Breast cancer is the most common cancer among women worldwide. Globally, in 2020, an estimated 2.3 million new cases of breast cancer were diagnosed, and there were approximately 685,000 deaths from the disease (Sung et al., 2021). The majority of breast cancer mortality is due to distant metastasis, with 5-year survival estimated at 30% (American Cancer Society, 2023). There is a critical need to identify early breast cancer metastasis using prognostic biomarkers to ensure patients receive effective anticancer therapies in a timely manner (Miglietta et al., 2022).

Epithelial–mesenchymal transition (EMT) plays a critical role in tumor progression and metastatic invasion in breast cancer. EMT describes the process by which epithelial cells lose their epithelial characteristics and cell–cell contact and increase their invasive potential (Singh and Chakrabarti, 2019). EMT is characterized by a loss of epithelial cell markers, such as cytokeratins and E-cadherin, followed by an upregulation in the expression of mesenchymal cell markers, such as N-cadherin and vimentin. EMT is regulated at different levels by factors involved in cell signaling, transcriptional control, and epigenetic and post-translational modifications (Lai et al., 2020).

Biomarkers of EMT may predict early breast cancer metastasis and facilitate clinical decision-making (Liu et al., 2016). The regulator of G protein signaling 10 (RGS10) belongs to the superfamily of RGS proteins that bind and deactivate heterotrimeric G proteins (Almutairi et al., 2020). RGS proteins are important mediators of essential cellular processes and may be tumor initiators or tumor suppressors (Li et al., 2023). The canonical function of RGS proteins is to act as GTPase-activating proteins (GAPs), accelerate GTP hydrolysis on G-protein alpha subunits, and terminate signaling pathways downstream of G protein-coupled receptors (Hunt et al., 1996). RGS proteins can have noncanonical GAP-independent functions, including the suppression of transforming growth factor beta (TGF-β)-induced EMT in non-small cell lung cancer by RGS6 (Wang et al., 2022).

RGS10 is a critical regulator of cell survival, polarization, adhesion, chemotaxis, and differentiation that exhibits tumor-suppressing effects in ovarian and colorectal cancer. In ovarian cancer cells, RGS10 suppression increases proliferation by phosphorylation of mTOR, 4E-BP1, p70S6K, and rProtein-S6, including in the presence of chemotherapy (Altman et al., 2015), loss of RGS10 expression contributes to the development of chemoresistance (Cacan et al., 2014), and modulating RGS10 expression can alter sensitivity to paclitaxel, cisplatin, and vincristine (Cacan et al., 2014; Hooks et al., 2010; Hooks and Murph, 2015). In ovarian tumors, transcription of RGS10 is regulated by DNA methylation and histone deacetylation (Nguyen et al., 2014). In colorectal cancer, RGS10 expression is suppressed, and inhibition of DNA methylation may contribute to improved prognosis (Caldiran and Cacan, 2022).

In breast cancer, RGS10 is downregulated in heavily metastatic human breast cancer cell populations compared to weakly metastatic human breast cancer cell populations (Montel et al., 2005; Steeg, 2005). The mechanisms underlying the metastasis-suppressing function of RGS10 in breast cancer remain to be elucidated. In this study, we investigated the function of RGS10 in breast cancer, specifically in breast cancer metastasis. The objectives were to (1) characterize the expression of RGS10 in freshly resected breast cancer and adjacent normal breast tissues; (2) determine the prognostic significance of RGS10 expression in patients with breast cancer; and (3) explore the role of RGS10 and upstream effectors in tumor progression and metastasis in breast cancer cells in vitro and in vivo.

Distant metastasis is a main cause of death in patients with breast cancer. Identification of prognostic biomarkers for early distant metastasis may inform clinical decision-making and improve patient outcomes. To the best of our knowledge, this is the first study to characterize the role of RGS10 as a tumor suppressor and biomarker of EMT in breast cancer. RGS10 was expressed at lower levels in breast cancer tissues than in adjacent normal breast tissues. RGS10 expression was associated with molecular subtypes of breast cancer, distant metastasis, and survival status. Patients with high compared to low RGS10 mRNA expression in breast cancer tissues had improved DFS and OS. RGS10 protein levels were lower in the highly aggressive breast cancer cell line MDA-MB-231 compared to the poorly aggressive and less invasive breast cancer cell lines MCF7 and SKBR3. RGS10 reduced breast cancer cell proliferation, colony formation, invasion, and migration by inhibiting EMT via a novel mechanism dependent on LCN2 and MIR539-5p.

