Dysregulation of pseudogene/lncRNA-hsa-miR-363-3p-SPOCK2 pathway fuels stage progression of ovarian cancer

Introduction

It is known to all that RNAs consist of coding RNAs (messenger RNAs, mRNAs) and noncoding RNAs (ncRNAs). In recent years, ncRNAs have been the centerpiece of human genome research [1]. NcRNAs have key implications for human health and dysregulation of ncRNAs causes a variety of human disorders, including cancer [2, 3]. There are many types of ncRNAs, including microRNA (miRNA), long ncRNA (lncRNA), pseudogene and circular RNA (circRNA) [4, 5]. In 2011, the team of Salmena et al. proposed competing endogenous RNA (ceRNA) hypothesis, which is a regulatory mechanism between mRNAs and ncRNAs [6]. ceRNA mechanism demonstrates that lncRNAs, pseudogenes, circRNAs and mRNAs can cross-talk by competitively binding to shared miRNAs, thereby exerting their biological functions [7]. Increasing ncRNAs have been discovered to act as important tumor promoters or suppressors [8–11]. Moreover, ncRNAs also serve as potential diagnostic and prognostic biomarkers for cancer [12–15]. To date, only an extremely limited number of ncRNAs have been characterized functionally in cancer, though thousands of ncRNAs have been annotated.

Ovarian cancer ranks the seventh most common neoplasm among the world and the eighth leading cause of cancer deaths in women [16, 17]. Globally, 239,000 new patients were affected by ovarian cancer and 152,000 deaths occurred every year [18]. It’s widely known that ovarian cancer can be classified into three subtypes, including epithelial, specialized stromal cell and germ cell tumors, among which epithelial ovarian cancer occupies largest ratio in ovarian cancer. Besides, epithelial ovarian cancer is the most lethal gynecologic malignancy. Different types of epithelial ovarian cancer have distinct mutational spectrum and different prognosis [19–21]. To date, surgery and platinum/ taxane chemotherapy combinate as the main treatment for ovarian cancer [22, 23]. It’s an effective method for early stage ovarian cancer patients, with the five-year survival rates up to 92% [24]. However, in spite of huge advances in various therapies in the past few decades, only 19% ovarian cancer patients are diagnosed at early stage, due to deficiency of typical clinical symptoms and manifestations and fast tumor progression, and the five-year survival rates of ovarian cancer are still only below 30% [25]. Hence, it’s extremely meaningful to rapidly seek potential biomarkers and therapeutic targets for improving outcomes of ovarian cancer patients.

In this study, we constructed a network linked to stage progression of ovarian cancer by a series of analytic processes (Figure 1). Firstly, we obtained differentially expressed genes (DEGs)-associated with stage progression of ovarian cancer using GEO dataset, GSE12470. Next, TCGA ovarian cancer cohort was employed to validate expression of these DEGs and perform survival analysis. Subsequently, upstream miRNAs of SPOCK2 were predicted. By expression correlation analysis and survival analysis, the most potential miRNA (hsa-miR-363-3p) binding to SPOCK2 was identified. Finally, the potential upstream dysregulated mechanisms (pseudogenes and lncRNAs) and downstream modulatory pathway were explored. The established pseudogene/lncRNA-hsa-miR-363-3p-SPOCK2 pathway shed novel insight into molecular mechanism of ovarian cancer progression and may provide effective therapeutic targets and promising prognostic biomarkers for ovarian cancer.

Figure 1. The visual flow-process diagram of this study.

