Identification of differentially expressed RNAs in breast cancer
Compared to adjacent tissues, a total of 2762 DEmRNAs (1118 upregulated and 1644 downregulated miRNAs) and 158 DEmiRNAs (71 upregulated and 87 downregulated miRNAs) were identified in BRCA with FDR<0.01 and |log2fold change|>1. A total of 72 DEcircRNAs (51 upregulated and 21 downregulated circRNAs) were obtained in BRCA compared to adjacent tissues with P value<0.01, |log2fold change|>1. The RNAs hierarchical clustering analyses are presented in Figure 2, and it was demonstrated that the expression levels of these three type of RNAs were significantly differentiated compared with the normal tissues. Finally, volcano plots were generated, and differences between the normal and tumor groups were identified (Figure 2). Table 1, 2 and 3 show the top 10 up and down regulation of DEmRNAs, DEmiRNAs and DEcircRNAs in BRCA, respectively.
Table 1 Top 10 upregulation and downregulation DEmRNAs in BRCA.
mRNA
|
logFC
|
P value
|
FDR
|
Regulation
|
COL10A1
|
6.870373
|
1.01E-60
|
1.67E-59
|
Up
|
CST1
|
6.616865
|
1.52E-56
|
2.15E-55
|
Up
|
MMP13
|
6.354082
|
8.94E-57
|
1.27E-55
|
Up
|
IBSP
|
5.863152
|
3.64E-104
|
1.91E-102
|
Up
|
MMP11
|
5.781753
|
2.35E-84
|
7.56E-83
|
Up
|
COL11A1
|
5.337783
|
1.91E-37
|
1.39E-36
|
Up
|
MMP1
|
5.235857
|
1.55E-39
|
1.21E-38
|
Up
|
PLPP4
|
5.184921
|
2.61E-88
|
9.12E-87
|
Up
|
SLC24A2
|
4.98746
|
2.18E-73
|
5.43E-72
|
Up
|
COMP
|
4.598021
|
7.01E-40
|
5.58E-39
|
Up
|
ADH1B
|
-6.86393
|
2.57E-114
|
1.69E-112
|
Down
|
TUSC5
|
-6.81912
|
1.32E-161
|
3.10E-159
|
Down
|
ADIPOQ
|
-6.79919
|
3.51E-120
|
2.65E-118
|
Down
|
CIDEC
|
-6.63588
|
3.51E-174
|
1.19E-171
|
Down
|
SCARA5
|
-6.48854
|
3.00E-239
|
1.53E-235
|
Down
|
FABP4
|
-6.14086
|
1.66E-110
|
1.01E-108
|
Down
|
PLIN1
|
-6.00856
|
4.59E-146
|
6.21E-144
|
Down
|
GPD1
|
-5.99957
|
5.75E-169
|
1.57E-166
|
Down
|
AQP7
|
-5.81861
|
1.75E-206
|
1.91E-203
|
Down
|
PLIN4
|
-5.79315
|
4.21E-115
|
2.84E-113
|
Down
|
Table 2 Top 10 upregulation and downregulation DEmiRNAs in BRCA.
