Liu Chang, Zhang Ning, Xu Zhihao, Wang Xiaofeng, Yang Yang, Bu Junming, Cao Huake, Xiao Jin, Xie Yinyin
College of Life Sciences, Anhui Medical University, Hefei, 230032, Anhui, China.
School of Basic Medical Sciences, Anhui Medical University, Hefei, 230032, Anhui, China.
Heliyon. 2023 Sep 5;9(9):e19856. doi: 10.1016/j.heliyon.2023.e19856. eCollection 2023 Sep.
Glioma is the most frequent malignant primary brain tumor, and mitochondria may influence the progression of glioma. The aim of this study was to analyze the role of nuclear mitochondria related genes (MTRGs) in glioma, identify subtypes and construct a prognostic model based on nuclear MTRGs and machine learning algorithms.
Samples containing both gene expression profiles and clinical information were retrieved from the TCGA database, CGGA database, and GEO database. We selected 16 nuclear MTRGs and identified two clusters of glioma. Prognostic features, microenvironment, mutation landscape, and drug sensitivity were compared between the clusters. A prognostic model based on multiple machine learning algorithms was then constructed and validated by multiple datasets.
We observed significant discrepancies between the two clusters. Cluster One had higher nuclear MTRG expression, a lower survival rate, and higher immune infiltration than Cluster Two. For the two clusters, we found distinct predictive drug sensitivities and responses to immune therapy, and the infiltration of immune cells was significantly different. Among the 22 combinations of machine learning algorithms we tested, LASSO was the most effective in constructing the prognostic model. The model's accuracy was further verified in three independent glioma datasets. We identified as a vital gene associated with infiltrating immune cells in multiple types of tumors.
In short, our research identified two clusters of glioma and developed a dependable prognostic model based on machine learning methods. was identified as a potential biomarker for multiple tumors. Our results will contribute to precise medicine and glioma management.
胶质瘤是最常见的原发性恶性脑肿瘤，线粒体可能影响胶质瘤的进展。本研究旨在分析细胞核线粒体相关基因（MTRGs）在胶质瘤中的作用，识别亚型，并基于细胞核MTRGs和机器学习算法构建预后模型。
从TCGA数据库、CGGA数据库和GEO数据库中检索包含基因表达谱和临床信息的样本。我们选择了16个细胞核MTRGs，并识别出胶质瘤的两个聚类。比较了两个聚类之间的预后特征、微环境、突变图谱和药物敏感性。然后构建了基于多种机器学习算法的预后模型，并通过多个数据集进行验证。
我们观察到两个聚类之间存在显著差异。聚类一的细胞核MTRG表达较高，生存率较低，免疫浸润高于聚类二。对于这两个聚类，我们发现了不同的预测药物敏感性和对免疫治疗的反应，并且免疫细胞的浸润存在显著差异。在我们测试的22种机器学习算法组合中，LASSO在构建预后模型方面最有效。该模型的准确性在三个独立的胶质瘤数据集中得到了进一步验证。我们确定 为与多种肿瘤中浸润免疫细胞相关的关键基因。
简而言之，我们的研究识别出了胶质瘤的两个聚类，并基于机器学习方法开发了一个可靠的预后模型。 被确定为多种肿瘤的潜在生物标志物。我们的结果将有助于精准医学和胶质瘤的管理。
