Mahmoudian-Hamedani Sharareh, Lotfi-Shahreza Maryam, Nikpour Parvaneh
Department of Genetics and Molecular Biology, Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Department of Computer Engineering, Shahreza Campus, University of Isfahan, Isfahan, Iran.
Biochem Biophys Rep. 2024 Dec 19;41:101897. doi: 10.1016/j.bbrep.2024.101897. eCollection 2025 Mar.
Gastric cancer (GC) is among the deadliest malignancies globally, characterized by hypoxia-driven pathways that promote cancer progression, including stemness mechanisms facilitating invasion and metastasis. This study aimed to develop a prognostic decision tree using genes implicated in hypoxia and stemness pathways to predict outcomes in GC patients.
GC RNA-seq data from The Cancer Genome Atlas (TCGA) were analyzed to compute hypoxia and stemness scores using Gene Set Variation Analysis (GSVA) and the mRNA expression-based stemness index (mRNAsi). Hierarchical clustering identified clusters with distinct survival outcomes, and differentially expressed genes (DEGs) between clusters were identified. Weighted Gene Co-expression Network Analysis (WGCNA) identified modules and hub genes associated with clinical traits. Overlapping DEGs and hub genes underwent functional enrichment, protein-protein interaction (PPI) network analysis, and survival analysis. A prognostic decision tree was constructed using survival-associated shared genes.
Hierarchical clustering identified six clusters among 375 TCGA GC patients, with significant survival differences between cluster 1 (low hypoxia, high stemness) and cluster 4 (high hypoxia, high stemness). Validation in the GSE62254 dataset corroborated these findings. WGCNA revealed modules linked to clinical traits and survival, with functional enrichment highlighting pathways like cell adhesion and calcium signaling. The decision tree, based on genes such as , , and , achieved an AUC of 0.81 (training) and 0.67 (test), demonstrating the utility of combined scores in patient stratification.
This study introduces a novel hypoxia-stemness-based prognostic decision tree for GC. The identified genes show promise as prognostic biomarkers, warranting further clinical validation.
胃癌（GC）是全球最致命的恶性肿瘤之一，其特征是由缺氧驱动的促进癌症进展的途径，包括促进侵袭和转移的干性机制。本研究旨在利用与缺氧和干性途径相关的基因开发一种预后决策树，以预测GC患者的预后。
分析来自癌症基因组图谱（TCGA）的GC RNA测序数据，使用基因集变异分析（GSVA）和基于mRNA表达的干性指数（mRNAsi）计算缺氧和干性评分。层次聚类识别出具有不同生存结果的簇，并识别簇之间的差异表达基因（DEG）。加权基因共表达网络分析（WGCNA）识别与临床特征相关的模块和枢纽基因。对重叠的DEG和枢纽基因进行功能富集、蛋白质-蛋白质相互作用（PPI）网络分析和生存分析。使用与生存相关的共享基因构建预后决策树。
层次聚类在375例TCGA GC患者中识别出六个簇，簇1（低缺氧，高干性）和簇4（高缺氧，高干性）之间存在显著的生存差异。在GSE62254数据集中的验证证实了这些发现。WGCNA揭示了与临床特征和生存相关的模块，功能富集突出了细胞粘附和钙信号等途径。基于、和等基因的决策树在训练集的AUC为0.81，在测试集的AUC为0.67，表明综合评分在患者分层中的效用。
本研究为GC引入了一种基于缺氧-干性的新型预后决策树。所识别的基因有望作为预后生物标志物，值得进一步的临床验证。