miRNA
|
logFC
|
P value
|
FDR
|
Regulation
|
hsa-miR-592
|
3.424794
|
2.41E-76
|
6.71E-75
|
Up
|
hsa-miR-1307-5p
|
3.197336
|
1.96E-88
|
6.73E-87
|
Up
|
hsa-miR-96-5p
|
3.155099
|
3.09E-90
|
1.25E-88
|
Up
|
hsa-miR-141-3p
|
3.087332
|
4.21E-97
|
1.88E-95
|
Up
|
hsa-miR-429
|
2.994871
|
1.10E-70
|
2.32E-69
|
Up
|
hsa-miR-190b
|
2.963382
|
6.87E-26
|
2.73E-25
|
Up
|
hsa-miR-183-5p
|
2.924208
|
6.80E-108
|
5.05E-106
|
Up
|
hsa-miR-200a-3p
|
2.638975
|
4.00E-63
|
6.60E-62
|
Up
|
hsa-miR-184
|
2.553121
|
1.13E-13
|
2.68E-13
|
Up
|
hsa-miR-200a-5p
|
2.512744
|
2.55E-65
|
4.53E-64
|
Up
|
hsa-miR-486-5p
|
-4.09846
|
1.82E-115
|
1.62E-113
|
Down
|
hsa-miR-139-3p
|
-3.60464
|
2.43E-132
|
5.41E-130
|
Down
|
hsa-miR-204-5p
|
-3.53102
|
4.25E-102
|
2.70E-100
|
Down
|
hsa-miR-139-5p
|
-3.19493
|
2.46E-156
|
1.10E-153
|
Down
|
hsa-miR-451a
|
-3.07530
|
1.49E-76
|
4.42E-75
|
Down
|
hsa-miR-5683
|
-2.96346
|
7.12E-51
|
7.73E-50
|
Down
|
hsa-miR-144-5p
|
-2.79822
|
2.63E-71
|
5.84E-70
|
Down
|
hsa-miR-1247-3p
|
-2.73934
|
1.42E-45
|
1.27E-44
|
Down
|
hsa-miR-452-5p
|
-2.48651
|
2.97E-54
|
3.67E-53
|
Down
|
hsa-miR-145-5p
|
-2.45593
|
1.41E-123
|
1.57E-121
|
Down
|
Table 3 Top 10 upregulation and downregulation DEcircRNAs in BRCA.
circRNA
|
Alias
|
logFC
|
P value
|
Regulation
|
hsa_circRNA_001846
|
hsa_circ_0000520
|
3.800033
|
0.0004347
|
Up
|
hsa_circRNA_000167
|
hsa_circ_0000518
|
3.742085
|
0.0016513
|
Up
|
hsa_circRNA_002172
|
hsa_circ_0000514
|
3.365067
|
0.0042528
|
Up
|
hsa_circRNA_002144
|
hsa_circ_0000511
|
2.995608
|
0.0044046
|
Up
|
hsa_circRNA_000166
|
hsa_circ_0000512
|
2.883005
|
0.0044287
|
Up
|
hsa_circRNA_000585
|
hsa_circ_0000515
|
2.788805
|
0.0012788
|
Up
|
hsa_circRNA_001678
|
hsa_circ_0000517
|
2.6775
|
0.0001633
|
Up
|
hsa_circRNA_101967
|
hsa_circ_0041732
|
2.335477
|
0.002966
|
Up
|
hsa_circRNA_101233
|
hsa_circ_0008784
|
1.958382
|
0.0035194
|
Up
|
hsa_circRNA_002178
|
hsa_circ_0000519
|
1.90691
|
0.00011
|
Up
|
hsa_circRNA_102049
|
hsa_circ_0043278
|
-3.924372
|
0.0099265
|
Down
|
hsa_circRNA_102619
|
hsa_circ_0000977
|
-3.646793
|
0.0080463
|
Down
|
hsa_circRNA_102051
|
hsa_circ_0006220
|
-3.398483
|
0.0091516
|
Down
|
hsa_circRNA_103345
|
hsa_circ_0065173
|
-2.507172
|
0.0025211
|
Down
|
hsa_circRNA_102651
|
hsa_circ_0008911
|
-2.179549
|
0.001517
|
Down
|
hsa_circRNA_001153
|
hsa_circ_0001455
|
-2.059051
|
0.0016539
|
Down
|
hsa_circRNA_104653
|
hsa_circ_0008303
|
-1.776765
|
0.0009081
|
Down
|
hsa_circRNA_101381
|
hsa_circ_0004781
|
-1.771974
|
0.0029019
|
Down
|
hsa_circRNA_100685
|
hsa_circ_0020080
|
-1.714172
|
0.0034233
|
Down
|
hsa_circRNA_000554
|
hsa_circ_0000376
|
