Development of a nine‑lncRNA signature as a novel prognostic marker of estrogen receptor‑negative breast cancer
- Authors:
- Published online on: February 13, 2020 https://doi.org/10.3892/ol.2020.11391
- Pages: 2979-2988
Abstract
Introduction
Breast cancer (BC) is the one of the most common malignancies in women worldwide, and the leading cause of cancer-associated mortality (1). In 2018, ~40,000 people died of breast cancer (2). Long-term survival rates decrease from 90 to 5% following the development of distant metastasis or recurrence, thus leading to poor prognosis in patients with BC (3). Tamoxifen monotherapy poses as an anti-estrogen agent in the mammary tissue and is considered effective in decreasing the recurrence rate and improving the overall survival (OS) of patients with BC (4). For patients with estrogen receptor (ER)-positive BC, the response rate of tamoxifen monotherapy decreased by 50–70 and 5–10% for patients with ER-negative BC. Thus, the development of novel prognostic biomarkers is critical in order to accurately assess the outcome and provide individualized treatment strategies for patients with ER-negative BC.
Long non-coding RNAs (lncRNAs) are defined as RNA transcripts >200 base pairs in length (5). Accumulating evidence demonstrates lncRNA dysregulation have in multiple human diseases, particularly different types of cancer (6–8), and their expression is associated with cancer development and metastasis (9). For example, lncRNA Inhibiting Metastasis is upregulated in BC and is associated the inhibition of cell proliferation and metastasis (10). Furthermore, lncRNA HOXA transcript induced by TGFβ plays important roles in regulating the epithelial-to-mesenchymal transition (EMT), invasion and metastasis of BC (11). Several lncRNA signatures have been developed for specific BC subtypes, based on the aberrant expression of these lncRNAs in BC (12). The results of these studies have demonstrated the potential role of lncRNAs as biomarkers for the clinical outcome of ER-negative BC (13,14).
A number of prognosis-associated lncRNAs have been identified as effective biomarkers via systematic, and subsequently used as lncRNA signatures for the prognosis of BC (15). For example, Wang et al (16) developed an 11-lncRNA signature using Cox regression analysis. Furthermore, Sun et al (17) developed an eight-lncRNA signature based on a weighted co-expressed network and competing endogenous RNA. Li et al (15) developed a five-lncRNA signature using the microarray re-annotation method. The aforementioned studies predominantly focused on all of the BC subtypes or patients with triple negative BC; however, very few focused on the identification of prognostic biomarkers for patients with ER-negative BC.
In order to develop an lncRNA signature for prognosis, the present study obtained samples from patients with ER-negative BC from multiple datasets archived in the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). The nine-lncRNA signature was developed using the Cox regression model, and validated in independent cohorts using the lncRNA expression profiles obtained using the microarray re-annotation method. Functional analyses demonstrated that these signature lncRNAs play important roles in tumor development and metastasis, which provides further evidence for the predictive ability of the signature.
Materials and methods
Data acquisition and pre-processing
A total of three datasets, GSE21653 (18,19), GSE58812 (20) and GSE19615 (21) were retrieved from the GEO database and measured using the Affymetrix Human Genome U133 Plus 2.0 Array platform (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL570). The GSE21653 dataset contained 266 BC samples, among which 110 were ER-negative BC samples and used as the discovery cohort. The GSE58812 and GSE19615 datasets contained 107 and 45 ER-negative BC samples, respectively, which were used as the validation cohorts. In the present study, the gene expression profiles (CEL. files) for each dataset, and the corresponding clinical information, were downloaded. The robust multichip average algorithm was used for background adjustment of the gene expression profiles. Each probe-set ID was mapped to its Entrez gene ID using the corresponding platform annotation files. In the case of multiple probes being mapped to the same gene, the expression value for the gene was summarized as the arithmetic mean value of the multiple probes. In the instance that a probe was mapped to multiple or no genes, this probe was deleted.
lncRNA probe re-annotation
Using the BLASTn tools (https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastn& PAGE_TYPE=BlastSearch&LINK_LOC=blasthome), the probe annotation sequences supported by Affymetrix were aligned to the human long non-coding transcript sequences and protein-coding transcript sequences from the GENCODE database (Release 30; http://www.gencodegenes.org/human/), respectively. The results of the sequence alignment were filtered as follows: Only the probes that matched to one long non-coding transcript were reserved, and each transcript should identically match with more than six probes. Gene expression profiles containing 15,942 mRNAs and 1,631 lncRNAs were obtained using the re-annotation method.
