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M2巨噬细胞相关基因对非小细胞肺癌患者预后及药物敏感性影响的研究:来自生物信息学和实验的证据

Investigation of M2 macrophage-related gene affecting patients prognosis and drug sensitivity in non-small cell lung cancer: Evidence from bioinformatic and experiments.

作者信息

Zeng Zhen, Yu Jiachen, Yang Zhuo, Du Kangming, Chen Yuewei, Zhou Lei

机构信息

Department of Emergency, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.

Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.

出版信息

Front Oncol. 2022 Dec 15;12:1096449. doi: 10.3389/fonc.2022.1096449. eCollection 2022.


DOI:10.3389/fonc.2022.1096449
PMID:36591493
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9797692/
Abstract

BACKGROUND: The progression process of lung cancer can be accelerated by M2 macrophages. However, genes that affect M2 macrophage polarization remain unidentified. METHODS: The Cancer Genome Atlas, Gene Expression Omnibus, and Arrayexpress databases were used to obtain open-access data. The analysis of public data was mostly performed with R studio. The RNA levels of specific genes were detected using quantitative real-time PCR. The proliferation ability of the cells was assessed by CCK8, colony formation, and EdU assays. RESULTS: Based on the multiple datasets, we noticed a poor prognosis in patients with high M2 macrophage infiltration. There were 114 genes differentially expressed between high and low M2 macrophages infiltrated samples, regarded as M2 macrophage-related genes. Subsequently, a prognosis prediction signature consisting of ABHD5, HS3ST2, TM6SF1, CAPZA2, LEPROT, HNMT, and MRO was identified and presented a satisfactory performance. The pathway enrichment results revealed a positive correlation between riskscore and enrichment scores for most immunotherapy-related positive terms. Also, there might be an increase in genomic instability among patients at high risk. Interestingly, low risk patients are most likely to benefit from PD-1 therapy, while high risk patients may benefit from CTLA-4 therapy. Meanwhile, the estimated IC50 of seven drugs differs significantly between two risk groups, including Cisplatin, Docetaxel, Doxorubicin, Gefitinib, Paclitaxel, Sunitinib and Vinorelbine. Moreover, further experiments indicated that HNMT was overexpressed and can enhance the proliferation ability in lung cancer cells. CONCLUSIONS: In summary, our study identified the molecules significantly affecting M2 macrophage infiltration and identified a prognosis signature that robustly indicated patients prognosis. Moreover, we validated the cancer-promoting effect of HNMT using experiments.

摘要

背景:M2巨噬细胞可加速肺癌的进展过程。然而,影响M2巨噬细胞极化的基因仍未明确。 方法:利用癌症基因组图谱、基因表达综合数据库和Arrayexpress数据库获取开放获取数据。公共数据的分析主要使用R studio进行。使用定量实时PCR检测特定基因的RNA水平。通过CCK8、集落形成和EdU试验评估细胞的增殖能力。 结果:基于多个数据集,我们注意到M2巨噬细胞浸润高的患者预后较差。在M2巨噬细胞浸润高和低的样本之间有114个基因差异表达,这些基因被视为M2巨噬细胞相关基因。随后,鉴定出由ABHD5、HS3ST2、TM6SF1、CAPZA2、LEPROT、HNMT和MRO组成的预后预测特征,其表现令人满意。通路富集结果显示,风险评分与大多数免疫治疗相关阳性术语的富集评分之间呈正相关。此外,高风险患者的基因组不稳定性可能增加。有趣的是,低风险患者最有可能从PD-1治疗中获益,而高风险患者可能从CTLA-4治疗中获益。同时,七种药物的估计IC₅₀在两个风险组之间有显著差异,包括顺铂、多西他赛、阿霉素、吉非替尼、紫杉醇、舒尼替尼和长春瑞滨。此外,进一步的实验表明,HNMT在肺癌细胞中过表达并能增强其增殖能力。 结论:总之,我们的研究确定了显著影响M2巨噬细胞浸润的分子,并鉴定了一个能有力指示患者预后的预后特征。此外,我们通过实验验证了HNMT的促癌作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2fd/9797692/7c4c9e506312/fonc-12-1096449-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2fd/9797692/20b48f144d15/fonc-12-1096449-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2fd/9797692/7330996a18c1/fonc-12-1096449-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2fd/9797692/fa0506e2d4fb/fonc-12-1096449-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2fd/9797692/31a9d1b4d704/fonc-12-1096449-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2fd/9797692/34d13eb08c97/fonc-12-1096449-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2fd/9797692/eda67f77b935/fonc-12-1096449-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2fd/9797692/f04ad12fd06b/fonc-12-1096449-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2fd/9797692/7c4c9e506312/fonc-12-1096449-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2fd/9797692/20b48f144d15/fonc-12-1096449-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2fd/9797692/7330996a18c1/fonc-12-1096449-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2fd/9797692/fa0506e2d4fb/fonc-12-1096449-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2fd/9797692/31a9d1b4d704/fonc-12-1096449-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2fd/9797692/34d13eb08c97/fonc-12-1096449-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2fd/9797692/eda67f77b935/fonc-12-1096449-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2fd/9797692/f04ad12fd06b/fonc-12-1096449-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c2fd/9797692/7c4c9e506312/fonc-12-1096449-g008.jpg

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本文引用的文献

[1]
Machine learning reveals two heterogeneous subtypes to assist immune therapy based on lipid metabolism in lung adenocarcinoma.

Front Immunol. 2022

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HNMT Upregulation Induces Cancer Stem Cell Formation and Confers Protection against Oxidative Stress through Interaction with HER2 in Non-Small-Cell Lung Cancer.

Int J Mol Sci. 2022-1-31

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Comprehensive Analysis of a Nine-Gene Signature Related to Tumor Microenvironment in Lung Adenocarcinoma.

Front Cell Dev Biol. 2021-9-1

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Identification of MX2 as a Novel Prognostic Biomarker for Sunitinib Resistance in Clear Cell Renal Cell Carcinoma.

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