Glioma is a common and aggressive primary malignant cancer known for its high morbidity, mortality, and recurrence rates. Despite this, treatment options for glioma are currently restricted. The dysregulation of RBPs has been linked to the advancement of several types of cancer, but their precise role in glioma evolution is still not fully understood. This study sought to investigate how RBPs may impact the development and prognosis of glioma, with potential implications for prognosis and therapy. RNA-seq profiles of glioma and corresponding clinical data from the CGGA database were initially collected for analysis. Unsupervised clustering was utilized to identify crucial tumor subtypes in glioma development. Subsequent time-series analysis and MS model were employed to track the progression of these identified subtypes. RBPs playing a significant role in glioma progression were then pinpointed using WGCNA and Lasso Cox regression models. Functional analysis of these key RBP-related genes was conducted through GSEA. Additionally, the CIBERSORT algorithm was utilized to estimate immune infiltrating cells, while the STRING database was consulted to uncover potential mechanisms of the identified biomarkers. Six tumor subgroups were identified and found to be highly homogeneous within each subgroup. The progression stages of these tumor subgroups were determined using time-series analysis and a MS model. Through WGCNA, Lasso Cox, and multivariate Cox regression analysis, it was confirmed that BCLAF1 is correlated with survival in glioma patients and is closely linked to glioma progression. Functional annotation suggests that BCLAF1 may impact glioma progression by influencing RNA splicing, which in turn affects the cell cycle, Wnt signaling pathway, and other cancer development pathways. The study initially identified six subtypes of glioma progression and assessed their malignancy ranking. Furthermore, it was determined that BCLAF1 could serve as an RBP-related prognostic marker, offering significant implications for the clinical diagnosis and personalized treatment of glioma.

中文翻译：

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基于RNA结合蛋白的神经胶质瘤亚型的鉴定和分类

胶质瘤是一种常见的侵袭性原发性恶性肿瘤，以其高发病率、死亡率和复发率而闻名。尽管如此，神经胶质瘤的治疗选择目前仍然受到限制。 RBP 的失调与几种癌症的进展有关，但它们在神经胶质瘤进化中的确切作用仍不完全清楚。本研究旨在探讨 RBP 如何影响神经胶质瘤的发展和预后，并对预后和治疗具有潜在影响。最初收集了 CGGA 数据库中神经胶质瘤的 RNA-seq 谱和相应的临床数据进行分析。利用无监督聚类来识别神经胶质瘤发展中的关键肿瘤亚型。随后的时间序列分析和 MS 模型被用来跟踪这些已识别亚型的进展。然后使用 WGCNA 和 Lasso Cox 回归模型确定了在神经胶质瘤进展中发挥重要作用的 RBP。通过 GSEA 对这些关键 RBP 相关基因进行功能分析。此外，还利用 CIBERSORT 算法来估计免疫浸润细胞，同时参考 STRING 数据库来揭示已识别生物标志物的潜在机制。鉴定出六个肿瘤亚组并发现每个亚组内具有高度同质性。使用时间序列分析和 MS 模型确定这些肿瘤亚组的进展阶段。通过WGCNA、Lasso Cox和多变量Cox回归分析，证实BCLAF1与胶质瘤患者的生存相关，并且与胶质瘤进展密切相关。功能注释表明，BCLAF1 可能通过影响 RNA 剪接来影响神经胶质瘤的进展，进而影响细胞周期、Wnt 信号通路和其他癌症发展通路。该研究最初确定了神经胶质瘤进展的六种亚型，并评估了它们的恶性程度。此外，确定BCLAF1可以作为RBP相关的预后标志物，为神经胶质瘤的临床诊断和个性化治疗提供重要意义。