Skip to main content

Lymphatic metastasis-associated circRNA‒miRNA‒mRNA network for exploring the pathogenesis and therapeutic target of triple negative breast cancer based on whole-transcriptome sequencing analysis: an experimental verification study

A Correction to this article was published on 07 March 2023

This article has been updated

Abstract

Background

The metastatic mechanisms of axillary lymph nodes (ALNs) in triple-negative breast cancer (TNBC) remain unclear. We aimed to identify the potential circRNA regulatory network in ALN metastasis.

Methods

We performed whole transcriptome sequencing (WTS) to determine the expression profiles of RNAs and screen out differentially expressed messenger RNAs (DEMs), microRNAs (DEMis), and circRNAs (DECs) between ALN-positive and ALN-negative TNBC patients. Functional enrichment analysis and Kaplan–Meier survival analysis were utilized to unearth the potential regulatory mechanisms of the DEMs. A competing endogenous RNA (ceRNA) network was constructed using computational biology. The expression levels of DECs in cell lines were confirmed by real-time polymerase chain reaction (RT‒PCR).

Results

Following WTS and differential expression analysis, 739 DEMs, 110 DEMis, and 206 DECs were identified between ALN-positive and ALN-negative TNBC patients. Functional analysis indicated that the DEMs mainly functioned in carcinogenesis and tumor progression-related pathways. ceRNA networks containing eight circRNAs, six miRNAs, and eighteen mRNAs were developed. In the ceRNA network, two mRNAs (RAB3D and EDARADD) that were significantly associated with better overall survival and one mRNA (GSR) that predicted favorable recurrence-free survival in TNBC patients were chosen for further analysis. Then, a survival-related ceRNA network containing two DECs (hsa_circ_0061260 and hsa_circ_0060876), two DEMis (hsa-miR-5000-3p and hsa-miR-4792), and three mRNAs (GSR, RAB3D, and EDARADD) was identified. Then, two candidate DECs were validated by real-time PCR.

Conclusion

Our research constructed a ceRNA network that provides novel insights into the molecular mechanism of ALN metastasis and potential therapeutic targets in TNBC.

Introduction

Triple-negative breast cancer (TNBC) accounts for approximately 15% of the total incidence of breast carcinoma diagnosed worldwide and has a relatively unfavorable prognosis [1]. For patients with TNBC, the risk of local or regional recurrence, distant metastasis, and death peaks approximately two to three years after diagnosis, and TNBC appears to be more likely to metastasize to visceral organs, such as the liver, lungs, and brain [2, 3]. In particular, metastasis of axillary lymph nodes (ALNs) in TNBC patients is a strong and independent prognostic factor that indicates a worse five-year survival rate [4]. Additionally, micrometastatic nodal involvement is associated with an increased risk of death compared with patients with negative lymph nodes [5]. However, the potential precise mechanisms of ALN metastasis remain unclear.

Noncoding RNAs (ncRNAs), mainly microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs), make up most of the transcriptome and play vital roles in the oncogenesis and progression of TNBC [6]. Previous studies found that circRNAs act as sponges of the corresponding miRNAs, thus leading to the progression and chemoresistance of TNBC [7,8,9]. Zeng et al. discovered that circANKS1B was overexpressed in TNBC tissue and was associated with lymph node metastasis [10]. circRNAs may also act as tumor suppressors. circTADA2A-E6 was remarkably upregulated in TNBC and inhibited tumor growth and metastasis [11]. In addition, circFBXW7 sponged miR-197-3p and further inhibited TNBC progression [12]. However, there are few available studies on the functions of circRNAs in ALN metastasis in TNBC. The rapid development of next-generation sequencing technologies has made whole-transcriptome sequencing (WTS) much easier to conduct. Through WTS, we can capture both noncoding RNAs and coding RNAs in cells and tissues to determine the genetic interaction networks and the corresponding functions [13]. However, few studies have applied WTS to explore the gene expression profile in detail and to better understand the transcriptomic landscape of ALN metastasis in TNBC.