EMT is involved in normal development and morphogenic processes, including embryogenesis and tissue regeneration. Pathological EMT promotes invasion and metastasis in tumors through intracellular signaling, transcription factors, miRNAs, and epigenetic and posttranslational regulators (Huang et al., 2022; Lamouille et al., 2014). Signaling pathways such as TGF-β, Wnt, Notch, and phosphoinositide 3-kinase/AKT contribute to EMT, often with cross-talk at various levels and feedback activation/repression mechanisms (Deshmukh et al., 2021; Huang et al., 2022). Transcription factors such as snail, slug, ZEBl/ZEB2, and Twist1/Twist2 induce EMT by acting on the E-box sequence of the CDH1 promoter. Noncoding miRNAs regulate EMT by selectively targeting mRNAs of cell receptors, signaling pathways, the cell cycle, or cell adhesion (Górecki and Rak, 2021; Huang et al., 2022; Li et al., 2019). Epigenetic modifications, including DNA methylation and histone modifications, alter the expression of EMT transcription factors involved in the molecular pathways of metabolism, transcription, differentiation, and apoptosis (Górecki and Rak, 2021).

The present study identifies RGS10 as an important mediator of EMT in breast cancer. Previous studies have demonstrated links between several RGS proteins and various cancers, with RGS proteins acting as tumor initiators or suppressors depending on the RGS protein and type of cancer (Li et al., 2023). RGS10 is the smallest protein of the RGS D/12 subfamily, which functions as GAPs for the Gi family Gα subunits (Almutairi et al., 2020). RGS10 has been linked to a poor prognosis in patients with laryngeal cancer (Yin et al., 2013), liver cancer (Wen et al., 2015), and childhood acute myeloid leukemia (Chaudhury et al., 2018). RGS10 may represent a biomarker of clinical staging for ovarian cancer and is one of five signature genes involved in the occurrence and development of ovarian cancer. This five-gene signature (RGS11, RGS10, RGS13, RGS4, and RGS3) is overexpressed in ovarian cancer and involved in extracellular matrix–receptor interaction, the TGF-β signaling pathway, the Wnt signaling pathway, and the chemokine signaling pathway. These pathways mediate the proliferation, migration, and invasion of ovarian cancer cells; in particular, TGF-β signaling plays an important role in EMT in ovarian cancer (Hu et al., 2021).

The novel action of RGS10 in EMT in breast cancer appears to be dependent on LNC2, also known as neutrophil gelatinase-associated lipocalin, siderocalin, uterocalin, and oncogene 24p3. LNC2 is a secreted glycoprotein of the adipokine superfamily. LCN2 expression levels are particularly high in breast, pancreatic, ovarian, colorectal, thyroid, and bile duct cancer tissues and tumor cell lines. Previous studies show LCN2 can promote tumorigenesis by increasing invasion, metastasis, and proliferation while decreasing apoptosis, possibly because LCN2 can facilitate iron intake to cancer cells and form a heterodimer with matrix metalloproteinase-9 (Santiago-Sánchez et al., 2020). In breast cancer, LCN2 can promote progression by inducing EMT through the estrogen receptor alpha/Slug axis (Morales-Valencia et al., 2022; Yang et al., 2009).

The regulation of EMT in breast cancer by RGS10 may rely on upstream regulation by MIR539-5p. miRNAs play a critical role in the cellular processes of breast cancer, including EMT (Zhang et al., 2013; Zhao et al., 2017). Previous reports indicate that MIR539 expression is downregulated in breast cancer tissues and cell lines (Guo et al., 2018), and MIR539 acts as a tumor suppressor by targeting epidermal growth factor receptor (Guo et al., 2018), specificity protein 1 (Cai et al., 2020), or laminin subunit alpha 4 (Cai et al., 2020) expression. In patients with breast cancer, decreased expression of MIR539 was significantly associated with lymph node metastasis. In breast cancer cells, overexpression of MIR539 inhibited the proliferation and promoted apoptosis of breast cancer cells, suppressed EMT, and sensitized cells to cisplatin treatment (Cai et al., 2020). Further studies are required to fully elucidate miRNA regulation of the gene expression networks that are essential to EMT in breast cancer.

