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Introduction

Similar to other human cancer types, breast cancer (BRCA) is one of the most frequently diagnosed cancer types and is the leading cause of cancer-associated mortality among females worldwide. BRCA comprises a heterogeneous group of neoplasms; the mechanisms underlying the development of this disease remain poorly understood (1). The prognosis of patients with BRCA varies and depends on the subtype, age and the extent of the disease (2). BRCA can be classified using several grading systems from the United States National Cancer Institute Breast/Ovarian Cancer Family Registry and systems based on histological type and grade (3,4), which may affect the outcome and response to treatment. At present, estrogen receptor (ER), progesterone receptor (PR) and human epidermal-growth factor receptor 2 (HER2) are considered as three biomarkers of cancer, and may be employed to aid the selection of treatment for BRCA (5). Patients with ER+ and/or PR+ BRCA, which are typical of luminal tumors, tend to have better outcomes; however, patients with BRCA of ER−, PR− and HER2− status, typical of triple-negative breast cancer (TNBC), have poorer outcomes (1). These two subtypes of BRCA also exhibit distinct responses to chemotherapy (6,7). Therefore, future studies focused on BRCA subtypes should be conducted to improve the treatment and prognosis of this disease.

Long non-coding RNAs (lncRNAs) are a form of RNA that do not encode proteins; the discovery of numerous lncRNAs in humans has notably improved the understanding of cancer and disease (8,9). With increasing interest in human lncRNAs and the availability of high-throughput technologies, the number of BRCA-lncRNA associations identified has rapidly increased. lncRNA metastasis associated lung adenocarcinoma transcript 1 (MALAT1) could suppress the metastasis of BRCA (10). lncRNA small nucleolar RNA host gene 1 induced trastuzumab resistance in BRCA by regulating polyadenylate-binding protein 1 expression via H3K27 acetylation (11). A limited number of previous studies have determined the expression and function of lncRNAs in certain subtypes of BRCA. For example, lncRNA AWPPH promoted the growth of TNBC by upregulating frizzled homolog 7 (12). Yang et al (13) performed a comprehensive analysis of the expression profiles of lncRNAs, mRNAs and microRNAs (miRNAs) associated with competing endogenous RNA networks in TNBC; however, the functions and mechanisms of the majority of lncRNAs have not been characterized in BRCA, particularly in various subtypes of this disease.

Transcription factors (TFs) strictly regulate gene transcription by binding to genomic cis-regulatory elements present in a specific sequence motif (14). Additionally, the efficacy of TFs to regulate their target genes is affected by a variety of genetic and epigenetic factors (15,16). The dysregulation of these global regulatory mechanisms could contribute to the development of diseases and cancer (17). Accumulating evidence has demonstrated that lncRNAs mediate gene expression and regulate transcription. For example, lncRNA MALAT1 controls the progression of the cell cycle by regulating the expression of the oncogenic TF Myb-related protein B (18). lncRNA MALAT1 could promote cellular proliferation by regulating the expression and pre-mRNA processing of cell cycle-regulated TFs. In addition, lncRNA colorectal cancer associated transcript 1-long isoform contributes to regulating long-range interactions at the locus of the TF MYC (19). These previous findings suggested that lncRNAs may serve essential roles in regulating TFs; however, only a few previous studies have determined the activity of TFs mediated by lncRNAs in the subtypes of BRCA.

In the present study, a comprehensive and computational approach was undertaken to systemically identify lncRNA-mediated transcriptional dysregulation triplets (lncTDTs) based on the expression profiles of TFs, genes and lncRNAs; their previously experimentally identified interactions in ER+/PR+, HER2− BRCA and TNBC were further analyzed in the present study using bioinformatics methods. A total of six regulatory profiles of lncTDTs were identified, which exhibited distinct features in the two BRCA subtypes. The diverse and common characteristics of the two subtypes were also analyzed. Functional analysis revealed that the lncTDTs were significantly associated with cancer-related Gene Ontology (GO) terms and the PI3K/AKT signaling pathway. Additionally, certain lncTDTs in ER+/PR+, HER2− BRCA and TNBCs were detected. Collectively, the results of the present study may provide further insight into the functions of lncRNAs and the underlying mechanisms, and may serve as a basis for the classification and treatment of BRCA.

