Dataset Information

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.

SUBMITTER: Li G

PROVIDER: S-EPMC7816205 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

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

Li Guanzhang G Wu Fan F Zeng Fan F Zhai You Y Feng Yuemei Y Chang Yuanhao Y Wang Di D Jiang Tao T Zhang Wei W

CNS neuroscience & therapeutics 20201016 2

<h4>Aims</h4>We aimed to create a tumor recurrent-based prediction model to predict recurrence and survival in patients with low-grade glioma.<h4>Methods</h4>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 ...[more]

PMID: 33063446

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Project description:Background : Autophagy plays a vital role in cancer development. However, the prognostic value of autophagy-related genes (ARGs) in low-grade gliomas (LGG) is unclear. This research aimed to investigate whether ARGs correlated with overall survival (OS) in LGG patients. Methods: RNA-sequencing data were obtained from The Cancer Genome Atlas (TCGA) TARGET GTEx database. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis of ARGs were performed by the "clusterprofile" R package. Cox regression with the wald χ2 test was employed to identify prognostic significant ARGs. Next, the receiver operator characteristic curves were established to evaluate the feasibility of risk score ( riskscore=h0(t)exp(∑j=1nCoefj×Xj) ) and other clinical risk factors to predict prognosis. A nomogram was constructed. Correlations between clinical features and ARGs were further verified by a t-test or Kruskal-Wallis test. In addition, the correlations between autophagy and immune cells were assessed through the single-sample gene set enrichment analysis (ssGSEA) and tumor immune estimation resource database. Last, the prediction model was verified by LGG data downloaded from the Chinese Glioma Genome Atlas (CGGA) database. Results: Overall, 35 DE-ARGs were identified. Functional enrichment analysis showed that these genes were mainly related to oxidative stress and regulation of autophagy. Nine ARGs (BAX, BIRC5, CFLAR, DIRAS3, GRID2, MAPK9, MYC, PTK6, and TP53) were significantly associated with OS. Age (Hazard ratio (HR) = 1.063, 95% CI: 1.046-1.080), grade (HR = 3.412, 95% CI: 2.164-5.379), histological type (HR = 0.556, 95% CI: 0.346-0.893), and risk score (HR = 1.135, 95% CI: 1.104-1.167) were independent prognostic risk factors (all p < 0.05). In addition, BIRC5, CFLAR, DIRAS3, TP53, and risk scores were found to correlate significantly with age and tumor grade (all p < 0.05). Immune cell enrichment analysis demonstrated that the types of immune cells and their expression levels in the high-risk group were significantly different from those in the low-risk group (all p < 0.05). A prognostic nomogram was constructed to predict 1-, 3-, and 5-year survival, and the prognostic value of sorted ARGs were verified in the CGGA database and clinical samples. Conclusion: Our findings suggest that the 9 DE-ARGs' risk score model could serve as diagnostic and prognostic biomarkers. The prognostic nomograms could be useful for individualized survival prediction and improved treatment strategies.

Dataset Information

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

Aims

Methods

Results

Conclusion

Publications

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

Similar Datasets

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