Abstract

   Ovarian cancer is the most lethal gynecologic malignancy, characterized
   by late diagnosis, frequent relapse, and easy development of
   chemoresistance. Recent studies suggest that lncRNAs are involved in
   ovarian cancer onset and progression, as well as the resistance in
   paclitaxel-containing chemotherapy. However, the genome-wide expression
   pattern and associated functional implications of lncRNAs in
   paclitaxel-resistant ovarian cancer cells remain undetermined. In the
   present study, we identified a panel of lncRNAs aberrantly expressed in
   both paclitaxel resistant ovarian cancer tissues and cell lines,
   including [32]XR_948297, [33]XR_947831, [34]XR_938728, [35]XR_938392,
   [36]NR_103801, [37]NR_073113, and [38]NR_036503. Moreover, the
   seven-lncRNA signature showed a relatively high predictive accuracy of
   chemoresistance with an area under the ROC curve (AUC) of 0.93, and was
   associated with progression-free survival inovarian cancer patients
   (HR=2.05, p=0.015). Our function prediction demonstrated that the
   seven-lncRNA signature was positively correlated with a cluster
   containing 129 genes enriched in insulin secretion-related pathway. Our
   findings suggest that the seven-lncRNA signature may be utilized as
   potent biomarkers for predicting chemoresistance for ovarian cancer
   patients with paclitaxel-containing chemotherapy.

   Keywords: long non-coding RNA, epithelial ovarian cancer, paclitaxel,
   chemoresistance

INTRODUCTION

   Epithelial ovarian cancer (EOC), accounting for the first place in
   mortality rate of all gynecologic cancers [[39]1], still has a poor
   prognosis, with a 5-year survival rate lower than 30%, despite the
   amelioration of modern therapeutic methods [[40]2]. The high mortality
   rate of EOC is mainly on account of the difficulty in early diagnosis,
   metastasis, and chemoresistance [[41]3]. Combination of cytoreductive
   surgery and post-operational chemotherapy is thecurrent standard
   treatment for advanced EOC. However, more than 70% patients exhibit
   chemoresistance after surgery, leading to adverse effects to subsequent
   curative results and survival [[42]4]. The mechanic research on
   chemoresistance remains clinically challenging, simply because it holds
   back a successful long-term efficacy of EOC [[43]5, [44]6]. Currently,
   the paclitaxel and platinum-containing chemotherapy is the main scheme
   for dealing with advanced EOC. Compared with platinum resistance which
   has been widely studied in EOC, much less mechanism of paclitaxel
   resistance has been clarified. Efforts in exploring molecular mechanism
   of paclitaxel resistance will definitely enhance the clinical treatment
   as well as improve the survival rate of EOC patients.

   Non-coding RNAs are drawing increasing attention in recent years for
   their pivotal regulating functions in various biological and
   pathological processes. As an important member of the non-coding RNAs,
   long non-coding RNAs (lncRNAs) have been identified as versatile
   players in multiple diseases by regulating target genes expression,
   directly either on transcripts originating from the same locus as the
   lncRNA itself (cis-acting) or on target transcripts originating on
   other loci (trans-acting) [[45]7]. Numerous evidences reveal that they
   are significant regulators of various biological processes including
   immunoreaction, tumorigenesis, genetic modification, and metabolism
   regulation [[46]8–[47]11], and play pivotal roles in various cellular
   functions, disease occurrence and progression, including drug
   resistance [[48]12]. To date, several lncRNAs have been found to
   mediate drug resistance. For example, studies revealed the harmful
   effects of a specific lncRNA, named homeobox transcript anti-sense RNA
   (HOTAIR), with an increasing prevalence of multiple neoplasm
   chemoresistance phenotypes [[49]13–[50]15]. Huang et al. identified
   that certain lncRNA could inhibit the transcription of tumor-suppressor
   genes by regulating protein activity to promote drug resistance in
   tumor cells [[51]16]. In addition, an eight-lncRNA signature was found
   to be eligible for classifying patients with poor and improved overall
   survival rate [[52]17]. Noticeably, two lncRNAs, RP11-284N8.3.1 and
   [53]AC104699.1.1, have been recently demonstrated to be potential
   prognosis indicators of EOC patients by lncRNA-mRNA co-regulation
   network [[54]18]. Collectively, these results confirmed the involvement
   of lncRNAs in the occurrence and progression of cancer and raised the
   possibility that lncRNAs could be used as biomarkers for diagnosis and
   prognosis as well as novel treatment targets [[55]19, [56]20]. However,
   so far there is no research available on whether lncRNAs play a role in
   paclitaxel resistance of EOC. We believe that identifying a paclitaxel
   resistance-specific signature of lncRNAs in EOC and elaborating the
   latent molecular mechanisms will broaden our understanding of human EOC
   and provide future clinical approaches to treating this disease.