Materials and methods

TF, gene and lncRNA expression profiles of ER+/PR+, HER2− BRCA and TNBC

Three same-sample expression profiles of TFs, genes and lncRNAs were downloaded from The Cancer Genome Atlas (TCGA; https://tcga-data.nci.nih.gov/; https://portal.gdc.cancer.gov/exploration?filters=%7B''op''%3A''and''%2C''content''%3A%5B%7B''op''%3A''in''%2C''content''%3A%7B''field''%3A''cases.primary_site''%2C''value''%3A%5B''Breast''%5D%7D%7D%5D%7D). The matched clinical information, including ER status, PR status, HER2 status and survival data were also obtained. All BRCA samples were divided into two sets: ER+/PR+, HER2− BRCA and TNBC, based on clinical information. In total, the ER+/PR+, HER2− BRCA dataset included 422 cancer samples and 22 matched normal samples, while the TNBC dataset contained 112 cancer samples and 3 matched normal samples.

Collection of cancer-associated lncRNAs and genes

To obtain more accurate information for analysis, the data of BRCA-associated lncRNAs and genes were extracted. Information regarding cancer-related lncRNAs was obtained from lnc2cancer 2.0, which is an updated database that provides comprehensive experimentally supported associations between lncRNAs and human cancer (20). Cancer-related genes were obtained from DisGeNET, which is the largest publicly available database of genes and variants associated with a variety of human diseases (21). A t-test was performed to identify differentially expressed cancer-related genes and lncRNAs in ER+/PR+, HER2− BRCA and TNBC. P<0.05 was considered to indicate a statistically significant difference. A total of 6,626 and 555 ER+/PR+, HER2− BRCA-specific genes and lncRNAs, and 5,576 and 326 TNBC-specific genes and lncRNAs were respectively obtained. These BRCA subtype-specific genes and lncRNAs were employed for subsequent analysis.

Identification of ER+/PR+, HER2− BRCA and TNBC-specific TF-gene interactions

TF-gene interaction data were obtained from TRANSFAC (22). ER+/PR+, HER2− BRCA- and TNBC-specific TF-gene interactions were extracted based on the aforementioned conditions for the retrieval of BRCA subtype-specific genes and co-expression data. Pearson correlation coefficient (PCC) values were calculated for all cancer-associated TF-gene interactions. Then, ER+/PR+, HER2− BRCA and TNBC-specific TF-gene interactions were considered, providing the absolute PCC values were >0.3. Following filtering, 494 and 476 ER+/PR+, HER2− BRCA- and TNBC-specific TF-gene interactions were respectively identified and employed for subsequent analysis.

Identification of lncTDTs associated with ER+/PR+, HER2− BRCA and TNBC

An integrated and computational approach was developed to identify lncTDTs associated with patients with ER+/PR+, HER2− BRCA and TNBC. For each BRCA subtype-specific lncRNA, the BRCA samples were sorted based on lncRNA expression; 25% of the samples exhibiting the highest and lowest lncRNA expression levels were selected. Additionally, interactions between TFs and genes were considered, providing the interactions were affected by a certain lncRNA, based on the independent analysis of each lncRNA-TF-gene triplet. The PPC values between each TF and gene were respectively calculated in the top and bottom 25% samples for a given lncRNA. A TF-gene interaction was considered to be affected by an lncRNA providing the absolute difference of the PCC values between the top and bottom 25% samples was >0.4. This lncRNA-TF-gene interaction was considered as a candidate lncTDT. For a particular lncRNA, all BRCA subtype-specific TF-gene interactions were calculated. Furthermore, 1,000 random alterations of the sample labels of expression profiles was performed to compare the absolute difference of PCC values with changes in the absolute difference of these values to determine significance. P<0.05 was selected as the threshold value to obtain significant lncTDTs associated with ER+/PR+, HER2− BRCA and TNBC.

