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Identification of transcription factor-miRNA-lncRNA feed-forward loops in breast cancer subtypes
Computational Biology and Chemistry 78 (2019) 1–7
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Computational Biology and Chemistry journal homepage: www.elsevier.com/locate/cbac
Identification of transcription factor-miRNA-lncRNA feed-forward loops in breast cancer subtypes
T
Leiming Jianga,b,1, Xuexin Yub,1, Xueyan Mab, Haizhou Liua, Shunheng Zhoua, Xu Zhoub, ⁎⁎ ⁎ Qianqian Mengb, Lihong Wangc, , Wei Jianga,b, a
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 21106, China College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China c Department of Pathophysiology, School of Medicine, Southeast University, Nanjing, 210009, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: lncRNA microRNA Feed-forward loop Regulatory network Breast cancer
Previous studies have demonstrated that transcription factor-miRNA-gene feed-forward loops (FFLs) played important roles in tumorigenesis. However, the lncRNA-involved FFLs have not been explored very well. Understanding the characteristics of lncRNA-involved FFLs in breast cancer subtypes may be a key question with clinical implications. In this study, we firstly constructed an integrated background regulatory network. Then, based on mRNA, miRNA, and lncRNA differential expression, we identified 147, 140, 284, 1031 dysregulated FFLs for luminal A, luminal B, HER2+ and basal-like subtype of breast cancer, respectively. Importantly, the known breast cancer-associated lncRNAs and miRNAs were enriched in the identified dysregulated FFLs. Through merging the dysregulated FFLs, we constructed the regulatory sub-network for each subtype. We found that all sub-networks were enriched in the well-known cancer-related pathways, such as cell cycle, pathways in cancer. Next, we also identified potential prognostic FFLs for subtypes of breast cancer, such as the hsa-miR-1825p_JUN_XIST in basal-like subtype. Finally, we also discussed the potential application of inferring the candidate drugs for breast cancer treatment through modulating the lncRNA expression in the dysregulated FFLs. Collectively, this study elucidated the roles of lncRNA-involved FFLs in breast cancer subtypes, which could contribute to understanding breast cancer pathogenesis and improving the treatment.
1. Introduction Breast cancer is the most widespread diagnosed cancer, and the second leading cause of cancer death among women worldwide (Siegel et al., 2016). According to the immunohistochemistry markers, estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER-2), breast cancer can be grouped into four basic subtypes, including luminal A (ER + and/or PR+, HER-2-), luminal B (ER + and/or PR+, HER-2+), HER2+ (ER-, PR-, HER-2+) and basal-like (ER-, PR-, HER-2-) (Spitale et al., 2009; Tang and Tse, 2016). In addition, different breast cancer subtypes have different risk factors, clinical presentation, especially the basal-like breast cancer have some biological characteristics of a higher aggressiveness, poorer prognosis compared to other subtypes (Tang and Tse, 2016). Recent studies provided valuable information on the molecular regulatory mechanism of breast cancer via transcriptome analysis, in
which transcription factor (TF), microRNA (miRNA) and long noncoding RNA (lncRNA) are three of the best-studied regulators (Volinia et al., 2012; Su et al., 2014; He et al., 2016). TFs are proteins that play important roles at the transcriptional level, abnormal TFs can lead to dysregulation of genes involved in almost all known cellular processes, such as apoptosis, proliferation (Libermann and Zerbini, 2006). In research on non-coding genes, miRNA (∼ 22 nucleotides), which is the most widely studied small non-coding RNA (ncRNA) subclass, act at the post-transcriptional level by degrading and silencing target mRNAs (Ziebarth et al., 2012; Deng et al., 2015; Feng et al., 2017a, 2017b, 2017c). Massed evidence has shown that miRNAs may play oncogenic and tumor-suppressive roles in breast cancer (Iorio and Croce, 2009; Yao et al., 2010). For example, miR-106b and miR-26a function as oncogenic and tumor suppressive role in human breast cancer, respectively (Ivanovska et al., 2008; Zhang et al., 2011). LncRNA ( > 200 nucleotides) can also function as oncogenes or tumor suppressors in the
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Corresponding author at: College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 21106, China. Corresponding author. E-mail addresses: [email protected] (L. Wang), [email protected] (W. Jiang). 1 These authors contributed equally to this work. ⁎⁎
https://doi.org/10.1016/j.compbiolchem.2018.11.008 Received 9 May 2018; Received in revised form 18 October 2018; Accepted 14 November 2018 Available online 16 November 2018 1476-9271/ © 2018 Elsevier Ltd. All rights reserved.
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development of human cancers (Zhou et al., 2007; He et al., 2017). In addition, it could act as competing endogenous RNAs (ceRNAs) to communicate with other RNA transcripts by competing miRNAs (Salmena et al., 2011). For example, the lncRNA MALAT1 has been shown to play a role in several cancers, which inhibits miR-124 expression and reduces miR-124-mediated translational repression of CDK4 in breast cancer (Feng et al., 2016). To date, there are many databases to collect regulations from different molecular levels. For example, starBase was intended for deciphering RNA-Protein and miRNA-target interactions from 108 CLIP-Seq datasets, such as miRNAlncRNA interactions and miRNA-mRNA interactions (Li et al., 2014a, 2014b). Moreover, ChIPBase was developed for discovering of TF binding maps and transcriptional regulatory relationships of miRNAs and lncRNAs from ChIP-Seq data (Yang et al., 2013). The two databases have provided abundant and comprehensive resource that makes the regulatory networks credible. Recently, the integrated analyses of these regulators acted as motif such as feed-forward loop (FFL), which is composed of two regulator factors, one of which regulates the other, both jointly regulating a target gene, have been proposed (Mangan and Alon, 2003). For example, the novel method that integrated node and edge scores into the regulatory networks, has been used for identifying the TF-miRNA-gene FFLs in pan-cancer (Jiang et al., 2016). However, TF-miRNA-lncRNA FFLs of breast cancer subtypes has not been explored, in which TF regulates miRNA or miRNA regulates TF, and both of them co-regulate target lncRNAs. In this study, we developed a computational approach to identify dysregulated FFLs in subtypes of breast cancer through integrating TF and miRNA regulations to lncRNA and their expression data. We found that the dysregulated FFLs enriched the known cancer-related lncRNAs, miRNAs and genes. In addition, the common and specific FFLs of breast cancer subtypes were investigated. Finally, we also predicted the survival-associated FFLs for each subtype and the drugs that could reverse the lncRNA expression in the dysregulated FFLs. Thus, this study would provide a novel perspective for pathogenesis and treatment of breast cancer.