Inflammation promotes EMT in tumors, and EMT induces the production of proinflammatory factors by cancer cells (Suarez-Carmona et al., 2017). The present study showed that RGS10 expression in SKBR3 cells was associated with the inflammatory response. Previous reports show that RGS10 regulates cellular physiology and fundamental signaling pathways in microglia, macrophages, and T-lymphocytes. In microglia and ovarian cancer cells, RGS10 regulates inflammatory signaling by a G protein-independent mechanism, linking RGS10 to the inflammatory signaling mediators tumor necrosis factor-alpha (TNFα) and cyclooxygenase-2 (Alqinyah et al., 2018). In macrophages, RGS10 regulates activation and polarization by suppressing the production of the inflammatory cytokines TNFα and IL6 (Dean and Hooks, 2020). These findings suggest a potential role for RGS10 in the development of an inflamed and immunosuppressive tumor microenvironment, which may have important implications for the progression in breast cancer (Suarez-Carmona et al., 2017).

Our study had some limitations. First, this was a retrospective study with a small sample size. Thus, the conclusions should be confirmed in meta-analyses or large randomized controlled trials. Second, the mechanism by which the MIR539-5p/RGS10/LCN2 axis may be related to outcomes in patients with breast cancer remains to be elucidated. Biochemical characterization of the molecular mechanisms of RGS10 in breast cancer should provide additional insight into the potential of RGS10 as a biomarker of EMT, metastasis, and prognosis in breast cancer and the role of RGS10 as a therapeutic target.

In conclusion, the results of this study show that RGS10 expression is related to survival outcomes in patients with breast cancer. RGS10 protein levels were lower in the highly aggressive breast cancer cell line MDA-MB-231 compared to the poorly aggressive and less invasive breast cancer cell lines MCF7 and SKBR3. Silencing RGS10 expression effectively increased the proliferation, colony formation, invasion, and migration ability of SKBR3 cells. The MIR539-5p/RGS10/LCN2 pathway was identified as an important regulatory axis of EMT in breast cancer. These data demonstrate that RGS10 may play a tumor suppressor role and be considered a biomarker of EMT and prognosis in breast cancer.

Sequencing data have been deposited in Dryad under the accession link: https://doi.org/10.5061/dryad.7h44j102r. The data title is the transcriptomes in RGS10-depleted SKBR3 cells. All data generated or analyzed during the study are included in the manuscript and figures. Source data files have been provided for Figures 1-6. Raw gel/blot data for Figure 2, Figure 3, Figure 4, and Figure 5 were uploaded as source data files corresponding to the figures.

1. 1. Liu Y
   2. Jiang Y
   3. Qui P
   4. Ma T
   5. Bai Y
   6. Bu J
   7. Hu Y
   8. Jin M
   9. Zhu T
   10. Gu X

   (2024) Dryad Digital Repository

   The transcriptomes in RGS10-depleted SKBR3 cells.

   https://doi.org/10.5061/dryad.7h44j102r

1. 1. Adnan M
   2. Morton G
   3. Hadi S

   (2011) Analysis of rpoS and bolA gene expression under various stress-induced environments in planktonic and biofilm phase using 2−ΔΔCT method

   Molecular and Cellular Biochemistry 357:275–282.

   https://doi.org/10.1007/s11010-011-0898-y

   • Google Scholar
2. 1. Almutairi F
   2. Lee JK
   3. Rada B

   (2020) Regulator of G protein signaling 10: Structure, expression and functions in cellular physiology and diseases

   Cellular Signalling 75:109765.

   https://doi.org/10.1016/j.cellsig.2020.109765

   • PubMed
   • Google Scholar
3. 1. Alqinyah M
   2. Almutairi F
   3. Wendimu MY
   4. Hooks SB

