Open Access

A four‑gene signature for prognosis in breast cancer patients with hypermethylated IL15RA

  • Authors:
    • Hui Yang
    • Li Zhou
    • Jianhua Chen
    • Jiang Su
    • Wei Shen
    • Biao Liu
    • Jundong Zhou
    • Shiyou Yu
    • Jun Qian
  • View Affiliations

  • Published online on: March 12, 2019     https://doi.org/10.3892/ol.2019.10137
  • Pages: 4245-4254
  • Copyright: © Yang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Previous studies have revealed that upregulation of interleukin 15 receptor α (IL15RA) contributes to improved prognosis of breast cancer. The present study aimed to elucidate the molecular mechanisms underlying the antitumor effect induced by IL15RA upregulation, and to identify a gene signature capable of predicting the survival of patients with breast cancer. Using paired gene expression and methylation data of breast cancer samples from The Cancer Genome Atlas data portal, differentially expressed genes (DEGs) were identified in hypermethylated and hypomethylated IL15RA breast cancer samples. Furthermore, a gene signature‑based risk‑scoring model was developed according to the Cox regression coefficients of survival‑associated DEGS. The gene signature was applied to classify patients with breast cancer and hypermethylated IL15RA into two risk groups via Kaplan‑Meier survival analysis of overall survival (OS) time. Functional enrichment analysis was conducted to decipher the biological roles of the DEGs between the two risk groups. A total of 326 DEGs were present in the hypomethylation and hypermethylation samples compared with in the normal samples. A four‑gene signature [SH3 and cysteine rich domain 2 (STAC2), proline rich 11 (PRR11), homeobox C11 (HOXC11) and nucleolar and spindle associated protein 1 (NUSAP1)] was identified as able to successfully separate patients with breast cancer and hypermethylated IL15RA into two risk groups with significantly different OS time. The signature revealed similar predictive performance in an independent set. Significant enrichment of the ‘receptor interaction’ and ‘cell adhesion molecules (CAM)’ pathways, which involved the DEGs, occurred between the two risk groups. These findings suggested that IL15RA may participate in the regulation of STAC2, PRR11, HOXC11, NUSAP1, and ‘ECM‑receptor interaction’ and ‘cell adhesion molecules’ pathways, and therefore in the suppression of breast cancer development and progression. The four‑gene signature may have potential prognostic value for breast cancer.

Introduction

Breast cancer is the most common type of cancer in women, accounting for 25% of all cases of cancer (1). According to gene expression profiles, the molecular features of breast cancer are divided as follows: Human epidermal growth factor receptor 2-positive, a basal subtype and two luminal subtypes (2). In addition, infiltration of various types of immune cells is commonly observed in breast cancer. For instance, emerging evidence has demonstrated that lymphocytic infiltration in breast cancer is generally associated with a favorable prognosis (36).

Interleukin 15 receptor α (IL15RA) is a component of the IL15R, which shares a β and γ subunit with the IL2 receptor (7). IL15-dependent signaling regulates the activation and proliferation of T cells and natural killer cells, and modulates the adaptive immune response (8). Marra et al (9) demonstrated that IL15 and IL15RA are frequently upregulated in breast cancer and breast cancer cell lines. The authors suggested that upregulation of IL15 and IL15RA induces an antitumor immune response by activating peripheral blood mononuclear cell synthesis, and subsequently improves the prognosis of patients with lymphocyte-enriched breast cancer. However, the underlying mechanisms of the effects of IL15RA expression on breast cancer remain unknown.