Results

Screen of candidate genes associated with stage progression of ovarian cancer

To identify the key genes involved in stage progression of ovarian cancer, GSE12470 dataset was selected to perform differential expression analysis using GEO2R. As described in materials and methods, all cases of GSE12470 were divided into three groups: “Nor” group, “Ear” group and “Adv” group. A lot of differentially expressed genes (DEGs) between “Nor” group and “Adv” group or between “Ear” group and “Adv” group were discovered as shown in Figure 2A and Figure 2B, respectively. A total of 2,555 and 2,310 DEGs were significantly upregulated and downregulated in early ovarian cancer samples compared with normal samples (Supplementary Table 1), respectively. And a total of 921 and 196 DEGs were significantly upregulated and downregulated in advanced ovarian cancer samples compared with early ovarian cancer samples, respectively (Supplementary Table 2). This study aims to find those genes associated with stage progression of ovarian cancer. Therefore, we obtained the upregulated and downregulated DEGs that were commonly appeared in two comparison sets. As presented in Figure 2C–D and Table 1, 35 upregulated DEGs and 2 downregulated DEGs were finally identified. The 37 DEGs were defined as candidate genes and selected for subsequent analysis.

Figure 2. Identification of differentially expressed genes (DEGs) between ovarian cancer and normal controls. (A) Volcano plot showing the DEGs between normal samples (n=10) and early ovarian cancer (n=8). (B) Volcano plot showing the DEGs between early ovarian cancer (n=8) and advanced ovarian cancer (n=35). Note: the black dots represent genes that are not significantly differentially expressed between two groups, and the green dots and red dots represent the downregulated and upregulated genes in early ovarian cancer (compared with normal samples) and advanced ovarian cancer (compared with early ovarian cancer), respectively. |log₂FC| > 1 and P-value < 0.05 were set as the cut-off criteria. FC = fold change. (C) The intersection of upregulated DEGs of “Nor vs Ear” and “Ear vs Adv”. (D) The intersection of downregulated DEGs of “Nor vs Ear” and “Ear vs Adv”. “Nor” vs Ear” represents the differential expression analysis between normal samples and early ovarian cancer. “Ear vs Adv” represents the differential expression analysis between early ovarian cancer and advanced ovarian cancer.

Table 1. Expression change of 37 significant candidate gene in ovarian cancer.

Gene symbol	Log2FCa	Log2FCb
ADAMDEC1	2.76	1.95
C11orf45	1.59	1.30
CCNA2	4.10	1.34
CHEK1	2.88	1.37
CLGN	3.32	1.81
DAPL1	5.59	2.17
EGFL6	2.68	1.79
FAM122B	1.30	1.13
FAM181A	1.70	1.30
FAM83D	2.46	1.10
FOXA2	3.19	1.57
FRAS1	2.83	2.02
GJB2	3.04	2.85
GMCL1	1.44	1.02
GPR19	3.20	1.32
HRK	2.22	1.53
HTR2C	2.61	3.12
L3MBTL1	2.33	1.39
LCT	5.25	6.82
LRRTM1	3.22	2.55
LYPD1	3.19	1.56
MAD2L1	3.01	1.05
NEIL3	1.98	1.10
PART1	3.13	1.92
PNOC	3.96	2.97
RBM11	1.65	1.01
S100A2	1.85	1.43
SPOCK2	1.02	1.53
ST6GALNAC2	1.42	1.59
TGM1	2.12	2.07
TMPO	1.18	1.09
TMPRSS6	1.91	2.94
XPR1	1.05	1.38
ZBED2	5.98	1.53
ZNF556	2.46	1.17
EPS8	-1.48	-1.17
FAXDC2	-2.23	-2.30
aFC = (Early ovarian cancer samples)/(Normal samples);
bFC = (Advanced ovarian cancer samples)/(Early ovarian cancer samples).

