Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Aug 12;11(20):5918-5928.
doi: 10.7150/jca.46328. eCollection 2020.

Development and validation of a DNA repair gene signature for prognosis prediction in Colon Cancer

Xin Wang  1   2   3 , Cong Tan  1   2   3 , Min Ye  1   2   3 , Xu Wang  1   2   3 , Weiwei Weng  1   2   3 , Meng Zhang  1   2   3 , Shujuan Ni  1   2   3 , Lei Wang  1   2   3 , Dan Huang  1   2   3 , Zhaohui Huang  4 , Midie Xu  1   2   3 , Weiqi Sheng  1   2   3
Affiliations

Development and validation of a DNA repair gene signature for prognosis prediction in Colon Cancer

Xin Wang et al. J Cancer. .

Abstract

Aberrant expression of DNA repair genes (DRGs) can be related to tumor progression and clinical outcomes in colon cancer. Here, we aimed to establish a DRGs signature to identify the vital prognostic DRGs in colon cancer. Firstly, gene set enrichment analysis (GSEA) was performed to demonstrate the association between abnormal expression level of DRGs and tumorigenesis. Then, a total of 476 DRGs were obtained for detecting candidate biomarkers in randomly selected 295 cases from The Cancer Genome Atlas (TCGA) colon cancer cohort. Eleven genes were screened by LASSO Cox regression analyses to develop the prognostic model. Then, the prognostic model and the expression levels of the eleven genes were validated using the internal validation dataset (the rest 125 cases in TCGA cohort) and an external validation dataset (obtained from Gene Expression Omnibus dataset). Further analysis revealed the independent prognostic capacity of the prognostic model in relation to other clinical characteristics. The receiver operating characteristic (ROC) curve analysis confirmed the good performance of the prognostic model. Furthermore, we provided a nomogram for interpreting the clinical application of the 11-DRG signature. In conclusion, we propose a newly developed 11-DRG signature as a practical prognostic predictor for patients with colon cancer, which can facilitate the individualized counselling and treatment.

Keywords: DNA repair; GEO; TCGA; colon cancer; prognosis.

PubMed Disclaimer

Conflict of interest statement

Competing Interests: The authors have declared that no competing interest exists.

Figures

Figure 1
Figure 1
The DNA repair signaling pathway is up-regulated in development in colon cancer. (A) Heatmaps of GSE21510, GSE24514 and GSE32323 data sets. Different patterns of transcriptional expression were observed in tumor and non-tumor samples. (B-D) Enriched pathways of GSEA, HALLMARK_DNA_REPAIR was activated in all three data sets.
Figure 2
Figure 2
Risk group identified by the DRG classifier, KM survival analysis and ROC curve of TCGA-COAD training set. (A) The relationship between alive/dead status with Risk Score and survival time (years). The cutoff of Risk Score was set at 0.18. (B) KM survival analysis of overall survival for high-risk or low-risk group patients. (C) ROC analysis of the eleven-DRG prognostic signature. The AUC for 1-year, 3-year, 5-year predicting were 0.773, 0.775 and 0.751, respectively. (D) Heatmap displayed the expression level of eleven DRGs.
Figure 3
Figure 3
Risk group identified by the DRG classifier, KM survival analysis and ROC curve of TCGA-COAD internal validation set. (A) KM survival analysis of overall survival for high-risk or low-risk group patients. (B) The relationship between alive/dead status with Risk Score and survival time (years). The cutoff of Risk Score was set at 0.18. (C) ROC analysis of the eleven-DRG prognostic signature. The AUC for 1-year, 3-year, 5-year predicting were 0.910, 0.599 and 0.827, respectively. (D) Heatmap displayed the expression level of eleven DRGs.
Figure 4
Figure 4
Subgroup KM analysis in high or low risk group patients of TCGA-COAD according to clinical characteristics. Significance differences of overall survival was detected in all subgroup analysis, including distinct gender, age, pathological T, N, M and stage.
Figure 5
Figure 5
Risk group identified by the DRG classifier, KM survival analysis and ROC curve of GSE39582 dataset. (A) The relationship between alive/dead status with Risk Score and survival time (years). The cutoff of Risk Score is set at 0.18. (B) KM survival analysis of overall survival for high-risk or low-risk group patients. (C) ROC analysis of the eleven-DRG prognostic signature. The AUC for 1-year, 3-year, 5-year predicting were 0.663, 0.610 and 0.622, respectively. (D) Heatmap displayed the expression level of eleven DRGs.
Figure 6
Figure 6
Comparisons of sensitivity and specificity for survival prediction by DRG signature and pathologic M stage as independent factors. The eleven-DRG signature showed a better capability for survival prediction than pathologic M stage. Significant differences reached at 1-year and 3-year prediction.
Figure 7
Figure 7
Nomogram and calibration analysis for the DRG prognostic signature. (A) Nomogram plotted by the independent factors of patients' survival. The probability of long-term survival can be calculated by adding the corresponding points of M stage and Risk Score in the nomogram. (B) Calibration plots displayed the relationship between actual and the nomogram-predicted survival, which indicated a powerful predicting capability of the nomogram.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians. 2018;68:394–424. - PubMed
    1. Pages F, Mlecnik B, Marliot F, Bindea G, Ou FS, Bifulco C. et al. International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study. Lancet (London, England) 2018;391:2128–39. - PubMed
    1. Zhou R, Zhang J, Zeng D, Sun H, Rong X, Shi M. et al. Immune cell infiltration as a biomarker for the diagnosis and prognosis of stage I-III colon cancer. Cancer immunology, immunotherapy: CII. 2019;68:433–42. - PMC - PubMed
    1. Uhlen M, Zhang C, Lee S, Sjostedt E, Fagerberg L, Bidkhori G, A pathology atlas of the human cancer transcriptome. Science (New York, NY) 2017. 357. - PubMed
    1. Bailey P, Chang DK, Nones K, Johns AL, Patch AM, Gingras MC. et al. Genomic analyses identify molecular subtypes of pancreatic cancer. Nature. 2016;531:47–52. - PubMed
  NODES
Association 1
INTERN 6
twitter 2