-1.615084
|
0.0001321
|
Down
|
Construction of ceRNA regulatory network in BRCA
To elucidate the regulatory mechanism of BRCA, a circRNA-miRNA-mRNA related ceRNA network of BRCA was developed according the above results. First, we searched for the target miRNAs of the 72 DEcircRNAs in the CircIteractome and CSCD databases, and found 295 interactive circRNAs-miRNAs pairs after intersecting with the DEmiRNAs. The circRNA-miRNA relationship pairs were screened according to a negative regulatory pattern, and positively co-expressed circRNA-miRNA pairs were discarded. The results showed that 162 interactive circRNA-miRNA pairs were screened, of which 72 DEmiRNAs were confirmed to interact with 59 DEcircRNAs. Following this, we predicted that 1626 mRNAs were targeted by these 72 DEmiRNAs in all three target predicting databases (TargetScan, miRTarBase and miRDB). these 1626 target mRNAs intersected with the 2762 DEmRNAs, and target mRNAs not contained in DEmRNAs was excluded, resulting in a total of 327 interactive miRNA-mRNA pairs. At the same time, we also screened miRNA-mRNA pairs based on negative regulatory patterns and discarded positively co-expressing pairs. The results showed that eventually 30 DEmiRNAs and 100 DEmRNAs formed 140 interactive miRNA-mRNA pairs. The circRNA-miRNA and miRNA –mRNA relationship pairs (Tables 4 and 5) were combined into the ceRNA network following the pattern of negative regulation. Finally, we constructed the ceRNA regulatory network of BRCA comprised of 200 edges among 40 DEcircRNAs, 30 DEmiRNAs and 100 DEmRNAs. The ceRNA network in BRCA was visualized using Cytoscape software (Figure 3).
Table 4 Interaction between circRNA and miRNA in the ceRNA network.
circRNA
|
miRNA
|
hsa_circ_0000069
|
hsa-miR-193a-5p
|
hsa_circ_0000376
|
hsa-miR-142-5p
|
hsa_circ_0000511
|
hsa-miR-296-5p
|
hsa_circ_0000512
|
hsa-miR-296-5p
|
hsa_circ_0000514
|
hsa-miR-296-5p
|
hsa_circ_0000515
|
hsa-miR-204-5p, hsa-miR-296-5p
|
hsa_circ_0000517
|
hsa-miR-193a-5p, hsa-miR-296-5p
|
hsa_circ_0000519
|
hsa-miR-204-5p, hsa-miR-296-5p
|
hsa_circ_0000520
|
hsa-miR-296-5p
|
hsa_circ_0001455
|
hsa-miR-142-5p
|
hsa_circ_0001806
|
hsa-miR-139-5p
|
hsa_circ_0002702
|
hsa-miR-100-5p, hsa-miR-99a-5p, hsa-miR-296-5p
|
hsa_circ_0003528
|
hsa-miR-224-5p
|
hsa_circ_0003645
|
hsa-miR-335-5p
|
hsa_circ_0004313
|
hsa-miR-365a-3p, hsa-miR-365b-3p
|
hsa_circ_0004315
|
hsa-miR-145-5p, hsa-miR-195-5p, hsa-miR-497-5p, hsa-miR-218-5p
|
hsa_circ_0004538
|
hsa-miR-503-5p, hsa-miR-7-5p
|
hsa_circ_0005273
|
hsa-miR-328-3p
|
hsa_circ_0005397
|
hsa-miR-10b-5p, hsa-miR-1-3p
|
hsa_circ_0005699
|
hsa-miR-143-3p
|
hsa_circ_0006220
