Skip to main content

Selection hub MicroRNAs as biomarkers in breast cancer stem cells in extracellular matrix using bioinformatics analyses

Abstract

Background

Breast cancer is one of the most common cancers in women, and many people get it every year. The cancer stem cells are maybe crucial role to exacerbates and relapse the breast cancer. Therefore, finding biomarkers in human secretions can be an suitable solution for early detection and neo adjuvant therapy. This study aimed to investigate the molecular events related to the cancer stem cells in breast cancer, after which we nominated a suitable MicroRNAs participates in breast cancer pathogenesis.

Methods

In this study, we investigated the relationship between molecular pathways using a bioinformatics approach. First, we selected the appropriate RNA-Seq datasets from the GEO database. We used Enrichr, KEGG, and Shiny GO databases to evaluate the signal pathways and gene ontology after isolating the gene expression profiles. In the next step, we used the STRING database to assess the protein network, and we used the Targetscan database to nominate the MicroRNA.

Results

510 high-expression genes and 460 low-expression genes were associated with breast cancer and the cancer stem cells. Highly expressed genes were involved in the cell cycle and cellular aging pathways. On the other hand, low-expression genes were involved in the RNA transports, spliceosome, and apoptosis pathways. After evaluating the ontology of genes and the relationship between proteins, high-expression SPARC, INHBA, FN1, and GBA proteins were nominated. In the next section, the MicroRNAs related to these genes were hsa miR-9.5p, hsa miR-203.3p, and hsa miR-429.

Conclusion

In general, we examined more closely and more the relationship between the cancer stem cells pathway and breast cancer using a regular and accurate bioinformatics framework. Finally, we nominated suitable MicroRNAs that were involved in breast cancer stem cells.

Highlights

  • Breast cancer stem cells play a significant role in the recurrence of the disease or even the development of secondary tumors.

  • SPARC, INHBA, FN1, and GBA genes play a significant role in breast cancer stem cells.

  • hsa miR-9.5p, hsa miR-203.3p, and hsa miR-429. associated with breast cancer stem cells can be detected in human secretions.

Background

Breast cancer is one of the most common cancers in women. Due to the high incidence of this type of cancer in women, which is progressing rapidly worldwide [1], various treatments for this cancer are performed. Despite these treatments, recurrence of the disease as a secondary tumor or more severe than breast cancer is still a major concern for all societies today [2, 3]. In this case, finding more effective and important factors will lead to more helpful answers for how to treat breast cancer better.

One of the most critical factors in the recurrence of breast cancer is cancer stem cells. These cells account for about 1% of the total tumor cell population [4]. With multiple treatments and even surgery, small amounts of cancer stem cells may remain in the patient [5]. This causes the cancer stem cells to redistribute, and this time the population of tumor cells formed by different therapies becomes more resistant [6]. Various studies have been performed in recent years and have tried to destroy breast cancer stem cells in different ways. Domenici et al. showed that the Sox2 and Sox9 genes could be potent markers for breast cancer stem cells [7]. Another study showed that the use of Doxycycline weakened mitochondria in breast cancer stem cells [8]. The survey by Palomeras et al. also showed that CD44 and CD24 markers are used to identify breast cancer stem cells more accurately [9]. These markers, and many of the other markers identified in various articles, are more effective in diagnosis and may not be used as a target marker to eliminate breast cancer stem cells. So, using bioinformatics analysis lets us look at the selection of miRNAs by looking at the gene expression profile of breast cancer stem cells and choosing the right candidate genes. This helps us find more solutions for both diagnosis and treatment.

Methods

Select the appropriate bioinformatics data

In this study, we selected an RNA-Seq dataset (GSE109798) from the Biojupies database. These data, which were obtained on triple-negative breast cancer patients, included a total of 6 samples, of which three samples were in the control group, and three samples were related to breast cancer. LogFC > 1,LogFC < − 1, and p value ˂ 0.05 were selected to evaluate cancer stem cells gene expression profiles more accurately. Then cluster the genes that had differential expression in an Excel file to use for other analyzes. Figures 1 and 2 indicate that the MA plot and heatmap for gene expression profiles (Additional file 1).

