Genomics & Informatics

Korea Genome Organization (한국유전체학회)
권호 표지
DOI QR코드

DOI QR Code

Analysis of H3K4me3-ChIP-Seq and RNA-Seq data to understand the putative role of miRNAs and their target genes in breast cancer cell lines

  • Kotipalli, Aneesh (HPC-Medical and Bioinformatics Applications Group, Centre for Development of Advanced Computing) ;
  • Banerjee, Ruma (HPC-Medical and Bioinformatics Applications Group, Centre for Development of Advanced Computing) ;
  • Kasibhatla, Sunitha Manjari (HPC-Medical and Bioinformatics Applications Group, Centre for Development of Advanced Computing) ;
  • Joshi, Rajendra (HPC-Medical and Bioinformatics Applications Group, Centre for Development of Advanced Computing)
  • Received : 2021.04.07
  • Accepted : 2021.05.25
  • Published : 2021.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Breast cancer is one of the leading causes of cancer in women all over the world and accounts for ~25% of newly observed cancers in women. Epigenetic modifications influence differential expression of genes through non-coding RNA and play a crucial role in cancer regulation. In the present study, epigenetic regulation of gene expression by in-silico analysis of histone modifications using chromatin immunoprecipitation sequencing (ChIP-Seq) has been carried out. Histone modification data of H3K4me3 from one normal-like and four breast cancer cell lines were used to predict miRNA expression at the promoter level. Predicted miRNA promoters (based on ChIP-Seq) were used as a probe to identify gene targets. Five triple-negative breast cancer (TNBC)-specific miRNAs (miR153-1, miR4767, miR4487, miR6720, and miR-LET7I) were identified and corresponding 13 gene targets were predicted. Eight miRNA promoter peaks were predicted to be differentially expressed in at least three breast cancer cell lines (miR4512, miR6791, miR330, miR3180-3, miR6080, miR5787, miR6733, and miR3613). A total of 44 gene targets were identified based on the 3'-untranslated regions of downregulated mRNA genes that contain putative binding targets to these eight miRNAs. These include 17 and 15 genes in luminal-A type and TNBC respectively, that have been reported to be associated with breast cancer regulation. Of the remaining 12 genes, seven (A4GALT, C2ORF74, HRCT1, ZC4H2, ZNF512, ZNF655, and ZNF608) show similar relative expression profiles in large patient samples and other breast cancer cell lines thereby giving insight into predicted role of H3K4me3 mediated gene regulation via the miRNA-mRNA axis.

Keywords

Acknowledgement

The authors thank the BRAF facility of C-DAC for HPC infrastructure. The authors would also like to deeply thank Dr. Janaki C.H. for her scientific review and critical comments. This research work is funded by the National Supercomputing Mission (NSM) of the Government of India. The authors thank anonymous reviewers for their valuable suggestions and constructive criticism in improving the manuscript.