|
hsa-miR-342-3p
|
hsa_circ_0006758
|
hsa-miR-224-5p, hsa-miR-1-3p
|
hsa_circ_0008365
|
hsa-miR-328-3p
|
hsa_circ_0008784
|
hsa-miR-193a-5p, hsa-miR-139-5p
|
hsa_circ_0014624
|
hsa-miR-7-5p
|
hsa_circ_0016201
|
hsa-miR-33a-5p, hsa-miR-33b-5p
|
hsa_circ_0020080
|
hsa-miR-7-5p
|
hsa_circ_0022587
|
hsa-miR-193a-5p
|
hsa_circ_0025388
|
hsa-miR-139-5p, hsa-miR-144-3p, hsa-miR-335-5p
|
hsa_circ_0028190
|
hsa-miR-451a
|
hsa_circ_0031724
|
hsa-miR-143-3p
|
hsa_circ_0041732
|
hsa-miR-193a-5p
|
hsa_circ_0041821
|
hsa-miR-144-3p
|
hsa_circ_0049998
|
hsa-miR-205-5p, hsa-miR-145-5p
|
hsa_circ_0054021
|
hsa-miR-141-3p, hsa-miR-200a-3p
|
hsa_circ_0058753
|
hsa-miR-218-5p
|
hsa_circ_0069104
|
hsa-miR-451a, hsa-miR-296-5p
|
hsa_circ_0082564
|
hsa-miR-205-5p
|
hsa_circ_0084429
|
hsa-miR-129-5p, hsa-miR-224-5p
|
hsa_circ_0084443
|
hsa-miR-129-5p
|
Table 5 Interaction between miRNA and mRNA in the ceRNA network.
miRNA
|
mRNA
|
hsa-miR-100-5p
|
FGFR3
|
hsa-miR-10b-5p
|
GATA3, SDC1
|
hsa-miR-129-5p
|
CBX4, COL1A1
|
hsa-miR-139-5p
|
TPD52, ZNF367
|
hsa-miR-1-3p
|
ADAM12, AP1S1, CERS2, E2F5, FAM102A, FN1, RIMS4, SLC25A22, GPR137C, TRPS1
|
hsa-miR-141-3p
|
EPHA2,QKI, STAT5A, USP53, YAP1, ZEB2
|
hsa-miR-142-5p
|
CREBRF, FIGN, SLITRK4, TNS1, ZBTB20
|
hsa-miR-143-3p
|
COL1A1, ERBB3, LIMK1, SERPINE1, TTYH3
|
hsa-miR-144-3p
|
EZH2, KPNA2, NACC1, SIX4
|
hsa-miR-145-5p
|
ABHD17C, ABRACL, RTKN, SERPINE1, SOX11, TPM3
|
hsa-miR-193a-5p
|
NUP210
|
hsa-miR-195-5p
|
CBX2, CBX4, CCNE1, CDC25A, CDCA4, CEP55, CHEK1, CLSPN, E2F7, ENTPD7, HMGA1, KIF23, MYB, RASEF, RET, SLC25A22, ZNF367
|
hsa-miR-200a-3p
|
DLC1, EPHA2, HGF, QKI, THRB, USP53, YAP1, ZEB2
|
hsa-miR-204-5p
|
AP1S1, EZR, RUNX2
|
hsa-miR-205-5p
|
CENPF, ERBB3, EZR, LPCAT1, PARD6B, RUNX2
|
hsa-miR-218-5p
|
BCL9, CDH2, CNTNAP2, FBN2, FBXO41, LMNB1, RET, NACC1, PRLR, RUNX2, TPD52, TTYH3
|
hsa-miR-224-5p
|
DIO1
|
hsa-miR-296-5p
|
HMGA1
|
hsa-miR-328-3p
|
H2AFX
|
hsa-miR-335-5p
|
CDH11
|
hsa-miR-33a-5p
|
ABCA1, ARID5B, DSC3, GAS1, ZC3H12C
|
hsa-miR-33b-5p
|
ABCA1, ARID5B, GAS1
|
hsa-miR-342-3p
|
FIGN, ID4, MBNL3
|
hsa-miR-365a-3p
|
MCOLN2, SIX4
|
hsa-miR-365b-3p
|
MCOLN2, SIX4
|
hsa-miR-451a
|
CDKN2D, MIF
|
hsa-miR-497-5p
|
ANLN, CBX2, CBX4, CCNE1, CDC25A, CDCA4, CEP55, CHEK1, CLSPN, E2F7, KIF23, RASEF, ZNF367
|
hsa-miR-503-5p
|
AKT3, CCND2, CYP26B1, FGF2, PIK3R1, RECK
|
hsa-miR-7-5p
|
EGFR, FNDC4, IRS1, IRS2, KLF4, RBMS3, RRAS2, SNCA, SOCS2
|
hsa-miR-99a-5p
|
FGFR3
|
Functional annotation of the DEGs in the ceRNA network
