LncRNAs KB-1836B5, LINC00566 and FAM27L are associated with the survival time of patients with ovarian cancer.

Ovarian cancer (OvCa) is the most common gynecological malignancy type in the United States in 2014. Functions of long non-coding RNAs (lncRNAs) in OvCa have attracted increasing attention from researchers. The present study aimed to identify an lncRNA-based signature for survival prediction in patients with OvCa. On the basis of lncRNA expression profiles from The Cancer Genome Atlas data portal, differentially expressed lncRNAs (DELs) were selected from patients with good prognosis and poor prognosis in the training set, from which the prognostic lncRNAs were identified using univariate and multivariate Cox regression analyses and used to construct a risk scoring system. The prognostic power of this lncRNA signature was tested in the training set and validated in validation dataset and entire dataset. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the genes significantly associated with ≥1 prognostic lncRNA, and a total of 112 DELs were identified. LncRNAs KB-1836B5, long intergenic non-protein coding RNA 566 (LINC00566) and family with sequence similarity E5 (FAM27L) were determined to be prognostic lncRNAs. A three-lncRNAs signature-based risk scoring system was developed, which classified the patients from the training set into high-risk and low-risk groups with significantly different overall survival time. Risk stratification capability of the three-lncRNAs signature was validated in the validation and entire set. Multivariate Cox regression and data stratification analyses determined that the three-lncRNAs signature was independent of other clinical variables. GO and KEGG pathway enrichment analyses determined that the three prognostic lncRNAs may be involved in a number of metabolic processes and signaling pathways, including the mechanistic target of rapamycin signaling pathway, ubiquitin-mediated proteolysis, and complement and coagulation cascades pathways. In conclusion, the results of the present study demonstrated that the three-lncRNAs signature may be an independent biomarker for predicting prognosis in patients with OvCa.

Introduction

Ovarian cancer (OvCa) has the highest incidence of mortality of any gynecological cancer type in the United States, and is the primary cause of female cancer-associated mortality in the United States in 2014 (1). Owing to the mild or absent signs and symptoms during early stage OvCa, and the lack of a reliable early detection test, OvCa has a disproportionately poor prognosis, with a 5-year survival rate of 44% between 1995–2007 in the United States (2), therefore further understanding of its regulatory mechanisms at the molecular level is vital, in order to identify reliable prognostic biomarkers for the prediction of survival times of patients with OvCa.

Long non-coding RNAs (lncRNAs) are a group of non-coding RNAs with >200 nucleotides (3). An increasing number of studies have demonstrated that lncRNAs are implicated in cancer development, and their dysregulated expression confers on the cancer cell capability for tumor initiation and development (4,5). Previous evidence indicated the functions of the lncRNAs H19 (H19, imprinted maternally expressed transcript), long stress-induced non-coding transcript 5 and X-inactive-specific transcript in the tumorigenesis and progression of OvCa (6). Cheng et al (7), demonstrated that upregulated lncRNA AB073614 predicts a poor prognosis; however, it has been indicated that lncRNA homeobox A11 antisense enhances cell proliferation and invasion in serous OvCa, and is associated with prognosis (8). Chen et al (9) determined the function of lncRNA nuclear paraspeckle assembly transcript 1 as a clinical prognostic biomarker for OvCa. Although previous studies have made notable progress, the prognostic functions of lncRNAs in OvCa and the underlying mechanisms remain poorly characterized.

In the present study, an in-depth analysis of lncRNA and mRNA expression profiles, and corresponding clinical characteristics of patients with OvCa from The Cancer Genome Atlas (TCGA) data portal was conducted. In contrast with the study by Zhou et al (10), which identifies a 10-lncRNA signature for predicting survival using the competing endogenous RNAs-network driven method, the present study searched for prognostic lncRNAs based on univariate and multivariate Cox regression analyses in a training set, and then validated their prognostic power in a validation set. Furthermore, Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed to determine the possible biological functions of identified prognostic lncRNAs. The results of the present study improved the understanding of the molecular mechanisms of OvCa.

Materials and methods

Data resource

lncRNA and mRNA expression data of OvCa samples, together with corresponding clinical information were downloaded from TCGA data portal (gdc-portal.nci.nih.gov). Each sample was annotated according to its barcode ID. Consequently, a total of 419 samples with lncRNA and mRNA data, based on the Illumina HiSeq 2000 RNA Sequencing platform, were collected. Out of these samples, the samples at early and middle stages according to American Joint Committee on Cancer standard (11) with available clinical information (n=353) were selected for the present study, and randomly and the validation set were combined and defined as the entire set. Clinical characteristics of these datasets are listed in Table I.

