Abstract
Dysregulated long noncoding RNAs (lncRNAs) are potential markers of
several tumor prognoses. This study aimed to develop a lncRNA
expression signature that can predict chemotherapeutic sensitivity for
patients with advanced stage and high-grade serous ovarian cancer
(HGS-OvCa) treated with platinum-based chemotherapy. The lncRNA
expression profiles of 258 HGS-OvCa patients from The Cancer Genome
Atlas were analyzed. Results revealed that an eight-lncRNA signature
was significantly associated with chemosensitivity in the multivariate
logistic regression model, which can accurately predict the
chemosensitivity of patients [Area under curve (AUC) = 0.83]. The
association of a chemosensitivity predictor with molecular subtypes
indicated the excellent prognosis performance of this marker in
differentiated, mesenchymal, and immunoreactive subtypes (AUC > 0.8).
The significant correlation between ZFAS1 expression and
chemosensitivity was confirmed in 233 HGS-OvCa patients from the Gene
Expression Omnibus datasets ([40]GSE9891, [41]GSE63885, and
[42]GSE51373). In vitro experiments demonstrated that the ZFAS1
expression was upregulated by cisplatin in A2008, HeyA8, and HeyC2 cell
lines. This finding suggested that ZFAS1 may participate in platinum
resistance. Therefore, the evaluation of the eight-lncRNA signature may
be clinically implicated in the selection of platinum-resistant
HGS-OvCa patients. The role of ZFAS1 in platinum resistance should be
further investigated.
Introduction
Ovarian cancer yields the highest mortality rate of all lethal
gynecologic cancers and represents approximately 3% of all cancers
diagnosed in women worldwide^[43]1,[44]2. The prognosis of ovarian
cancer is unsatisfactory, with a 5-year survival rate of approximately
30%^[45]3. Approximately 70% of patient deaths are advanced stage and
high-grade serous ovarian cancers (HGS-OvCa)^[46]4. Despite
advancements in surgery and chemotherapy, platinum-resistant cancer
recurs in approximately 25% of patients within 6 months after they
undergo initial standard treatments consisting of aggressive surgery
and platinum-based chemotherapy^[47]5. Some patients with a complete
response to first-line chemotherapy develop acquired drug
resistance^[48]6. Several molecular mechanisms, including drug efflux
and tolerance, increased DNA repair, and increased cellular glutathione
levels^[49]7–[50]9, are implicated in chemosensitivity. However, exact
mechanisms have yet to be fully investigated. Clinical biomarkers that
accurately predict sensitivity to chemotherapy have yet to be
developed^[51]10,[52]11. These factors should be understood to identify
prognostic signatures, which can be utilized to develop effective
treatment modalities for stratified patients who unlikely respond to
platinum-based chemotherapy and thus can benefit from alternative
strategies^[53]10.
Dysregulated and functional long noncoding RNAs (lncRNAs) are
associated with the tumorigenesis and progression of various human
cancers^[54]12–[55]14. lncRNAs are mRNA-like transcripts range from 200
nucleotides (bp) to multiple kilobases (kb) in length but lack a coding
capacity^[56]15. In ovarian cancer, some dysregulated lncRNAs function
as tumor suppressor genes, proto-oncogenes, and metastatic
transformation stimulator^[57]16–[58]22. Increased HOTAIR,
[59]AB073614, and CCAT2 expression levels are associated with poor
prognosis and high metastatic probability^[60]16,[61]17,[62]20. LSINCT5
is overexpressed in ovarian cancer cell lines and tumor tissues and
implicated in the cellular proliferation and development of ovarian
cancer^[63]18. The downregulation of BC200 in ovarian cancer is
involved in cancer cell proliferation and mediation of
carboplatin-induced cancer cell death^[64]19. Zhou et al. ^[65]21
identified an eight-lncRNA signature that can be used to classify
patients with poor and improved overall survival rates. Two
immune-related lncRNAs, namely, RP11-284N8.3.1 and [66]AC104699.1.1,
have been identified as predictors of an ovarian cancer patient’s
survival rates by using lncRNA–mRNA coexpression network
methods^[67]22. Similar to protein-coding genes and miRNAs, lncRNAs can
be utilized as biomarkers for diagnosis and prognosis. However, the
prognostic significance of lncRNAs in the chemotherapeutic sensitivity
of HGS-OvCa treated with platinum-based chemotherapy has yet to be
investigated.