References

  1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2015. CA Cancer J Clin 2015;65:5-29. https://doi.org/10.3322/caac.21254
  2. Carey LA, Perou CM, Livasy CA, Dressler LG, Cowan D, Conway K, et al. Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study. JAMA 2006;295:2492-2502. https://doi.org/10.1001/jama.295.21.2492
  3. Collignon J, Lousberg L, Schroeder H, Jerusalem G. Triple-negative breast cancer: treatment challenges and solutions. Breast Cancer (Dove Med Press) 2016;8:93-107. https://doi.org/10.1007/BF02967486
  4. Luan QX, Zhang BG, Li XJ, Guo MY. MiR-129-5p is downregulated in breast cancer cells partly due to promoter H3K27m3 modification and regulates epithelial-mesenchymal transition and multi-drug resistance. Eur Rev Med Pharmacol Sci 2016; 20:4257-4265.
  5. Yan H, Tian S, Slager SL, Sun Z. ChIP-seq in studying epigenetic mechanisms of disease and promoting precision medicine: progresses and future directions. Epigenomics 2016;8:1239-1258. https://doi.org/10.2217/epi-2016-0053
  6. Furey TS. ChIP-seq and beyond: new and improved methodologies to detect and characterize protein-DNA interactions. Nat Rev Genet 2012;13:840-852. https://doi.org/10.1038/nrg3306
  7. Dawson MA, Kouzarides T. Cancer epigenetics: from mechanism to therapy. Cell 2012;150:12-27. https://doi.org/10.1016/j.cell.2012.06.013
  8. Zhou VW, Goren A, Bernstein BE. Charting histone modifications and the functional organization of mammalian genomes. Nat Rev Genet 2011;12:7-18. https://doi.org/10.1038/nrg2905
  9. Prabhu KS, Raza A, Karedath T, Raza SS, Fathima H, Ahmed EI, et al. Non-coding RNAs as regulators and markers for targeting of breast cancer and cancer stem cells. Cancers (Basel) 2020;12:351. https://doi.org/10.3390/cancers12020351
  10. Bartel DP. MicroRNAs: target recognition and regulatory functions. Cell 2009;136:215-233. https://doi.org/10.1016/j.cell.2009.01.002
  11. Place RF, Li LC, Pookot D, Noonan EJ, Dahiya R. MicroRNA-373 induces expression of genes with complementary promoter sequences. Proc Natl Acad Sci U S A 2008;105:1608-1613. https://doi.org/10.1073/pnas.0707594105
  12. Brennecke J, Stark A, Russell RB, Cohen SM. Principles of microRNA-target recognition. PLoS Biol 2005;3:e85. https://doi.org/10.1371/journal.pbio.0030085
  13. Iorio MV, Ferracin M, Liu CG, Veronese A, Spizzo R, Sabbioni S, et al. MicroRNA gene expression deregulation in human breast cancer. Cancer Res 2005;65:7065-7070. https://doi.org/10.1158/0008-5472.CAN-05-1783
  14. Khalife H, Skafi N, Fayyad-Kazan M, Badran B. MicroRNAs in breast cancer: new maestros defining the melody. Cancer Genet 2020;246-247:18-40. https://doi.org/10.1016/j.cancergen.2020.08.005
  15. Ramos J, Felty Q, Roy D. Integrated Chip-Seq and RNA-Seq data analysis coupled with bioinformatics approaches to investigate regulatory landscape of transcription modulators in breast cancer cells. Methods Mol Biol 2020;2102:35-59. https://doi.org/10.1007/978-1-0716-0223-2_3
  16. Franco HL, Nagari A, Malladi VS, Li W, Xi Y, Richardson D, et al. Enhancer transcription reveals subtype-specific gene expression programs controlling breast cancer pathogenesis. Genome Res 2018;28:159-170. https://doi.org/10.1101/gr.226019.117
  17. Andrews S. FASTQC: a quality control tool for high throughput sequence data. Cambridge: Babraham Institute, 2010. Accessed 2021 May 30. Available from: https://www.bioinformatics.babraham.ac.uk/projects/fastqc/.
  18. Li H, Durbin R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 2010;26:589-595. https://doi.org/10.1093/bioinformatics/btp698
  19. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 2009;25:2078-2079. https://doi.org/10.1093/bioinformatics/btp352
  20. Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol 2008;9:R137. https://doi.org/10.1186/gb-2008-9-9-r137
