High-throughput methylation sequencing reveals novel biomarkers for the early detection of renal cell carcinoma
Abstract Purpose Renal cell carcinoma (RCC) is a common malignancy, with patients frequently diagnosed at an advanced stage due to the absence of sufficiently sensitive detection technologies, significantly compromising patient survival and quality of life. Advances in cell-free DNA (cfDNA) methylat...
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BMC
2025-01-01
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| Series: | BMC Cancer |
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| Online Access: | https://doi.org/10.1186/s12885-024-13380-6 |
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| author | Wenhao Guo Weiwu Chen Jie Zhang Mingzhe Li Hongyuan Huang Qian Wang Xiaoyi Fei Jian Huang Tongning Zheng Haobo Fan Yunfei Wang Hongcang Gu Guoqing Ding Yicheng Chen |
| author_facet | Wenhao Guo Weiwu Chen Jie Zhang Mingzhe Li Hongyuan Huang Qian Wang Xiaoyi Fei Jian Huang Tongning Zheng Haobo Fan Yunfei Wang Hongcang Gu Guoqing Ding Yicheng Chen |
| author_sort | Wenhao Guo |
| collection | DOAJ |
| description | Abstract Purpose Renal cell carcinoma (RCC) is a common malignancy, with patients frequently diagnosed at an advanced stage due to the absence of sufficiently sensitive detection technologies, significantly compromising patient survival and quality of life. Advances in cell-free DNA (cfDNA) methylation profiling using liquid biopsies offer a promising non-invasive diagnostic option, but robust biomarkers for early detection are current not available. This study aimed to identify methylation biomarkers for RCC and establish a DNA methylation signature-based prognostic model for this disease. Methods High-throughput methylation sequencing was performed on peripheral blood samples obtained from 49 primarily Stage I RCC patients and 44 healthy controls. Comparative analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression methods were employed to identify RCC methylation signatures.Subsequently, methylation markers-based diagnostic and prognostic models for RCC were independently trained and validated using random forest and Cox regression methodologies, respectively. Results Comparative analysis revealed 864 differentially methylated CpG islands (DMCGIs), 96.3% of which were hypermethylated. Using a training set from The Cancer Genome Atlas (TCGA) dataset of 443 early-stage RCC tumors and matched normal tissues, we applied LASSO regression and identified 23 methylation signatures. We then constructed a random forest-based diagnostic model for early-stage RCC and validated the model using two independent datasets: a TCGA set of 460 RCC tumors and controls, and a blood sample set from our study of 15 RCC cases and 29 healthy controls. For Stage I RCC tissue, the model showed excellent discrimination (AUC-ROC: 0.999, sensitivity: 98.5%, specificity: 100%). Blood sample validation also yielded commendable results (AUC-ROC: 0.852, sensitivity: 73.9%, specificity: 89.7%). Further analysis using Cox regression identified 7 of the 23 DMCGIs as prognostic markers for RCC, allowing the development of a prognostic model with strong predictive power for 1-, 3-, and 5-year survival (AUC-ROC > 0.7). Conclusions Our findings highlight the critical role of hypermethylation in RCC etiology and progression, and present these identified biomarkers as promising candidates for diagnostic and prognostic applications. |
| format | Article |
| id | doaj-art-84294bc5bb0d4e5a8e06c592c543bf7f |
| institution | Kabale University |
| issn | 1471-2407 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj-art-84294bc5bb0d4e5a8e06c592c543bf7f2025-01-19T12:26:53ZengBMCBMC Cancer1471-24072025-01-0125111410.1186/s12885-024-13380-6High-throughput methylation sequencing reveals novel biomarkers for the early detection of renal cell carcinomaWenhao Guo0Weiwu Chen1Jie Zhang2Mingzhe Li3Hongyuan Huang4Qian Wang5Xiaoyi Fei6Jian Huang7Tongning Zheng8Haobo Fan9Yunfei Wang10Hongcang Gu11Guoqing Ding12Yicheng Chen13Department of Urology, Sir Run-Run Shaw Hospital, College of Medicine, Zhejiang UniversityDepartment of Urology, Sir Run-Run Shaw Hospital, College of Medicine, Zhejiang UniversityDepartment of Urology, Sir Run-Run Shaw Hospital, College of Medicine, Zhejiang UniversityDepartment of Urology, Sir Run-Run Shaw Hospital, College of Medicine, Zhejiang UniversityDepartment of Urology, Jinjiang Municipal HospitalHangzhou Shengting Medical Technology Co., Ltd.Hangzhou Shengting Medical Technology Co., Ltd.Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of SciencesDepartment of Urology, Ningbo Zhenhai People’s HospitalDepartment of Urology, Sir Run-Run Shaw Hospital, College of Medicine, Zhejiang UniversityHangzhou Shengting Medical Technology Co., Ltd.Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of SciencesDepartment of Urology, Sir Run-Run Shaw Hospital, College of Medicine, Zhejiang UniversityDepartment of Urology, Sir Run-Run Shaw Hospital, College of Medicine, Zhejiang UniversityAbstract Purpose Renal cell carcinoma (RCC) is a common malignancy, with patients frequently diagnosed at an advanced stage due to the absence of sufficiently sensitive detection technologies, significantly compromising patient survival and quality of life. Advances in cell-free DNA (cfDNA) methylation profiling using liquid biopsies offer a promising non-invasive diagnostic option, but robust biomarkers for early detection are current not available. This study aimed to identify methylation biomarkers for RCC and establish a DNA methylation signature-based prognostic model for this disease. Methods High-throughput methylation sequencing was performed on peripheral blood samples obtained from 49 primarily Stage I RCC patients and 44 healthy controls. Comparative analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression methods were employed to identify RCC methylation signatures.Subsequently, methylation markers-based diagnostic and prognostic models for RCC were independently trained and validated using random forest and Cox regression methodologies, respectively. Results Comparative analysis revealed 864 differentially methylated CpG islands (DMCGIs), 96.3% of which were hypermethylated. Using a training set from The Cancer Genome Atlas (TCGA) dataset of 443 early-stage RCC tumors and matched normal tissues, we applied LASSO regression and identified 23 methylation signatures. We then constructed a random forest-based diagnostic model for early-stage RCC and validated the model using two independent datasets: a TCGA set of 460 RCC tumors and controls, and a blood sample set from our study of 15 RCC cases and 29 healthy controls. For Stage I RCC tissue, the model showed excellent discrimination (AUC-ROC: 0.999, sensitivity: 98.5%, specificity: 100%). Blood sample validation also yielded commendable results (AUC-ROC: 0.852, sensitivity: 73.9%, specificity: 89.7%). Further analysis using Cox regression identified 7 of the 23 DMCGIs as prognostic markers for RCC, allowing the development of a prognostic model with strong predictive power for 1-, 3-, and 5-year survival (AUC-ROC > 0.7). Conclusions Our findings highlight the critical role of hypermethylation in RCC etiology and progression, and present these identified biomarkers as promising candidates for diagnostic and prognostic applications.https://doi.org/10.1186/s12885-024-13380-6Renal cell carcinomaCell-free DNADNA methylationCpG islandsLiquid biopsy |
| spellingShingle | Wenhao Guo Weiwu Chen Jie Zhang Mingzhe Li Hongyuan Huang Qian Wang Xiaoyi Fei Jian Huang Tongning Zheng Haobo Fan Yunfei Wang Hongcang Gu Guoqing Ding Yicheng Chen High-throughput methylation sequencing reveals novel biomarkers for the early detection of renal cell carcinoma BMC Cancer Renal cell carcinoma Cell-free DNA DNA methylation CpG islands Liquid biopsy |
| title | High-throughput methylation sequencing reveals novel biomarkers for the early detection of renal cell carcinoma |
| title_full | High-throughput methylation sequencing reveals novel biomarkers for the early detection of renal cell carcinoma |
| title_fullStr | High-throughput methylation sequencing reveals novel biomarkers for the early detection of renal cell carcinoma |
| title_full_unstemmed | High-throughput methylation sequencing reveals novel biomarkers for the early detection of renal cell carcinoma |
| title_short | High-throughput methylation sequencing reveals novel biomarkers for the early detection of renal cell carcinoma |
| title_sort | high throughput methylation sequencing reveals novel biomarkers for the early detection of renal cell carcinoma |
| topic | Renal cell carcinoma Cell-free DNA DNA methylation CpG islands Liquid biopsy |
| url | https://doi.org/10.1186/s12885-024-13380-6 |
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