Work

Precision Tumor Recognition in Clear Cell Renal Cell Carcinoma: A qHCR Platform Leveraging Tetrahedral DNA Nanostructures for Rapid Molecular Diagnosis

Year: 2025

Type: article

Abstract: ABSTRACT Clear cell renal cell carcinoma (ccRCC) is the most prevalent and aggressive subtype of kidney cancer, demanding rapid and precise diagnostic tools to guide clinical decision‐making. Current methods, such as immunohistochemistry (IHC) and frozen section analysis, face limitations in speed, sensitivity, and workflow complexity. To address these challenges, we developed a tetrahedral DNA nanostructure (TDN)‐enhanced quadrivalent hybridization chain reaction (qHCR) platform targeting carbonic anhydrase IX (CAIX), a highly specific biomarker for ccRCC. This system integrates aptamer‐based molecular recognition with enzyme‐free signal amplification, leveraging the spatial confinement and multivalent effects of TDNs to achieve ultrasensitive detection. The qHCR platform demonstrated remarkable performance, with a reaction rate 95 times faster than traditional HCR and sustained signal stability in serum for over 36 h. In clinical validation using 40 ccRCC tissue samples, the platform enabled Precision Tumor Recognition within 25 min for frozen sections, yielding a tumor‐to‐normal tissue signal ratio of 3.35:1, and completed molecular profiling within 2.5 h for formalin‐fixed, paraffin‐embedded (FFPE) samples, showing full concordance with histopathological diagnoses. Its modular design allows seamless target switching by replacing aptamer sequences, as confirmed by successful detection of protein tyrosine kinase (PTK7) in an acute lymphoblastic leukemia model. With its cost‐effectiveness ($0.15 per test), streamlined workflow, and compatibility with both intraoperative margin assessment and postoperative pathological analysis, the qHCR platform represents a transformative advancement in molecular diagnostics for ccRCC management, offering a robust solution for precision oncology and time‐critical surgical decision‐making.

Source: Aggregate

DOI: 10.1002/agt2.70151