A panel of 7 prognosis-related long non-coding RNAs to improve platinum-based chemoresistance prediction in ovarian cancer
- Authors:
- Published online on: May 11, 2018 https://doi.org/10.3892/ijo.2018.4403
- Pages: 866-876
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Copyright: © Song et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
In the past 30 years, ovarian cancer has exhibited the highest mortality rate of all types of lethal gynecological cancer worldwide, with a 5-year survival rate of <30% (1,2). The characteristics of ovarian carcinoma encompass the development of chemoresistance (3), recurrence (4) and poor prognosis (5), which pose huge challenges for drug development and therapeutics (6). Once chemoresistance occurs, patients may not only experience adverse effects, but they may also eventually relapse (7). Although ~75% of patients initially respond to the platinum-based chemotherapy, the majority relapse with resistance, which leads to treatment failure and causes >90% of mortalities (8). To date, patients with ovarian cancer have been defined as treatment-resistant if the duration of disease-free survival (DFS) or the platinum-free interval was <6 months, while patients with longer durations have been defined as treatment-sensitive (3,9). However, this approach cannot provide guidance for individualized treatment of patients with drug resistance.
Long non-coding (lnc)RNA is a type of RNA that has no protein coding potential (10). However, lncRNAs have been reported to be critical in multiple stages of tumorigenesis and progression, including apoptosis, DNA damage response and metastasis (11,12).
lncRNAs may also be potential markers for the prediction of platinum-based chemoresistance (11,12). LncRNA ABHD11 antisense RNA 1 (tail to tail) was demonstrated to promote the proliferation, invasion and migration of ovarian cancer cells (13). LncRNA nuclear enriched abundant transcript 1 was reported to interact with BAI1-associated protein 1, which may contribute to the sensitivity against gemcitabine in cholangiocarcinoma (14). The role of lncRNAs in drug resistance of high-grade serous ovarian cancer (HGS-OvCa) remains to be fully investigated. Therefore, examining the possibilities of individualized treatment in patients with chemoresistance to platinum-based therapeutics and ovarian cancer from the perspective of lncRNAs may be useful to improve our understanding of drug resistance (15,16). The present study investigated the role of lncRNAs in platinum-based chemo-resistance and survival in patients with HGS-OvCa in order to contribute to current research on individualized therapies.
Materials and methods
Training and validation datasets
Raw human microarray expression profiles were retrieved and downloaded from Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih. gov/geo/). First of all, the key words 'high-grade serous ovarian cancer' and 'chemotherapeutic resistance' were used to search for samples. Secondly, as more lncRNAs could be detected using the Affymetrix Human Genome U133 Plus2.0 micro-array (Affynetrix; Thermo Fisher Scientific, Inc., Waltham, MA, USA), the samples not from that platform were filtered. Finally, the samples with data associated with chemotherapeutic response were selected. In total, 328 microarray profiles with corresponding clinical information [i.e. age, tumor grade, International Federation of Gynecology and Obstetrics (FIGO) stage (17), residual tumor size and platinum sensitivity] were downloaded. The accession numbers of GEO for the datasets were as follows: GSE63885 (18) (n=70); GSE51373 (19) (n=28) and GSE15372 (20) (n=10). Another dataset GSE9891 (21) (n=220) was used as a GEO validation dataset. The expression levels were normalized using the robust multi-array average (RMA) algorithm (22). Batch effects were removed using the Combat algorithm (23).
The expression profiles and clinical information of 233 patients with HGS-OvCa were downloaded from The Atlas of Noncoding RNAs in Cancer (24) and cBioportal (25) databases, respectively, as The Cancer Genome Atlas (TCGA) validation dataset. The expression levels were processed using the same methods as aforementioned. All datasets were analyzed to determine the differences in expression (Table I).
