一种新的缺氧特征基因预后模型用于鉴别弥漫性大b细胞淋巴瘤的缺氧亚型。

IF 1.8 4区 生物学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY
Geng Lyu, Ruixin Sun, Xiaxin Liu, Zizhen Xu
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引用次数: 0

摘要

弥漫性大b细胞淋巴瘤(DLBCL)是侵袭性b细胞淋巴瘤的主要类型,具有生物学和临床异质性。不同亚型DLBCL的预后差异较大。缺氧是肿瘤微环境的关键因素之一,通过多种机制促进肿瘤的进展,如增殖增加、代谢改变、血管生成增强、迁移能力增强等。本研究的主要目的是探讨缺氧特征基因(HFGs)与DLBCL预后之间的关系及其与免疫微环境的相关性。通过无监督共识聚类算法,从GEO和TCGA数据库中识别出DLBCL患者的各种缺氧相关模式。采用CIBERSORT和IOBR包识别不同的免疫浸润状态。为了利用缺氧相关基因建立预测模型,我们进行了单因素Cox回归、多因素Cox回归和LASSO回归评估。随后,我们证实了这些低氧相关基因的预测重要性,强调了低氧相关特征,并探索了低氧模型与免疫环境之间的联系。确定了三个缺氧簇。我们还观察到,每种缺氧反应模式与不同的预后显著相关。研究发现,不同缺氧群的免疫状态不同。在使用5个缺氧相关基因建立预后风险模型后,我们发现风险评分与免疫因素和药物治疗DLBCL的有效性有关。在DLBCL患者中,不同的缺氧模式与预后结果和免疫微环境相关。在这些患者中,缺氧特征基因(HFGs)是一个独立的预测因素。它也可能是预测ICI治疗和传统药物临床反应的可靠指标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Hypoxia-Featured Genes Prognostic Model for Identification of Hypoxia Subtypes in Diffuse Large B-Cell Lymphoma.

Diffuse large B-cell lymphoma (DLBCL), known as the predominant type of aggressive B-cell lymphoma, is biologically and clinically heterogeneous. The prognosis of DLBCL is quite different among subtypes. Hypoxia is one of the key elements in tumor microenvironment, promoting tumor progression by means of various mechanisms, such as increased proliferation, altered metabolism, enhanced angiogenesis, and greater migratory capability, among others. The primary purpose of this research is to investigate the connection between hypoxia-featured genes (HFGs), prognosis in DLBCL, and their capacity association with the immune microenvironment. Various hypoxia-associated patterns for DLBCL patients from GEO and TCGA databases were identified by means of an unsupervised consensus clustering algorithm. CIBERSORT and IOBR package is used to identify different immune infiltration status. To develop a predictive model using hypoxia-related genes, we conducted univariate Cox regression, multivariate Cox regression, and LASSO regression assessment. Subsequently, we confirmed the predictive importance of these hypoxia-associated genes, highlighting hypoxia-associated characteristics, and explored the connection between the hypoxia model and the immune environment. Three hypoxia clusters were identified. We also observed that each pattern of hypoxia response was significantly related to different prognoses. It was found that the immune status among hypoxia clusters is different. After developing a prognostic risk model using 5 hypoxia-related genes, we discovered that the risk score is related to immune factors and how effective drugs are in treating DLBCL. In DLBCL patients, varying hypoxia patterns correlate with both prognostic outcomes and the immune microenvironment. Hypoxia-featured genes (HFGs) function as a standalone predictive element in these patients. It is also potentially a reliable indicator for predicting clinical responses to ICI therapy and traditional drugs.

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来源期刊
Cell Biochemistry and Biophysics
Cell Biochemistry and Biophysics 生物-生化与分子生物学
CiteScore
4.40
自引率
0.00%
发文量
72
审稿时长
7.5 months
期刊介绍: Cell Biochemistry and Biophysics (CBB) aims to publish papers on the nature of the biochemical and biophysical mechanisms underlying the structure, control and function of cellular systems The reports should be within the framework of modern biochemistry and chemistry, biophysics and cell physiology, physics and engineering, molecular and structural biology. The relationship between molecular structure and function under investigation is emphasized. Examples of subject areas that CBB publishes are: · biochemical and biophysical aspects of cell structure and function; · interactions of cells and their molecular/macromolecular constituents; · innovative developments in genetic and biomolecular engineering; · computer-based analysis of tissues, cells, cell networks, organelles, and molecular/macromolecular assemblies; · photometric, spectroscopic, microscopic, mechanical, and electrical methodologies/techniques in analytical cytology, cytometry and innovative instrument design For articles that focus on computational aspects, authors should be clear about which docking and molecular dynamics algorithms or software packages are being used as well as details on the system parameterization, simulations conditions etc. In addition, docking calculations (virtual screening, QSAR, etc.) should be validated either by experimental studies or one or more reliable theoretical cross-validation methods.
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