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. 2021 Dec 21:14:10083-10101.
doi: 10.2147/IJGM.S343839. eCollection 2021.

A Novel DNA Damage Repair-Related Gene Signature for Predicting Glioma Prognosis

Jiaoyang Zhan  1 , Shuang Wu  2 , Xu Zhao  3 , Jingjing Jing  4   5   6
Affiliations

A Novel DNA Damage Repair-Related Gene Signature for Predicting Glioma Prognosis

Jiaoyang Zhan et al. Int J Gen Med. .

Abstract

Background: Glioma is one of the most prevalent tumors in the central nervous system of adults and shows a poor prognosis. This study aimed to develop a DNA damage repair (DDR)-related gene signature to evaluate the prognosis of glioma patients.

Methods: Differentially expressed genes (DEGs) were extracted based on 276 DDR genes. Then, a gene signature was developed for the survival prediction in glioma patients by means of univariate, multivariate Cox, and least absolute shrinkage and selector operation (Lasso) analyses. After analyzing the clinical parameters, a nomogram was constructed and assessed. A total of 693 gliomas from the Chinese Glioma Genome Atlas (CGGA) were used for external validation. In addition, we used glioma tumor tissues for qPCR experiment to verify.

Results: A 12-DDR-related gene signature was identified from the 75 DEGs to stratify the survival risk of glioma patients. The overall survival of high-risk group was significantly shorter than that of low-risk group (P < 0.001). Besides, according to the risk score assessment, patients in high- or low-risk group also had significant correlations with clinicopathological parameters, including age (P < 0.01), grade (P < 0.001), IDH status (P < 0.001) and 1p19q codeletion status (P < 0.001). The nomogram provided favorable C-index and calibration plots. The C-index of training set and verification set was 0.761 and 0.746, respectively, and the calibration curve also showed that both training set and verification set were close to the standard curve. The qPCR results showed that there were significant differences in the expression of some typical DDR-related genes in tumor tissues and paracancer tissues (P(WEE1)=0.0002, P(RECQL)=0.0117, P(RPA1)=0.021, P(RRM1)=0.0035, P(PARP4)=0.0006, P(ELOA)=0.0023).

Conclusion: Our study developed a novel 12 DDR-related gene signature as a practical prognostic predictor for glioma patients. A nomogram combining the signature and clinical parameters was established as an individual clinical prediction tool.

Keywords: DNA damage repair-related gene signature; glioma; nomogram; prognosis; qPCR.

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Conflict of interest statement

The authors declare that there is no conflict of interest regarding the publication of this paper.

Figures

Figure 1
Figure 1
A heatmap of 75 differential DDR-related genes.
Figure 2
Figure 2
Functional and pathway enrichment of 75 differential DDR-related genes. (A) The bar chart shows the GO function enrichment results of the top ten DDR-related genes. (B) The circle diagram shows the specific functions of each DDR-related gene. (C) The bar chart shows the KEGG pathway enrichment results of the top ten DDR-related genes. (D) The circle diagram shows the specific related pathway of each DDR-related gene.
Figure 3
Figure 3
Consensus clustering of DDR-related genes in glioma samples. (A) Consensus clustering of DDR-related genes clustered glioma samples into two clusters with distinct clinical outcomes. The heatmap shows the consensus matrix when k = 2. (B) Consensus clustering cumulative distribution function (CDF) under k = 2–9. (C) Relative change in area under CDF curve. (D) Kaplan-Meier OS curve of cluster 1 and 2.
Figure 4
Figure 4
Construction of the prognostic signature based on 12 DDR-related genes. (A) Tuning parameter (lambda) screening in the Lasso regression model. (B) The Lasso coefficient profiles of the common genes (1- RECQL; 2- BARD1; 3-CDC5L; 4- CETN2; 5- RFC2; 6- ERCC5; 7- RRM1; 8- ELOA; 9-WEE1; 10- GADD45G; 11- RPA1; 12- PARP4; 13- DCLRE1B; 14-TOP3B; 15- CDC25A).
Figure 5
Figure 5
Evaluation and survival analysis of the DDR-related gene prognostic signature. (A–C) The receiver operator characteristic (ROC) curve to predict 1-, 3-, and 5-year OS according to risk score in CGGA and TCGA cohort. (D) Kaplan-Meier survival curve demonstrating the OS differences between high- and low- risk groups.
Figure 6
Figure 6
Clinical features of the DDR-related gene prognostic signature. (A) The correlations between the DDR-related gene signature and clinicopathological parameters, including radiotherapy, chemotherapy, 1p19q codeletion, IDH mutation status, grade, gender and age. (B) Heatmap depicting the expression patterns in the 12 DNA repair genes between high- and low- risk groups. (C) The distribution of the risk scores among all glioma samples. According to the median value (dotted line), glioma samples were divided into high- (red dot) and low- risk (green dot) groups. (D) The distribution of survival status of all glioma samples. Red dot is indicative of dead status and green dot indicates alive status. (***Represented P < 0.001, **Represented P < 0.01).
Figure 7
Figure 7
The best cutoff value of age for survival of the glioma patients. (A–C) The best cutoff value of age for optional survival difference was determined, tagged by the black circle and presented by a histogram of the whole cohort, which was 61.
Figure 8
Figure 8
Screening and evaluation of the indicators of prognostic model. (A) Univariate Cox regression analyses of DDR gene signature (risk score) and several other clinical variables. (B) Tuning parameter (lambda) screening in the Lasso regression model. (C) The Lasso coefficient profiles of the indicators (1-Age; 2-Grade; 3-IDH status; 4-1p19q codeletion; 5-Risk).
Figure 9
Figure 9
Construction and evaluation of the nomogram for predicting overall survival of patients with gliomas. (A) A nomogram integrating the signature risk score and the clinicopathologic characteristics in the training cohort. The line determines the “point” received for the value of each variable. The sum of these numbers is presented as “total points”, while the line drawn down to the survival axis determines the likelihood of different survival rate. (B) The calibration curve for the nomogram in the training set based on data from TCGA database. (C) The calibration curve for the nomogram in the validation set based on data from CGGA database.
Figure 10
Figure 10
The analysis of signaling pathways between the high- and low-groups. (A) The signaling pathways of the high groups. (B) The signaling pathways of the low groups.
Figure 11
Figure 11
qPCR verification results of the mRNA expression of six DDR-related genes. (A) WEE1, (B) RECQL, (C) RPA1, (D) RRM1, (E) PARP4, (F) ELOA.
Figure 12
Figure 12
Differential expression analysis of DDR-related genes using GEPIA. (A) WEE1, (B) RECQL, (C) RPA1, (D) RRM1, (E) PARP4, (F) ELOA.
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