A novel DNA repair-related nomogram predicts survival in low-grade gliomas

Abstract

Aims: We aimed to create a tumor recurrent-based prediction model to predict recurrence and survival in patients with low-grade glioma.

Methods: This study enrolled 291 patients (188 in the training group and 103 in the validation group) with clinicopathological information and transcriptome sequencing data. LASSO-COX algorithm was applied to shrink predictive factor size and build a predictive recurrent signature. GO, KEGG, and GSVA analyses were performed for function annotations of the recurrent signature. The calibration curves and C-Index were assessed to evaluate the nomogram's performance.

Results: This study found that DNA repair functions of tumor cells were significantly enriched in recurrent low-grade gliomas. A predictive recurrent signature, built by the LASSO-COX algorithm, was significantly associated with overall survival and progression-free survival in low-grade gliomas. Moreover, function annotations analysis of the predictive recurrent signature exhibited that the signature was associated with DNA repair functions. The nomogram, combining the predictive recurrent signature and clinical prognostic predictors, showed powerful prognostic ability in the training and validation groups.

Conclusion: An individualized prediction model was created to predict 1-, 2-, 3-, 5-, and 10-year survival and recurrent rate of patients with low-grade glioma, which may serve as a potential tool to guide postoperative individualized care.

Keywords: DNA repair functions; low-grade glioma; prognosis prediction; recurrence prediction; tumor recurrence.

Figure 1

The landscape of highly activated biological processes in recurrent low‐grade gliomas. A, Highly activated biological processes in recurrent LGGs compared to primary tumors. Red dots were significantly elevated BPs. Gray dots represented the non‐significant changed BPs. B, Classification of the recurrent LGGs enriched BPs. Number of BPs in a certain group divided by the total number of significantly changed BPs to get the percentage of each group. C, BPs with the most prognostic value in proliferation and cell cycle group. D, BPs with the most prognostic value in the transcription group. E, BPs with the most prognostic value in the metabolic process group. F, BPs with the most prognostic value in response to the stimulus group

Figure 2

Building a recurrent signature by LASSO‐COX analysis. A, BPs with independent prognostic value in LGGs. Red dots were BPs independent prognostic value. BPs stained gray was not an independent prognostic factor. B, Screening the most representative 4 genes in response to DNA damage stimulus‐related genes by LASSO‐COX analysis

Figure 3

The relationship between the recurrent score and clinical characteristics and survival in patients with LGGs. (A and B) The heatmap showed the clinical‐pathologic factors and 4 representative genes for each LGG in ascending order of the recurrence score in training and validation groups. C, The Kaplan‐Meier curves indicated that patients in high‐risk group have shorter OS and PFS than patients in the low‐risk group. The line chart showed the P values of survival analysis between patients with lower and higher recurrence scores with various cutoff. D, The recurrence score showed good predictive accuracy in the validation group

Figure 4

The relationship between recurrent scores and clinical‐pathologic characteristics of LGGs. (A and B) Violin charts showed the distribution of recurrent scores between different clinical‐pathologic characteristics of LGGs in training and validation groups. The significance of the difference between the two groups was verified by ^a Mann Whitney test or ^c Student's t‐test. The significance of the difference between the three groups was verified by ^b Kruskal‐Wallis test

Figure 5

Biological functions associated with the recurrent scores. (A and B) The recurrent score related biological process and pathways revealed by Gene ontology analysis and KEGG analysis in the training group. (C and D) The recurrent score related biological process and pathways revealed by Gene ontology analysis and KEGG analysis in the validation group. (E and F) GSEA analysis showed that the recurrent score was closely related to DNA damage repair functions. G, The recurrent score was significantly positively correlated with most DNA repair‐related functions. The R‐value of pearson correlation analysis of recurrent scores and DNA repair functions enrichment scores were showed in the inner circle. The strength of the correlation was represented by the shade of red

Figure 6

The individualized prediction models for PFS in LGGs. A, The 1‐, 2‐ 3‐, 5‐, and 10‐year recurrent rate of LGG patients after tumor resection could exactly be predicted by the nomogram. B, The Calibration plots showed the comparison between predicted and actual PFS for 1‐, 2‐ 3‐, 5‐, and 10‐year survival probabilities in training and validation groups. C, The predictive effect of the individualized prediction model, recurrent score, prediction model without the recurrent score, and clinical prognostic factors of LGGs on PFS was evaluated by C‐Index