Molecular Basis of Aggressiveness in Pituitary Adenomas and Its Association With the Immune Microenvironment

Xiaoyan Chen, Jingnan Wang, Qianqian Guo
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Abstract

Background: Pituitary adenomas (PAs) are common intracranial tumors, and their aggressive phenotype exhibits a poor prognosis. We aimed to explore the aggressive feature of PAs and discover novel diagnostic markers.

Method: The datasets of GSE260487 and GSE169498, which contained invasive and noninvasive samples, were downloaded from the Gene Expression Omnibus (GEO) database. Aggressive phenotype-related gene modules were classified using the “WGCNA” package. Differentially expressed genes (DEGs) in each module were identified by the “limma” package. Next, a protein–protein interaction (PPI) network was used in the construction and identification process of key genes, and the CytoHubba tool was utilized to analyze the subnetwork and select the top 10 genes. Diagnostic markers were selected using two machine learning algorithms: support vector machine (SVM) and Lasso. Finally, the ESTIMATE and “GSVA” were applied for immune infiltration assessment.

Results: WGCNA showed that the turquoise module was closely associated with the aggressive phenotype and enriched in neural differentiation and cell migration pathways. A total of 521 DEGs were intersected with the turquoise module genes to obtain 187 overlapping genes, from which 10 hub genes related to tumor proliferation were selected to develop a PPI network. Next, we determined MYH7 as an accurate diagnostic marker, and the immune infiltration analysis revealed that MYH7 expression was negatively correlated with stromal score and immune score but positively correlated with the infiltration of antitumor cells.

Conclusion: We developed a novel marker with a strong diagnostic performance for PAs, providing novel insights for the detection and individualized treatment of PAs.

Abstract Image

Abstract Image

垂体腺瘤侵袭性的分子基础及其与免疫微环境的关系
背景:垂体腺瘤(PAs)是一种常见的颅内肿瘤,其侵袭性表型表现为预后不良。我们旨在探索PAs的侵袭性特征并发现新的诊断标志物。方法:从Gene Expression Omnibus (GEO)数据库中下载GSE260487和GSE169498的数据集,其中包含侵入性和非侵入性样本。利用“WGCNA”包对侵略性表型相关基因模块进行分类。每个模块中的差异表达基因(deg)通过“limma”包进行鉴定。接下来,利用蛋白-蛋白相互作用(protein-protein interaction, PPI)网络构建和鉴定关键基因,并利用CytoHubba工具对子网络进行分析,筛选出前10位基因。使用支持向量机(SVM)和Lasso两种机器学习算法选择诊断标记。最后应用ESTIMATE和GSVA进行免疫浸润评价。结果:WGCNA显示,绿松石模块与侵袭性表型密切相关,并在神经分化和细胞迁移途径中富集。共521个deg与绿松石模块基因相交,得到187个重叠基因,从中选择10个与肿瘤增殖相关的枢纽基因构建PPI网络。接下来,我们确定MYH7作为准确的诊断标志物,免疫浸润分析显示MYH7表达与基质评分和免疫评分呈负相关,而与抗肿瘤细胞浸润呈正相关。结论:我们开发了一种对PAs具有较强诊断性能的新型标志物,为PAs的检测和个体化治疗提供了新的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Comparative and Functional Genomics
Comparative and Functional Genomics 生物-生化与分子生物学
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