Table 1 Summary of the TF-miRNA-lncRNA regulatory network. Database
# Interaction
# TF
StarBase
960 1473 33,988 6455 42,876
81 102 99 103
miRNA-lncRNA miRNA-TF ChIPBase TF-lncRNA TF-miRNA Total number
# miRNA
# lncRNA
125 114
155 1590
220 223
1689
start site (Yang et al., 2013). The regulations of miRNAs to mRNAs and lncRNAs were collected from starBase (version 2.0) with the number of supporting experiments > = 3 (Li et al., 2014a, 2014b). In this regulatory network, the TF and gene names were mapped to Entrez IDs, the miRNA names were mapped to miRBase accession numbers of mature miRNAs, and the lncRNA names were mapped to Ensembl IDs (the TFs, miRNAs and lncRNAs that did not map to these IDs were discarded). Moreover, the retained TFs, miRNAs and lncRNAs should also have expression data in TCGA database. As a result, the final TF-miRNAlncRNA regulatory network in Table 1, including 42,876 regulations among 103 TFs, 223 miRNAs and 1689 lncRNAs, was visualized with the Cytoscape v2.8.3 (Killcoyne et al., 2009).
2.3. Identification of the TF-miRNA-lncRNA dysregulated FFLs We just considered the three-node FFLs, which included a TF, a miRNA and a lncRNA. According to the regulations between TF and miRNA, the FFLs can be classified into three types. The first type of FFL is termed as TF-FFL, TF regulates lncRNA and miRNA at transcriptional level, miRNA represses lncRNA expression at posttranscriptional level. Similarly, the miRNA-FFL is defined as the structure that miRNA represses TF and lncRNA, while TF regulates lncRNA. In third type of FFL, TF and miRNA mutually regulate each other to form a feed-back loop, and both of them regulate the shared lncRNA. Thus, we define it as Composite-FFL. To evaluate the biological activity of a particular FFL, we performed the differential expression analysis of all nodes between the corresponding breast subtype samples and the normal samples. Firstly, the node score was calculated through the formula (1), which was based on the significance of differential expression between breast cancer subtype samples and normal samples (Ideker et al., 2002).
2. Materials and methods 2.1. Data collection and processing We collected the miRNA-seq and RNA-SeqV2 level 3 data of breast cancer from The Cancer Genome Atlas (TCGA) database (Tomczak et al., 2015). The lncRNA expression data of breast cancer was downloaded from The Atlas of non-coding RNA in Cancer (TANRIC) database (Li et al., 2015). The clinical data of breast cancer, including the immunohistochemical information and survival time, was also obtained from TCGA database. The samples with the expression of mRNAs, miRNAs and lncRNAs were retained for the following analysis. In this analysis, based on the immunohistochemical status, we divided the breast cancer samples into four breast cancer subtypes, including luminal A, luminal B, HER2+, and basal-like subtype (Spitale et al., 2009). As a result, we obtained 211 luminal A, 48 luminal B, 20 HER2+, 58 basal-like and 84 normal samples (see Supplementary Table S1). For miRNA expression, we calculated reads per million (RPM) values of each mature miRNA from the isoform quantification files. For gene expression, normalized gene data was obtained from the rsem normalized files. Furthermore, the lncRNAs, miRNAs or genes that expressed (expression value > 0) in at least half of the samples of each subtype were retained for further analysis.
Zi = φ−1 (1 − pi )
(1)
where pi represents the significance of expression change determined by T-test. The φ−1 represents the inverse normal cumulative distribution function. Next, the FFL score is the weighted sum of node scores as follows:
ZA =
1 k
∑ Zi i∈A
(2)
where k is the number of nodes in the FFL (k = 3). We performed permutation analysis to estimate the significance of each FFL score. We firstly randomly selected three molecules to construct a random FFL. This process was repeated 100,000 times. Next, we calculated the FFL score for each random FFL according to the equations above and generated the null distribution of random FFL scores. The p-value for an observed FFL was defined as the proportion of random FFL scores (random) larger than the observed FFL score (S): pvalue = (Nrandom > S)/Np, where Nrandom > S is the number of random FFLs that have larger scores than the particular FFL, and Np is the number of permutations (here it is 100,000). In this study, only FFLs with p-value < 0.05 were considered as the dysregulated FFLs in the breast cancer subtypes.
2.2. Construction of the TF-miRNA-lncRNA regulatory network The regulations of TFs to miRNAs and lncRNAs were obtained from ChIPBase (release1.1), the promoter region used in this study was defined as a 5 kb upstream and 1 kb downstream region of transcription 2
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We firstly obtained an integrated regulatory network with 118,637 regulations from starBase and ChIPBase (Yang et al., 2013; Li et al., 2014a, 2014b). After discarding the lncRNAs, miRNAs and TFs without expression data in TCGA, we finally gained 33,988 TF-lncRNA, 6455 TF-miRNA, 960 miRNA-lncRNA and 1473 miRNA-mRNA interactions to construct the background regulatory network, including 103 TFs, 223 miRNAs and 1689 lncRNAs. To have an overview of this background regulatory network, we examined the degree distribution of the network by Network Analyzer, which is a plug-in of Cytoscape (Killcoyne et al., 2009). The result indicated that this TF-miRNAlncRNA regulatory network presented approximate scale-free topological character (Fig. 1A), which is the common architectural feature of biological molecular networks (Barabasi and Oltvai, 2004). Then, to better clarify the regulations of the TF and miRNA to lncRNA, the relationships among the number of TFs, miRNAs and their target lncRNAs were further investigated. Many lncRNAs are regulated by a small number of miRNAs and TFs, respectively (Fig. 1B and C).
