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. 2022 May 3:13:879299.
doi: 10.3389/fgene.2022.879299. eCollection 2022.

A Panel of E2F Target Gene Signature Predicting the Prognosis of Hepatocellular Carcinoma

Wenmin Hu  1 , Yongmei Shi  2 , Tongqin Han  3 , Caiyun Liu  4 , Xipeng Cao  5 , Guangjun Shi  4 , Wenjing Zhu  5   6
Affiliations

A Panel of E2F Target Gene Signature Predicting the Prognosis of Hepatocellular Carcinoma

Wenmin Hu et al. Front Genet. .

Abstract

Hepatocellular carcinoma is one of the most malignant tumors, and the therapeutic effects of traditional treatments are poor. It is urgent to explore and identify new biomarkers and therapeutic targets to develop novel treatments which are individualized and effective. Three hallmarks, including E2F targets, G2M checkpoint and DNA repair, were collected by GSEA analysis. The panel of E2F-related gene signature consisted of five genes: HN1, KIF4A, CDCA3, CDCA8 and SSRP1. They had various mutation rates ranging from 0.8 to 5% in hepatocellular carcinoma, and patients with gene mutation had poorer prognosis. Among these genes, HN1 has the greatest mutation rate, and SSRP1 has the greatest impact on the model with a B (COX) value of 0.8842. Patients with higher expression of these genes had poorer prognosis. Kaplan-Meier curves in stratified survival analysis confirmed that patients with high risk scores had poor prognosis (p < 0.05). The results of univariate and multivariate COX survival analysis showed that risk score was closely related to the overall survival of patients with hepatocellular carcinoma. For clinical validation, we found that all the genes in the model were upregulated in hepatocellular carcinoma tissues compared to normal liver tissues, which was consistent with the previous results we obtained. Our study demonstrated that a panel of E2F target genes signature including five genes could predict the prognosis of hepatocellular carcinoma. This panel gene signature can facilitate the development of individualized and effective treatment for hepatocellular carcinoma.

Keywords: E2F target gene; gene signature; hepatocellular carcinoma; prognosis; risk.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Three hallmarks, including E2F targets, G2M checkpoint and DNA repair, were collected by GSEA analysis (A) E2F target gene set. (B) G2M checkpoint gene set. (C) DNA repair gene set.
FIGURE 2
FIGURE 2
The panel of E2F target genes signature (A) The distribution of risk scores in patients with HCC. (B) The survival time and survival status of patients with HCC ranked by risk scores (C) The distribution of expression of the five genes in heatmap in patients with HCC ranked by risk scores. (D) The coefficients of five genes. (E) Genetic alterations of five E2F target genes from TCGA PanCan and TCGA in patients with hepatocellular carcinoma.
FIGURE 3
FIGURE 3
The expression of E2F target genes in tumor tissues versus normal tissues from TCGA (A-E) Wilcoxon rank-sum test, the expression of HN1, CDCA3, CDCA8, KIF4A and SSRP1 in tumor tissues versus normal tissues, respectively. (F-J) Wilcoxon signed-rank test, the expression of HN1, CDCA3, CDCA8, KIF4A and SSRP1 in tumor tissues versus normal tissues, respectively.
FIGURE 4
FIGURE 4
The alterations of E2F target genes in tumor tissues from cBioportal database (A) All alteration types across five genes in patients with hepatocellular carcinoma. (B–F) Genetic alterations of CDCA3, CDCA8, HN1, KIF4A, and SSRP1 were respectively described in (B–F) with specific alteration frequencies (G) The overall survival of hepatocellular carcinoma patients in unaltered and altered groups. (H) The disease-free survival of hepatocellular carcinoma patients in unaltered and altered groups.
FIGURE 5
FIGURE 5
Analysis of clinicopathological parameters affecting the prognosis of hepatocellular carcinoma patients (A) K–M survival curves of patients in high-risk and low-risk groups. (B–C) The effects of different clinicopathological parameters including cancer and stage on patients’ Kaplan–Meier survival curves (D) The expression levels of HN1, KIF4A, CDCA3, SSRP1 and CDCA8 in low- and high-risk groups. (E) A ROC curve of patients with HCC from TCGA.
FIGURE 6
FIGURE 6
Validation of the risk scores for a panel of E2F target gene signature by KM plot. (A-J) KM survival analysis was used to explore the impact of risk score on the prognosis of HCC patients stratified by the clinical and pathological parameters, including age, gender, stage, cancer status, family cancer history, and risk score.
FIGURE 7
FIGURE 7
Validation of the panel of E2F target genes signature by test set and validation set (A) The distribution of risk scores in patients with liver cancer by TCGA test set (n = 245) (B) The survival time and survival status of patients with liver cancer ranked by risk scores by TCGA test set (n = 245) (C) The distribution of expression of the five genes in heatmap in patients with liver cancer ranked by risk scores by TCGA test set (n = 245) (D) The distribution of risk scores in patients with liver cancer by TCGA validation set (n = 123) (E) The survival time and survival status of patients with liver cancer ranked by risk scores by TCGA validation set (n = 123) (F) The distribution of expression of the five genes in heatmap ranked by risk score in patients with liver cancer by TCGA validation set (n = 123).
FIGURE 8
FIGURE 8
Validation of the mRNA panel signature in HCC patients from clinical tissue specimens (A) The mRNA relative expression of CDCA3, CDCA8, SSRP1, KIF4A and HN1 in HCC tissues was evaluated by qRT-PCR. (B) Representative hematoxylin-eosin (H&E) and immunohistochemistry (IHC) staining of GPC3, Ki67 and PHH3 in HCC patients. (C) There was significant difference between the Sample1 and Sample2 by Ki67 expression. All data are shown as the mean ± SEM.
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