circRNA profiles in GC patients with different prognoses
The flowchart for circRNA screening in this study is shown in Fig. 1a. We performed RNA sequencing to characterize the circRNA expression profiles of GC patients with different RFS. A total of 23,995 circRNAs were detected in the discovery set, among which 9,443 circRNAs were included in the circBase database and annotated (Fig. 1b). Consistent with previous studies, circRNAs were distributed in all human chromosomes, and their splicing length was mostly less than 1,000 nucleotides (Fig. 1c-d). Differential analysis was performed on the circRNA expression profile of GC patients with different prognoses. A total of 259 differentially expressed circRNAs were identified with the threshold of padj < 0.05, of which 192 circRNAs were upregulated, and 67 circRNAs were downregulated in GC patients with good prognoses (Fig. 1e).
Screening and validation of prognostic circRNAs
Then, we evaluated whether circRNAs could be used as prognostic biomarkers in GC patients. The top 20 up- and downregulated circRNAs with extremely significant differences obtained by RNA-seq were included in the preliminary screening (Fig. 2a). Considering the contingency of RNA sequencing samples, 12 circRNAs with significant expression differences in at least 4 of 5 pairs of samples were selected for further validation. The circRNA ID, genome position, spliced length, and gene symbols of these 12 candidate circRNAs are shown in Table 1.
Table 1
The information of 12 candidate circRNAs
circRNA ID | Genome position | Strand | Spliced length | Gene name | Log2FC | P Value | Regulation |
Hsa_circ_0005729 | chr18:29691716–29693823 | + | 282 | RNF138 | 5.974 | 9.58E-05 | Up |
Hsa_circ_0005092 | chr11:9451220–9452550 | + | 290 | IPO7 | 4.9603 | 0.002134 | Up |
Hsa_circ_0001965 | chr3:169854206–169863309 | - | 346 | PHC3 | 4.7984 | 0.003229 | Up |
Hsa_circ_0002647 | chr9:140611077–140646860 | + | 1163 | EHMT1 | 4.7371 | 0.003767 | Up |
Hsa_circ_0063604 | chr22:42204878–42206004 | + | 241 | CCDC134 | 4.6347 | 0.00505 | Up |
Hsa_circ_0105599 | chr16:53967896–53971745 | + | 3849 | FTO | 4.5821 | 0.005785 | Up |
Hsa_circ_0008197 | chr1:51032748–51061888 | - | 524 | FAF1 | 4.5784 | 0.005578 | Up |
Hsa_circ_0099642 | chr12:9833518–9847565 | + | 692 | CLEC2D | -4.8723 | 0.001811 | Down |
Hsa_circ_0006847 | chr16:29916172–29917280 | + | 286 | ASPHD1 | -4.8243 | 0.002375 | Down |
Hsa_circ_0072697 | chr5:64863339–64868113 | + | 773 | PPWD1 | -4.6578 | 0.003623 | Down |
Hsa_circ_0015491 | chr1:180047597–180049796 | + | 357 | CEP350 | -4.4914 | 0.005258 | Down |
Hsa_circ_0008403 | chr5:141732790–141733148 | + | 358 | TCONS_l2_00023557 | -4.268 | 0.008983 | Down |
FC, fold change. |
The relative expression levels of 12 circRNAs were detected by qRT-PCR assay in the training set (n = 136). The median circRNA expression level was used as the cutoff to divide GC patients into two groups with high and low expression. Kaplan-Meier survival analysis showed that four circRNAs (hsa_circ_0005092, hsa_circ_0002647, hsa_circ_0008197 and hsa_circ_0105599) were significantly associated with RFS in GC patients and were upregulated in patients with good prognoses, consistent with the sequencing results (Fig. 2b-e, Appendix Figure S1). Among them, hsa_circ_0005092 and hsa_circ_0002647 were confirmed to be independent prognostic factors of RFS in GC patients by univariate and multivariate Cox regression analysis (Table 2).
Table 2
Univariate and multivariate Cox regression analysis of prognostic factors for RFS in the training set
Factors | Univariate analysis | Multivariate analysis |
HR (95% CI) | P value | HR (95% CI) | P value |
Age | 0.997 (0.976–1.018) | 0.774 | 1.010 (0.987–1.034) | 0.405 |
Sex | 1.221 (0.730–2.043) | 0.446 | 1.095 (0.637–1.885) | 0.742 |
Tumor size | 1.024 (0.940–1.114) | 0.589 | 0.998 (0.901–1.105) | 0.967 |
Hsa_circ_0005092 | 1.531 (1.219–1.922) | 0.000*** | 1.784 (1.256–2.534) | 0.001** |
Hsa_circ_0008197 | 1.121 (0.942–1.334) | 0.198 | 0.960 (0.811–1.137) | 0.637 |
Hsa_circ_0002647 | 1.345 (1.139–1.589) | 0.001** | 1.294 (1.072–1.563) | 0.007** |
Hsa_circ_0105599 | 1.176 (1.003–1.378) | 0.045* | 0.872 (0.706–1.078) | 0.206 |
RFS, recurrence-free survival; HR, Hazard ratio; CI, confidence interval; *p༜0.05, **p༜0.01, ***p༜0.001. |
Construction of prognostic model based on hsa_circ_0005092 and hsa_circ_0002647
Next, the training set and validation set were combined to construct a postoperative recurrence model based on the regression coefficients of hsa_circ_0005092 and hsa_circ_0002647 for statistical significance (Appendix Table S3):
![](data:image/png;base64,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)
A total of 303 gastric cancer patients were included in the statistics, and were divided into high- and low-risk groups according to the median. Survival analysis showed that patients with circPanellow had a shorter RFS than those with circPanelhigh (hazard ratio [HR]: 2.229, 95% confidence interval [CI]: 1.662-2.989, p<0.0001, Figure 4a). The ROC curve and the area under the ROC curve (AUC) were further used to analyze the prognostic value of circPanel, as shown in Figure 4c. Compared with the hsa_circ_0005092 and hsa_circ_0002647, circPanel had a larger AUC (0.709, 95% CI: 0.607-0.742), with sensitivity of 69.9% and specificity of 58.1%.
