Liu Liwen, Hu Qiuyue, Zhang Yize, Sun Xiangyi, Sun Ranran, Ren Zhigang
Precision Medicine Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Front Pharmacol. 2023 Feb 22;14:1145408. doi: 10.3389/fphar.2023.1145408. eCollection 2023.
Recent studies highlighted the functional role of protein arginine methyltransferases (PRMTs) catalyzing the methylation of protein arginine in malignant progression of various tumors. Stratification the subtypes of hepatocellular carcinoma (HCC) is fundamental for exploring effective treatment strategies. Here, we aim to conduct a comprehensive analysis of PRMTs with bioinformatic tools to identify novel biomarkers for HCC subtypes classification and prognosis prediction, which may be potential ideal targets for therapeutic intervention. The expression profiling of PRMTs in HCC tissues was evaluated based on the data of TCGA-LIHC cohort, and further validated in HCC TMA cohort and HCC cell lines. HCC was systematically classified based on PRMT family related genes. Subsequently, the differentially expressed genes (DEGs) between molecular subtypes were identified, and prognostic risk model were constructed using least absolute shrinkage and selection operator (LASSO) and Cox regression analysis to evaluate the prognosis, gene mutation, clinical features, immunophenotype, immunotherapeutic effect and antineoplastic drug sensitivity of HCC. PRMTs expression was markedly altered both in HCC tissues and HCC cell lines. Three molecular subtypes with distinct immunophenotype were generated. 11 PRMT-related genes were enrolled to establish prognostic model, which presented with high accuracy in predicting the prognosis of two risk groups in the training, validation, and immunotherapy cohort, respectively. Additionally, the two risk groups showed significant difference in immunotherapeutic efficacy. Further, the sensitivity of 72 anticancer drugs was identified using prognostic risk model. In summary, our findings stratified HCC into three subtypes based on the PRMT-related genes. The prognostic model established in this work provide novel insights into the exploration of related therapeutic approaches in treating HCC.
最近的研究强调了蛋白质精氨酸甲基转移酶(PRMTs)催化蛋白质精氨酸甲基化在各种肿瘤恶性进展中的功能作用。对肝细胞癌(HCC)亚型进行分层是探索有效治疗策略的基础。在此,我们旨在利用生物信息学工具对PRMTs进行全面分析,以识别用于HCC亚型分类和预后预测的新型生物标志物,这些标志物可能是治疗干预的潜在理想靶点。基于TCGA-LIHC队列的数据评估了PRMTs在HCC组织中的表达谱,并在HCC组织芯片队列和HCC细胞系中进一步验证。根据PRMT家族相关基因对HCC进行系统分类。随后,鉴定了分子亚型之间的差异表达基因(DEGs),并使用最小绝对收缩和选择算子(LASSO)和Cox回归分析构建预后风险模型,以评估HCC的预后、基因突变、临床特征、免疫表型、免疫治疗效果和抗肿瘤药物敏感性。PRMTs的表达在HCC组织和HCC细胞系中均有明显改变。产生了三种具有不同免疫表型的分子亚型。纳入11个与PRMT相关的基因建立预后模型,该模型在训练、验证和免疫治疗队列中分别对两个风险组的预后预测具有较高的准确性。此外,两个风险组在免疫治疗效果上显示出显著差异。进一步地,使用预后风险模型鉴定了72种抗癌药物的敏感性。总之,我们的研究结果基于与PRMT相关的基因将HCC分为三种亚型。本研究建立的预后模型为探索HCC相关治疗方法提供了新的见解。