Fig. 1
figure 1

The figure contains an interactive scatter plot which displays the average expression and statistical significance of each gene calculated by performing differential gene expression analysis. Every point in the plot represents a gene. Red points indicate significantly upregulated genes, and blue points indicate downregulated genes

Fig. 2
figure 2

The figure contains an interactive heatmap displaying gene expression for each sample in the RNA-seq dataset. Every row of the heatmap represents a gene, every column represents a sample, and every cell displays normalized gene expression values. The heatmap additionally features color bars beside each column which represent prior knowledge of each sample, such as the tissue of origin or experimental treatment

Investigation of gene ontology and signaling pathways

This section loaded high-expression and low-expression genes separately into the Enrichr database and used signaling pathways libraries and the ontology section to evaluate signaling pathways, molecular functions, biological processes, and cellular components. KEGG library was used for molecular pathways. In this part, the p value ˂ 0.05 was considered.

Communication network between proteins

Uploaded the genes with high expression in this step and were present in important signaling pathways in the STRING database. We then plotted the protein network of these genes. Then isolated the most closely related proteins to other proteins and were shown to play a more important role for further evaluation.

A closer look at the candidate proteins in patients with breast cancer

We used the GEPIA database to analyze this part. We placed each of the candidate genes in this database separately and measured their expression, stage plot, and survival in patients with breast cancer compared to the control group.

Examination of miRNAs associated with breast cancer stem cells

We uploaded the previously evaluated genes in patients with breast cancer to the control group to select miRNAs in the MienTURNET database and plotted the relationship between the genes and miRNAs as a network.

Results

Breast cancer stem cells were observed in the ECM receptor, proteoglycans, and Gap junctions signaling pathways

After performing gene expression profile analysis, data on breast cancer stem cells with the control group, 510 genes with high expression, and 460 genes with low expression were obtained. High-expression genes consist of ECM receptor, proteoglycans in cancer, phagosome, focal adhesion, PI3K/AKT, adherent junctions, FoxO signaling pathways. Low-expression genes were present in ribosome, RNA transports, spliceosome, apoptosis, Gap junctions, cell cycles, and biosynthesis in amino acids signaling pathways. Also, the genes that had the highest score in terms of expression differentiation are shown in Table 1.

Table 1 Top 10 up- and downregulated genes in breast cancer stem cells

Gene ontology in breast cancer stem cells

We examined high-expression and low-expression genes separately for molecular functions, biological processes, and cellular components. High-expression genes were involved in regulatory of transcription factors, positive regulation of transcription, extracellular matrix disassembly, positive regulation of cell cycle, positive regulation of proliferation, and positive regulation of exocytosis pathways, in biological process. Transcription regulatory DNA binding, transcription coactivator activity, protein kinase binding, cadherin binding, collagen binding, and integrin binding in molecular functions. Low-expression genes were involved in translation, SRP co translational proteins targeting, peptides biosynthesis process, protein targeting to ER, cellular macromolecules biosynthesis process, rRNA metabolic pathways, and ribosome biogenesis pathways in biological processes. Also RNA binding, GTP binding, translation initiation factor function, ubiquitin ligase activity, and purine binding indicated that molecular functions. More information is shown in Fig. 3.

Fig. 3
figure 3

The figure contains interactive bar charts displaying the results of the gene ontology enrichment analysis generated using Enrichr. The x axis indicates the − log10 (p value) for each term. Significant terms are highlighted in bold

The communication network of proteins in the extracellular matrix

In this part of the study, proteins in the extracellular matrix were examined to find more accurate markers of cancer stem cells in breast cancer patients' blood or other secretions. Accordingly, the communication network between the proteins is plotted in Fig. 4. This protein network consists of 53 nodes and 93 edges. Based on the average correlation between proteins, four proteins showed significant SPARC, INHBA, FN1, and GBA compared to other proteins in this network.