The present study aimed to investigate the molecular mechanisms underlying the antitumor effects induced by IL15RA upregulation and to identify a gene signature for breast cancer prognosis. Since IL15RA/cg09290866 hypermethylation leads to the suppression of IL15RA expression, and hypomethylation elevates the expression of IL15RA, the present study aimed to highlight the differentially expressed genes (DEGs) detected in hypermethylated and hypomethylated IL15RA breast cancer samples compared to normal samples. Univariate Cox regression analysis identified the prognosis-associated DEGs. In silico analyses of the expression profiles of these prognosis-associated DEGs enabled the generation of a four-gene signature, capable of predicting the odds of patient outcome, in the training and test sets. The four-gene signature may allow the classification of patients with IL15RA hypermethylation in the training set into a high-risk group and a low-risk group with significantly different survival times. In addition, the DEGs between the two risk groups were identified, and their possible biological roles were studied using Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses.

Materials and methods

The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets

The present study acquired the gene expression and methylation data of 316 breast cancer samples and 21 normal tissue samples from The Cancer Genome Atlas (TCGA) data portal (https://portal.gdc.cancer.gov/projects) based on Illumina Methylation 450 platform and Illumina HiSeq 2000 RNA Sequencing (Illumina, Inc., San Diego, CA, USA). These samples were defined as the training set (TCGA set). The corresponding clinical characteristics are shown in Table I.

Table I.

Clinical summary of patients in TCGA and GEO datasets.

Table I.

Clinical summary of patients in TCGA and GEO datasets.

Clinical featuresTCGA (n=316)GSE39004 (n=57)
Pathologic_M (M0/M1/MX)299/9/8
Pathologic_N (N0/N1/N2/N3/NX)167/95/25/21/8
Pathologic_T (M1/M2/M3/M4/MX)84/187/28/16
Pathologic_stage (I/II/III/IV/–)57/185/52/9/133/40/14/0/0
Age (>60/≤60/–, years)154/16217/38/2
ER_Status (positive/negative/–)238/69/929/28
HER2_Status (positive/negative/–)56/243/17
PR_Status (positive/negative/–)201/106/9
Radiation_therapy (Yes/No/–)6/18/292
Pharmaceutical_therapy (Yes/No)11/14/291
Hormone_therapy (Yes/No)23/33/1
Mortality (Dead/Alive)36/28024/33
Overall survival days (months, mean ± SD)29.07±34.7357.61±38.03

[i] ‘-’, data unavailable. ER, estrogen receptor; HER2, human epidermal growth factor receptor; GEO, Gene Expression Omnibus; PR, progesterone receptor; SD, standard deviation; TCGA, The Cancer Genome Atlas.

The GSE37751 dataset (10), including the gene expression data of human breast tumor samples, and the GSE39004 dataset, (10) consisting of DNA methylation profiles of the same human breast tumor samples, were downloaded from the National Center of Biotechnology Information (NCBI) GEO database (http://www.ncbi.nlm.nih.gov/geo/). A total of 57 samples with paired gene expression and DNA methylation data were selected as the test set (GEO set). Clinical characteristics of the test set are displayed in Table I.

DEGs screening

According to the methylation value of IL15RA/cg09290866 (β-value), samples from the training set were categorized into a hypermethylation group (β-value≥0.3) and a hypomethylation group (β-value<0.3). A false discovery rate (FDR)<0.05 and |log fold change (FC)|>0.585 were considered as the strict cutoff thresholds, and the DEGs were subsequently screened by comparing the DEGs in the hypermethylation or hypomethylation samples with those in the normal samples using Limma package (11) (http://www.bioconductor.org/packages/release/bioc/html/limma.html) in R3.1.0 language. The overlapping DEGs in both hypermethylation and hypomethylation samples were selected for further analysis.

Association of DEGs with patient survival

Univariate Cox regression analysis (12) was performed to evaluate the association of the overlapping DEGs with patient survival using survival package (https://cran.r-project.org/web/packages/survival/index.html) in R3.1.0 language. The DEGs that were significantly associated with patient survival were identified (log-rank P<0.05) and ordered.

Risk score

The risk score was calculated for each patient based on the linear combination of expression levels of the DEGs with the regression coefficient as follows:

Risk score = βgene 1 × exprgene 1 + βgene 2 × exprgene 2 +···+ βgene n × exprgene n

where ‘β gene n’ describes the estimated regression coefficient of the gene n derived from the univariate Cox regression analysis, and ‘exprgene n’ describes the expression level of the gene n.

