# Clinicopathological and Prognostic Significance of ABCC3 in Human Glioma

Authors: Dan-Dong Fang; Wei Huang; Gang Cheng; Xiao-Nan Liu; Shi-Min Liu; Bao-Sen Hou; Jian Mao; Hu Zhou
Journal: Journal of Oncology
 (2021)

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## 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.


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## Body

## 1. Introduction

Glioma is the most commonly seen malignancy primary cerebroma and the most fatal type of cerebroma in adults [1]. Of the 4 categories of glioma categorized by the WHO, the most severe gradation (gradation IV) is GBM [2]. The morbidity and mortality of GBM cases have also increased year by year. GBM displays elevated aggressive proliferation and a tendency to invade and metastasize [3, 4]. Despite the fact that remarkable progresses have been made in the diagnoses and target treatment of this disease, the prognostic results of sufferers remain unsatisfactory [5, 6]. For that reason, more and more studies are conducted to find promising markers for tumor identification or forecast results, particularly in the early phases.The progress in biological information and high-flux sequencing has realized the determination of various cancer markers which might assist the prognostic accurateness of GBM, which might give rise to more valid interventions in this regard [7, 8]. Tan et al. reported that serum long noncoding RNA HOTAIR was highly expressed in glioblastoma, and its positive association with long-term survival in tumors patients was also confirmed, indicating sera HOTAIR could be utilized as a novel prognosis and diagnostic marker for GBM [9]. Stanniocalcin 1 was reported to be overexpressed in glioma, and its upregulation in glioma patients predicted a poor prognosis [10]. Moreover, LAMC1 was also reported to be vital for the development of this disease and might be utilized in the diagnoses, prognoses, and target treatment of sufferers [11]. On the other hand, more and more prognostic models based on multiple genes were also developed [12, 13]. Those biomarkers might be utilized for future sophisticated diagnosis and decision-making processes [14]. Despite these advances, more reliable prognostic indicators are needed for glioma.Herein, we searched CGGA datasets and identified many survival-related genes based on several conditions. Finally, we identified 132 genes which may be the most important survival-related genes in glioma. Among those genes, our attention focused on ABCC3. Recently, some studies have discovered the dysregulation of ABCC3 in many cancers, including glioma [15–18]. However, its clinical significance in glioma patients was rarely reported.


## 2. Materials and Methods

### 2.1. Data Collection

We collected 1018 glioma sufferers for the following investigation. Clinic feature data and transcriptomic sequencing results of CGGA microarray and RNA-sequencing cohorts were acquired from the CGGA dataset [19]. Our team utilized FPKM to speculate the expression of RNA. Each sufferer without prognosis data was excluded at first. As the data were acquired from TCGA and the CGGA, the acceptance from the ethical board was not needed.


### 2.2. Survival Analysis Filtration

Survival and survminer packages were used in R program [20], and K–M and univariable Cox analysis were utilized for filtering genetic expression and survival data, with P<0.001 being significant on statistics.


### 2.3. Analyses of the Expression of the Survival-Associated Genes in GBM

The information of differentially expressed survival-associated genes between tumor and matched normal tissues was from TCGA and GTEx databases. GEPIA was applied to analyze the expressions of the survival-associated genes in GBM [21].


### 2.4. Independent Prognosis Role of ABCC3 in Glioma Sufferers

To determine the impact of ABCC3 expression on prognoses, our team has to evaluate if the ABCC3 expression was related to the rest of the factors clinically, such as sex, age, IDH1 variant phase, and cancer WHO gradation. For that reason, univariable and multivariable Cox proportion assays were finished to identify the independent prognosis effects of ABCC3 with the forward stepwise procedure. The ABCC3 expression and clinical factors were considered to be independence factors when the modifiedP result was <0.05.


### 2.5. Clinic Relevance Filtration

The data of gene expressions acquired from ROC curve filtration and the relevant clinic data were studied via R program and subjected to filtration viaP<0.05.