In this study, the association between lncRNA expression profiles and
platinum-based chemotherapy sensitivity for HGS-OvCa patients from the
Cancer Genome Atlas (TCGA) Research Network was investigated to
determine whether lncRNA expression profiling can be used as a
prognostic predictive signature for chemotherapeutic sensitivity. Our
findings were validated on the basis of independent datasets from Gene
Expression Omnibus (GEO).
Results
Identification of lncRNAs from the training sets
The TCGA dataset with 258 HGS-OvCa patients was used for the detection
of lncRNAs related with platinum chemotherapeutic sensitivity. By
subjecting the lncRNA expression data to univariate and multivariate
logistic regression models, we identified a set of eight lncRNAs that
were significantly correlated with the patients’ chemotherapeutic
sensitivity (p < 0.003 in the univariate model and p < 0.01 in the
multivariate model; Table [68]1). The higher expression levels of
ZFAS1, RP5-1061H20.5, RP11-489O18.1, and RP11-16E12.1 were associated
with the lower probability of chemotherapeutic sensitivity (OR < 1 in
both the univariate and multivariate models). On the other hand, the
higher expression levels of LINC01514, TUG1, RP11-136I14.5, and
CTD-2555A7.3 were associated with the higher probability of
chemotherapeutic sensitivity (OR > 1 in both the univariate and
multivariate models) (Fig. [69]1A and Table [70]1). The complete list
of lncRNAs that were associated with the patients’ chemotherapeutic
sensitivity with p < 0.05 in the univariate model of the training
dataset is shown in Table [71]S1.
Table 1.
Logistic regression model for chemosensitive patients with complete
clinical and genomic data in the training dataset (n = 258).
Gene id Gene symbol Chromosome Univariate model Multivariate model
OR 95% CI P value OR 95% CI P value
ENSG00000177410.12 ZFAS1 chr20: 49278178–49295738 (+) 0.65 0.49–0.85
2.74 × 10^−3 0.61 0.43–0.87 6.72 × 10^−3
ENSG00000233920.1 RP5-1061H20.5 chr1: 229223461–229227562 (−) 0.59
0.43–0.79 7.57 × 10^−4 0.53 0.37–0.74 3.36 × 10^−4
ENSG00000237579.2 LINC01514 chr10: 101176323–101194147 (+) 1.56
1.19–2.06 1.64 × 10^−3 1.78 1.28–2.53 8.19 × 10^−4
ENSG00000253352.8 TUG1 chr22: 30970677–30979395 (+) 1.64 1.24–2.20
7.10 × 10^−4 1.67 1.19–2.41 4.23 × 10^−3
ENSG00000253988.1 RP11-489O18.1 chr8: 138063268–138073240 (+) 0.63
0.47–0.83 1.19 × 10^−3 0.60 0.42–0.83 3.13 × 10^−3
ENSG00000255689.1 RP11-136I14.5 chr11: 115582297–115600339 (+) 1.61
1.22–2.18 1.14 × 10^−3 1.72 1.25–2.43 1.34 × 10^−3
ENSG00000259448.2 RP11-16E12.1 chr15: 31216020–31224445 (+) 0.64
0.48–0.85 2.44 × 10^−3 0.57 0.40–0.81 2.04 × 10^−3
ENSG00000261546.1 CTD-2555A7.3 chr16: 89113175–89115279 (−) 1.51
1.16–2.00 2.77 × 10^−3 1.61 1.17–2.26 4.33 × 10^−3
[72]Open in a new tab
Figure 1.
[73]Figure 1
[74]Open in a new tab
Unsupervised clustering heatmap and ROC curves for the eight- lncRNA
signature. Heatmap based on eight lncRNAs (rows) of HGS-OvCa patients
(columns) in the TCGA datasets (n = 258). Red and blue indicate high
and low expression levels, respectively (A). ROC curves represent the
accuracy of the eight-lncRNA signature in the TCGA dataset and
different subtypes (B), and ROC curves represent the accuracy of our
defined signature, the lncRNA signature developed by Zhou et al., and
the TCGA mRNA prognostic signature (C). True positive rate represents
sensitivity, whereas false positive rate is one minus the specificity.