   The present study aims to demonstrate whether lncRNAs play a vital role
   in paclitaxel-containing chemotherapies of EOC patients and to assess
   the prognostic values of identified lncRNAs. Furthermore, we also
   attempt to explore the underlying molecular mechanisms and involved
   pathways in paclitaxel resistance of EOC patients.

RESULTS

Identification of differentially expressed lncRNAs from the datasets

   The [57]GSE30161, a gene expression profile consisting of 46 patient
   samples, was used for the detection of lncRNAs related to incomplete
   response (IR) and complete response (CR) of EOC patients. With
   analyzing package GEO2R, we identified a total of 50 up-regulated genes
   and 181 down-regulated genes (p<0.05 and FC>1.2) (Figure [58]1A),
   including both mRNAs (35 up-regulated mRNAs and 114 down-regulated
   mRNAs) and lncRNAs (15 up-regulated lncRNAs and 67 down-regulated
   lncRNAs) from [59]GSE30161 (Figure [60]1B and [61]1C). Then, we
   analyzed [62]GSE54772, another gene expression profile consisting of
   samples of 2 paclitaxel-resistant ovarian cancer cell lines as well as
   2 paclitaxel-sensitive ovarian cancer cell lines, and found
   approximately 400 differentially expressed genes between the two groups
   (p<0.05 and FC>2 were considered statistically significant) (Figure
   [63]2A). We further analyzed the combination of [64]GSE30161 and
   [65]GSE54772 and identified a panel of seven lncRNAs([66]XR_948297,
   [67]XR_947831, [68]XR_938728, [69]XR_938392, [70]NR_103801,
   [71]NR_073113, and [72]NR_036503) that were significantly correlated
   with EOC patients’ chemotherapeutic sensitivity and efficacy (Figure
   [73]2B). Distribution patterns of the seven enrolled lncRNAs in
   different sample groups were presented (Figure [74]2C).

Figure 1. Identification of differentially expressed genes from the EOC
dataset [75]GSE30161.

   [76]Figure 1
   [77]Open in a new tab

   (A) A total of 231 differentially expressed genes were picked up from
   EOC patients treated with paclitaxel. (B) Within the 231 differentially
   expressed genes, there were both mRNAs (35 up-regulated mRNAs and 114
   down-regulated mRNAs) and lncRNAs (15 up-regulated lncRNAs and 67
   down-regulated lncRNAs). (C) A diagrammatic sketch demonstrating the
   specific pattern of differentially expressed genes from [78]GSE30161.
   IR: incomplete response; CR: complete response.

Figure 2. Identification of differentially expressed genes from the EOC
dataset [79]GSE54772.

   [80]Figure 2
   [81]Open in a new tab

   (A) Approximately 400 differentially expressed genes were picked up
   from paclitaxel-sensitive and paclitaxel-resistant EOC cell lines. (B)
   A panel of seven lncRNAs([82]XR_948297, [83]XR_947831, [84]XR_938728,
   [85]XR_938392, [86]NR_103801, [87]NR_073113, and [88]NR_036503) that
   were significantly correlated with EOC patients’ chemotherapeutic
   sensitivity and efficacy were picked up when EOC dataset [89]GSE30161
   and [90]GSE54772 were analyzed simultaneously. (C) Distribution
   patterns of the seven enrolled lncRNAs. OV: ovarian cancer; IR:
   incomplete response; CR: complete response; Res: resistant; Sens:
   sensitive.

