Pan-cancer analysis of ZC3H13 expression
To explore possible roles of ZC3H13 in carcinogenesis, we first analyzed its expression in 20 types of human cancer using Oncomine database. As shown in Figure 1A, compared with normal samples, ZC3H13 was significantly upregulated in 5 cancer types, including colorectal cancer, kidney cancer, lymphoma, melanoma, and sarcoma, and was markedly downregulated in 4 cancer types, involving brain and CNS cancer, liver cancer, lung cancer, and lymphoma. Next, we further validated the expression of ZC3H13 in these 33 cancer types using GEPIA database. As presented in Figure 1B, ZC3H13 expression in DLBC, ESCA, LAML, PAAD, SKCM, STAD, and THYM was statistically increased when compared with corresponding normal controls. And in LIHC, ZC3H13 was obviously decreased (Figure 1C). Taken together, ZC3H13 was downregulated in LIHC, indicating that ZC3H13 may function as crucial regulator in carcinogenesis of the liver cancer.
The prognostic values of ZC3H13 in human cancer
Next, survival analysis for ZC3H13 in BRCA, KIRC, LIHC, LUAD and STAD was conducted. Two prognostic indices, consisting of overall survival (OS) and progression-free survival (RFS), were included. For OS, KIRC and LIHC patients with higher expression of ZC3H13 indicated better prognosis (Figures 2C, E). For RFS, among all cancer types, only increased expression of ZC3H13 indicated poor prognosis in LIHC (Figure 2F). No statistical significance of ZC3H13 for predicting prognosis of patients in other cancer types was observed. By combination of OS and RFS, ZC3H13 may be utilized as an unfavorable prognostic biomarker in patients with HCC.
Prediction and analysis of upstream miRNAs of ZC3H13
It has been widely acknowledged that ncRNAs are responsible for the regulation of gene expression. To ascertain whether ZC3H13 was modulated by some ncRNAs, we first predicted upstream miRNAs that could potentially bind to ZC3H13 and finally found 12 miRNAs (Table 1). To improve visualization, a miRNA-ZC3H13 regulatory network was established using cytoscape software (Figure 3A). Based on the action mechanism of miRNA in regulation of target gene expression, there should be negative correlation between miRNA and ZC3H13.Thus, the expression correlation analysis was performed. As showed in Figures 3B and 3E ZC3H13 was significantly negatively correlated with miR-362-3p or miR-425-5p in HCC. There were no statistical expression relationships between ZC3H13 and the other 9 predicted miRNAs. Based on the ceRNA hypothesis, miRNA expression was also upregulated in liver cancer wherein it negatively correlates with ZC3H13. These results are consistent with bioinformatics analysis. Expression analysis of miR-362-3p/ miR-425-5p based on the TCGA database revealed miR-362-3p/miR-425-5p was upregulated in HCC tissue. As Figures 3C and 3F shows, there is a significant difference between the two groups. Finally, the expression and prognostic value of miR-362-3p/ miR-425-5p in HCC were determined. As presented in Figures 3C and 3G, miR-362-3p/ miR-425-5p was markedly upregulated in HCC and its downregulation was positively linked to patients’prognosis. Huh7 and Hep3B cells were transfected with miR-362-3p/ miR-425-5p and checked for the impact on ZC3H13 levels. We found that miR-362-3p/ miR-425-5p mimics significantly reduced the ZC3H13 level compared with NC, and the reduction was repealed by miR-362-3p/ miR-425-5p inhibitors as evidenced by qRT-PCR (Figures 3H, I). Additionally, we performed a western blotting analysis to examine the ZC3H13 protein level. The results showed that miR-362-3p/ miR-425-5p mimics and inhibitors significantly decreased and increased ZC3H13 protein levels, respectively (Figure 3J). The most striking result to emerge from the data in Figure 3 is that ZC3H13 is a direct target of miR-362-3p/ miR-425-5p and is tumor suppressor gene involved in HCC tumor progression, which further confirmed RNA-seq results.
Table 1
The expression correlation between predicted miRNAs and ZC3H13 in HCC.
|
geneName
|
miRNAname
|
R-value
|
P-value
|
ZC3H13
|
hsa-miR-15a-5p
|
0.069
|
0.183
|
ZC3H13
|
hsa-miR-16-5p
|
0.173a
|
8.29E-04a
|
ZC3H13
|
hsa-miR-23a-3p
|
0.081
|
0.119
|
ZC3H13
|
hsa-miR-7-5p
|
0.026
|
0.616
|
ZC3H13
|
hsa-miR-15b-5p
|
0.021
|
0.691
|
ZC3H13
|
hsa-miR-23b-3p
|
0.036
|
0.492
|
ZC3H13
|
hsa-miR-195-5p
|
0.173a
|
8.35E-04a
|
ZC3H13
|
hsa-miR-424-5p
|
0.086
|
0.1
|
ZC3H13
|
hsa-miR-329-3p
|
0.006
|
0.905
|
ZC3H13
|
hsa-miR-497-5p
|
0.079
|
0.130
|
ZC3H13
|
hsa-miR-425-5p
|
-0.129a
|
0.0128a*
|
ZC3H13
|
hsa-miR-362-3p
|
-0.106a
|
0.0417a*
|
aThese results are statistially significant. *p value < 0.05; **p value < 0.01; ***p value < 0.001.
