Cross-validation of genes potentially associated with overall survival and drug resistance in ovarian cancer
- Authors:
- Published online on: March 27, 2017 https://doi.org/10.3892/or.2017.5534
- Pages: 3084-3092
Abstract
Introduction
Ovarian cancer is the most lethal cancer among gynecologic malignancies (1). Early-stage ovarian cancer is frequently asymptomatic and difficult to detect, and thus diagnosis usually occurs after the disease has disseminated beyond the ovaries (2). The current standard treatment for advanced ovarian cancer is surgical debulking followed by platinum/taxane-based chemotherapy (3). Although this standard treatment significantly reduces the mortality rates and prolongs the survival time, the majority of patients will eventually relapse (4). The main obstacle to a successful treatment for ovarian cancer is the development of drug resistance that finally leads to fatal disease (5), and 5-year survival rates of ovarian cancer are less than 40%, with only modest improvement over the past 40 years (2). Therefore, there is a consistent and urgent need to understand the mechanism of drug resistance and to identify useful biomarkers for overall survival in ovarian cancer (6).
The present study was based on profiles of 1648 ovarian cancer patients, in microarrays from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). We identified several genes significantly associated with overall survival (OS); thus, these genes are potential biomarkers for prognosis in OC. We also found that these genes were potentially involved in regulation of drug resistance in OC.
Materials and methods
Cell culture
The human epithelial OC cell lines SKOV3 and A2780 were maintained in our laboratory and propagated in vitro by serial passage in RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS). The cisplatin-resistant cell line SKOV3-DDP and the carboplatin-resistant cell line A2780-CBP were established by sequential exposure of cells to increasing concentrations of cisplatin and carboplatin, respectively (7).
Real-time quantitative polymerase chain reaction analysis
Total RNA was isolated from the cell lines SKOV3, SKOV3-DDP, A2780 and A2780-CBP, using TRIzol reagent (Life Technologies, Grand Island, NY, USA). The quantity and quality of the RNA were measured using a Thermo Scientific NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA). cDNA was synthesized from 2 µg of RNA using the SuperScript III First-Strand synthesis system (Life Technologies). mRNA expression was measured using real-time quantitative polymerase chain reaction (RT-qPCR) and the Power SYBR-Green PCR Master Mix (Applied Biosystems, Waltham, MA, USA). Data were collected with the Applied Biosystems StepOne RT-PCR system in accordance with the manufacturers instructions. The RT-qPCR gene-specific primers were: CAKP2: forward primer, 5-AA GCCACAAAACCTCAGCCT-3 and reverse primer, 5-CA TGAGGCCCTTTCCGGATT-3. BCHE: forward primer, 5-GGCCTGTCTTCAAAAGCACTG-3 and reverse primer, 5-TCCTGCTTTCCACTCCCATTC-3. CLDN10: forward primer, 5-GGAGGCTCCGATAAAGCCAA-3 and reverse primer, 5-GTGGCCCCGTTGTATGTGTA-3. OVGP1: forward primer, 5-AGCGAAGAAGCACTGGATTGA-3 and reverse primer, 5-ATTCACAGCAGATGACAGCCA-3. For GAPDH, used as the control, the forward primer was 5-GA GGTGAAGGTCGGAGT-3 and the reverse primer 5-GAA GATGGTGATGGGATTT-3.
Gene expression profiles
Gene expression data and survival information of the 1648 OC patients were deposited in the KM plotter, which was established based on the microarrays from GEO and TCGA (8). Twelve independent microarrays from GEO profiles covered 1083 patients, including GSE14764 (n=80), GSE15622 (n=36), GSE18520 (n=63), GSE19829 (n=28), GSE23554 (n=28), GSE26193 (n=107), GSE26712 (n=195), GSE27651 (n=49), GSE30161 (n=58), GSE3149 (n=166), GSE51373 (n=28) and GSE9891 (n=285); the TCGA ovarian cohort covered 565 patients. Detailed information of gene expression and clinical characteristics of the 565 OC patients in TCGA cohort were retrieved from the cBioPortal for cancer genomics (http://cbioportal.org) (9,10).