Risk assessment and predictive value

   We assessed the risk score on the basis of the expression levels of
   seven enrolled lncRNAs between the two patient groups (IR vs. CR)
   (Figure [91]3). Besides the relevance of sensitivity to EOC
   chemotherapy, the relationships between the identified seven lncRNAs
   and chemotherapeutic effects were also explored. ROC analysis was
   performed to assess the predictive accuracy of each individual lncRNA,
   respectively (Figure [92]4). Furthermore, we also analyzed the
   predictive accuracy of the combined seven-lncRNA signature. The seven
   lncRNAs were integrated into risk score to predict the risk of tumor
   recurrence of EOC patients receiving paclitaxel-containing
   chemotherapy. The IR group showed a higher risk score than the CR group
   (p<0.0001) (Figure [93]5A). Compared with each individual lncRNA
   signature, the combination of all 7-identified-lncRNA signature showed
   a much better performance testified by a significantly higher AUC value
   (AUC = 0.93, 95% CI = 0.86 - 1.00) (Figure [94]5B).

Figure 3. Dendrogram and trait heatmap of seven enrolled lncRNAs between IR
and CR EOC patient groups.

   [95]Figure 3
   [96]Open in a new tab

   Risk scores were assessed on the basis of the expression levels of
   seven enrolled lncRNAs between IR and CR EOC patient groups.

Figure 4. ROC analysis of the predictive accuracy of each individual lncRNA.

   [97]Figure 4
   [98]Open in a new tab

   (A-G) The predictive accuracy of each individual lncRNA of the seven
   enrolled lncRNAs was assessed by ROC analysis, respectively. OV:
   ovarian cancer.

Figure 5. Predictive and prognostic value of each individual lncRNA as well
as the combined seven-lncRNA signature.

   [99]Figure 5
   [100]Open in a new tab

   (A) Risk scores of the combined seven-lncRNA signature between IR and
   CR EOC patient groups. (B) Sensitivity analysis of the combined
   seven-lncRNA signature between IR and CR EOC patient groups. (C)
   Correlation between risk scores and progression-free survival, as well
   as overall survival. (D) The Kaplan-Meier plotter analysis of overall
   survival between low risk and high risk EOC patient groups on the basis
   of the combined seven-lncRNA signature. (E) The Kaplan-Meier plotter
   analysis of progression-free survival between low risk and high risk
   EOC patient groups on the basis of the combined seven-lncRNA signature.
   IR: incomplete response; CR: complete response; OS: overall survival;
   OV: ovarian cancer; PFS: progression-free survival; MST: mean survival
   time.

Prognostic value of the combined seven-lncRNA signature

   According to the risk score, patients in the dataset [101]GSE30161 were
   further divided into low-score and high-score groups, with the median
   risk score taken as the cut-off point (Figure [102]5C). As presented in
   Figure [103]5C, our results showed that patients with low risk scores
   always had a longer progression-free survival (PFS) time. In contrast,
   the overall survival (OS) time between high- and low- risk groups did
   not reach a significant difference. In order to confirm whether the
   seven-lncRNA expression signature was an independent predictor of
   ovarian patients’ sensitivity to platinum-based paclitaxel
   chemotherapy, the Kaplan-Meier plotter analysis was conducted. The
   patients with a high risk score showed a shorter OS time, compared with
   their counterparts in the low risk group (MST = 40.40 vs 50.03 months).
   However, the association between seven-lncRNA expression signature and
   OS did not reach a significant difference (HR = 1.29, 95% CI =
   0.63-2.64, p = 0.490). Interestingly, when analyzing the association
   between seven-lncRNA expression signature and PFS, we found that
   patients with higher risk score had a shorter PFS time (MST = 10.30 vs
   15.13 months) with a significant LogRankp-value (R = 2.05, 95% CI =
   1.20-4.23, p = 0.015) (Figure [104]5D). Together, these results showed
   that the combined seven-lncRNA signature is an independent predictor of
   ovarian patients’ sensitivity to platinum-basedpaclitaxel-containing
   chemotherapy.