Pattern classification of lncTDTs for ER+/PR+, HER2− BRCA and TNBC

In order to further investigate the mechanism of lncTDTs, the lncTDTs were separated into six profiles based on their mode of regulation: i) Weaken inhibition, TFs can suppress the expression of a gene, while an lncRNA could weaken this inhibitory action; ii) strengthen inhibition, TFs inhibit the expression of a gene, while an lncRNA could promote this inhibitory action; iii) reverse inhibition, TFs induce the expression of gene, while an lncRNA can invert these activating effects favoring inhibition; iv) reverse activation, TFs inhibit the expression of a gene, while an lncRNA can invert these inhibitory effects favoring activation; v) weaken activation, TFs induce the expression of a gene, but the effects of the TF are weakened by an lncRNA; and vi) strengthen activation, TFs can induce the expression of a gene, which is promoted by an lncRNA.

Construction and analysis of an lncTDT network for ER+/PR+, HER2− BRCA and TNBC

The networks of lncTDTs for ER+/PR+, HER2− BRCA and TNBC were constructed and the degree analysis was performed using Cytoscape 3.0 (http://www.cytoscape.org/).

Functional enrichment analysis for lncTDTs in ER+/PR+, HER2− BRCA and TNBC

For all TFs and genes associated with ER+/PR+, HER2− BRCA and TNBC, functional enrichment analyses were performed with the Enrichr tool online web server using default parameters (23). GO terms (http://geneontology.org/; Accessed May 9, 2019; P<0.01) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (https://www.kegg.jp/; release 90.1; P<0.05) associated with lncTDTs in ER+/PR+, HER2− BRCA and TNBC were determined.

Prognosis analysis for lncTDTs in ER+/PR+, HER2− BRCA and TNBC

The regression coefficient was employed to verify whether each lncTDT in ER+/PR+, HER2− BRCA and TNBC was associated with survival based on expression data. A multivariate cox regression model was generated for each lncTDT and standardized Cox regression coefficients were obtained. Then, a risk score was established for each sample based on the expression profile of an lncTDT weighted by the standardized Cox regression coefficient. Furthermore, the BRCA samples were divided to high- and low-risk groups based on the median risk score. Finally, Kaplan-Meier survival analysis of the two groups was performed followed by a log-rank test. All analyses were performed using R 3.3.2 software (https://www.r-project.org/).

Results

lncTDTs in TNBC and ER+/PR+, HER2− BRCA

An integrated and computational approach was performed to identify lncTDTs in TNBC and ER+/PR+, HER2− BRCA based on expression profiles and experimentally verified interactions. A total of 2,645 and 1,417 lncTDTs were identified in TNBC and ER+/PR+, HER2− BRCA; two lncTDT networks were also constructed for TNBC and ER+/PR+, HER2− BRCA (Fig. 1A and B; Table SI). There were 325 nodes and 1,417 edges in the TNBC lncTDT network; there were 583 nodes and 2,645 edges were identified in the ER+/PR+, HER2− BRCA lncTDT network. The nodes included 218 lncRNAs, 107 TF-gene interactions, 33 TFs and 83 genes in the TNBC lncTDT network (Fig. 1C). The nodes included 410 lncRNAs, 173 TF-gene interactions, 69 TFs and 142 genes in the ER+/PR+, HER2− BRCA lncTDT network (Fig. 1C). The degrees of all nodes in the TNBC network (R2=0.61; Fig. 1D) and the ER+/PR+, HER2− BRCA network (R2=0.85; Fig. 1E) exhibited scale-free distribution, indicating that the networks were biological regulatory networks. In addition, degree analysis of the lncRNAs for each lncRNA in the networks for TNBC and ER+/PR+, HER2− BRCA. The results revealed that the lncRNA with the highest degree was RP11-82L18.2 in the TNBC lncTDT network. lncRNA RP11-82L18.2 was determined to mediate 18 TF-gene interactions (Fig. 1F). The lncRNA with the highest degree was RAMP2 antisense RNA 1 (RAMP2-AS1) in the ER+/PR+, HER2− BRCA lncTDT network; lncRNA RAMP2-AS1 mediated 30 TF-gene interactions (Fig. 1G). These results indicated that lncRNAs serve essential and specific roles in the lncTDT networks in TNBC and ER+/PR+, HER2− BRCA.