Integration and probe annotation of lncRNA datasets
LNCipeida (26) is a publicly available database for annotated human lncRNA transcript sequences and structures. The public repository produced by Du et al (27) provides a resource of clinically relevant lncRNAs for the development of lncRNA biomarkers and the identification of lncRNA therapeutic targets. The present study integrated the records of lncRNAs from GENCODE (28), LNCipedia, NONCODE (29), TCGA and the public repository by Du et al (27). In total, 24,372 lncRNAs were included in the present study. To avoid inaccurate annotations, the probes were cross-referenced by gene name and Ensembl transcript ID (release 89). For multiple probes of a single lncRNA, the median expression of the unique transcript ID was preserved. Finally, the expression profiles of 6,955 lncRNAs were obtained.
Clustering and differential expression analysis
The platinum-based sensitivity of patients with HGS-OvCa varies greatly, and the DFS ranged from 180 days to several thousand days. In this context, unsupervised hierarchical clustering based on Pearson distance for platinum-sensitive samples were performed using the ConcensusClusterPlus package in R (30).
The differential expression analysis was performed using R/Limma package (31). The common differentially expressed lncRNAs between each sensitive subgroup and resistant group were screened [differential expressed (DE) set A]. The differentially expressed lncRNAs between the complete sensitive group and the resistant group were obtained (DE set B); DE sets A and B were then merged.
Univariate and multivariate logistic regression analysis and survival analysis
To investigate the platinum-based chemoresistance-associated lncRNAs and the clinical characteristics of patients with HGS-OvCa, an independent univariate logistic regression was performed on 6,955 lncRNAs and clinical characteristics. The lncRNAs and clinical characteristics with P<0.05 were considered statistically significant and associated with chemoresistance. The survival package in R (32) was used to investigate DFS-associated lncRNAs. The Kaplan-Meier survival curves of highly expressed and weakly expressed lncRNAs were constructed. The median expression level was used as a cut-off for determining high and low expression.
The lncRNAs and clinical characteristics that were statistically significant in the univariate logistic regression were used as candidate parameters for constructing the multivariate logistic regression model. A best multivariate logistic regression model was generated after fitting the model thousands of times. The expression levels of each lncRNA and its coefficient in the best multivariate logistic model were used to calculate the platinum-based chemoresistance risk score of patients with HGSC-OvCa. The risk score was computed by the following formula:
where n is the number of prognostic lncRNAs in the model, 'expri' is the expression level of lncRNAi and coefi is the estimated regression coefficient of lncRNAi in the multivariate logistic regression model. Patients who have higher risk scores are expected to have higher probability of platinum-based chemoresistance. The median risk score was used as a cut-off to divide the patients in the GEO validation dataset into low-risk and high-risk groups. Furthermore, the multivariate logistic regression analysis was performed to test whether the risk score was independent of clinical covariates.Subtype classification of patients with HGS-OvCa
The 100-gene set containing four gene signatures corresponding to four HGS-OvCa subtypes (differentiated, immunoreactive, proliferative and mesenchymal) (33) were used to train the support vector machine for subtype classification. The univariate and multivariate logistic regression were performed to investigate whether the HGS-OvCa subtypes were associated with platinum-based chemoresistance, and whether these subtypes associated with our signature, respectively.
Classifier performance evaluation
To assess the sensitivity and specificity of the risk scores in prediction of chemoresistance in patients with HGS-OvCa, ROC (34) and ROCR package (35) in R software were used to construct the receiver operating characteristic curve and perform the area under curve (AUC) analysis, respectively. Furthermore, the predictive performance of the risk score and three signatures previously developed by Zhou et al (36), Liu et al (37) and TCGA mRNA signatures (38) was compared.
Verification of the expression level of 7 lncRNAs
Microarrays and RNA-seq data were used to validate the expression of lncRNAs. The RNA-seq data of 136 patients with HGS-OvCa and platinum-based chemosensitivity was derived from the Gaucher Disease Advisory Committee (39). The reads per kilobase per million mapped reads were obtained by chromosome location (GRCH37, hg19) of lncRNAs and normalized using Z-scores.
