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Introduction

Epithelial ovarian cancer (EOC) has the highest mortality rate among gynecological malignancies, and remains the most lethal type that threatens the life and health of females (1,2). The majority of patients with EOCs are diagnosed at an advanced stage of the disease (3–6), and numerous studies have demonstrated that the outcome of patients with EOC depends on the tumor grade (7). Following surgery and platinum-based combination chemotherapy, the recurrence rate of low-grade EOCs (LG-EOCs) was lower and the survival rate was higher compared with high-grade EOCs (HG-EOCs) (8). Therefore, the EOC grade is considered to serve as a distinctive risk factor. At present, the assessment of grade in EOC samples is based on a dualistic classification system proposed by Shih Ie and Kurman in 2004 (9). EOC is divided into types I and II; type I (low-grade) EOC presents with a good prognosis; however, it is unresponsive to chemotherapy. Type II (high-grade) EOC has a poor prognosis, yet it is sensitive to chemotherapy (9). The evaluation of ovarian cancer grade currently relies solely on clinicopathological parameters; a molecular standard for diagnosis is yet to be established (10). Therefore, identifying effective biomarkers associated with the EOC grade is of clinical significance for developing effective therapeutic strategies for patients with EOC, and may contribute to the prediction of prognosis.

Gene microarrays are valued for their strong application prospects, as they can monitor expression levels of thousands of genes simultaneously. At present, due to the publication of gene microarray information, an increasing number of researchers are devoted to exploring unknown mechanisms with this methodology. However, limited sample sizes, different microarray platforms and different statistical methods are limitations of this approach (11). Bioinformatics analysis may be conducted to overcome these drawbacks.

In the past decades, several studies investigated dysregulated genes and their potential functions in EOC (12,13). However, to the best of our knowledge, few studies investigating molecular biomarkers associated with EOCs grade have been reported. Therefore, there is a requirement for the identification of reliable biomarkers to distinguish between LG-EOCs and HG-EOCs. The present study used bioinformatics methods to investigate and identify differentially expressed genes in different EOC grades.

Materials and methods

Microarray datasets filtering

To analyze the differentially expressed genes between HG-EOCs and LG-EOCs, EOC datasets were downloaded from the Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/) database, using the keywords ‘ovarian cancer’ and ‘GPL570’. Subsequently, four public microarray datasets, including GSE26193 (14), GSE63885 (15), GSE30161 (16) and GSE9891 (17), were selected based on the following criteria (11): i) Expression profiling by array; ii) samples obtained from from Homo sapiens; iii) availability of raw CEL files; iv) GPL570 platforms; and v) EOC samples associated with EOC grades. As a result, the GSE26193, GSE63885, GSE30161 and GSE9891 datasets, consisting of 107, 80, 54 and 280 EOC samples associated with EOC grades, respectively, were included in the present study. The GSE26193 dataset consisted of 40 LG-EOCs and 67 HG-EOCs samples, GSE63885 contained 10 LG-EOCs and 70 HG-EOCs samples, GSE30161 included 21 LG-EOCs and 33 HG-EOCs samples, and GSE9891 was composed of 119 LG-EOCs and 161 HG-EOCs samples.

Data analysis

The publicly available raw CEL files downloaded from the GEO database pre-treated by robust multichip average (RMA) analysis in the affy package (version 3.9; http://www.bioconductor.org/packages/release/bioc/html/affy.html). In order to analyze dysregulated genes in each dataset, the limma package (version 3.9; http://bioconductor.org/packages/release/bioc/html/limma.html) was used. The upregulated or downregulated probes, where P<0.05 and log2 fold change (FC)>1 (upregulated genes) or <-1(downregulated genes), were listed. Venn diagrams (bioinfogp.cnb.csic.es/tools/venny) were used to analyze the consistently differentially expressed genes in the datasets in the present study. To further expand the sample size, the InSilicoMerging (18) approach was used to merge the normalized datasets selected for inclusion in the current study and the ‘RankProd’ (19) approach was applied to identify the dysregulated genes in the merged datasets.

Gene enrichment analysis of dysregulated genes

Gene Ontology analysis for the list of differentially expressed genes identified by RankProd was performed to identify their prevalence in biological processes and in molecular functions and pathways, using the Database for Annotation, Visualization and Integrated Discovery (DAVID Bioinformatics Resources; version 6.8; http://david.ncifcrf.gov) (20). The ggplot2 package (21) was used to visualize the main functional pathways of dysregulated genes.

