Departments of Lymphatic and Hematological Oncology, Jiangxi Cancer Hospital, Nanchang, Jiangxi, China.
Queen Mary College of Nanchang University, Nanchang, Jiangxi, China.
Cancer Biomark. 2024;40(1):125-139. doi: 10.3233/CBM-230325.
Therapies for diffuse large B-cell lymphoma (DLBCL) are limited due to the diverse gene expression profiles and complicated immune microenvironments, making it an aggressive lymphoma. Beyond this, researches have shown that ferroptosis contributes to tumorigenesis, progression, and metastasis. We thus are interested to dissect the connection between ferroptosis and disease status of DLBCL. We aim at generating a valuable prognosis gene signature for predicting the status of patients of DLBCL, with focus on ferroptosis-related genes (FRGs).
To examine the connection between ferroptosis-related genes (FRGs) and clinical outcomes in DLBCL patients based on public datasets.
An expression profile dataset for DLBCL was downloaded from GSE32918 (https://www.ncbi.nlm.nih.gov/geo/ query/acc.cgi?acc=gse32918), and a ferroptosis-related gene cluster was obtained from the FerrDb database (http://www. zhounan.org/ferrdb/). A prognostic signature was developed from this gene cluster by applying a least absolute shrinkage and selection operator (LASSO) Cox regression analysis to GSE32918, followed by external validation. Its effectiveness as a biomarker and the prognostic value was determined by a receiver operator characteristic curve mono factor analysis. Finally, functional enrichment was evaluated by the package Cluster Profiler of R.
Five ferroptosis-related genes (FRGs) (GOP1, GPX2, SLC7A5, ATF4, and CXCL2) associated with DLBCL were obtained by a multivariate analysis. The prognostic power of these five FRGs was verified by TCGA (https://xenabrowser.net/datapages/?dataset=TCGA.DLBC.sampleMap%2FHiSeqV2_PANCAN&host=https%3A%2F%2Ftcga.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A44) and GEO (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse 32918) datasets, with ROC analyses. KEGG and GO analyses revealed that upregulated genes in the high-risk group based on the gene signature were enriched in receptor interactions and other cancer-related pathways, including pathways related to abnormal metabolism and cell differentiation.
The newly developed signature involving GOP1, GPX2, SLC7A5, ATF4, and CXCL2 has the potential to serve as a prognostic biomarker. Furthermore, our results provide additional support for the contribution of ferroptosis to DLBCL.
弥漫性大 B 细胞淋巴瘤（DLBCL）的治疗方法有限，这是由于其基因表达谱的多样性和复杂的免疫微环境，使其成为一种侵袭性淋巴瘤。除此之外，研究表明铁死亡有助于肿瘤的发生、发展和转移。因此，我们有兴趣剖析铁死亡与 DLBCL 疾病状态之间的联系。我们旨在为 DLBCL 患者生成一个有价值的预后基因特征，重点关注铁死亡相关基因（FRGs）。
基于公共数据集，研究铁死亡相关基因（FRGs）与 DLBCL 患者临床结局之间的关系。
从 GSE32918 数据库（https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse32918）下载 DLBCL 的表达谱数据集，并从 FerrDb 数据库（http://www.zhounan.org/ferrdb/）获得铁死亡相关基因簇。通过 LASSO Cox 回归分析，对 GSE32918 数据集进行分析，得到一个由铁死亡相关基因簇组成的预后特征，并进行外部验证。通过单因素分析的受试者工作特征曲线来确定其作为生物标志物和预后价值的有效性。最后，使用 R 软件包 Cluster Profiler 评估功能富集情况。
通过多变量分析，获得了与 DLBCL 相关的 5 个铁死亡相关基因（FRGs）（GOP1、GPX2、SLC7A5、ATF4 和 CXCL2）。这些 5 个 FRGs 的预后能力通过 TCGA（https://xenabrowser.net/datapages/?dataset=TCGA.DLBC.sampleMap%2FHiSeqV2_PANCAN&host=https%3A%2F%2Ftcga.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A44）和 GEO（https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=gse32918）数据集的 ROC 分析得到了验证。KEGG 和 GO 分析显示，基于基因特征的高风险组中上调的基因富集在受体相互作用和其他癌症相关途径中，包括与异常代谢和细胞分化相关的途径。
新开发的涉及 GOP1、GPX2、SLC7A5、ATF4 和 CXCL2 的特征签名具有成为预后生物标志物的潜力。此外，我们的结果为铁死亡对 DLBCL 的贡献提供了额外的支持。