Microarray GDS3754 (Human U133 plus 2.0 GeneChips; Affymetrix, Inc., Santa Clara, CA, USA) was deposited in GEO Profiles (11), and used for the analyses of gene expression in drug-resistant OC cells. In this analysis, in the case of two probe sets that target one gene, only the probe set with significant variability was retained. In the case of more than three probe sets that target one gene, the set exhibiting the most divergent expression was excluded and the set with significant variability was retained (12).
The TCGA ovarian cohort and GEO profiles are well known publicly available cohorts that can be downloaded by researchers for further analysis. No changes were made to mRNA expression values used in the analysis.
Bioinformatics analysis
Protein/gene interaction network was generated using the GeneMANIA (http://www.genemania.org/), which is a web-based database and a tool for the prediction of gene functions on the basis of multiple networks derived from different genomic or proteomic data/sources (13). It is fast enough to predict gene functions with great accuracy using GeneMANIA because hundreds of datasets from GEO, BioGRID, Pathway Commons and I2D, as well as organismspecific functional genomics data sets have been collected in this software (14).
Biological process annotation was performed using the Coremine Medical (http://www.coremine.com/medical/), which is a gene/protein database and a web-based tool for text mining. It carries out automated extraction of experimental and theoretical biomedical knowledge from publicly available gene and text databases to create a gene-to-gene co-citation network for millions of named human genes by automated analysis of titles and abstracts in over 10 million MEDLINE records (15).
Data analysis
The data were analyzed using SPSS 20.0 software. The probability of survival and significance were calculated using the Kaplan-Meier method and KM plotter (8). The correlation between gene expression and the clinicopathological characteristics was evaluated by Pearsons χ2 test (two-sided). Gene expression was dichotomized into high and low values using the median as a cut-off in all above analyses, in accordance with previous studies (16). Gene mRNA expression levels are shown as mean ± SD. Homogeneity of the variance was analyzed using the t-test. P<0.05 was considered significant. The desired Affymetrix ID for CKAP2, BCHE, CLDN10, and OVGP1 is 218252_at, 205433_at, 205328_at, and 205432_at, respectively.
Results
Determination of genes associated with OS in OC
We previously reported that 60 genes were upregulated, and 126 genes were downregulated by at least 4-fold in 594 ovarian serous cystadenocarcinomas compared with 8 normal ovaries, respectively, according to TCGA ovarian statistics data deposited in the Oncomine database (17). Of these, we selected 136 genes (38 upregulated and 98 downregulated genes) for which we found ≤2 articles in PubMed using a search with (ovarian[Title]) and ‘gene’[Title/Abstract], for further analysis of their relationships with OS. The mRNA expression data of the 136 genes and related clinical data for 489 OC patients in the TCGA cohort were retrieved from cBioPortal for Cancer Genomics (9,10). Gene expression was dichotomized into high and low values using the median as a cut-off in all above analyses, in accordance with a previous study (16). As shown in Table I, among all 136 genes, 11 were closely associated with OS, as determined by Kaplan-Meier analysis. CKAP2 is upregulated, and ATXN10, BCHE, CLDN10, CRISP3, FCGBP, LYVE1, NDNF, OVGP1, PTGIS and REEP1 are downregulated in OC (17). We found that the upregulation of CKAP2, and downregulation of CLDN10, CRISP3 and OVGP1 were associated with shorter OS, whereas the downregulation of ATXN10, BCHE, FCGBP, LYVE1, NDNF, PTGIS and REEP1 were associated with longer OS. To elucidate whether any of the above genes was an independent factor for predicting OS, we performed multivariate analyses of histological grade, tumor stage, residual tumor and platinum status for the 11 genes, using a Cox proportional hazards model (Table II). The results indicated that BCHE (P=0.026), CRISP3 (P=0.031), LYVE1 (P=0.014) and OVGP1 (P=0.001) were independent prognostic factors for OS in 489 OC patients.
