High-throughput methylation sequencing reveals novel biomarkers for the early detection of renal cell carcinoma

• Published in: BMC cancer 2025-01, Vol.25 (1), p.96-14, Article 96
• Main Authors: Guo, Wenhao, Chen, Weiwu, Zhang, Jie, Li, Mingzhe, Huang, Hongyuan, Wang, Qian, Fei, Xiaoyi, Huang, Jian, Zheng, Tongning, Fan, Haobo, Wang, Yunfei, Gu, Hongcang, Ding, Guoqing, Chen, Yicheng
• Format: Article
• Language: English
• Subjects: Adult; Aged; Analysis; Biological markers; Biomarkers, Tumor - blood; Biomarkers, Tumor - genetics; Cancer; Carcinoma, Renal cell; Carcinoma, Renal Cell - blood; Carcinoma, Renal Cell - diagnosis; Carcinoma, Renal Cell - genetics; Care and treatment; Case-Control Studies; Cell-free DNA; CpG Islands; Development and progression; Diagnosis; DNA Methylation; Early Detection of Cancer - methods; Female; Genetic aspects; Genomes; Genomics; Health aspects; High-Throughput Nucleotide Sequencing; High-throughput screening (Biochemical assaying); Histology, Pathological; Humans; Kidney Neoplasms - blood; Kidney Neoplasms - diagnosis; Kidney Neoplasms - genetics; Liquid biopsy; Male; Methylation; Middle Aged; Neoplasm Staging; Prognosis; Renal cell carcinoma
• ISSN: 1471-2407; 1471-2407
• DOI: 10.1186/s12885-024-13380-6

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. 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. 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). 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.