Tissue sample collection

A total of 82 EOC tissue samples (including 36 LG-EOCs and 46 HG-EOCs) were collected from patients with an age range of 35–73 years who had received surgery at The First Affiliated Hospital of Nanjing Medical University (Nanjing, China) between July 2011 to December 2018. The resected tissues were assessed by histological analysis. The patients enrolled had been histopathologically diagnosed with primary ovarian cancer, and had not received any other treatment prior to surgical resection. The tissue samples were immediately stored at −80°C until subsequent analysis. Patients were followed up every 3 months after surgery, during which no patients were lost to follow-up. The follow-up information was recorded comprehensively. Written informed consent for the collection and analysis of tissue specimens in the present study was obtained from every patient; the study was approved by the Research Ethics Committee of Nanjing Medical University. The clinical and pathological characteristics of the patients with EOC are presented in Table I.

Table I. Association between CTCFL expression and clinical pathological characteristics of patients with epithelial ovarian cancer (n=82).

Table I.

Association between CTCFL expression and clinical pathological characteristics of patients with epithelial ovarian cancer (n=82).

		CTCFL expression
Clinicopathological feature	Number of cases	Low (n=41)	High (n=41)	P-value
Age (years)				0.2672
<50	37	16	21
≥50	45	25	20
Histological subtype				0.1052
Serous	71	33	38
Others	11	8	3
Tumor size (cm)				0.0344a
<8	27	18	9
≥8	55	23	32
FIGO stage				0.0343a
I–II	13	10	3
III–IV	69	31	38
Histological grade				0.0004a
Low-grade	36	26	10
High-grade	46	15	31
Lymph node metastasis				0.3769
Absent	40	18	22
Present	42	23	19
Ascites				0.1109
Absent	31	12	19
Present	51	29	22

{ label (or @symbol) needed for fn[@id='tfn1-ol-0-0-10736'] } CTCFL, CCCTC-binding factor like; FIGO, International Federation of Gynecology and Obstetrics.

a P<0.05.

RNA isolation and reverse-transcription polymerase chain reaction (RT-qPCR)

Total RNA in the EOC tissues was extracted by TRIzol® reagent (Invitrogen, CA, USA) according to the manufacturer's protocol. The cDNA reactions prepared using the reverse transcriptase kit (Takara Bio, Inc.) according to the manufacturer's protocol. The mRNA expression of CCCTC-binding factor like (CTCFL) was detected using a standard SYBR Green permix Ex Taq kit (Takara Bio, Inc.) on the 7900 HT real-time instrument (Applied Biosytems; Thermo Fisher Scientific, Inc.). The amplification of CTCFL was performed with an initial step at 94°C for 30 sec, followed by 40 cycles of denaturation at 95°C for 5 sec, annealing at 60°C for 30 sec, and extension at 95°C for 15 sec. The sequences of the primers used were as follows: CTCFL forward, 5′-GTACTCCCCGCAAGAGATGG-3′ and reverse, 5′-TCACCGCTAACTTACTGTCTTCA-3′; and GAPDH forward, 5′-CCCACTCCTCCACCTTTGAC-3′ and reverse, 5′-GGATCTCGCTCCTGGAAGATG-3′. CTCFL mRNA levels were quantified using the 2−ΔΔCq method and normalized to GAPDH (22).

Immunohistochemical analysis

Immunohistochemical analysis was performed to detect CTCFL protein expression in the tissue specimens. Analysis revealed that CTCFL was the most upregulated gene in the four datasets and was subsequently selected for immunohistochemical analysis. Briefly, paraffin-embedded tissue blocks were cut into 4-µm thick sections and then placed in a constant temperature box at 65°C for 30 min to deparaffinize. The sections were submerged in the ethylenediaminetetraacetic acid buffer and microwaved for 8 min for antigenic retrieval. 3% hydrogen peroxide in methanol was used to quench the endogenous peroxidase activity. Then 1% goat serum albumin (Abcam) was incubated at room temperature for 5 min to block nonspecific binding. The sections were subsequently stained with an anti-BORIS (CTCFL) primary antibody (1:200; cat. no. ab187163; Abcam) and incubated overnight at 4°C, and horseradish peroxidase-conjugated goat anti-rabbit IgG secondary antibody (1:1,000; cat. no. ab6721; Abcam) were then incubated at room temperature for 1 h. Finally, images were captured using a high-capacity digital slide scanner (Pannoramic SCAN, 3DHISTECH) at ×200 magnification. The sections were evaluated independently by two experienced pathologists. A total of 12 patients with paired serous ovarian cancer patients, were selected from patients enrolled in our study to conduct this experiment.