Eight-lncRNA signature and chemotherapeutic sensitivity
We created a risk-score formula according to the expression levels of
eight lncRNAs for the chemotherapeutic sensitivity prediction as
follows: predictive score = (−0.4410 × expression level of
ZFAS1) − (0.6380 × expression level of
RP5-1061H20.5) + (0.5775 × expression level of
LINC01514) + (0.5143 × expression level of TUG1) − (0.5167 × expression
level of RP11-489O18.1) + (0.5425 × expression level of
RP11-136I14.5) − (0.5595 × expression level of
RP11-16E12.1) + (0.4771 × expression level of CTD-2555A7.3). According
to this risk score, patients in the training set were divided into
low-score and high-score groups using the median risk score as the
cut-off. The high-score group showed a higher probability of
sensitivity (OR = 9.06, 95% CI = 4.77–18.35, p = 1.07 × 10^−10 in the
univariate model; OR = 9.58, 95% CI = 4.97–19.73, p = 1.05 × 10^−10 in
the multivariate model). In addition, ROC analysis was performed to
assess the predictive accuracy of the eight-lncRNA signature. The
lncRNA signature showed a predictive power in distinguishing sensitive
from resistance either in the training dataset (AUC = 0.83,
Fig. [75]1B) or in different molecular subtypes (AUC > 0.7,
Fig. [76]1B). Furthermore, compared with the two published tests
(signature by Zhou et al. and TCGA), our defined lncRNA signature
showed a better performance as demonstrated by higher AUC values
(Fig. [77]1C).
Eight-lncRNA signature and chemotherapeutic response
In addition to the association of chemotherapeutic sensitivity in
ovarian cancer, the significant associations between the identified
eight-lncRNA signature and chemotherapeutic response were also
investigated. ROC analysis showed that our defined lncRNA signature was
predictive of a complete response in the whole training dataset
(AUC = 0.67) and across different subtypes (AUC ≥ 0.6, Fig. [78]2A). In
addition, the eight-lncRNA signature showed higher AUC values than the
two published signatures developed by Zhou et al. and the TCGA group
(Fig. [79]2B).
Figure 2.
[80]Figure 2
[81]Open in a new tab
ROC curves for the eight-lncRNA signature in predicting chemoresponses.
ROC curves represents the accuracy of the lncRNA signature in the
training dataset and different subtypes in predicting chemoresponses
(A), and the accuracy of our defined lncRNA signature, the lncRNA
signature developed by Zhou et al., and the TCGA mRNA prognostic
signature (B). True positive rate represents sensitivity, whereas false
positive rate is one minus the specificity.
Prognostic value of the eight-lncRNA signature that is independent of
clinical information
The multivariate logistic regression analysis was conducted to confirm
whether the eight-lncRNA expression signature was an independent
predictor of HGS-OvCa patients’ sensitivity after platinum-based
chemotherapy. In the model, chemotherapeutic sensitivity was a
dependent variable, and stage, grade, molecular subtypes, and lncRNA
predictive score were covariates. Specifically, results showed that the
eight-lncRNA signature is an independent predictor of chemotherapeutic
sensitivity when adjusted using the above-mentioned covariates
(OR = 9.58, 95% CI = 4.97–19.73; p = 1.05 × 10^−10) (Table [82]2).
Table 2.
Univariable and multivariable logistic regression models in the
training dataset.