In our study, WTS was conducted on the tumor tissues from three ALN-positive and three ALN-negative patients with TNBC. Next, differential expression analysis was conducted to recognize the significant differentially expressed messenger RNAs (DEMs), miRNAs (DEMis), and circRNAs (DECs) between the ALN-positive and ALN-negative groups. Furthermore, we built a circRNA-miRNA‒mRNA network and performed functional analysis to elucidate the potential mechanisms of ALN metastasis in TNBC, which provides novel insight into the prognostic prediction and potential therapeutic application of circRNAs in the ALN metastasis of TNBC.

Materials and methods

Three pairs of tumor tissues were acquired from six early TNBC patients (three ALN-positive and three ALN-negative patients) who underwent surgical treatment at our hospital with approval from the Ethics Committee of our hospital. Participants in this study signed informed consent forms. All samples were preserved in liquid nitrogen.

Total RNA was extracted from three ALN-positive and three ALN-negative tumor tissues using TRIzol reagent (Life Technologies, USA) according to the manufacturer’s protocol. WTS of mRNAs, miRNAs, and circRNAs was conducted by Aksomics (Guangzhou, China) using the Illumina HiSeq 3000 platform. DEMs, DEMis, and DECs were screened between ALN-positive and ALN-negative TNBC tumor tissues using the Limma package in R. A threshold of a 2-fold change and p < 0.01 was considered significantly different. The DEMs, DEMis, and DECs were visualized on heatmaps and volcano plots. The biological functions of the DEMs were further explored by functional enrichment analysis on the Kyoto Encyclopedia of Genes and Genomes (KEGG) dataset and Hallmark’s gene datasets using the cluster Profiler package in R. p values < 0.05 were considered to indicate significantly enriched pathways.

Potential miRNA‒mRNA interaction networks were explored using the miRTarBase and TargetScan databases. Then, the predicted miRNA‒mRNA regulatory relationship was integrated with the DEM data to acquire the DEMi-DEM regulatory relationship. Then, the ceRNA networks were developed using Cytoscape (version 3.6.1; http://www.cytoscape.org/).

Real-time PCR was utilized to validate the expression levels of the candidate DECs in the ceRNA networks in the following cell lines (MCF-10 A, MB231, and MB453). Specifically, total RNA was extracted from tissues with TRIzol (Life Technologies, USA), as mentioned above. Then, the PrimeScript Reagent Kit (Vazyme Biotech Co., Ltd, China) was utilized to synthesize the single-stranded cDNA. cDNA amplification was performed starting from 100 ng of total RNA. The relative expression levels of circRNAs were detected by SYBR Green Master Mix (Vazyme Biotech Co., Ltd, China) in the RT‒PCR system (Applied Biosystems Step One Plus, USA). Both divergent and convergent primers were used to validate the circular structures of circRNAs. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was utilized as an internal control. To identify whether the circRNAs contained back-splice sites, Sanger sequencing was utilized (Beijing Genomics institution, China).

Stata/MP, version 14.0 (Stata Corp LP, College Station, TX), and R software (version 4.1.3) were employed to carry out the statistical analyses. A paired design Wilcoxon signed-rank test was conducted to accomplish the differential expression analyses in paired tumor tissue samples. Correlation analyses were carried out by Pearson’s correlation test. Quantitative real-time PCR was conducted in three replicates, and the data are expressed as the mean ± SEM. The Kaplan‒Meier method and log-rank test were utilized to compare the recurrence-free survival (RFS) and overall survival (OS) of TNBC patients based on the Kaplan‒Meier Plotter database. p < 0.05 was considered statistically significant.

Results

Firstly, to identify the potential ALN metastasis-associated miRNAs, circRNAs, and mRNAs, WTS was conducted on three pairs of ALN-positive and ALN-negative TNBC tissues. In total, we identified 739 mRNAs, 206 circRNAs, and 110 miRNAs that were differentially expressed, and they are presented in heatmaps (Fig. 1).

Fig. 1
figure 1

Heatmaps of differentially expressed RNAs between ALN-positive and ALN-negative TNBC patients. The differential expression of circRNAs (A), miRNAs (B), and mRNAs (C)

In brief, 206 DECs consisting of 41 upregulated and 165 downregulated circRNAs were identified (Fig. 2A). A total of 110 DEMis with 53 upregulated miRNAs and 57 downregulated miRNAs were identified (Fig. 2B). Meanwhile, among the 739 DEMs, 371 mRNAs were overexpressed, and 368 mRNAs were downregulated (Fig. 2C).