 | Table I.Eleven genes were determined by Kaplan-Meier analysis to be closely associated with overall survival in ovarian cancer. |
| Table II.Multivariate analysis of overall survival of ovarian cancer patients using Cox proportional hazard model. |
The associations of the 11 genes with OS were further validated using KM plotter which included gene expression data and survival information of 1287 OC patients downloaded from GEO and TCGA ovarian data in 2012 (8), and updated to 1648 patients for the 2015 version. As shown in Fig. 1, among the 11 genes, CKAP2, BCHE, CLDN10 and OVGP1 were consistently associated with OS when the number of patients increased from 489 in TCGA cohort to 1583 in KM plotter (Table I and Fig. 1). Of these, upregulation of CKAP2, and downregulation of CLDN10 and OVGP1 were associated with shorter OS, whereas downregulation of BCHE was associated with longer OS. When the four genes were combined, we found that their mean expression in combination was greatly more associated with OS than any of the genes used separately (Fig. 2).
Verification of genes associated with OS in OC patients treated with different chemotherapy regimens
As shown in Table III, in subgroups of patients treated with different regimens, we found that the high expression of CKAP2 was associated with shorter OS in 752 patients treated with taxol-based regimens, and in 763 treated with regimens containing both platin and taxol, but the association was not significant in 1364 patient treated with only platins; downregulation of BCHE predicts a longer OS in patients treated with platin, but not in patients treated with taxols or taxols with platin; downregulation of CLDN10 was associated with shorter OS in patients treated with platin or platin with taxol, but not in the taxol-only group, and downregulation of OVGP1 predicted a shorter OS in all three subgroups of ovarian patients treated with different regimens.
| Table III.Four genes were associated with overall survival in subgroups of ovarian patients treated with different chemotherapy regiments, as determined by KM plotter. |
Verification of genes associated with OS in subgroups of OC patients
The relationships between dysregulation of the four genes (CKAP2, BCHE, CLDN10 and OVGP1) with OS in subgroups of OC patients with different CA125 levels, grades, stages and TP53 mutation status were further investigated. We revealed that the high CKAP2 expression significantly associated with shorter OS in 515 OC patients having low CA125 levels (the average CA125 below lower quartile) (Fig. 3). Besides, as shown in Table IV, all four genes were significantly associated with OS in 968 OC patients with grade 3 disease, but only BCHE and OVGP1 were associated with OS in 371 patients with grade 1–2 disease. CLDN10 and OVGP1 were associated with OS in 1148 patients with stage III–IV OC, but none of the genes was associated with OS in 133 patients with stage I–II OC. In the subgroup of 439 patients with TP53 mutation, downregulation of OVGP1 predicted shorter OS, whereas downregulation of CLDN10 predicted shorter OS in the TP53 wild-type group. In addition, downregulation of BCHE was associated with longer PFS and PPS in cohorts of 1307 and 708 patients, respectively; whereas downregulation of CLDN10 and OVGP1 were associated with shorter PFS and PPS (Fig. 4).
| Table IV.The four genes were associated with overall survival in subgroups of ovarian patients with different grades, stages and TP53 mutation status, as determined by KM plotter. |
Clinical importance of the analysis
The four OS-related genes, CKAP2, BCHE, CLDN10 and OVGP1, with clinical characteristics such as tumor stage, histological grade, and primary therapy outcome were checked in the TCGA cohort (n=489). As shown in Table V, BCHE and OVGP1 were closely associated with primary therapy outcomes. Downregulation of BCHE was associated with good complete response, whereas downregulation of OVGP1 was associated with poor complete response. Besides, downregulation of BCHE was associated with lower tumor stage, in particular, stage II. Only CLDN10 was associated with platinum status; its downregulation might predict drug resistance. The 4 genes were not significantly associated with histological grade in this analysis.
| Table V.Correlation between gene expression and the clinicopathological characteristics of 489 patients with ovarian cancer in TCGA cohort. |
Possible associations with drug resistance
The four genes, CKAP2, BCHE, CLDN10 and OVGP1, were dysregulated in platinum-resistant OC cells, as determined using microarray data retrieved from GEO Profiles and RT-qPCR analysis (Fig. 5). Compared with their expression in sensitive cells, expression of CKAP2 was increased in cisplatin-resistant cells, but decreased in carboplatin-resistant cells; BCHE, CLDN10 and OVGP1 all were decreased in cisplatin- and carboplatin-resistant cells, although the changes in CLDN10 and OVGP1 were not significant in one analysis (Fig. 5A).
