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Identification of Prognostic Values of Neutrophil Extracellular Traps-Related Genes in Glioma Based on Bioinformatics
Xiaobing Guo1, Xiaowen Li1, Hengxi Li1
1Department of Anatomy and Histology & Embryology, Faculty of Basic Medical Science, Kunming Medical University, Kunming, Yunnan, China.
Background:
Glioma is a highly invasive and drug-resistant malignant primary tumor. Increasing research is focusing on the function of neutrophil extracellular traps (NETs) in glioma progress. We aimed to explore the mechanism of NETs-related genes (NETs-RGs) in glioma to find potential biomarkers for glioma.
Methods:
The GSE16011 data set was downloaded from the GEO database, and the gene expression matrix and clinical data of glioma patients were downloaded from the TCGA database, the cbioportal website, and the CGGA database, as the training and validation sets. The NETs-RGs were obtained from previous studies. Subsequently, differential expression analysis, WGCNA, GO enrichment, and GSEA analysis. The risk model was established for Cox, LASSO, survival, and independent prognostic analyses. The CIBERSORT algorithm was used for immune infiltration analysis, and pRRophetic was used for drug sensitivity analysis. Finally, the expression levels of genes were validated by data set, glioma patients' tissue samples, and glioma cells, and evaluating cell biological behavior.
Results:
A total of 57 differential expression genes between Glioma and Normal samples were obtained. Then, two modules with the highest positive correlation with NETs-RGs by WGCNA, the NETs-RGs were obtained from previous studies. Six candidate genes were obtained for subsequent analysis. Then, we conducted functional enrichment of candidate genes and constructed a glioma prognosis model. The prognosis model was indicated as a good predictor of a patient's glioma risk. These genes were related to immune cells significantly. And drug sensitivity analysis predicted 128 differences in chemotherapy drugs and found that MICALL2 had a significant correlation with multiple drugs. Finally, only NFIL3 had the same trend of significantly high expression levels. Moreover, knockdown NFIL3 can inhibit glioma cell malignant growth, and promote apoptosis.
Conclusion:
Three prognosis-related genes have better prognosis values for glioma patients and may be the potential biomarkers for the treatment of glioma.
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