Fig. 2
figure 2

Volcano plots of differentially expressed RNAs between ALN-positive and ALN-negative TNBC patients. The differential expression of circRNAs (A), miRNAs (B), and mRNAs (C)

Then, functional enrichment analysis of DEMs was conducted. KEGG pathway analysis was performed to identify the significantly enriched pathways via clusterProfiler (version 3.14.3). Specifically, DEMs were mainly enriched in the TGF-beta signaling pathway, drug metabolism-cytochrome P450 pathway, cell cycle, p53 signaling pathway, breast cancer, TNF signaling pathway, IL-17 signaling pathway, and hippo signaling pathway (Fig. 3A). In addition, hallmark gene analysis was conducted to identify meaningful pathways based on the Molecular Signatures Database using clusterProfiler. DEMs were mainly involved in E2F targets, notch signaling, angiogenesis, the G2M checkpoint, glycolysis, the early estrogen response, the late estrogen response, and IL2 STAT5 signaling (Fig. 3B). Among these pathways, the TNF signaling pathway, cell cycle, p53 signaling pathway, breast cancer, and NOTCH signaling pathway were associated with the carcinogenesis and progression of TNBC.

Fig. 3
figure 3

Functional enrichment analysis of DEMs. DEMs are associated with signaling pathways in KEGG (A) and hallmark datasets (B)

Next, to explore the potential vital roles of DECs and DEMis in the ceRNA network of TNBC, we constructed a circRNA-miRNA‒mRNA (ceRNA) network. First, we extracted data related to the eight top-ranked DECs (hsa_circRNA_061260, hsa_circRNA_060876, hsa_circRNA_046265, hsa_circRNA_087060, hsa_circRNA_007336, hsa_circRNA_007333, hsa_circRNA_044837, and hsa_circRNA_000150) selected from the RNA-seq data. Then, we screened the potential miRNAs targeted by the eight DECs using the circBank and CircInteractome online datasets and overlapped them with the identified DEMis. As a result, a circRNA-miRNA network was generated that consisted of 8 DECs and 6 DEMis (hsa-miR-1207-5p, hsa-miR-4763-3p, hsa-miR-326, hsa-miR-885-3p, hsa-miR-5000-3p, and hsa-miR-4792). Subsequently, miRTarBase and TargetScan were utilized to recognize mRNAs targeted by these 6 DEMis. Next, targeted mRNAs were crosschecked against the DEMs identified previously, and 18 DEMs were recognized. Finally, we established a ceRNA regulatory network containing 8 DECs, 6 DEMis, and 18 DEMs in TNBC using Cytoscape 3.7.1 (Fig. 4).

Fig. 4
figure 4

The ALN metastasis-related ceRNA network of circRNA–miRNA–mRNA in TNBC.

The next step was to perform survival analysis of related DEMs. To investigate the expression of the DEMs and their prognostic value in BC, we retrieved mRNA expression profile data and corresponding overall survival (OS) and recurrence-free survival (RFS) data of clinical samples from the Kaplan‒Meier Plotter database. Then, a survival analysis was performed for the 18 DEMs from the ceRNA network. RAB3D and EDARADD were significantly correlated with OS, and GSR was significantly correlated with RFS in patients with BC (Fig. 5). High expression levels of RAB3D and EDARADD predicted better OS. Meanwhile, high expression levels of GSR predicted better RFS. Therefore, these three mRNAs and their corresponding genes were the most likely to be target genes regulated by DECs through the ceRNA network in TNBC.

Fig. 5
figure 5

Kaplan‒Meier curves of the survival-related differentially expressed mRNAs in the ceRNA network. The OS of the RAB3D gene (A); the RFS of the GSR gene (B); the OS of the EDARADD gene (C)

Based on the results of survival-related DEMs, we identified the corresponding genes that were also associated with survival outcomes. The three identified genes (RAB3D, EDARADD, and GSR) may be important biomarkers in the prognosis of TNBC. Then, we constructed a survival-related ceRNA network based on these th–ree genes. As shown in Fig. 6, three ceRNA regulatory axes containing two DECs (hsa_circRNA_061260 and hsa_circRNA_060876), two DEMis (hsa-miR-5000-3p and hsa-miR-4792), and three mRNAs (GSR, RAB3D, and EDARADD) were identified. These regulatory modules from the survival-related ceRNA network included the hsa_circRNA_060876/hsa-miR-4792/GSR regulatory axis, hsa_circRNA_060876/hsa-miR-4792/RAB3D regulatory axis, and hsa_circRNA_061260/hsa-miR-5000-3p/EDARADD regulatory axis.