Figure 1 Identification of lncTDTs associated with TNBC and ER+/PR+, HER2− BRCA. (A) lncTDT network for TNBC. (B) lncTDT network for ER+/PR+, HER2− BRCA. Green and dark orange nodes represent the TF-gene pairs and lncRNAs, respectively. The different colored edges represent various regulatory patterns. Light blue (1), purple (2), dark blue (3), light green (4), dark green (5) and pink (6) represent weaken inhibition, strengthen inhibition, reverse inhibition, reverse activation, weaken activation and strengthen activation, respectively. The size of the nodes indicates the degree of nodes. (C) Bar plots indicate the number of lncRNAs, TF-gene pairs, TFs and genes. Plots indicate the degree distribution for (D) TNBC and (E) ER+/PR+, HER2− BRCA. Bar plots demonstrate the degree of lncRNAs in (F) TNBC and (G) ER+/PR+, HER2− BRCA. Teal and orange represent TNBC and ER+/PR+, HER2− BRCA, respectively. lncTDT, lncRNA-mediated transcriptional dysregulation triplet; TNBC, triple-negative breast cancer; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal-growth factor receptor 2; BRCA, breast cancer; TF, transcription factor; lncRNA, long non-coding RNA; RAMP2-AS1, RAMP2 antisense RNA 1.

Complex patterns of lncTDTs in TNBC and ER+/PR+, HER2− BRCA

TFs are known to activate or inhibit gene expression; however, this is a complex process in diseases, such as cancer. In addition, lncRNAs can influence the effects of TFs on their target genes; thus, these lncRNA-mediated TFs-gene interactions were defined as lncTDTs. The regulatory patterns of lncTDTs in TNBC and ER+/PR+, HER2− BRCA are complex. The lncTDTs were divided into six patterns based on the regulatory action of lncRNAs and TFs (Fig. 2A). In total, there were, 1.27, 0.42, 18.77, 13.20, 24.49 and 41.85% lncTDTs involving lncRNAs that could weaken inhibition, strengthen inhibition, reverse inhibition, reverse activation, weaken activation and strengthen activation of TF-regulated gene expression, respectively for TNBC. In addition, there were 0.076, 0.87, 7.18, 4.23, 33.50 and 54.14 lncTDTs involving lncRNAs that could weaken inhibition, strengthen inhibition, reverse inhibition, reverse activation, weaken activation and strengthen activation of the effects of TF on gene expression for ER+/PR+, HER2− BRCA (Fig. 2B). In TNBC and ER+/PR+, HER2− BRCA, the most common regulatory pattern was strengthened activation, while the least common were weakened inhibition and strengthened inhibition. Additionally, it was determined that an lncRNA can exhibit various regulatory patterns; for example, a particular lncRNA could weaken, strengthen or reverse the effects of different TFs on gene expression. The cancer-associated lncRNA HOX transcript antisense RNA (HOTAIR), which is an important cancer-related lncRNA (24), could weaken activation and reverse inhibition of gene expression in TNBC (Fig. 2D). Furthermore, HOTAIR was proposed to reverse inhibition, and strengthen or weaken activation in ER+/PR+, HER2− BRCA (Fig. 2E); only the most significant HOTAIR-related lncTDTs are shown. The most common patterns of HOTAIR in TNBC and ER+/PR+, HER2− BRCA were weaken activation and strengthen activation, respectively. These results indicated the complexity of lncTDTs in TNBC and ER+/PR+, HER2− BRCA.

