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基于生物信息学分析和实验验证的细胞焦亡相关基因signature 对肝癌预后预测能力及其与肝癌免疫微环境关系的分析。

Analysis of the Prognosis Prediction Ability of a Necroptosis-Related Gene Signature and its Relationship With the Hepatocellular Carcinoma Immune Microenvironment Using Bioinformatics Analysis and Experimental Validation.

机构信息

Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China.

State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.

出版信息

Technol Cancer Res Treat. 2023 Jan-Dec;22:15330338231182208. doi: 10.1177/15330338231182208.

DOI:10.1177/15330338231182208
PMID:37335078
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10286176/
Abstract

Hepatocellular carcinoma (HCC) is one of the most malignant cancers and has a poor prognosis. The immune microenvironment is closely related to the drug sensitivity of a tumor. Necroptosis was reported to be a key factor for HCC. The prognostic value of necroptosis-related genes and their association with the tumor immune microenvironment are still unknown. Necroptosis-related genes that could comprise a signature for predicting the prognosis of HCC cases were identified using univariate analysis and least absolute shrinkage and selection operator Cox regression analysis. The association between this prognosis prediction signature and HCC immune microenvironment was analyzed. The immunological activities and drug sensitivities were compared between different risk score groups identified using the prognosis prediction signature. The expression levels of the five genes comprising the signature were validated using RT-qPCR. A prognosis prediction signature consisting of five necroptosis-related genes was constructed and validated. Its risk score was = (0.1634 × PGAM5 expression) + (0.0134 × CXCL1 expression) - (0.1007 × ALDH2 expression) + (0.2351 × EZH2 expression) - (0.0564 × NDRG2 expression). The signature was found to be significantly associated with the infiltration of B cells, CD4 T cells, neutrophils, macrophages, and myeloid dendritic cells into the HCC immune microenvironment. The number of infiltrating immune cells and the expression levels of immune checkpoints in the immune microenvironment of high-risk score patients were higher. Sorafenib and immune checkpoint blockade were determined to be ideally suited for treating high-risk score patients and low-risk score patients, respectively. Finally, RT-qPCR results confirmed that the expression levels of EZH2, NDRG2, and ALDH2 were significantly down-regulated in HuH7 and HepG2 cells compared to those in LO2 cells. The necroptosis-related gene signature developed herein can classify patients with HCC according to prognosis risk well and is associated with infiltration of immune cells into the tumor immune microenvironment.

摘要

肝细胞癌(HCC)是最恶性的癌症之一,预后不良。免疫微环境与肿瘤的药物敏感性密切相关。坏死性凋亡被报道是 HCC 的关键因素。坏死性凋亡相关基因的预后价值及其与肿瘤免疫微环境的关系尚不清楚。使用单因素分析和最小绝对收缩和选择算子 Cox 回归分析,确定了可能构成预测 HCC 病例预后的特征的坏死性凋亡相关基因。分析了该预后预测特征与 HCC 免疫微环境的关联。使用预后预测特征确定不同风险评分组之间的免疫活性和药物敏感性进行比较。使用 RT-qPCR 验证构成特征的五个基因的表达水平。构建并验证了由五个坏死性凋亡相关基因组成的预后预测特征。其风险评分=(0.1634×PGAM5 表达)+(0.0134×CXCL1 表达)-(0.1007×ALDH2 表达)+(0.2351×EZH2 表达)-(0.0564×NDRG2 表达)。结果表明,该特征与 HCC 免疫微环境中 B 细胞、CD4 T 细胞、中性粒细胞、巨噬细胞和髓样树突状细胞的浸润显著相关。高风险评分患者的免疫浸润细胞数量和免疫微环境中免疫检查点的表达水平更高。索拉非尼和免疫检查点阻断被确定分别适合治疗高风险评分患者和低风险评分患者。最后,RT-qPCR 结果证实 EZH2、NDRG2 和 ALDH2 的表达水平在 HuH7 和 HepG2 细胞中明显低于 LO2 细胞。本研究开发的坏死性凋亡相关基因特征可以很好地根据预后风险对 HCC 患者进行分类,并与肿瘤免疫微环境中免疫细胞的浸润有关。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9133/10286176/af6d54b78ad5/10.1177_15330338231182208-fig11.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9133/10286176/af6d54b78ad5/10.1177_15330338231182208-fig11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9133/10286176/2f2ea1cd48fd/10.1177_15330338231182208-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9133/10286176/d88af3355816/10.1177_15330338231182208-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9133/10286176/cbfe19db6773/10.1177_15330338231182208-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9133/10286176/bd69e81a0b79/10.1177_15330338231182208-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9133/10286176/9e3b5ff532f1/10.1177_15330338231182208-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9133/10286176/0410e67382f1/10.1177_15330338231182208-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9133/10286176/365c637c1abc/10.1177_15330338231182208-fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9133/10286176/6703654f39a6/10.1177_15330338231182208-fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9133/10286176/d42c2cd88931/10.1177_15330338231182208-fig9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9133/10286176/7169b0861783/10.1177_15330338231182208-fig10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9133/10286176/af6d54b78ad5/10.1177_15330338231182208-fig11.jpg

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