Survival analysis
The univariate Cox proportional hazards regression model was used to identify lncRNAs significantly associated with disease-free survival (DFS) of patients in the discovery cohort (22), with P<0.05 as the threshold. Subsequently, the multivariate Cox proportional hazards regression model was used to obtain robust prognosis-associated lncRNAs, which were used to develop the risk-score model according to the following equation:
RiskScore=∑k=1nExpk*eHRkWhere n is the number of prognostic lncRNAs, Expk is the expression value of the prognostic lncRNAs and eHRk is the estimated regression coefficient of the lncRNAs in the multivariate Cox regression analysis.
The present study used a stepwise regression method (23) in order to perform multivariate regression analysis on 16 lncRNAs, from which nine stable molecules were obtained. Stepwise regression is commonly used to introduce variables, one by one, into the model. Following each introduction of an explanatory variable, an F-test is performed, and the explanatory variables that have been selected are subjected to a t-test, respectively. When an explanatory variable is no longer significant due to the introduction of a subsequent explanatory variable, the former is deleted in order to ensure that only the significant variables are included in the regression equation prior to the introduction of each new variable. This procedure is an iterative process until neither a significant explanatory variable is selected into the regression equation, nor an insignificant explanatory variable is removed from the regression equation, in order to ensure that the resulting set of explanatory variables is optimal. The present study used the R step function to implement the stepwise regression process.
Consistency evaluation of DEGs
The reproducibility of the DEGs identified in the different datasets (GSE21653, GSE58812 and GSE19615) was evaluated using consistency analysis. The concordance score was calculated as k/n × 100% for several DEG lists extracted separately randomly from two of the three datasets (GSE21653, GSE58812 and GSE19615) which shared n genes, of which k genes demonstrated the same deregulation directions (up- or down-regulation). The score evaluates the consistency of DEGs by randomly extracting two independent datasets from two of the three datasets (GSE21653, GSE58812 and GSE19615).
The probability of observing a concordance score of k/n by chance was evaluated using the cumulative binomial distribution model as follows:
p=1-∑i=0k-1(ni)P0i(1-P0)n-iWhere P0 (here, 0.5) is the probability of one gene having the concordant association between the two lists of genes by chance.
Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis
KEGG pathway enrichment analysis was performed using the R package clusterProfiler (24) for DEGs and genes associated with the signature lncRNAs, and visualized using the R package DOSE (25). For both analyses, P<0 .05 was considered to indicate a statistically significant difference.
Statistical analysis
The median risk-score evaluated in each dataset by the signature for ER-negative BC samples was used as the cut-off (median score=26.1) in order to classify these samples into high- and low-risk groups. Kaplan-Meier (KM) plots were generated in order to assess DFS, followed by the log-rank test for the statistical comparison of the two groups. The predictive performance of the signature was further assessed using the pROC package (version 1.15.3). All statistical analyses were performed using the R software package (version 3.1.2;).
Results
Identification of prognosis-associated lncRNAs
For the present study, the gene expression profiles and clinical follow-up data of datasets GSE21653, GSE58812 and GSE19615 were downloaded, resulting in a total of 262 ER-negative BC samples. Subsequently, gene expression profiles containing 15,942 mRNAs and 1,631 lncRNAs were obtained via the probe re-annotation method. The univariate Cox proportional hazards regression model identified 16 prognosis-associated lncRNAs in the GSE21653 dataset, among which five were protective factors and 11 were risk factors (P<0.05; Table I). A number of lncRNAs have been reported to be associated with the invasion and metastasis of various types of cancer. For example, lncRNA Long Intergenic Non-Protein Coding RNA, P53 Induced Transcript (LINC-PINT) has been reported to inhibit cancer cell proliferation, invasion and migration in osteosarcoma by downregulating micro (miR)-21 (26). Furthermore, LINC-PINT is known to inhibit tumor cell invasion through a highly conserved sequence element (27). The lncRNA LINC00324 has been demonstrated to promote cell proliferation in gastric cancer by binding with human antigen R and stabilizing FAM83B expression (28). In addition, the lncRNA Homeobox A11 antisense (HOXA11-AS) regulates the JAK-STAT signaling pathway via the miR-15a-3p/STAT3 axis in order to promote liver cancer growth and metastasis (29).