First, the gene expression in the Cancer Cell Line Encyclopedia (CCLE) (40) database had been pre-processed using the RMA algorithm. The chemotherapeutic status of 52 ovarian cancer cell lines was investigated. The data regarding the cell lines of clear cell, granulosa cell, undifferentiated cell, mixed cell, endometrioid cell, Brenner cell, mucinous cell and cross-contaminated cell were filtered. The cell lines that were treated with cisplatin or carboplatin under-went >3 cycles of treatment. The cell lines exhibited resistance to platinum and the degree of resistance varied. Secondly, gene expression levels in 46 ovarian cancer cell lines with or without cisplatin treatment were obtained from GSE47856 (41). To investigate the changes in the expression of lncRNAs that was induced by platinum, cell lines from the CCLE were analyzed using the Integrative Genomics Viewer (IGV) software (version 2.3) (42). In addition, cell lines from GSE47856 with ≥3 replicates were analyzed. For multi-probe lncRNA, the median expression was calculated.
Functional prediction analysis
To investigate the biological processes and cellular components implicated in the drug resistance mechanisms, the enriched Gene Ontology (GO) (43) terms of 7 lncRNAs were identified using the NONCODE data-base and were visualized using Cytoscape (version 3.2.1) (44). The potential functions of the lncRNAs were further analyzed using non-coding RNA in the Drug Resistance (ncDR) data-base (45); P<0.05 was used to identify resistant compounds of lncRNAs in cancer.
Statistical analysis
The empirical Bayes method and a two-tailed Student's t-test were used in differential expression analysis. The log-rank test was used for survival analysis, with Kaplan-Meier curves. Clinical characteristics and molecular subtypes of three datasets were tested using the χ2 tests or two-way analysis of variance, where appropriate. In multivariate logistic regression, the χ2 test was used to add or filter features and avoid overdispersion. The features were also filtered using the methods of Watt et al (46). P<0.05 was considered to indicate a statistically significant difference. All statistical analyses were conducted using the R software (version 3.2.1).
Results
Cluster analysis of chemosensitive patients reveals three subgroups
The results of unsupervised hierarchical clustering were obtained when clustering center k was set from 2 to 5 (Fig. 1). The specimens were perfectly divided into two clusters (Fig. 1A); a third cluster was observed when k=3 (Fig. 1B). However, fourth stable clusters were not found when the number of cluster centers k continued to increase (Fig. 1C and D). These results indicate that there may be three stable subgroups with different expression patterns in chemosensitive patients.
Identification and assessment of chemoresistance-associated lncRNA signature
In the present study, 386 differential expressed lncRNAs were obtained subsequent to merging 262 lncRNAs in DE set A and 365 lncRNAs in DE set B (Fig. 2A). In univariate regression, 337 lncRNAs (288 plus 43 plus 7 minus 1) were associated with platinum-based chemoresistance after merging the lncRNA transcripts corresponding to the same lncRNA gene, 50 lncRNAs (43 plus 7) (Fig. 2B) were associated with DFS (log-rank P<0.05). Next, a 7-lncRNA signature was identified (Table II and Fig. 2C), which was associated with platinum-based chemoresistance in the univariate and multivariate logistic regression model. The risk scores based on the 7-lncRNA signature for the prediction of platinum-based chemoresistance in patients with HGS-OvCa were also calculated. According to the risk scores, patients in the GEO training dataset were divided into low-score and high-score groups using the median risk score as a cut-off in the multivariate logistic regression model (Fig. 2D). The high-score group exhibited a higher probability of drug resistance [univariate model: Odds ratio (OR), 2.675; 95% confidence interval (CI), 1.841-3.897; P=6.36×10−7; multivariate model: OR, 2.859; 95% CI, 1.801-5.257; P=9.36×10−5). The results of Kaplan-Meier curves also indicated that the high risk score was significantly associated with the poor survival of patients with HGS-OvCa and drug resistance (n=47; log-rank, P<0.05; Fig. 2E).
 | Table IIUnivarate and multivariate logistic regression model of 7 long non-coding RNAs in the Gene Expression Omnibus training dataset. |
Risk score can be used as an independent predictor for drug resistance
In the multivariate logistic regression model of the training datasets, platinum-based chemosensitivity was a dependent variable. Platinum-based chemosensitivity has multiple covariates, including tumor stage, grade, tumor size, molecular subtypes and risk score. However, the results indi-cated that the risk score was an independent variable when adjusting using the covariates as aforementioned (OR, 2.859; 95% CI, 1.801-5.257; P=9.36×10−5; Table III).