Expression validation and survival analysis of candidate genes in ovarian cancer

Next, for improving accuracy of analysis, TCGA ovarian cancer samples and GTEx normal data were introduced to validate expression levels of 37 candidate genes via GEPIA database. As shown in Figure 3, among the 37 candidate genes, expression of 26 genes (ADAMDEC1, C11orf15, CCNA2, CHEK1, CLGN, DAPL1, EGFL6, FAM181A, FAM83D, FOXA2, GJB2, GPR19, LRRTM1, LYPD1, MAD2L1, NEIL3, PART1, PNOC, S100A2, SPOCK2, ST6GALNAC2, TGM1, XPR1, ZBED2, EPS8 and FAXDC2) were in accordance with the results acquired from GSE12470. Notably, only two genes (EPS8 and FAXDC2) were downregulated in cancer tissues compared with normal controls. In the following survival analysis, we focused on the 26 candidate genes. Firstly, TCGA ovarian cancer cohort was introduced to assess the prognostic values (including OS and RFS) of the 26 candidate genes. As shown in Figure 4A, high expression of GJB2, S100A2, SPOCK2, TGM1or EPS8 indicated a poor OS of patients with ovarian cancer, whereas ovarian cancer patients with higher expression of ADAMDEC1, CHEK1, EGFL6, FAM181A, FOXA2, LYPD1, PART1 or XPR1 possessed better OS. For RFS of patients with ovarian cancer, SPOCK2 and FAXDC2 were unfavorable prognostic biomarkers but ADAMDEC1, CCNA2, FAM181A, FOXA2, LRRTM1, NEIL3 and XPR1 were favorable prognostic biomarkers (Figure 4B). Among these genes, only expression levels of GJB2, S100A2, SPOCK2 and TGM1 were significantly upregulated in cancer samples and their high expression indicated poor prognosis (OS or RFS). The detailed information of survival analysis was listed in Figure 4C and Figure 4D. Subsequently, we introduced the ovarian cancer data other than TCGA to further assess the survival effects of GJB2, S100A2, SPOCK2 and TGM1 on prognosis (OS) by Kaplan-Meier plotter. As presented in Figure 4E–4G, ovarian cancer patients with higher expression of GJB2, S100A2 and SPOCK2 showed better OS, which was identical with the analytic results obtained from TCGA cohort. However, in 206008_at cohort, increased expression of TGM1 linked to favorable OS of ovarian cancer (Figure 4H), contrary to TCGA cohort. Taken together, GJB2, S100A2 and SPOCK2 might be three key genes in mediating tumor stage progression of ovarian cancer and causing poor prognosis.

Figure 3. Expression levels of 26 key genes (ADAMDEC1 (A), C11orf15 (B), CCNA2 (C), CHEK1 (D), CLGN (E), DAPL1 (F), EGFL6 (G), FAM181A (H), FAM83D (I), FOXA2 (J), GJB2 (K), GPR19 (L), LRRTM1 (M), LYPD1 (N), MAD2L1 (O), NEIL3 (P), PART1 (Q), PNOC (R), S100A2 (S), SPOCK2 (T), ST6GALNAC2 (U), TGM1 (V), XPR1 (W), ZBED2 (X), EPS8 (Y) and FAXDC2 (Z)) in ovarian cancer determined by GEPIA database. “*” represents “P-value < 0.05”. Y axis indicates relative expression value, log₂(TPM+1). TPM=Transcript per million.

Figure 4. Prognostic values of 26 candidate genes in ovarian cancer assessed by Kaplan-Meier plotter database. (A) Prognostic values (overall survival, OS) of the 26 key genes in TCGA ovarian cancer cohort. (B) Prognostic values (relapse free survival, RFS) of the 26 key genes in TCGA ovarian cancer cohort. Green bars indicate a favorable prognosis; red bars indicate an unfavorable prognosis; black bars represent no statistical significance. (C) The detailed information of (A). (D) The detailed information of (B). (E) The prognostic value of GJB2 in 223278_at ovarian cancer cohort. (F) The prognostic value of S100A2 in 204268_at ovarian cancer cohort. (G) The prognostic value of SPOCK2 in 202524_s_at ovarian cancer cohort. (H) The prognostic value of TGM1 in 206008_at ovarian cancer cohort. Logrank P < 0.05 was considered as statistically significant.

hsa-miR-363-3p-SPOCK2 axis is identified as a potential pathway linked to ovarian cancer progression

