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
. 2022 Apr 28;12(1):6946.
doi: 10.1038/s41598-022-10561-w.

Identification and validation of prognosis-associated DNA repair gene signatures in colorectal cancer

Dingli Song #  1 , Dai Zhang #  2 , Sisi Chen  1 , Jie Wu  1 , Qian Hao  2 , Lili Zhao  3 , Hong Ren  4 , Ning Du  5
Affiliations

Identification and validation of prognosis-associated DNA repair gene signatures in colorectal cancer

Dingli Song et al. Sci Rep. .

Abstract

Colorectal cancer (CRC) is the third most common malignant tumor. DNA damage plays a crucial role in tumorigenesis, and abnormal DNA repair pathways affect the occurrence and progression of CRC. In the current study, we aimed to construct a DNA repair-related gene (DRG) signature to predict the overall survival (OS) of patients with CRC patients. The differentially expressed DRGs (DE-DRGs) were analyzed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The prognostic gene signature was identified by univariate Cox regression and least absolute shrinkage and selection operator (LASSO)-penalized Cox proportional hazards regression analysis. The predictive ability of the model was evaluated using the Kaplan-Meier curves and time-dependent receiver operating characteristic (ROC) curves. The gene set enrichment analysis (GSEA) was performed to explore the underlying biological processes and signaling pathways. ESTIMATE and CIBERSORT were implemented to estimate the tumor immune score and immune cell infiltration status between the different risk group. The half-maximal inhibitory concentration (IC50) was evaluated to representing the drug response of this signature. Nine DE-DRGs (ESCO2, AXIN2, PLK1, CDC25C, IGF1, TREX2, ALKBH2, ESR1 and MC1R) signatures was constructed to classify patients into high- and low-risk groups. The risk score was an independent prognostic indicator of OS (hazard ratio > 1, P < 0.001). The genetic alteration analysis indicated that the nine DE-DRGs in the signature were changed in 63 required samples (100%), and the major alteration was missense mutation. Function enrichment analysis revealed that the immune response and mtotic sister chromatid segregation were the main biological processes. The high-risk group had higher immune score than the low-risk group. What's more, low-risk patients were more sensitive to selumetinib and dasatinib. The nine DE-DRGs signature was significantly associated with OS and provided a new insight for the diagnosis and treatment of CRC.

PubMed Disclaimer

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
The flowchart of the research design.
Figure 2
Figure 2
Identification of prognostic DNA repair-related DEGs in TCGA. (A) Venn plot to identify prognostic DE-DRGs in CRC based on data from TCGA; (B) the expression patterns of the 12 DE-DRGs in a heatmap; (C) forest plots of 12 DE-DRGs associated with OS by univariate Cox regression. (D) The 12 DE-DRGs interactions of PPI network downloaded from STRING database. (E) The correlation heatmap of 12 DE-DRGs. The different colors presented correlation coefficients.
Figure 3
Figure 3
Construction of a prognostic model in TCGA by LASSO Cox regression analysis. (A,B) Selection of the optimal parameter (lambda) in the LASSO model for colorectal cancer. (C) The distribution of risk score and patient’s survival time. The black dotted line is the optimum cutoff dividing patients into low-risk and high-risk groups. The red curve represents high risk and the blue curve represents low risk. (D) The distribution of risk score and patient’s survival status. (E) The high-risk score was related to poorer OS. F ROC analysis of the sensitivity and specificity of the OS.
Figure 4
Figure 4
Independent prognostic analysis of 9 DE-DRG signature in the TCGA cohort. (A) Heatmap of the DE-DRGs in prognostic signature for TCGA. (B,C) Forest plot of the association between risk factors and survival of TCGA-CRC by univariate and multivariate Cox regression analysis.
Figure 5
Figure 5
Analysis of genetic alterations, involved signaling pathways and immune correlation of DRGs in CRC. (A) Genetic alterations of the 9 DE-DRGs in the CRC cohort. X axis represents cancer type, sky blue indicates COAD, light blue indicates READ. The left Y axis represents ratio of gene mutation, right Y axis represents gene names. Dark blue, cyan, and pink small rectangles indicate the type of gene mutation. (B–D) GO and KEGG enrichment analysis of the 9 DE-DRGs. (D,E) The scores of 16 immune cells and 13 immune-related functions are displayed in boxplots. CCR, cytokine-cytokine receptor. Adjusted P values were showed as: ns not significant; *P < 0.05; **P < 0.01; ***P < 0.001.
Figure 6
Figure 6
Estimation of the correlation between risk score with TME. (A–C) Comparison the stromal score, immune score and ESTIMATE score between high-risk and low-risk groups. (D) The scores of 22 immune cells in high-risk and low-risk groups.
Figure 7
Figure 7
Analysis of drug sensitivity in risk model. (A) The correlation between GDSC drug sensitivity and 9 DE-DRGs mRNA expression. (B–D) The drug sensitivity of selumetinib, Dasatinib and Vorinostat in high-risk and low-risk groups.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Bray F, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2018;68(6):394–424. doi: 10.3322/caac.21492. - DOI - PubMed
    1. Dekker E, et al. Colorectal cancer. Lancet. 2019;394(10207):1467–1480. doi: 10.1016/S0140-6736(19)32319-0. - DOI - PubMed
    1. Fiorentini G, et al. Updates of colorectal cancer liver metastases therapy: review on DEBIRI. Hepat. Oncol. 2020;7(1):16. doi: 10.2217/hep-2019-0010. - DOI - PMC - PubMed
    1. Rawla P, Sunkara T, Barsouk A. Epidemiology of colorectal cancer: Incidence, mortality, survival, and risk factors. Prz. Gastroenterol. 2019;14(2):89–103. - PMC - PubMed
    1. Patel, J. et al. DNA damage and mitochondria in cancer and aging.Carcinogenesis (2020). - PMC - PubMed

Publication types

  NODES
Association 1
twitter 2