The results of the univariate and multivariate logistic regression analysis for the GEO validation set (n=220) and the TCGA validation set (n=233) confirmed that the risk score of the training set was associated with platinum-based chemore-sistance of patients with HGS-OvCa (univariate model of the GEO dataset: OR, 4.190; 95% CI, 2.974-4.685; P<4.68×10−14; multivariate model of the GEO dataset: OR, 4.210; 95% CI, 2.967-6.366; P<1.15×10−13; univariate model of TCGA dataset: OR, 2.315; 95% CI, 1.852-2.956; P=2.33×10−5; and multivariate model of TCGA dataset: OR, 2.514; 95% CI, 1.817-3.354; P=2.65×10−5; Table III). Furthermore, clinical characteristics and HGS-OvCa subtypes (differentiated, immunoreactive, proliferative and mesenchymal) were not associated with platinum-based resistance in two validation datasets. Collectively, these results suggest that risk score has the potential to be an independent predictor for chemoresistance of HGS-OvCa cancer.
Predictive lncRNA signature performance evaluation
The risk score of the 7-lncRNA signature indicated a high sensitivity and specificity in predicting platinum-based chemoresistance in the GEO training (AUC=0.848), GEO validation (AUC=0.901) and TCGA validation (AUC=0.818) datasets (Fig. 3A). Compared with the lncRNA signature of the study by Liu et al (37), the lncRNA signature developed in the study by Zhou et al (36) and the TCGA mRNA signature, the 7-lncRNA signature exhibited an improved predictive performance (Fig. 3B–D).
Verification of the expression level of 7 lncRNAs in microarray and RNA-seq data
A total of 7 lncRNAs were observed in the GEO validation dataset, while F11-AS1 and RP11-348N5.7 were not observed in the TCGA validation dataset and RNA-seq dataset, respectively. However, all of the 7 lncRNAs were covered by validation datasets. Notably, the expression levels of 7 lncRNAs were concordant in at least two validation datasets (P<0.05; Fig. 3E).
Predicted function of the 7 lncRNAs may be associated with drug resistance mechanisms
With several cycles of cisplatin or carboplatin treatment, ZBED1-AS1 was upregulated, while F11-AS1 was downregulated by platinum in OV56 and OVKATE cell lines (P<0.05; Fig. 3F). Growth arrest specific 5 (GAS5) was downregulated by cisplatin in A2008, DOV13B and HeyA8 cell lines (P<0.05; Fig. 3G). This indicates that these lncRNAs may be involved in drug resistance mechanisms. Therefore, the functional prediction analysis of 7 lncRNAs based on NONCODE database was further performed. The results showed that a total of 42 GO terms were involved in the biological functions of 7 lncRNAs, which were distributed in 'biological processes' (91.3%) and 'cellular components' (8.7%). Furthermore, 16 of the 42 GO terms was associated with >1 lncRNA (Fig. 4). Based on ncDR, the aberrant regulation of lncRNAs was predicted to be resistant to dozens of compounds across various cancer types. For example, the upregulation of ZBED3-AS1 is associated with resistance to cisplatin, dasatinib and bicalutamide in low-grade glioma. The downregulation of RNF144A-AS1 is associated with resistance to dacinostat in sarcoma, and the downregulation of GAS5 is associated with resistance to cisplatin and cytarabine in pancreatic adenocarcinoma.