Bioinformatics analyses were performed to further explain the associations of the four genes with drug resistance in OC. As shown in Fig. 6A, text mining indicated that BCHE had direct associations with drug resistance and OC, and all the four genes were indirectly associated with drug resistance and OC through six biological processes (P<0.05), including cell proliferation, apoptosis, mitotic nuclear division, phosphorylation, growth and gene expression. We further generated a protein/gene interaction network for the four genes and their products with 49 drug-resistance proteins/genes in OC, which including 15 tumor suppressors (19): BRCA1, BRCA2, CHEK2, FBXO32, MLH1, SULF1, IL24, CDKN2A, CDKN1A, TP53, TP73, PDCD4, PTEN, RASSF1 and WWOX, 25 oncoproteins (20): ACTN4, AKT1, AKT2, BAX, BCL2, MIEN1, CLU, CUZD1, DAXX, EGFR, ERBB2, FOS, JUN, IKBKE, KRAS, MET, MYC, NFKB1, NINL, NOTCH3, PIK3CA, RSF1, SRC, STAT3 and URI1, NEK2 (21), NEK11 (22), TRPC1 (12), CCL21 and SPARCL1 (23), GGNBP2 and RNASET2 (7), ALDH1A2 and ADH1B (17). As shown in Fig. 6B, CKAP2, BCHE, CLDN10 and OVGP1 had direct and indirect interactions with all of the 49 drug-resistant proteins/genes, and directly interacted with 11, 23, 19 and 13 of those 49 proteins/genes, respectively. The four genes and their products also directly interacted with each other, with OVGP1 and BCHE both directly interacting with CKAP2 and CLDN10.
Discussion
Biomarkers not only have prognostic implications, but are also helpful in monitoring treatment responses, surveillance for tumor recurrence and guidance of clinical decisions (24). Long-term survival of OC patients remains very poor as a result of recurrence and emergence of drug resistance, and the 5-year survival rates is only ~40% (2). Thus, prognostic biomarkers for OC patients are particularly necessary and crucial, and there is ongoing search for predictive biomarkers. In the present study, we identified a group of genes significantly associated with OS in 489 OC patients from a TCGA cohort (Table I), of which BCHE, CRISP3, LYVE1 and OVGP1 were identified as independent risk prognostic factors for OS (Table II). Four genes (CKAP2, BCHE, CLDN10 and OVGP1) were consistently associated with OS when the number of patients increased from 489 to 1583 (Fig. 1). Of these, upregulation of CKAP2, and downregulation of CLDN10 and OVGP1 were associated with shorter OS, whereas downregulation of BCHE was associated with longer OS. With the exception of CKAP2, the other three genes all were significantly related to PFS and PPS, with decreased BCHE expression associated with longer PFS and PPS in 1307 and 708 OC patients, respectively; and decreased expression of CLDN10 and OVGP1 were associated with shorter PFS and PPS (Fig. 4). In subgroups of patients with OC, we further verified that downregulation of OVGP1 was significantly associated with shorter OS in all OC subgroups, including the 752 patients treated with chemotherapy regimens that contained taxol, 763 treated with both platin and taxol, 1364 treated with platin, 371 patients with grade 1–2 disease, 968 with grade 3 disease, 1148 with stage III–IV disease, and 439 with TP53 mutations (Tables III and IV). All these results together suggest that CKAP2, BCHE, CLDN10 and OVGP1, (especially OVGP1), are potential biomarkers for predicting OS in OC.
The relationship between these genes and prognosis in OC is poorly studied. Upregulation of chromatin CKAP2 is an independent prognostic marker for shorter relapse-free survival in early-stage breast cancer (25), and for significantly worse prognoses in male patients with T1-2 gastric cancer (26). Downregulation of CLDN10 indicates shorter OS in some patients with lung adenocarcinoma (27). All these reports were consistent with our findings in this study. However, low levels of preoperative serum BCHE were validated as an independent negative prognostic factor for prostate cancer patients who undergo radical prostatectomy (28), and a predictor of shorter OS and DFS in patients with muscle-invasive bladder cancer who undergo radical cystectomy (29). These results were contrary to our findings that downregulation of BCHE was associated with longer OS. The associations of the four genes with OS in OC is rare, with only one study reporting that the downregulation of BCHE predicts a longer OS in OC patients, which was also based on the analysis of data in the TCGA cohort (30).