By considering the median risk score of the hypermethylation group as the cutoff value, the hypermethylation samples in the training set were divided into a high-risk and a low-risk subgroup. By applying the β-value derived from the training set, the samples in the test set were also classified by risk score into a high-risk and a low-risk subgroup.

Statistical analysis

The overall survival (OS) time of the risk groups was analyzed using Kaplan-Meier (KM) survival analysis followed by log-rank test. Multivariate Cox regression analysis and data stratification analysis were conducted to determine the association of risk score with other clinical characteristics in TCGA and GEO sets. P<0.05 was considered to indicate a statistically significant difference. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated.

Functional enrichment analysis

In the training set, the DEGs between the high-risk and the low-risk groups were also identified using the Limma package, with the threshold set at FDR<0.05. Correlations of the DEGs selected with risk score were characterized using cor function (https://www.rdocumentation.org/packages/stats/versions/3.4.1/topics/cor) in R language. The DEGs with correlation coefficient 0–1 were defined as positive DEGs, whereas the DEGs with correlation coefficient −1–0 were defined as negative DEGs. The positive or negative DEGs were ranked according to their r. The top 20 positive DEGs and the top 20 negative DEGs underwent GO (13) function and KEGG (14) pathway enrichment analyses using the Database for Annotation, Visualization and Integrated Discovery (DAVID) (15). There are three types of GO terms, including biological processes (BP), cellular compartment (CC), and molecular function (MF). GO terms and KEGG pathways with FDR<0.05 were considered as significant functional annotations.

Results

Identification of DEGs

There were 226 hypermethylation samples (β-value≥0.3), 90 hypomethylation samples (β-value<0.3) and 21 normal samples in the training set. A total of 326 overlapping DEGs (FDR<0.05, |logFC|>0.585) were identified in the hypomethylation and hypermethylation samples relative to the normal samples.

Development of a four-gene signature-based risk-scoring model

The genes associated with prognosis were selected from the overlapping DEGs previously identified in the training set using univariate Cox regression analysis. These genes were then used to build a risk-scoring model for prognosis. Eventually, the SRC homology 3 (SH3) domain, SH3 and cysteine rich domain 2 (STAC2), proline rich 11 (PRR11), homeobox C11 (HOXC11) and nucleolar and spindle associated protein 1 (NUSAP1) genes were selected to calculate the risk score based on their Cox regression coefficients as follows:

Risk score = −1.744862×Exp STAC2 + −0.969505×Exp KCND3 + 1.404081×Exp HOXC11 + −4.4925×Exp NUSAP1

A risk score was assigned to each patient in the hypermethylation group. By using the 50th percentile cutoff of risk score as the threshold, all hypermethylation samples were classified into a high-risk and a low-risk subgroup. The low-risk subgroup exhibited significantly elongated survival time compared with the high-risk subgroup (median OS: 647 days vs. 92 days; P=0.00042; Fig. 1A).

To validate the prognostic performance of the four-gene signature in the test set, the patients with breast cancer in the test set were also dichotomized into low-risk and high-risk subgroups using the 50th percentile cutoff of risk score as the threshold. Similarly, better survival was observed in the low-risk group compared with the high-risk group (median OS: 1,792 days vs. 1,209 days; P=0.031; Fig. 1B).

Expression of the four prognostic genes in different risk groups

As shown in Fig. 2, the high-risk group presented significantly increased expression levels of PRR11, HOXC11 and NUSAP1, and significantly decreased expression of STAC2 compared to the low-risk subgroup in the training set (Fig. 2A; PRR11, P<0.005; STAC2, P<0.005; HOXC11, P<0.005; NUSAP1, P<0.005) and the test set (Fig. 2B; PRR11, P<0.005; STAC2, P<0.005; HOXC11, P<0.05; NUSAP1, P<0.001).