Figure 2 Complex regulatory patterns. (A) A total of six regulatory patterns were identified. Green, burgundy and orange represent lncRNAs, TFs and genes, respectively. The pie charts demonstrate the distribution of the regulatory patterns in (B) TNBC and (C) ER+/PR+, HER2− BRCA. Light blue (1), purple (2), dark blue (3), light green (4), dark green (5) and pink (6) represent weaken inhibition, strengthen inhibition, reverse inhibition, reverse activation, weaken activation and strengthen activation, respectively. Regulatory patterns of the most significant HOTAIR-mediated lncRNA-mediated transcriptional dysregulation triplets in (D) TNBC and (E) ER+/PR+, HER2− BRCA. TNBC, triple-negative breast cancer; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal-growth factor receptor 2; BRCA, breast cancer; TF, transcription factor; lncRNA, long non-coding RNA; HOTAIR, HOX transcript antisense RNA.

Diverse and common characteristics of lncTDTs in TNBC and ER+/PR+, HER2− BRCA

TNBC and ER+/PR+, HER2− BRCA are two important subtypes of BRCA. The mechanisms underlying the development and progression of these subtypes differ. Therefore, investigating the common characteristics of lncTDTs in TNBC and ER+/PR+, HER2− BRCA may improve the understanding of this disease. In the present study, the lncRNAs, TFs, genes and TF-gene interactions in TNBC and ER+/PR+, HER2− BRCA were analyzed. The majority of these molecules differed between TNBC and ER+/PR+, HER2− BRCA; however, 142 lncRNAs, 18 TFs, 23 genes and 23 TF-gene interactions were reported in the two BRCA subtypes (Fig. 3A-D). In addition, five common lncTDTs were identified between TNBC and ER+/PR+, HER2− BRCA (Fig. 3E). In the present study, 80% of the common lncTDTs exhibited the same regulation pattern (Fig. 3F); these lncTDTs included: NCK1-antisense RNA 1 (NCK1-AS1)/nuclear factor of activated T cells 2 (NFATC2)/CD3g molecule, NCK1-AS1/NFATC2/cytotoxic T-lymphocyte associated protein 4, small nucleolar RNA host gene 6 (SNHG6)/interferon regulatory factor 1 (IRF1)/interleukin 12B (IL12B) and SNHG6.1/IRF1/IL12B, which exhibited weakened activation (Fig. 3G). Of all the common lncTDTs, only PRC1 antisense RNA 1 (PRC1-AS1)/NK2 homeobox 5 (NKX2-5)/procollagen-lysine, 2-oxoglutarate 5-dioxygenase 1 (PLOD1) revealed a different regulatory pattern in TNBC and ER+/PR+, HER2− BRCA. In addition, PRC1-AS1/NKX2-5/PLOD1 exhibited reversed inhibition and strengthened inhibition patterns (Fig. 3H). It was identified that lncRNA NCK1-AS1 serves an important role in the two BRCA subtypes; lncRNA NCK1-AS1 could promote proliferation and induce cell cycle progression in cervical cancer (25). Downregulation of lncRNA NCK1-AS1 was reported to increase chemosensitivity to cisplatin in cervical cancer (26). In the present study, lncRNA NCK1-AS1 was determined to regulate 12 and 14 TF-gene interactions in TNBC and ER+/PR+, HER2− BRCA, respectively (Fig. 3I). The regulatory patterns in ER+/PR+, HER2− BRCA are varied and complex. The present results indicated that the lncTDTs possessed common and specific characteristics in TNBC and ER+/PR+, HER2− BRCA.