In order to better understand the potential functional significance of differentially expressed genes in the ceRNA network, we performed GO and KEGG functional enrichment analysis. In the GO analysis we identified a total of 162 enriched GO terms (FDR<0.01). The top 8 significantly enriched GO terms in the biological process (BP), cellular components (CC) and molecular function (MF) are shown in Figure 4. The biological processes of these differentially expressed genes were primarily associated with regulated by protein kinase B signaling, phosphatidylinositol phosphorylation, protein kinase B signaling and lipid phosphorylation. Meanwhile, the genes related to cellular components were mostly involved in nuclear transcription factor complex, focal adhesion, cell-substrate adherens junction and cell-substrate junction. In terms of molecular function, these differential genes were mostly enriched in phosphatidylinositol-4,5-bisphosphate 3-kinase activity, phosphatidylinositol bisphosphate kinase activity, phosphatidylinositol 3-kinase activity and 1-phosphatidylinositol-3-kinase activity.
Additionally, KEGG signal pathway analysis showed that 24 signal pathways were significantly enriched (FDR<0.01). The top 15 significantly enriched pathways are shown in Figure 5. Among these pathways, the ‘PI3K-Akt signaling pathway’, ‘MicroRNAs in cancer’, ‘Proteoglycans in cancer’, ‘Cellular senescence’, ‘FoxO signaling pathway’, ‘Central carbon metabolism in cancer’ and ‘Cell cycle’ are closely correlated with the carcinogenesis and development of BRCA.
Prognostic characteristics of RNAs in the ceRNA regulatory network
Survival analysis based on Survival package of R found that 13 mRNAs (CCNE1, TPD52, SDC1, ANLN, ZNF367, SOX11, IRS2, EZR, DSC3, CCND2, KPNA2, CBX2 and CEP55)among the 100 DEmRNAs in the ceRNA network were closely associated with the overall survival of breast cancer patients. The low expression of CCNE1, TPD52, SDC1, ANLN, ZNF367, SOX11, EZR, KPNA2, CBX2 and CEP55 was associated with high survival, whereas for IRS2, DSC3 and CCND2, high expression was associated with high survival. Six miRNAs (hsa-miR-204-5p, hsa-miR-335-5p, hsa-miR-100-5p, hsa-miR-195-5p, hsa-miR-328-3p and hsa-miR-342-3p) of 30 DEmiRNAs were associated with prognosis. High expression of hsa-miR-204-5p, hsa-miR-335-5p, hsa-miR-100-5p, hsa-miR-195-5p and hsa-miR-342-3p indicated long survival time, while high expression of hsa-miR-328-3p indicated a relatively short survival time. Survival analysis results are shown in Table 6 and Figure 6. Notably, based on the ceRNA network, we found that the hsa_circ_0004315-hsa-miR195-5p axis was associated with four mRNAs associated with breast cancer prognosis.
Table 6 Prognostic value of the differentially expressed mRNAs and miRNAs.