Fig. 4
figure 4

In this image, the genes involved in the extracellular matrix are isolated, and then, a protein network is drawn between them

Evaluation of proteins in human data in databases

In this part of the study, SPARC, INHBA, FN1, and GBA proteins in the GEPIA database were evaluated in breast cancer samples compared to controls. Accordingly, similar to bioinformatics data, SPARC, INHBA, FN1, and GBA proteins in the breast cancer sample showed a significant increase in expression compared to the control sample. In the plot diagram, higher data density is directly related to increased gene expression. In the survival chart, on average, SPARC, INHBA, FN1, and GBA proteins have reduced the survival of patients by about 60% over time, which is a significant rate (Fig. 5).

Fig. 5
figure 5

This section identified the proteins most associated with other proteins. In the sample of patients and healthy individuals with three approaches, we examined the difference in gene expression, gene expression at different stages, and survival. As can be seen in the box plots, the expression of genes is significantly higher in breast cancer patients than in healthy individuals. The same has been confirmed in the stage plot, which shows that the expression of these four genes is high in all the main stages of breast cancer. The survival chart also shows the mortality of people up to 40%, which is the importance of the pathogenicity of these genes in breast cancer, especially in breast cancer stem cells. BRCA (Breast invasive carcinoma). A: FN1, B: SPARC, C: INHBA, D: GBA

The candidacy of miRNAs associated with proteins in the extracellular matrix

Following the bioinformatics analyses performed in this step, we uploaded the SPARC, INHBA, FN1, and GBA in the MienTURNET database and selected the miRNAs related to these genes. hsa miR-9.5p, hsa miR-203.3p, hsa miR-429, hsa miR-200c, hsa miR-1, hsa miR-206, and hsa miR-613 miRNAs are significantly identified, as shown in Fig. 6 of its communication network.

Fig. 6
figure 6

Here, mapped important miRNAs and related candidate genes

Discussion

There are many challenges today in dealing with breast cancer recurrence and the increasing severity of the disease [10, 11]. Cancer stem cells play a vital role in this phenomenon. Cancer stem cells have become resistant to treatments such as chemotherapy and radiotherapy. In the event of a recurrence of the disease, it becomes complicated to manage breast cancer treatment [12]. Because of this, finding biomarkers, especially miRNAs, can help make new drugs and find better ways to kill cancer stem cells.

In this study, which was performed through continuous bioinformatics analysis, after evaluating the molecular pathways associated with breast cancer stem cells, different genes, proteins, and miRNAs involved in better identification and targeting of breast cancer stem cells were chosen. For this purpose, SPARC, INHBA, FN1, GBA proteins and hsa miR-9.5p, hsa miR-203.3p, hsa miR-429, hsa miR-200c, hsa miR-1, hsa miR-206, and hsa miR-613 were selected in our study. In the following, we examined these important biomarkers is the first SPARC gene. This gene encodes a cysteine-rich acidic matrix-associated protein. The encoded protein is required for the collagen in bone to become calcified but is also involved in extracellular matrix synthesis and the promotion of changes to cell shape. The gene product has been associated with tumor suppression but has also been correlated with metastasis based on changes to cell shape which can promote tumor cell invasion [13, 14]. Various studies have shown that SPARC plays a crucial role in tumorigenesis, breast cancer progression, and other cancers. For example, a study showed that SPARC significantly increased expression in breast cancer patients compared to healthy individuals. When the gene was inhibited, the invasion of breast cancer cells decreased [15]. In the study, Sanita et al. used nanoparticles to target SPARC albumin. Inhibition of the SPARC gene in the breast cancer cell line has been shown to reduce the survival of these cells and induce apoptosis and cell invasion [16]. A study by Bawazeer et al. also showed that polymorphisms in the SPARC gene could affect VEGF and exacerbate breast cancer [17]. However, various studies have shown SPARC activity in breast cancer. But in breast cancer stem cells, examining the traces of this gene can help to better target breast cancer stem cells. On the other hand, SPARC plays a significant role in other cancers, including prostate [18], colon [19], and lung [20].