   (2018) RGS10 Regulates the Expression of Cyclooxygenase-2 and Tumor Necrosis Factor Alpha through a G Protein-Independent Mechanism

   Molecular Pharmacology 94:1103–1113.

   https://doi.org/10.1124/mol.118.111674

   • PubMed
   • Google Scholar
4. 1. Altman MK
   2. Alshamrani AA
   3. Jia W
   4. Nguyen HT
   5. Fambrough JM
   6. Tran SK
   7. Patel MB
   8. Hoseinzadeh P
   9. Beedle AM
   10. Murph MM

   (2015) Suppression of the GTPase-activating protein RGS10 increases Rheb-GTP and mTOR signaling in ovarian cancer cells

   Cancer Letters 369:175–183.

   https://doi.org/10.1016/j.canlet.2015.08.012

   • PubMed
   • Google Scholar
5. Website

   1. American Cancer Society

   (2023) Survival Rates for Breast Cancer

   Accessed January 17, 2024.

   https://www.cancer.org/cancer/types/breast-cancer/understanding-a-breast-cancer-diagnosis/breast-cancer-survival-rates.html
6. 1. Bianchini G
   2. Balko JM
   3. Mayer IA
   4. Sanders ME
   5. Gianni L

   (2016) Triple-negative breast cancer: challenges and opportunities of a heterogeneous disease

   Nature Reviews. Clinical Oncology 13:674–690.

   https://doi.org/10.1038/nrclinonc.2016.66

   • PubMed
   • Google Scholar
7. 1. Cacan E
   2. Ali MW
   3. Boyd NH
   4. Hooks SB
   5. Greer SF

   (2014) Inhibition of HDAC1 and DNMT1 modulate RGS10 expression and decrease ovarian cancer chemoresistance

   PLOS ONE 9:e87455.

   https://doi.org/10.1371/journal.pone.0087455

   • PubMed
   • Google Scholar
8. 1. Cai F
   2. Chen L
   3. Sun Y
   4. He C
   5. Fu D
   6. Tang J

   (2020) MiR-539 inhibits the malignant behavior of breast cancer cells by targeting SP1

   Biochemistry and Cell Biology = Biochimie et Biologie Cellulaire 98:426–433.

   https://doi.org/10.1139/bcb-2019-0111

   • PubMed
   • Google Scholar
9. 1. Caldiran FY
   2. Cacan E

   (2022) RGS10 suppression by DNA methylation is associated with low survival rates in colorectal carcinoma

   Pathology, Research and Practice 236:154007.

   https://doi.org/10.1016/j.prp.2022.154007

   • PubMed
   • Google Scholar
10. 1. Chaudhury S
    2. O’Connor C
    3. Cañete A
    4. Bittencourt-Silvestre J
    5. Sarrou E
    6. Prendergast Á
    7. Choi J
    8. Johnston P
    9. Wells CA
    10. Gibson B
    11. Keeshan K

    (2018) Age-specific biological and molecular profiling distinguishes paediatric from adult acute myeloid leukaemias

    Nature Communications 9:5280.

    https://doi.org/10.1038/s41467-018-07584-1

    • PubMed
    • Google Scholar
11. 1. Coates AS
    2. Winer EP
    3. Goldhirsch A
    4. Gelber RD
    5. Gnant M
    6. Piccart-Gebhart M
    7. Thürlimann B
    8. Senn HJ
    9. Panel Members

    (2015) Tailoring therapies--improving the management of early breast cancer: St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2015

    Annals of Oncology 26:1533–1546.

    https://doi.org/10.1093/annonc/mdv221

    • PubMed
    • Google Scholar
12. 1. Dean P
    2. Hooks S

    (2020) Role of RGS10 in the ovarian cancer microenvironment

    The FASEB Journal 34:05803.

    https://doi.org/10.1096/fasebj.2020.34.s1.05803

    • Google Scholar
13. 1. Deshmukh AP
    2. Vasaikar SV
    3. Tomczak K
    4. Tripathi S
    5. den Hollander P
    6. Arslan E
    7. Chakraborty P
    8. Soundararajan R
    9. Jolly MK
    10. Rai K
    11. Levine H
    12. Mani SA