|
Determining the function of ZC3H13 in liver cancer
To understand the function of ZC3H13 in LIHC, we utilized available TCGA sequencing data using LinkedOimcs online tool. The association results and top 50 positively and negatively correlated genes are shown in Additional file 1. As plotted in Figure 4A, it shows that red dots positively correlated with ZC3H13, and green dots negatively correlated (p-value < 0.05). KEGG analysis showed genes were primarily enriched in the JAK-STAT signaling pathway, hippo signaling pathway, inositol phosphate metabolism, chagas disease, propanoate metabolism and transcriptional misregulation in cancer.
ZC3H13 positively correlates with immune cell infiltration in HCC
ZC3H13 is a member of m6A-related genes, which have an immunoglobulin domain and are reported to play a critical role in the immune system. As shown in Figure 5A, no significant change of immune cell infiltration level under various copy numbers of ZC3H13 in HCC was observed. Correlation analysis could provide key clues for studying the function and mechanism of ZC3H13. Thus, the correlation of ZC3H13 expression level with immune cell infiltration level was evaluated. As presented in Figures 5B–5G, ZC3H13 expression was significantly positively associated with four kinds of immune cells, including CD4+T cell, macrophage, neutrophil, and dendritic cell in HCC, such a correlation was not seen for CD8+T cell and B cell.
Expression correlation of ZC3H13 and biomarkers of immune cells in HCC
To further explore the role of ZC3H13 in tumor immune, we determined the expression correlation of ZC3H13 with biomarkers of immune cells in HCC using GEPIA database. As listed in Table 2, ZC3H13 was significantly positively correlated with CD4+T cell’s biomarker (CD4), M1 macrophage’s biomarkers (NOS2, IRF5, and PTGS2), M2 macrophage’s biomarkers (VSIG4, and MS4A4A), neutrophil’s biomarkers (ITGAM and CCR7), and dendritic cell’s biomarkers (HLA-DPB1, HLA-DRA, HLA-DPA1, CD1C, NRP1, and ITGAX) in HCC, but B cell’s biomarkers (CD79A), CD8+T cell’s biomarkers need further validation. These findings partially support that ZC3H13 is positively linked to immune cell infiltration.
Table 2
Correlation analysis between ZC3H13 and biomarkers of immune cells in HCC.
|
|
|
Immune cell
|
Biomarker
|
R value
|
p value
|
|
B cell
|
CD19
|
-0.013
|
8.00E-01
|
|
CD79A
|
0.1a
|
4.80E-02* a
|
|
CD8+ T cell
|
CD8A
|
0.15a
|
4.20E-03** a
|
|
CD8B
|
0.033
|
5.20E-01
|
|
CD4+ T cell
|
CD4
|
0.25a
|
1.60E-06*** a
|
|
M1 macrophage
|
NOS2
|
0.29a
|
1.70E-08*** a
|
|
IRF5
|
0.2a
|
7.50E-05*** a
|
|
PTGS2
|
0.4a
|
1.20E-15*** a
|
|
M2 macrophage
|
CD163
|
0.07
|
1.80E-01
|
|
VSIG4
|
0.18a
|
3.90E-04*** a
|
|
MS4A4A
|
0.25a
|
1.10E-06*** a
|
|
Neutrophil
|
CEACAM8
|
0.078
|
1.30E-01
|
|
ITGAM
|
0.24a
|
2.30E-06*** a
|
|
CCR7
|
0.25a
|
7.50E-07*** a
|
|
Dendritic cell
|
HLA-DPB1
|
0.17a
|
1.10E-03** a
|
|
HLA -DQB1
|
-0.078
|
1.30E-01
|
|
HLA-DRA
|
0.22a
|
1.60E-05*** a
|
|
HLA-DPA1
|
0.26a
|
4.00E-07*** a
|
|
CD1C
|
0.2a
|
1.20E-04*** a
|
|
NRP1
|
0.5a
|
1.40E-24*** a
|
|
ITGAX
|
0.28a
|
7.60E-08*** a
|
|
aThese results are statistially significant. *p value < 0.05; **p value < 0.01; ***p value < 0.001.
|
|
|
Relationship between ZC3H13 and immune checkpoints in HCC
PD1/PD-L1 and CTLA-4 are important immune checkpoints that are responsible for tumor immune escape. Considering the potential tumor suppressor gene of ZC3H13 in HCC, the relationship of ZC3H13 with PD1, PD-L1, or CTLA-4 was assessed. As suggested in Additional file 2, ZC3H13 expression was significantly positively correlated with PD-L1, in HCC, which was adjusted by purity. Similar to TIMER data analysis, we also found that there was significant positive correlation of ZC3H13 with PD1, PD-L1, or CTLA-4 in HCC. Additionally, a strong correlation exists between PD-L1 and ZC3H13. These results demonstrate that tumor immune escape might be involved in ZC3H13 mediated carcinogenesis of HCC.