### 2.6. Analyses of the Association between the Expression of ABCC3 and Clinical Features

Genetic expression and relevant clinic data acquired from ROC curve filtration were studied in R to abstract the clinic data related to the ABCC3 gene. The association between the expression of ABCC3 and a variety of clinic features was identified via beeswarm.


### 2.7. Statistical Analysis

All the statistical analyses were performed using R version 3.4.2 software. A two-tailedP<0.05 was considered statistically significant.


## 2.1. Data Collection

We collected 1018 glioma sufferers for the following investigation. Clinic feature data and transcriptomic sequencing results of CGGA microarray and RNA-sequencing cohorts were acquired from the CGGA dataset [19]. Our team utilized FPKM to speculate the expression of RNA. Each sufferer without prognosis data was excluded at first. As the data were acquired from TCGA and the CGGA, the acceptance from the ethical board was not needed.


## 2.2. Survival Analysis Filtration

Survival and survminer packages were used in R program [20], and K–M and univariable Cox analysis were utilized for filtering genetic expression and survival data, with P<0.001 being significant on statistics.


## 2.3. Analyses of the Expression of the Survival-Associated Genes in GBM

The information of differentially expressed survival-associated genes between tumor and matched normal tissues was from TCGA and GTEx databases. GEPIA was applied to analyze the expressions of the survival-associated genes in GBM [21].


## 2.4. Independent Prognosis Role of ABCC3 in Glioma Sufferers

To determine the impact of ABCC3 expression on prognoses, our team has to evaluate if the ABCC3 expression was related to the rest of the factors clinically, such as sex, age, IDH1 variant phase, and cancer WHO gradation. For that reason, univariable and multivariable Cox proportion assays were finished to identify the independent prognosis effects of ABCC3 with the forward stepwise procedure. The ABCC3 expression and clinical factors were considered to be independence factors when the modifiedP result was <0.05.


## 2.5. Clinic Relevance Filtration

The data of gene expressions acquired from ROC curve filtration and the relevant clinic data were studied via R program and subjected to filtration viaP<0.05.


## 2.6. Analyses of the Association between the Expression of ABCC3 and Clinical Features

Genetic expression and relevant clinic data acquired from ROC curve filtration were studied in R to abstract the clinic data related to the ABCC3 gene. The association between the expression of ABCC3 and a variety of clinic features was identified via beeswarm.


## 2.7. Statistical Analysis

All the statistical analyses were performed using R version 3.4.2 software. A two-tailedP<0.05 was considered statistically significant.


## 3. Results

### 3.1. Identification of Survival-Related Genes in Glioma

We performed the K–M, univariable Cox method, and multivariable Cox analyses to screen the survival-related genes. Then, AUC >0.7 was taken as the liminal value for ROC curve analyses (Supplementary TableS1). Eventually, the association between genes and clinic features was studied, with P<0.05 being the threshold. As shown in Figures 1(a)–1(f), we showed the top 6 survival-related genes including MED8 (Figure 1(a), P<0.001), HIST1H2BK8 (Figure 1(b), P<0.001), ANXA1 (Figure 1(c), P<0.001), AK2 (Figure 1(d), P<0.001), ABCC3 (Figure 1(e), P<0.001), and ABRACL (Figure 1(f), P<0.001).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.
(a)(b)(c)(d)(e)(f)


### 3.2. The Distinct Upregulation of ABCC3 in GBM Specimens

Then, we used GEPIA to study the expressions of the abovementioned 6 genes and found that ABCC3 (Figure2(a)), HIST1H2BK83 (Figure 2(b)), AK2 (Figure 2(c)), and ANXA1 (Figure 2(d)) exhibited an increased level in GBM specimens in contrast to healthy cerebrum specimens. However, the expressions of ABRACL and MED8 remained unchanged between GBM specimens and nontumor specimens (Figures 2(e) and 2(f)). Our attention focused on ABCC3.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.
(a)(b)(c)(d)(e)(f)


