Department of Oncology, The Second Affiliated Hospital of Zunyi Medical University, Zunyi, China.
Shanghai Molecular Medicine Engineering Technology Research Center, Shanghai, 201203, China.
Biol Direct. 2024 Nov 7;19(1):103. doi: 10.1186/s13062-024-00554-2.
Accurately identifying effective biomarkers and translating them into clinical practice have significant implications for improving clinical outcomes in hepatocellular carcinoma (HCC). In this study, our objective is to explore appropriate methods to improve the accuracy of biomarker identification and investigate their clinical value.
Concentrating on the N6-methyladenosine (m6A) modification regulators, we utilized dozens of multi-omics HCC datasets to analyze the expression patterns and genetic features of m6A regulators. Through the integration of big data analysis with function experiments, we have redefined the biological roles of m6A regulators in HCC. Based on the key regulators, we constructed m6A risk models and explored their clinical value in estimating prognosis and guiding personalized therapy for HCC.
Most m6A regulators exhibit abnormal expression in HCC, and their expression is influenced by copy number variations (CNV) and DNA methylation. Large-scale data analysis has revealed the biological roles of many key m6A regulators, and these findings are well consistent with experimental results. The m6A risk models offer significant prognostic value. Moreover, they assist in reassessing the therapeutic potential of drugs such as sorafenib, gemcitabine, CTLA4 and PD1 blockers in HCC.
Our findings suggest that the mutual validation of big data analysis and functional experiments may facilitate the precise identification and definition of biomarkers, and our m6A risk models may have the potential to guide personalized chemotherapy, targeted treatment, and immunotherapy decisions in HCC.
准确识别有效的生物标志物并将其转化为临床实践，对改善肝细胞癌（HCC）的临床结局具有重要意义。在这项研究中，我们的目的是探索改进生物标志物识别准确性的适当方法，并研究其临床价值。
我们专注于 N6-甲基腺苷（m6A）修饰调节剂，利用数十个多组学 HCC 数据集来分析 m6A 调节剂的表达模式和遗传特征。通过大数据分析与功能实验的整合，我们重新定义了 m6A 调节剂在 HCC 中的生物学作用。基于关键调节剂，我们构建了 m6A 风险模型，并探讨了它们在估计 HCC 预后和指导个体化治疗方面的临床价值。
大多数 m6A 调节剂在 HCC 中表现出异常表达，其表达受拷贝数变异（CNV）和 DNA 甲基化的影响。大规模数据分析揭示了许多关键 m6A 调节剂的生物学作用，这些发现与实验结果高度一致。m6A 风险模型具有显著的预后价值。此外，它们有助于重新评估索拉非尼、吉西他滨、CTLA4 和 PD1 阻滞剂等药物在 HCC 中的治疗潜力。
我们的研究结果表明，大数据分析和功能实验的相互验证可能有助于准确识别和定义生物标志物，我们的 m6A 风险模型可能具有指导 HCC 个体化化疗、靶向治疗和免疫治疗决策的潜力。