Statistical analysis

SPSS software (version 18.0; SPSS, Inc., Chicago, IL, USA) and GraphPad Prism software (version 5; GraphPad Software, Inc., La Jolla, CA, USA) were used for all statistical analysis. Data are presented as the mean ± standard error of mean (SEM). The differences between groups were analyzed by the Student's t-test. For the analysis on the clinicopathological parameters, χ2 test and Fisher exact probability method were applied. And Kaplan-Meier analysis and the log-rank test were used for survival analysis. P<0.05 was considered to indicate a statistically significant difference. All experiments were repeated in triplicate.

Results

Dysregulated genes between high-and low-grade EOC

The present study initially analyzed the differentially expressed genes between HG-EOCs and LG-EOCs in each dataset with the limma software package. The three datasets (GSE26193, GSE63885 and GSE30161) were then employed to analyze the consistently differential expressed genes (Fig. 1A). As illustrated in Fig. 1B, there were 90, 733 and 1,489 upregulated genes in the GSE26193, GSE30161 and GSE63885 datasets, respectively. CTCFL (fold change/FC=2.676; percentage of false prediction/pfp<0.01), EGFL6 (FC=2.140; pfp<0.01), radical S-adenosyl methionine domain containing 2 (FC=1.776; pfp<0.01) and SAM and HD domain containing deoxynucleoside triphosphate triphosphohydrolase 1 (SAMHD1; FC=1.639; pfp<0.01) were consistently upregulated among these three datasets. Furthermore, 415, 79 and 41 genes were identified as downregulated in the GSE26193, GSE30161 and GSE63885 datasets, respectively. Only ALK and LTK ligand 2 (FC=0.504; pfp<0.01) was downregulated among the three aforementioned datasets (Fig. 1C). In order to decrease the number of differentially expressed genes, and therefore identify more reliable potential molecular markers for EOC grade, the present study superadded an additional dataset (GSE9891). As presented in Fig. 2, only one overlapping candidate probe, CTCFL, was identified to be upregulated in the four datasets and no probes were consistently downregulated in the four examined datasets.

Figure 1. Intersection of differentially expressed probes identified from three individual datasets using the limma package. (A) Heat map of the differentially expressed probes in three individual datasets. The color key indicates the expression of gene, the expression increases gradually from bottom to top. (B) A total of four probes were upregulated (P<0.01; logFC>1) and (C) one probe was downregulated (P<0.01; logFC<-1) in the three datasets. FC, fold change.

Figure 2. Intersection of differentially expressed probes identified from four individual datasets using the limma package. (A) Heat map of the differentially expressed probes in GSE9891. The color key indicates the expression of gene, the expression increases gradually from bottom to top. (B) Upregulated probes (P<0.01; logFC>1) and (C) downregulated probes (P<0.01; logFC<-1). One probe was found to be consistently upregulated in each of the four datasets. FC, fold change.

Genome-wide analysis of differential gene expression in merged datasets

To further enlarge the sample size, the present study initially merged the three datasets by using the InSilicoMerging method. The ‘RankProd’ approach was subsequently used to analyze the differentially expressed genes in the merged datasets. As a result, a total of 6,103 upregulated probes corresponding with 5,766 genes (FC>1; pfp<0.01), and 4,004 downregulated probes corresponding with 3,707 genes (FC<1; pfp<0.01) were identified from the GSE26193, GSE63885 and GSE30161 datasets. The top 20 upregulated and downregulated genes are presented in Tables II and III, respectively.

Table II. Top 20 most significantly upregulated probes identified from merged three datasets by RankProd in high grade-EOCs.

Table II.

Top 20 most significantly upregulated probes identified from merged three datasets by RankProd in high grade-EOCs.