Moreover, we observed a similar effect of circPanel on OS in GC patients. Patients with circPanellow had a shorter OS (HR: 2.025, 95% CI: 1.362-3.010, p=0.0004, Figure 4b). As shown in Figure 4d, the AUCs of hsa_circ_0002647, hsa_circ_0005092 and circPanel were 0.612 (95% CI: 0.533-0.691), 0.631 (95% CI: 0.553-0.709) and 0.638 (95% CI: 0.56-0.716), respectively. The sensitivity and specificity of circPanel for predicting OS were 51.0% and 75.5%, respectively. In short, circPanel could be used as a biomarker for the prognostic evaluation of GC patients, with better predictive performance than single circRNA.
Comparative analysis of circRNA and other clinical indicators
We also analyzed the correlation between circPanel and the clinicopathological features of GC patients. The results suggested that circPanel was related to the tumor differentiation and T stage in GC patients (Table 4). Patients with circPanelhigh might have higher tumor differentiation and lower T stages. No significant association was observed between circPanel and other characteristics (including sex, age, tumor size, N stage and TNM stage).
In addition, we compared the prognostic value of circPanel with some traditional tumor markers (including CEA, CA19-9 and CA724) by ROC curve analysis. As a result, the AUCs of circPanel, CEA, CA19-9 and CA724 were 0.67, 0.508, 0.56, and 0.493 for RFS, respectively, and 0.638, 0.485, 0.566, and 0.524 for OS. CircPanel has greater prognostic value than CEA, CA19-9 and CA724 (Figure 4e-f). In summary, these results strongly confirmed the clinical application value of circPanel in the prognostic assessment of GC patients.
Table 4. Correlation between circRNAs and clinicopathologic features of GC patients
|
|
|
circPanel
|
Variables
|
Group
|
Cases
|
High expression
|
Low expression
|
P value
|
|
|
n=303
|
n = 151
|
n = 152
|
|
Age
|
≤60 years
|
155
|
73
|
88
|
0.0958
|
|
>60 years
|
148
|
78
|
64
|
|
Sex
|
Male
|
222
|
111
|
111
|
0.9242
|
|
Female
|
81
|
40
|
41
|
|
Tumor size
|
≤5 cm
|
143
|
80
|
73
|
0.3885
|
|
>5 cm
|
150
|
71
|
79
|
|
T stage
|
T1-3
|
41
|
26
|
15
|
0.0359*
|
|
T4a
|
181
|
93
|
88
|
|
|
T4b
|
82
|
32
|
49
|
|
N stage
|
N0
|
73
|
36
|
37
|
0.1369
|
|
N1
|
73
|
41
|
32
|
|
|
N2
|
76
|
42
|
34
|
|
|
N3
|
81
|
32
|
49
|
|
TNM stage
|
I-II
|
75
|
40
|
35
|
0.1041
|
|
IIIA
|
113
|
64
|
49
|
|
|
IIIB
|
63
|
26
|
38
|
|
|
IIIC
|
52
|
21
|
30
|
|
Differentiation
|
Poor-undifferentation
|
158
|
69
|
89
|
0.0251*
|
|
Well-moderate differentation
|
145
|
82
|
63
|
|
*p<0.05.
CeRNA network of hsa_circ_0005092 and hsa_circ_0002647
Furthermore, we attempted to explore the biological function of these two selected circRNAs. CeRNA regulation is currently considered one of the main mechanisms for circRNA functioning. Through the prediction of the CircInteractome, 6 miRNAs (miR-616,miR-599,miR-409-3p,miR-217,miR-513a-5p and miR-890) targeted by hsa_circ_0005092 and 6 miRNAs (miR-370, miR-626, miR-637, miR-648, miR-326 and miR-574-5p) targeted by hsa_circ_0002647, with more binding sites and lower context scores, were screened. Then, we predicted the target mRNAs of these miRNAs by TargetScan (cumulative weighted context score ≤-0.3) and miRDB (target score ≥70) and obtained 259 and 434 mRNAs for hsa_circ_0005092 and hsa_circ_0002647, respectively. Cytoscape was further used to describe the regulatory network of circRNA-miRNA-mRNA (Figure 5a-b).
GO and KEGG analysis of hsa_circ_0005092 and hsa_circ_0002647
To predict the potential function of circRNAs, GO and KEGG enrichment analyses were performed on the ceRNA regulatory networks of these two circRNAs. The significantly enriched biological processes (BPs), cellular components (CCs) and molecular functions (MFs) are shown in Figure 6a-b. The main annotations of the hsa_circ_0005092 network included protein processing in the endoplasmic reticulum, protein binding and regulation of epidermal growth factor receptor signaling, while hsa_circ_0002647 was mainly involved in RNA transcriptional regulation. The pathways enriched for the two circRNAs based on KEGG analysis are shown in Figure 6c-d. The metabolic pathways, chemokine signaling pathways, and proteoglycans in cancer for hsa_circ_0005092 and the cell adhesion molecules, cAMP signaling pathway, and transcriptional misregulation in cancer for hsa_circ_0002647 are all cancer-related.