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MicroRNAs and Their Targets Could Have a Crucial Role in Breast Cancer Drug Resistance: A Bioinformatics Research

Yıl 2024, Cilt: 34 Sayı: 4, 458 - 464, 31.08.2024
https://doi.org/10.54005/geneltip.1431670

Öz

Background: MicroRNAs(miRNAs) have been demonstrated to contribute to cancer development by playing essential roles in processes including proliferation, migration, invasion, and metastasis. One of the most serious issues in breast cancer (BRCA) is drug resistance. Recent research suggests that miRNAs may possibly play a role in drug resistance. Using diverse datasets and in silico approaches, we focused on the BRCA/drug resistance/miRNA/mRNA link in our study.
Methods: GSE73736 and GSE71142 geo datasets (for miRNAs) and GSE162187 geodataset (for genes) were obtained from the GEO database to detect differently expressed miRNAs and genes using the R software “LIMMA” package. Potential target genes of screened differentially expressed miRNAs (DE-miRNAs) were predicted using miRMap, miRTarbase, and miRNet tools. Differently expressed genes (DE-genes) were filtered and common DE-genes were identified via TCGA data and miRNet. Afterward, Enrichr, and Funrich tools were used to perform GO annotation and KEGG pathway enrichment analysis. KMplot and GEPIA2 web tools were utilized to investigate further hub miRNAs and genes' expression and prognostic effects.
Results: 3 miRNAs that were considerably downregulated and had prognostic significance in BRCA were identified using the criteria defined in the investigated geo datasets. MiR-586, which is expected to be more closely linked to BRCA, has been found to have the ability to target 5 genes involved in BRCA resistance to therapy. GO, KEGG, and survival analysis showed that the probable target genes of miR-586 could be closely connected to BRCA.
Conclusion: In this study, a comprehensive BRCA-drug resistance-miRNA-gene network was established and new targets for the treatment and prognosis of BRCA were revealed using bioinformatics data.