  21. Bailey T, Krajewski P, Ladunga I, Lefebvre C, Li Q, Liu T, et al. Practical guidelines for the comprehensive analysis of ChIP-seq data. PLoS Comput Biol 2013;9:e1003326. https://doi.org/10.1371/journal.pcbi.1003326
  22. Feng J, Liu T, Qin B, Zhang Y, Liu XS. Identifying ChIP-seq enrichment using MACS. Nat Protoc 2012;7:1728-1740. https://doi.org/10.1038/nprot.2012.101
  23. Li Q, Brown JB, Huang H, Bickel PJ. Measuring reproducibility of high-throughput experiments. Ann Appl Stat 2011;5:1752-1779. https://doi.org/10.1214/11-AOAS466
  24. Landt SG, Marinov GK, Kundaje A, Kheradpour P, Pauli F, Batzoglou S, et al. ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res 2012;22:1813-1831. https://doi.org/10.1101/gr.136184.111
  25. Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell 2010;38:576-589. https://doi.org/10.1016/j.molcel.2010.05.004
  26. Shin H, Liu T, Manrai AK, Liu XS. CEAS: cis-regulatory element annotation system. Bioinformatics 2009;25:2605-2606. https://doi.org/10.1093/bioinformatics/btp479
  27. Kim D, Paggi JM, Park C, Bennett C, Salzberg SL. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol 2019;37:907-915. https://doi.org/10.1038/s41587-019-0201-4
  28. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 2014;30:923-930. https://doi.org/10.1093/bioinformatics/btt656
  29. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014;15:550. https://doi.org/10.1186/s13059-014-0550-8
  30. Hollbacher B, Balazs K, Heinig M, Uhlenhaut NH. Seq-ing answers: current data integration approaches to uncover mechanisms of transcriptional regulation. Comput Struct Biotechnol J 2020;18:1330-1341. https://doi.org/10.1016/j.csbj.2020.05.018
  31. Rehmsmeier M, Steffen P, Hochsmann M, Giegerich R. Fast and effective prediction of microRNA/target duplexes. RNA 2004; 10:1507-1517. https://doi.org/10.1261/rna.5248604
  32. Augustin R, Endres K, Reinhardt S, Kuhn PH, Lichtenthaler SF, Hansen J, et al. Computational identification and experimental validation of microRNAs binding to the Alzheimer-related gene ADAM10. BMC Med Genet 2012;13:35.
  33. Chandrashekar DS, Bashel B, Balasubramanya SA, Creighton CJ, Ponce-Rodriguez I, Chakravarthi B, et al. UALCAN: a portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia 2017;19:649-658. https://doi.org/10.1016/j.neo.2017.05.002
  34. Shi D, Li Y, Fan L, Zhao Q, Tan B, Cui G. Upregulation of miR153 inhibits triple-negative breast cancer progression by targeting ZEB2-mediated EMT and contributes to better prognosis. Onco Targets Ther 2019;12:9611-9625. https://doi.org/10.2147/OTT.S223598
  35. Hsu PC, Ho JY, Yu CP. RERG involvement in the RAS pathway and ER-dependent transcription in breast cancer. J Clin Oncol 2019;37(15 Suppl):e14638. https://doi.org/10.1200/JCO.2019.37.15_suppl.e14638
  36. Nikulin SV, Raigorodskaya MP, Poloznikov AA, Zakharova GS, Schumacher U, Wicklein D, et al. In vitro model for studying of the role of IGFBP6 gene in breast cancer metastasizing. Bull Exp Biol Med 2018;164:688-692. https://doi.org/10.1007/s10517-018-4060-7
  37. Bornstein C, Brosh R, Molchadsky A, Madar S, Kogan-Sakin I, Goldstein I, et al. SPATA18, a spermatogenesis-associated gene, is a novel transcriptional target of p53 and p63. Mol Cell Biol 2011;31:1679-1689. https://doi.org/10.1128/MCB.01072-10
  38. Colavito SA. AXL as a target in breast cancer therapy. J Oncol 2020;2020:5291952. https://doi.org/10.1155/2020/5291952
  39. Hornsveld M, Tenhagen M, van de Ven RA, Smits AM, van Triest MH, van Amersfoort M, et al. Restraining FOXO3-dependent transcriptional BMF activation underpins tumour growth and metastasis of E-cadherin-negative breast cancer. Cell Death Differ 2016;23:1483-1492. https://doi.org/10.1038/cdd.2016.33
  40. Lee YK, Lee SY, Park JR, Kim RJ, Kim SR, Roh KJ, et al. Dysadherin expression promotes the motility and survival of human breast cancer cells by AKT activation. Cancer Sci 2012;103: 1280-1289. https://doi.org/10.1111/j.1349-7006.2012.02302.x