Table I.

Clinical characteristics of patients in the training set, the validation set and the entire set.

Characteristics	Training set (n=160)	Validation set (n=177)	Entire set (n=337)
Age at diagnosis (mean ± SD)	59.63±11.18	60.11±11.86	59.88±11.53
Clinical stage (I/II/III)	0/7/153	0/15/162	0/22/315
Neoplasm histological grade (G1/G2/G3/G4/-)	0/12/145/1/2	0/32/142/0/3	0/44/287/1/5
Lymphatic invasion (yes/no/-)	33/28/99	54/21/102	87/49/201
Tumor recurrence (yes/no)	96/64	101/76	197/140
Status (deceased/alive)	86/74	107/70	193/144
Overall survival time (mean ± SD, months)	38.18±27.14	34.58±26.99	36.29±27.08

SD, standard deviation.

Screening for differentially expressed lncRNAs (DELs)

A total of 16 patients with prognosis of <6 months were excluded from the training set (remaining patients, n=160). No patients were excluded from the validation set. Good prognosis was defined as patients alive after >24 months, and poor prognosis was defined as patients who succumbed within 24 months. Patients with good prognosis and poor prognosis were selected from the training set, and defined as the good prognosis and poor prognosis groups, respectively. DELs between the groups were screened using two packages in R3.1.0 (https://www.r-project.org/), Differential Expression for Sequence Count Data (DESeq) (12) and edgeR (13). An lncRNA was considered a significant DEL when false discovery rate (FDR) <0.05 and |fold change|>1.3. The overlapping DELs between the two methods were incorporated into the subsequent analyses.

Identification of survival-associated lncRNAs

Univariate Cox proportional hazards regression model was employed to evaluate the associations between these significant DELs and the overall survival (OS) time of the patients with OvCa in the training set, followed by the log-rank test. The DELs with log-rank P<0.05 were defined as survival-associated lncRNAs, which were then subjected to multivariate Cox regression analysis with OS time as the dependent variable.

Construction of an lncRNA-based risk scoring system

On the basis of the linear combination of expression levels of the predictive lncRNAs selected by the multivariate Cox regression analysis with the regression coefficient, a risk scoring system was produced to calculate the risk score for each sample using the following equation (10,14): Risk score=β1 × Expr1 + β2 × Expr2 + ··· + βn × Exprn, where βn represents estimated regression coefficient of lncRNAn, and Exprn represents lncRNAn expression level.

All samples in the training and validation set were classified into a high-risk (>median risk score) and a low-risk group (≤median risk score), with the median risk score of the dataset as the cut-off value.

Prognosis correlation analysis

Univariate Cox regression analysis and Student's t-test (unpaired) was used to characterize OS time of each risk group. The log-rank test was then used following univariate analysis to compare the difference between the two risk groups. The results were shown by Kaplan-Meier survival curve. With OS time as the dependent variable, and risk score and clinical variables as explanatory variables, multivariate Cox regression analysis and data stratification analysis were conducted to evaluate whether the risk score was independent of other clinical variables, from which hazard ratios and 95% confidence intervals (CI) were calculated. P<0.05 was considered to indicate a statistically significant difference. To appraise the prognostic power of the lncRNAs-based risk scoring system, the time-dependent receiver operating characteristic (ROC) curve analyses were carried out using the pROC package (15), followed by calculation of the area under the ROC curves (AUC). All analyses were conducted by R 3.0.1 software and Bioconductor 1.14.3. The data are presented as mean ± standard deviation.

GO function and KEGG pathway enrichment analyses

Spearman correlation coefficients were computed to assess the association between the prognostic lncRNAs and corresponding mRNAs, by analyzing expression profiles of the paired lncRNA and protein-coding genes in all OvCa samples. The protein-coding genes significantly correlated with at least one prognostic lncRNA with a Spearman correlation coefficient >0.4 were selected and underwent functional enrichment analyses using the Database for Annotation, Visualization and Integrated Discovery (16) tool limited to GO biological process terms (17) and KEGG pathway categories (18). GO terms or KEGG pathways with P<0.05 were considered to indicate a statistically significant difference.