Kaynakça

  • Dong X, Bai X, Ni J, Zhang H, Duan W, Graham P, Li Y. Exosomes and breast cancer drug resistance. Cell Death Dis. 2020; 11(11):987.
  • Kaya M, Suer İ. The Effect of miR-34a-5p on Overexpressed AML Associated Genes. Journal of Istanbul Faculty of Medicine. 2023; 86(1):59-68.
  • Kaya M, Karataş ÖF. The relationship between larynx cancer and microRNAs. Van medical journal. 2020; 27(4):535-41.
  • Kaya M, Suer I, Ozgur E, Capik O, Karatas OF, Ozturk S, et al. miR-145-5p suppresses cell proliferation by targeting IGF1R and NRAS genes in multiple myeloma cells. Turkish Journal of Biochemistry. 2023; 48(5):563-9.
  • Capik O, Sanli F, Kurt A, Ceylan O, Suer I, Kaya M, et al. CASC11 promotes aggressiveness of prostate cancer cells through miR-145/IGF1R axis. Prostate Cancer and Prostatic Diseases. 2021; 24(3):891-902.
  • Cosentino G, Plantamura I, Tagliabue E, Iorio MV, Cataldo A. Breast Cancer Drug Resistance: Overcoming the Challenge by Capitalizing on MicroRNA and Tumor Microenvironment Interplay. Cancers (Basel). 2021; 13(15).
  • Chen L, Heikkinen L, Wang C, Yang Y, Sun H, Wong G. Trends in the development of miRNA bioinformatics tools. Brief Bioinform. 2019; 20(5):1836-52.
  • Luna Buitrago D, Lovering RC, Caporali A. Insights into Online microRNA Bioinformatics Tools. Noncoding RNA. 2023; 9(2).
  • Kaya M. A Bioinformatics Approach to Male Infertility, MicroRNAs, and Targeted Genes. Ahi Evran Medical Journal. 2023; 7(3):296-303.
  • Banwait JK, Bastola DR. Contribution of bioinformatics prediction in microRNA-based cancer therapeutics. Adv Drug Deliv Rev. 2015; 81:94-103.
  • Lánczky A, Győrffy B. Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation. J Med Internet Res. 2021; 23(7):e27633.
  • Vejnar CE, Zdobnov EM. MiRmap: comprehensive prediction of microRNA target repression strength. Nucleic Acids Res. 2012; 40(22):11673-83.
  • Tang Z, Kang B, Li C, Chen T, Zhang Z. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 2019; 47(W1):W556-w60.
  • Chang L, Zhou G, Soufan O, Xia J. miRNet 2.0: network-based visual analytics for miRNA functional analysis and systems biology. Nucleic Acids Res. 2020; 48(W1):W244-w51.
  • Huang HY, Lin YC, Cui S, Huang Y, Tang Y, Xu J, et al. miRTarBase update 2022: an informative resource for experimentally validated miRNA-target interactions. Nucleic Acids Res. 2022; 50(D1):D222-d30.
  • Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016; 44(W1):W90-7.
  • Fonseka P, Pathan M, Chitti SV, Kang T, Mathivanan S. FunRich enables enrichment analysis of OMICs datasets. J Mol Biol. 2021; 433(11):166747.
  • Wang X, Zhang H, Chen X. Drug resistance and combating drug resistance in cancer. Cancer Drug Resist. 2019; 2(2):141-60.
  • Zhong L, Li Y, Xiong L, Wang W, Wu M, Yuan T, et al. Small molecules in targeted cancer therapy: advances, challenges, and future perspectives. Signal Transduct Target Ther. 2021; 6(1):201.
  • Han YH, Wang Y, Lee SJ, Mao YY, Jiang P, Sun HN, et al. Identification of Hub Genes and Upstream Regulatory Factors Based on Cell Adhesion in Triple-negative Breast Cancer by Integrated Bioinformatical Analysis. Anticancer Res. 2023; 43(7):2951-64.
  • Huang X, Taeb S, Jahangiri S, Korpela E, Cadonic I, Yu N, et al. miR-620 promotes tumor radioresistance by targeting 15-hydroxyprostaglandin dehydrogenase (HPGD). Oncotarget. 2015; 6(26):22439-51.
  • Kim DH, Park S, Kim H, Choi YJ, Kim SY, Sung KJ, et al. Tumor-derived exosomal miR-619-5p promotes tumor angiogenesis and metastasis through the inhibition of RCAN1.4. Cancer Lett. 2020; 475:2-13.