Discussion
The compilation of multiple datasets offers a unique opportunity to discover transcriptional variation in HGS-OvCa. This type of combinatorial computational analysis must take the context of numerous confounding factors, including distinct platforms, different protocols of sample collection and processing, and different normalization algorithms of expression level, which may not associate with tumor biology or consider cancer behavior (45). Therefore, in the present study, the independent analysis of multiple datasets was performed, and batch effect was removed. Furthermore, the verification of the expression levels of the identified lncRNAs was conducted using multiple datasets and cell lines. The results from multiple datasets were concordant and verified that the 4 upregulated lncRNAs (RP11-126K1.6, ZBED3-AS1, RP11-439E19.10 and RP11-348N5.7) and 3 downregulated lncRNAs (RNF144A-AS1, GAS5 and F11-AS1) were differentially expressed in response to platinum. In addition, not only the 7-lncRNA signature, but also the individual lncRNAs themselves were associated with the prognosis of patients with HGS-OvCa.
The molecular mechanisms of drug resistance in ovarian cancer are mainly involved in apoptosis evasion, escape from immune surveillance and natural killer cell-mediated oncolysis, promotion of DNA repair, and sustained angiogenesis, proliferation, invasion and metastasis (6,47,48). To date, the majority of the lncRNAs reported have not been functionally characterized in drug-resistant ovarian cancer.
GAS5 has been reported as a biomarker of drug resistance in non-small cell lung cancer (49) and breast cancer (50). The downregulation of GAS5 promotes the proliferation, migration and invasion of ovarian cancer cells, and inhibits apoptosis, which leads to a poor prognosis (51,52). From the results of functional prediction in the present study, the downregulation of RNF144A-AS1 and GAS5, and the upregulation of RP11-126K1.6, RP11-439E19.10 and RP11-348N5.7 in patients with ovarian cancer may contribute to platinum resistance by affecting the regulation of tumor growth. RNF144A-AS1, F11-AS1, ZBED3-AS1, RP11-439E19.10 and RP11-348N5.7 were predicted to participate in the acute-phase response. The acute-phase response serves as a core of the innate immune response, which can be triggered by inflammation (53). It has been confirmed that inflammation and immunogenic-tumor microenvironment interactions will increase the risk of ovarian tumor initiation and progression by overexpression of several proinflammatory cytokines (54,55). For example, the complex formed by the binding of the proinflammatory cytokine inter-leukin-6 (IL-6) to its receptor IL-6R activates Janus kinase and various downstream effectors, including signal transducer and activator of transcription 3 and mitogen-activated protein kinase, which are critical for cell proliferation, apoptosis evasion, survival and drug resistance in ovarian cancer (6,56,57). Taken together, the present results indicated that the 5 lncRNAs (RNF144A-AS1, F11-AS1, ZBED3-AS1, RP11-439E19.10 and RP11-348N5.7) may interact with proinflammatory cytokines to promote ovarian tumor initiation and progression. Additionally, the ncDR database also indicated that the identified lncRNAs were involved in resistance to platinum-based chemotherapeu-tics across various cancer types.
The present study has a number of limitations. Once multiple-transcript probes have been identified as candidate features of the multivariate logistic regression model, multi-collinearity could be a concern for the accuracy of predictors. Multicollinearity does not reduce the predictive reliability of the entire signature. In the present study, there was no collinearity between other lncRNAs and the 7 candidate lncRNAs. In addition, lncRNA records vary in well-known biological databases. Although in the present study the lncRNAs records from multiple databases were re-integrated for lncRNA identification, the omission of identification of lncRNAs may still occur.
In the present study, a 7-lncRNA signature for predicting platinum-based chemoresistance in patients with HGS-OvCa was developed. The signature is independent of clinical characteristics and molecular subtypes of HGS-OvCa, and can be used as a novel prognostic marker, with the potential to be a therapeutic molecular target in the future. Further clinical studies are required for validating the lncRNAs identified in the present study and for investigating the underlying mechanisms of drug resistance in HGS-OvCa.
Acknowledgments
Not applicable.
Funding
The present study was supported by a grant from the Chongqing Science and Technology Foundation of Yuzhong District (no. 20150107).
Availability of data and materials
All data generated or analyzed during this study are included in this published article.
Authors' contributions
JS designed the study, analyzed and interpreted the data, and was a major contributor in writing the manuscript. WZ made critical advices to the conception and design of the study. SW, KL and FS contributed to data collection. LR designed the study, supervised the project and revised the manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no conflict of interests.
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