FFL was dysregulated in one subtype if the score of the FFL in this subtype was significantly larger than the scores of 100,000 random FFLs (experimental p-value < 0.05). The intersections number of FFLs, lncRNAs, TFs and miRNAs in breast cancer subtypes were depicted in Fig. 2D–G (detailed information in Table S2). Overall, we identified that 1279 FFLs were dysregulated in at least one breast cancer subtype. Here, if the FFL was dysregulated in at least two breast cancer subtypes it was designated as “common FFL”, while the FFL that was dysregulated in only one subtype was considered as “specific FFL”. In total, 252 FFLs were identified as common FFLs, 1027 FFLs were specific FFLs. Here, ten common FFLs were dysregulated in all four breast cancer subtypes, such as hsa-let-7c-5p_JUN_XIST. The TF JUN is important for cell proliferation, survival and apoptosis, and regulates cell growth in breast cancer (Vleugel et al., 2006). The expression of the lncRNA XIST as a tumor suppressor lncRNA has been demonstrated to be downregulated in breast cancer (Huang et al., 2016). Thus, we inferred that the hsa-let-7c-5p_JUN_XIST might also be implicated in the occurrence of breast cancer. On the other hand, most of dysregulated FFLs were specific. For example, the SP1_hsa-miR-93-5p_XIST was only dysregulated in basal-like breast cancer. The hsa-miR-93-5p expression was reported to be significantly upregulated in basal-like subtype and its upregulation promote cell proliferation and migration in MCF-7 cells (Hu et al., 2015). We further analyzed the dysregulated FFLs from multiple functional properties in breast cancer subtypes. Firstly, we obtained the experimentally confirmed breast cancer-related lncRNAs from Lnc2cancer (Ning et al., 2016). We found that these lncRNAs were significantly involved in the dysregulated FFLs (Fisher’s exact test p-value = 5.2 × 10−11). Meanwhile, the proportion of breast cancer-related lncRNAs in dysregulated FFLs and all FFLs were significantly higher than that in the background network (p-value < 0.01, Fig. 2H). The results indicated that lncRNAs in dysregulated FFLs were preferred to play important roles in the pathogenesis of breast cancer. What’s more, we got the breast cancer-related miRNAs from the HMDD database, and the breast cancer-related genes from MalaCards database (Li et al., 2014a, 2014b; Rappaport et al., 2017). Interestingly, the similar results were found in breast cancer-related miRNAs and genes (Fig. 2I–J). In addition, the proportion of breast cancer-related genes in the dysregulated FFLs was higher than in all FFLs, although it was not significant. Thus, the FFLs might be an efficient way for inferring some potential and valuable targets for treating breast cancer.
3.2. Common and specific FFLs in breast cancer subtypes
3.3. TF-miRNA-lncRNA regulatory sub-networks for each subtype
Based on the regulations of TFs and miRNAs to lncRNAs, there are three types of three-node FFLs, TF-mediated FFL (Fig. 2A), miRNAmediated FFL (Fig. 2B), and composite FFL (Fig. 2C). Here, we identified 7947 FFLs from the background regulatory network, including 6155 TF-FFLs, 1004 miRNA-FFLs and 778 composite-FFLs (Supplementary Table S2). Furthermore, combined with the expression data of TF, miRNA and lncRNA for each subtype, we identified the dysregulated FFLs for four breast cancer subtypes, respectively. We defined an
Through merging the significant FFLs of each subtype, we constructed the corresponding TF-miRNA-lncRNA regulatory sub-network for each subtype (Fig. 3), which consisted of 11 (15, 21, 16) lncRNAs, 26 (37, 47, 76) miRNAs and 35 (19, 44, 54) TFs in luminal A (luminal B, HER2+, basal-like) breast cancer, respectively (Fig. 2E–G). We found that all sub-networks showed approximated the scale-free network topology characteristic. In all the sub-networks, we defined the nodes with degree > 15 as hubs. Interestingly, we found that these lncRNA
2.4. Survival analysis In this study, for each dysregulated FFL in the breast cancer subtypes, a Cox multivariate regression analysis was used to evaluate the association between survival and expression of the FFL. The risk scores were calculated based on the combination of the expressions and the values of the regression coefficients from the Cox multivariate regression analysis, which takes into account the strength and positive/negative association of each node with survival, as follows: n
Risk
score =
∑ ri × Exp (i) i=1
(3)
where ri is the Cox regression coefficient of node i in the FFL (n = 3), n is the number of nodes in the FFL and Exp(i) is the expression value of node i. The median risk score firstly was used as cut-off to classify patients into high-risk and low-risk groups. Then, a Kaplan-Meier survival analysis was used for the two groups with the statistical significance using the log-rank test. 3. Results 3.1. Characteristics of TF-miRNA-lncRNA regulatory network
Fig. 1. The characteristics of TF-miRNA-lncRNA regulatory network. (A) The log-log plots show that the degree distributions follow the power law. (B) The relationship between the number of TFs and the number of their target lncRNAs. (C) The relationship between the number of miRNAs and the number of their target lncRNAs. 3
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Fig. 2. The three types of FFLs and the overview of dysregulated FFLs in all subtypes of breast cancer. (A) TF-FFL: TF and miRNA regulate lncRNA, and TF regulates miRNA. (B) miRNA-FFL: TF and miRNA regulate lncRNA, and miRNA regulates TF. (C) Composite-FFL: TF and miRNA regulate lncRNA, and TF and miRNA mutually regulate each other. (D–G) represent the intersection of FFLs, lncRNAs, miRNAs, TF in all subtypes of breast cancer, respectively. From the inner to the outer layer, the blocks represent the luminal A, luminal B, HER2+, basal-like subtypes and the number of intersection, respectively. The green block indicates the “presence” of the FFL, lncRNA, miRNA or TF in the corresponding breast cancer subtype. The height of the bars in the outermost layer is corresponding to the number of intersection. (H–J) The proportion of breast cancer-related lncRNAs, miRNA and TFs in dysregulated FFLs, all FFLs and background regulatory network. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
3.4. Prognostic FFLs in all four subtypes of breast cancer
hubs were significantly enriched in the known breast cancer-related lncRNAs (hypergeometric test p-value < 0.05), which were marked with the red border in Fig. 3. For example, the lncRNA XIST was the hub node in all sub-networks except HER2 + . As described above, XIST is a well-known tumor suppressor lncRNA. Functional enrichment analysis was widely used to investigate the biological functions of the interested gene set (Huang da et al., 2009; Yang et al., 2017). Here, by using the DAVID tool (Huang da et al., 2009), we performed the functional enrichment analysis of TFs to identify the significantly enriched pathways in these regulatory subnetworks. The top 10 significantly enriched KEGG pathways of the subnetworks were shown in Fig. 4. We observed that all sub-networks were commonly enriched in the following pathways, including cell cycle, pathways in cancer. These pathways were closely necessary to progression of breast cancer (Eroles et al., 2012; Bower et al., 2017). Interestingly, we also found that the thyroid related pathways were significantly enriched in all sub-networks, such as thyroid cancer, thyroid hormone signaling pathway. Previous researches have demonstrated that thyroid hormone is important in breast cancer initiation and progression (Nielsen et al., 2016). Thus, the common functions in all sub-networks played crucial roles in breast cancer pathogenesis. In addition, there were some subtype specific pathways existed in subtypes. For example, adherens junction, one of the epithelial characteristics, is specific in the basal-like subtype. That breast epithelial cells lose their adherens junctions can play a relevant role in breast cancer progression and metastasis, especially occurred in basal-like subtype (Sarrio et al., 2008).
