背景:胶质瘤是一种常见且侵袭性的原发性恶性肿瘤,以其高发病率而闻名,死亡率,和复发率。尽管如此,目前神经胶质瘤的治疗方案受到限制.RBP的失调与几种癌症的发展有关,但是它们在神经胶质瘤进化中的确切作用仍未完全了解。本研究旨在探讨RBPs如何影响胶质瘤的发展和预后。对预后和治疗有潜在影响。
方法:首先从CGGA数据库中收集神经胶质瘤的RNA-seq谱和相应的临床数据进行分析。无监督聚类用于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模型用于跟踪这些鉴定的亚型的进展。然后使用WGCNA和LassoCox回归模型确定在神经胶质瘤进展中起重要作用的RBPs。通过GSEA对这些关键的RBP相关基因进行功能分析。此外,CIBERSORT算法用于估计免疫浸润细胞,同时查阅STRING数据库以发现已鉴定的生物标志物的潜在机制.
结果:确定了六个肿瘤亚组,发现每个亚组中都是高度同质的。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,LassoCox,和多变量Cox回归分析,已证实BCLAF1与神经胶质瘤患者的生存率相关,并且与神经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,和其他癌症发展途径。
结论:该研究最初确定了神经胶质瘤进展的六种亚型,并评估了其恶性程度。此外,确定BCLAF1可以作为RBP相关的预后标志物,对脑胶质瘤的临床诊断和个性化治疗具有重要意义。
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.