Univariate model Multivariable model
OR 95% CI P value OR 95% CI P value
LncRNA signature (high/low) 9.06 4.77–18.35 1.07 × 10^−10 9.58
4.97–19.73 1.05 × 10^−10
Stage (ref = 2)
3 0.16 0.01–0.85 8.38 × 10^−2 0.16 0.01–1.03 1.05 × 10^−1
4 0.18 0.01–1.13 1.29 × 10^−1 0.16 0.01–1.19 1.20 × 10^−1
Grade (ref = 2)
3 0.65 0.26–1.44 3.10 × 10^−1 0.69 0.25–1.78 4.53 × 10^−1
Molecular subtypes (ref = differentiated)
Immunoreactive 2.12 0.99–4.68 5.46 × 10^−2 2.51 1.06–6.14 3.94 × 10^−2
Mesenchymal 1.35 0.63–2.95 4.39 × 10^−1 1.63 0.68–3.96 2.76 × 10^−1
Proliferative 1.55 0.77–3.12 2.18 × 10^−1 1.45 0.64–3.30 3.68 × 10^−1
[83]Open in a new tab
LncRNA ZFAS1 association with chemotherapeutic sensitivity in ovarian cancer
subtypes
Among the eight-lncRNAs, only three lncRNAs (ZFAS1, LINC01514, and
TUG1) were observed in the validation dataset, and the role of ZFAS1
was confirmed in the validation dataset (OR = 0.67, 95% CI = 0.48–0.94;
p = 2.12×10^−2, Fig. [84]3A). The probe name of ZFAS1 by Affymetrix
U133 Plus 2 platform is 224915_x_at. In addition to the association of
chemotherapeutic sensitivity in ovarian cancer, the associations
between ZFAS1 and molecular subtypes were also studied. Results show
that the increased expression level of ZFAS1 can be accomplished with a
low probability of sensitivity for all subtypes (OR < 1). However,
accounting for the small sample size within molecular subtypes, the
relationship between ZFAS1 and probability of sensitivity is only
statistically significant in the training dataset (OR = 0.58, p
value = 4.83 × 10^−2) of differentiated subtypes (Table [85]3).
Figure 3.
[86]Figure 3
[87]Open in a new tab
Associations between ZFAS1 and chemosensitivity are observed in the
validating datasets. Heatmap based on the genes (rows) of patients with
ovarian cancer (columns) for the ZFAS1 in the validating dataset (A).
Red and blue indicate high and low expression levels, respectively. The
expression values of ZFAS1 in A2008, HeyA8, and HeyC2 cell lines
treated with or without cisplatin treatment. p values were calculated
by independent two-tailed t test. Error bars represent the mean ± SD
(B). The functional map of enriched GO terms with each node indicates
an enriched GO term, and each edge represents the common genes shared
between connecting and enriched GO terms (C).
Table 3.
Relationship between ZFAS1 with chemosensitivity in ovarian cancer
molecular subtypes.
Molecular subtype Training dataset Validating dataset
OR 95% CI P value OR 95% CI P value
Proliferative 0.58 0.32–0.97 4.83 × 10^−2 0.53 0.24–1.04 8.65 × 10^−2
Mesenchymal 0.64 0.33–1.20 1.71 × 10^−1 0.69 0.21–2.00 5.18 × 10^−1
Differentiated 0.59 0.30–1.04 8.76 × 10^−2 0.75 0.38–1.45 3.93 × 10^−1
Immunoreactive 0.71 0.36–1.35 3.12 × 10^−1 0.61 0.28–1.22 1.82 × 10^−1
[88]Open in a new tab
LncRNA ZFAS1 may be associated with platinum resistance
From the above-mentioned results, we can conclude that the high
expression level of ZFAS1 correlate with low sensitivity in HGS-OvCa
patients treated with platinum, suggesting that ZFAS1 might be
associated with platinum resistance. To validate this hypothesis,
[89]GSE47856^[90]23 was downloaded from GEO and analyzed. The probe
that corresponded to ZFAS1 by Human Gene ST 1.0 arrays was 8063337. In
vitro experiment results showed that the ZFAS1 expression level was
upregulated in A2008, HeyA8, and HeyC2 cell lines treated with
cisplatin compared with the control group (Fig. [91]3B), which
indicates that cisplatin could increase the ZFAS1 expression level in
ovarian cancer cells. Results for the 17 cell lines with at least three
replicates are illustrated in Table [92]S2.