### 3.3. The Prognostic Significance of ABCC3 in Glioma Patients from the CGGA Database

To investigate the prognostic value of the expression of ABCC3 in glioma patients, our team performed univariable Cox analyses and observed that ABCC3 (HR = 1.369; 95% CI = 1.306–1.435;P<0.001), PRS types, histological status, gradation, ages, and chemotherapy were factors related to higher risks and IDH variant and 1p19q codeletion were related to lower risks (Figure 3(a)). Multivariable Cox analyses revealed that ABCC3 (HR = 1.118; 95% CI = 1.052–1.188; P<0.001) was related to OS in an independent way, which unveiled that ABCC3 could serve as an independence marker for the clinical outcome of this disease. Moreover, PRS types, gradation, ages, chemotherapy, IDH variant, and 1p19q codeletion might be independent prognosis factors as well (Figure 3(b)). ROC curve analyses revealed that ABCC3 was a predicting factor of 1-year (AUC = 0.717), 3-year (AUC = 0.757), and 5-year survival (AUC = 0.755) (Figure 4). Finally, we analyzed the relationship between ABCC3 expressions and clinic characteristics of glioma patients, finding that differentially expressing ABCC3 was remarkably related to age (Figure 5(a)), 1p19q codeletion (Figure 5(b)), PRS type (Figure 5(c)), chemo status (Figure 5(d)), grade (Figure 5(e)), IDH mutation status (Figure 5(f)), and histology (Figure 5(g)). Our findings suggested ABCC3 might participate in the clinical development of glioma and may be a novel biomarker.Figure 3
(a) Univariate and (b) multivariate analysis of ABCC3 expression and its correlation in patients with glioma based on CGGA data.
(a)(b)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.
(a)(b)(c)(d)(e)(f)(g)


## 3.1. Identification of Survival-Related Genes in Glioma

We performed the K–M, univariable Cox method, and multivariable Cox analyses to screen the survival-related genes. Then, AUC >0.7 was taken as the liminal value for ROC curve analyses (Supplementary TableS1). Eventually, the association between genes and clinic features was studied, with P<0.05 being the threshold. As shown in Figures 1(a)–1(f), we showed the top 6 survival-related genes including MED8 (Figure 1(a), P<0.001), HIST1H2BK8 (Figure 1(b), P<0.001), ANXA1 (Figure 1(c), P<0.001), AK2 (Figure 1(d), P<0.001), ABCC3 (Figure 1(e), P<0.001), and ABRACL (Figure 1(f), P<0.001).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.
(a)(b)(c)(d)(e)(f)


## 3.2. The Distinct Upregulation of ABCC3 in GBM Specimens

Then, we used GEPIA to study the expressions of the abovementioned 6 genes and found that ABCC3 (Figure2(a)), HIST1H2BK83 (Figure 2(b)), AK2 (Figure 2(c)), and ANXA1 (Figure 2(d)) exhibited an increased level in GBM specimens in contrast to healthy cerebrum specimens. However, the expressions of ABRACL and MED8 remained unchanged between GBM specimens and nontumor specimens (Figures 2(e) and 2(f)). Our attention focused on ABCC3.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.
(a)(b)(c)(d)(e)(f)


