研究动态
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基于 MAPK 信号通路的神经胶质瘤亚型、机器学习风险模型和关键枢纽蛋白识别。

MAPK signaling pathway-based glioma subtypes, machine-learning risk model, and key hub proteins identification.

发表日期:2023 Nov 04
作者: Hengrui Liu, Tao Tang
来源: GENES & DEVELOPMENT

摘要:

早期诊断和准确的预后对于神经胶质瘤的治疗至关重要。丝裂原激活蛋白激酶(MAPK)信号通路可能影响神经胶质瘤,但仍缺乏对该通路临床价值的探索。我们获取了来自 TCGA、GTEx、CGGA 等的数据。鉴定了神经胶质瘤中上调的 MAPK 信号通路基因,并使用共识聚类对神经胶质瘤亚型进行聚类。分析了生存、癌症干性和免疫微环境方面的亚型差异。使用 LASSO 方法使用已识别的基因训练预后模型,并通过三个外部队列进行验证。分析了风险模型和癌症相关特征之间的相关性。通过中心基因分析和生存分析鉴定了基因组的关键中心基因。 47% 的 MAPK 信号通路基因在神经胶质瘤中过度表达。基于这些基因的亚型在生存、癌症干性和免疫微环境方面进行了区分。风险模型的计算对总体生存率的预测具有很高的可信度,并且与多种癌症相关特征相关。鉴定出 12 个枢纽基因,其中 8 个与生存相关。 MAPK 信号通路在神经胶质瘤中过度表达,具有预后价值。© 2023。作者。
An early diagnosis and precise prognosis are critical for the treatment of glioma. The mitogen‑activated protein kinase (MAPK) signaling pathway potentially affects glioma, but the exploration of the clinical values of the pathway remains lacking. We accessed data from TCGA, GTEx, CGGA, etc. Up-regulated MAPK signaling pathway genes in glioma were identified and used to cluster the glioma subtypes using consensus clustering. The subtype differences in survival, cancer stemness, and the immune microenvironment were analyzed. A prognostic model was trained with the identified genes using the LASSO method and was validated with three external cohorts. The correlations between the risk model and cancer-associated signatures in cancer were analyzed. Key hub genes of the gene set were identified by hub gene analysis and survival analysis. 47% of the MAPK signaling pathway genes were overexpressed in glioma. Subtypes based on these genes were distinguished in survival, cancer stemness, and the immune microenvironment. A risk model was calculated with high confidence in the prediction of overall survival and was correlated with multiple cancer-associated signatures. 12 hub genes were identified and 8 of them were associated with survival. The MAPK signaling pathway was overexpressed in glioma with prognostic value.© 2023. The Author(s).