Name
|
HR (95% Cl)
|
P value
|
CCNE1
|
1.606 (1.169-2.208)
|
0.0038
|
TPD52
|
1.578 (1.148-2.169)
|
0.0053
|
SDC1
|
1.516 (1.102-2.086)
|
0.0102
|
ANLN
|
1.489 (1.083-2.046)
|
0.0154
|
ZNF367
|
1.475 (1.073-2.025)
|
0.0176
|
SOX11
|
1.443 (1.050-1.983)
|
0.0244
|
IRS2
|
0.705 (0.512-0.971)
|
0.0299
|
EZR
|
1.418 (1.030-1.950)
|
0.0311
|
DSC3
|
0.718 (0.521-0.988)
|
0.0398
|
CCND2
|
0.718 (0.522-0.987)
|
0.0400
|
KPNA2
|
1.395 (1.015-1.917)
|
0.0410
|
CBX2
|
1.391 (1.012-1.912)
|
0.0433
|
CEP55
|
1.384 (1.007-1.902)
|
0.0474
|
hsa-miR-195-5p
|
0.629 (0.455-0.870)
|
0.0046
|
hsa-miR-204-5p
|
0.648 (0.469-0.894)
|
0.0086
|
hsa-miR-335-5p
|
0.664 (0.481-0.916)
|
0.0134
|
hsa-miR-342-3p
|
0.696 (0.504-0.961)
|
0.0280
|
hsa-miR-100-5p
|
0.704 (0.509-0.972)
|
0.0323
|
hsa-miR-328-3p
|
1.410 (1.021-1.947)
|
0.0356
|
Interaction between miRNA and mRNA from the ceRNA network
According to ceRNA theory, circRNA could indirectly affect mRNA through miRNA. At the expression level, miRNA was negatively correlated with circRNA and mRNA. In order to verify that the network we built was consistent with ceRNA theory, we needed to perform correlation analysis on different kinds of RNA. The expression information of circRNA in this study was from the GSE101123 dataset, while the expression information of miRNA and mRNA were from the TCGA dataset. Since the expression information of RNAs in the correlation analysis must be from the same sample, this study could only analyze the correlation between the expression levels of miRNA and mRNA. We performed a correlation analysis of miRNA-mRNA pairs in the ceRNA network based on R software, and the results showed that there were 48 miRNA-mRNA pairs with strong negative correlation (r<-0.3, P<0.001) (Table 7). For instance, hsa-miR-141-3p negatively correlated with ZEB2 (r=-0.599, P<0.001) and QKI (r=-0.535, P<0.001), hsa-miR-195-5p negatively correlated with CEP55 (r=-0.547, P<0.001) and CLSPN (r=-0.525, P<0.001), hsa-miR-200a-3p negatively correlated with ZEB2 (r=-0.520, P<0.001) as well as QKI (r=-0.513, P=0.001) (Figure 7).
Table 7 Correlation analysis of the relationship between miRNA and mRNA.
miRNA
|
mRNA
|
R
|
P_vlaue
|
hsa-miR-141-3p
|
ZEB2
|
-0.59875
|
0
|
hsa-miR-195-5p
|
CEP55
|
-0.54744
|
0
|
hsa-miR-141-3p
|
QKI
|
-0.53509
|
0
|
hsa-miR-195-5p
|
CLSPN
|
-0.52524
|
0
|
hsa-miR-200a-3p
|
ZEB2
|
-0.52049
|
0
|
hsa-miR-200a-3p
|
QKI
|
-0.51254
|
0
|
hsa-miR-195-5p
|
HMGA1
|
-0.49915
|
0
|
hsa-miR-497-5p
|
CEP55
|
-0.49525
|
0
|
hsa-miR-195-5p
|
CHEK1
|
-0.49341
|
0
|
hsa-miR-195-5p
|
CDC25A
|
-0.49147
|
0
|
hsa-miR-195-5p
|
CCNE1
|
-0.48761
|
0
|
hsa-miR-497-5p
|
ANLN
|
-0.45814
|
0
|
hsa-miR-139-5p
|
TPD52
|
-0.44566