Results

Identification of significant DELs

Following the deletion of lncRNAs with notably low expression, 2,035 lncRNAs were acquired from TCGA. According to the survival time information, 28 patients were included in the poor prognosis group and 41 patients were included in the good prognosis group. As presented in Fig. 1, the edgeR method detected 145 DELs between the two groups, whereas the DESeq method identified 133 DELs. A total of 112 DELs were shared by the two methods and were selected for further analyses.

Figure 1.

DEL analysis. (A) DELs identified using the DESeq package. (B) DELs identified using the edgeR package. (C) Overlapping DELs. DESeq, Differential Expression for Sequence Count Data; FDR, false discovery rate; DEL, differentially expressed long non-coding RNA.

Three-lncRNAs signature for survival prediction

Out of the 112 overlapping DELs, 33 lncRNAs were determined to be significantly associated with survival (P<0.05; Table II), and were then included in the multivariate Cox regression analysis. Subsequently, the lncRNAs KB-1836B5, long intergenic non-protein coding RNA 566 (LINC00566) and FAM27L were selected following multivariate Cox regression analysis and used to construct a risk scoring system as follows: Risk score=[(−1.10205) × Exp KB-1836B5] + [(−0.58589) × Exp LINC00566] + [(−1.69273) × Exp FAM27L]. Exp was taken as the expression level of the lncRNA.

Table II.

Three prognostic lncRNAs analyzed by multivariate Cox regression analysis.

LncRNA	Cox regression coefficient	95% CI	P-value
KB-1836B5	−1.102	0.163–0.676	0.002[a]
LINC00566	−0.586	0.362–0.857	0.008[a]
FAM27L	−1.693	0.044–0.761	0.020[a]
LINC00706	0.71872	0.9464–4.4483	0.039[a]
LINC01040	0.74652	0.93732–4.7482	0.047[a]
RP11-49K4	−0.18254	0.67114–1.0343	0.050
LINC01109	0.39972	0.80386–2.767	0.205
KB-1254G8	0.71192	0.66577–6.2379	0.212
GM140	0.92532	0.54335–11.7124	0.238
LINC01195	0.31406	0.77485–2.4187	0.280
GHET1	−0.70253	0.13395–1.8317	0.292
LINC01447	−0.26729	0.46046–1.2724	0.303
RP1-84O15	−0.3337	0.37818–1.3566	0.306
FOXD1-AS1	−0.88184	0.07468–2.2952	0.313
LINC00032	−0.95171	0.05269–2.8292	0.349
DIRC3-AS1	0.83933	0.31433–17.0467	0.410
RP11-490B18	0.07483	0.88091–1.3185	0.467
LINC01135	−0.25925	0.37569–1.5848	0.480
LINC01162	0.14533	0.74264–1.8008	0.520
LINC00364	−0.304	0.28455–1.9133	0.532
RP11-61L19	−0.11059	0.62889–1.2746	0.540
LINC00562	−0.15952	0.5056–1.4376	0.550
RP11-680F200	0.11108	0.77155–1.6185	0.557
LINC01514	0.15865	0.63662–2.1573	0.610
MIR219A2	0.10826	0.72297–1.7175	0.624
LINC00701	0.15113	0.58805–2.3007	0.664
LINC01349	−0.14309	0.37503–2.0029	0.738
TTTY1B	0.43506	0.06779–35.2169	0.785
TTTY23	−0.26192	0.0745–7.9493	0.826
TTTY3	−0.19517	0.10562–6.4082	0.852
LINC00366	−0.03284	0.6815–1.3741	0.854
LINC01034	−0.03165	0.48084–1.9521	0.929
D21S2088E	0.0173	0.36988–2.7988	0.973

lncRNA, long non-coding RNA; CI, confidence interval

P<0.05.

The 160 patients in the training set were classified into a high-risk (n=80) and low-risk group (n=80) by the three-lncRNAs panel-based risk scoring system, with the median risk score as the cut-off value.