  • Gao Y, Zhang S, Gao X. TP73-AS1 rs3737589 Polymorphism is Associated With the Clinical Stage of Colorectal Cancer. Evid Based Complement Alternat Med. 2023; 2023:3931875.
  • Zhang D, Liu X, Li Y, Sun L, Liu SS, Ma Y, et al. LINC01189-miR-586-ZEB1 feedback loop regulates breast cancer progression through Wnt/β-catenin signaling pathway. Mol Ther Nucleic Acids. 2021; 25:455-67.
  • Liu C, Yang J, Zhu F, Zhao Z, Gao L. Exosomal circ_0001190 Regulates the Progression of Gastric Cancer via miR-586/SOSTDC1 Axis. Biochem Genet. 2022; 60(6):1895-913.
  • Shao X, Liu Y, Huang H, Zhuang L, Luo T, Huang H, Ge X. Down-regulation of G protein-coupled receptor 137 by RNA interference inhibits cell growth of two hepatoma cell lines. Cell Biol Int. 2015; 39(4):418-26.
  • Ma B, Ma Q, Jin C, Wang X, Zhang G, Zhang H, et al. ADAM12 expression predicts clinical outcome in estrogen receptor-positive breast cancer. Int J Clin Exp Pathol. 2015; 8(10):13279-83.
  • Wang X, Wang Y, Gu J, Zhou D, He Z, Wang X, Ferrone S. ADAM12-L confers acquired 5-fluorouracil resistance in breast cancer cells. Sci Rep. 2017; 7(1):9687.
  • Hisada T, Kondo N, Wanifuchi-Endo Y, Osaga S, Fujita T, Asano T, et al. Co-expression effect of LLGL2 and SLC7A5 to predict prognosis in ERα-positive breast cancer. Sci Rep. 2022; 12(1):16515.
  • Törnroos R, Tina E, Göthlin Eremo A. SLC7A5 is linked to increased expression of genes related to proliferation and hypoxia in estrogen‑receptor‑positive breast cancer. Oncol Rep. 2022; 47(1).
  • Li Y, Wang W, Wu X, Ling S, Ma Y, Huang P. SLC7A5 serves as a prognostic factor of breast cancer and promotes cell proliferation through activating AKT/mTORC1 signaling pathway. Ann Transl Med. 2021; 9(10):892.
  • He TG, Xiao ZY, Xing YQ, Yang HJ, Qiu H, Chen JB. Tumor Suppressor miR-184 Enhances Chemosensitivity by Directly Inhibiting SLC7A5 in Retinoblastoma. Front Oncol. 2019; 9:1163.
  • Sato M, Harada-Shoji N, Toyohara T, Soga T, Itoh M, Miyashita M, et al. L-type amino acid transporter 1 is associated with chemoresistance in breast cancer via the promotion of amino acid metabolism. Sci Rep. 2021; 11(1):589.
  • Liu Z, Xie Y, Xiong Y, Liu S, Qiu C, Zhu Z, et al. TLR 7/8 agonist reverses oxaliplatin resistance in colorectal cancer via directing the myeloid-derived suppressor cells to tumoricidal M1-macrophages. Cancer Lett. 2020; 469:173-85.
  • Yi SA, Kim GW, Yoo J, Han JW, Kwon SH. HP1γ Sensitizes Cervical Cancer Cells to Cisplatin through the Suppression of UBE2L3. Int J Mol Sci. 2020; 21(17).
  • Zhang Y, Talmon G, Wang J. MicroRNA-587 antagonizes 5-FU-induced apoptosis and confers drug resistance by regulating PPP2R1B expression in colorectal cancer. Cell Death Dis. 2015; 6(8):e1845.
  • He X, Sun H, Jiang Q, Chai Y, Li X, Wang Z, et al. Hsa-miR-4277 Decelerates the Metabolism or Clearance of Sorafenib in HCC Cells and Enhances the Sensitivity of HCC Cells to Sorafenib by Targeting cyp3a4. Front Oncol. 2021; 11:735447.
  • Wu C, Zhao A, Tan T, Wang Y, Shen Z. Overexpression of microRNA-620 facilitates the resistance of triple negative breast cancer cells to gemcitabine treatment by targeting DCTD. Exp Ther Med. 2019; 18(1):550-8.
  • Song A, Wu Y, Chu W, Yang X, Zhu Z, Yan E, et al. Involvement of miR-619-5p in resistance to cisplatin by regulating ATXN3 in oral squamous cell carcinoma. Int J Biol Sci. 2021; 17(2):430-47.

