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()的表达增加预示着预后不良,且与胶质瘤中的免疫细胞浸润相关。

Increased expression of () predicts poor prognosis and is correlated with immune-cell infiltration in glioma.

作者信息

Shi Linyang, Chen Hui, Chen Junmin, Palmer Joshua D, Wang Lin, Sheng Li

机构信息

Department of Medical Oncology, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China.

Department of Neurosurgery, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China.

出版信息

Transl Cancer Res. 2024 Nov 30;13(11):6298-6314. doi: 10.21037/tcr-24-1982. Epub 2024 Nov 27.


DOI:10.21037/tcr-24-1982
PMID:39697738
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11651804/
Abstract

BACKGROUND: The poor response of patients with gliomas to existing immunotherapy has resulted in negligible improvement in prognosis. It is widely acknowledged that serves as a transcriptional activator implicated in tumorigenesis across various cancer types. However, its specific role within glioma remains unclear. The aim of this study was to determine the association between expression and survival and tumor-infiltrating immune cells. In addition, to construct a prognostic model to predict the overall survival (OS) of patients with glioma. METHODS: Transcriptome sequencing data of 325 patients with glioma in the Chinese Glioma Genome Atlas (CGGA) database and 702 patients with glioma in The Cancer Genome Atlas (TCGA) were included for retrospective analysis and were used as the training group and the validation group, respectively. The expression of in pancancer was detected in the database. A -test and one-way analysis of variance were used to determine the differential expression levels of across distinct subgroups of glioma. Functional annotations pertaining specifically to 's biological relevance underwent scrutiny via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The prognostic significance of in glioma was ascertained through Kaplan-Meier curves and Cox regression models, a nomogram was used to establish a prognostic model, and the predictive power was evaluated with calibration curves and the concordance index. 's association with inhibitory immune checkpoints and tumor immune cell infiltration was examined using Pearson correlation coefficients via Tumor Immune Estimation Resource database. RESULTS: We found that was upregulated in glioma, and that elevated expression correlated significantly with adverse clinicopathological features and decreased OS. Multivariate analysis showed that was an independent prognostic biomarker for glioma, and the established prognostic model could accurately predict the OS of patients. We also found that expression was positively correlated with inhibitory immune checkpoint expression, overexpression was associated with increased levels of tumor immunoinfiltrating cells in glioma that resulted in poor survival, and demonstrated a positive correlation with the expression of immune cell marker genes. CONCLUSIONS: is closely related to the clinicopathologic factors in glioma and may function as an oncogene. Its high expression is associated with poor prognosis, which may potentially be linked to immune escape and immune cell infiltration. is a potential biomarker of prognostic and immune infiltration in glioma.

摘要

背景:胶质瘤患者对现有免疫疗法反应不佳,导致预后改善甚微。人们普遍认为,它作为一种转录激活因子,与多种癌症类型的肿瘤发生有关。然而,其在胶质瘤中的具体作用仍不清楚。本研究的目的是确定其表达与生存及肿瘤浸润免疫细胞之间的关联。此外,构建一个预后模型来预测胶质瘤患者的总生存期(OS)。 方法:纳入中国胶质瘤基因组图谱(CGGA)数据库中325例胶质瘤患者和癌症基因组图谱(TCGA)中702例胶质瘤患者的转录组测序数据进行回顾性分析,分别作为训练组和验证组。在数据库中检测其在泛癌中的表达。采用t检验和单因素方差分析确定其在不同胶质瘤亚组中的差异表达水平。通过基因本体论(GO)和京都基因与基因组百科全书(KEGG)分析,对其生物学相关性的功能注释进行详细审查。通过Kaplan-Meier曲线和Cox回归模型确定其在胶质瘤中的预后意义,使用列线图建立预后模型,并通过校准曲线和一致性指数评估预测能力。通过肿瘤免疫评估资源数据库,使用Pearson相关系数检验其与抑制性免疫检查点和肿瘤免疫细胞浸润的关联。 结果:我们发现其在胶质瘤中上调,且其表达升高与不良临床病理特征和总生存期降低显著相关。多因素分析表明,它是胶质瘤的独立预后生物标志物,所建立的预后模型能够准确预测患者的总生存期。我们还发现其表达与抑制性免疫检查点表达呈正相关,其过表达与胶质瘤中肿瘤免疫浸润细胞水平增加相关,导致生存不良,且与免疫细胞标志物基因的表达呈正相关。 结论:它与胶质瘤的临床病理因素密切相关,可能作为一种癌基因发挥作用。其高表达与预后不良相关,这可能与免疫逃逸和免疫细胞浸润有关。它是胶质瘤预后和免疫浸润的潜在生物标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cf1/11651804/5919ad3acbcf/tcr-13-11-6298-f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cf1/11651804/e4ec3eec02cf/tcr-13-11-6298-f1.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cf1/11651804/6a60dcee221d/tcr-13-11-6298-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cf1/11651804/334f5b384f35/tcr-13-11-6298-f6.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cf1/11651804/5b0cc92ab59c/tcr-13-11-6298-f8.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cf1/11651804/5919ad3acbcf/tcr-13-11-6298-f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cf1/11651804/e4ec3eec02cf/tcr-13-11-6298-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cf1/11651804/3a5949f671fc/tcr-13-11-6298-f2.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cf1/11651804/68a91d792d63/tcr-13-11-6298-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cf1/11651804/6a60dcee221d/tcr-13-11-6298-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cf1/11651804/334f5b384f35/tcr-13-11-6298-f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cf1/11651804/fc0f20d771cb/tcr-13-11-6298-f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cf1/11651804/5b0cc92ab59c/tcr-13-11-6298-f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cf1/11651804/ace014e32194/tcr-13-11-6298-f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3cf1/11651804/5919ad3acbcf/tcr-13-11-6298-f10.jpg

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