To identify prognostic FFLs in breast subtype patients, we further performed Cox multivariate regression analysis to explore whether dysregulated FFLs were associated with the survival of each subtype patients (see Materials and methods). We found some FFLs that were correlated with overall survival (OS) in subtypes of breast cancer (Supplementary Table S3). For example, MYC_hsa-miR-106b5p_HOTAIR was correlated with the overall survival of luminal A and luminal B patients (Fig. 5A, B). The TF-FFL SP1_hsa-miR-26a5p_MALAT1 and the miRNA-FFL hsa-miR-182-5p_JUN_XIST were correlated with overall survival in HER2+ and basal-like patients, respectively (Fig. 5C, D). Among those FFLs, the miRNA-FFL hsa-miR182-5p_JUN__XIST could significantly distinguish a total of 58 samples with high risk group (n = 29) and low risk group (n = 29) in basal-like patients according to the median risk score (Fig. 5D, Log rank p = 0.031). The previous studies showed JUN and hsa-miR-182-5p were prognostic markers of cancer (Jiang et al., 2010; Huhe et al., 2017). These results suggested the FFLs may serve as prognostic biomarkers for breast cancer subtypes.
4. Conclusions and discussion Breast cancer is the most widespread cancers among women (Siegel et al., 2016). Immunohistochemistry markers, including ER, PR and HER2, are commonly used for subtyping four basic subgroups (Spitale et al., 2009). The TF-miRNA-lncRNA FFLs in breast cancer subtypes have not been explored yet. In this study, we firstly constructed the human global TF-miRNA-lncRNA regulatory network based on the regulations from ChIPBase and starBase (Yang et al., 2013; Li et al., 4
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Fig. 3. The sub-networks for the breast cancer subtypes. (A), (B), (C), (D) represent sub-networks for luminal A, luminal B, HER2+, basal-like subtypes, respectively. Purple triangle, green diamond and gray round node nodes represent TFs, miRNAs and target lncRNAs, respectively. Gray round nodes with the red border represent the known breast cancer–related lncRNAs. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
subtypes shared well-known cancer pathways, such as cell cycle, pathways in cancer, while the basal-like subtype was specifically associated with adherens junction. Recent studies verified that Cancerlectin, one kind of lectins, plays a critical role in the process of tumor cells interactions, such as cell adhesion (Lin et al., 2015; Lai et al., 2017), which
2014a, 2014b). Through integrating the expression profiles, we identified the dysregulated FFLs that were common and specific to breast cancer subtypes. Interestingly, we found that the known breast cancerrelated lncRNAs and miRNAs were enriched in the identified dysregulated FFLs. The KEGG functional enrichment analysis showed that four
Fig. 4. Significantly enriched KEGG pathways in TF-miRNA-lncRNA regulatory sub-networks. (A), (B), (C), (D) represent the top 10 enriched KEGG pathways in the luminal A, luminal B, HER2+, basal-like, respectively. Red pentagram represent the shared pathways in the four regulatory sub-networks. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article). 5
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Fig. 5. The FFLs that were significantly correlated with OS of breast cancer subtype patients. (A), (B), (C) and (D) represent luminal A, luminal B, HER2+ and basallike subtypes, respectively.
RNA function (Chen et al., 2015a, 2015b, 2015c; Chen et al., 2016, 2017; Feng et al., 2017a, 2017b, 2017c), which might make our study more credible. In addition, with more reliable regulatory interactions and the abundant samples of breast cancer subtypes, we believed that the accuracy and stability of our approach would be improved. In summary, we identified dysregulated TF-miRNA-lncRNA FFLs that are common or specific to breast cancer subtypes, and analyzed the FFL sub-networks in breast cancer subtypes. Our analysis might provide novel insights into the roles of TF-miRNA-lncRNA FFLs in breast cancer subtypes, and will be helpful for drug repositioning of breast cancer.
might be associated with malignancy of basal-like subtype. Next, survival analysis suggested that the FFLs may serve as prognostic biomarkers for breast cancer subtypes, such as hsa-miR-182-5p_JUN_XIST in basal-like subtype. We found that many lncRNAs were deregulated in breast cancer subtypes. For example, XIST was down-regulated and HOTAIR was up-regulated in basal-like breast cancer, which was consistent with previous studies (Supplementary Fig. S1) (Huang et al., 2016; Xue et al., 2016). Meanwhile, many researchers have found that lncRNAs could act as drug targets for treatment of diseases (Lavorgna et al., 2016). Moreover, small-molecule drugs can influence lncRNAs expression (Chen et al., 2014). Therefore, based on the literature search, we predicted some potential small-molecule drugs for the treatment of basal-like subtype through reversing lncRNAs expression in the dysregulated FFLs (Supplementary Fig. S2) (Chen et al., 2014; Chen et al., 2015a, 2015b, 2015c; Liu et al., 2015; Yan-Fang et al., 2015; Kern et al., 2016), such as Genistein, the combination of Carboplatin and Docetaxel to perturb expression of the lncRNA XIST and HOTAIR, respectively. However, there are still some limitations in study of the FFL roles in breast cancer subtypes. Here, we did not consider where these regulations between TF, miRNA and lncRNA took place in cell, thus RNA subcellular localization should be incorporated in future (Feng et al., 2017a, 2017b, 2017c; Zhang et al., 2017). RNA modification is the abundant post transcriptional modification, and plays important role in
Conflict of interest The authors declare that they have no conflict of interest.