Function annotation of seven-lncRNA signature

   To predict the biological function ofthe seven identified lncRNAs, we
   performed WGCNA based on the mRNA and lncRNA profiles derived from
   [105]GSE30161 (Figure [106]6A). The co-expressed relationships between
   the expression levels of seven lncRNAs and genome-wide genes were
   investigated by module-trait relationship analysis (Figure [107]6B),
   and we identified 129 genes (pink module) with which the expression of
   seven lncRNAs were all significantly and positively correlated (Figures
   [108]6B and [109]7A). Then we made use of Gene Ontology (GO) analysis
   and KEGG analysis to analyze the potential biological functions of the
   129 picked-up genes. The results revealed these genes are closely
   involved in biological processes including insulin secretion,
   regulation of exocytosis, cyclic Adenosine monophosphate (cAMP)
   -mediated signaling, synapse assembly, calcium ion regulated exocytosis
   (Figure [110]7B), and cellular components such as receptor complex,
   nucleoplasm and membrane (Figure [111]7C). KEGG pathway enrichment
   analysis further suggested that those genes were associated with cancer
   and insulin secretion (Figure [112]7D). Interestingly, both GO and KEGG
   pathway enrichment analyses suggested the insulin secretion related
   pathways were involved in the predicted functions of the sevenlncRNAs.
   Above findings provide evidence to explore molecular mechanism of
   paclitaxel resistance in EOC patients.

Figure 6. The WGCNA gene-coexpression analysis of differentially expressed
genes from the EOC dataset [113]GSE30161.

   [114]Figure 6
   [115]Open in a new tab

   (A) The cluster dendrogram differentially expressed mRNAs and lncRNAs
   derived from the EOC dataset [116]GSE30161. (B) The module-trait
   relationship analysis of the co-expressed patterns between the seven
   lncRNAs and genome-wide genes. ME: module eigengenes.

Figure 7. Function annotation of seven-lncRNA signature.

   [117]Figure 7
   [118]Open in a new tab

   (A) Correlation of each individual lncRNA with the differentially
   expressed genes from the EOC dataset [119]GSE30161. (B-C) Gene Ontology
   analysis of biological processes (B) and cellular components (C)
   related to the 129-seven lncRNAs-co-expressed genes. (D) The KEGG
   pathway enrichment analysis of the predicted functions of the seven
   lncRNAs. GO: Gene Ontology.

DISCUSSION

   Most EOC patients respond well at the initial of chemotherapy, but
   quickly develop drug resistance, which finally leads to treatment
   failure. To date, there are still no effective methods to prevent drug
   resistance in EOC. To design new and more effective treatments, it is
   essential to understand the underlying molecular mechanism inducing
   drug resistance. In this study, a comprehensive analysis of lncRNA
   expression profiles in EOC patients was conducted. To the best of our
   knowledge, our study is the first to explore the association between
   lncRNAs expression signature and paclitaxel treatment in EOC patients.