Figure 3 Common and specific lncTDTs for TNBC and ER+/PR+, HER2− BRCA. Venn diagrams demonstrating the association between (A) lncRNAs, (B) TFs, (C) genes, (D) TF-gene pairs and (E) lncTDTs. Teal indicates TNBC and orange indicates ER+/PR+, HER2- BRCA. (F) Pie chart indicates the percentage of the same regulatory pattern among common lncTDTs. (G) Regulatory patterns of common lncTDTs. The nodes represent various regulatory patterns. Light blue, purple and dark green represent weaken inhibition, strengthen inhibition and weaken activation, respectively. (H) Regulatory pattern of lncTDT PRC1-AS1/NKX2-5/PLOD1. Green, burgundy and orange represent lncRNAs, TFs and genes, respectively. (I) Subnetworks of lncRNA NCK1-AS1 in TNBC and ER+/PR+, HER2− BRCA. Green and dark orange nodes represent the TF-gene pairs and lncRNAs, respectively. The different colored edges represent various regulatory patterns. Dark blue, dark green and pink represent reverse inhibition, weaken activation and strengthen activation, respectively. lncTDTs, long non-coding RNA-mediated transcriptional dysregulation triplets; TNBC, triple-negative breast cancer; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal-growth factor receptor 2; BRCA, breast cancer; lncRNAs, long non-coding RNAs; TF, transcription factor; PRC1-AS1, PRC1 antisense RNA 1; NKX2-5, NK2 homeobox 5; PLOD1, procollagen-lysine,2-oxoglutarate 5-dioxygenase 1.

lncTDTs exhibit cancer-associated functions in TNBC and ER+/PR+, HER2− BRCA

To further understand the functions and mechanisms of lncTDTs in TNBC and ER+/PR+, HER2− BRCA, GO and KEGG pathway functional enrichment analyses were performed for genes and TFs associated with lncTDTs. The lncTDTs in TNBC or ER+/PR+, HER2− BRCA were enriched for certain cancer-associated functions, including 'positive regulation of transcription, DNA-templated', 'negative regulation of transcription, DNA-templated', 'positive regulation of gene expression' and 'cellular response to cytokine stimulus' (Fig. 4A and B). Additionally, the lncTDTs in TNBC and ER+/PR+, HER2− BRCA were both enriched in the PI3K/AKT signaling pathway which is a key pathway for cancer development and treatment (data not shown) and this pathway is key in the development and treatment of cancer (27-29); alterations in the PI3K/AKT signaling pathway also contribute to drug resistance in BRCA (30,31). In the present study, numerous genes in lncTDTs were enriched in this pathway for TNBC and ER+/PR+, HER2− BRCA (Fig. 4C). Of note, lncTDTs were associated with the PI3K/AKT signaling pathway in the two BRCA subtypes; however, the genes differed. Furthermore, a common gene, CD19, was determined to be associated with the PI3K/AKT signaling pathway in TNBC and ER+/PR+, HER2− BRCA. These results indicated that lncTDTs may serve essential roles in TNBC and ER+/PR+, HER2− BRCA by affecting certain important biological processes and pathways.

Figure 4 Functional analysis for lncTDTs in TNBC and ER+/PR+, HER2− BRCA. Gene Ontology terms enriched for genes and transcription factors in lncTDTs for (A) TNBC and (B) ER+/PR+, HER2− BRCA are presented and ranked by -log10(P) values. The purple lines represent the number of enriched genes. (C) PI3K/AKT signaling pathway and the genes associated with TNBC and ER+/PR+, HER2− BRCA, which are presented as teal and orange, respectively. The purple, blue, green and orange shapes represent the genes in the cell membrane, transmembrane, intracellular membrane and biology process, repectively. lncTDTs, long non-coding RNA-mediated transcriptional dysregulation triplets; TNBC, triple-negative breast cancer; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal-growth factor receptor 2; BRCA, breast cancer.

