Bioinformatics analysis of in glioblastoma multiforme to evaluate immune features and predict prognosis.

BACKGROUND

Glioblastoma multiforme (GBM) is the most common and aggressive primary brain cancer in adults. This study aimed to obtain data on immune cell infiltration based on public datasets and to examine the prognostic significance of SH2 domain containing 4A () for GBM.

METHODS

expression in GBM was analyzed using a Tumor Immunity Estimation Resource (TIMER) 2.0 dataset, and a gene expression profile interaction analysis (GEPIA), and the results were validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR). The Chinese Glioma Genome Atlas (CGGA) dataset was used to assess the effect of on GBM patient survival. The co-expression network of the LinkedOmics dataset and GeneMANIA dataset was also investigated. Least absolute shrinkage and selection operator (LASSO) regression models and a nomogram were constructed to assess the prognosis of GBM patients. A Gene Set Enrichment Analysis (GSEA) was performed using The Cancer Genome Atlas (TCGA) dataset to find functional differences. The relationship between expression and tumor-infiltrating immune cells was analyzed using xCELL, the Cell Type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm, and the TIMER dataset.

RESULTS

We discovered that expression was upregulated in GBM patients, and elevated SH2D4A expression was also substantially correlated with tumor grade. The survival curve analysis and multivariate Cox regression analysis showed that high expression was a significant independent predictor of poor overall survival (OS) in GBM patients. The immunoassay results suggested that altered expression may affect the immune infiltration of GBM tissues and thus the survival outcomes of GBM patients.

CONCLUSIONS

In addition to being a possible prognostic marker and therapeutic target for GBM, may also accelerate the progression of GBM.