MikroRNA’lar ve Hedefleri Meme Kanseri İlaç Direncinde Önemli Bir Role Sahip Olabilir: Biyoinformatik Bir Araştırma

Yıl 2024, Cilt: 34 Sayı: 4, 458 - 464, 31.08.2024
https://doi.org/10.54005/geneltip.1431670

Öz

Amaç: MikroRNA’ların (miRNA’ların) hücre çoğalması, göç, istila ve metastaz gibi süreçlerde önemli roller oynayarak kanser gelişimine katkıda bulunduğu gösterilmiştir. Meme kanserinde (MK) en ciddi sorunlardan biri ilaç direncidir. Son araştırmalar, miRNA’ların ilaç direncinde rol oynayabileceğini öne sürmektedir. Çalışmamızda çeşitli veri setleri ve in silico yaklaşımlar kullanılarak MK/ilaç direnci/miRNA bağlantısı araştırılmıştır.
Gereç ve Yöntem: Geo veritabanından GSE73736 ve GSE71142 veri setleri (miRNA’lar için) ve GSE162187 veri seti (genler için) indirilerek R yazılımı “LIMMA” paketi aracılığıyla farklı şekilde ifade edilen miRNA’lar ve genler tespit edilmiştir. Farklı şekilde eksprese edilen miRNA’ların (DE-miRNA’lar) potansiyel hedef genleri, miRMap, miRTarbase ve miRNet araçları kullanılarak tahmin edildi. İfade düzeyi farklı olan genler (DE-genler) filtrelenmiş olup TCGA verileri ve miRNet’te ortak olan genler belirlenmiştir. Daha sonra GO ve KEGG ilişkilendirme analizleri Enrichr ve Funrich araçlarıyla yapılmıştır. Hub miRNA ve genlerin ekspresyon düzeyleri ve prognostik etkileri KMplot ve GEPIA2 web araçları kullanılarak araştırılmıştır.
Bulgular: MK’da önemli ölçüde ifadesi azalmış ve prognostik önemi olan 3 miRNA tespit edilmiştir. MK ile daha yakından bağlantılı olabileceği düşünülen miR-586’nın, MK’nın tedaviye direncinde rol oynayan 5 geni hedefleme poansiyeline sahip olduğu görülmüştür. GO ve KEGG analizlerinde, miR-586’nın olası hedef genlerinin MK ile yakından ilişkili olabileceği gösterilmiştir.
Sonuç: Bu çalışmada kapsamlı bir MK ilaç direnci-miRNA-gen ağları araştırılmıştır. Çalışmada biyoinformatik veriler kullanılarak MK’nın tedavi ve prognozuna yönelik yeni veriler ortaya çıkarılmıştır.

Etik Beyan

Bu çalışmanın, özgün bir çalışma olduğunu; çalışmanın hazırlık, veri toplama, analiz ve bilgilerin sunumu olmak üzere tüm aşamalarından bilimsel etik ilke ve kurallarına uygun davrandığımı; bu çalışma kapsamında elde edilmeyen tüm veri ve bilgiler için kaynak gösterdiğimi ve bu kaynaklara kaynakçada yer verdiğimi; kullanılan verilerde herhangi bir değişiklik yapmadığımı, çalışmanın Committee on Publication Ethics (COPE)' in tüm şartlarını ve koşullarını kabul ederek etik görev ve sorumluluklara riayet ettiğimi beyan ederim. Herhangi bir zamanda, çalışmayla ilgili yaptığım bu beyana aykırı bir durumun saptanması durumunda, ortaya çıkacak tüm ahlaki ve hukuki sonuçlara razı olduğumu bildiririm.