Development of the nine-lncRNA signature
Using the multivariate Cox proportional hazards regression model, a total of nine lncRNAs were identified from the 16 DFS-associated lncRNAs with optimal predictive performance in the discovery cohort, which were defined as the nine-lncRNA signature. The multivariate regression results of the nine identified lncRNAs are presented in Table II. Subsequently, the risk-score equations were formulated as follows: Risk-score=2.4651*exp (CPS1-IT1) + 0.5640*exp (FOXD2-AS1) + 0.8631*exp (HOXA11-AS)-1.4586*exp (LINC00324) + 0.8866*exp (CKMT2-AS1)-0.6698*exp (BISPR) + 1.3965*exp (SNAP25-AS1) + 1.1144*exp (LOC101929340) + 0.4768*exp (LINC02544). The risk-score for each ER-negative BC sample in the discovery cohort was calculated for the nine-lncRNA signature. Receiver operating characteristic (ROC) curve analysis demonstrated that the 3-year area under curve reached 0.908 (Fig. 1A). Samples were divided into high- and low-risk groups for the nine-lncRNAs signature, with the median risk-scores as threshold. The 53 patients in the high-risk group had significantly worse DFS compared with the 57 patients in the low-risk group [hazard ratio (HR)=2.718, 95% confidence interval (CI)=2.115–3.494, P<0.0001; Fig. 1B].
Validation of the nine-lncRNA signature
Risk-scores for samples in the validation cohort of the GSE19615 dataset were calculated using the nine-lncRNA signature. Subsequently, 23 and 22 samples were divided into low- and high-risk groups, respectively, with the median risk-score as the threshold. The DFS of patients in the high-risk group was significantly worse than that of the patients in the low-risk group (HR=1.499, 95% CI=0.950–2.365, P=0.025; Fig. 2A). Similarly, in the validation cohort of the GSE58812 dataset, 54 and 53 samples were classified into low- and high-risk groups, respectively. The OS rate of patients in the high-risk group was significantly worse compared with patients in the low-risk group (HR=1.316, 95% CI=1.094–1.582, P=0.0035; Fig. 2B). Furthermore, patients in the high-risk group demonstrated significantly worse DFS than those in the low-risk group (HR=1.262, 95% CI=1.056–1.510, P=0.015; Fig. 2C). Overall, the nine-lncRNA characteristic risk-score κ had a good prognostic stratification in both the training set (GSE21653) and the independent validation sets.
Association between risk-score and the expression levels of the signature lncRNAs
The distribution of risk-scores for each patient calculated by the nine-lncRNA signature were analyzed. Together with patients' survival status, this identified a total of seven risk-associated lncRNAs (CKMT2-AS1, HOXA11-AS, CPS1-IT1, LOC101929340, FOXD2-AS1, SNAP25-AS1 and LINC02544), which were demonstrated to be overexpressed in patients with high risk-scores, and two protective lncRNAs (BISPRL and INC00324) were expressed at a lesser degree in patients with low risk-scores (Fig. 3A). Furthermore, eight of the nine lncRNAs were demonstrated to be significantly differentially expressed between the high- and low-risk groups classified by the nine-lncRNAs signature (Fig. 3B, C and E-J); only HOXA11-AS exhibited marginally differential expression (P=0.057; Fig. 3D).
Association between the nine-lncRNA signature and clinical features
It has been reported that lymph node status, age, histodifferentiation, progesterone receptor (PR) status and human epidermal growth factor receptor 2 (HER-2) status may influence the prognosis of patients with BC (30,31). Using the multivariate Cox regression model, the present study demonstrated that the predictive performance of the nine-lncRNA signature was independent of the aforementioned clinical features. The results of the univariate and multivariate analyses for the nine-lncRNA signature and other clinical features are presented in Table III.
