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

• DOI: 10.1007/s12672-024-01653-2
• Publication Date: 2025-01-13T02:52:42Z
• Authors: Hanzhang Yuan; Jingsheng Cheng; Jun Xia; Zeng Yang; Lixin Xu
• Keywords: Immune infiltration; Anticancer agents; Research; Neoplasms. Tumors. Oncology. Including cancer and carcinogens; Glioma; Biomarker; Prognosis; Hub genes; RC254-282

Glioma is the most prevalent tumor of central nervous system. The poor clinical outcomes and limited therapeutic efficacy underscore urgent need for early diagnosis an optimized prognostic approach glioma. Therefore, aim this study was to identify sensitive biomarkers Differentially expressed genes (DEGs) glioma were downloaded from Cancer Genome Atlas (TCGA) Gene Expression Omnibus (GEO) databases. potential identified using weighted gene co-expression network analysis (WGCNA) least absolute shrinkage selection operator (LASSO) regression. ability evaluated by Cox regression survival curve. CellMiner used access correlation between expression anticancer drug sensitivity. We then explored association immune infiltration single-sample GSEA (ssGSEA) CIBERSORT. Immune staining in patient samples cell experiments further verified their function. Ultimately, we three biomarkers: SLC8A2, ATP2B3, SRCIN1. These 3 found significantly correlated with clinicopathological features (age, WHO grade, IDH mutation status, 1p19q codeletion status). Furthermore, overall (OS), disease-specific (DSS), progression-free (PFS) be positively high these biomarkers. Besides, there a substantial relationship sensitivity drugs expression. More importantly, negative cells also established. Moreover, decreased samples. Finally, confirmed that might promotes proliferation migration vitro. SRCIN1 as underlying associated prognosis assessments personal immunotherapy.