The INHBA gene encodes a member of the TGF-beta (transforming growth factor-beta) superfamily of proteins. The encoded preproprotein is proteolytically processed to generate a subunit of the dimeric activin and inhibin protein complexes. These complexes activate and inhibit, respectively, follicle stimulating hormone secretion from the pituitary gland. The encoded protein also plays a role in eye, tooth, and testis development [21, 22]. The study by Hamalian et al. showed that INHBA in the SNAI2/PEAK1/INHBA signaling pathway plays a vital role in the invasion of HER2 + breast cancer cells. This signaling pathway is associated with the actin cytoskeleton and is in contact with the microenvironment and extracellular matrix. This signaling pathway is also involved in the integrin growth factor, in which any disruption can disrupt cell connections and initiate cell invasion [23]. Wang et al.’s study showed a clear association between circulating tumor cells and INHBA and that both were more active in breast cancer patients than in healthy individuals. But after using chemotherapy drugs, the activity of both of them decreased significantly [24]. The study by Yu et al. showed that INHBA significantly increased expression in breast cancer patients compared to healthy individuals. It was also found that INHBA activated the TGFB signal pathway, which intensified the invasion of breast cancer cells by activating the EMT pathway [22]. The study by Xueqin et al. showed that INHBA could play a major role in the division of breast cancer cells by affecting the Wnt/B catenin signaling pathway [25]. The study also showed that the function of the INHBA gene in breast cancer stem cells was not clearly defined, which could be further tested if a study shows that INHBA is involved in the invasion and division of gastric cancer stem cells.

The FN1 gene encodes fibronectin, a glycoprotein present in a soluble dimeric form in plasma, and in a dimeric or multimeric form at the cell surface and in the extracellular matrix. The encoded preproprotein is proteolytically processed to generate the mature protein. Fibronectin is involved in cell adhesion and migration processes, including embryogenesis, wound healing, blood coagulation, host defense, and metastasis [26, 27]. A few studies have closely examined the association of FN1 with breast cancer. The study by Yang et al. showed that FN1 was involved in the induction of the EMT pathway and the invasiveness of breast cancer cells by regulation by miR-200b [28]. The study by Wang et al. using bioinformatics analysis showed that FN1 is involved in the progression and invasion of breast cancer [29]. The survey by Hellinger et al. also showed that FN1 plays a role in the acute stage of breast cancer and plays an essential role in promoting breast cancer [30].

The GBA gene encodes a lysosomal membrane protein that cleaves the beta-glucosidic linkage of glycosylceramide, an intermediate in glycolipid metabolism. Mutations in this gene cause Gaucher disease, a lysosomal storage disorder characterized by glucocerebroside accumulation [31, 32]. The study by Zhou et al. showed that GBA plays a key role in reducing the sensitivity of breast cancer tumor cells in response to chemotherapy, and by increasing GBA expression, the PI3K/AKT/mTOR signal pathway in reducing this sensitivity to drugs poses many challenges for management of breast cancer treatment [33]. The study by Moro et al. also showed that GBA plays an important role in the invasion of breast cancer cells [34]. This gene has not been studied in detail in breast cancer stem cells, which could be a good option for targeting breast cancer stem cells.

After studies performed using bioinformatics, SPARC, INHBA, FN1, and GBA were specifically selected in breast cancer stem cells in this study. As you can see in Fig. 5, the effectiveness of these genes is critical in the survival of breast cancer, and as the disease progresses to an invasive phase, the number of patients with high gene expression increases. In this regard, the study of these four genes or their protein products, especially miRNAs in breast cancer, can be a strong point for managing breast cancer treatment.

Conclusion

Subsequently, we identified SPARC, INHBA, FN1, and GBA and their associated miRNAs in breast cancer stem cells in this study. Since these genes were studied only in breast cancer and less on cancer stem cells, the importance of these genes was investigated. Accordingly, targeting these SPARC, INHBA, FN1, and GBA genes or protein products could be used as a neoadjuvant treatment for breast cancer. Candidate miRNAs can also be evaluated better to detect breast cancer stem cells in various human secretions.