Nine-lncRNA signature-associated biological processes
The present study identified 1,276 DEGs between the 57 low-risk samples and the 53 high-risk samples classified by the nine-lncRNA signature in the discovery cohort (Table SI). As predicted, hierarchical clustering analysis of the top 30 most significant DEGs (Fig. 4A) demonstrated that samples in the high- and low-risk groups were divided into two clusters, respectively. Furthermore, KEGG pathway function analysis demonstrated that these DEGs were significantly enriched in 32 signaling pathways (Table SII), including signaling pathways associated with cancer cell growth, proliferation, migration and angiogenesis. The associated signaling pathways in the present study included ‘cytokine-cytokine receptor interaction’ and ‘cell adhesion molecules’ (CAMs), and signaling pathways associated with the immune system, such as ‘T-cell receptor signaling pathway’, ‘NF-κB signaling pathway’, ‘Th17 cell differentiation’, and ‘Th1 and Th2 cell differentiation’. Immune escape mechanisms have been reported to play an important role in the development and metastasis of cancer (32), which indicated the dysregulation of important carcinogenesis-associated biological processes between the classified high- and low-risk groups in the present study. With regards to the 110 samples assessed in the discovery cohort of the present study, the expression levels of 4,195 mRNAs were demonstrated to be significantly associated with the nine lncRNAs, among which 3,159 mRNA-lncRNA pairs were positively associated and 1,036 mRNA-lncRNA pairs were negatively associated (Pearson's correlation analysis, FDR<0.01) (Table SIII). These mRNAs were predicted to be co-expressed with the corresponding lncRNAs. The present study performed KEGG pathway function analysis using the co-expressed mRNAs, for each lncRNAs. A total of 37 KEGG pathways (Table SIV) were obtained for six lncRNAs (Fig. 4C), including cancer-associated signaling pathways, such as ‘cell cycle’, ‘DNA replication’ and ‘p53 signaling pathway’, and immune-associated signaling pathways including, ‘Th1 and Th2 cell differentiation’, ‘B-cell receptor signaling pathway’ and ‘T-cell receptor signaling pathway’. Overall, the nine lncRNAs were associated with the carcinogenesis and development of patients with ER-negative BC.
Discussion
In recent years, a number of studies have demonstrated that dysregulated lncRNAs play an important role in the carcinogenesis, development and metastasis of different types of tumor (33–35). A number of lncRNAs have been reported to be associated with the recurrence, metastasis and resistance to adjuvant therapy of patients with BC (36,37). For example, lncRNA HOX Transcript Antisense Intergenic RNA has been demonstrated to enhance ER signaling and confer tamoxifen resistance in BC (38). Furthermore, upregulation of the lncRNA SRY-box 2 has been demonstrated to promote BC cell growth and invasion by activating the expression of the lncRNA Plasmacytoma variant translocation 1 (39). The degradation of the histone-lysine N-methyltransferase EZH2, mediated by lncRNA Antidifferentiation noncoding RNA, has been indicated to decrease the invasion and metastasis of BC (40). With the development of genomic diagnostic tests such as Oncotype DX (41) and Coloprint (42), high-throughput gene expression profiles have become the principle method of identifying novel prognostic biomarkers of cancer. Although several lncRNA signatures have been developed for the prognosis of patients with BC (43,44), there are currently none in use for clinical practice, particularly for ER-negative BC. Thus, the present study used publicly available gene expression profiles, together with the probe re-annotation method, in order to develop a DFS-associated nine-lncRNA signature, using the correlation analysis of the lncRNA expression data and clinical information from patients with ER-negative BC. The nine-lncRNA signature classified patients with ER-negative BC into high- and low-risk groups with significantly different DFS, which was validated in independent datasets. The results of the present study provide evidence for the repeatability and clinical value of the nine-lncRNA signature.