Fig. 3 displayed the four-gene risk score distribution, OS time of patients and a heatmap showing the expression of STAC2, PRR11, HOXC11 and NUSAP1 in the training set (Fig. 3A) and the test set (Fig. 3B). In the group of patients with high-risk score, PRR11, HOXC11 and NUSAP1 were upregulated, whereas STAC2 was downregulated.

Four-gene signature is an independent predictor for OS. The results of univariate Cox regression analysis for the training set and the test set demonstrated that the four-gene risk score (training set: P=3.01×10−05, HR=1.086, CI=1.044–1.128; test set: P=0.0288, HR=0.827, CI=0.582–1.175) and the pathological stage (training set: P=0.00472, HR=1.813, CI=1.200–2.738; test set: P=0.0448, HR=2.646, CI=1.023–3.845) were significantly associated with the survival of patients with breast cancer (Table II). Furthermore, the four-gene risk score (training set: P=0.0032, HR=1.069, CI=1.023–1.117; test set: P=0.0205, HR=0.780, CI=0.534–1.141) and the pathological stage (training set: P=0.0047, HR=1.198, CI=0.487–2.947; test set: P=0.0663, HR=2.458, CI=0.941–3.42) were independent predictors of survival according to the multivariate Cox regression analysis, which included clinical variables for both sets (Table II).

Table II.

Regression coefficients from univariate and multivariate Cox regression analysis.

Table II.

Regression coefficients from univariate and multivariate Cox regression analysis.

A, TCGA

Univariate CoxMultivariable Cox


VariableP-valueHR (CI 95%)P-valueHR (CI 95%)
pathologic_T0.1561.398 (0.881–2.220)
ER_Status0.5490.771 (0.328–1.808)
HER2_Status0.7310.831 (0.288–2.396)
pathologic_M0.0143.480 (1.284–9.430)0.0942.941 (1.676–2.947)
pathologic_N0.0171.517 (1.077–2.136)0.0721.009 (0.656–1.846)
Age0.0481.030 (1.000–1.060)0.1261.026 (0.993–1.061)
PR_Status0.0250.426 (0.202–0.897)0.0530.304 (0.132–0.698)
pathologic_stage<0.0051.813 (1.200–2.738)<0.0051.198 (0.487–2.947)
Risk score<0.0051.086 (1.044–1.128)<0.0051.069 (1.023–1.117)

B, GSE39004

Univariate CoxMultivariable Cox


VariableP-valueHR (CI)P-valueHR (CI)

Ethnicity0.1850.515 (0.193–1.376)
ER_Status0.1710.521 (0.205–1.326)
Triple0.8181.128 (0.402–3.167)
Grade0.3021.424 (0.728–2.787)
Neoadjuvant-therapy0.6210.601 (0.0797–1.527)
Hormone-therapy0.2620.581 (0.225–1.501)
Chemotherapy0.2040.547 (0.216–1.387)
Stage0.0452.646 (1.023–3.845)0.06632.458 (0.941–3.42)
Age0.0161.039 (1.007–1.071)0.01251.036 (1.008–1.128)
Risk score0.0290.827 (0.582–1.175)0.02050.780 (0.534–1.141)

[i] Bold indicates significant differences. CI, confidence interval; ER, estrogen receptor; HER2, human epidermal growth factor receptor; HR, hazard ratio; PR, progesterone receptor; SD, standard deviation; TCGA, The Cancer Genome Atlas.