Fig. 6
figure 6

Survival-associated circRNA-miRNA‒mRNA regulatory network in TNBC. The network consists of two DECs (hsa_circRNA_061260 and hsa_circRNA_060876), two DEMis (hsa-miR-5000-3p and hsa-miR-4792), and three mRNAs (GSR, RAB3D, and EDARADD)

Finally, to validate the results of candidate DECs potentially dysregulated in TNBC cells, we first performed RT‒qPCR in the following cell lines: MCF-10 A, MDA-MB-231, and MDA-MB-453. We selected two DECs for further analysis and conducted Sanger sequencing to confirm the unique back-splicing junction sites of these two DECs (Fig. 7A–B). The DEC hsa_circRNA_061260 is located on chr21: 11,047,480–11,049,621, composed of exon 4 and exon 5. The DEC hsa_circRNA_060876 is located on chr20: 50,226,639–50,292,747, composed of exons 10 to 24. The PCR results revealed that the expression levels of both hsa_circRNA_061260 and hsa_circRNA_060876 were downregulated in the MDA-MB-231 and MDA-MB-453 cell lines compared to the MCF-10 A cell line (p < 0.01; Fig. 7C–D).

Fig. 7
figure 7

Detailed information on the circRNAs and their back-splice junction sites. The location and corresponding splicing junction site of hsa_circ_0060876 (A) and hsa_circ_0061260 (B). The expression levels of hsa_circ_0060876 (C) and hsa_circ_0061260 (D) in MCF-10 A, MDA-MB-231 and MDA-MB-453 cell lines by RT‒PCR. **P ≤ 0.01

Discussion

ALN metastasis in TNBC predicts poor prognosis. Understanding gene expression regulation may contribute to a promising new direction in therapy. However, the underlying metastatic mechanism is not well defined. In recent years, WTS has become one of the most practical and effective techniques that has enabled researchers to identify driver genes in several types of malignant tumors [14,15,16,17]. Previous studies have demonstrated that circRNAs may play vital roles in gene expression and signaling pathways and are involved in the activation and progression of tumors [18]. However, the role of circRNAs in the ALN metastasis of TNBC remains unknown. Herein, we profiled mRNAs, miRNAs, and circRNAs in tumor samples of both nonmetastatic ALN and metastatic ALN patients and focused on circRNAs that are associated with the development of ALN metastasis in TNBC.

In our study, we first employed comprehensive WTS to obtain differentially expressed mRNA, miRNA, and circRNA expression data for paired ALN-positive and ALN-negative TNBC tumor tissues. As a result, we identified several DEMs, DEMis, and DECs in ALN-positive compared with ALN-negative TNBC tumor tissues. Functional enrichment analysis of the DEMs revealed that these mRNAs mainly function in the cell cycle, TNF signaling pathway, breast cancer, p53 signaling pathway, and NOTCH signaling. In cancer cells, cell cycle control is lost, thus allowing continuous replication, and cell cycle control may play a vital role in cancer treatment [19]. CDK4/6 inhibitors act on cell cycle checkpoints, and several preclinical studies have presented their therapeutic capability in TNBC [20]. The Notch signaling pathway also affects cancer cell activation, migration, invasion, metastasis, and resistance to therapy in TNBC. This pathway is also a potential therapeutic target for TNBC [21]. DEMs that are enriched in these pathways might also be associated with the carcinogenesis and tumor progression of TNBC, and further investigation of these DEMs may identify therapeutic targets to treat TNBC.