Growing evidences have suggested that miRNAs mainly participate in negatively regulating gene expression and thereby play important roles in a variety of human biological processes, including cancer initiation and development [4, 26, 27]. Thus, we first predicted upstream miRNAs of GJB2, S100A2 and SPOCK2 through seven predicting programs as mentioned above (Table 2). Finally, 8, 6 and 20 upstream miRNAs were found to potentially target GJB2, S100A2 and SPOCK2, respectively. For better visualization, miRNA-GJB2, miRNA-S100A2 and miRNA-SPOCK2 sub-networks were established as shown in Figure 5A–5C. Then, we evaluated the prognostic values of these miRNAs in ovarian cancer using TCGA data. As shown in Figure 5D, among all the predicted miRNAs of GJB2, high expression of hsa-miR-522-3p and hsa-miR-105-5p indicated favorable OS of patients with ovarian cancer but hsa-miR-485-5p was an unfavorable prognostic biomarker for ovarian cancer. For S100A2, upregulation of hsa-miR-421 and hsa-miR-367-3p correlated with good and bad prognosis, respectively (Figure 5E). As presented in Figure 5F, regarding to these miRNAs of SPOCK2, high expression of hsa-miR-363-3p, hsa-miR-362-3p and hsa-miR-942-5p showed favorable OS but increased expression of hsa-miR-137, hsa-miR-367-3p, hsa-miR-377-3p, hsa-miR-329-3p, hsa-miR-1297, hsa-miR-1197, hsa-miR-2278, hsa-miR-4465 and hsa-miR-3194-3p linked to unfavorable OS. In view of miRNA functional mechanism and oncogenic roles of GJB2, S100A2 and SPOCK2, upstream miRNAs of the three genes should be tumor suppressive miRNAs. Therefore, six miRNA-mRNA pairs, including hsa-miR-105-5p-GJB2, hsa-miR-522-3p-GJB2, hsa-miR-421-S100A2, hsa-miR-363-3p-SPOCK2, hsa-miR-362-3p-SPOCK2 and hsa-miR-942-5p-SPOCK2, were selected for subsequent expression correlation analysis. Figure 5G–5L told us that only hsa-miR-363-3p was significantly negatively correlated with SPOCK2 in ovarian cancer. Altogether, hsa-miR-363-3p-SPOCK2 axis may be the most potential pathway in mediating tumor stage progression of ovarian cancer.

Table 2. Prediction of miRNAs binding to GJB2, S100A2 or SPOCK2.

Gene name	miRNA name	Predicting program	Number
GJB2	hsa-miR-23a-3p	PITA, miRanda	2
GJB2	hsa-miR-105-5p	PITA, miRmap, microT, PicTar	4
GJB2	hsa-miR-23b-3p	PITA, miRanda	2
GJB2	hsa-miR-485-5p	PITA, miRmap	2
GJB2	hsa-miR-522-3p	PITA, miRmap, microT	3
GJB2	hsa-miR-642a-5p	PITA, miRmap	2
GJB2	hsa-miR-140-3p	PITA, miRmap	2
GJB2	hsa-miR-224-3p	miRmap, microT	2
S100A2	hsa-miR-25-3p	RNA22, TargetScan	2
S100A2	hsa-miR-32-5p	RNA22, TargetScan	2
S100A2	hsa-miR-212-3p	PITA, miRanda	2
S100A2	hsa-miR-132-3p	PITA, miRanda	2
S100A2	hsa-miR-367-3p	RNA22, TargetScan	2
S100A2	hsa-miR-421	PITA, miRmap, microT, miRanda	4
SPOCK2	hsa-miR-25-3p	PITA, microT, miRanda, TargetScan	4
SPOCK2	hsa-miR-26a-5p	PITA, RNA22, PicTar, TargetScan	4
SPOCK2	hsa-miR-26b-5p	PITA, RNA22, PicTar, TargetScan	4
SPOCK2	hsa-miR-32-5p	PITA, microT, miRanda, TargetScan	4
SPOCK2	hsa-miR-92a-3p	PITA, microT, miRanda, TargetScan	4
SPOCK2	hsa-miR-137	PITA, TargetScan	2
SPOCK2	hsa-miR-363-3p	PITA, microT, miRanda, TargetScan	4
SPOCK2	hsa-miR-367-3p	PITA, microT, miRanda, TargetScan	4
SPOCK2	hsa-miR-377-3p	PITA, miRmap, microT, miRanda	4
SPOCK2	hsa-miR-379-5p	PITA, miRmap	2
SPOCK2	hsa-miR-329-3p	PITA, microT	2
SPOCK2	hsa-miR-92b-3p	PITA, microT, miRanda, TargetScan	4
SPOCK2	hsa-miR-362-3p	PITA, microT	2
SPOCK2	hsa-miR-942-5p	PITA, miRmap	2
SPOCK2	hsa-miR-1287-5p	PITA, miRmap	2
SPOCK2	hsa-miR-1297	PITA, PicTar, TargetScan	3
SPOCK2	hsa-miR-1197	PITA, miRmap, microT	3
SPOCK2	hsa-miR-2278	miRmap, PicTar, RNA22	3
SPOCK2	hsa-miR-4465	RNA22, PicTar, TargetScan	3
SPOCK2	hsa-miR-3194-3p	miRmap, microT	2