## 3.3. The Prognostic Significance of ABCC3 in Glioma Patients from the CGGA Database

To investigate the prognostic value of the expression of ABCC3 in glioma patients, our team performed univariable Cox analyses and observed that ABCC3 (HR = 1.369; 95% CI = 1.306–1.435;P<0.001), PRS types, histological status, gradation, ages, and chemotherapy were factors related to higher risks and IDH variant and 1p19q codeletion were related to lower risks (Figure 3(a)). Multivariable Cox analyses revealed that ABCC3 (HR = 1.118; 95% CI = 1.052–1.188; P<0.001) was related to OS in an independent way, which unveiled that ABCC3 could serve as an independence marker for the clinical outcome of this disease. Moreover, PRS types, gradation, ages, chemotherapy, IDH variant, and 1p19q codeletion might be independent prognosis factors as well (Figure 3(b)). ROC curve analyses revealed that ABCC3 was a predicting factor of 1-year (AUC = 0.717), 3-year (AUC = 0.757), and 5-year survival (AUC = 0.755) (Figure 4). Finally, we analyzed the relationship between ABCC3 expressions and clinic characteristics of glioma patients, finding that differentially expressing ABCC3 was remarkably related to age (Figure 5(a)), 1p19q codeletion (Figure 5(b)), PRS type (Figure 5(c)), chemo status (Figure 5(d)), grade (Figure 5(e)), IDH mutation status (Figure 5(f)), and histology (Figure 5(g)). Our findings suggested ABCC3 might participate in the clinical development of glioma and may be a novel biomarker.Figure 3
(a) Univariate and (b) multivariate analysis of ABCC3 expression and its correlation in patients with glioma based on CGGA data.
(a)(b)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.
(a)(b)(c)(d)(e)(f)(g)


## 4. Discussion

Amongst inhomogeneous primary cancers of the CNS, gliomas are the most common type, with GBM featured by the most unsatisfactory prognoses [22, 23]. In the past 10 years, the variant in epigenesis modulator genes has been discovered to be crucial driving factor of the glioma subgroups with different clinic characteristics [24, 25]. More and more potential regulators display the potential to be used as novel diagnostic and prognostic biomarkers for glioma [26–28]. Among them, the dysregulated genes with positive regulatory functions in the tumor growth and metastasis were the most hopeful biomarkers [29, 30]. However, the expression and function of most genes remained largely unclear.In this study, we analyzed CGGA datasets and identified 132 possible survival-related genes with a high score of ROC. Among the 132 genes, we showed the top 6 genes, including MED8, ABCC3, ABRACL, AK2, ANXA1, and HIST1H2BK. However, only ABCC3, HIST1H2BK, AK2, and ANXA1 exhibited a high level in GBM. Previously, several studies have reported the expressing pattern and function of HIST1H2BK and ANXA1 in glioma [31, 32]. For instance, knockdown of ANXA1 was reported to suppress the proliferation and metastasis of glioma cells via regulating the PI3K/Akt signaling pathway [33]. High HIST1H2BK expression predicted a shorter OS of glioma sufferers [34]. Nevertheless, the clinical significance of ABCC3 in glioma has not been investigated. Herein, our team offered proofs that the expression of ABCC3 was an independent prognostic marker for overall survival of glioma sufferers.Previously, some studies have reported the effects of ABCC3 in many types of tumors. For instance, ABCC3 was found to be involved in the regulation of the sensitivity of doxorubicin in triple-negative breast cancer [17]. ABCC3 was highly expressed in urinary bladder cancer, and its knockdown inhibited cell growth, drug-resistant ability, and aerobic glycolysis of bladder oncocytes [35]. In glioma, ABCC3 was shown to predict reactions in GBM sufferers receiving combined chemo and DC immune therapy [36]. Those discoveries revealed the vital role of ABCC3 in cancer development. Herein, our team analyzed the clinical feature of glioma patients with ABCC3 expression, finding that differentially expressed ABCC3 was remarkably related to PRS types, histological status, gradations, ages, chemotherapy states, IDH variant, and 1p19q codeletion. Our findings suggested ABCC3 exhibited a prognostic value in glioma and may be involved in clinical progression of glioma via complex mechanisms.


## 5. Conclusions

ABCC3 is upregulated in patients with glioma. Its abnormal expression can be utilized as an independent diagnostic and prognostic biomarker for this tumor. Nevertheless, its mechanisms and other effects remain unknown. Moreover, this study is limited by its small sample. Hence, further studies are needed.



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