The Kaplan-Meier survival analysis demonstrated notable differences in OS time between the two risk groups (28.29±21.25 months for the high-risk group vs. 48.07±28.85 months for the low-risk group; log-rank P<0.001; Fig. 2A). The AUC for the three-lncRNAs panel in the training set was 0.924 (Fig. 2B). In order to validate the prognostic capability of the three-lncRNAs panel, 177 patients of the validation set were grouped into high-risk and low-risk groups with the risk scoring system based on the three-lncRNAs derived from the training set. The results of the validation set (32.47±21.02 months for the high-risk group vs 36.66±31.79 months for the low-risk group; log-rank P<0.001; Fig. 2A) were similar to the observations for the training set. The AUC of the validation set was 0.826 (Fig. 2B). The entire set was also classified into a high-risk and low-risk group with the same three-lncRNAs panel and the β value derived from the training set. Similarly, the OS time was significantly different between the two risk groups (32.47±21.02 months for the high-risk group vs. 36.66±31.79 months for the low-risk group; log-rank P<0.001; Fig. 2A), with an AUC of 0.861 (Fig. 2B).

Figure 2.

Three-lncRNAs signature for predicting survival in patients with OvCa. (A) The Kaplan-Meier curves for the high-risk and low-risk groups in the training, validation and entire dataset. (B) The receiver operating characteristic curves for survival prediction by the three-lncRNAs signature in the training dataset, the validation set and the entire set. AUC, area under the receiver operating characteristic curve; lncRNA, long non-coding RNA; OvCa, ovarian cancer.

Association of the expression levels of the three prognostic lncRNAs with OS time

Expression levels of KB-1836B5, LINC00566 and FAM27L were significantly different between the high-risk and low-risk group in the training set, the validation set and the entire set (P<0.005, Fig. 3). All samples in the training set (n=160) were assorted into a high-expression and a low-expression group. There were notable differences in the OS time between the two expression groups for each prognostic lncRNA (P=0.0079 and AUC=0.763 for KB-1836B5; P=0.043 and AUC=0.777 for LINC00566; and P=0.023 and AUC=0.704 for FAM27L; Fig. 4).

Figure 3.

Comparison of expression levels of KB-1836B5, LINC00566 and FAM27L between the high-risk and low risk-groups in the (A) training, (B) validation and (C) entire dataset. ***P<0.005. Student's t-test was used for comparison. LINC00566, long intergenic non-protein coding RNA 566; lncRNA, long non-coding RNA.

Figure 4.

Risk stratification according to the expression levels of the three prognostic lncRNAs. Patients in the training set were stratified by expression level of one prognostic lncRNA into a high-expression and a low-expression group. (A) The Kaplan-Meier curves for the high-expression and low-expression groups of KB-1836B5, LINC00566 or FAM27L, individually. (B) The receiver operating characteristic curves for survival prediction by the expression levels of KB-1836B5, LINC00566 or FAM27L, individually. LNC00566, long intergenic non-protein coding RNA 566; lncRNA, long non-coding RNA; AUC, area under receiver operating characteristic curve.

The distribution of the three-lncRNAs risk score, OS time of patients and the expression profiles of the three prognostic lncRNAs in the training, validation and entire sets is presented in Fig. 5. The expression levels of KB-1836B5, LINC00566 and FAM27L were upregulated in the low-risk patients, compared with the high-risk patients.

Figure 5.

Three-lncRNAs risk score distribution, overall survival time of patients and heatmap of the three prognostic lncRNAs expression profiles in the (A) training, (B) validation and entire dataset (C). lncRNA, long non-coding RNA; OS, overall survival; LNC00566, long intergenic non-protein coding RNA 566.

Independence of the prognostic performance of the three-lncRNAs panel from other clinical variables

Univariate and multivariate Cox regression, and data stratification analyses were performed in order to determine whether the prognostic power of the three-lncRNAs signature was independent of other clinical variables, including neoplasm histological grade, lymphatic invasion and tumor recurrence. Univariate Cox regression analysis determined that the three-lncRNAs risk score was significantly associated with OS time in the training set (P<0.001; 95% CI=1.736–4.258), the validation set (P=0.034; 95% CI=0.969–2.135) and the entire set (P<0.001; 95% CI=1.476–2.636; Table III). Furthermore, via multivariate Cox regression analyses, the three-lncRNAs risk score was determined to be an independent predictor of survival in the training set (P<0.001; 95% CI=2.787–15.389), the validation set (P=0.013; 95% CI=1.241–6.200) and the entire set (P≤0.001; 95% CI=2.487–7.549; Table III).

Table III.

Univariate and multivariate Cox regression analyses with three-lncRNAs risk score and other clinical variables.