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

References

  1. Sopik V (2021) International variation in breast cancer incidence and mortality in young women. Breast Cancer Res Treat 186:497–507

    Article  CAS  Google Scholar 

  2. Mirzaei M, Sheikholeslami SA, Jalili A, Bereimipour A, Sharbati S, Kaveh V, Salari S. Investigating the molecular mechanisms of Tamoxifen on the EMT pathway among patients with breast cancer. J Med Life. 2022;15(6):835.

    Article  Google Scholar 

  3. Cerkauskaite D, Zilinskas K, Varnelis P, El Oreibi M, Asejev V, Dulskas A (2021) Ovarian metastases from breast cancer: a report of 24 cases. J Gynecol Obstet Hum Reprod 50:102075

    Article  Google Scholar 

  4. Butti R, Gunasekaran VP, Kumar TVS, Banerjee P, Kundu GC (2019) Breast cancer stem cells: biology and therapeutic implications. Int J Biochem Cell Biol 107:38–52

    Article  CAS  Google Scholar 

  5. De Angelis ML, Francescangeli F, Zeuner A (2019) Breast cancer stem cells as drivers of tumor chemoresistance, dormancy and relapse: new challenges and therapeutic opportunities. Cancers (Basel) 11:1569

    Article  Google Scholar 

  6. Turdo A, Veschi V, Gaggianesi M, Chinnici A, Bianca P, Todaro M, Stassi G (2019) Meeting the challenge of targeting cancer stem cells. Front Cell Dev Biol 7:16

    Article  Google Scholar 

  7. Domenici G, Aurrekoetxea-Rodr’iguez I, Simões BM, Rábano M, Lee SY, San Millan J, Comaills V, Oliemuller E, López-Ruiz JA, Zabalza I (2019) A Sox2–Sox9 signalling axis maintains human breast luminal progenitor and breast cancer stem cells. Oncogene 38:3151–3169

    Article  CAS  Google Scholar 

  8. Scatena C, Roncella M, Di Paolo A, Aretini P, Menicagli M, Fanelli G, Marini C, Mazzanti CM, Ghilli M, Sotgia F (2018) Doxycycline, an inhibitor of mitochondrial biogenesis, effectively reduces cancer stem cells (CSCs) in early breast cancer patients: a clinical pilot study. Front Oncol 8:452

    Article  Google Scholar 

  9. Palomeras S, Ruiz-Mart’inez S, Puig T (2018) Targeting breast cancer stem cells to overcome treatment resistance. Molecules 23:2193

    Article  Google Scholar 

  10. Al-mahmood S, Sapiezynski J, Garbuzenko OB, Minko T (2018) Metastatic and triple-negative breast cancer: challenges and treatment options. Drug Deliv Trans Res 8:1483–1507

    Article  Google Scholar 

  11. de Wiel M, Dockx Y, den Wyngaert T, Stroobants S, Tjalma WAA, Huizing MT (2017) Neoadjuvant systemic therapy in breast cancer: challenges and uncertainties. Eur J Obstet Gynecol Reprod Biol 210:144–156

    Article  Google Scholar 

  12. Dittmer J (2018) Breast cancer stem cells: features, key drivers and treatment options. Semin Cancer Biol 53:59–74

    Article  Google Scholar 

  13. Arqueros C, Salazar J, Arranz MJ, Sebio A, Mora J, Sullivan I, Tobeña M, Mart’in-Richard M, Barnadas A, Baiget M (2017) SPARC gene variants predict clinical outcome in locally advanced and metastatic pancreatic cancer patients. Med Oncol 34:1–8

    Article  CAS  Google Scholar 

  14. Bao J-M, Dang Q, Lin C-J, Lo U-G, Feldkoren B, Dang A, Hernandez E, Li F, Panwar V, Lee C-F (2021) SPARC is a key mediator of TGF-β-induced renal cancer metastasis. J Cell Physiol 236:1926–1938