lncTDTs may serve as candidate prognostic biomarkers in TNBC and ER+/PR+, HER2− BRCA

A risk score-based analysis on the expression of the lncRNA, TF and gene of an lncTDT was performed for the prediction of survival to evaluate the potential of lncTDTs as prognostic biomarkers in the two BRCA subtypes. A total of 16.08 and 7.86% lncTDTs were significantly associated with survival in TNBC and ER+/PR+, HER2− BRCA, respectively (Fig. 5A and B); few lncTDTs (0.28 and 1.36%) exhibited a highly significant association with survival in the two respective BRCA subtypes. In addition, the prognostic value of HOTAIR-mediated lncTDTs in the two BRCA subtypes was investigated. Certain HOTAIR-mediated lncTDTs in TNBC were associated with survival (data not shown). A total of seven HOTAIR-mediated lncTDTs in ER+/PR+, HER2− BRCA were significantly related to survival (Fig. 5C), including HOTAIR/E2F transcription factor 1 (E2F1)/cyclin E1 (CCNE1), HOTAIR/CCAAT enhancer binding protein α (CEBPA)/apolipoprotein B, HOTAIR/CEBPA/carboxylesterase 1, HOTAIR/signal transducer and activator of transcription 5A/insulin like growth factor 1, HOTAIR/estrogen receptor 2/membrane metalloendopeptidase, HOTAIR/Kruppel like factor 11/hemoglobin subunit β and HOTAIR/early growth response 2/ATP binding cassette subfamily B member 1. Of these HOTAIR-mediated lncTDTs, HOTAIR/E2F1/CCNE1 was determined to be related to survival with the highest degree of significance (P=0.02); the lncTDTs that were associated with survival in TNBC and ER+/PR+, HER2− BRCA with a high degree of significance are presented in Fig. 5D and E. The patients in the high-risk group exhibited shorter survival than those in the low-risk group. The results of the present study indicated that lncTDTs may collectively influence the survival of patients with TNBC or ER+/PR+, HER2− BRCA; thus, lncTDTs could be considered as potential biomarkers for these subtypes of BRCA.

Figure 5 lncTDTs are associated with survival in TNBC and ER+/PR+, HER2− BRCA. Pie charts indicate the percentage of survival-related lncTDTs in (A) TNBC and (B) ER+/PR+, HER2− BRCA. (C) Survival analyses for HOX transcript antisense RNA-mediated lncTDTs in ER+/PR+, HER2− BRCA; highly significant associations are presented in red. Orange/red indicate significant associations and yellow/green indicate non-significant associations. Examples of survival-related lncTDTs in (D) TNBC and (E) ER+/PR+, HER2− BRCA. lncTDTs, long non-coding RNA-mediated transcriptional dysregulation triplets; TNBC, triple-negative breast cancer; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal-growth factor receptor 2; BRCA, breast cancer.

Discussion

In present study, an integrated and computational approach was developed to identify lncTDTs in ER+/PR+, HER2− BRCA and TNBC based on expression profiles and experimentally verified TF-gene interactions. The regulatory patterns of these lncTDTs were complex and could be divided into six types, including weaken inhibition, strengthen inhibition, reverse inhibition, reverse activation, weaken activation and strengthen activation. In addition, it was proposed that an lncRNA may regulate different TF-gene interactions, while an interaction could also be regulated by various lncRNAs. In addition, the common and diverse characteristics of ER+/PR+, HER2− BRCA and TNBC were investigated. The majority of lncTDTs were BRCA subtype-specific and only five common lncTDTs were reported; four of these five common lncTDTs exhibited a common regulatory pattern. However, lncTDT PRC1-AS1/NKX2-5/PLOD1 revealed a reverse inhibition pattern in TNBC and a strengthen inhibition pattern in ER+/PR+, HER2− BRCA. Therefore, the regulatory pattern for PRC1-AS1/NKX2-5/PLOD1 was reversed in ER+/PR+, HER2− BRCA and TNBC. The functional analysis demonstrated that the lncTDTs were associated with the PI3K/AKT signaling pathway in ER+/PR+, HER2− BRCA and TNBC. Survival analysis revealed that lncTDTs could serve as candidate prognostic biomarkers in these two subtypes of BRCA.