Correlation analysis between risk score and clinical characteristics of patients

To explore whether the prognostic power of the four-gene signature was independent of other clinical features, including age, pathologic_M, N, stage and progesterone receptor (PR) status, the data stratification analysis was carried out for each clinical feature in the training set (Table III and Figs. 4 and 5). Firstly, all hypermethylated samples of the training set were stratified by age into an older dataset (>60 years, n=132) and a younger dataset (≤60 years, n=132). In addition, the older dataset was classified into a high-risk group and a low-risk group: however, no significant difference in OS time was observed between the two risk groups (P=0.273; Table III; Fig. 4A and C). The younger dataset was classified according to the risk score and divided into a high-risk and a low-risk group with significantly different OS time (P=0.0015; Table III, Fig. 4B and C). Subsequently, all hypermethylated samples were stratified by pathologic_M into an M0 dataset (n=284) and an M1 dataset (n=9). The M0 dataset was divided according to the risk score into a high-risk and a low-risk group. Difference in OS time between the two risk groups was significant (P=0.032; Table III, Fig. 4D and F). Nevertheless, the risk score that classified the M1 dataset into a high-risk and a low-risk group did not present significant difference in the OS time (P=0.3766; Table III, Fig. 4E and F). In addition, all hypermethylated samples were stratified by pathologic_N into an N0 + N1 dataset (n=227) and an N2 + N3 dataset (n=37). A significant difference in OS time between the high-risk and the low-risk group was observed in the N0 + N1 dataset (P=0.0032; Table III, Fig. 4G and I) but not in the N2 + N3 dataset (P=0.897; Table III, Fig. 4H and I). Based on the pathologic_stage, these samples were categorized into a stage I + II dataset (n=215) and a stage III + IV dataset (n=49). In the stage I + II dataset, the difference in OS time was significantly different between the two risk groups (P=0.0032; Table III, Fig. 5A). However, in the stage III + IV dataset, the survival time was not significantly different between the high-risk and the low-risk group (P=0.6115; Table III, Fig. 5B and C). These samples were eventually grouped into a PR_positive (n=173) and a PR_negative dataset (n=91) according to their PR_status. Difference in OS time between the high-risk and the low-risk group was significant in the PR_positive dataset (P=0.0283; Table III, Fig. 5D) but not in the PR_negative dataset (P=0.0513; Table III, Fig. 5E and F). These results indicated that the predictive capacity of the four-gene risk score for prognosis may be associated with the aforementioned clinical features.

Table III.

Results of data stratification analysis for the training set.

Table III.

Results of data stratification analysis for the training set.

VariableP-value
Age (>60 years, n=132)0.273
Age (<60 years, n=132)<0.005
pathologic_M (M0, n=284)0.032
pathologic_M (M1, n=9)0.377
pathologic_N (N0 + N1, n=227)<0.005
pathologic_N (N2 + N3, n=37)0.897
pathologic_stage (I + II, n=215)<0.005
pathologic_stage (III + IV, n=49)0.612
PR_Status (Positive, n=173)<0.005
PR_Status (Negative, n=91)0.051

[i] Bold indicates significant differences. PR, progesterone receptor.

Data stratification analysis was also conducted for the GEO dataset. All breast cancer samples in the GEO dataset were stratified by age and pathological stage (Fig. 6). The difference in the OS time between the high-risk and the low-risk group was not significant in the younger dataset (P=0.22; Fig. 6A), however, was significant in the older dataset (P=0.0372; Fig. 6B and C). In addition, the difference in the OS time between the high-risk and the low-risk group was significant in the stage I + II dataset (P=0.048; Fig. 6D), however, was not significant in the stage III + IV dataset (P=0.29; Fig. 6E and F).

Functional analysis

In order to identify the molecular mechanisms underlying the prognostic performance of this four-gene signature, the present study investigated the DEGs between the high-risk and the low-risk group of the IL15RA hypermethylated samples in the training set. Subsequently, among the 308 DEGs retrieved, 224 were negatively associated with the risk score (negative DEGs) and 84 were positively associated with the risk score (positive DEGs). These positive or negative DEGs were ranked according to the correlation coefficient. A list of the top 20 positive DEGs and top 20 negative DEGs is shown in Table IV, and includes STAC2, PRR11 and HOXC11. Hierarchical cluster analysis revealed that the gene expression pattern of high-risk samples was different from that of low-risk samples (Fig. 7A). In order to determine the possible biological roles of the top 20 positive and top 20 negative DEGs, GO function and KEGG pathway enrichment analysis were carried out. As shown in Fig. 7B, positive DEGs were mainly associated with several transcription-related GO BP terms, whereas negative DEGs were mostly associated with many cellular homeostasis-related GO BP terms. These genes were significantly associated with several KEGG pathways, such as ‘ECM-receptor interaction’ pathway and ‘cell adhesion molecules (CAMs)’ pathway (Fig. 7C).

