Department of Infectious Diseases, The First Affiliated Hospital of Xi'an Jiaotong University, No. 277 Yanta Road(W), Xi'an, 710061, Shaanxi, China.
Health Science Center, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Hum Cell. 2024 May;37(3):752-767. doi: 10.1007/s13577-024-01044-3. Epub 2024 Mar 27.
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.
近年来，肝细胞癌（HCC）中异常的 m6A 改变一直是研究的重点，以探讨其生物学意义。在这项研究中，我们的目的是确定 m6A 修饰是否有助于乙型肝炎病毒（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 调节剂。随后，我们开发了一个列线图模型，利用这 8 个特征 m6A 调节剂来预测 HBV 相关 HCC 的患病率。根据决策曲线分析，患者可能受益于列线图模型。临床影响曲线显示列线图模型具有强大的预测能力。此外，共识分子亚型分析用于识别 m6A 修饰模式和 m6A 相关基因特征。通过 ssGSEA 算法实现免疫细胞亚群的定量。采用 PCA 算法计算个体肿瘤的 m6A 评分。两个不同的 m6A 修饰模式，即簇 A 和簇 B，与不同的免疫浸润模式和生物学途径显著相关。值得注意的是，通过 m6A 评分指标，簇 B 中的患者的 m6A 评分明显高于簇 A 中的患者。此外，通过免疫荧光验证了 IGFBP3 蛋白的表达，发现在肿瘤组织中其表达明显降低。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。这项研究有可能为 HBV 相关 HCC 的识别提供新的预后生物标志物和治疗靶点。