Acknowledgements This work was supported by National Natural Science Foundation of China [61571169]; Natural Science Foundation of Heilongjiang Province of China [QC2014C017]; University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province [UNPYSCT-2015037]; Fundamental Research Funds for the Central Universities [NE2018101]. 6
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Appendix A. Supplementary data
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Computational Biology and Chemistry journal homepage: www.elsevier.com/locate/cbac
Identification of transcription factor-miRNA-lncRNA feed-forward loops in breast cancer subtypes
T
Leiming Jianga,b,1, Xuexin Yub,1, Xueyan Mab, Haizhou Liua, Shunheng Zhoua, Xu Zhoub, ⁎⁎ ⁎ Qianqian Mengb, Lihong Wangc, , Wei Jianga,b, a
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 21106, China College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China c Department of Pathophysiology, School of Medicine, Southeast University, Nanjing, 210009, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: lncRNA microRNA Feed-forward loop Regulatory network Breast cancer
Previous studies have demonstrated that transcription factor-miRNA-gene feed-forward loops (FFLs) played important roles in tumorigenesis. However, the lncRNA-involved FFLs have not been explored very well. Understanding the characteristics of lncRNA-involved FFLs in breast cancer subtypes may be a key question with clinical implications. In this study, we firstly constructed an integrated background regulatory network. Then, based on mRNA, miRNA, and lncRNA differential expression, we identified 147, 140, 284, 1031 dysregulated FFLs for luminal A, luminal B, HER2+ and basal-like subtype of breast cancer, respectively. Importantly, the known breast cancer-associated lncRNAs and miRNAs were enriched in the identified dysregulated FFLs. Through merging the dysregulated FFLs, we constructed the regulatory sub-network for each subtype. We found that all sub-networks were enriched in the well-known cancer-related pathways, such as cell cycle, pathways in cancer. Next, we also identified potential prognostic FFLs for subtypes of breast cancer, such as the hsa-miR-1825p_JUN_XIST in basal-like subtype. Finally, we also discussed the potential application of inferring the candidate drugs for breast cancer treatment through modulating the lncRNA expression in the dysregulated FFLs. Collectively, this study elucidated the roles of lncRNA-involved FFLs in breast cancer subtypes, which could contribute to understanding breast cancer pathogenesis and improving the treatment.
1. Introduction Breast cancer is the most widespread diagnosed cancer, and the second leading cause of cancer death among women worldwide (Siegel et al., 2016). According to the immunohistochemistry markers, estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER-2), breast cancer can be grouped into four basic subtypes, including luminal A (ER + and/or PR+, HER-2-), luminal B (ER + and/or PR+, HER-2+), HER2+ (ER-, PR-, HER-2+) and basal-like (ER-, PR-, HER-2-) (Spitale et al., 2009; Tang and Tse, 2016). In addition, different breast cancer subtypes have different risk factors, clinical presentation, especially the basal-like breast cancer have some biological characteristics of a higher aggressiveness, poorer prognosis compared to other subtypes (Tang and Tse, 2016). Recent studies provided valuable information on the molecular regulatory mechanism of breast cancer via transcriptome analysis, in
which transcription factor (TF), microRNA (miRNA) and long noncoding RNA (lncRNA) are three of the best-studied regulators (Volinia et al., 2012; Su et al., 2014; He et al., 2016). TFs are proteins that play important roles at the transcriptional level, abnormal TFs can lead to dysregulation of genes involved in almost all known cellular processes, such as apoptosis, proliferation (Libermann and Zerbini, 2006). In research on non-coding genes, miRNA (∼ 22 nucleotides), which is the most widely studied small non-coding RNA (ncRNA) subclass, act at the post-transcriptional level by degrading and silencing target mRNAs (Ziebarth et al., 2012; Deng et al., 2015; Feng et al., 2017a, 2017b, 2017c). Massed evidence has shown that miRNAs may play oncogenic and tumor-suppressive roles in breast cancer (Iorio and Croce, 2009; Yao et al., 2010). For example, miR-106b and miR-26a function as oncogenic and tumor suppressive role in human breast cancer, respectively (Ivanovska et al., 2008; Zhang et al., 2011). LncRNA ( > 200 nucleotides) can also function as oncogenes or tumor suppressors in the
⁎
Corresponding author at: College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 21106, China. Corresponding author. E-mail addresses: [email protected] (L. Wang), [email protected] (W. Jiang). 1 These authors contributed equally to this work. ⁎⁎
https://doi.org/10.1016/j.compbiolchem.2018.11.008 Received 9 May 2018; Received in revised form 18 October 2018; Accepted 14 November 2018 Available online 16 November 2018 1476-9271/ © 2018 Elsevier Ltd. All rights reserved.
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development of human cancers (Zhou et al., 2007; He et al., 2017). In addition, it could act as competing endogenous RNAs (ceRNAs) to communicate with other RNA transcripts by competing miRNAs (Salmena et al., 2011). For example, the lncRNA MALAT1 has been shown to play a role in several cancers, which inhibits miR-124 expression and reduces miR-124-mediated translational repression of CDK4 in breast cancer (Feng et al., 2016). To date, there are many databases to collect regulations from different molecular levels. For example, starBase was intended for deciphering RNA-Protein and miRNA-target interactions from 108 CLIP-Seq datasets, such as miRNAlncRNA interactions and miRNA-mRNA interactions (Li et al., 2014a, 2014b). Moreover, ChIPBase was developed for discovering of TF binding maps and transcriptional regulatory relationships of miRNAs and lncRNAs from ChIP-Seq data (Yang et al., 2013). The two databases have provided abundant and comprehensive resource that makes the regulatory networks credible. Recently, the integrated analyses of these regulators acted as motif such as feed-forward loop (FFL), which is composed of two regulator factors, one of which regulates the other, both jointly regulating a target gene, have been proposed (Mangan and Alon, 2003). For example, the novel method that integrated node and edge scores into the regulatory networks, has been used for identifying the TF-miRNA-gene FFLs in pan-cancer (Jiang et al., 2016). However, TF-miRNA-lncRNA FFLs of breast cancer subtypes has not been explored, in which TF regulates miRNA or miRNA regulates TF, and both of them co-regulate target lncRNAs. In this study, we developed a computational approach to identify dysregulated FFLs in subtypes of breast cancer through integrating TF and miRNA regulations to lncRNA and their expression data. We found that the dysregulated FFLs enriched the known cancer-related lncRNAs, miRNAs and genes. In addition, the common and specific FFLs of breast cancer subtypes were investigated. Finally, we also predicted the survival-associated FFLs for each subtype and the drugs that could reverse the lncRNA expression in the dysregulated FFLs. Thus, this study would provide a novel perspective for pathogenesis and treatment of breast cancer.
