Molecular and clinical characterization of PARP9 in gliomas: A potential immunotherapeutic target

Abstract

Background: Glioma is a primary malignancy of the central nervous system (CNS). As biomedicine advances, an efficient molecular target is urgently needed for the diagnosis and treatment of glioma. Meanwhile, several studies have demonstrated that glioma development is closely related to immunity. PARP9 is an inactive mono-ADP-ribosyltransferase belonging to the poly-ADP ribosyltransferase (ARTD) family. In this article, we aimed to reveal the relationship between PARP9 and glioma and explore the potential prognostic value and immunotherapeutic targetability of PARP9 in glioma.

Methods: PARP9 transcript levels were analyzed with TCGA and GEO databases. The clinicopathological information of patients with glioma in the TCGA database and gene expression profiles were analyzed to determine the relationship between the expression of PARP9 and clinicopathologic characteristics. Kaplan-Meier survival analysis, univariate Cox regression analysis, and multivariate Cox regression analysis were used for survival analysis. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used for bioinformatics analysis. Correlation analysis explored the relationships between PARP9, infiltrating inflammatory immune cells, and immune checkpoint molecules.

Results: PARP9 is highly expressed in glioma, and high expression of PARP9 is associated with poor prognosis and advanced clinicopathological features. Bioinformatics analysis showed that some immune-related pathways were closely associated with high expression of PARP9. Correlation analysis indicated that PARP9 was closely related to inflammatory and immune responses, high immune cell infiltration, and immune checkpoint molecules.

Conclusions: PARP9 may serve as an unfavorable prognosis predictor for glioma and a potential immunotherapeutic target.

Keywords: PARP9; biomarker; glioma; immunotherapeutic targets; prognosis.

FIGURE 1

A and B, PARP9 was highly expressed in glioma for GSE50161 from GEO database and TCGA database. C and D, ROC curve analysis revealed the predictive value of PARP9 in diagnosis of glioma for GSE50161 from GEO database and TCGA database

FIGURE 2

Relationship between the expression of PARP9 and clinicopathologic features in TCGA cohort. (A): age, (B): gender, (C): IDH mutation, (D): tumor status. (E): vital status, (F): histological type, (G): KPS, (H): grade

FIGURE 3

Survival analysis of PARP9 in all‐grade glioma in the TCGA cohort

FIGURE 4

Gene set enrichment analysis (GSEA) for PARP9

FIGURE 5

PARP9‐related inflammatory response. A, Heat map involved the clinicopathological features, PARP9 expression, and seven related metagenes from the TCGA datasets. B, Correlograms were generated based on the relationship between PARP9 expression and GSVA enrichment scores for these seven metagenes. The circles were filled in blue clockwise for positive values and in red anticlockwise for negative values

FIGURE 6

A, Correlations of PARP9 expression with immune cell markers. B, Relationship between PARP9 and immune checkpoints in the TCGA databases