Predictive value of single-nucleotide polymorphism signature for recurrence in localised renal cell carcinoma: a...

Area of Science:

• Oncology
• Genetics
• Bioinformatics

Background:

• Accurate identification of high-risk localized renal cell carcinoma (RCC) is crucial for effective adjuvant treatment selection.
• Developing predictive models to improve recurrence risk assessment in RCC patients is an ongoing challenge.

Purpose of the Study:

• To develop and validate a novel classifier based on single-nucleotide polymorphisms (SNPs) for predicting recurrence in localized clear cell renal cell carcinoma (ccRCC).
• To investigate the impact of intratumor heterogeneity on the classifier's predictive accuracy.

Main Methods:

• A six-SNP classifier was developed using LASSO Cox regression on a training set of 227 localized ccRCC patients.
• The classifier's performance was evaluated in internal, independent Chinese, and TCGA validation sets.
• Intratumor heterogeneity was assessed in different tumor regions.

Main Results:

• The six-SNP classifier accurately predicted recurrence-free survival across all validation sets (p<0.0001).
• The classifier demonstrated consistent predictive accuracy despite observed intratumor heterogeneity.
• A combined nomogram incorporating the SNP classifier and clinicopathological factors showed superior predictive accuracy (AUC at 5 years: 0.811).

Conclusions:

• The developed six-SNP classifier serves as a practical and reliable predictor for localized RCC recurrence post-surgery.
• This tool can enhance existing staging systems and inform adjuvant therapy decisions.
• Intratumor heterogeneity does not compromise the classifier's accuracy, highlighting its potential clinical utility.