Functional annotation
The coexpressed relationships between the expression levels of eight
lncRNAs and protein-coding genes (PCGs) were investigated by
determining Pearson’s correlation coefficients in the TCGA dataset to
further investigate the potential biological roles involving the
prognostic lncRNA biomarkers. The expression level of 24 PCGs was
highly correlated with that of ZFAS1 (R ≥ 0.4, Table [93]S3). Gene
ontology (GO) function enrichment analysis of these PCGs was then
performed with the whole human genome as the background. GO functional
annotation suggested that these PCGs were significantly enriched in 14
GO terms (Table [94]S4, Fig. [95]3C, Bonferroni p value of <0.05), and
the translation process (Bonferroni p value = 9.54 × 10^−13) is the
most significant. The KEGG pathway enrichment analysis of
ZFAS1-correlated PCGs showed that the pathway ribosome was
significantly enriched (Bonferroni p value = 1.68 × 10^−16). The
functional analysis shows that ZFAS1 is implicated in ovarian cancer
tumorigenesis via the positive regulation of protein-coding genes that
affect translational and ribosome processes.
Discussion
Conventionally, the study of gene regulation in biology has focused on
protein-coding genes and miRNAs until the discovery of multiple
functional regulatory lncRNAs. LncRNAs had increased disease- and
tissue-specific expression levels than protein-coding genes, and their
expression levels are more closely associated with its biological
function^[96]24. Previous studies on tissue-specific lncRNAs in normal
tissues and dysregulated lncRNA expression across various cancer types
indicate that altered lncRNAs play critical roles in
tumorigenesis^[97]25 via multiple cancer-related biological processes,
such as apoptosis, cell cycle regulation, metastasis, and DNA damage
response^[98]26,[99]27. Furthermore, these dysregulated lncRNAs could
mark the spectrum of tumor progression and have a great potential in
the diagnosis and prognosis of cancer as novel independent molecular
biomarkers^[100]28,[101]29. Several dysregulated lncRNAs, such as
HOTAIR and LSINCT5, are associated with ovarian cancer survival.
However, to date, the expression profile-based prognostic lncRNA
signatures for the prediction of chemotherapeutic sensitivity in
ovarian cancer patients have not been developed.
In this study, a comprehensive analysis of lncRNA expression profiles
in HGS-OvCa patients from TCGA was conducted. An eight-lncRNA
predictive signature of chemotherapeutic sensitivity was identified via
the logistic regression analysis. The increased expression levels of
six lncRNAs were associated with the low probability of sensitivity,
and three lncRNAs were correlated with the high probability of
sensitivity. The eight-lncRNA signature is predictive of different
molecular subtypes and better than the two published signatures. We
also observed a close association between the eight-lncRNA signature
and chemotherapeutic response within the TCGA dataset and four
molecular subtypes. Furthermore, the eight-lncRNA signature is
independent of other clinicopathological covariates, such as stage,
grade, and molecular subtypes. To our knowledge, this study first
showed the correlation of lncRNA expression profiles with
chemotherapeutic sensitivity after platinum-based chemotherapy of
HGS-OvCa.
To date, although an increased numbers of lncRNAs have been discovered
and recorded in biological databases, such as GENCODE^[102]30, most of
the lncRNAs were not functionally characterized. Only one of eight
prognostic lncRNAs, namely, ZFAS1, has been reported as a prognostic
biomarker and target of hepatocellular carcinoma^[103]31, colorectal
cancer^[104]32,[105]33, and gastric cancer^[106]34. According to the
publication by Li et al., ZFAS1 gene amplification is related with
intrahepatic and extrahepatic metastasis and the poor prognosis of
hepatocellular carcinoma, which functions as an oncogene by binding
miR-150 and abolishing its tumor-suppressive roles^[107]31. ZFAS1 is
significantly up-regulated in colorectal cancer tissues and may be an
oncogene in colorectal cancer by the destabilization of p53 and
interaction with CDK1/cyclin B1 complex, thus leading to cell cycle
progression and apoptosis inhibition^[108]32. Furthermore, ZFAS1
expression is also overexpressed in gastric cancer, and its increased
level is correlated with a shorter survival and poor prognosis and
promotes the proliferation of gastric cancer cells by epigenetically
repressing the KLF2 and NKD2 expression levels^[109]34. Our analysis
identified the association of ZFAS1 with chemotherapy sensitivity in
the training and validation datasets. The increased expression level of
ZFAS1 was associated with the lower probability of sensitivity in
patients with proliferative, mesenchymal, and differentiated subtypes.
