肺癌中参与M2巨噬细胞与肿瘤代谢串扰的长链非编码rna的探索。

IF 1.4 4区 生物学 Q4 GENETICS & HEREDITY
Fang Fang, Yuanshan Yao, Zhe Ma
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引用次数: 1

摘要

背景:肿瘤代谢与M2巨噬细胞之间存在复杂的调控关系。长链非编码rna (lncrna)因其广泛的调控作用而闻名。本研究旨在鉴定参与肿瘤代谢与M2巨噬细胞间串扰的lncrna。方法:癌症基因组图谱负责公共数据。R软件负责公共数据的分析。结果:基于输入表达谱,我们使用CIBERSORT算法量化M2巨噬细胞浸润,发现M2巨噬细胞是肺癌的危险因素。此外,我们发现M2巨噬细胞与多种代谢途径相关。然后,鉴定出67个同时参与M2巨噬细胞和相关代谢途径的lncrna。建立了基于AC027288.3、AP001189.3、FAM30A、GAPLINC、LINC00578、LINC01936的预后信号,具有较好的预后预测能力。为了更好地预测患者的预后,将临床参数和风险评分合并成一个nomogram plot。使用校准图发现实际生存期和nomogram- prediction生存期高度吻合。此外,在低危患者中,免疫治疗更有效,而在高危患者中,顺铂和多西他赛更有效。生物富集分析表明,高危组notch信号通路、TGF-β信号通路、干扰素α反应通路、干扰素γ反应通路被激活。同时,风险评分与肿瘤代谢和M2巨噬细胞相关。此外,我们发现CAPLINC对M2巨噬细胞极化的促进作用可能通过多种代谢途径发挥作用。结论:我们的研究结果可以为M2巨噬细胞与肿瘤代谢的相互作用以及所涉及的lncrna提供新的认识,为今后的研究提供方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Exploration of the Long Noncoding RNAs Involved in the Crosstalk between M2 Macrophages and Tumor Metabolism in Lung Cancer.

Exploration of the Long Noncoding RNAs Involved in the Crosstalk between M2 Macrophages and Tumor Metabolism in Lung Cancer.

Exploration of the Long Noncoding RNAs Involved in the Crosstalk between M2 Macrophages and Tumor Metabolism in Lung Cancer.

Exploration of the Long Noncoding RNAs Involved in the Crosstalk between M2 Macrophages and Tumor Metabolism in Lung Cancer.

Background: Complex regulation exists between tumor metabolism and M2 macrophages. Long noncoding RNAs (lncRNAs) are famous for their wide regulatory role. This study aimed to identify the lncRNAs involved in the crosstalk between tumor metabolism and M2 macrophages.

Methods: The Cancer Genome Atlas was responsible for the public data. R software was responsible for the analysis of public data.

Results: Based on the input expression profile, we quantified the M2 macrophage infiltration using the CIBERSORT algorithm and found that M2 macrophages were a risk factor for lung cancer. Also, we found that M2 macrophages were correlated with multiple metabolism pathways. Then, 67 lncRNAs involved in both M2 macrophages and related metabolism pathways were identified. A prognosis signature based on AC027288.3, AP001189.3, FAM30A, GAPLINC, LINC00578, and LINC01936 was established, which had good prognosis prediction ability. The clinical parameters and risk score were combined into a nomogram plot for better prediction of the patient's prognosis. A high fit of actual survival and nomogram-predicted survival was found using the calibration plot. Moreover, in low-risk patients, immunotherapy was more effective, while cisplatin and docetaxel were more effective in high-risk patients. Biological enrichment analysis indicated pathways of notch signaling, TGF-β signaling, interferon alpha response, and interferon-gamma response were activated in the high-risk group. Meanwhile, the risk score was associated with tumor metabolism and M2 macrophages. Also, we found that the promoting effect of CAPLINC on M2 macrophage polarization might act through multiple metabolism pathways.

Conclusions: Our result can provide new insights into the interaction between M2 macrophages and tumor metabolism, as well as the involved lncRNAs, which can provide the direction for future studies.

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来源期刊
Genetics research
Genetics research 生物-遗传学
自引率
6.70%
发文量
74
审稿时长
>12 weeks
期刊介绍: Genetics Research is a key forum for original research on all aspects of human and animal genetics, reporting key findings on genomes, genes, mutations and molecular interactions, extending out to developmental, evolutionary, and population genetics as well as ethical, legal and social aspects. Our aim is to lead to a better understanding of genetic processes in health and disease. The journal focuses on the use of new technologies, such as next generation sequencing together with bioinformatics analysis, to produce increasingly detailed views of how genes function in tissues and how these genes perform, individually or collectively, in normal development and disease aetiology. The journal publishes original work, review articles, short papers, computational studies, and novel methods and techniques in research covering humans and well-established genetic organisms. Key subject areas include medical genetics, genomics, human evolutionary and population genetics, bioinformatics, genetics of complex traits, molecular and developmental genetics, Evo-Devo, quantitative and statistical genetics, behavioural genetics and environmental genetics. The breadth and quality of research make the journal an invaluable resource for medical geneticists, molecular biologists, bioinformaticians and researchers involved in genetic basis of diseases, evolutionary and developmental studies.
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