    (2021) Identification of EMT signaling cross-talk and gene regulatory networks by single-cell RNA sequencing

    PNAS 118:e2102050118.

    https://doi.org/10.1073/pnas.2102050118

    • PubMed
    • Google Scholar
14. 1. Górecki I
    2. Rak B

    (2021) The role of microRNAs in epithelial to mesenchymal transition and cancers; focusing on mir-200 family

    Cancer Treatment and Research Communications 28:100385.

    https://doi.org/10.1016/j.ctarc.2021.100385

    • PubMed
    • Google Scholar
15. 1. Gradishar WJ
    2. Moran MS
    3. Abraham J
    4. Aft R
    5. Agnese D
    6. Allison KH
    7. Blair SL
    8. Burstein HJ
    9. Dang C
    10. Elias AD
    11. Giordano SH
    12. Goetz MP
    13. Goldstein LJ
    14. Hurvitz SA
    15. Isakoff SJ
    16. Jankowitz RC
    17. Javid SH
    18. Krishnamurthy J
    19. Leitch M
    20. Lyons J
    21. Matro J
    22. Mayer IA
    23. Mortimer J
    24. O’Regan RM
    25. Patel SA
    26. Pierce LJ
    27. Rugo HS
    28. Sitapati A
    29. Smith KL
    30. Smith ML
    31. Soliman H
    32. Stringer-Reasor EM
    33. Telli ML
    34. Ward JH
    35. Wisinski KB
    36. Young JS
    37. Burns JL
    38. Kumar R

    (2021) NCCN Guidelines Insights: Breast Cancer, Version 4.2021

    Journal of the National Comprehensive Cancer Network 19:484–493.

    https://doi.org/10.6004/jnccn.2021.0023

    • PubMed
    • Google Scholar
16. 1. Guo J
    2. Gong G
    3. Zhang B

    (2018) miR-539 acts as a tumor suppressor by targeting epidermal growth factor receptor in breast cancer

    Scientific Reports 8:2073.

    https://doi.org/10.1038/s41598-018-20431-z

    • PubMed
    • Google Scholar
17. 1. Győrffy B

    (2021) Survival analysis across the entire transcriptome identifies biomarkers with the highest prognostic power in breast cancer

    Computational and Structural Biotechnology Journal 19:4101–4109.

    https://doi.org/10.1016/j.csbj.2021.07.014

    • PubMed
    • Google Scholar
18. 1. Hooks SB
    2. Callihan P
    3. Altman MK
    4. Hurst JH
    5. Ali MW
    6. Murph MM

    (2010) Regulators of G-Protein signaling RGS10 and RGS17 regulate chemoresistance in ovarian cancer cells

    Molecular Cancer 9:289.

    https://doi.org/10.1186/1476-4598-9-289

    • PubMed
    • Google Scholar
19. 1. Hooks SB
    2. Murph MM

    (2015) Cellular deficiency in the RGS10 protein facilitates chemoresistant ovarian cancer

    Future Medicinal Chemistry 7:1483–1489.

    https://doi.org/10.4155/fmc.15.81

    • PubMed
    • Google Scholar
20. 1. Hu Y
    2. Zheng M
    3. Wang S
    4. Gao L
    5. Gou R
    6. Liu O
    7. Dong H
    8. Li X
    9. Lin B

    (2021) Identification of a five-gene signature of the RGS gene family with prognostic value in ovarian cancer

    Genomics 113:2134–2144.

    https://doi.org/10.1016/j.ygeno.2021.04.012

    • Google Scholar
21. 1. Huang Y
    2. Hong W
    3. Wei X

    (2022) The molecular mechanisms and therapeutic strategies of EMT in tumor progression and metastasis

    Journal of Hematology & Oncology 15:129.

    https://doi.org/10.1186/s13045-022-01347-8

    • PubMed
    • Google Scholar
22. 1. Hunt TW
    2. Fields TA
    3. Casey PJ
    4. Peralta EG

    (1996) RGS10 is a selective activator of Gαi GTPase activity

    Nature 383:175–177.

    https://doi.org/10.1038/383175a0

    • Google Scholar
23. 1. Lai X
    2. Li Q
    3. Wu F
    4. Lin J
    5. Chen J
    6. Zheng H
    7. Guo L

