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. 2021 Jul 12;13(13):17707-17733.
doi: 10.18632/aging.203260. Epub 2021 Jul 12.

Screening and identification of angiogenesis-related genes as potential novel prognostic biomarkers of hepatocellular carcinoma through bioinformatics analysis

Zili Zhen  1   2   3 , Zhemin Shen  2   3 , Yanmei Hu  4 , Peilong Sun  1   2
Affiliations

Screening and identification of angiogenesis-related genes as potential novel prognostic biomarkers of hepatocellular carcinoma through bioinformatics analysis

Zili Zhen et al. Aging (Albany NY). .

Abstract

Hepatocellular carcinoma (HCC) is a malignant tumor with high morbidity and mortality, which makes the prognostic prediction challenging. Angiogenesis appears to be of critical importance in the progression and metastasis of HCC. Some of the angiogenesis-related genes promote this process, while other anti-angiogenesis genes suppress tumor growth and metastasis. Therefore, the comprehensive prognostic value of multiple angiogenesis-related genes in HCC needs to be further clarified. In this study, the mRNA expression profile of HCC patients and the corresponding clinical data were acquired from multiple public databases. Univariate Cox regression analysis was utilized to screen out differentially expressed angiogenesis-related genes with prognostic value. A multigene signature was established with the least absolute shrinkage and selection operator Cox regression in the Cancer Genome Atlas cohort, and validated through an independent cohort. The results suggested that a total of 16 differentially expressed genes (DEGs) were associated with overall survival (OS) and a 7-gene signature was constructed. The risk score of each patient was calculated using this signature, the median value of which was used to divide these patients into a high-risk group and a low-risk group. Compared with the low-risk group, the patients in the high-risk group had a poor prognosis. The risk score was an independent predictor for OS through multivariate Cox regression analysis. Then, unsupervised learning was used to verify the validity of this 7-gene signature. A nomogram by further integrating clinical information and the prognostic signature was utilized to predict prognostic risk and individual OS. Functional enrichment analyses demonstrated that these DEGs were enriched in the pathways of cell proliferation and mitosis, and the immune cell infiltration was significantly different between the two risk groups. In summary, a novel angiogenesis-related genes signature could be used to predict the prognosis of HCC and for targeted therapy.

Keywords: angiogenesis; bioinformatics analysis; gene signature; hepatocellular carcinoma; prognosis.

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Conflict of interest statement

CONFLICTS OF INTEREST: The authors declare no conflicts of interest related to this study.

Figures

Figure 1
Figure 1
The flow chart of data collection and analyses.
Figure 2
Figure 2
Identification of the differentially expressed angiogenesis-related genes with prognostic value in the TCGA cohort. (A) Forest plots showing the results of the univariate Cox regression analysis between gene expression and OS. (B) The heatmap showing the expression of 16 overlapping genes in tumor tissues. (C) The DEGs with prognostic value were obtained by the intersection of the two groups of genes in the venn diagram. (D) The PPI network downloaded from the STRING database indicated the interactions among the candidate genes. (E) The correlation network of the candidate genes.
Figure 3
Figure 3
Establishment and prognostic analysis of a 7-gene signature in the TCGA cohort. (A) The distribution and median value of the risk scores in the TCGA cohort. (B) Kaplan-Meier curves for the difference in OS of HCC patients between the high-risk group and low-risk group in the TCGA cohort. (C) The AUC of time-dependent ROC curves verified the prognostic performance of the 7-gene signature in the TCGA cohort. (D) The PCA plot of the TCGA cohort. (E) The t-SNE analysis of the TCGA cohort. (F) The distributions of OS status, OS and risk score in the TCGA cohort.
Figure 4
Figure 4
The relative expression of 7 angiogenesis-related genes between normal liver cell lines and hepatocarcinoma cell lines. ANGTP1 (A), COL18A (B), ITGAV (C), PGF (D) were relatively highly expressed in hepatocarcinoma cell lines, while ENG (E), PON1 (F) had higher expression in normal liver cell lines. (G) There is no significant difference in the expression of PDCD10 in the two cell lines.
Figure 5
Figure 5
Human Protein Atlas immunohistochemistry of normal sample and tumor sample. The expression levels of ANGTP1 (A, B), ENG (C, D), PDCD10 (E, F), COL18A (G, H), ITGAV (I, J) and PON1 (K, L) in tumor and normal tissues were validated in the TCGA cohort, using the paired expression of the same individual normal tissue and tumor tissue.
Figure 6
Figure 6
Validation of the 7-gene signature in the ICGC cohort. (A) The distribution and median value of the risk scores in the ICGC cohort. (B) Kaplan-Meier curves for the difference in OS of HCC patients between the high-risk group and low-risk group in the ICGC cohort. (C) The AUC of time-dependent ROC curves verified the prognostic performance of the 7-gene signature in the ICGC cohort. (D) The PCA plot of the TCGA cohort. (E) The t-SNE analysis of the ICGC cohort. (F) The distributions of OS status, OS and risk score in the ICGC cohort.
Figure 7
Figure 7
The relationship between the signature and clinical characteristics of HCC patients. There was no difference in risk scores for patients of different ages (A) and genders (B). With the increase of grade (C, D), stage (E, F) and T classification (G, H), the risk had an upward trend. There was a significant difference in the with tumor and tumor free patients (I, J). The stage difference could be verified in the ICGC cohort (K, L).
Figure 8
Figure 8
The nomogram to predict the survival probabilities in the TCGA cohort. (A) The nomogram for predicting OS of HCC patients in the TCGA cohort. The calibration plots for predicting 1-year (B), 2-year survival (C) and 3-year survival (D) in the TCGA dataset.
Figure 9
Figure 9
Functional enrichment analyzes of DEGs. The most significant or shared GO enrichment and KEGG pathways in the TCGA cohort (A, C) and the ICGC cohort (B, D). From top to bottom, the barplot represents the biological process, cellular component, and molecular function, respectively.
Figure 10
Figure 10
Comparison of the ssGSEA scores between different risk groups in the TCGA cohort and the ICGC cohort. The scores of 16 immune cells (A, C) and 13 immune-related functions (B, D) are displayed in boxplots. P values were showed as: ns: not significant; *P < 0.05; **P < 0.01; ***P < 0.001.
Figure 11
Figure 11
Comparison of immunophenoscore (IPS) between high and low risk groups under different immune checkpoint states. In the case of both CTLA4 and PD-1 double-positive (A) or double-negative (B), or PD-1 positive but CTLA4 negative (C), the low-risk group had higher IPS. (D) The high-risk group with CTLA4 positive but PD-1 negative had higher IPS. (E) The expression level of CTLA4 in different risk groups. (F) The risk scores under different immune subtypes.
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