Li Jian, Pan Hong, Wang Yangyang, Chen Haixin, Song Zhaopeng, Wang Zheng, Li Jinxing
Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
Department of Neurosurgery, Linyi People's Hospital, Linyi, Shandong, China.
Biomed Res Int. 2025 Mar 10;2025:2004975. doi: 10.1155/bmri/2004975. eCollection 2025.
Abnormalities in the extracellular matrix (ECM) have been shown to play a crucial role in promoting the formation, progression, and metastasis of glioblastoma multiforme (GBM). Therefore, our study is aimed at constructing a prognostic model based on ECM-related factors, to predict the prognosis of patients with GBM. We employed single-sample gene set enrichment analysis (ssGSEA) to establish the ECM index of GBM. The identification of candidate genes was achieved by differential analysis conducted between ECM index groups, as well as through the utilization of weighted gene coexpression network analysis (WGCNA) and gene enrichment analysis. We conducted functional validation to confirm the significance of five biomarkers that were tested in the U251 cell line. The screening of prognostic genes was conducted using least absolute shrinkage and selection operator (LASSO) and univariate Cox analysis. The predictive relevance of the risk score model was assessed by using Kaplan-Meier (KM) survival curves in both The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) cohorts. In addition, we conducted immunological studies, created and verified a nomogram, and constructed a network involving long noncoding RNA (lncRNA), microRNA (miRNA), and messenger RNA (mRNA). We identified 45 candidate genes by overlapping the 59 WGCNA core genes with the 855 differentially expressed genes (DEGs) between ECM index groups. These candidate genes were significantly enriched in 254 biological processes (BPs), 18 cellular components (CCs), 27 molecular functions (MFs), and 11 KEGG pathways. We identified a prognostic ECM-related five-gene signature using these candidate genes and constructed a risk model. Furthermore, we generated a nomogram model with excellent predictive ability. We also found significant differences between risk groups in six cell types and 29 immune checkpoints. Finally, we constructed a lncRNA-miRNA-mRNA network consisting of 27 lncRNAs, 73 miRNAs, and 5 model mRNAs. Our study developed a prognostic model based on the ECM-related five-gene signature, which can serve as a valuable reference for the treatment and prophetic prediction of GBM.
细胞外基质（ECM）异常已被证明在多形性胶质母细胞瘤（GBM）的形成、进展和转移中起关键作用。因此，我们的研究旨在构建一个基于ECM相关因素的预后模型，以预测GBM患者的预后。我们采用单样本基因集富集分析（ssGSEA）来建立GBM的ECM指数。通过在ECM指数组之间进行差异分析，以及利用加权基因共表达网络分析（WGCNA）和基因富集分析来鉴定候选基因。我们进行了功能验证，以确认在U251细胞系中测试的五个生物标志物的意义。使用最小绝对收缩和选择算子（LASSO）和单变量Cox分析进行预后基因的筛选。通过在癌症基因组图谱（TCGA）和中国胶质瘤基因组图谱（CGGA）队列中使用Kaplan-Meier（KM）生存曲线来评估风险评分模型的预测相关性。此外，我们进行了免疫学研究，创建并验证了列线图，并构建了一个涉及长链非编码RNA（lncRNA）、微小RNA（miRNA）和信使RNA（mRNA）的网络。我们通过将59个WGCNA核心基因与ECM指数组之间的855个差异表达基因（DEG）重叠，鉴定出45个候选基因。这些候选基因在254个生物学过程（BP）、18个细胞成分（CC）、27个分子功能（MF）和11条KEGG途径中显著富集。我们使用这些候选基因鉴定了一个与预后相关的ECM五基因特征，并构建了一个风险模型。此外，我们生成了一个具有出色预测能力的列线图模型。我们还发现六个细胞类型和29个免疫检查点在风险组之间存在显著差异。最后，我们构建了一个由27个lncRNA、73个miRNA和5个模型mRNA组成的lncRNA-miRNA-mRNA网络。我们的研究开发了一个基于与ECM相关的五基因特征的预后模型，可为GBM的治疗和预后预测提供有价值的参考。