  41. Bai L, Deng X, Li Q, Wang M, An W, Deli A, et al. Down-regulation of the cavin family proteins in breast cancer. J Cell Biochem 2012;113:322-328. https://doi.org/10.1002/jcb.23358
  42. McDonald L, Ferrari N, Terry A, Bell M, Mohammed ZM, Orange C, et al. RUNX2 correlates with subtype-specific breast cancer in a human tissue microarray, and ectopic expression of Runx2 perturbs differentiation in the mouse mammary gland. Dis Model Mech 2014;7:525-534. https://doi.org/10.1242/dmm.015040
  43. Cao Q, Chen X, Wu X, Liao R, Huang P, Tan Y, et al. Inhibition of UGT8 suppresses basal-like breast cancer progression by attenuating sulfatide-alphaVbeta5 axis. J Exp Med 2018;215:1679-1692. https://doi.org/10.1084/jem.20172048
  44. Suman S, Basak T, Gupta P, Mishra S, Kumar V, Sengupta S, et al. Quantitative proteomics revealed novel proteins associated with molecular subtypes of breast cancer. J Proteomics 2016;148:183-193. https://doi.org/10.1016/j.jprot.2016.07.033
  45. Hollmen M, Karaman S, Schwager S, Lisibach A, Christiansen AJ, Maksimow M, et al. G-CSF regulates macrophage phenotype and associates with poor overall survival in human triple-negative breast cancer. Oncoimmunology 2016;5:e1115177. https://doi.org/10.1080/2162402X.2015.1115177
  46. Zhao YR, Wang JL, Xu C, Li YM, Sun B, Yang LY. HEG1 indicates poor prognosis and promotes hepatocellular carcinoma invasion, metastasis, and EMT by activating Wnt/beta-catenin signaling. Clin Sci (Lond) 2019;133:1645-1662. https://doi.org/10.1042/CS20190225
  47. Lin M, Zhang Z, Gao M, Yu H, Sheng H, Huang J. MicroRNA-193a-3p suppresses the colorectal cancer cell proliferation and progression through downregulating the PLAU expression. Cancer Manag Res 2019;11:5353-5363. https://doi.org/10.2147/CMAR.S208233
  48. Hung CM, Liu LC, Ho CT, Lin YC, Way TD. Pterostilbene enhances TRAIL-induced apoptosis through the induction of death receptors and downregulation of cell survival proteins in TRAIL-resistance triple negative breast cancer cells. J Agric Food Chem2017;65:11179-11191. https://doi.org/10.1021/acs.jafc.7b02358
  49. Gianni M, Terao M, Kurosaki M, Paroni G, Brunelli L, Pastorelli R, et al. S100A3 a partner protein regulating the stability/activity of RARalpha and PML-RARalpha in cellular models of breast/lung cancer and acute myeloid leukemia. Oncogene 2019;38:2482-2500. https://doi.org/10.1038/s41388-018-0599-z
  50. Saville B, Poukka H, Wormke M, Janne OA, Palvimo JJ, Stoner M, et al. Cooperative coactivation of estrogen receptor alpha in ZR75 human breast cancer cells by SNURF and TATA-binding protein. J Biol Chem 2002;277:2485-2497. https://doi.org/10.1074/jbc.M109021200
  51. Garcia E, Machesky LM, Jones GE, Anton IM. WIP is necessary for matrix invasion by breast cancer cells. Eur J Cell Biol 2014;93:413-423. https://doi.org/10.1016/j.ejcb.2014.07.008
  52. He W, Wang Q, Xu J, Xu X, Padilla MT, Ren G, et al. Attenuation of TNFSF10/TRAIL-induced apoptosis by an autophagic sur vival pathway involving TRAF2- and RIPK1/RIP1-mediated MAPK8/JNK activation. Autophagy 2012;8:1811-1821. https://doi.org/10.4161/auto.22145
  53. Guo L, Zhang K, Bing Z. Application of a coexpression network for the analysis of aggressive and nonaggressive breast cancer cell lines to predict the clinical outcome of patients. Mol Med Rep 2017;16:7967-7978. https://doi.org/10.3892/mmr.2017.7608
  54. Kumar D, Hassan MK, Pattanaik N, Mohapatra N, Dixit M. IQGAP2 acts as a tumor suppressor in breast cancer and its reduced expression promotes cancer growth and metastasis by MEK/ERK signalling pathways. Preprint at https://doi.org/10.1101/651034 (2019).
  55. Hou L, Chen M, Zhao X, Li J, Deng S, Hu J, et al. FAT4 functions as a tumor suppressor in triple-negative breast cancer. Tumour Biol 2016;37:16337-16343. https://doi.org/10.1007/s13277-016-5421-3