   So far, although increasing numbers of lncRNAs have been discovered and
   recorded in biological databases, most of their functions still remain
   unclear. Previous studies found aberrant lncRNA expressions could
   indicate the range of tumor progression and have a great power in the
   diagnosis and prognosis of cancer as novel independent molecular
   biomarkers [[120]26, [121]27]. Certain lncRNA, such as HOTAIR, has been
   identified as the survival biomarker of EOC patients. However, whether
   lncRNA expression signatures could be used as predictors of paclitaxel
   chemo-sensitivity in EOC patients have not been determined. Qiao. et
   al. identified a lncRNA, CCAT1, regulates paclitaxel chemo-sensitivity
   in nasopharynx cancer cells by manipulating the miR-181a/CPEB2 axis
   [[122]28]. It is demonstrated that lncRNA H19 is an important element
   related to paclitaxel chemo-resistance in the ER-positive breast
   carcinoma cells [[123]29]. In our study, informatics prediction results
   showed that the genes associated with the seven lncRNAs([124]XR_948297,
   [125]XR_947831, [126]XR_938728, [127]XR_938392, [128]NR_103801,
   [129]NR_073113, and [130]NR_036503) significantly participated in
   cancer pathways. The predictive capacity of the seven-lncRNA signature
   in chemotherapeutic sensitivity was identified via ROC analysis. Our
   findings revealed that the seven-lncRNA signature had a relatively high
   predictive accuracy of chemoresistance in EOC patients with
   paclitaxel-containing chemotherapy and was associated with patients’
   PFS time. Therefore, based on those findings as well as our analysis,
   we concluded the seven identified lncRNAsmight be potential biomarkers
   for chemoresistance in EOC patients. Admittedly, more and further
   studies are in urgent need to confirm the connection between the
   seven-lncRNA signature and relevant pathways.

   Biologically diverse mechanisms are involved in the development of
   chemoresistant phenotype in EOC. Among the various biological functions
   predicted by bioinformatics, insulin and Insulin-like growth factor
   (IGF) signaling pathways stood out from the crowd to be the topof the
   list. Actually, insulin and IGF signaling have long been discovered to
   regulate cellular growth, proliferation, metabolism, and survival
   [[131]30]. Interestingly, chemo-resistant cells also displayed an
   enhanced proliferative response to insulin [[132]31]. Insulin and
   insulin analogs are reported to be associated with a high proliferation
   rate and chemoresistance in patients with acute lymphoblastic leukemia
   [[133]32]. Previous study found that high levels of insulin conferred
   AKT signaling activation and resistance to oxaliplatin in colon cancer
   cell lines [[134]33]. A recently published research suggests that
   stroma-derived IGFs can blunt the response to chemotherapy in
   pancreatic cancer via an IGF-insulin/IGF1R paracrine signaling axis
   [[135]34]. A recent study also demonstrated that exogenous IGF-I
   increased cell growth in the ovarian cancer cell line OVCAR-3 in a
   manner equivalent to 10% FCS [[136]35]. Hyperactivation of the IGF-IR
   and PI3K signaling pathways has been revealed as an essential event for
   cisplatin resistance in ovarian cancer cells [[137]36]. Furthermore,
   PI3KCA is regarded as an oncogene in EOC onset and development, while
   PIK3R1 is constitutively activated via mutations [[138]37, [139]38].
   Together, these findings indicate a close interaction between
   chemoresistance and insulin and IGF signaling pathways in EOC.

   Considering the regulatory relationship between the seven lncRNAs and
   prognosis of EOC patients, we further studied the prognostic value of
   the combination of these seven lncRNAs in EOC patients. As
   expected,survival analysis confirmed the significant association
   between the seven-lncRNA signature and EOC patients’ PFS. However, we
   did not find a significant association between the seven-lncRNA
   signature and OS. The possible explanation might be that the
   sevenlncRNAs were identified as paclitaxel-resistance-related
   biomarkers. Their expression levels could be used to predict
   chemoresistance but not death. Admittedly, the moderate sample size in
   our study limited the validity of some stratified analyses to some
   extent. Although the diagnostic power for these seven lncRNAs in
   combination is rather modest, our present study offers evidence for the
   future investigation on whether these seven lncRNAs could be used as
   prognostic markers in EOC management. Thereby, numerous prospective
   validation studies need to be performed before they are applied to
   clinical practice. Besides, another limitation of our present study is
   the lack of serum levels of those seven lncRNAs. Since blood-based
   biomarkers are minimally invasive and more important for monitoring
   therapeutic effects of chemotherapy, We plan to explore the clinical
   values of serum lncRNAs in the next step.

