Aptasensors in colorectal Cancer: Emerging tools for precision diagnostics

Nanomaterial integration has significantly advanced the development of aptamer-based biosensors (aptasensors) for colorectal cancer (CRC) biomarker detection, offering increased sensitivity, stability, and signal transduction in complex biological environments. This review comprehensively examines the roles and contributions of key nanomaterial classes, including gold nanoparticles (AuNPs), carbon-based nanomaterials, metal-organic frameworks (MOFs), covalent organic frameworks (COFs), and emerging nanostructures (e.g., nanochannels, nanocomposites, and quantum dots), in CRC aptasensor design. AuNPs remain foundational owing to their high surface-to-volume ratio, surface plasmonic properties, and versatility as both immobilization platforms and signal reporters; however, concerns regarding batch variability, colloidal instability, and limited clinical validation persist. Carbon nanomaterials spanning quantum dots, carbon nanotubes, graphene, and 3D porous architectures provide exceptional electrical conductivity, biocompatibility, and modular functionalization, with successful detection of CRC-associated markers such as CEA, CA19-9, and inflammatory cytokines (e.g., IL-6, TNF-α, and CRP). MOFs and COFs offer ultrahigh porosity and tunable catalytic/redox properties, enabling multiplexed detection strategies, although their clinical translation is hindered by stability and reproducibility challenges. In addition to nanomaterial engineering, DNA amplification techniques, including hybridization chain reaction (HCR), exonuclease-assisted recycling, and G-quadruplex/hemin DNAzymes, have pushed detection limits to subng·mL^-1 levels. However, their complexity, long assay times, and reliance on multistep enzymatic processes limit their suitability for point-of-care use. Collectively, while nanomaterial-enhanced aptasensors exhibit extraordinary analytical promise for early CRC diagnosis, critical gaps remain in standardization, scalability, biocompatibility, and clinical validation. Future efforts must prioritize simplifying architectures, ensuring batch-to-batch reproducibility, and validating performance in real-world samples to enable translational impact in precision oncology.