Exploring the prognosis, immune response, and therapeutic prospects of Parthanatos-related miRNAs in low-grade gliomas

The low-grade gliomas (LGG) are chronic aggressive brain tumors that often result in shortened survival rates due to recurrences and disease progression. Parthanatos, a programmed cell death pathway associated with PARP-1 activation and AIF, is potentially instrumental in tumor development. miRNAs play a key role in regulating tumourigenesis and progression, but few studies have been conducted on Parthanatos-associated miRNAs. We collected miRNA and mRNA datasets from LGG patients from TCGA and CGGA databases and corresponding clinical information. Gene sets related to Parthanatos were obtained through the Genecard database. We used IHC data to validate the differences in Parthanatos-related genes (PRGs) expression in glioma and normal brain tissue. A protein-protein interaction network of PRGs was established using the STRING database, and significantly related miRNAs were screened by Spearman correlation analysis. NMF clustering analysis and a Parthanatos-related miRNA-based prognostic index (PCMI) explored the heterogeneity of miRNA expression patterns. In addition, we assessed the potential role of miRNAs in immune invasion and therapeutic response in the tumor microenvironment and predicted the sensitivity of common chemotherapeutic and targeted therapeutic agents by prophetic and OncoPredict using the CMap database to identify possible therapeutic agents. The miRNAs associated with Parthanatos were successfully identified, and a prognostic model consisting of 9 miRNAs was constructed. The model showed good predictive efficacy in both training and validation datasets. Significant differences in survival, clinicopathological characteristics, and immune microenvironment invasion were observed between patients in the high-risk and low-risk groups. Drugs such as Fasudil were identified as possible therapeutic candidates by drug sensitivity analysis and CMap prediction. The miRNAs associated with Parthanatos were successfully identified, and a prognostic model consisting of 9 miRNAs was constructed. The model showed good predictive efficacy in both training and validation datasets. In the low-risk group, immune microenvironment invasion and survival differed significantly from the high-risk group . Drugs such as Fasudil were identified as possible therapeutic candidates by drug sensitivity analysis and CMap prediction.

低级别胶质瘤（low-grade gliomas, LGG）是一类慢性侵袭性脑肿瘤，常因复发与疾病进展导致患者生存期缩短。帕拉塔托斯细胞死亡（Parthanatos）是一种依赖多聚ADP核糖聚合酶1（PARP-1）激活与凋亡诱导因子（AIF）的程序性细胞死亡通路，可能在肿瘤发生发展中发挥关键作用。微小RNA（microRNAs, miRNAs）在调控肿瘤发生与进展中扮演核心角色，但目前针对帕拉塔托斯相关微小RNA的研究尚少。本研究从癌症基因组图谱（TCGA）与中国胶质瘤基因组图谱（CGGA）数据库中收集了低级别胶质瘤患者的微小RNA与信使RNA（mRNA）数据集，以及对应的临床信息。通过Genecard数据库获取与帕拉塔托斯相关的基因集。利用免疫组化（IHC）数据验证帕拉塔托斯相关基因（Parthanatos-related genes, PRGs）在胶质瘤与正常脑组织中的表达差异。使用STRING数据库构建帕拉塔托斯相关基因的蛋白质-蛋白质相互作用网络，并通过斯皮尔曼（Spearman）相关分析筛选出显著关联的微小RNA。采用非负矩阵分解聚类分析（NMF clustering analysis）与基于帕拉塔托斯相关微小RNA的预后指数（Parthanatos-related miRNA-based prognostic index, PCMI），探究微小RNA表达模式的异质性。此外，本研究评估了微小RNA在肿瘤微环境中免疫浸润与治疗响应中的潜在作用，并通过CMap数据库结合Prophetic与OncoPredict工具预测常见化疗及靶向治疗药物的敏感性，以筛选潜在治疗药物。本研究成功鉴定出与帕拉塔托斯相关的微小RNA，并构建了包含9种微小RNA的预后模型。该模型在训练集与验证集数据中均表现出优异的预测效能。高风险组与低风险组患者在生存期、临床病理特征及免疫微环境浸润方面均存在显著差异。通过药物敏感性分析与CMap预测，确定了法舒地尔（Fasudil）等药物作为潜在治疗候选药物。