Table 1 Summary of the TF-miRNA-lncRNA regulatory network. Database
# Interaction
# TF
StarBase
960 1473 33,988 6455 42,876
81 102 99 103
miRNA-lncRNA miRNA-TF ChIPBase TF-lncRNA TF-miRNA Total number
# miRNA
# lncRNA
125 114
155 1590
220 223
1689
start site (Yang et al., 2013). The regulations of miRNAs to mRNAs and lncRNAs were collected from starBase (version 2.0) with the number of supporting experiments > = 3 (Li et al., 2014a, 2014b). In this regulatory network, the TF and gene names were mapped to Entrez IDs, the miRNA names were mapped to miRBase accession numbers of mature miRNAs, and the lncRNA names were mapped to Ensembl IDs (the TFs, miRNAs and lncRNAs that did not map to these IDs were discarded). Moreover, the retained TFs, miRNAs and lncRNAs should also have expression data in TCGA database. As a result, the final TF-miRNAlncRNA regulatory network in Table 1, including 42,876 regulations among 103 TFs, 223 miRNAs and 1689 lncRNAs, was visualized with the Cytoscape v2.8.3 (Killcoyne et al., 2009).
2.3. Identification of the TF-miRNA-lncRNA dysregulated FFLs We just considered the three-node FFLs, which included a TF, a miRNA and a lncRNA. According to the regulations between TF and miRNA, the FFLs can be classified into three types. The first type of FFL is termed as TF-FFL, TF regulates lncRNA and miRNA at transcriptional level, miRNA represses lncRNA expression at posttranscriptional level. Similarly, the miRNA-FFL is defined as the structure that miRNA represses TF and lncRNA, while TF regulates lncRNA. In third type of FFL, TF and miRNA mutually regulate each other to form a feed-back loop, and both of them regulate the shared lncRNA. Thus, we define it as Composite-FFL. To evaluate the biological activity of a particular FFL, we performed the differential expression analysis of all nodes between the corresponding breast subtype samples and the normal samples. Firstly, the node score was calculated through the formula (1), which was based on the significance of differential expression between breast cancer subtype samples and normal samples (Ideker et al., 2002).
2. Materials and methods 2.1. Data collection and processing We collected the miRNA-seq and RNA-SeqV2 level 3 data of breast cancer from The Cancer Genome Atlas (TCGA) database (Tomczak et al., 2015). The lncRNA expression data of breast cancer was downloaded from The Atlas of non-coding RNA in Cancer (TANRIC) database (Li et al., 2015). The clinical data of breast cancer, including the immunohistochemical information and survival time, was also obtained from TCGA database. The samples with the expression of mRNAs, miRNAs and lncRNAs were retained for the following analysis. In this analysis, based on the immunohistochemical status, we divided the breast cancer samples into four breast cancer subtypes, including luminal A, luminal B, HER2+, and basal-like subtype (Spitale et al., 2009). As a result, we obtained 211 luminal A, 48 luminal B, 20 HER2+, 58 basal-like and 84 normal samples (see Supplementary Table S1). For miRNA expression, we calculated reads per million (RPM) values of each mature miRNA from the isoform quantification files. For gene expression, normalized gene data was obtained from the rsem normalized files. Furthermore, the lncRNAs, miRNAs or genes that expressed (expression value > 0) in at least half of the samples of each subtype were retained for further analysis.
Zi = φ−1 (1 − pi )
(1)
where pi represents the significance of expression change determined by T-test. The φ−1 represents the inverse normal cumulative distribution function. Next, the FFL score is the weighted sum of node scores as follows:
ZA =
1 k
∑ Zi i∈A
(2)
where k is the number of nodes in the FFL (k = 3). We performed permutation analysis to estimate the significance of each FFL score. We firstly randomly selected three molecules to construct a random FFL. This process was repeated 100,000 times. Next, we calculated the FFL score for each random FFL according to the equations above and generated the null distribution of random FFL scores. The p-value for an observed FFL was defined as the proportion of random FFL scores (random) larger than the observed FFL score (S): pvalue = (Nrandom > S)/Np, where Nrandom > S is the number of random FFLs that have larger scores than the particular FFL, and Np is the number of permutations (here it is 100,000). In this study, only FFLs with p-value < 0.05 were considered as the dysregulated FFLs in the breast cancer subtypes.
2.2. Construction of the TF-miRNA-lncRNA regulatory network The regulations of TFs to miRNAs and lncRNAs were obtained from ChIPBase (release1.1), the promoter region used in this study was defined as a 5 kb upstream and 1 kb downstream region of transcription 2
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We firstly obtained an integrated regulatory network with 118,637 regulations from starBase and ChIPBase (Yang et al., 2013; Li et al., 2014a, 2014b). After discarding the lncRNAs, miRNAs and TFs without expression data in TCGA, we finally gained 33,988 TF-lncRNA, 6455 TF-miRNA, 960 miRNA-lncRNA and 1473 miRNA-mRNA interactions to construct the background regulatory network, including 103 TFs, 223 miRNAs and 1689 lncRNAs. To have an overview of this background regulatory network, we examined the degree distribution of the network by Network Analyzer, which is a plug-in of Cytoscape (Killcoyne et al., 2009). The result indicated that this TF-miRNAlncRNA regulatory network presented approximate scale-free topological character (Fig. 1A), which is the common architectural feature of biological molecular networks (Barabasi and Oltvai, 2004). Then, to better clarify the regulations of the TF and miRNA to lncRNA, the relationships among the number of TFs, miRNAs and their target lncRNAs were further investigated. Many lncRNAs are regulated by a small number of miRNAs and TFs, respectively (Fig. 1B and C).