   In summary, we identified seven lncRNAs which were shown to be
   distinctively expressed in most ovarian tumor samples and significantly
   correlated with a poor chemotheraputic response of EOC patients.
   Mechanistically, lncRNA promotes the malignant activities of ovarian
   cancer cells via cancer-related pathways and insulin secretion. In
   addition, our study indicates a joint effect of lncRNA-gene markers in
   predicting the prognosis of EOC patients. Our data also shed light on
   the potential of the seven lncRNAs as novel therapeutic targets for
   future treatment of EOC.

MATERIALS AND METHODS

Microarray data

   The Gene Expression Omnibus (GEO, [140]http://www.ncbi.nlm.nih.gov/geo)
   is a public database available. Two gene expression profiles
   ([141]GSE54772 and [142]GSE30161) were obtained from GEO database.
   [143]GSE54772 consisted of 2 paclitaxel-resistant ovarian cancer cell
   line samples and 2 paclitaxel-sensitive ovarian cancer cell line
   samples [[144]21]. [145]GSE30161 included 21 IR (incomplete response)
   ovarian cancer samples and 25 CR (complete response) ovarian cancer
   samples [[146]22].

Weighted correlation network analysis (WGCNA)

   A total of 3187probe sets with 2730corresponding differential expressed
   geneswere annotated to construct a co-expression network using the
   Rpackage “WGCNA” according to previous reports [[147]23]. The soft
   thresholding power was selected to 6 to produce a weightednetwork. The
   enrolled genes were hierarchically clustered based on their topological
   overlap (TO). Modules of clustered genes were then selected using the
   Dynamic Tree Cut algorithm. The module eigengenes (ME) were calculated
   by principal component analysis and clustered according to their
   correlation to quantify the co-expression similarity of entire modules.

Receiver operating characteristic analysis

   In order to explore the possibility whether lncRNAs could be potential
   biomarkers for EOC diagnosis and prognosis prediction and assess the
   risk between two groups of patients, we did receiver operating
   characteristic (ROC) analysis. The area under the ROC curve (AUC) was
   used to evaluate the classification performance of the lncRNA signature
   according to their capability to distinguish IR patients from CR
   patients.

Pathway enrichment analysis

   Gene ontology analysis (GO) is the most common useful method of
   annotating genes and gene products and of identifying characteristic
   biological properties for high-throughput genome or transcriptome data
   [[148]24]. Kyoto Encyclopedia of Genes and Genomes (KEGG) is a tool
   about systematic analysis of gene functions, connecting genomic
   information with higher-order functional information [[149]25]. We used
   GO analysis and KEGG analysis to identify the biology processes,
   cellular components and related pathways of the seven candidate
   lncRNAs-associated genes. p<0.05 was considered as statistically
   significant.

Survival analysis

   Survival analyses for high- and low- risk EOC patients were carried out
   based on prognostic data derived from [150]GSE30161, respectively. The
   prognosis survival and progression free survival analysis for each
   eligible lncRNA and also the combination of all the seven candidate
   lncRNAs were performed, respectively.

Footnotes

   CONFLICTS OF INTEREST

   The authors declare no potential conflicts of interest.

   GRANT SUPPORT

   This work was supported by National Science Foundation of China (Nos.
   81201549, 81402313 and 81602374); the Fundamental Research Funds for
   the Central Universities (Nos. 2016qngz05and xjj2016111); the Clinical
   Research Award of the First Affiliated Hospital of Xi'an Jiaotong
   University, China (No. XJTU1AF-CRF-2015-011).

   Author contributions

   Kai Qu and Yue Teng: Designed the research and wrote the paper; Yanjun
   Hu and Luqing Wang: Collected and analyzed data; Kai Qu: Constructed
   figures; Xiaohong Xiang and Yue Teng: Drafted and revised the
   manuscript.

REFERENCES