Abstract

In recent years, abnormal m6A alteration in hepatocellular carcinoma (HCC) has been a focus on investigating the biological implications. In this study, our objective is to determine whether m6A modification contributes to the progression of HBV-related HCC. To achieve this, we employed a random forest model to screen top 8 characteristic m6A regulators from 19 candidate genes. Subsequently, we developed a nomogram model that utilizes these 8 characteristic m6A regulators to predict the prevalence of HBV-related HCC. According to decision curve analysis, patients may benefit from the nomogram model. The clinical impact curves exhibited a robust predictive capability of the nomogram models. Additionally, consensus molecular subtyping was employed to identify m6A modification patterns and m6A-related gene signature. The quantification of immune cell subsets was accomplished through the implementation of ssGSEA algorithms. PCA algorithms were developed to compute the m6A score for individual tumors. Two distinct m6A modification patterns, namely cluster A and cluster B, exhibited significant correlations with distinct immune infiltration patterns and biological pathways. Notably, patients belonging to cluster B demonstrated higher m6A scores compared to those in cluster A, as determined by the m6A score metric. Furthermore, the expression of IGFBP3 proteins was validated through immunofluorescence, revealing their pronounced lower expression in tumor tissues. In summary, our study underscores the importance of m6A modification in the advancement of HBV-related HCC. This research has the potential to yield novel prognostic biomarkers and therapeutic targets for the identification of HBV-related HCC.

中文翻译：

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m6A RNA甲基化调节因子在HBV相关肝细胞癌诊断及亚型分类中的意义

摘要

近年来，肝细胞癌 (HCC) 中 m6A 的异常改变一直是研究其生物学意义的焦点。在本研究中，我们的目标是确定 m6A 修饰是否会导致 HBV 相关 HCC 的进展。为了实现这一目标，我们采用随机森林模型从 19 个候选基因中筛选出前 8 个特征 m6A 调节因子。随后，我们开发了一个列线图模型，利用这 8 个特征性 m6A 调节因子来预测 HBV 相关 HCC 的患病率。根据决策曲线分析，患者可能会从列线图模型中受益。临床影响曲线表现出列线图模型强大的预测能力。此外，还采用共识分子亚型来识别 m6A 修饰模式和 m6A 相关基因特征。免疫细胞亚群的量化是通过实施 ssGSEA 算法来完成的。 PCA 算法被开发来计算单个肿瘤的 m6A 评分。两种不同的 m6A 修饰模式，即 A 簇和 B 簇，与不同的免疫浸润模式和生物途径表现出显着的相关性。值得注意的是，根据 m6A 评分指标确定，与 A 组患者相比，B 组患者表现出更高的 m6A 评分。此外，通过免疫荧光验证了 IGFBP3 蛋白的表达，表明它们在肿瘤组织中的表达明显较低。总之，我们的研究强调了 m6A 修饰在 HBV 相关 HCC 进展中的重要性。这项研究有可能产生新的预后生物标志物和治疗靶点，用于鉴定 HBV 相关的 HCC。