Kaynakça

  • Dong X, Bai X, Ni J, Zhang H, Duan W, Graham P, Li Y. Exosomes and breast cancer drug resistance. Cell Death Dis. 2020; 11(11):987.
  • Kaya M, Suer İ. The Effect of miR-34a-5p on Overexpressed AML Associated Genes. Journal of Istanbul Faculty of Medicine. 2023; 86(1):59-68.
  • Kaya M, Karataş ÖF. The relationship between larynx cancer and microRNAs. Van medical journal. 2020; 27(4):535-41.
  • Kaya M, Suer I, Ozgur E, Capik O, Karatas OF, Ozturk S, et al. miR-145-5p suppresses cell proliferation by targeting IGF1R and NRAS genes in multiple myeloma cells. Turkish Journal of Biochemistry. 2023; 48(5):563-9.
  • Capik O, Sanli F, Kurt A, Ceylan O, Suer I, Kaya M, et al. CASC11 promotes aggressiveness of prostate cancer cells through miR-145/IGF1R axis. Prostate Cancer and Prostatic Diseases. 2021; 24(3):891-902.
  • Cosentino G, Plantamura I, Tagliabue E, Iorio MV, Cataldo A. Breast Cancer Drug Resistance: Overcoming the Challenge by Capitalizing on MicroRNA and Tumor Microenvironment Interplay. Cancers (Basel). 2021; 13(15).
  • Chen L, Heikkinen L, Wang C, Yang Y, Sun H, Wong G. Trends in the development of miRNA bioinformatics tools. Brief Bioinform. 2019; 20(5):1836-52.
  • Luna Buitrago D, Lovering RC, Caporali A. Insights into Online microRNA Bioinformatics Tools. Noncoding RNA. 2023; 9(2).
  • Kaya M. A Bioinformatics Approach to Male Infertility, MicroRNAs, and Targeted Genes. Ahi Evran Medical Journal. 2023; 7(3):296-303.
  • Banwait JK, Bastola DR. Contribution of bioinformatics prediction in microRNA-based cancer therapeutics. Adv Drug Deliv Rev. 2015; 81:94-103.
  • Lánczky A, Győrffy B. Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation. J Med Internet Res. 2021; 23(7):e27633.
  • Vejnar CE, Zdobnov EM. MiRmap: comprehensive prediction of microRNA target repression strength. Nucleic Acids Res. 2012; 40(22):11673-83.
  • Tang Z, Kang B, Li C, Chen T, Zhang Z. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 2019; 47(W1):W556-w60.
  • Chang L, Zhou G, Soufan O, Xia J. miRNet 2.0: network-based visual analytics for miRNA functional analysis and systems biology. Nucleic Acids Res. 2020; 48(W1):W244-w51.
  • Huang HY, Lin YC, Cui S, Huang Y, Tang Y, Xu J, et al. miRTarBase update 2022: an informative resource for experimentally validated miRNA-target interactions. Nucleic Acids Res. 2022; 50(D1):D222-d30.
  • Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016; 44(W1):W90-7.
  • Fonseka P, Pathan M, Chitti SV, Kang T, Mathivanan S. FunRich enables enrichment analysis of OMICs datasets. J Mol Biol. 2021; 433(11):166747.
  • Wang X, Zhang H, Chen X. Drug resistance and combating drug resistance in cancer. Cancer Drug Resist. 2019; 2(2):141-60.
  • Zhong L, Li Y, Xiong L, Wang W, Wu M, Yuan T, et al. Small molecules in targeted cancer therapy: advances, challenges, and future perspectives. Signal Transduct Target Ther. 2021; 6(1):201.
  • Han YH, Wang Y, Lee SJ, Mao YY, Jiang P, Sun HN, et al. Identification of Hub Genes and Upstream Regulatory Factors Based on Cell Adhesion in Triple-negative Breast Cancer by Integrated Bioinformatical Analysis. Anticancer Res. 2023; 43(7):2951-64.
  • Huang X, Taeb S, Jahangiri S, Korpela E, Cadonic I, Yu N, et al. miR-620 promotes tumor radioresistance by targeting 15-hydroxyprostaglandin dehydrogenase (HPGD). Oncotarget. 2015; 6(26):22439-51.
  • Kim DH, Park S, Kim H, Choi YJ, Kim SY, Sung KJ, et al. Tumor-derived exosomal miR-619-5p promotes tumor angiogenesis and metastasis through the inhibition of RCAN1.4. Cancer Lett. 2020; 475:2-13.
  • Gao Y, Zhang S, Gao X. TP73-AS1 rs3737589 Polymorphism is Associated With the Clinical Stage of Colorectal Cancer. Evid Based Complement Alternat Med. 2023; 2023:3931875.