Further, based on in vitro experimental data, we concluded that the
expression level of ZFAS1 could be regulated by cisplatin. Thus, ZFAS1
might play an important role in cisplatin resistance. Gene functional
annotation revealed that ZFAS1 were likely involved in the
translational process. To gain a deeper understanding of ZFAS1 roles
and the effects of the other seven lncRNAs in response to chemotherapy
in HGS-OvCa patients, the underlying regulatory mechanisms should be
further explored.
Based on the molecular and genetic heterogeneity characteristics of
ovarian cancer, we tested whether the prognostic value of the
eight-lncRNA signature was independent of clinical characteristics. The
multivariable logistic regression analysis revealed that the prognostic
value of the eight-lncRNA signature was independent of stage, grade,
and molecular subtypes. The eight-lncRNA signature might be used to
update the current prognostic model and contribute to the strata of
patients in future clinical trials.
The limitations of this study need to be presented. First, owing to the
restricted availability of data, only a fraction of human lncRNAs (7740
out of 15000+) were included in our study. Second, although the
biological functions of ZFAS1 have been inferred by gene functional
annotation analysis, the mechanisms behind the predictive values of
these eight lncRNAs in response to the chemotherapy of HGS-OvCas are
still not clear, and their functional roles should be further explored
in experimental studies. Finally, because other independent datasets
are not available to validate our model, the significance and
robustness of the eight-lncRNA signature for the prediction of
chemotherapeutic sensitivity should be further investigated in clinical
trials.
In summary, via probing and integrating available microarray expression
data, our study presents a set of eight-lncRNA signature that is
associated with chemotherapeutic sensitivity of HGS-OvCas. This
signature might contribute to the identification of the low survival
probability of patients who are likely to develop chemotherapy
resistance. Gene functional annotation indicates that ZFAS1 might
participate in the translational biological process. Our results
confirmed that the identified signature lncRNAs might play potential
roles in chemotherapeutic resistance mechanisms of HGS-OvCa tumors and
are also considered as molecular diagnostic biomarkers and therapeutic
targets in clinical practice.
Materials and Methods
Sources of data
Only HGS-OvCa specimens were used in the study that include the
following datasets.
Training dataset
The clinical information on HGS-OvCas (stages II, III, and IV and
grades 2, 3, and 4) were obtained from Supplementary Table [110]S1.2
([111]http://www.nature.com/nature/journal/v474/n7353/extref/nature1016
6-s2.zip) of TCGA’s publication^[112]35. Up to 258 of patients received
at least six cycles of platinum treatment, and chemotherapeutic
sensitivity information were used in this study. The clinical
information of patients, including age, tumor stage and grade,
chemosensitivity, chemoresponses, and molecular subtypes, are listed in
Table [113]4 and Table [114]S5.
Table 4.
Patient characteristics of the training and validating datasets.
Characteristics Training dataset Validating dataset P value^$
Sample size 258 233
Age, year mean (SD) 59.8 (11.2) 60.34 (9.9) 0.59
Histologic grade (%) 6.61 × 10^−12
2 35 (13.6) 80 (34.3)
3 223 (86.4) 138 (59.2)
4 0 15 (6.4)
Stage ^#(%) 0.10
II 14 (5.4) 14 (6.0)
III 206 (79.8) 199 (85.4)
IV 38 (14.7) 20 (8.6)
Platinum sensitivity (%) 0.02
Sensitive 190 (68.1) 185 (79.3)
Response to therapy^& —
CR 194 (69.5) 0
Non-CR 63 (22.6) 0
Unknown 22 (7.9) 233 (100)
Molecular subtypes 0.47
Proliferative 77 (29.8) 71 (30.5)
Mesenchymal 51 (19.7) 39 (16.7)
Immunoreactive 62 (24.0) 69 (29.6)
Differentiated 68 (26.3) 54 (23.2)
[115]Open in a new tab
^#Stage based on the International Federation of Gynecology &
Obstetrics (FIGO).
^&CR means the complete response, and Non-CR depicts a non-complete
response, including partial response, progressive disease, and stable
disease.
^$p values for the difference between the derivation and validation
cohorts were calculated using independent sample t-test (for age and
height) and Chi square test (for histologic grade, stage, platinum
sensitivity, response to therapy, and molecular subtypes).
