基于多数据库的胶质瘤关键生物标志物及免疫格局模式的识别

Identification of critical biomarkers and immune landscape patterns in glioma based on multi-database.

作者信息

Yuan Hanzhang, Cheng Jingsheng, Xia Jun, Yang Zeng, Xu Lixin

机构信息

Department of Neurosurgery, Yueyang Central Hospital, Yueyang, 414020, Hunan, China.

Department of Neurosurgery, Changde Hospital, Xiangya School of Medicine, Central South University (The First People's Hospital of Changde City), Changde, 415003, Hunan, China.

出版信息

Discov Oncol. 2025 Jan 13;16(1):35. doi: 10.1007/s12672-024-01653-2.

DOI:10.1007/s12672-024-01653-2

PMID:39800804

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11725551/

Abstract

PURPOSE

Glioma is the most prevalent tumor of the central nervous system. The poor clinical outcomes and limited therapeutic efficacy underscore the urgent need for early diagnosis and an optimized prognostic approach for glioma. Therefore, the aim of this study was to identify sensitive biomarkers for glioma.

PATIENTS AND METHODS

Differentially expressed genes (DEGs) of glioma were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The potential biomarkers were identified using weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression. The prognostic ability of the potential biomarkers was evaluated by Cox regression and survival curve. CellMiner was used to access the correlation between the expression of potential biomarkers and anticancer drug sensitivity. We then explored the association of potential biomarkers and tumor immune infiltration by single-sample GSEA (ssGSEA) and CIBERSORT. Immune staining in glioma patient samples and cell experiments of potential biomarkers further verified their expression and function.

RESULTS

Ultimately, we identified three potential biomarkers: SLC8A2, ATP2B3, and SRCIN1. These 3 genes were found significantly correlated with clinicopathological features (age, WHO grade, IDH mutation status, 1p19q codeletion status). Furthermore, the overall survival (OS), disease-specific survival (DSS), and progression-free survival (PFS) were found to be positively correlated with high expression of these 3 potential biomarkers. Besides, there was a substantial relationship between the sensitivity of anticancer drugs and these biomarkers expression. More importantly, the negative association between the 3 genes with most tumor immune cells was also established. Moreover, the decreased expression of the biomarkers was also verified in glioma patient samples. Finally, we confirmed that these 3 genes might promotes glioma proliferation and migration in vitro.

CONCLUSION

SLC8A2, ATP2B3, and SRCIN1 were identified as underlying biomarkers for glioma associated with prognosis assessments and personal immunotherapy.

摘要

目的

胶质瘤是中枢神经系统最常见的肿瘤。临床预后差和治疗效果有限凸显了对胶质瘤进行早期诊断和优化预后方法的迫切需求。因此，本研究的目的是识别胶质瘤的敏感生物标志物。

患者和方法

从癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）下载胶质瘤的差异表达基因（DEG）。使用加权基因共表达网络分析（WGCNA）和最小绝对收缩和选择算子（LASSO）回归识别潜在的生物标志物。通过Cox回归和生存曲线评估潜在生物标志物的预后能力。使用CellMiner评估潜在生物标志物的表达与抗癌药物敏感性之间的相关性。然后，我们通过单样本基因集富集分析（ssGSEA）和CIBERSORT探索潜在生物标志物与肿瘤免疫浸润的关联。胶质瘤患者样本中的免疫染色和潜在生物标志物的细胞实验进一步验证了它们的表达和功能。

结果

最终，我们确定了三个潜在的生物标志物：SLC8A2、ATP2B3和SRCIN1。发现这三个基因与临床病理特征（年龄、世界卫生组织分级、异柠檬酸脱氢酶（IDH）突变状态、1p19q共缺失状态）显著相关。此外，总生存期（OS）、疾病特异性生存期（DSS）和无进展生存期（PFS）与这三个潜在生物标志物的高表达呈正相关。此外，抗癌药物的敏感性与这些生物标志物的表达之间存在实质性关系。更重要的是，还建立了这三个基因与大多数肿瘤免疫细胞之间的负相关关系。此外，在胶质瘤患者样本中也验证了生物标志物表达的降低。最后，我们证实这三个基因可能在体外促进胶质瘤的增殖和迁移。