Identify truly high-risk TP53-mutated diffuse large B cell lymphoma patients and explore the underlying biological mechanisms.

IF 8.2 2区 生物学 Q1 CELL BIOLOGY
Kai-Xin Du, Yi-Fan Wu, Wei Hua, Zi-Wen Duan, Rui Gao, Jun-Heng Liang, Yue Li, Hua Yin, Jia-Zhu Wu, Hao-Rui Shen, Li Wang, Yang Shao, Jian-Yong Li, Jin-Hua Liang, Wei Xu
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引用次数: 0

Abstract

TP53 mutation (TP53-mut) correlates with inferior survival in many cancers, whereas its prognostic role in diffuse large B-cell lymphoma (DLBCL) is still in controversy. Therefore, more precise risk stratification needs to be further explored for TP53-mut DLBCL patients. A set of 2637 DLBCL cases from multiple cohorts, was enrolled in our analysis. Among the 2637 DLBCL patients, 14.0% patients (370/2637) had TP53-mut. Since missense mutations account for the vast majority of TP53-mut DLBCL patients, and most non-missense mutations affect the function of the P53 protein, leading to worse survival rates, we distinguished patients with missense mutations. A TP53 missense mutation risk model was constructed based on a 150-combination machine learning computational framework, demonstrating excellent performance in predicting prognosis. Further analysis revealed that patients with high-risk missense mutations are significantly associated with early progression and exhibit dysregulation of multiple immune and metabolic pathways at the transcriptional level. Additionally, the high-risk group showed an absolutely suppressed immune microenvironment. To stratify the entire cohort of TP53-mut DLBCL, we combined clinical characteristics and ultimately constructed the TP53 Prognostic Index (TP53PI) model. In summary, we identified the truly high-risk TP53-mut DLBCL patients and explained this difference at the mutation and transcriptional levels.

确定真正的高风险 TP53 突变弥漫大 B 细胞淋巴瘤患者,并探索其潜在的生物学机制。
TP53突变(TP53-mut)与许多癌症的低生存率相关,但它在弥漫大B细胞淋巴瘤(DLBCL)中的预后作用仍存在争议。因此,需要进一步探索对TP53突变的DLBCL患者进行更精确的风险分层。我们分析了来自多个队列的2637例DLBCL病例。在这2637例DLBCL患者中,14.0%的患者(370/2637)有TP53突变。由于错义突变占 TP53 突变 DLBCL 患者的绝大多数,而大多数非错义突变会影响 P53 蛋白的功能,导致生存率降低,因此我们对错义突变患者进行了区分。基于150种组合的机器学习计算框架,我们构建了一个TP53错义突变风险模型,该模型在预测预后方面表现出色。进一步的分析表明,高风险错义突变患者与疾病的早期进展密切相关,并在转录水平上表现出多种免疫和代谢通路的失调。此外,高风险组显示出绝对抑制的免疫微环境。为了对整个TP53突变DLBCL队列进行分层,我们结合了临床特征,最终构建了TP53预后指数(TP53PI)模型。总之,我们确定了真正的高风险TP53突变DLBCL患者,并从突变和转录水平解释了这种差异。
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
CiteScore
11.00
自引率
0.00%
发文量
180
期刊介绍: Cell Communication and Signaling (CCS) is a peer-reviewed, open-access scientific journal that focuses on cellular signaling pathways in both normal and pathological conditions. It publishes original research, reviews, and commentaries, welcoming studies that utilize molecular, morphological, biochemical, structural, and cell biology approaches. CCS also encourages interdisciplinary work and innovative models, including in silico, in vitro, and in vivo approaches, to facilitate investigations of cell signaling pathways, networks, and behavior. Starting from January 2019, CCS is proud to announce its affiliation with the International Cell Death Society. The journal now encourages submissions covering all aspects of cell death, including apoptotic and non-apoptotic mechanisms, cell death in model systems, autophagy, clearance of dying cells, and the immunological and pathological consequences of dying cells in the tissue microenvironment.
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