	Univariate analysis				Multivariate analysis
Variables	HR	95% CI	P-value	HR	95% CI	P-value
Training set (n=160)
Three-lncRNAs risk score (low/high risk)	2.718	1.736–4.258	<0.001[a]	6.548	2.787–15.389	<0.001[a]
Age (≤60/>60)	1.171	0.763–1.798	0.471	1.879	0.938–3.764	0.075
Neoplasm histological grade (G1+G2/G3+G4)	0.978	0.370–2.584	0.964	1.207	0.476–3.063	0.692
Lymphatic invasion (yes/no)	1.281	0.659–2.488	0.466	1.115	0.556–2.235	0.759
Tumor recurrence (yes/no)	1.139	0.686–1.888	0.615	0.355	0.149–0.849	0.020
Validation set (n=177)
Three-lncRNAs risk score (low/high risk)	1.439	0.969–2.135	0.033[a]	2.773	1.241–6.200	0.013[a]
Age, years (≤60/>60)	1.350	0.913–1.997	0.133	1.195	0.489–2.920	0.696
Neoplasm histological grade (G1+G2/G3+G4)	1.887	1.115–3.192	0.016[a]	4.229	0.828–5.613	0.083
Lymphatic invasion (yes/no)	1.129	0.399–1.965	0.765	0.998	0.388–2.570	0.997
Tumor recurrence (yes/no)	1.193	0.552–1.273	0.408	0.809	0.353–1.856	0.617
Entire set (n=337)
Three-lncRNAs risk score (low/high risk)	1.972	1.476–2.636	<0.001[a]	4.333	2.487–7.549	<0.001[a]
Age, years (≤60/>60)	1.260	0.947–1.686	0.112	1.454	0.871–2.428	0.152
Neoplasm histological grade (G1+G2/G3+G4)	1.584	1.063–2.362	0.024[a]	1.931	0.928–4.020	0.079
Lymphatic invasion (yes/no)	1.133	0.681–1.884	0.632	1.148	0.670–1.966	0.615
Tumor recurrence (yes/no)	1.059	0.686–1.300	0.726	0.620	0.357–1.079	0.091

HR, hazard ratio; CI, confidence interval; lncRNA, long non-coding RNA

P<0.05.

Data stratification analyses were carried out according to age, neoplasm histological grade, lymphatic invasion and tumor recurrence, individually (Table IV; Fig. 6). All patients in the entire set were stratified by age into a younger (≤60 years) and elder dataset (>60 years). The younger dataset was further divided according to the three-lncRNAs signature into a low-risk and a high-risk group, which had significantly different OS time (median, 42.57 vs. 29.29 months, respectively; log-rank P=0.001; Fig. 6A). Similarly, the elder dataset was further classified into a low-risk and high-risk group, and the difference in OS time between the two risk groups was also significant (median, 41.73 vs. 28.98 months, respectively; log-rank P<0.001; Fig. 6A). Subsequently, all patients of the entire set were stratified by neoplasm histological grade into a grade 1+2 and grade 3+4 dataset. These datasets was classified by the three-lncRNAs signature into a low-risk group with longer OS time and a high-risk group with shorter OS time (median, 49.74 vs. 41.65 months, respectively; log-rank P=0.012, for the grade 1+2 dataset; median, 41.25 vs. 28.37 months, respectively; log-rank P<0.001, for the grade 3+4 dataset; Fig. 6B). Similarly, all patients were stratified by lymphatic invasion into a non-lymphatic invasion and lymphatic invasion dataset. In the two datasets, differences in OS time between the low-risk and high-risk groups were significant (median, 39.97 vs. 23.30 months, respectively; log-rank P=0.022, for the non-lymphatic invasion dataset; median, 35.58 vs. 20.58 months, respectively; log-rank P≤0.001, for the lymphatic invasion dataset; Fig. 6C). Subsequently, all patients were stratified by tumor recurrence into a no recurrence and a recurrence dataset. Significant differences between the low-risk and high-risk groups were observed in the two datasets (median 34.15 vs. 21.38 months, respectively; log-rank P=0.005, for the no recurrence dataset; median, 46.78 vs. 21.38 months, respectively; log-rank P<0.001, for the recurrence dataset; Fig. 6D). These data indicated that the prognostic value of the three-lncRNAs panel is independent from age, neoplasm histological grade, lymphatic invasion and tumor recurrence.

Table IV.

Results of data stratification analysis.

