Guo Youwei, Zhou Quanwei, Wei Min, Fan Jianfeng, Huang He
Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan Province, China.
National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
Heliyon. 2024 Mar 20;10(7):e28445. doi: 10.1016/j.heliyon.2024.e28445. eCollection 2024 Apr 15.
TNF family members (TFMs) play a crucial role in different types of cancers, with TNF Receptor Superfamily Member 19 (TNFRSF19) standing out as a particularly important member in this category. Further research is necessary to investigate the potential impact of TFMs on prognosis prediction and to elucidate the function and potential therapeutic targets linked to TNFRSF19 expression in gliomas.
Three databases provided the data on gene expression and clinical information. Fourteen prognostic members were found through univariate Cox analysis and subsequently utilized to construct TFMs-based model in LASSO and multivariate Cox analyses. TFMs-based subtypes based on the expression profile were identified using an unsupervised clustering method. Machine learning algorithm identified key genes linked to prognostic model and subtype. A sequence of immune infiltrations was evaluated using the ssGSEA and ESTIMATE algorithms. Immunohistochemistry was used to examine the patterns of expression and the clinical significance of TNFRSF19.
Our development of a prognostic model and subtypes based on the TNF family was successful, resulting in accurate predictions of prognosis. The findings indicate that TNFRSF19 exhibited strong performance. Upregulation of TNFRSF19 was correlated with malignant phenotypes and poor prognosis, which was confirmed through immunohistochemistry. TNFRSF19 played a role in reshaping the immunosuppressive microenvironment in gliomas, and multiple drug-targeted TNFRSF19 molecules were identified.
The TMF-based prognostic model and subtype can facilitate treatment decisions for glioma. TNFRSF19 is an outstanding representative of a predictor of prognosis and immunotherapy effect in gliomas.
肿瘤坏死因子（TNF）家族成员（TFMs）在不同类型的癌症中发挥着关键作用，其中肿瘤坏死因子受体超家族成员19（TNFRSF19）是该类别中尤为重要的成员。有必要进一步研究TFMs对预后预测的潜在影响，并阐明与胶质瘤中TNFRSF19表达相关的功能及潜在治疗靶点。
三个数据库提供了基因表达和临床信息数据。通过单变量Cox分析发现了14个预后相关成员，随后在LASSO和多变量Cox分析中用于构建基于TFMs的模型。使用无监督聚类方法基于表达谱确定基于TFMs的亚型。机器学习算法确定了与预后模型和亚型相关的关键基因。使用ssGSEA和ESTIMATE算法评估一系列免疫浸润情况。采用免疫组织化学法检测TNFRSF19的表达模式及其临床意义。
我们成功开发了基于TNF家族的预后模型和亚型，从而实现了对预后的准确预测。研究结果表明TNFRSF19表现出强大的性能。TNFRSF19的上调与恶性表型和不良预后相关，这一点通过免疫组织化学得到了证实。TNFRSF19在重塑胶质瘤的免疫抑制微环境中发挥作用，并鉴定出多种靶向TNFRSF19的药物分子。
基于TMF的预后模型和亚型有助于胶质瘤的治疗决策。TNFRSF19是胶质瘤预后和免疫治疗效果预测指标的杰出代表。
