cuprotosis相关lncrna与肿瘤代谢和免疫微环境相关,并预测胰腺癌患者预后

IF 1.9 4区 生物学 Q4 CELL BIOLOGY IET Systems Biology Pub Date : 2023-06-21 DOI:10.1049/syb2.12068
Yanling Wang, Weiyu Ge, Shengbai Xue, Jiujie Cui, Xiaofei Zhang, Tiebo Mao, Haiyan Xu, Shumin Li, Jingyu Ma, Ming Yue, Daiyuan Shentu, Liwei Wang
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引用次数: 1

摘要

cuprotosis是一种新的细胞死亡途径,在胰腺癌(PC)中的调控机制尚不清楚。作者旨在了解cuprotossis相关的lncRNAs (CRLs)是否可以预测PC的预后及其机制。首先,构建基于最小绝对收缩和选择算子Cox分析筛选的7个crl的预后模型。随后,计算胰腺癌患者的风险评分,并将患者分为高危组和低危组。在我们的预后模型中,风险评分较高的PC患者预后较差。基于几个预后特征,建立了预测图。此外,我们还对不同风险组间差异表达基因进行了功能富集分析,表明内分泌和代谢途径是不同风险组间潜在的调控途径。TP53、KRAS、CDKN2A、SMAD4是高危组的显性突变基因,肿瘤突变负担与风险评分呈正相关。最后,肿瘤免疫景观显示,高危组患者的免疫抑制程度高于低危组,CD8+ T细胞浸润较低,M2巨噬细胞较高。综上所述,crl可用于预测PC预后,与肿瘤代谢和免疫微环境密切相关。
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Cuproptosis-related lncRNAs are correlated with tumour metabolism and immune microenvironment and predict prognosis in pancreatic cancer patients

Cuproptosis is a novel cell death pathway, and the regulatory mechanism in pancreatic cancer (PC) is unclear. The authors aimed to figure out whether cuproptosis-related lncRNAs (CRLs) could predict prognosis in PC and the underlying mechanism. First, the prognostic model based on seven CRLs screened by the least absolute shrinkage and selection operator Cox analysis was constructed. Following this, the risk score was calculated for pancreatic cancer patients and divided patients into high and low-risk groups. In our prognostic model, PC patients with higher risk scores had poorer outcomes. Based on several prognostic features, a predictive nomogram was established. Furthermore, the functional enrichment analysis of differentially expressed genes between risk groups was performed, indicating that endocrine and metabolic pathways were potential regulatory pathways between risk groups. TP53, KRAS, CDKN2A, and SMAD4 were dominant mutated genes in the high-risk group and tumour mutational burden was positively correlated with the risk score. Finally, the tumour immune landscape indicated patients in the high-risk group were more immunosuppressive than that in the low-risk group, with lower infiltration of CD8+ T cells and higher M2 macrophages. Above all, CRLs can be applied to predict PC prognosis, which is closely correlated with the tumour metabolism and immune microenvironment.

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来源期刊
IET Systems Biology
IET Systems Biology 生物-数学与计算生物学
CiteScore
4.20
自引率
4.30%
发文量
17
审稿时长
>12 weeks
期刊介绍: IET Systems Biology covers intra- and inter-cellular dynamics, using systems- and signal-oriented approaches. Papers that analyse genomic data in order to identify variables and basic relationships between them are considered if the results provide a basis for mathematical modelling and simulation of cellular dynamics. Manuscripts on molecular and cell biological studies are encouraged if the aim is a systems approach to dynamic interactions within and between cells. The scope includes the following topics: Genomics, transcriptomics, proteomics, metabolomics, cells, tissue and the physiome; molecular and cellular interaction, gene, cell and protein function; networks and pathways; metabolism and cell signalling; dynamics, regulation and control; systems, signals, and information; experimental data analysis; mathematical modelling, simulation and theoretical analysis; biological modelling, simulation, prediction and control; methodologies, databases, tools and algorithms for modelling and simulation; modelling, analysis and control of biological networks; synthetic biology and bioengineering based on systems biology.
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