  56. Chowdhury UR, Samant RS, Fodstad O, Shevde LA. Emerging role of nuclear protein 1 (NUPR1) in cancer biology. Cancer Metastasis Rev 2009;28:225-232. https://doi.org/10.1007/s10555-009-9183-x
  57. Li C, Cui J, Zou L, Zhu L, Wei W. Bioinformatics analysis of the expression of HOXC13 and its role in the prognosis of breast cancer. Oncol Lett 2020;19:899-907.
  58. Dong J, Lv Z, Chen Q, Wang X, Li F. PRRX1 drives tamoxifen therapy resistance through induction of epithelial-mesenchymal transition in MCF-7 breast cancer cells. Int J Clin Exp Pathol 2018;11:2629-2635.
  59. Hou J, Wang Z, Xu H, Yang L, Yu X, Yang Z, et al. Stanniocalicin 2 suppresses breast cancer cell migration and invasion via the PKC/claudin-1-mediated signaling. PLoS One 2015;10:e0122179. https://doi.org/10.1371/journal.pone.0122179
  60. Ji XW, Zhou TY, Lu Y, Wei MJ, Lu W, Cho WC. Breast cancer treatment and sulfotransferase. Expert Opin Ther Targets 2015; 19:821-834. https://doi.org/10.1517/14728222.2015.1014803
  61. Li Z, Guo X, Wu Y, Li S, Yan J, Peng L, et al. Methylation profiling of 48 candidate genes in tumor and matched normal tissues from breast cancer patients. Breast Cancer Res Treat 2015;149:767-779. https://doi.org/10.1007/s10549-015-3276-8
  62. Debily MA, Camarca A, Ciullo M, Mayer C, El Marhomy S, Ba I, et al. Expression and molecular characterization of alternative transcripts of the ARHGEF5/TIM oncogene specific for human breast cancer. Hum Mol Genet 2004;13:323-334. https://doi.org/10.1093/hmg/ddh024
  63. Pangeni RP, Channathodiyil P, Huen DS, Eagles LW, Johal BK, Pasha D, et al. The GALNT9, BNC1 and CCDC8 genes are frequently epigenetically dysregulated in breast tumours that metastasise to the brain. Clin Epigenetics 2015;7:57. https://doi.org/10.1186/s13148-015-0089-x
  64. Bademler S, Ucuncu MZ, Tilgen Vatansever C, Serilmez M, Ertin H, Karanlik H. Diagnostic and prognostic significance of carboxypeptidase A4 (CPA4) in breast cancer. Biomolecules 2019;9:103. https://doi.org/10.3390/biom9030103
  65. Sun C, Gu Y, Chen G, Du Y. Bioinformatics analysis of stromal molecular signatures associated with breast and prostate cancer. J Comput Biol 2019;26:1130-1139. https://doi.org/10.1089/cmb.2019.0045
  66. Sanlioglu AD, Korcum AF, Pestereli E, Erdogan G, Karaveli S, Savas B, et al. TRAIL death receptor-4 expression positively correlates with the tumor grade in breast cancer patients with invasive ductal carcinoma. Int J Radiat Oncol Biol Phys 2007;69:716-723. https://doi.org/10.1016/j.ijrobp.2007.03.057
  67. Zhang T, Jing L, Li H, Ding L, Ai D, Lyu J, et al. MicroRNA-4530 promotes angiogenesis by targeting VASH1 in breast carcinoma cells. Oncol Lett 2017;14:111-118. https://doi.org/10.3892/ol.2017.6102
  68. Svoboda M, Sana J, Redova M, Navratil J, Palacova M, Fabian P, et al. MiR-34b is associated with clinical outcome in triple-negative breast cancer patients. Diagn Pathol 2012;7:31. https://doi.org/10.1186/1746-1596-7-31
  69. Xie Y, Du J, Liu Z, Zhang D, Yao X, Yang Y. MiR-6875-3p promotes the proliferation, invasion and metastasis of hepatocellular carcinoma via BTG2/FAK/Akt pathway. J Exp Clin Cancer Res 2019;38:7. https://doi.org/10.1186/s13046-018-1020-z
  70. Zhang KJ, Hu Y, Luo N, Li X, Chen FY, Yuan JQ, et al. miR5745p attenuates proliferation, migration and EMT in triplenegative breast cancer cells by targeting BCL11A and SOX2 to inhibit the SKIL/TAZ/CTGF axis. Int J Oncol 2020;56:1240-1251.
  71. Mesci A, Huang X, Taeb S, Jahangiri S, Kim Y, Fokas E, et al. Targeting of CCBE1 by miR-330-3p in human breast cancer promotes metastasis. Br J Cancer 2017;116:1350-1357. https://doi.org/10.1038/bjc.2017.105
  72. Liu Y, Yang Y, Du J, Lin D, Li F. MiR-3613-3p from carcinoma-associated fibroblasts exosomes promoted breast cancer cell proliferation and metastasis by regulating SOCS2 expression. IUBMB Life 2020;72:1705-1714. https://doi.org/10.1002/iub.2292