B-372 The diagnostic value of serum RNA m6A methylation and microRNAs in hepatocellular carcinoma

Background Hepatocellular carcinoma (HCC) is a malignant neoplastic disease characterized by high clinical incidence and mortality. The prognosis for HCC remains poor mainly due to the challenging in early diagnosis. It is urgent to identify reliable biomarkers for early detection to improve prognosis of HCC. N6-methyladenine (m6A) modification of RNA has emerged as an important regulatory mechanism in various cancers, including HCC. It alters the stability, translation, and splicing of RNA and further influences development and progression of cancers. The microRNAs are involved in the initiation and progression of HCC through regulating gene expression. Abnormal expression of specific microRNAs can serve as biomarkers for HCC diagnosis and prognosis. This study aims to investigate the role of m6A modification in HCC development and identify microRNAs with significant expression differences in HCC patients. A nomogram model based on serum expression levels was developed to predict HCC risk and assess its diagnostic efficacy, providing valuable insights for early detection and more personalized management of HCC. Methods Twenty-four HCC patients and 22 matched healthy individuals were enrolled. The overall level of serum m6A was measured using an RNA m6A level detection kit. The expression levels of m6A modifying enzymes METTL3, BCDIN3D, as well as liver cancer-related microRNAs (microRNA-122, microRNA-198, microRNA-361, microRNA-378 and microRNA-532) were assessed by qRT-PCR. Each index was compared between patients and controls. Risk factors related to the incidence of HCC were identified using multivariate logistic regression analysis, and a nomogram model was constructed to predict HCC risk. Results Univariate analysis revealed that the overall level of m6A modification was down-regulated in HCC patients (0.0015 vs 0.0030, p=0.0015). The expression of microRNA-122, microRNA-198 and microRNA-532 was significantly higher in HCC patients (p<0.05). Multivariate logistic regression analysis identified m6A level (OR=0.288, 95%CI:0.123-0.676, P=0.004), microRNA-122 expression (OR=1.338, 95%CI:1.006-1.779, P=0.045) and microRNA-532 expression (OR=1.403, 95%CI:1.011-1.947, P=0.043) as independent risk factors for HCC. Two prediction models for HCC based on these indicators were developed: the m6A + microRNA-122 model (AUC=0.849, 95%CI=0.738-0.961) and the m6A + microRNA-532 model (AUC=0.847, 95%CI=0.730-0.963). Both models were internally verified by Bootstrap self-sampling method with the C-index value of 0.85, indicating good discrimination. Calibration curves showed a good fit, and decision curve analysis confirmed that the nomogram model provided greater clinical benefit than using single risk factors. Conclusion Serum RNA m6A modification is significantly decreased in HCC patients, while the expression of microRNA-122, microRNA-198, and microRNA-532 is elevated, which could be of value for the diagnosis of HCC. In addition, RNA m6A, microRNA-122 and microRNA-532 are independent risk factors for HCC patients. The nomogram diagnose model based on the three indicators can effectively predict the risk of HCC, aiding in early screening and intervention for high-risk individuals.