    (2020) Epithelial-Mesenchymal Transition and Metabolic Switching in Cancer: Lessons From Somatic Cell Reprogramming

    Frontiers in Cell and Developmental Biology 8:760.

    https://doi.org/10.3389/fcell.2020.00760

    • PubMed
    • Google Scholar
24. 1. Lamouille S
    2. Xu J
    3. Derynck R

    (2014) Molecular mechanisms of epithelial-mesenchymal transition

    Nature Reviews. Molecular Cell Biology 15:178–196.

    https://doi.org/10.1038/nrm3758

    • PubMed
    • Google Scholar
25. 1. Lee AV
    2. Oesterreich S
    3. Davidson NE

    (2015) MCF-7 Cells--Changing the Course of Breast Cancer Research and Care for 45 Years

    JNCI Journal of the National Cancer Institute 107:djv073.

    https://doi.org/10.1093/jnci/djv073

    • Google Scholar
26. 1. Li CJ
    2. Chu PY
    3. Yiang GT
    4. Wu MY

    (2019) The Molecular Mechanism of Epithelial-Mesenchymal Transition for Breast Carcinogenesis

    Biomolecules 9:476.

    https://doi.org/10.3390/biom9090476

    • PubMed
    • Google Scholar
27. 1. Li L
    2. Xu Q
    3. Tang C

    (2023) RGS proteins and their roles in cancer: friend or foe?

    Cancer Cell International 23:81.

    https://doi.org/10.1186/s12935-023-02932-8

    • PubMed
    • Google Scholar
28. 1. Liberzon A
    2. Subramanian A
    3. Pinchback R
    4. Thorvaldsdóttir H
    5. Tamayo P
    6. Mesirov JP

    (2011) Molecular signatures database (MSigDB) 3.0

    Bioinformatics 27:1739–1740.

    https://doi.org/10.1093/bioinformatics/btr260

    • PubMed
    • Google Scholar
29. 1. Liu F
    2. Gu LN
    3. Shan BE
    4. Geng CZ
    5. Sang MX

    (2016) Biomarkers for EMT and MET in breast cancer: An update

    Oncology Letters 12:4869–4876.

    https://doi.org/10.3892/ol.2016.5369

    • PubMed
    • Google Scholar
30. 1. Merlin JL
    2. Barberi-Heyob M
    3. Bachmann N

    (2002) In vitro comparative evaluation of trastuzumab (Herceptin) combined with paclitaxel (Taxol) or docetaxel (Taxotere) in HER2-expressing human breast cancer cell lines

    Annals of Oncology 13:1743–1748.

    https://doi.org/10.1093/annonc/mdf263

    • PubMed
    • Google Scholar
31. 1. Miglietta F
    2. Bottosso M
    3. Griguolo G
    4. Dieci MV
    5. Guarneri V

    (2022) Major advancements in metastatic breast cancer treatment: when expanding options means prolonging survival

    ESMO Open 7:100409.

    https://doi.org/10.1016/j.esmoop.2022.100409

    • PubMed
    • Google Scholar
32. 1. Montel V
    2. Huang TY
    3. Mose E
    4. Pestonjamasp K
    5. Tarin D

    (2005) Expression profiling of primary tumors and matched lymphatic and lung metastases in a xenogeneic breast cancer model

    The American Journal of Pathology 166:1565–1579.

    https://doi.org/10.1016/S0002-9440(10)62372-3

    • PubMed
    • Google Scholar
33. 1. Morales-Valencia J
    2. Lau L
    3. Martí-Nin T
    4. Ozerdem U
    5. David G

    (2022) Therapy-induced senescence promotes breast cancer cells plasticity by inducing Lipocalin-2 expression

    Oncogene 41:4361–4370.

    https://doi.org/10.1038/s41388-022-02433-4

    • PubMed
    • Google Scholar
34. 1. Nguyen HT
    2. Tian G
    3. Murph MM

    (2014) Molecular epigenetics in the management of ovarian cancer: are we investigating a rational clinical promise?