    Article  CAS  Google Scholar 

  15. Ma J, Gao S, Xie X, Sun E, Zhang M, Zhou Q, Lu C (2017) SPARC inhibits breast cancer bone metastasis and may be a clinical therapeutic target. Oncol Lett 14:5876–5882

    Google Scholar 

  16. Sanità G, Armanetti P, Silvestri B, Carrese B, Cal’i G, Pota G, Pezzella A, d’Ischia M, Luciani G, Menichetti L (2020) Albumin-modified melanin-silica hybrid nanoparticles target breast cancer cells via a SPARC-dependent mechanism. Front Bioeng Biotechnol 8:765

    Article  Google Scholar 

  17. Bawazeer S, Sabry D, Mahmoud RH, Elhanbuli HM, Yassen NN, Abdelhafez MN (2018) Association of SPARC gene polymorphisms rs3210714 and rs7719521 with VEGF expression and utility of Nottingham prognostic index scoring in breast cancer in a sample of Egyptian women. Mol Biol Rep 45:2313–2324

    Article  CAS  Google Scholar 

  18. Ding X, Li X, Qin A, Zhou J, Yan D, Stevens C, Krauss D, Kabolizadeh P (2018) Have we reached proton beam therapy dosimetric limitations?–a novel robust, delivery-efficient and continuous spot-scanning proton arc (SPArc) therapy is to improve the dosimetric outcome in treating prostate cancer. Acta Oncol (Madr) 57:435–437

    Article  Google Scholar 

  19. Zhong M-E, Chen Y, Xiao Y, Xu L, Zhang G, Lu J, Qiu H, Ge W, Wu B (2019) Serum extracellular vesicles contain SPARC and LRG1 as biomarkers of colon cancer and differ by tumour primary location. EBioMedicine 50:211–223

    Article  CAS  Google Scholar 

  20. Hung J-Y, Yen M-C, Jian S-F, Wu C-Y, Chang W-A, Liu K-T, Hsu Y-L, Chong I-W, Kuo P-L (2017) Secreted protein acidic and rich in cysteine (SPARC) induces cell migration and epithelial mesenchymal transition through WNK1/snail in non-small cell lung cancer. Oncotarget 8:63691

    Article  Google Scholar 

  21. Bao Y, Yao X, Li X, Samahy MA, Yang H, Liang Y, Liu Z, Wang F (2021) INHBA transfection regulates proliferation, apoptosis and hormone synthesis in sheep granulosa cells. Theriogenology 175:111

    Article  CAS  Google Scholar 

  22. Yu Y, Wang W, Lu W, Chen W, Shang A (2021) Inhibin β-A (INHBA) induces epithelial–mesenchymal transition and accelerates the motility of breast cancer cells by activating the TGF-β signaling pathway. Bioengineered 12:4681–4696

    Article  CAS  Google Scholar 

  23. Hamalian S, Güth R, Runa F, Sanchez F, Vickers E, Agajanian M, Molnar J, Nguyen T, Gamez J, Humphries JD (2021) others, A SNAI2-PEAK1-INHBA stromal axis drives progression and lapatinib resistance in HER2-positive breast cancer by supporting subpopulations of tumor cells positive for antiapoptotic and stress signaling markers. Oncogene 40:5224–5235

    Article  CAS  Google Scholar 

  24. Wang XQ, Liu B, Li BY, Wang T, Chen DQ (2020) Effect of CTCs and INHBA level on the effect and prognosis of different treatment methods for patients with early breast cancer. Eur Rev Med Pharmacol Sci 24:12735–12740

    Google Scholar 

  25. Xueqin T, Jinhong M, Yuping H (2021) INHBA promotes cell proliferation and metastasis of breast cancer through Wnt/β-catenin signaling pathway