FFL was dysregulated in one subtype if the score of the FFL in this subtype was significantly larger than the scores of 100,000 random FFLs (experimental p-value < 0.05). The intersections number of FFLs, lncRNAs, TFs and miRNAs in breast cancer subtypes were depicted in Fig. 2D–G (detailed information in Table S2). Overall, we identified that 1279 FFLs were dysregulated in at least one breast cancer subtype. Here, if the FFL was dysregulated in at least two breast cancer subtypes it was designated as “common FFL”, while the FFL that was dysregulated in only one subtype was considered as “specific FFL”. In total, 252 FFLs were identified as common FFLs, 1027 FFLs were specific FFLs. Here, ten common FFLs were dysregulated in all four breast cancer subtypes, such as hsa-let-7c-5p_JUN_XIST. The TF JUN is important for cell proliferation, survival and apoptosis, and regulates cell growth in breast cancer (Vleugel et al., 2006). The expression of the lncRNA XIST as a tumor suppressor lncRNA has been demonstrated to be downregulated in breast cancer (Huang et al., 2016). Thus, we inferred that the hsa-let-7c-5p_JUN_XIST might also be implicated in the occurrence of breast cancer. On the other hand, most of dysregulated FFLs were specific. For example, the SP1_hsa-miR-93-5p_XIST was only dysregulated in basal-like breast cancer. The hsa-miR-93-5p expression was reported to be significantly upregulated in basal-like subtype and its upregulation promote cell proliferation and migration in MCF-7 cells (Hu et al., 2015). We further analyzed the dysregulated FFLs from multiple functional properties in breast cancer subtypes. Firstly, we obtained the experimentally confirmed breast cancer-related lncRNAs from Lnc2cancer (Ning et al., 2016). We found that these lncRNAs were significantly involved in the dysregulated FFLs (Fisher’s exact test p-value = 5.2 × 10−11). Meanwhile, the proportion of breast cancer-related lncRNAs in dysregulated FFLs and all FFLs were significantly higher than that in the background network (p-value < 0.01, Fig. 2H). The results indicated that lncRNAs in dysregulated FFLs were preferred to play important roles in the pathogenesis of breast cancer. What’s more, we got the breast cancer-related miRNAs from the HMDD database, and the breast cancer-related genes from MalaCards database (Li et al., 2014a, 2014b; Rappaport et al., 2017). Interestingly, the similar results were found in breast cancer-related miRNAs and genes (Fig. 2I–J). In addition, the proportion of breast cancer-related genes in the dysregulated FFLs was higher than in all FFLs, although it was not significant. Thus, the FFLs might be an efficient way for inferring some potential and valuable targets for treating breast cancer.
3.2. Common and specific FFLs in breast cancer subtypes
3.3. TF-miRNA-lncRNA regulatory sub-networks for each subtype
Based on the regulations of TFs and miRNAs to lncRNAs, there are three types of three-node FFLs, TF-mediated FFL (Fig. 2A), miRNAmediated FFL (Fig. 2B), and composite FFL (Fig. 2C). Here, we identified 7947 FFLs from the background regulatory network, including 6155 TF-FFLs, 1004 miRNA-FFLs and 778 composite-FFLs (Supplementary Table S2). Furthermore, combined with the expression data of TF, miRNA and lncRNA for each subtype, we identified the dysregulated FFLs for four breast cancer subtypes, respectively. We defined an
Through merging the significant FFLs of each subtype, we constructed the corresponding TF-miRNA-lncRNA regulatory sub-network for each subtype (Fig. 3), which consisted of 11 (15, 21, 16) lncRNAs, 26 (37, 47, 76) miRNAs and 35 (19, 44, 54) TFs in luminal A (luminal B, HER2+, basal-like) breast cancer, respectively (Fig. 2E–G). We found that all sub-networks showed approximated the scale-free network topology characteristic. In all the sub-networks, we defined the nodes with degree > 15 as hubs. Interestingly, we found that these lncRNA
2.4. Survival analysis In this study, for each dysregulated FFL in the breast cancer subtypes, a Cox multivariate regression analysis was used to evaluate the association between survival and expression of the FFL. The risk scores were calculated based on the combination of the expressions and the values of the regression coefficients from the Cox multivariate regression analysis, which takes into account the strength and positive/negative association of each node with survival, as follows: n
Risk
score =
∑ ri × Exp (i) i=1
(3)
where ri is the Cox regression coefficient of node i in the FFL (n = 3), n is the number of nodes in the FFL and Exp(i) is the expression value of node i. The median risk score firstly was used as cut-off to classify patients into high-risk and low-risk groups. Then, a Kaplan-Meier survival analysis was used for the two groups with the statistical significance using the log-rank test. 3. Results 3.1. Characteristics of TF-miRNA-lncRNA regulatory network
Fig. 1. The characteristics of TF-miRNA-lncRNA regulatory network. (A) The log-log plots show that the degree distributions follow the power law. (B) The relationship between the number of TFs and the number of their target lncRNAs. (C) The relationship between the number of miRNAs and the number of their target lncRNAs. 3
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Fig. 2. The three types of FFLs and the overview of dysregulated FFLs in all subtypes of breast cancer. (A) TF-FFL: TF and miRNA regulate lncRNA, and TF regulates miRNA. (B) miRNA-FFL: TF and miRNA regulate lncRNA, and miRNA regulates TF. (C) Composite-FFL: TF and miRNA regulate lncRNA, and TF and miRNA mutually regulate each other. (D–G) represent the intersection of FFLs, lncRNAs, miRNAs, TF in all subtypes of breast cancer, respectively. From the inner to the outer layer, the blocks represent the luminal A, luminal B, HER2+, basal-like subtypes and the number of intersection, respectively. The green block indicates the “presence” of the FFL, lncRNA, miRNA or TF in the corresponding breast cancer subtype. The height of the bars in the outermost layer is corresponding to the number of intersection. (H–J) The proportion of breast cancer-related lncRNAs, miRNA and TFs in dysregulated FFLs, all FFLs and background regulatory network. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
3.4. Prognostic FFLs in all four subtypes of breast cancer
hubs were significantly enriched in the known breast cancer-related lncRNAs (hypergeometric test p-value < 0.05), which were marked with the red border in Fig. 3. For example, the lncRNA XIST was the hub node in all sub-networks except HER2 + . As described above, XIST is a well-known tumor suppressor lncRNA. Functional enrichment analysis was widely used to investigate the biological functions of the interested gene set (Huang da et al., 2009; Yang et al., 2017). Here, by using the DAVID tool (Huang da et al., 2009), we performed the functional enrichment analysis of TFs to identify the significantly enriched pathways in these regulatory subnetworks. The top 10 significantly enriched KEGG pathways of the subnetworks were shown in Fig. 4. We observed that all sub-networks were commonly enriched in the following pathways, including cell cycle, pathways in cancer. These pathways were closely necessary to progression of breast cancer (Eroles et al., 2012; Bower et al., 2017). Interestingly, we also found that the thyroid related pathways were significantly enriched in all sub-networks, such as thyroid cancer, thyroid hormone signaling pathway. Previous researches have demonstrated that thyroid hormone is important in breast cancer initiation and progression (Nielsen et al., 2016). Thus, the common functions in all sub-networks played crucial roles in breast cancer pathogenesis. In addition, there were some subtype specific pathways existed in subtypes. For example, adherens junction, one of the epithelial characteristics, is specific in the basal-like subtype. That breast epithelial cells lose their adherens junctions can play a relevant role in breast cancer progression and metastasis, especially occurred in basal-like subtype (Sarrio et al., 2008).
