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通过调节胆固醇代谢参与CD8+ T细胞抗结肠癌反应的关键标志物

Key Markers Involved in the Anticolon Cancer Response of CD8+ T Cells through the Regulation of Cholesterol Metabolism.

作者信息

Dong Liang, Yang Xi, Wang Yangyanqiu, Jin Yin, Zhou Qing, Chen Gong, Han Shuwen

机构信息

Department of Gastroenterology, Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, Sanhuan North Road No. 1558, Wuxing District, Huzhou 313000, Zhejiang, China.

Department of Intervention and Radiotherapy, Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, Huzhou 313000, Zhejiang, China.

出版信息

J Oncol. 2021 Nov 23;2021:9398661. doi: 10.1155/2021/9398661. eCollection 2021.

DOI:10.1155/2021/9398661
PMID:34858500
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8632400/
Abstract

BACKGROUND

T cell-mediated antitumor immune response is the basis of colorectal cancer (CRC) immunotherapy. Cholesterol plays an important role in T cell signal transduction and function. Apolipoprotein (APOE) plays a major role in cholesterol metabolism.

OBJECTIVE

To screen and analyze key markers involved in the anticolon cancer response of CD8+ T cells through the regulation of cholesterol metabolism.

METHODS

Based on the median cutoff of the expression value of according to the data downloaded from The Cancer Genome Atlas and Gene Expression Omnibus database, patients were grouped into low and high expression groups. Differences in clinical factors were assessed, and survival analysis was performed. Differentially expressed genes (DEGs) in the high and low expression groups were screened, followed by the analysis of differences in tumor-infiltrating immune cells and weighted gene coexpression network analysis results. The closely related genes to were identified, followed by enrichment analysis, protein-protein interaction (PPI) network analysis, and differential expression analysis. Immunohistochemical staining (IHC) was used to detect the expression of CD8 in CRC tissues.

RESULTS

There were significant differences in prognosis and pathologic_N between the APOE low and high expression groups. A total of 2,349 DEGs between the high and low expression groups were selected. A total of 967 genes were obtained from the blue and brown modules. The probability of distribution of CD8+ T cells differed significantly between the two groups, and 320 closely related DEGs of were screened. Genes including the gene family, , , and had a higher degree in the PPI network. GEO datasets verified the prognosis and the related DEGs of . IHC staining verified the relationship between the distribution of CD8+ T cells and expression.

CONCLUSION

Genes including the gene family, , , and might be the key genes involved in the anticolon cancer response of CD8+ T cells through the regulation of cholesterol metabolism.

摘要

背景

T细胞介导的抗肿瘤免疫反应是结直肠癌(CRC)免疫治疗的基础。胆固醇在T细胞信号转导和功能中起重要作用。载脂蛋白E(APOE)在胆固醇代谢中起主要作用。

目的

通过调节胆固醇代谢来筛选和分析参与CD8+T细胞抗结肠癌反应的关键标志物。

方法

根据从癌症基因组图谱和基因表达综合数据库下载的数据,以APOE表达值的中位数为界,将患者分为低表达组和高表达组。评估临床因素的差异,并进行生存分析。筛选高、低表达组中的差异表达基因(DEGs),随后分析肿瘤浸润免疫细胞的差异以及加权基因共表达网络分析结果。鉴定与APOE密切相关的基因,随后进行富集分析、蛋白质-蛋白质相互作用(PPI)网络分析和差异表达分析。采用免疫组织化学染色(IHC)检测CRC组织中CD8的表达。

结果

APOE低表达组和高表达组在预后和病理_N方面存在显著差异。高、低表达组共筛选出2349个DEGs。从蓝色和棕色模块中获得了967个基因。两组之间CD8+T细胞的分布概率差异显著,筛选出320个与APOE密切相关的DEGs。包括APOE基因家族、COL1A1、COL1A2和FAP在内的基因在PPI网络中的程度较高。GEO数据集验证了APOE的预后和相关DEGs。IHC染色验证了CD8+T细胞分布与APOE表达之间的关系。

结论

包括APOE基因家族、COL1A1、COL1A2和FAP在内的基因可能是通过调节胆固醇代谢参与CD8+T细胞抗结肠癌反应的关键基因。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1020/8632400/6022052e85b5/JO2021-9398661.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1020/8632400/a0598cf34965/JO2021-9398661.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1020/8632400/0ffd0959ca9b/JO2021-9398661.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1020/8632400/e6c325b7915d/JO2021-9398661.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1020/8632400/2008919ef30d/JO2021-9398661.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1020/8632400/a866aa427e54/JO2021-9398661.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1020/8632400/c0e4117ba61b/JO2021-9398661.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1020/8632400/6022052e85b5/JO2021-9398661.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1020/8632400/a0598cf34965/JO2021-9398661.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1020/8632400/0ffd0959ca9b/JO2021-9398661.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1020/8632400/e6c325b7915d/JO2021-9398661.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1020/8632400/2008919ef30d/JO2021-9398661.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1020/8632400/a866aa427e54/JO2021-9398661.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1020/8632400/c0e4117ba61b/JO2021-9398661.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1020/8632400/6022052e85b5/JO2021-9398661.007.jpg

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