	Univariate analysis
Variables	HR	95% CI	P-value
Age, years
≤60 (n=173)	2.012	1.323–3.061	0.001
>60 (n=164)	2.085	1.380–3.148	<0.001
Neoplasm histological grade
G1+G2 (n=44)	1.949	0.844–4.500	0.012
G3+G4 (n=288)	1.993	1.460–2.721	<0.001
Lymphatic invasion
Yes (n=49)	3.940	2.032–7.642	<0.001
No (n=87)	2.761	1.161–6.563	0.022
Tumor recurrence
Yes (n=197)	1.845	1.313–2.594	<0.001
No (n=140)	2.274	1.290–4.009	0.005

HR, hazard ratio; CI, confidence interval. n values varied as some clinical features for certain samples were unavailable in the TCGA database.

Figure 6.

Kaplan-Meier survival analyses of all patients with OvCa stratified by age, neoplasm histological grade, lymphatic invasion and tumor recurrence with the three-long non-coding RNAs signature. (A) Kaplan-Meier curves for the younger (age, ≤60 years; n=173) and elder dataset (age, >60 years; n=164). (B) Kaplan-Meier curves for the neoplasm histological grade 1+2 dataset (n=44) and the neoplasm histological grade 3+4 dataset (n=208). (C) Kaplan-Meier curves for the non-lymphatic invasion (n=49) and lymphatic invasion dataset (n=87). (D) Kaplan-Meier curves for the no recurrence (n=140) and recurrence (n=197) dataset. OvCa, ovarian cancer.

Potential functions of the three-lncRNAs signature in OvCa tumorigenesis

It has been demonstrated that lncRNAs affect various cellular processes such as organ or tissue development, cellular transport or metabolic processes via regulating protein-coding genes (19). For the purpose of determining the potential functions of the three prognostic lncRNAs in OvCa, the protein-coding genes associated with ≥1 of the three prognostic lncRNAs with a Spearman correlation coefficient >0.4 were selected. Subsequently, GO function and KEGG pathway enrichment analyses were performed for these genes. As presented in Fig. 7A, these genes were significantly associated with 13 GO terms (P<0.05), which were categorized into four functional clusters, ATP metabolic process, transport, electron transport chain and cellular metabolic process. Furthermore, 10 pathways were significantly associated with these genes (P<0.05), consisting of oxidative phosphorylation, prion diseases, RNA polymerase, apoptosis, ubiquitin-mediated proteolysis, complement and coagulation cascades, pyrimidine metabolism, systemic lupus erythematosus, mechanistic target of rapamycin (mTOR) signaling pathway and amyotrophic lateral sclerosis pathways (Fig. 7B).

Figure 7.

Functional enrichment analysis for the genes associated with the three prognostic long non-coding RNAs. (A) The functional enriched GO terms. Each node represents a GO term. An edge represents the overlapped genes between two connecting terms. Node size is positively associated with the number of genes enriched in each GO term. Color intensity is positively associated with enrichment significance. (B) Significantly enriched KEGG pathway. Gene count represents the number of genes significantly enriched in each pathway. FDR, false discovery rate; ALS, amyotrophic lateral sclerosis; mTOR, mechanistic target of rapamycin; NADH, reduced nicotinamide-adenine dinucleotide; GO, Gene Ontology; Kyoto Encyclopedia of Genes and Genomes.

Discussion

OvCa has the highest malignancy incidence of all gynecological malignancy types globally in 2013 (20). LncRNAs may serve as independent biomarkers for survival prediction, due to lncRNAs not coding proteins (21). To investigate an lncRNA-based signature for predicting the prognosis of patients with OvCa, the present study initially selected 112 DELs between patients with good prognosis and poor prognosis using DESeq and edgeR methods. On the basis of the results of univariate and multivariate Cox regression analysis, a three-lncRNAs signature (lncRNA KB-1836B5, LINC00566 and FAM27L) for survival prediction was determined and used to construct a risk scoring system. The three-lncRNAs signature-based risk scoring system classified the patients in the training set into a high-risk and low-risk group, indicating significantly different OS time. The risk stratification capability of the three-lncRNAs signature was confirmed in the validation and entire set. Furthermore, the results of the multivariate Cox regression analysis and data stratification analysis demonstrated that the prognostic value of the three-lncRNAs signature was independent of age, neoplasm histological grade, lymphatic invasion and tumor recurrence. It is notable that histology was not a significant predictor of survival, and established risk factors for the patient's outcome, including tumor stage and residual tumor, were not included in the multivariate model. Although tumor stage is an important prognostic factor in OvCa (20), only early-stage samples were selected for analysis in the present study, with the majority of samples being stage II and III; therefore, the samples were considered to be at a ‘fixed’ stage, and were not used in the analysis. Furthermore, there are numerous clinical features that are associated with the prognosis of patients with OvCa (22,23), but clinical information for these features were not available for all patients in the datasets downloaded from TCGA. Furthermore, the aim of the multivariate Cox regression analyses was to investigate whether the three-lncRNAs signature was a significant variable, and whether it was independent of other clinical variables; thus, only available clinical features were analyzed in the present study. The results of the present study demonstrated that the three-lncRNAs panel may be a promising independent biomarker to predict the OS time of patients with OvCa.

