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. 2021 Jan 30;21(1):81.
doi: 10.1186/s12935-021-01779-1.

Identification of DNA repair-related genes predicting pathogenesis and prognosis for liver cancer

Wenjing Zhu  1 , Qiliang Zhang  2 , Min Liu  1 , Meixing Yan  3 , Xiao Chu  4 , Yongchun Li  5
Affiliations

Identification of DNA repair-related genes predicting pathogenesis and prognosis for liver cancer

Wenjing Zhu et al. Cancer Cell Int. .

Abstract

Background: Liver cancer (LC) is one of the most fatal cancers throughout the world. More efficient and sensitive gene signatures that could accurately predict survival in LC patients are vitally needed to promote a better individualized and effective treatment.

Material/methods: 422 LC and adjacent normal tissues with both RNA-Seq and clinical data in TCGA were embedded in our study. Gene set enrichment analysis (GSEA) was applied to identify genes and hallmark gene sets that are more valuable for liver cancer therapy. Cox regression analysis was used to identify genes related to overall survival (OS) and build the prediction model. cBioPortal database was used to examine the alterations of the panel mRNA signature. ROC curves and Kaplan-Meier curves were used to validate the prediction model. Besides, the expression of the genes in the model were validated using quantitative real-time PCR in clinical tissue specimens.

Results: The panel of DNA repair-related mRNA signature consisted of seven mRNAs: RFC4 (replication factor C subunit 4), ZWINT (ZW10 interacting kinetochore protein), UPF3B (UPF3B regulator of nonsense mediated mRNA decay), NCBP2 (nuclear cap binding protein subunit 2), ADA (adenosine deaminase), SF3A3 (splicing factor 3a subunit 3) and GTF2H1 (general transcription factor IIH subunit 1). On-line analysis of cBioPortal database found that the expression of the panel mRNA has a wide variation ranging from 7 to 10%. All the mRNAs were significantly upregulated in LC tissues compared to normal tissues (P < 0.05). The risk model is closely related to the OS of LC patients. The hazard ratio (HR) is 2.184 [95% CI (confidence interval) 1.523-3.132] and log-rank P-value < 0.0001. For clinical specimen validation, we found that all of the genes in the model upregulated in liver cancer tissues versus normal liver tissues, which was consistent with the results predicted.

Conclusions: Our study demonstrated a mRNA signature including seven mRNA for prognosis prediction of LC. This panel gene signature provides a new criterion for accurate diagnosis and therapeutic target of LC.

Keywords: Biomarker; Liver cancer; Prognosis; TCGA; mRNA.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
GSEA revealed three gene sets which were significantly differentiated in normal liver tissues versus liver cancer tissues from TCGA. a DNA repair gene sets. b E2F targets gene sets. c G2M checkpoint gene sets. d, the alteration of the selected genes in patients with liver cancer
Fig. 2
Fig. 2
Non-paired t test to detect the different expression of the selected seven genes between LC tissues and normal tissues from TCGA database. a RF4. b ZWINT. c UPF3B. d NCBP2. e ADA. f SF3A3. g GTF2H1
Fig. 3
Fig. 3
Paired t test to detect the different expression of the selected seven genes in 50 paired samples from TCGA. a RFC4. b ZWINT. c UPF3B. d NCBP2. e ADA. f SF3A3. g GTF2H1
Fig. 4
Fig. 4
Analysis of clinicopathological parameters affecting prognosis of liver cancer. The data obtained from TGCA databases. a the distribution of risk score in patients with liver cancer. b the overall survival time and survival status of patients with liver cancer ranked by risk score. c the heatmap of the selected seven mRNAs’ expression in patients with liver cancer. d–f the effect of different clinicopathological parameters including T stage, stage and cancer status on patients’ survival by Kaplan–Meier
Fig. 5
Fig. 5
Validation of the mRNA signature panel in patients with LC from TCGA and clinical tissue specimens. a ROC curve of patients with liver cancer from TCGA. b K–M plot curve for patients with high-risk and low-risk. c–h stratified analysis of LC patients’ prognosis according to clinicopathological parameters, including sex, age, person neoplasm cancer status, new tumor event, relative family cancer history, race, and risk score. i the relative expression of the key genes in clinical liver cancer tissues versus normal liver tissues collected from Chinese Institution
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