  26. Matalliotaki C, Matalliotakis M, Rahmioglu N, Mavromatidis G, Matalliotakis I, Koumantakis G, Zondervan K, Spandidos DA, Goulielmos GN, Zervou MI (2019) Role of FN1 and GREB1 gene polymorphisms in endometriosis. Mol Med Rep 20:111–116

    CAS  Google Scholar 

  27. Koivisto O, Hanel A, Carlberg C (2020) Key vitamin D target genes with functions in the immune system. Nutrients 12:1140

    Article  CAS  Google Scholar 

  28. Yang X, Hu Q, Hu L-X, Lin X-R, Liu J-Q, Lin X, Dinglin X-X, Zeng J-Y, Hu H, Luo M-L (2017) miR-200b regulates epithelial-mesenchymal transition of chemo-resistant breast cancer cells by targeting FN1. Discov Med 24:75–85

    Google Scholar 

  29. Wang Y, Xu H, Zhu B, Qiu Z, Lin Z (2018) Systematic identification of the key candidate genes in breast cancer stroma. Cell Mol Biol Lett 23:1–15

    Article  Google Scholar 

  30. Hellinger JW, Schömel F, Buse JV, Lenz C, Bauerschmitz G, Emons G, Gründker C (2020) Identification of drivers of breast cancer invasion by secretome analysis: insight into CTGF signaling. Sci Rep 10:1–21

    Article  Google Scholar 

  31. Malek N, Weil RS, Bresner C, Lawton MA, Grosset KA, Tan M, Bajaj N, Barker RA, Burn DJ, Foltynie T (2018) others, Features of GBA-associated Parkinson’s disease at presentation in the UK tracking Parkinson’s study. J Neurol Neurosurg Psychiatry 89:702–709

    Article  Google Scholar 

  32. Garc’ia-Sanz P, Orgaz L, Fuentes JM, Vicario C, Moratalla R (2018) Cholesterol and multilamellar bodies: lysosomal dysfunction in GBA-Parkinson disease. Autophagy 14:717–718

    Article  Google Scholar 

  33. Zhou X, Huang Z, Yang H, Jiang Y, Wei W, Li Q, Mo Q, Liu J (2017) β-Glucosidase inhibition sensitizes breast cancer to chemotherapy. Biomed Pharmacother 91:504–509

    Article  CAS  Google Scholar 

  34. Moro K, Kawaguchi T, Tsuchida J, Gabriel E, Qi Q, Yan L, Wakai T, Takabe K, Nagahashi M (2018) Ceramide species are elevated in human breast cancer and are associated with less aggressiveness. Oncotarget 9:19874

    Article  Google Scholar 

Download references

Acknowledgements

We thank all colleagues in the core facilities for their support.

Funding

Not applicable.

Author information

Authors and Affiliations

Authors

Contributions

AS, AJ, AES, ASP, ST, AB participated in study design, data collection and evaluation, drafting and statistical analysis and contributed extensively in interpretation of the data and the conclusion and figure design. ZA modified the revision manuscript procedure. All authors performed editing and approving the final version of this paper for submission and also participated in the finalization of the manuscript and approved the final draft. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ahmad Bereimipour.

Ethics declarations

Ethical approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

All authors declare that they have no competing interests.

Additional information

Publisher's Note

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

Supplementary Information

Additional file 1

: In the Excel file that is included in the appendix, we first isolated the complete expression profile of the genes. Then, based on significance, we kept only the genes that had p value less than 0.05. In the next step, the genes were divided into two separate pages, and based on the level of gene expression and the LogFC parameter, we separated the genes with high and low expression.

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/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Shirinsokhan, A., Azarmehr, Z., Jalili, A. et al. Selection hub MicroRNAs as biomarkers in breast cancer stem cells in extracellular matrix using bioinformatics analyses. Egypt J Med Hum Genet 23, 159 (2022). https://doi.org/10.1186/s43042-022-00359-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s43042-022-00359-w

Keywords

  • MicroRNAs
  • Biomarkers
  • Cancer stem cells
  • Breast cancer
  • Extracellular matrix
  • Bioinformatics analyses