Figure 5. Identification of upstream potential miRNAs of GJB2, S100A2 and SPOCK2 by combination of miRNA prediction, survival analysis and correlation analysis. (A) The miRNA-GJB2 network established by Cytoscape. (B) The miRNA-S100A2 network established by Cytoscape. (C) The miRNA-SPOCK2 network established by Cytoscape. (D) Prognostic values (overall survival, OS) of potential upstream miRNAs of GJB2 in TCGA ovarian cancer cohort. (E) Prognostic values (overall survival, OS) of potential upstream miRNAs of S100A2 in TCGA ovarian cancer cohort. (F) Prognostic values (overall survival, OS) of potential upstream miRNAs of SPOCK2 in TCGA ovarian cancer cohort. Green bars indicate a favorable prognosis; red bars indicate an unfavorable prognosis; black bars represent no statistical significance. (G) The expression correlation of hsa-miR-105-5p and GJB2 in ovarian cancer. (H) The expression correlation of hsa-miR-522-3p and GJB2 in ovarian cancer. (I) The expression correlation of hsa-miR-421 and S100A2 in ovarian cancer. (J) The expression correlation of hsa-miR-363-3p and SPOCK2 in ovarian cancer. (K) The expression correlation of hsa-miR-362-3p and SPOCK2 in ovarian cancer. (L) The expression correlation of hsa-miR-942-5p and SPOCK2 in ovarian cancer.

Co-expression and enrichment analyses reveal that hsa-miR-363-3p-SPOCK2 axis is involved in regulation of actin cytoskeleton

Co-expression analysis was performed using two databases, namely UALCAN and GEPIA. 100 and 200 co-expressed genes were obtained from UALCAN and GEPIA as listed in Supplementary Table 3. By intersection of two gene sets, we found that 77 co-expressed genes of SPOCK2 were commonly appeared in the two databases (Figure 6A and Supplementary Table 4). The 77 genes and their correlation coefficients were presented in Table 3. GO functional annotation and KEGG pathway enrichment analysis were introduced to better understand these genes via Enrichr database. The top 10 enriched GO terms were shown in Figure 6B–6D, containing membrane raft organization, membrane raft assembly and positive regulation of protein acetylation in the BP category, actin cytoskeleton, membrane raft and focal adhesion in the CC category, and cadherin binding involved in cell-cell adhesion, protein binding involved in cell-cell adhesion and cadherin binding in the MF category. The top 10 enriched KEGG pathways were presented in Figure 6E, including bacterial invasion of epithelial cells, leukocyte transendothelial migration and regulation of actin cytoskeleton. These findings demonstrate that hsa-miR-363-3p-SPOCK2 axis may regulate actin cytoskeleton and thereby take part in cell adhesion, invasion and migration, and finally suppress progression of ovarian cancer.

Figure 6. Enrichment analysis of co-expressed genes of SPOCK2 in ovarian cancer. (A) Identification of co-expressed genes of SPOCK2 in ovarian cancer using UALCAN and GEPIA database. (B) The top 10 enriched biological process (BP) items for the co-expressed genes of SPOCK2. (C) The top 10 enriched cellular component (CC) items for the co-expressed genes of SPOCK2. (D) The top 10 enriched molecular function (MF) items for the co-expressed items for the co-expressed genes of SPOCK2. (E) The top 10 enriched KEGG items for the co-expressed items for the co-expressed genes of SPOCK2.

