Hubei Provincial Key Laboratory of Precision Radiation Oncology, Wuhan 430022, China; The First Clinical School, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430023, China.
The First Clinical School, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430023, China; Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Gene. 2025 Jan 30;935:149077. doi: 10.1016/j.gene.2024.149077. Epub 2024 Nov 3.
Oxidative stress is a cellular characteristic that might induce the proliferation and differentiation of tumor cells and promote tumor progression in diffuse large B-cell lymphoma (DLBCL).
The DLBCL gene sequencing dataset, tumor mutation burden data, copy number variation data of Somatic cell mutation data in TCGA were downloaded for data training analysis, along with four DLBCL datasets in GEO for validation analysis. The known oxidative stress related genes (OSRGs) were collected from websites. The weighted gene co-expression network analysis (WGCNA) was conducted on the TCGA DLBCL dataset to obtain gene modules related to oxidative stress and intersected with the known OSRGs to obtain the hub genes, which were used to perform consensus clustering on the samples to obtain new phenotypes. Next, the prognosis related OSRGs were selected through regression analysis algorithms and key genes were identified. These genes were used to establish the prognostic risk model and predictive model, and to compare functional and pathway differences among different risk groups.
Through website search, we obtained 297 known OSRGs, and after intersecting with WGCNA results, we obtained 26 OSRGs. The TCGA-DLBC samples were clustered into 2 subtypes with these genes and there were significant differences in immune infiltration between subtypes. After regression analysis, we obtained a total of four key genes, BMI1, CDKN1A, NOX1, and SESN1. The risk prediction model established with these four genes as variables has accurate prognostic prediction ability. The key genes interact with 65 miRNAs, 57 TFs, 47 RBPs, and 62 drugs, respectively, and are closely related to immune infiltration of the disease. Among them, CDKN1A and SESN1 had the highest variability.
The key genes involved in oxidative stress could predict the prognosis of DLBCL and potentially become therapeutic targets.
氧化应激是一种可能诱导肿瘤细胞增殖和分化并促进弥漫性大 B 细胞淋巴瘤（DLBCL）进展的细胞特征。
从 TCGA 下载了 DLBCL 基因测序数据集、肿瘤突变负担数据、体细胞突变拷贝数变异数据，并结合 GEO 中的四个 DLBCL 数据集进行数据训练分析。从网站上收集了已知的氧化应激相关基因（OSRGs）。对 TCGA-DLBCL 数据集进行加权基因共表达网络分析（WGCNA），获得与氧化应激相关的基因模块，并与已知的 OSRGs 进行交集，获得枢纽基因，用于对样本进行共识聚类，获得新的表型。然后，通过回归分析算法选择与预后相关的 OSRGs，并确定关键基因。这些基因用于建立预后风险模型和预测模型，并比较不同风险组之间的功能和途径差异。
通过网站搜索，我们获得了 297 个已知的 OSRGs，在与 WGCNA 结果相交后，我们获得了 26 个 OSRGs。利用这些基因，TCGA-DLBC 样本聚类为 2 个亚型，亚型间免疫浸润存在显著差异。经过回归分析，我们共获得了 4 个关键基因，BMI1、CDKN1A、NOX1 和 SESN1。以这 4 个基因为变量建立的风险预测模型具有准确的预后预测能力。关键基因分别与 65 个 miRNA、57 个 TF、47 个 RBP 和 62 种药物相互作用，与疾病的免疫浸润密切相关。其中，CDKN1A 和 SESN1 的变异性最高。
涉及氧化应激的关键基因可预测 DLBCL 的预后，并可能成为治疗靶点。
