← 返回

成釉细胞型颅咽管瘤生物标志物与机制的综合分析

英文原题:Integrative Analysis of Biomarkers and Mechanisms in Adamantinomatous Craniopharyngioma.

查看英文原题

Integrative Analysis of Biomarkers and Mechanisms in Adamantinomatous Craniopharyngioma.

PubMed 2022/03/30(内容时间) Front Genet Q2 · IF 3(JCR 2025)

分数与星级只用于站内排序 —— 不代表疗效、安全性或个人适用性。

中文摘要

颅咽管瘤是一种良性肿瘤,主要治疗方法是手术切除和放疗。然而,这两种治疗方式均可能导致复杂的并发症,严重影响患者的生存率和生活质量。造釉细胞型颅咽管瘤(ACP)作为颅咽管瘤的组织学亚型之一,发病率高、预后差,且ACP免疫相关基因的靶向治疗尚存空白。

本研究从基因表达综合数据库(GEO)下载了ACP的两个基因表达谱,即GSE68015和GSE94349。通过Limma包鉴定差异表达基因(DEGs),并从Immport数据库获得271个差异表达免疫相关基因(DEIRGs)。对基因本体论(GO)、京都基因与基因组百科全书(KEGG)和基因集富集分析(GSEA)进行注释、可视化和整合发现。通过蛋白质-蛋白质相互作用(PPI)网络互作构建,筛选出五个枢纽基因,包括CXCL6、CXCL10、CXCL11、CXCL13和SAA1。通过机器学习算法筛选出两个诊断标志物,即S100A2和SDC1(两者的曲线下面积值均为1)。CIBERSORT分析显示,M2巨噬细胞、活化NK细胞和γδT细胞在ACP浸润中丰度较高,而CD8+ T细胞、调节性T细胞和中性粒细胞在ACP浸润中丰度较低。γδT细胞的表达与CXCL6、S100A2、SDC1和SAA1呈正相关,而CD8+ T细胞的表达与CXCL6、S100A2、SDC1和CXCL10呈负相关。通过CellMiner数据库分析,CXCL6高表达的ACP对Pentostatin和Wortmannin表现出显著的药物敏感性。

我们的结果加深了对ACP分子免疫机制的理解,并为ACP的精准靶向治疗提供了潜在的生物标志物。

展开英文摘要原文

Craniopharyngioma is a benign tumor, and the predominant treatment methods are surgical resection and radiotherapy.

However, both treatments may lead to complex complications, seriously affecting patients' survival rate and quality of life. Adamantinomatous craniopharyngioma (ACP), as one of the histological subtypes of craniopharyngioma, is associated with a high incidence and poor prognosis, and there is a gap in the targeted therapy of immune-related genes for ACP. In this study, two gene expression profiles of ACP, namely GSE68015 and GSE94349, were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified by the Limma package, and 271 differentially expressed immune-related genes (DEIRGs) were obtained from the Immport database. The gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were performed for annotation, visualization, and integrated discovery.

Five hub genes, including CXCL6, CXCL10, CXCL11, CXCL13, and SAA1, were screened out through protein-protein interaction (PPI) network interaction construction. Two diagnostic markers, namely S100A2 and SDC1 (both of which have the Area Under Curve value of 1), were screened by the machine learning algorithm. CIBERSORT analysis showed that M2 macrophages, activated NK cells, and gamma delta T cells had higher abundance in ACP infiltration, while CD8+ T cells, regulatory T cells, and Neutrophils had less abundance in ACP infiltration.

The expression of gamma delta T cells was positively correlated with CXCL6, S100A2, SDC1, and SAA1, while CD8+ T cells expression was negatively correlated with CXCL6, S100A2, SDC1, and CXCL10. ACP with high CXCL6 showed remarkable drug sensitivity to Pentostatin and Wortmannin via CellMiner database analysis.

Our results deepened the understanding of the molecular immune mechanism in ACP and provided potential biomarkers for the precisely targeted therapy for ACP.

论文信息

作者
Lin D、Zhao W、Yang J、Wang H、Zhang H
单位
Department of Neurosurgery, Beijing Luhe Hospital, Capital Medical University, Beijing, China.China
期刊
Frontiers in genetics2022
原文标识
PubMed 35432485 · DOI 10.3389/fgene.2022.830793