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基于加权基因共表达网络分析鉴定嗜铬细胞瘤和副神经节瘤中与肿瘤微环境相关的关键预后基因。

Identification of vital prognostic genes related to tumor microenvironment in pheochromocytoma and paraganglioma based on weighted gene co-expression network analysis.

机构信息

Department of General Medicine, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China.

Department of Urology, The First Affiliated Hospital of Fujian Medical University, Fuzhou 350005, China.

出版信息

Aging (Albany NY). 2021 Mar 26;13(7):9976-9990. doi: 10.18632/aging.202754.

DOI:10.18632/aging.202754
PMID:33795528
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8064173/
Abstract

Pheochromocytoma and paraganglioma (PCPG) is a rare neuroendocrine tumor. This study aims to identify vital prognostic genes which were associated with PCPG tumor microenvironment (TME). We downloaded transcriptome data of PCPG from TCGA database and calculated the immune scores and stromal scores by using the ESTIMATE algorithm. DEGs related to TMB were then identified. We conducted WGCNA to further extract the TME-related modules. GO, KEGG pathway analysis, and PPI network were performed. Survival analysis was conducted to identify the hub genes associated with the prognosis of PCPG. A total of 150 PCPG samples were included in this study. We obtained 1507 and 2067 DEGs based on immune scores and stromal scores, respectively. WGCNA analysis identified the red module and brown module were correlated with immune sores while the turquoise module and red module were significantly associated with stromal scores. Functional enrichments analysis revealed that 307 TME-related genes were correlated with the inflammation or immune response. Survival analysis showed that three TME-relate genes (ADGRE1, CCL18, and LILRA6) were associated with PCPG prognosis. These three hub genes including ADGRE1, CCL18, and LILRA6 might be involved in the progression of PCPG and could serve as potential biomarkers and novel therapeutic targets.

摘要

嗜铬细胞瘤和副神经节瘤(PCPG)是一种罕见的神经内分泌肿瘤。本研究旨在鉴定与 PCPG 肿瘤微环境(TME)相关的重要预后基因。我们从 TCGA 数据库下载了 PCPG 的转录组数据,并使用 ESTIMATE 算法计算了免疫评分和基质评分。然后确定了与 TMB 相关的 DEGs。我们进行了 WGCNA 以进一步提取与 TME 相关的模块。进行了 GO、KEGG 通路分析和 PPI 网络分析。进行了生存分析以确定与 PCPG 预后相关的关键基因。本研究共纳入了 150 例 PCPG 样本。我们分别基于免疫评分和基质评分获得了 1507 个和 2067 个 DEGs。WGCNA 分析表明红色模块和棕色模块与免疫评分相关,而绿松石模块和红色模块与基质评分显著相关。功能富集分析显示 307 个与 TME 相关的基因与炎症或免疫反应相关。生存分析表明,三个与 TME 相关的基因(ADGRE1、CCL18 和 LILRA6)与 PCPG 的预后相关。这三个关键基因包括 ADGRE1、CCL18 和 LILRA6,可能参与了 PCPG 的进展,可作为潜在的生物标志物和新的治疗靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/32a1/8064173/27e1900d494d/aging-13-202754-g007.jpg
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