LncRNA expression profiles by repurposing the probes from Affymetrix
Human Exon 1.0 ST microarray of HGS-OvCa patients were downloaded from
[116]http://cistrome.org/lncRNA/lncRNA_data_repository.html ^[117]36.
The probe sets that were not assigned for mRNAs but uniquely and
perfectly mapped for noncoding RNA sequences that represent lncRNAs.
The lncRNA expression levels were used as the background-corrected
intensity of all probes mapped to this lncRNA. To reduce the
heterogeneity of different batches and biological samples, the lncRNA
expression value was standardized using the quantile-normalized method
and Combat algorithm^[118]37. To reduce inaccurate annotations, the
lncRNAs obtained from Du’s study and lncRNAs from the GENCODE project
([119]http://www.gencodegenes.org/, release 25)^[120]30 were
cross-referenced by Ensembl id and gene name. Finally, we obtained the
expression profiles of 7739 lncRNAs. The lncRNA expression levels were
modified with a mean of 0 and a standard deviation (SD) of 1.
Validating datasets from GEO
Three datasets with the profiling data of gene expression obtained by
using pretreatment biopsies in patients who received platinum-based
chemotherapy and corresponding clinical data were downloaded from the
GEO database ([121]http://www.ncbi.nlm.nih.gov/geo/). All data were
obtained with Affymetrix Human U133 Plus 2.0 arrays (Affymetrix). After
the removal of the samples without progression-free survival
information, a total of 233 advanced stage (stage > I) and high-grade
(grade > 1) serous ovarian cancer patients were observed. A total of
141 patients from [122]GSE9891 (24), 70 patients from [123]GSE63885
(25), and 22 patients from [124]GSE51373 (27) were included. The
clinical information of the patients is listed in Table [125]4 and
Supplementary Table [126]S6.
The probe sets of Affymetrix Human U133 Plus 2.0 arrays that were not
assigned for protein-coding transcripts and pseudogene transcripts but
were uniquely and perfectly mapped for noncoding RNA sequences that
were downloaded from
[127]http://cistrome.org/lncRNA/lncRNA_data_repository.html (file
Array.probe.alignment/U133p2.lncRNA.uniq). Each lncRNA should include
at least four probe mappings in the corresponding ncRNA entity. Up to
2654 probes corresponding to 2183 lncRNAs were left. The raw CEL files
were downloaded from GEO, and all gene expression data were normalized
with the MAS5 algorithm using the “simpleaffy” R Bioconductor package
([128]http://www.bioconductor.org/packages/release/bioc/html/simpleaffy
.html) with the mean expression focused at 600. The validating dataset
was adjusted, which consists of three datasets for potential batch
effects with the ComBat algorithm^[129]37. Furthermore, the probe-level
expression profiles were converted into lncRNA-based expressions via
probe merging with the collapse row function^[130]38. Finally, the
lncRNA expression level of Affymetrix microarray datasets was scaled
with a mean of 0 and an SD of 1.
OVCA cultured cell lines
Forty-six ovarian cancer cell lines were subjected to treatment with
cisplatin at the 50% growth inhibition concentration dosage. To explore
transcriptomic responses to cisplatin, genome-wide expression changes
were measured serially before and after cisplatin treatment. The gene
expression was obtained using Human Gene ST 1.0 arrays (Affymetrix,
Santa Clara, CA, USA), which was downloaded from the GEO with the
accession number of [131]GSE47856^[132]23. The cell lines with no less
than three replicates (A2008, A2780, C13, CH1, DOV13, DOV13B, FU-OV-1,
HeyA8, HeyC2, IGROV-1, OV90, OVCA420, OVCA429, OVCA433, OVCAR-8, PA-1,
and TYK-nu) were tested in our study.
Clinical outcomes
In the TCGA dataset, the platinum-free interval was the interval from
the date of the last primary platinum chemotherapy to the date of
recurrence, date of progression, or date of last follow-up if the
patient is alive and did not experience recurrence. Platinum status was
defined as resistant if the platinum-free interval was less than 6
months and was defined as sensitive if the platinum-free interval is 6
months or longer. However, no evidence on recurrence or progression
existed, and the follow-up interval was at least 6 months from the date
of the last primary platinum treatment. Patients who were monitored for
less than 6 months from the date of the last primary platinum treatment
and did not experience recurrence or progression were excluded from the
analyses regardless of platinum status.
