Identification of a prognostic signature based on five ferroptosis-related genes for diffuse large B-cell lymphoma.

IF 2.2 4区医学 Q3 ONCOLOGY

Cancer Biomarkers Pub Date : 2024-01-01 DOI:10.3233/CBM-230325

Wuping Li, Ruizhe Yao, Nasha Yu, Weiming Zhang

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引用次数: 0

Abstract

Background: 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).

Objective: To examine the connection between ferroptosis-related genes (FRGs) and clinical outcomes in DLBCL patients based on public datasets.

Methods: 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.

Results: 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.

Conclusion: 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.

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基于五个铁蛋白沉积相关基因确定弥漫大 B 细胞淋巴瘤的预后特征。

背景：弥漫大 B 细胞淋巴瘤（DLBCL）的基因表达谱多样，免疫微环境复杂，是一种侵袭性淋巴瘤，因此治疗方法有限。除此以外，研究表明铁蛋白沉积有助于肿瘤的发生、发展和转移。因此，我们有兴趣研究铁蛋白沉积与 DLBCL 疾病状态之间的联系。我们的目标是生成一个有价值的预后基因特征，用于预测 DLBCL 患者的病情，重点是铁蛋白沉积相关基因（FRGs）：基于公开数据集，研究铁突变相关基因（FRGs）与DLBCL患者临床预后之间的联系：从GSE32918（https://www.ncbi.nlm.nih.gov/geo/ query/acc.cgi?acc=gse32918）下载了DLBCL的表达谱数据集，并从FerrDb数据库（http://www. zhounan.org/ferrdb/）获得了铁突变相关基因簇。通过对GSE32918进行最小绝对收缩和选择算子（LASSO）Cox回归分析，并进行外部验证，从该基因簇中得出了预后特征。其作为生物标志物的有效性和预后价值是通过接收者操作者特征曲线单因子分析确定的。最后，利用 R 软件包 Cluster Profiler 对功能富集进行了评估：结果：通过多变量分析得出了五个与 DLBCL 相关的铁蛋白沉积相关基因（FRGs）（GOP1、GPX2、SLC7A5、ATF4 和 CXCL2）。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=gse 32918) 数据集通过 ROC 分析验证了这五个 FRG 的预后能力。KEGG和GO分析显示，基于基因特征的高危组上调基因富集于受体相互作用和其他癌症相关通路，包括与代谢异常和细胞分化相关的通路：结论：新开发的基因特征包括 GOP1、GPX2、SLC7A5、ATF4 和 CXCL2，具有作为预后生物标志物的潜力。此外，我们的研究结果还为铁变态反应对 DLBCL 的贡献提供了更多支持。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cancer Biomarkers ONCOLOGY-

CiteScore

5.20

自引率

3.20%

发文量

195

审稿时长

3 months

期刊介绍： Concentrating on molecular biomarkers in cancer research, Cancer Biomarkers publishes original research findings (and reviews solicited by the editor) on the subject of the identification of markers associated with the disease processes whether or not they are an integral part of the pathological lesion. The disease markers may include, but are not limited to, genomic, epigenomic, proteomics, cellular and morphologic, and genetic factors predisposing to the disease or indicating the occurrence of the disease. Manuscripts on these factors or biomarkers, either in altered forms, abnormal concentrations or with abnormal tissue distribution leading to disease causation will be accepted.