  • Zhang D, Liu X, Li Y, Sun L, Liu SS, Ma Y, et al. LINC01189-miR-586-ZEB1 feedback loop regulates breast cancer progression through Wnt/β-catenin signaling pathway. Mol Ther Nucleic Acids. 2021; 25:455-67.
  • Liu C, Yang J, Zhu F, Zhao Z, Gao L. Exosomal circ_0001190 Regulates the Progression of Gastric Cancer via miR-586/SOSTDC1 Axis. Biochem Genet. 2022; 60(6):1895-913.
  • Shao X, Liu Y, Huang H, Zhuang L, Luo T, Huang H, Ge X. Down-regulation of G protein-coupled receptor 137 by RNA interference inhibits cell growth of two hepatoma cell lines. Cell Biol Int. 2015; 39(4):418-26.
  • Ma B, Ma Q, Jin C, Wang X, Zhang G, Zhang H, et al. ADAM12 expression predicts clinical outcome in estrogen receptor-positive breast cancer. Int J Clin Exp Pathol. 2015; 8(10):13279-83.
  • Wang X, Wang Y, Gu J, Zhou D, He Z, Wang X, Ferrone S. ADAM12-L confers acquired 5-fluorouracil resistance in breast cancer cells. Sci Rep. 2017; 7(1):9687.
  • Hisada T, Kondo N, Wanifuchi-Endo Y, Osaga S, Fujita T, Asano T, et al. Co-expression effect of LLGL2 and SLC7A5 to predict prognosis in ERα-positive breast cancer. Sci Rep. 2022; 12(1):16515.
  • Törnroos R, Tina E, Göthlin Eremo A. SLC7A5 is linked to increased expression of genes related to proliferation and hypoxia in estrogen‑receptor‑positive breast cancer. Oncol Rep. 2022; 47(1).
  • Li Y, Wang W, Wu X, Ling S, Ma Y, Huang P. SLC7A5 serves as a prognostic factor of breast cancer and promotes cell proliferation through activating AKT/mTORC1 signaling pathway. Ann Transl Med. 2021; 9(10):892.
  • He TG, Xiao ZY, Xing YQ, Yang HJ, Qiu H, Chen JB. Tumor Suppressor miR-184 Enhances Chemosensitivity by Directly Inhibiting SLC7A5 in Retinoblastoma. Front Oncol. 2019; 9:1163.
  • Sato M, Harada-Shoji N, Toyohara T, Soga T, Itoh M, Miyashita M, et al. L-type amino acid transporter 1 is associated with chemoresistance in breast cancer via the promotion of amino acid metabolism. Sci Rep. 2021; 11(1):589.
  • Liu Z, Xie Y, Xiong Y, Liu S, Qiu C, Zhu Z, et al. TLR 7/8 agonist reverses oxaliplatin resistance in colorectal cancer via directing the myeloid-derived suppressor cells to tumoricidal M1-macrophages. Cancer Lett. 2020; 469:173-85.
  • Yi SA, Kim GW, Yoo J, Han JW, Kwon SH. HP1γ Sensitizes Cervical Cancer Cells to Cisplatin through the Suppression of UBE2L3. Int J Mol Sci. 2020; 21(17).
  • Zhang Y, Talmon G, Wang J. MicroRNA-587 antagonizes 5-FU-induced apoptosis and confers drug resistance by regulating PPP2R1B expression in colorectal cancer. Cell Death Dis. 2015; 6(8):e1845.
  • He X, Sun H, Jiang Q, Chai Y, Li X, Wang Z, et al. Hsa-miR-4277 Decelerates the Metabolism or Clearance of Sorafenib in HCC Cells and Enhances the Sensitivity of HCC Cells to Sorafenib by Targeting cyp3a4. Front Oncol. 2021; 11:735447.
  • Wu C, Zhao A, Tan T, Wang Y, Shen Z. Overexpression of microRNA-620 facilitates the resistance of triple negative breast cancer cells to gemcitabine treatment by targeting DCTD. Exp Ther Med. 2019; 18(1):550-8.
  • Song A, Wu Y, Chu W, Yang X, Zhu Z, Yan E, et al. Involvement of miR-619-5p in resistance to cisplatin by regulating ATXN3 in oral squamous cell carcinoma. Int J Biol Sci. 2021; 17(2):430-47.
Toplam 39 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Klinik Tıp Bilimleri (Diğer)
Bölüm Original Article
Yazarlar

Murat Kaya 0000-0003-2241-7088

Erken Görünüm Tarihi 30 Ağustos 2024
Yayımlanma Tarihi 31 Ağustos 2024
Gönderilme Tarihi 5 Şubat 2024
Kabul Tarihi 3 Haziran 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 34 Sayı: 4

Kaynak Göster

Vancouver Kaya M. MicroRNAs and Their Targets Could Have a Crucial Role in Breast Cancer Drug Resistance: A Bioinformatics Research. Genel Tıp Derg. 2024;34(4):458-64.