Chemotherapy response, the success of the primary therapy, was defined
as the response to treatment determined after the primary surgery and
subsequent adjuvant platinum chemotherapy. Following the primary
therapy to determine the response, patients were evaluated with a
combination of imaging (CT scan) and blood (CA125) tests. Patients with
normalized CA125 and who did not show radiographic evidence of the
disease were defined as complete responses^[133]39.
As for the dataset downloaded from GEO, platinum status was defined as
resistant if the disease did not respond or progress during treatment
or recur within 6 months of treatment^[134]40, and the status was
defined as sensitive if the progression-free survival was 6 months or
longer.
Classification of HGS-OvCa subtypes
HGS-OvCas in the TCGA dataset were divided into proliferative,
mesenchymal, immunoreactive, and differentiated subtypes according to
the expression level of 100 genes by Verhaak et al. ^[135]41.
Furthermore, the 100-gene set (Supplemental Table [136]7 from the
publication by Verhaak et al.) was used to train support vector
machines for the classification of samples in the validation datasets
from GEO. The sample sizes for each subtype in the training and
validating datasets are shown in Tables [137]S5 and [138]S6.
Statistical analysis
To identify predictive lncRNAs, a univariate logistic regression
analysis was performed to assess the relationship between the
continuous expression level of each lncRNA and chemosensitivity. The
lncRNAs with p values less than 0.003 were considered statistically
significant and associated with chemosensitivity. Multivariate logistic
regression was performed for the above-mentioned selected lncRNAs, and
those lncRNAs with a p value of less than 0.01 were left for the
predictive score calculation. The predictive score was computed to
evaluate each patient’s probability of chemosensitivity according to
the following formula:
[MATH:
predict<
mi>ivescore(PS)=∑i
=1n(Expi⁎Coei), :MATH]
1
where n stands for the number of prognostic lncRNA genes in the model;
Exp[i] is the expression level of lncRNA[i]; Coe[i] is the estimated
regression coefficient of lncRNA[i] in the multivariable logistic
regression model. Patients who have higher predictive scores are
expected to have a higher probability of response. Furthermore, the
multivariate logistic regression analysis was conducted to test whether
the predictive score was independent of clinical covariates.
Statistical computations were conducted using the R statistical
software version 3.2.2^[139]42 with related packages or customized
functions.
Classifier performance evaluation
The area under the receiver operator characteristic curve (AUC) was
used to evaluate the classification performance of the signatures
according to their capability to distinguish between chemotherapeutic
sensitivity and resistance. Moreover, AUC was calculated by R-package
ROCR. The performance of our defined lncRNAs signature and two
previously published signatures developed by Zhou et al. ^[140]21 and
TCGA signature^[141]35 was compared.
Coexpression and functional annotation
First, the expression profiles of 16936 PCGs in 258 HGS-OvCa patients
were obtained from Du’s study^[142]36. The biological functions of
lncRNAs are associated with the coexpressed PCGs^[143]43. Thus, the
expression correlation between lncRNAs and PCGs with the expression
profiles of paired lncRNA and PCG was tested. The PCGs were lncRNA
correlated if their correlation coefficients with this lncRNA were not
less than 0.4.
The GO biological process (GOTERM-BP-ALL) and Kyoto encyclopedia of
genes and genomes (KEGG) pathway enrichment analyses of the PCGs
coexpressed with prognostic lncRNAs were performed to predict the
function of prognostic lncRNAs via the DAVID annotation tool
([144]http://david.abcc.ncifcrf.gov/) with the functional annotation
clustering option^[145]44. The enriched GO terms and KEGG pathway with
a Bonferroni p value of <0.05 were considered as a potential function
of prognostic lncRNAs. The significantly enriched GO terms with a
similar function were visualized using the Enrichment Map Plugin in
Cytoscape^[146]45.
Electronic supplementary material
[147]Supplementary Table S2^ (42KB, doc)
[148]Supplementary Tables^ (210.5KB, xls)
Acknowledgements