Recently, some research on circRNA-related ceRNA networks in breast cancer has been reported [7, 9, 22,23,24]. However, the involvement of this kind of ceRNA network in ALN metastasis in TNBC remains unclear. Based on the candidate top-ranked DECs and the bioinformatic predictions of the interactions between mRNAs, miRNAs, and circRNAs, a ceRNA network was constructed. We found that three DEMs (RAB3D, EDARADD, and GSR) contained in the ceRNA network were significantly associated with OS or RFS in TNBC. Then, a survival-related ceRNA regulatory network was established. As shown in the survival-related ceRNA network, hsa_circRNA_060876 might function as a ceRNA of two DEMs (GSR and RAB3D) when it is bound to hsa-miR-4792, and hsa_circRNA_061260 might function as a ceRNA of one DEM (EDARADD) when it is bound to hsa-miR-5000-3p. Although research on the roles that the above two DECs play in cancer is limited, their targeted DEMis and DEMs have been shown to have important roles in many cancers. For example, GSR was found to act as an oncogene in many cancers, such as cervical cancer, hepatocellular carcinoma, and colorectal cancer [25,26,27]. Evidence has shown that the expression of RAB3D is upregulated by the ceRNA mechanism in non-small cell lung cancer, osteosarcoma, renal cell carcinoma, and melanoma, leading to tumor progression [28,29,30,31,32]. RAB3D was reported as a hub gene that is involved in many ceRNA networks. It may also play an important role in the ALN metastasis of TNBC. Meng Li et al. found that EDARADD expression is involved in the progression of tongue squamous cell carcinoma [33]. Future mechanistic studies are needed to clarify the role of these DEMs in the ALN metastasis of TNBC.

This study has some limitations. Although candidate DECs were found to be strongly related to ALN metastasis in TNBC, further in vitro and in vivo experimental validation is required to explore the underlying biological mechanisms of the DECs, DEMis, and DEMs. Additionally, our data were extracted from WTS profiles based on three paired tumor samples, and the results were obtained by bioinformatic analysis. Large-scale clinical data are needed for further validation.

In conclusion, our study investigated the molecular mechanism of ALN metastasis in TNBC using WTS. A survival-related ceRNA network was established, and two novel DECs were identified as possible prognostic predictors and potential therapeutic targets of ALN metastasis in TNBC. These findings provide novel ideas for clarifying the mechanisms, achieving early diagnosis and identifying therapeutic targets of ALN metastasis in TNBC

Availability of data and materials

All data were obtained from whole-transcriptome sequencing.

Change history

Abbreviations

ALN:

axillary lymph node

TNBC:

triple-negative breast cancer

WTS:

whole transcriptome sequencing

DEMs:

differentially expressed messenger RNAs

DEMis:

differentially expressed microRNAs

DECs:

differentially expressed circular RNAs

ceRNA:

competing endogenous RNA

KEGG:

Kyoto Encyclopedia of Genes and Genomes

OS:

overall survival

RFS:

recurrence-free survival

PCR:

polymerase chain reaction

References

  1. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics. CA Cancer J Clin. 2021;71(1):7–33.

    Article  PubMed  Google Scholar 

  2. Lin NU, Vanderplas A, Hughes ME, Theriault RL, Edge SB, Wong YN, et al. Clinicopathologic features, patterns of recurrence, and survival among women with triple-negative breast cancer in the National Comprehensive Cancer Network. Cancer. 2012;118(22):5463–72.

    Article  PubMed  Google Scholar 

  3. Liedtke C, Mazouni C, Hess KR, André F, Tordai A, Mejia JA, et al. Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer. J Clin Oncol. 2008;26(8):1275–81.

    Article  PubMed  Google Scholar 

  4. Siegel RL, Miller KD, Jemal A. Cancer statistics. CA Cancer J Clin. 2017;67(1):7–30.

    Article  PubMed  Google Scholar 

  5. de Boer M, van Dijck JA, Bult P, Borm GF, Tjan-Heijnen VC. Breast cancer prognosis and occult lymph node metastases, isolated tumor cells, and micrometastases. J Natl Cancer Inst. 2010;102(6):410–25.

