Comprehensive Analysis of Inflammatory Response–Related Genes, and Prognosis and Immune Infiltration in Patients With Low-Grade Glioma - Han, Tao; Zuo, Zhifan; Qu, Meilin; Zhou, Yinghui; Li, Qing; Wang, Hongjin (Frontiers in Pharmacology, 2021)
Abstract Background: Although low-grade glioma (LGG) has a good prognosis, it is prone to malignant transformation into high-grade glioma. It has been confirmed that the characteristics of inflammatory factors and immune microenvironment are closely related to the occurrence and development of tumors. It is necessary to clarify the role of inflammatory genes and immune infiltration in LGG. Methods: we downloaded the transcriptome gene expression data and corresponding clinical data of LGG patients from the TCGA and GTEX databases to screen prognostic-related differentially expressed inflammatory genes with the difference analysis and single-factor COX regression analysis. The prognostic risk model was constructed by LASSO COX regression analysis, which enable us to compare the overall survival rate of high and low risk groups in the model by Kaplan-Meier analysis and subsequently draw the risk curve and survival status diagram. we analysed the accuracy of the prediction model via ROC curves and performed GSEA enrichment analysis. The ssGSEA algorithm was used to calculate the score of immune cell infiltration and the activity of immune-related pathways. Use CellMiner database to study drug sensitivity. Results: In this study, 3 genes (CALCRL, MMP14 and SELL) were selected from 9 prognostic-related differential inflammation genes through LASSO COX regression analysis to construct a prognostic risk model. Further analysis showed that the risk score was negatively correlated with the prognosis, and the ROC curve showed that the accuracy of the model is better. Age, grade and risk score can be used as independent prognostic factors (P<0.001). GSEA analysis confirmed that 6 immune-related pathways were enriched in the high-risk group. we found that the degree of infiltration of 12 immune cell subpopulations and the scores of 13 immune functions and pathways in the high-risk group were significantly increased by applying the ssGSEA method(P <0.05) . Finally, we explored the relationship of drug sensitivity between the genes in the model and 16 drugs. Conclusion: This study analyzed the correlation between the inflammation-related risk model and the immune microenvironment. It is expected to provide a reference for the screening of LGG prognostic markers and the evaluation of immune response.