It has been demonstrated that lncRNAs serve a function in a number of biological processes by functioning as important regulators of gene regulation at transcriptional, posttranscriptional and epigenetic levels (24,25); therefore, the present study investigated the protein-coding genes regulated by the three prognostic lncRNAs, in order to determine their possible biological function in the molecular mechanisms of OvCa. In the present study, the protein-coding genes associated with ≥1 of the three prognostic lncRNAs (Spearman correlation coefficient >0.4) were selected. Results of GO function enrichment analysis demonstrated that these genes were significantly associated with ATP metabolic process, transport, electron transport chain and cellular metabolic process. Furthermore, these genes were significantly enriched in a number of KEGG signaling pathways, including the mTOR signaling pathway, ubiquitin-mediated proteolysis, and complement and coagulation cascade pathways. mTOR, a member of phosphoinositide 3-kinase-associated kinase family of protein kinases, is a serine/threonine protein kinase (26). The mTOR signaling pathway serves a central function in a number of major cellular processes, including cell growth, cell proliferation and cell survival, and is being identified to be involved in an increasing number of diseases, including cancer, obesity and type 2 diabetes (27,28). Previous studies have demonstrated that mTOR pathway activation is frequently observed in OvCa and is involved in tumorigenesis and progression, indicating this pathway as a potential therapeutic target for OvCa (29–31). Ubiquitin-mediated proteolysis serves a pivotal function in protein turnover, thereby exerting a regulatory effect on carcinogenesis-associated cellular processes, including cell cycle, apoptosis and gene transcription (32). Furthermore, proteasome inhibitors have attracted considerable interest as a treatment option for solid tumor types (33). Complement is a central part of innate immunity, and is also involved in the adaptive immune response, inflammation and other biological processes (34). Emerging studies have demonstrated that complement activation exerts a tumor-promoting effect by strengthening tumor growth and metastasis (35,36). Results of the present study indicated that the lncRNAs KB-1836B5, LINC00566 and FAM27L may exert an effect on mTOR signaling pathway, ubiquitin-mediated proteolysis, and complement and coagulation cascade pathways via gene regulation, thus influencing OvCa cancerogenesis and progression.

Currently, the investigation of lncRNAs is in the early stages. According to the literature surveyed, there are few studies involving lncRNAs KB-1836B5, LINC00566 and FAM27L. To the best of our knowledge, their involvement in OvCa has not been reported previously. The present study determined and validated a three-lncRNAs predictive signature via comprehensive analysis, based on lncRNA expression files downloaded from TCGA.

However, two limitations of the present study should be mentioned. First, the number of patients in the training set (n=160) and validation set (n=177) is limited, which may affect the prediction accuracy of this three-lncRNAs prognostic signature; therefore, further work is required to verify the results of the present study in a larger cohort of patients prior to applications of these data in the clinic. Secondly, the present results were all derived from bioinformatics analysis of clinical data, and no direct in vitro experimental validations were performed. Although bioinformatics has been a reliable method to select the genetic factors implicated in the cellular progress, molecular function, and even tumorigenesis and its prognosis, and provides a possible method to screen the majority of potential factors from a huge information pool, it is considered that the present study will be more reliable following validation with cell or animal experiments; therefore, determining the potential factors is the first step to accelerate the study of tumor mechanisms, and further in vitro analyses required to validate the present results.

In conclusion, the present study identified and validated a three-lncRNAs signature for survival prediction in OvCa. The prognostic capability of this signature was independent of other clinical variables and may be recommended as a promising prognostic biomarker for OvCa. The three prognostic lncRNAs are associated with several cellular processes and signaling pathways, including the mTOR signaling pathway, ubiquitin-mediated proteolysis, and complement and coagulation cascades pathways. The present study provides an insight into the involvement of lncRNAs into the molecular mechanisms of OvCa.