Ultrasensitive Detection of m 6 A-Modified RNA Using CRISPR/Cas12a-Integrated Iontronic Biosensor with Hydrophobized Nanochannels: Toward Early Cancer Diagnosis by Machine Learning

N⁶ -methyladenosine (m⁶ A), the most prevalent internal modification in eukaryotic RNAs, has emerged as a focal point of intensive research in recent years owing to its pivotal regulatory roles in carcinogenesis, progression, and metastasis. However, conventional methods for site-specific detection of m⁶ A modifications are plagued by operational complexity, pose challenges for quantitative assessment of methylation levels, and exhibit elevated false-positive rates, severely limiting their utility in clinical and mechanistic studies. In this study, we engineered an ultrasensitive iontronic biosensor leveraging a hydrophobized anodic aluminum oxide (AAO) nanochannel platform, synergistically integrating the precise target recognition capability of the CRISPR/Cas12a system with the efficient signal amplification of the clamped hybridization chain reaction (CHCR). This integration enables ultrasensitive and specific detection of m⁶ A-modified RNA with a low detection limit of 32 aM. Validation experiments targeting MALAT1 and HOTAIR lncRNAs demonstrated that the sensor achieves exceptional specificity in qualitative analysis of m⁶ A modifications. Furthermore, combinatorial detection of these two lncRNAs enables robust discrimination between cancer patients and healthy individuals. Through in-depth mining of latent data patterns via machine learning, the random forest (RF) model yielded a cancer diagnostic accuracy of 96.7%. This study establishes a novel and potent paradigm for early cancer diagnosis, with far-reaching implications for epitranscriptomic research and clinical translation.