at the mRNA and protein levels.ConclusionOur findings indicate that CCL5, LOX, C3, and PLG are significantly associated with the progression and prognosis of ccRCC, highlighting their potential as prognostic biomarkers. These results provide a foundation for future research aimed at uncovering the underlying mechanisms and identifying potential therapeutic targets for ccRCC. |
| format | Article |
| id | doaj-art-9a58b5a9d7f94ebe8977bb29c1d5aac8 |
| institution | DOAJ |
| issn | 2296-889X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Molecular Biosciences |
| spelling | doaj-art-9a58b5a9d7f94ebe8977bb29c1d5aac82025-08-20T03:11:01ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2025-05-011210.3389/fmolb.2025.15871961587196Identification of key genes CCL5, PLG, LOX and C3 in clear cell renal cell carcinoma through integrated bioinformatics analysisZhenwei Xie0Cheng Feng1Yude Hong2Libo Chen3Mingyong Li4Weiming Deng5Department of Kidney Transplantation, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, ChinaDepartment of Thyroid and Galactophore Surgery, People’s Hospital of Longhua, Shenzhen, ChinaDepartment of Urology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, ChinaDepartment of Urology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, ChinaDepartment of Urology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, ChinaDepartment of Urology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, ChinaBackgroundClear Cell Renal Cell Carcinoma (ccRCC) is a malignant tumor with high mortality and recurrence rates and the molecular mechanism of ccRCC genesis remains unclear. In this study, we identified several key genes associated with the prognosis of ccRCC by using integrated bioinformatics.MethodsTwo ccRCC expression profiles were downloaded from Gene Expression Omnibus and one dataset was gained from The Cancer Genome Atlas The Robust Rank Aggregation method was used to analyze the three datasets to gain integrated differentially expressed genes The Gene Ontology and KEGG analysis were performed to explore the potential functions of DEGs. The Search Tool for the Retreival of Interacting Genes/Proteins (STRING) and Cytoscape software were used to construct protein-protein interaction network and module analyses to screen the hub genes. Spearman’s correlation analysis was conducted to evaluate the interrelationships among the hub genes. The prognostic value was evaluated through K-M survival analysis, Cox regression analysis, and receiver operating characteristic curve analysis to determine their potential as prognostic biomarkers in ccRCC. The expression of hub genes between ccRCC and adjacent normal tissues was analyzed by RT-qPCR, Western blotting, and immunohistochemical (IHC).Result125 DEGs were identified using the limma package and RRA method, including 62 up-expressed genes and 63 down-expressed genes. GO and KEGG analysis showed some associated pathways. Spearman’s correlation analysis revealed that the hub genes are not only interrelated but also closely associated with immune cell infiltration. Gene expression analysis of the hub genes based on the TCGA-KIRC cohort, along with K-M survival analysis, Cox regression, and ROC curve analysis, consistently demonstrated that CCL5, LOX, and C3 are significantly upregulated in ccRCC and are associated with poor clinical outcomes. In contrast, PLG showed opposite result. These results were further validated at the mRNA and protein levels.ConclusionOur findings indicate that CCL5, LOX, C3, and PLG are significantly associated with the progression and prognosis of ccRCC, highlighting their potential as prognostic biomarkers. These results provide a foundation for future research aimed at uncovering the underlying mechanisms and identifying potential therapeutic targets for ccRCC.https://www.frontiersin.org/articles/10.3389/fmolb.2025.1587196/fullccRCCintegrated bioinformatics analysishub genesprognosisbiomarkers |
| spellingShingle | Zhenwei Xie Cheng Feng Yude Hong Libo Chen Mingyong Li Weiming Deng Identification of key genes CCL5, PLG, LOX and C3 in clear cell renal cell carcinoma through integrated bioinformatics analysis Frontiers in Molecular Biosciences ccRCC integrated bioinformatics analysis hub genes prognosis biomarkers |
| title | Identification of key genes CCL5, PLG, LOX and C3 in clear cell renal cell carcinoma through integrated bioinformatics analysis |
| title_full | Identification of key genes CCL5, PLG, LOX and C3 in clear cell renal cell carcinoma through integrated bioinformatics analysis |
| title_fullStr | Identification of key genes CCL5, PLG, LOX and C3 in clear cell renal cell carcinoma through integrated bioinformatics analysis |
| title_full_unstemmed | Identification of key genes CCL5, PLG, LOX and C3 in clear cell renal cell carcinoma through integrated bioinformatics analysis |
| title_short | Identification of key genes CCL5, PLG, LOX and C3 in clear cell renal cell carcinoma through integrated bioinformatics analysis |
| title_sort | identification of key genes ccl5 plg lox and c3 in clear cell renal cell carcinoma through integrated bioinformatics analysis |
| topic | ccRCC integrated bioinformatics analysis hub genes prognosis biomarkers |
| url | https://www.frontiersin.org/articles/10.3389/fmolb.2025.1587196/full |
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