Table 3. The co-expressed genes of SPOCK2 commonly appeared in UALCAN database and GEPIA database.

Common co-expressed genes of SPOCK2	Ra	Rb
RASGRP4	0.59	0.62
SLC48A1	0.57	0.58
ADORA1	0.53	0.50
UNC5B	0.53	0.58
OXTR	0.52	0.52
CRB2	0.52	0.55
TRIM36	0.50	0.47
CADM3	0.48	0.46
SLC22A18AS	0.48	0.41
ZBED2	0.47	0.53
TNS3	0.47	0.49
KIF21A	0.46	0.43
S100A10	0.45	0.43
CLIC5	0.45	0.44
CAMK2G	0.44	0.46
RAB19	0.42	0.46
CACNG4	0.42	0.41
TBC1D2	0.41	0.43
UPK3B	0.41	0.41
MYADM	0.41	0.42
ANXA7	0.40	0.39
CLDN15	0.40	0.40
WNT10A	0.40	0.41
CCDC85A	0.39	0.39
CD151	0.39	0.38
CASKIN2	0.39	0.37
AMOTL2	0.38	0.43
DTX4	0.38	0.40
IGFBP6	0.37	0.34
ANXA2P2	0.37	0.36
ST6GAL2	0.37	0.34
ANXA2	0.37	0.46
KRT80	0.37	0.39
CLSTN2	0.37	0.37
ANXA9	0.37	0.37
DNM3	0.37	0.41
SHISA4	0.36	0.37
PLA2G7	0.36	0.34
INF2	0.36	0.35
CHRDL1	0.36	0.42
RAPGEF3	0.36	0.40
SLC4A11	0.36	0.35
PNPLA2	0.36	0.32
RNASEL	0.35	0.36
TPRN	0.35	0.36
OLFML2A	0.35	0.35
TGM1	0.35	0.37
VSIG10L	0.35	0.32
SERPINB5	0.35	0.32
LRRN4	0.34	0.35
FNDC4	0.34	0.37
PRSS33	0.34	0.32
ST5	0.34	0.35
GNG12	0.33	0.33
VSTM2L	0.33	0.35
TNNT2	0.33	0.31
BAIAP2	0.33	0.37
ARL13B	0.32	0.37
SERPINA5	0.32	0.31
ZMIZ1	0.32	0.36
CHST11	0.32	0.34
SLC29A3	0.32	0.37
ANO9	0.32	0.31
PKP3	0.31	0.31
VCL	0.31	0.34
ARNTL	0.31	0.32
BET1L	0.31	0.35
TMEM9B	0.31	0.31
GDPD5	0.30	0.31
SCD5	0.30	0.33
FAM69A	0.30	0.31
TNS1	0.30	0.32
ARPC1B	0.30	0.31
SMPD1	0.30	0.32
PXN	0.30	0.35
RHOF	0.30	0.35
RIC8A	0.30	0.34
aCorrelation coefficient determined by UALCAN database.
bCorrelation coefficient determined by GEPIA database.

