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弥漫性大B细胞淋巴瘤(DLBCL)14基因预后特征的鉴定

Identification of a 14-Gene Prognostic Signature for Diffuse Large B Cell Lymphoma (DLBCL).

作者信息

Feng Pengcheng, Li Hongxia, Pei Jinhong, Huang Yan, Li Guixia

机构信息

Department of Basic Medicine, Changzhi Medical College, Changzhi, China.

Affiliated Hospital of Changzhi Institute of Traditional Chinese Medicine, Changzhi, China.

出版信息

Front Genet. 2021 Feb 10;12:625414. doi: 10.3389/fgene.2021.625414. eCollection 2021.

DOI:10.3389/fgene.2021.625414
PMID:33643388
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7902938/
Abstract

Although immunotherapy is a potential strategy to resist cancers, due to the inadequate acknowledge, this treatment is not always effective for diffuse large B cell lymphoma (DLBCL) patients. Based on the current situation, it is critical to systematically investigate the immune pattern. According to the result of univariate and multivariate cox proportional hazards, LASSO regression and Kaplan-Meier survival analysis on immune-related genes (IRGs), a prognostic signature, containing 14 IRGs (AQP9, LMBR1L, FGF20, TANK, CRP, ORM1, JAK1, BACH2, MTCP1, IFITM1, TNFSF10, FGF12, RFX5, and LAP3), was built. This model was validated by external data, and performed well. DLBCL patients were divided into low- and high-risk groups, according to risk scores from risk formula. The results of CIBERSORT showed that different immune status and infiltration pattern were observed in these two groups. Gene set enrichment analysis (GSEA) indicated 12 signaling pathways were significantly enriched in the high-risk group, such as natural killer cell-mediated cytotoxicity, toll-like receptor signaling pathway, and so on. In summary, 14 clinically significant IRGs were screened to build a risk score formula. This formula was an accurate tool to provide a certain basis for the treatment of DLBCL patients.

摘要

尽管免疫疗法是抵抗癌症的一种潜在策略,但由于认识不足,这种治疗方法对弥漫性大B细胞淋巴瘤(DLBCL)患者并不总是有效。基于目前的情况,系统地研究免疫模式至关重要。根据对免疫相关基因(IRGs)的单因素和多因素Cox比例风险、LASSO回归和Kaplan-Meier生存分析结果,构建了一个包含14个IRGs(AQP9、LMBR1L、FGF20、TANK、CRP、ORM1、JAK1、BACH2、MTCP1、IFITM1、TNFSF10、FGF12、RFX5和LAP3)的预后特征。该模型通过外部数据进行了验证,表现良好。根据风险公式的风险评分,将DLBCL患者分为低风险和高风险组。CIBERSORT结果显示,两组观察到不同的免疫状态和浸润模式。基因集富集分析(GSEA)表明,高风险组中有12条信号通路显著富集,如自然杀伤细胞介导的细胞毒性、Toll样受体信号通路等。总之,筛选出14个具有临床意义的IRGs构建风险评分公式。该公式是为DLBCL患者治疗提供一定依据的准确工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43b5/7902938/a535c9fe3570/fgene-12-625414-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43b5/7902938/29476771c0f4/fgene-12-625414-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43b5/7902938/a4e5d96d5599/fgene-12-625414-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43b5/7902938/349e8ea66097/fgene-12-625414-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43b5/7902938/ed257fa43c31/fgene-12-625414-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43b5/7902938/68104882bf98/fgene-12-625414-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43b5/7902938/40892ff55ab5/fgene-12-625414-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43b5/7902938/ee5bbc662515/fgene-12-625414-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43b5/7902938/8097da37f301/fgene-12-625414-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43b5/7902938/a535c9fe3570/fgene-12-625414-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43b5/7902938/29476771c0f4/fgene-12-625414-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43b5/7902938/a4e5d96d5599/fgene-12-625414-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43b5/7902938/349e8ea66097/fgene-12-625414-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43b5/7902938/ed257fa43c31/fgene-12-625414-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43b5/7902938/68104882bf98/fgene-12-625414-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43b5/7902938/40892ff55ab5/fgene-12-625414-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43b5/7902938/ee5bbc662515/fgene-12-625414-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43b5/7902938/8097da37f301/fgene-12-625414-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43b5/7902938/a535c9fe3570/fgene-12-625414-g009.jpg

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