At present, the status of ER, PR and HER2 serves as a major reference for the administration of targeted adjuvant therapy for BRCA (32,33). TNBC is a distinct subclass of BRCA with a high degree of aggressiveness (34). Classifying the two BRCA subtypes may improve the understanding of the mechanism underlying the formation of BRCA and aid the development of targeted treatment. Li et al (35) revealed the vascular features of triple negative breast carcinomas using dynamic magnetic resonance imaging. Li et al (36) reported that patients with TNBC had unique clinicopathological characteristics and poorer prognosis. Previous studies have employed high-throughput molecular profiles or other genetic risk factors to classify BRCA subtypes (37,38). Jiang et al (39) analyzed the subtypes and treatment strategies of TNBC. These previous findings indicated that TNBC and other subtypes exhibit specific molecular and therapeutic features. In addition, the results of the present study may provide novel insight for the classification of ER+/PR+, HER2− BRCA and TNBC. It was identified that the majority of lncTDTs were specific to BRCA subtypes, and only five common lncTDTs were observed in ER+/PR+, HER2− BRCA and TNBC. The results indicated the high degree of variation between ER+/PR+, HER2− BRCA and TNBC at the lncTDT level.

The regulatory patterns of lncTDTs were complex and indicated that lncRNAs can serve a variety of functions. Previous studies have employed several approaches, such as co-expression analyses and investigations into interactions with miRNAs to determine the functions of lncRNAs in TNBC (40-42). In the present study, the roles of lncRNAs in ER+/PR+, HER2− BRCA and TNBC were further examined based on lncTDTs. Approximately one-half of the lncTDTs exhibited strengthened activation regulation, in which lncRNAs could promote the ability of TFs to activate gene expression. Reverse inhibition and reverse activation were two major regulatory patterns by which lncRNAs reversed the regulatory effects of TF-gene interactions. The percentage of lncTDTs in ER+/PR+, HER2− BRCA and TNBC notably differed. The lncRNA HOTAIR, a cancer-related lncRNA, was investigated, which had been verified to be associated with BRCA in 36 previous studies retrieved via lnc2cancer 2.0 (20). In TNBC, HOTAIR regulated eight TF-gene interactions and the major regulation pattern was weakened activation. In ER+/PR+, HER2− BRCA, HOTAIR regulated 14 TF-gene interactions and the major regulation pattern was strengthened activation. These results indicated the different functions of HOTAIR in ER+/PR+, HER2− BRCA and TNBC. However, the number of control samples in the present study was small and experiments are required to verify the present results. Future studies should aim to conduct analysis using data from more control samples to validate the present computational approach and findings.

Collectively, the present study performed an integrated and computational approach to identify lncTDTs in ER+/PR+, HER2− BRCA and TNBC. A total of six regulatory patterns for lncTDTs were proposed; the mechanisms underlying the modes of regulation were notably complex. The common and specific characteristics of lncTDTs between ER+/PR+, HER2− BRCA and TNBC were also determined. Functional and survival analyses revealed lncTDTs as potential biomarkers for the prognosis of ER+/PR+, HER2− BRCA and TNBC.

Supplementary Materials

Funding

No funding was received.

Availability of data and materials

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

Authors' contributions

ZD, WG and SG conceived and designed the present study. ZD, WG, YL and JS performed the experiments and analyzed the data. YS and TC validated and improved the computational approach in the present study. ZD wrote the manuscript. 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.

Acknowledgments

Not applicable.

References