    Article  PubMed  Google Scholar 

  6. Mei J, Hao L, Wang H, Xu R, Liu Y, Zhu Y, et al. Systematic characterization of non-coding RNAs in triple-negative breast cancer. Cell Prolif. 2020;53(5):e12801.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Yang R, Xing L, Zheng X, Sun Y, Wang X, Chen J. The circRNA circAGFG1 acts as a sponge of miR-195-5p to promote triple-negative breast cancer progression through regulating CCNE1 expression. Mol Cancer. 2019;18(1):4.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Tang H, Huang X, Wang J, Yang L, Kong Y, Gao G, et al. circKIF4A acts as a prognostic factor and mediator to regulate the progression of triple-negative breast cancer. Mol Cancer. 2019;18(1):23.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Wang L, Zhou Y, Jiang L, Lu L, Dai T, Li A, et al. CircWAC induces chemotherapeutic resistance in triple-negative breast cancer by targeting miR-142, upregulating WWP1 and activating the PI3K/AKT pathway. Mol Cancer. 2021;20(1):43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Zeng K, He B, Yang BB, Xu T, Chen X, Xu M, et al. The pro-metastasis effect of circANKS1B in breast cancer. Mol Cancer. 2018;17(1):160.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Xu JZ, Shao CC, Wang XJ, Zhao X, Chen JQ, Ouyang YX, et al. circTADA2As suppress breast cancer progression and metastasis via targeting miR-203a-3p/SOCS3 axis. Cell Death Dis. 2019;10(3):175.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Ye F, Gao G, Zou Y, Zheng S, Zhang L, Ou X, et al. circFBXW7 Inhibits Malignant Progression by Sponging miR-197-3p and Encoding a 185-aa Protein in Triple-Negative Breast Cancer. Mol therapy Nucleic acids. 2019;18:88–98.

    Article  CAS  Google Scholar 

  13. Altman RB, Raychaudhuri S. Whole-genome expression analysis: challenges beyond clustering. Curr Opin Struct Biol. 2001;11(3):340–7.

    Article  CAS  PubMed  Google Scholar 

  14. Kim S, Kim N, Kang K, Kim W, Won J, Cho J. Whole Transcriptome Analysis identifies TNS4 as a key effector of cetuximab and a regulator of the oncogenic activity of KRAS mutant colorectal cancer cell lines. Cells. 2019;8(8):878.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Siena ÁDD, Plaça JR, Araújo LF, de BII, Peronni, Molfetta K. G, et al. Whole transcriptome analysis reveals correlation of long noncoding RNA ZEB1-AS1 with invasive profile in melanoma. Sci Rep. 2019;9(1):11350.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Zheng Q, Capell BC, Parekh V, O’Day C, Atillasoy C, Bashir HM, et al. Whole-Exome and transcriptome analysis of UV-Exposed epidermis and carcinoma in situ reveals early drivers of carcinogenesis. J Invest Dermatol. 2021;141(2):295–307.e13.

    Article  CAS  PubMed  Google Scholar 

  17. Jia Q, Wu W, Wang Y, Alexander PB, Sun C, Gong Z, et al. Local mutational diversity drives intratumoral immune heterogeneity in non-small cell lung cancer. Nat Commun. 2018;9(1):5361.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Meng S, Zhou H, Feng Z, Xu Z, Tang Y, Li P, et al. CircRNA: functions and properties of a novel potential biomarker for cancer. Mol Cancer. 2017;16(1):94.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Matthews HK, Bertoli C, de Bruin RAM. Cell cycle control in cancer. Nat Rev Mol Cell Biol. 2022;23(1):74–88.

    Article  CAS  PubMed  Google Scholar 

  20. Wang R, Xu K, Gao F, Huang J, Guan X. Clinical considerations of CDK4/6 inhibitors in triple-negative breast cancer. Biochim et Biophys acta Reviews cancer. 2021;1876(2):188590.

    Article  CAS  Google Scholar 

  21. Giuli MV, Giuliani E, Screpanti I, Bellavia D, Checquolo S. Notch signaling activation as a hallmark for triple-negative breast cancer subtype. J Oncol. 2019;2019:8707053.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Sang Y, Chen B, Song X, Li Y, Liang Y, Han D, et al. circRNA_0025202 Regulates Tamoxifen sensitivity and tumor progression via regulating the miR-182-5p/FOXO3a Axis in Breast Cancer. Mol Ther. 2019;27(9):1638–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Ma X, Liu C, Gao C, Li J, Zhuang J, Liu L, et al. circRNA-associated ceRNA network construction reveals the circRNAs involved in the progression and prognosis of breast cancer. J Cell Physiol. 2020;235(4):3973–83.