    Frontiers in Oncology 4:71.

    https://doi.org/10.3389/fonc.2014.00071

    • PubMed
    • Google Scholar
35. 1. Rahman R
    2. Dhruba SR
    3. Matlock K
    4. De-Niz C
    5. Ghosh S
    6. Pal R

    (2019) Evaluating the consistency of large-scale pharmacogenomic studies

    Briefings in Bioinformatics 20:1734–1753.

    https://doi.org/10.1093/bib/bby046

    • PubMed
    • Google Scholar
36. 1. Santiago-Sánchez GS
    2. Pita-Grisanti V
    3. Quiñones-Díaz B
    4. Gumpper K
    5. Cruz-Monserrate Z
    6. Vivas-Mejía PE

    (2020) Biological Functions and Therapeutic Potential of Lipocalin 2 in Cancer

    International Journal of Molecular Sciences 21:4365.

    https://doi.org/10.3390/ijms21124365

    • PubMed
    • Google Scholar
37. 1. Singh S
    2. Chakrabarti R

    (2019) Consequences of EMT-Driven Changes in the Immune Microenvironment of Breast Cancer and Therapeutic Response of Cancer Cells

    Journal of Clinical Medicine 8:642.

    https://doi.org/10.3390/jcm8050642

    • PubMed
    • Google Scholar
38. 1. Steeg PS

    (2005) New insights into the tumor metastatic process revealed by gene expression profiling

    The American Journal of Pathology 166:1291–1294.

    https://doi.org/10.1016/S0002-9440(10)62348-6

    • PubMed
    • Google Scholar
39. 1. Suarez-Carmona M
    2. Lesage J
    3. Cataldo D
    4. Gilles C

    (2017) EMT and inflammation: inseparable actors of cancer progression

    Molecular Oncology 11:805–823.

    https://doi.org/10.1002/1878-0261.12095

    • PubMed
    • Google Scholar
40. 1. Sung H
    2. Ferlay J
    3. Siegel RL
    4. Laversanne M
    5. Soerjomataram I
    6. Jemal A
    7. Bray F

    (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

    CA 71:209–249.

    https://doi.org/10.3322/caac.21660

    • PubMed
    • Google Scholar
41. 1. Szklarczyk D
    2. Franceschini A
    3. Wyder S
    4. Forslund K
    5. Heller D
    6. Huerta-Cepas J
    7. Simonovic M
    8. Roth A
    9. Santos A
    10. Tsafou KP
    11. Kuhn M
    12. Bork P
    13. Jensen LJ
    14. von Mering C

    (2015) STRING v10: protein-protein interaction networks, integrated over the tree of life

    Nucleic Acids Research 43:D447–D452.

    https://doi.org/10.1093/nar/gku1003

    • PubMed
    • Google Scholar
42. 1. Wang Z
    2. Chen J
    3. Wang S
    4. Sun Z
    5. Lei Z
    6. Zhang H-T
    7. Huang J

    (2022) RGS6 suppresses TGF-β-induced epithelial-mesenchymal transition in non-small cell lung cancers via a novel mechanism dependent on its interaction with SMAD4

    Cell Death & Disease 13:656.

    https://doi.org/10.1038/s41419-022-05093-0

    • PubMed
    • Google Scholar
43. 1. Wen L
    2. Li J
    3. Guo H
    4. Liu X
    5. Zheng S
    6. Zhang D
    7. Zhu W
    8. Qu J
    9. Guo L
    10. Du D
    11. Jin X
    12. Zhang Y
    13. Gao Y
    14. Shen J
    15. Ge H
    16. Tang F
    17. Huang Y
    18. Peng J

    (2015) Genome-scale detection of hypermethylated CpG islands in circulating cell-free DNA of hepatocellular carcinoma patients

    Cell Research 25:1376.

    https://doi.org/10.1038/cr.2015.141

    • PubMed
    • Google Scholar
44. 1. Wolff AC
    2. Hammond MEH
    3. Hicks DG
    4. Dowsett M
    5. McShane LM
    6. Allison KH
    7. Allred DC
    8. Bartlett JMS
    9. Bilous M
    10. Fitzgibbons P
    11. Hanna W
    12. Jenkins RB
    13. Mangu PB
    14. Paik S
    15. Perez EA
    16. Press MF
    17. Spears PA
    18. Vance GH
    19. Viale G
    20. Hayes DF
    21. American Society of Clinical Oncology
    22. College of American Pathologists