|
0
|
hsa-miR-195-5p
|
E2F7
|
-0.44235
|
0
|
hsa-miR-139-5p
|
ZNF367
|
-0.43672
|
0
|
hsa-miR-497-5p
|
CLSPN
|
-0.43102
|
0
|
hsa-miR-145-5p
|
TPM3
|
-0.42875
|
0
|
hsa-miR-195-5p
|
CBX2
|
-0.42701
|
0
|
hsa-miR-145-5p
|
RTKN
|
-0.42632
|
0
|
hsa-miR-195-5p
|
ZNF367
|
-0.42269
|
0
|
hsa-miR-200a-3p
|
DLC1
|
-0.4224
|
0
|
hsa-miR-497-5p
|
CCNE1
|
-0.42016
|
0
|
hsa-miR-7-5p
|
RBMS3
|
-0.41862
|
0
|
hsa-miR-497-5p
|
CDC25A
|
-0.4139
|
0
|
hsa-miR-342-3p
|
ID4
|
-0.4063
|
0
|
hsa-miR-497-5p
|
CHEK1
|
-0.40444
|
0
|
hsa-miR-141-3p
|
STAT5A
|
-0.39451
|
0
|
hsa-miR-218-5p
|
LMNB1
|
-0.3945
|
0
|
hsa-miR-141-3p
|
YAP1
|
-0.3803
|
0
|
hsa-miR-497-5p
|
CBX2
|
-0.37969
|
0
|
hsa-miR-200a-3p
|
HGF
|
-0.37823
|
0
|
hsa-miR-497-5p
|
E2F7
|
-0.3743
|
0
|
hsa-miR-218-5p
|
TPD52
|
-0.37324
|
0
|
hsa-miR-195-5p
|
CDCA4
|
-0.3683
|
0
|
hsa-miR-204-5p
|
AP1S1
|
-0.36607
|
0
|
hsa-miR-497-5p
|
CDCA4
|
-0.36528
|
0
|
hsa-miR-204-5p
|
EZR
|
-0.35209
|
0
|
hsa-miR-497-5p
|
ZNF367
|
-0.34916
|
0
|
hsa-miR-7-5p
|
SNCA
|
-0.33958
|
0
|
hsa-miR-145-5p
|
ABRACL
|
-0.33926
|
0
|
hsa-miR-365b-3p
|
MCOLN2
|
-0.33306
|
0
|
hsa-miR-365a-3p
|
MCOLN2
|
-0.33285
|
0
|
hsa-miR-141-3p
|
USP53
|
-0.31765
|
0
|
hsa-miR-200a-3p
|
YAP1
|
-0.3142
|
0
|
hsa-miR-145-5p
|
ABHD17C
|
-0.3107
|
0
|
hsa-miR-33b-5p
|
GAS1
|
-0.3097
|
0
|
hsa-miR-195-5p
|
ENTPD7
|
-0.30541
|
0
|
hsa-miR-33b-5p
|
ARID5B
|
-0.3005
|
0
|
Construction of PPI network and module analysis
The STRING database was used to unveil the interrelationships between the DEmRNAs in the ceRNA network by constructing PPI network. This PPI network involves a total of 75 nodes and 283 edges. Visualization was performed with Cytoscape (Figure 8A). In order to identify hub genes in the process of BRCA carcinogenesis, the MCODE plugin in Cytoscape was used to identify the core subnetwork in the PPI network. Two core sub-networks were obtained, including 21 genes and 49 edges (Figure 8B). We used these 21 genes as potential hub genes.
Quantitative real-time PCR validation
Finally, we randomly selected four DEcircRNAs, DEmiRNAs and DEmRNAs respectively in the ceRNA network to verify the reliability and validity of the above analysis results. These results showed that CCNE1, CEP55, ANLN, hsa-miR-592, hsa-miR-141-3p, hsa_circ_0000069, hsa_circ_0000518 and has_circ_0000520 were up-regulated in BRCA tumor tissues compared to adjacent non-tumor tissues, while ADIPOQ, hsa-miR-195-5p, hsa-miR-204-5p and has_circ_0000977 were down-regulated in BRCA tumor tissues (Figure 9). The results of qRT-PCR validation from new breast cancer patients were consistent with the above bioinformatics results, indicating that our bioinformatics analysis was credible.