PROBEID	SYMBOL	Fold change (class 1/class 2)	Pfp	P-value
1552368_at	CTCFL	2.675943	<0.001	<0.001
211430_s_at	MIR8071-2	2.242152	<0.001	<0.001
211430_s_at	MIR8071-1	2.242152	<0.001	<0.001
211430_s_at	IGHV4-31	2.242152	<0.001	<0.001
211430_s_at	IGHM	2.242152	<0.001	<0.001
211430_s_at	IGHG3	2.242152	<0.001	<0.001
211430_s_at	IGHG2	2.242152	<0.001	<0.001
211430_s_at	IGHG1	2.242152	<0.001	<0.001
214677_x_at	IGLJ3	2.159827	<0.001	<0.001
214677_x_at	IGLC1	2.159827	<0.001	<0.001
210809_s_at	POSTN	2.145002	<0.001	<0.001
219454_at	EGFL6	2.139953	<0.001	<0.001
204533_at	CXCL10	2.119093	<0.001	<0.001
210096_at	CYP4B1	2.111486	<0.001	<0.001
219768_at	VTCN1	2.05719	<0.001	<0.001
202575_at	CRABP2	2.042067	<0.001	<0.001
209138_x_at	IGLC1	1.982947	<0.001	<0.001
206067_s_at	WT1	1.968892	<0.001	<0.001
230720_at	RNF182	1.965409	<0.001	<0.001
224795_x_at	IGKC	1.958864	<0.001	<0.001

[i] Class 1 represents high-grade EOCs and class 2 represents low-grade EOCs. EOC, epithelial ovarian cancer; pfp, percentage of false prediction.

Table III. The top 20 most significantly downregulated probes identified from merged three datasets by RankProd in high grade-EOCs.

Table III.

The top 20 most significantly downregulated probes identified from merged three datasets by RankProd in high grade-EOCs.

Probe ID	Gene symbol	Fold change (class 1/class 2)	pfp	P-value
1552283_s_at	ZDHHC11B	0.842815	<0.001	<0.001
1552283_s_at	ZDHHC11	0.842815	<0.001	<0.001
1552348_at	PRSS33	0.683901	<0.001	<0.001
1552365_at	SCIN	0.926526	<0.001	<0.001
1552496_a_at	COBL	0.83375	<0.001	<0.001
1552532_a_at	ATP6V1C2	0.855798	<0.001	<0.001
1552670_a_at	PPP1R3B	0.79164	<0.001	<0.001
1552767_a_at	HS6ST2	0.826037	<0.001	<0.001
1552790_a_at	SEC62	0.893735	<0.001	<0.001
1552797_s_at	PROM2	0.846668	<0.001	<0.001
1552910_at	SIGLEC11	0.827746	<0.001	<0.001
1553062_at	MOGAT1	0.872144	<0.001	<0.001
1553589_a_at	PDZK1IP1	0.808865	<0.001	<0.001
1553613_s_at	FOXC1	0.617665	<0.001	<0.001
1553655_at	CDC20B	0.740247	<0.001	<0.001
1553986_at	RASEF	0.720305	<0.001	<0.001
1553989_a_at	ATP6V1C2	0.800128	<0.001	<0.001
1553995_a_at	NT5E	0.740796	<0.001	<0.001
1553997_a_at	ASPHD1	0.813537	<0.001	<0.001
1554067_at	C12orf66	0.847529	<0.001	<0.001

[i] Class 1 represents high-grade EOCs and class 2 represents low-grade EOCs. EOC, epithelial ovarian cancer; pfp, percentage of false prediction.

Consistent with the intersection results among the aforementioned four datasets, CTCFL was the most upregulated gene in HG-EOCs from the merged GSE26193, GSE63885 and GSE30161 datasets (Table I). Therefore, CTCFL may serve as a potent biomarker and CTCFL was subsequently selected as a candidate gene for distinguishing HG-EOCs from LG-EOCs.

The top 100 dysregulated probes in the merged datasets are presented in Fig. 3, and the hierarchical cluster analysis revealed that the expression profiles of HG-EOCs were similar to those of LG-EOCs.

Figure 3. Heatmap analysis was performed to illustrate the top 100 differentially expressed probes in low grade-EOC and high grade-EOC from three microarray datasets. Yellow represents normal samples and blue represents cancer samples. The color key indicates the expression of gene, the expression increases gradually from left to right. EOC, epithelial ovarian cancer.