Serum CA125 is widely used to distinguish malignant from benign pelvic masses, to monitor response to treatment, to assess prognosis and to detect disease recurrence. Low levels of CA125 are normally an optimistic indicator of OC progression and prognosis. For example, preoperative CA125 <65 U/ml correlates with longer survival in OC patients (31); CA125 <50 U/ml correlates with significantly longer DFS and OS in patient with borderline ovarian tumors (32). However, no data are available that further predicts OS in subgroups of patients with low levels of CA125. CKAP2 expression was significantly associated with shorter OS in 515 OC patients who had low CA125 levels (the average CA125 below lower quartile) (Fig. 3), which suggests that this gene could be used for prognosis prediction with CA125. However, this study measured tissue levels of CA125, whereas the FDA-approved test for OC is serum-based (8). Associations between CKAP2 and OS in patients with low CA125 levels should be further studied.
The relationships of the four genes with drug resistance in cancer has not been widely reported. In the present study, we found that CKAP2, BCHE, CLDN10 and OVGP1 all were significantly dysregulated in cisplatin- and carboplatin-resistant cells (Fig. 5). Text mining indicated that the four genes were directly and indirectly associated with drug resistance through several biological processes. A protein/gene interaction network indicated that the four genes interacted with 49 drug-resistant proteins/genes in OC. In particular, BCHE and CLDN10 directly interacted with 23 and 19 of those 49 proteins/genes (Fig. 6). Downregulation of CLDN10 was also an indicator of drug resistance, and downregulation of OVGP1 was significantly associated with poor complete response (Table IV). All these results imply that dysregulation of the four genes affects drug resistance in OC. Furthermore, among patients treated with different regimens, high expression of CKAP2 was associated with shorter OS in patients treated with taxol-containing regimens, whereas downregulation of BCHE was associated with OS in patient treated by regimens without taxol; downregulation of CLDN10 was associated with shorter OS in patients treated with platin, and downregulation of OVGP1 predicted a shorter OS in patients treated with either platin or taxol (Table III). These results indicated that CKAP2 is associated with taxol resistance, BCHE and CLDN10 with platin resistance, and OVGP1 with both platin and taxol resistance.
The roles of CKAP2, BCHE, CLDN10 and OVGP1 in OC have rarely been studied. We previously reported that CKAP2 was upregulated, and BCHE, CLDN10 and OVGP1 were downregulated in OC, by a 4.26, −8.39, −7.04 and −102.95-fold changes, respectively (17). Downregulation of OVGP1 in OC was recently confirmed in a study that showed the protein to be less abundant in high-grade serous ovarian tumor fluids (malignant) than in benign serous cystadenoma tumor fluids (33). CLDN10 mRNA was specifically detected in five cancerous ovaries compared with three normal controls in experimental hens, which suggests that CLDN10 is a novel biomarker for detecting OC in chickens (34); however, its association with human OC was not discussed.
In summary, we identified 11 genes significantly related to OS in 489 OC patients. Of these, BCHE, CRISP3, LYVE1 and OVGP1 were identified as independent risk prognostic factors. Four genes CKAP2, BCHE, CLDN10, and particularly the OVGP1, were remarkably associated with OS in total of 1583 OC patients, and combination of these genes had much better prediction potential. Besides, comprehensive analysis indicated that CKAP2, BCHE, CLDN10 and OVGP1 might contribute to drug resistance in OC. This study has implicated genes that might be both prognostic markers and potential therapeutic targets to pursue in the treatment of ovarian cancer.
Acknowledgements
This study was supported by the National Natural Science Foundation of China (grant nos. 81302283, 81560424, 81660606 and 81460397), the China Postdoctoral Science Foundation (nos. 2014M552535XB and 2014M552291), the Natural Science Foundation of Guangxi (nos. 2014GXNSFCA118010, 2015GXNSFBA139115, 2015GXNSFAA139151 and 2014GXNSFBA118155).
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