To identify prognostic FFLs in breast subtype patients, we further performed Cox multivariate regression analysis to explore whether dysregulated FFLs were associated with the survival of each subtype patients (see Materials and methods). We found some FFLs that were correlated with overall survival (OS) in subtypes of breast cancer (Supplementary Table S3). For example, MYC_hsa-miR-106b5p_HOTAIR was correlated with the overall survival of luminal A and luminal B patients (Fig. 5A, B). The TF-FFL SP1_hsa-miR-26a5p_MALAT1 and the miRNA-FFL hsa-miR-182-5p_JUN_XIST were correlated with overall survival in HER2+ and basal-like patients, respectively (Fig. 5C, D). Among those FFLs, the miRNA-FFL hsa-miR182-5p_JUN__XIST could significantly distinguish a total of 58 samples with high risk group (n = 29) and low risk group (n = 29) in basal-like patients according to the median risk score (Fig. 5D, Log rank p = 0.031). The previous studies showed JUN and hsa-miR-182-5p were prognostic markers of cancer (Jiang et al., 2010; Huhe et al., 2017). These results suggested the FFLs may serve as prognostic biomarkers for breast cancer subtypes.
4. Conclusions and discussion Breast cancer is the most widespread cancers among women (Siegel et al., 2016). Immunohistochemistry markers, including ER, PR and HER2, are commonly used for subtyping four basic subgroups (Spitale et al., 2009). The TF-miRNA-lncRNA FFLs in breast cancer subtypes have not been explored yet. In this study, we firstly constructed the human global TF-miRNA-lncRNA regulatory network based on the regulations from ChIPBase and starBase (Yang et al., 2013; Li et al., 4
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Fig. 3. The sub-networks for the breast cancer subtypes. (A), (B), (C), (D) represent sub-networks for luminal A, luminal B, HER2+, basal-like subtypes, respectively. Purple triangle, green diamond and gray round node nodes represent TFs, miRNAs and target lncRNAs, respectively. Gray round nodes with the red border represent the known breast cancer–related lncRNAs. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article).
subtypes shared well-known cancer pathways, such as cell cycle, pathways in cancer, while the basal-like subtype was specifically associated with adherens junction. Recent studies verified that Cancerlectin, one kind of lectins, plays a critical role in the process of tumor cells interactions, such as cell adhesion (Lin et al., 2015; Lai et al., 2017), which
2014a, 2014b). Through integrating the expression profiles, we identified the dysregulated FFLs that were common and specific to breast cancer subtypes. Interestingly, we found that the known breast cancerrelated lncRNAs and miRNAs were enriched in the identified dysregulated FFLs. The KEGG functional enrichment analysis showed that four
Fig. 4. Significantly enriched KEGG pathways in TF-miRNA-lncRNA regulatory sub-networks. (A), (B), (C), (D) represent the top 10 enriched KEGG pathways in the luminal A, luminal B, HER2+, basal-like, respectively. Red pentagram represent the shared pathways in the four regulatory sub-networks. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article). 5
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Fig. 5. The FFLs that were significantly correlated with OS of breast cancer subtype patients. (A), (B), (C) and (D) represent luminal A, luminal B, HER2+ and basallike subtypes, respectively.
RNA function (Chen et al., 2015a, 2015b, 2015c; Chen et al., 2016, 2017; Feng et al., 2017a, 2017b, 2017c), which might make our study more credible. In addition, with more reliable regulatory interactions and the abundant samples of breast cancer subtypes, we believed that the accuracy and stability of our approach would be improved. In summary, we identified dysregulated TF-miRNA-lncRNA FFLs that are common or specific to breast cancer subtypes, and analyzed the FFL sub-networks in breast cancer subtypes. Our analysis might provide novel insights into the roles of TF-miRNA-lncRNA FFLs in breast cancer subtypes, and will be helpful for drug repositioning of breast cancer.
might be associated with malignancy of basal-like subtype. Next, survival analysis suggested that the FFLs may serve as prognostic biomarkers for breast cancer subtypes, such as hsa-miR-182-5p_JUN_XIST in basal-like subtype. We found that many lncRNAs were deregulated in breast cancer subtypes. For example, XIST was down-regulated and HOTAIR was up-regulated in basal-like breast cancer, which was consistent with previous studies (Supplementary Fig. S1) (Huang et al., 2016; Xue et al., 2016). Meanwhile, many researchers have found that lncRNAs could act as drug targets for treatment of diseases (Lavorgna et al., 2016). Moreover, small-molecule drugs can influence lncRNAs expression (Chen et al., 2014). Therefore, based on the literature search, we predicted some potential small-molecule drugs for the treatment of basal-like subtype through reversing lncRNAs expression in the dysregulated FFLs (Supplementary Fig. S2) (Chen et al., 2014; Chen et al., 2015a, 2015b, 2015c; Liu et al., 2015; Yan-Fang et al., 2015; Kern et al., 2016), such as Genistein, the combination of Carboplatin and Docetaxel to perturb expression of the lncRNA XIST and HOTAIR, respectively. However, there are still some limitations in study of the FFL roles in breast cancer subtypes. Here, we did not consider where these regulations between TF, miRNA and lncRNA took place in cell, thus RNA subcellular localization should be incorporated in future (Feng et al., 2017a, 2017b, 2017c; Zhang et al., 2017). RNA modification is the abundant post transcriptional modification, and plays important role in
Conflict of interest The authors declare that they have no conflict of interest.
Acknowledgements This work was supported by National Natural Science Foundation of China [61571169]; Natural Science Foundation of Heilongjiang Province of China [QC2014C017]; University Nursing Program for Young Scholars with Creative Talents in Heilongjiang Province [UNPYSCT-2015037]; Fundamental Research Funds for the Central Universities [NE2018101]. 6
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Appendix A. Supplementary data
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