    (2013) Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update

    Journal of Clinical Oncology 31:3997–4013.

    https://doi.org/10.1200/JCO.2013.50.9984

    • PubMed
    • Google Scholar
45. 1. Yang J
    2. Bielenberg DR
    3. Rodig SJ
    4. Doiron R
    5. Clifton MC
    6. Kung AL
    7. Strong RK
    8. Zurakowski D
    9. Moses MA

    (2009) Lipocalin 2 promotes breast cancer progression

    PNAS 106:3913–3918.

    https://doi.org/10.1073/pnas.0810617106

    • PubMed
    • Google Scholar
46. 1. Yin W
    2. Wang P
    3. Wang X
    4. Song W
    5. Cui X
    6. Yu H
    7. Zhu W

    (2013) Identification of microRNAs and mRNAs associated with multidrug resistance of human laryngeal cancer Hep-2 cells

    Brazilian Journal of Medical and Biological Research = Revista Brasileira de Pesquisas Medicas e Biologicas 46:546–554.

    https://doi.org/10.1590/1414-431X20131662

    • PubMed
    • Google Scholar
47. 1. Zhang K
    2. Corsa CA
    3. Ponik SM
    4. Prior JL
    5. Piwnica-Worms D
    6. Eliceiri KW
    7. Keely PJ
    8. Longmore GD

    (2013) The collagen receptor discoidin domain receptor 2 stabilizes SNAIL1 to facilitate breast cancer metastasis

    Nature Cell Biology 15:677–687.

    https://doi.org/10.1038/ncb2743

    • PubMed
    • Google Scholar
48. 1. Zhao M
    2. Ang L
    3. Huang J
    4. Wang J

    (2017) MicroRNAs regulate the epithelial–mesenchymal transition and influence breast cancer invasion and metastasis

    Tumor Biology 39:101042831769168.

    https://doi.org/10.1177/1010428317691682

    • Google Scholar

Author details

1. Yang Liu

   Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, China

   Contribution

   Conceptualization, Data curation, Software, Funding acquisition, Writing – original draft, Project administration, Resources, Writing – review and editing

   Contributed equally with

   Yi Jiang, Peng Qiu, Tie Ma and Yang Bai

   Competing interests

   No competing interests declared

2. Yi Jiang

   Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, China

   Contribution

   Data curation, Software, Investigation, Writing – original draft

   Contributed equally with

   Yang Liu, Peng Qiu, Tie Ma and Yang Bai

   Competing interests

   No competing interests declared

3. Peng Qiu

   Department of Anesthesiology, Shengjing Hospital of China Medical University, Shenyang, China

   Contribution

   Investigation, Writing – original draft

   Contributed equally with

   Yang Liu, Yi Jiang, Tie Ma and Yang Bai

   Competing interests

   No competing interests declared

4. Tie Ma

   Department of Pathology, Shengjing Hospital of China Medical University, Shenyang, China

   Contribution

   Data curation, Investigation

   Contributed equally with

   Yang Liu, Yi Jiang, Peng Qiu and Yang Bai

   Competing interests

   No competing interests declared

5. Yang Bai

   Department of Nursing, Shengjing Hospital of China Medical University, Shenyang, China

   Contribution

   Writing – review and editing

   Contributed equally with

   Yang Liu, Yi Jiang, Peng Qiu and Tie Ma

   Competing interests

   No competing interests declared

6. Jiawen Bu

   Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, China

   Contribution

   Investigation

   Competing interests

   No competing interests declared

7. Yueting Hu

   Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, China

   Contribution

   Investigation

   Competing interests

   No competing interests declared

8. Ming Jin

   Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, China

   Contribution

   Investigation

   Competing interests

   No competing interests declared

9. Tong Zhu

   Breast Surgery of Panjin Central Hospital, Panjin, China

   Contribution

   Investigation

   Competing interests

   No competing interests declared

10. Xi Gu

    Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, China

    Contribution

    Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Writing – original draft, Writing – review and editing

    For correspondence

    jadegx@163.com

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-4172-9821

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