Table IV.

Top 20 negative DEGs and top 20 positive DEGs.

Table IV.

Top 20 negative DEGs and top 20 positive DEGs.

A, Negative DEGs

GeneCorrelation coefficients
STAC2−0.626
SFRP1−0.470
MSRA−0.435
KCNMB1−0.425
SAA1−0.421
KRT17−0.419
PDLIM4−0.409
TRIM29−0.409
KRT14−0.409
TMEM220−0.408
SPRY2−0.406
KRT5−0.400
TP63−0.398
SAA2−0.394
COL17A1−0.391
KLK5−0.389
KRT15−0.389
TINAGL1−0.384
NTRK2−0.382
ID4−0.381

B, Positive DEGs

GeneCorrelation coefficients

PRR110.562
INTS20.523
SERINC50.417
HOXC110.406
TAOK10.401
UHMK10.383
BRIP10.374
GAS2L30.370
CASC50.358
CCNT10.358
DDI20.353
CLSPN0.345
HOXC130.342
CENPI0.338
HOXC100.333
SHCBP10.331
EPN30.329
CCNE20.323
ZBTB370.322
E2F80.319

[i] DEGs, differentially expressed genes.

Discussion

Breast cancer is a common type of cancer in woman (16). It has been reported that high expression of IL15RA contributes to better survival outcomes for patients with breast cancer (9). The present study explored the molecular mechanisms underlying the positive effect of IL15RA upregulation on patient prognosis. The results detected 326 DEGs in the hypomethylation and hypermethylation samples compared to normal samples. A four-gene signature was then identified and risk score for prognosis was calculated. The gene signature consisted of STAC2, PRR11, HOXC11 and NUSAP1. The four-gene signature-based risk score enabled classification of patients with IL15RA hypermethylated breast cancer in the training set into a high-risk and a low-risk group that had significantly different survival times. The prognostic performance of this four-gene signature-based risk score was verified in the GEO set. Furthermore, the four-gene risk score was a significant prognostic factor in the multivariate Cox regression analysis. These results suggested that the use of this four-gene signature may allow for prognostic prediction in patients with breast cancer. This also suggested that IL15RA may affect the development and progression of breast cancer partly via modulating the expression of these DEGs, thus influencing the prognosis of patients.

In the present study, the prognostic gene signature for breast cancer included STAC2, PRR11, HOXC11 and NUSAP1. STAC2 belongs to a small family of SH3 and cysteine-rich containing adaptor proteins (STAC1, STAC2 and STAC3), and is expressed in various types of tissue (17). Few reports concern STAC2 in breast cancer. PRR11 belongs to the family of proline-rich proteins. Zhou et a (18) reported that PRR11 promotes the invasion of breast cancer cells by regulating epithelial-to-mesenchymal transition (EMT). The findings from the present study suggested that IL15RA may regulate EMT by controlling PRR11 expression. HOXC11 is a member of the homeobox family of genes, which encode transcription factors involved in morphogenesis (19). It has been demonstrated that HOX genes serve important roles in cancer development by regulating several biological processes, including apoptosis, differentiation and angiogenesis (20). Makiyama et al (21) reported that HOXC11 expression is decreased in breast cancer tissues compared to non-cancerous tissues. Soon et al (22) revealed that the interaction of HOXC11 with the steroid receptor coactivator SRC-1 is responsible for the development of endocrine therapy resistance in breast cancer and suggested that nuclear HOXC11 is associated with survival. Nucleolar-spindle associated protein (NuSAP1), encoded by the NUSAP1 gene, is a microtubule- and chromatin-binding protein that is critical for the spindle assembly in the cytokinesis stages of mitosis (23). Subsequently, the dysregulated expression of NuSAP1 leads to cancer development (24). A growing body of evidence also reported that NuSAP1 is associated with various types of cancer, including cervical cancer, lung adenocarcinoma and prostate cancer (2527). Furthermore, it has been demonstrated that elevated expression of NuSAP1 is predictive of poor prognosis in triple-negative breast cancer (28). The present study also demonstrated that NuSAP1 was associated with survival of patients with breast cancer.

