Clinicopathological and Prognostic Significance of ABCC3 in Human Glioma

Abstract

Glioma is the most common malignant primary brain tumor with an inferior survival period and unsatisfactory prognoses. Identification of novel biomarkers is important for the improvements of clinical outcomes of glioma patients. In recent years, more and more biomarkers were identified in many types of tumors. However, the sensitive markers for diagnoses and prognoses of patients with glioma remained unknown. In the present research, our team intended to explore the expression and clinical significance of ABCC3 in glioma patients. Sequential data filtration (survival analyses, independent prognosis analyses, ROC curve analyses, and clinical association analyses) was completed, which gave rise to the determination of the relationship between glioma and the ABCC3 gene. Clinical assays on the foundation of CGGA and TCGA datasets unveiled that ABCC3 expression was distinctly upregulated in glioma and predicted a shorter overall survival. In the multivariable Cox analysis, our team discovered that the expression of ABCC3 was an independent prognosis marker for both 5-year OS (HR = 1.118, 95% CI: 1.052-1.188; P < 0.001). Moreover, our team also studied the association between ABCC3 expression and clinical features of glioma patients, finding that differential expression of ABCC3 was remarkably related to age, 1p19q codeletion, PRS type, chemo status, grade, IDH mutation state, and histology. Overall, our findings suggested ABCC3 might be a novel prognosis marker in glioma.

Figure 1

Kaplan–Meier curve of correlation between the top 6 genes including (a) MED8, (b) HIST1H2BK8, (c) ANXA1, (d) AK2, (e) ABCC3, and (f) ABRACL of glioma samples and overall survival of patients based on CGGA datasets.

Figure 2

The expressing pattern of the top 6 genes in GBM based on TCGA datasets. The expression of (a) ABCC3, (b) HIST1H2BK8, (c) AK2, and (d) ANXA1 was distinctly increased in GBM samples. (e, f) The expression of ABRACL and MED8 remained unchanged between GBM samples and nontumor samples.

Figure 3

(a) Univariate and (b) multivariate analysis of ABCC3 expression and its correlation in patients with glioma based on CGGA data.

Figure 4

Time-dependent ROC curve for the patients in the CGGA dataset.

Figure 5

Association analyses between the expression of ABCC3 and clinical characteristics by virtue of the CGGA database. The upregulation of ABCC3 was remarkably associated with (a) ages, (b) 1p19q codeletion, (c) PRS types, (d) chemotherapy, (e) grade, (f) IDH variant, and (g) histology.