A number of signature lncRNAs have been reported to be associated with different types of tumor (45,46). For example, CPS1 Intronic Transcript 1 has been demonstrated to suppress cell proliferation, invasion and metastasis in colorectal, ovarian, lung and liver cancer (28–31,47–50). Furthermore, upregulation of FOXD2 Adjacent Opposite Strand RNA 1 has been associated with poor prognosis in various types of tumor (51–53). HOXA11-AS has been indicated to promote BC invasion and metastasis by regulating EMT (54). Furthermore, LINC00324 acts as an oncogene involved in the tumorigenesis and progression of gastric and lung cancer (55). The lncRNA BST2 Interferon Stimulated Positive Regulator has been demonstrated to promote the progression of thyroid papillary carcinoma by regulating miR-21-5p (56). Despite numerous studies, the biological functions of several lncRNAs remain unclear, thus further research is required.
In the present study, DEGs between the high- and low-risk groups were identified in the discovery and validation cohorts, and high consistency scores were obtained between these cohorts (Table SV). KEGG pathway function analysis demonstrated that these DEGs were significantly enriched in signaling pathways associated with cancer cell growth, proliferation, migration and angiogenesis, such as ‘cytokine-cytokine receptor interaction’ and ‘CAMs’, and signaling pathways associated with the immune system, such as ‘T-cell receptor signaling pathway’, ‘NF-κB signaling pathway’, ‘Th17 cell differentiation’ and ‘Th1 and Th2 cell differentiation’. Furthermore, genes co-expressed with these signature lncRNAs were significantly enriched in signaling pathways associated with cancer cell proliferation, migration and angiogenesis, such as ‘cell cycle’, ‘DNA replication’ and ‘p53 signaling pathway’, and signaling pathways associated with the immune system, such as ‘Th1 and Th2 cell differentiation’, ‘B-cell receptor signaling pathway’ and ‘T-cell receptor signaling pathway’. Altogether, the nine lncRNAs assessed in the present study were associated with the carcinogenesis and development of tumors, which provides evidence for the prognostic ability of the nine-lncRNA signature.
Although candidate lncRNA biomarkers involved in tumorigenesis were identified by bioinformatics analysis, some limitations exist in the present study. Firstly, since clinical follow-up information was missing, certain factors, including the presence of patient's other health status, including ER, PR and HER2 expression, were not considered to determine prognostic biomarkers. Secondly, the signature obtained by bioinformatics analysis was insufficient for clinical practice and requires external experimental verification, such as in vitro/in vivo validation. Therefore, further work with complete clinical information and a larger sample size is required for future genetic and experimental studies.
Overall, the present study used the available microarray data and clinical information to develop a nine-lncRNA signature, in order to promote personalized treatment for patients with ER-negative BC. Functional analysis demonstrated that this signature was involved in several signaling pathways associated with cancer recurrence and metastasis, which provides evidence for the prediction of post-surgery relapse risk for patients with ER-negative BC. Large-scale prospective studies are required in the future, in order to further evaluate and validate the robustness of the signature prior to its clinical application. Furthermore, future studies are required in order to understand the molecular mechanisms underlying the nine-lncRNA signature.
Supplementary Material
Supporting Data
Supporting Data
Supporting Data
Supporting Data
Supporting Data
Acknowledgements
Not applicable.
Funding
The present study was funded by the International Peace Maternity and Child Health Hospital (grant no. GFY 9307).
Availability of data and materials
The datasets used and/or analyzed during the present study are available from the corresponding author upon reasonable request.
Authors' contributions
ZW designed the present study. ZW, JW, LL and QH were responsible for acquisition, analysis and interpretation of data. ZW conducted the bioinformatics analyses. ZW and QH were responsible for drafting the manuscript and revising it critically. MW contributed to the conception of the present study and was responsible for approving the version to be published. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Glossary
Abbreviations
Abbreviations:
|
lncRNA |
long non-coding RNAs |
|
BC |
breast cancer |
|
ER |
estrogen receptor |
|
ROC |
receiver operating characteristic |
|
DEGs |
differentially expressed genes |
|
KEGG |
Kyoto Encyclopedia of Genes and Genomes |
|
DFS |
disease-free survival |
|
HR |
hazard ratio |
|
CI |
confidence interval |
|
GEO |
Gene Expression Omnibus |
|
FDR |
false discovery rate |
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