In the present study, the DEGs between the high-risk and the low-risk group of the training set were also investigated in order to determine the underlying mechanisms of the four-gene signature to predict prognosis. Among these DEGs, the top 20 DEGs positively or negatively associated with risk score were further selected. Results of GO analysis revealed that the top 20 positive DEGs were mainly involved in transcription-related biological processes, whereas the top 20 negative DEGs were mostly associated with cellular homeostasis-related biological processes. These DEGs were significantly associated with a number of signaling pathways, including ‘ECM-receptor interaction’ and ‘cell adhesion molecule (CAMs)’ pathways. It has been demonstrated that extracellular matrix (ECM) is involved in breast cancer development and progression (29). In addition, Emery and Tripathi reported that dysregulation of the adhesion and ECM pathways serves a role in breast cancer progression (30). A recent study by He et al (31) identified the role of ECM-receptor interaction in the crosstalk between tumor stroma and the peripheral blood mononuclear cells in breast cancer by using an integrated bioinformatics approach. The findings from the present study indicated that IL15RA may exert an effect on breast cancer development and progression partly by modulating ‘ECM-receptor interaction’ and ‘cell adhesion molecules (CAMs)’ pathways.

In conclusion, the present study suggested that IL15RA may affect breast cancer development and progression by regulating expression of STAC2, PRR11, HOXC11 and NUSAP1, and ‘ECM-receptor interaction’ and ‘cell adhesion molecules (CAMs)’ pathways. A four-gene signature was proposed for the prediction of prognosis in patients with breast cancer. The present study improved the understanding of the potent biological roles of IL15RA in the development of breast cancer. Further validation of this prognostic four-gene signature in a large cohort of patients with breast cancer, and experimental validation of these microarray-based results are required.

Acknowledgements

Not applicable.

Funding

The present study was supported by Suzhou Commission of Health and Family Planning (grant no. LCZX201713) and Foundation of Nanjing Medical University (no. 2017NJMU164).

Availability of data and materials

The datasets used and/or analyzed during the present study are available from the corresponding author on reasonable request.

Authors' contributions

HY and LZ performed data analyses and wrote the manuscript. JHC, JS, WS, BL and JDZ contributed significantly to data analyses and manuscript revision. SYY and JQ conceived and designed the study. 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.

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May 2019
Volume 17 Issue 5

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Online ISSN:1792-1082

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APA
Yang, H., Zhou, L., Chen, J., Su, J., Shen, W., Liu, B. ... Qian, J. (2019). A four‑gene signature for prognosis in breast cancer patients with hypermethylated IL15RA. Oncology Letters, 17, 4245-4254. https://doi.org/10.3892/ol.2019.10137
MLA
Yang, H., Zhou, L., Chen, J., Su, J., Shen, W., Liu, B., Zhou, J., Yu, S., Qian, J."A four‑gene signature for prognosis in breast cancer patients with hypermethylated IL15RA". Oncology Letters 17.5 (2019): 4245-4254.
Chicago
Yang, H., Zhou, L., Chen, J., Su, J., Shen, W., Liu, B., Zhou, J., Yu, S., Qian, J."A four‑gene signature for prognosis in breast cancer patients with hypermethylated IL15RA". Oncology Letters 17, no. 5 (2019): 4245-4254. https://doi.org/10.3892/ol.2019.10137