Gene enrichment analysis of dysregulated genes

To effectively analyze the function of the dysregulated genes in the merged datasets, DAVID was utilized to process the functional enrichment analysis. The biological process (BP) terms associated with the top 3,000 dysregulated genes were downloaded. There were 370 significant BP terms associated with the genes upregulated in HG-EOCs and 380 significant BP terms associated with the genes that were downregulated in HG-EOCs. The upregulated genes were enriched in the ‘positive regulation of transcription from RNA polymerase II’, whilst the downregulated genes were enriched in ‘signal transduction’ and ‘positive regulation of transcription from RNA polymerase II promoter’. The top 20 BP terms are presented in Fig. 4A and B.

Figure 4. Top 20 BP functional analysis of dysregulated genes. (A) The top 20 significant BP terms associated with genes upregulated in HG-EOCs. (B) The top 20 significant BP terms associated with genes downregulated in HG-EOCs. The color key indicates the -Log10 (P-value). BP, biological process; GO, Gene Ontology; HG-EOC, high grade-epithelial ovarian cancer.

mRNA expression of CTCFL between HG-EOCs and LG-EOCs

In order to verify the differential expression of CTCFL in LG-EOCs and HG-EOCs, RT-qPCR was performed in 36 LG-EOCs and 46 HG-EOCs tissue samples. As illustrated in Fig. 5A, the mRNA level of CTCFL was significantly upregulated in HG-EOCs compared with LG-EOCs (P=0.0007). This results indicated that upregulated CTCFL may be implicated in HG-EOCs. In addition, the present study divided the 82 patients with EOC into two groups on the basis of CTCFL expression in tumor tissues (Fig. 5B; cut-off, 0.1152275). By using the log-rank test, patients with high expression levels of CTCFL were observed to have a poor outcome compared with patients with low expression levels of CTCFL (P=0.0084, Fig. 5C). Notably, the association between clinical pathological characteristics and CTCFL expression revealed that high expression levels of CTCFL were significantly associated with tumor size (P=0.0344), the International Federation of Gynecology and Obstetrics stage (P=0.0343) and histological grade (P=0.0004). However, highly-expressed CTCFL was not associated with the other examined clinical characteristics (Table I).

Figure 5. CTCFL was upregulated in HG-EOC samples. (A) The mRNA level of CTCFL was upregulated in HG-EOC samples compared with LG-EOC samples. (B) A total of 82 patients with EOC were divided into high- and low-expression groups using the median value of relative CTCFL expression (cut-off, 0.1152275). (C) Postoperative Kaplan Meier analysis based on CTCFL expression in 82 patients with EOC. ***P<0.001. CTCFL, CCCTC-binding factor like; HG, high grade; EOC, epithelial ovarian cancer; LG, low grade.

Protein expression of CTCFL between HG-EOCs and LG-EOCs

For further validation, the protein expression of CTCFL in LG-EOCs EOCs and HG-EOCs was analyzed by immunohistochemistry. As hypothesized, the protein expression of CTCFL in HG-EOCs samples was markedly higher than LG-EOCs samples (Fig. 6A and B). Taken together, these results suggested that CTCFL was associated with EOC grade and may serve as a promising biomarker and therapeutic target for HG-EOCs.

Figure 6. Immunostaining of CTCFL in HG-EOC and LG-EOC samples. (A) Representative immunohistochemical staining of CTCFL in serous type LG-EOCs tissues (n=4; scale bar, 100 µm; magnification, ×200). (B) Representative immunohistochemical staining of CTCFL in serous type HG-EOCs (n=4; scale bar, 100 µm; magnification, ×200). CTCFL, CCCTC-binding factor like; HG, high grade; EOC, epithelial ovarian cancer; LG, low grade.

Discussion

As gene chip technology has advanced, genome-wide analysis of microarrays have been increasingly applied to medical research in order to identify differentially expressed genes in a variety of diseases including EOCs, as well as to explore the potential underlying molecular mechanisms of pathogenesis (11). An increasing number of microarray gene analysis investigating various diseases have been reported (11,23–25). For example, Singh et al (26) analyzed the genome-wide profile of the PIWI-interacting RNA-mRNA regulatory networks in EOCs. The study of Shi and Zhang (27) utilized microarray analysis to screen genes and regulatory factors involved in EOCs. Januchowski et al (28) used microarrays to identify novel genes associated with drug resistance in ovarian cancer cell lines. Wei et al (29) investigated the sequential gene changes in EOC induced by carboplatin via microarray analysis. However, few studies investigating biomarkers associated with EOC grade have been reported. Additionally, these studies have a limited sample size and different platforms resources, which lead to inconsistent results. Consequently, merging several eligible datasets together with normalization by using RMA analysis may produce more reliable results.