Upstream potential pseudogenes and lncRNAs of hsa-miR-363-3p

Pseudogene and lncRNA are two considerable subtypes of noncoding RNA (ncRNA), which may function as ceRNAs to interact with mRNAs by competing for shared miRNAs. Therefore, starBase database was first utilized to predict the upstream potential pseudogenes that could potentially bind to hsa-miR-363-3p. 56 pseudogenes were obtained as shown in Figure 7A. Based on ceRNA mechanism, these pseudogenes should be oncogenes in ovarian cancer. The expression levels of the 56 pseudogenes were detected using GEPIA database. Finally, only 6 pseudogenes, RPS26P15 (Figure 7B), AC004057.1 (Figure 7C), RPS26P31 (Figure 7D), RPS26P6 (Figure 7E), RPS26P3 (Figure 7F) and RPS26P47 (Figure 7G,) were significantly upregulated in cancer tissues when compared with normal controls. Then, expression differences of the 6 pseudogenes among various major stages were also determined. As shown in Figure 7H–7M, all of the 6 pseudogenes expression were higher in advanced stage than that in early stage of ovarian cancer in general. Expression correlation analysis revealed that hsa-miR-363-3p was significantly correlated with RPS26P15 (Figure 7N), AC004057.1 (Figure 7O), RPS26P6 (Figure 7Q) or RPS26P3 (Figure 7R). For hsa-miR-363-3p-RPS26P31 (Figure 7P) and hsa-miR-363-3p-RPS26P47 (Figure 7S) pairs, no significant relationship was observed but still presented negative correlations. All these findings indicated that RPS26P15, AC004057.1, RPS26P31, RPS26P6, RPS26P3 and RPS26P47 were the most potential pseudogenes in regulating hsa-miR-363-3p in ovarian cancer. Subsequently, those lncRNAs that could potentially bind to hsa-miR-363-3p were also predicted. 90 and 33 upstream lncRNAs were found in starBase and miRNet as presented in Figure 8A and Figure 8B, respectively. The detailed lncRNAs were listed in Supplementary Table 5. By intersection of the two databases, eight lncRNAs (SNHG5, DAPK1-IT1, MALAT1, TBX5-AS1, SNHG14, OIP5-AS1, PITPNA-AS1 and XIST) were commonly appeared. Taken together, overexpressed lncRNAs/pseudogenes-mediated downregulation of hsa-miR-363-3p leads to increased expression of SPOCK2, modulating actin cytoskeleton and thereby resulting in progression of ovarian cancer (Figure 9).

Figure 7. Identification of upstream potential pseudogenes of hsa-miR-363-3p in ovarian cancer. (A) The pseudogenes-hsa-miR-363-3p network constructed by Cytoscape. The expression levels of RPS26P15 (B), AC004057.1 (C), RPS26P31 (D), RPS26P6 (E), RPS26P3 (F) and RPS26P47 (G) in ovarian cancer compared with normal controls. “*” represents “P-value < 0.05”. Y axis indicates relative expression value, log₂(TPM+1). TPM=Transcript per million. Expression differences of RPS26P15 (H), AC004057.1 (I), RPS26P31 (J), RPS26P6 (K), RPS26P3 (L) and RPS26P47 (M) among various major stage in ovarian cancer. P-value < 0.05 was considered as statistically significant. (N) The expression correlation of hsa-miR-363-3p and RPS26P15 in ovarian cancer. (O) The expression correlation of hsa-miR-363-3p and AC004057.1 in ovarian cancer. (P) The expression correlation of hsa-miR-363-3p and RPS26P31 in ovarian cancer. (Q) The expression correlation of hsa-miR-363-3p and RPS26P6 in ovarian cancer. (R) The expression correlation of hsa-miR-363-3p and RPS26P3 in ovarian cancer. (S) The expression correlation of hsa-miR-363-3p and RPS26P47 in ovarian cancer.

Figure 8. Screening upstream potential lncRNAs of hsa-miR-363-3p. (A) The potential lncRNAs of hsa-miR-363-3p predicted by starBase database. (B) The potential lncRNAs of hsa-miR-363-3p predicted by miRNet database. (C) 8 intersected lncRNAs (SNHG5, DAPK1-IT1, MALAT1, TBX5-AS1, SNHG14, OIP5-AS1, PITPNA-AS1 and XIST) from starBase and miRNet databases.

Figure 9. Model of the pseudogene/lncRNA-hsa-miR-363-3p-SPOCK2 network and its expression and potential roles in ovarian cancer progression.

Author Contributions

WYL, WMF and PFF designed and conceived this project. WYL performed in silico analysis of the data and wrote the manuscript. BSD and GSZ performed some in silico analysis. PFF and CYD revised the manuscript. All authors have read and approved the final manuscript.

Conflicts of Interest

The authors have no conflicts of interest to declare.

Funding

This work was supported by the National Natural Science Foundation of China (81372462, 81572987).

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