    Article  CAS  PubMed  Google Scholar 

  24. Wang L, Yi J, Lu LY, Zhang YY, Wang L, Hu GS, et al. Estrogen-induced circRNA, circPGR, functions as a ceRNA to promote estrogen receptor-positive breast cancer cell growth by regulating cell cycle-related genes. Theranostics. 2021;11(4):1732–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Wang YN, Lu YX, Liu J, Jin Y, Bi HC, Zhao Q, et al. AMPKα1 confers survival advantage of colorectal cancer cells under metabolic stress by promoting redox balance through the regulation of glutathione reductase phosphorylation. Oncogene. 2020;39(3):637–50.

    Article  CAS  PubMed  Google Scholar 

  26. Xia Y, Wang G, Jiang M, Liu X, Zhao Y, Song Y, et al. A novel biological activity of the STAT3 Inhibitor stattic in inhibiting glutathione reductase and suppressing the tumorigenicity of human cervical cancer cells via a ROS-Dependent pathway. Onco Targets Ther. 2021;14:4047–60.

    Article  PubMed  PubMed Central  Google Scholar 

  27. McLoughlin MR, Orlicky DJ, Prigge JR, Krishna P, Talago EA, Cavigli IR, et al. TrxR1, Gsr, and oxidative stress determine hepatocellular carcinoma malignancy. Proc Natl Acad Sci U S A. 2019;116(23):11408–17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Li L, Wan K, Xiong L, Liang S, Tou F, Guo S. CircRNA hsa_circ_0087862 Acts as an oncogene in non-small cell lung cancer by targeting miR-1253/RAB3D Axis. Onco Targets Ther. 2020;13:2873–86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Ma J, Li Q, Li Y. CircRNA PRH1-PRR4 stimulates RAB3D to regulate the malignant progression of NSCLC by sponging miR-877-5p. Thorac Cancer. 2022;13(5):690–701.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Cao K, Fang Y, Wang H, Jiang Z, Guo L, Hu Y. The lncRNA HOXA11-AS regulates Rab3D expression by sponging miR-125a-5p promoting metastasis of osteosarcoma. Cancer Manag Res. 2019;11:4505–18.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Xie J, Zheng Y, Xu X, Sun C, Lv M. Long noncoding RNA CAR10 contributes to melanoma progression by suppressing miR-125b-5p to induce RAB3D expression. Onco Targets Ther. 2020;13:6203–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Zhai X, Wu Y, Wang Z, Zhao D, Li H, Chong T, et al. Long Noncoding RNA LINC01133 promotes the malignant behaviors of renal cell carcinoma by regulating the miR-30b-5p/Rab3D Axis. Cell Transpl. 2020;29:963689720964413.

    Article  Google Scholar 

  33. Li M, Bai YT, Han K, Li XD, Meng J. Knockdown of ectodysplasin-A receptor-associated adaptor protein exerts a tumor-suppressive effect in tongue squamous cell carcinoma cells. Exp Ther Med. 2020;19(5):3337–47.

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This study was supported by the National Natural Science Foundation of China (grant number 82002928, Jianguo Lai), the Doctor Launch Fund of Guangdong Provincial People’s Hospital (grant number 2020bq11, Jianguo Lai), and the Guangzhou Science and Technology Project (grant number 202201011184, Jianguo Lai and 202102020032, Jiayue Luo).

Author information

Authors and Affiliations

Authors

Contributions

JGL and JYL designed the study, analyzed the data, and wrote the manuscript. JGL provided funding acquisition. JGL and JYL supervised the research, analyzed the data, and wrote the manuscript. CH and DC performed the experiments and analyzed the data. ZL and YZ collected the samples. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Jianguo Lai.

Ethics declarations

Ethics approval and consent to participate

Ethics approval was obtained from the Ethics Committee of our Hospitals. Participants in this study signed informed consent forms.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Luo, J., Cao, D., Hu, C. et al. Lymphatic metastasis-associated circRNA‒miRNA‒mRNA network for exploring the pathogenesis and therapeutic target of triple negative breast cancer based on whole-transcriptome sequencing analysis: an experimental verification study. J Transl Med 20, 508 (2022). https://doi.org/10.1186/s12967-022-03728-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12967-022-03728-6

Keywords