The present study was based on four public microarray datasets downloaded from the GEO database (GSE26193, GSE63885, GSE30161 and GSE9891), which collectively included 521 EOC samples. Initially, the differentially expressed genes in each dataset were analyzed, and an intersection was obtained. CTCFL, EGFL6 and SAMHD1 were identified to be consistently upregulated among the GSE26193, GSE63885 and GSE30161 datasets. Following the addition of the GSE9891 dataset, only one overlapping candidate probe was identified to be upregulated among all the datasets. To compensate for the shortage of limited sample size and different platforms resources, the GSE26193, GSE63885 and GSE30161 datasets were merged for subsequent analysis and CTCFL was revealed to be the most upregulated gene in high-grade EOC. Based on gene enrichment analysis of dysregulated genes, the upregulated genes were most enriched in the ‘positive regulation of transcription from RNA polymerase II promoter’, while the downregulated genes were enriched in ‘signal transduction’ and ‘positive regulation of transcription from RNA polymerase II promoter’. These results indicated that the dysregulated genes in HG-EOCs may serve an underlying role in oncogene development and progression. Finally, RT-qPCR and immunohistochemical analysis demonstrated that the EOC grade was closely associated with the CTCFL level.

The aforementioned results demonstrated that CTCFL was the most upregulated gene in HG-EOCs. CTCFL belongs to the cancer testis antigen family (30), which is typically expressed in the testes (31). However, numerous studies investigating the high expression of CTCFL in multiple carcinomas have been reported, including breast cancer, lung cancer, endometrial carcinoma, prostate cancer and colon cancer, and the expression of CTCFL was associated with tumor size and histological differentiation (32–35). D'Arcy et al (32) reported that the CTCFL protein is closely associated with the occurrence and progression of breast cancer. Risinger et al (34) identified a similar expression profile of CTCFL in uterine cancer. The aforementioned studies suggested that CTCFL promoted tumorigenesis. Previous studies demonstrated that CTCFL was highly expressed in ovarian cancer, and the dysregulation was associated with DNA hypomethylation (36–38). At present, the pathophysiological role of CTCFL in tumor formation and progression is yet to be fully elucidated. To further investigate whether CTCFL was differentially expressed between HG-EOCs and LG-EOCs, the present study performed immunohistochemical analysis, and the results were consistent with the result of microarray datasets analysis. The results obtained in the current study suggested that CTCFL was the most upregulated gene in HG-EOCs compared with LG-EOCs. Taken together, the results of the present study indicated that CTCFL may act as an oncogene in the progression of EOC and may be a potential diagnostic biomarker and therapeutic target of EOC.

However, there are limitations to the present study. Due to the small sample size of EOC tissues in the experiment, further studies are required to verify the oncogenic role of CTCFL in EOC grade. Additionally, the mechanism of CTCFL which promotes malignant behaviors of ovarian cancer cells should be explored in depth. Finally, although the statistical analysis indicated a significant upregulation of CTFCL in HG-EOCs when compared with LG-EOCs, the RT-qPCR results demonstrated that the majority of patients had a value falling within the error bars for the LG-EOCs. Therefore, a larger sample size is required to define the CTCFL values that distinguish HG-EOCs from LG-EOCs.

In conclusion, CTCFL was the most upregulated gene in the selected datasets and may serve as a potential molecular biomarker to distinguish HG-EOCs from LG-EOCs. However, further investigations are required to explore the underlying mechanisms of CTCFL in HG-EOCs.

Acknowledgements

Not applicable.

Funding

This work was supported by the National Nature Science Foundation of China (grant. no. 81472442) and the Jiangsu Province Medical Innovation Team (grant. no. CXTDA2017008).

Availability of data and materials

The datasets analyzed during the current study are available in the GEO repository, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE26193, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63885, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE30161 and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE9891.

Authors' contributions

WC designed the study. MG, CY and YJ analyzed and interpreted the microarray datasets, and produced the manuscript. MG wrote the paper and submitted the manuscript. HM and MF performed the experiments. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The present study was approved by the Research Ethics Committee of Nanjing Medical University. Written informed consent for the analysis of tissue specimens in this study was obtained from the patients.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References