Wu Yue, Cai Xiaoyan, Hu Menghan, Cao Runyan, Wang Yong
Department of Ophthalmology, The First Affiliated Hospital of Anhui Medical University, Anhui, China.
Front Oncol. 2025 Aug 1;15:1618601. doi: 10.3389/fonc.2025.1618601. eCollection 2025.
BACKGROUND: Uveal melanoma (UVM) is a rare yet aggressive form of ocular cancer with a poor prognosis. This study aims to investigate the role of oxidative stress-related genes (OSGs) in UVM, focusing on their involvement in key signaling pathways and immune infiltration and their potential as prognostic biomarkers and therapeutic targets. METHOD: Differential gene expression analysis was conducted using 175 samples of normal retinal pigmented epithelium-choroid complex samples and 63 samples from UVM. Protein-protein interaction (PPI) networks were constructed to identify hub genes, and machine learning algorithms were utilized to screen for diagnostic genes, employing methods such as least absolute shrinkage and selection operator (LASSO) regression, random forest, support vector machine (SVM), gradient boosting machine (GBM), neural network algorithm (NNET), and eXtreme gradient boosting (XGBoost). A risk signature model was developed using data from The Cancer Genome Atlas (TCGA) cohort and validated using the International Cancer Genome Consortium (ICGC), GSE84976 dataset. Clinical samples were used to validate the diagnostic value. Experimental validation encompassed HO-induced oxidative stress assays and CALM1 overexpression analysis in UVM cells to evaluate its protective effects. RESULTS: A total of 2,576 differentially expressed genes (DEGs) were identified, with 185 overlapping OSGs enriched in pathways such as HIF-1, FoxO, PI3K-Akt, and apoptosis. Prognostic hub OSGs, including ACACA, CALM1, and DNM2, were associated with poor survival outcomes in the training set and multiple validation data. Revalidation using clinically collected samples confirmed that CALM1 exhibits superior diagnostic value. The risk signature model demonstrated strong predictive accuracy for a 5-year overall survival (AUC = 0.844). Immune infiltration analysis revealed increased CD4 memory-activated T cells and mast resting cells in the high-risk group. Additionally, CALM1 overexpression attenuated HO-induced oxidative stress and apoptosis in UVM cells. CALM1 upregulation also mitigated the inhibitory effects of HO on key cellular processes, including proliferation, migration, and invasion. CONCLUSION: This study underscores the critical role of OSGs in the progression of UVM and their potential as prognostic biomarkers and therapeutic targets. The identified risk signature model and the protective role of CALM1 offer valuable insights for developing targeted therapies and enhancing patient clinical outcomes in UVM.
背景:葡萄膜黑色素瘤(UVM)是一种罕见但侵袭性强的眼部癌症,预后较差。本研究旨在探讨氧化应激相关基因(OSGs)在UVM中的作用,重点关注它们在关键信号通路和免疫浸润中的参与情况,以及它们作为预后生物标志物和治疗靶点的潜力。 方法:使用175份正常视网膜色素上皮-脉络膜复合体样本和63份UVM样本进行差异基因表达分析。构建蛋白质-蛋白质相互作用(PPI)网络以识别枢纽基因,并利用机器学习算法筛选诊断基因,采用最小绝对收缩和选择算子(LASSO)回归、随机森林、支持向量机(SVM)、梯度提升机(GBM)、神经网络算法(NNET)和极端梯度提升(XGBoost)等方法。使用来自癌症基因组图谱(TCGA)队列的数据开发风险特征模型,并使用国际癌症基因组联盟(ICGC)、GSE84976数据集进行验证。使用临床样本验证诊断价值。实验验证包括HO诱导的氧化应激测定和UVM细胞中CALM1过表达分析,以评估其保护作用。 结果:共鉴定出2576个差异表达基因(DEGs),其中185个重叠的OSGs富集于HIF-1、FoxO、PI3K-Akt和凋亡等通路。预后枢纽OSGs,包括ACACA、CALM1和DNM2,在训练集和多个验证数据中与较差的生存结果相关。使用临床收集的样本重新验证证实CALM1具有卓越的诊断价值。风险特征模型对5年总生存期显示出强大的预测准确性(AUC = 0.844)。免疫浸润分析显示高危组中CD4记忆激活T细胞和肥大静止细胞增加。此外,CALM1过表达减轻了HO诱导的UVM细胞氧化应激和凋亡。CALM1上调还减轻了HO对关键细胞过程(包括增殖、迁移和侵袭)的抑制作用。 结论:本研究强调了OSGs在UVM进展中的关键作用及其作为预后生物标志物和治疗靶点的潜力。所鉴定的风险特征模型和CALM1的保护作用为开发靶向治疗和改善UVM患者的临床结局提供了有价值的见解。
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