Meta-Analysis of Transcriptome Profiles of B16 Melanoma Cells After In Vivo Treatment With Dacarbazine



Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription or Fee Access

Abstract

BACKGROUND: Phenotypic plasticity and heterogeneity of melanoma cells arising from epigenetic regulation and the activity of transcription factors is pivotal in chemoresistance. Therefore, there it is necessary to explore the molecular mechanisms underlying resistance to alkylating agents, such as dacarbazine.

AIM: This study aimed to analyze transcriptome profiles of B16 melanoma cells after in vivo treatment with dacarbazine to identify key clusters of differentially expressed genes and regulatory transcription factors associated with chemoresistance.

METHODS: The study used a C57Bl/6 mouse model of B16 melanoma. The animals were randomly allocated into a control group and an experimental group, each with 12 mice. The experimental group was treated with dacarbazine at 50 mg/kg. Total RNA was extracted from tumor tissue, before high-throughput sequencing was performed. Data were subjected to bioinformatic processing for gene clustering and prediction of transcription factors for analyzing motifs in RNA sequences.

RESULTS: NGS sequencing and bioinformatic analysis yielded 670 differentially expressed genes, which were organized into 12 functional clusters related to DNA repair, apoptosis, and cell cycle. A comprehensive analysis identified key transcription factors (RELB, IRF5/7/4, NANOG, SOX2, LEF1, and NFKB2) that regulate signaling pathways essential for maintaining pluripotency (p = 0.000001), Wnt (p = 0.000753), TGF-β (p = 0.002631), Toll-like receptors (p = 0.000776), and NF-κB (p = 0.044609) associated with B16 melanoma cell resistance to the alkylating agent dacarbazine in vivo.

CONCLUSION: The findings of this study indicated that the epigenetic and transcription mechanisms of chemoresistance in melanoma cells involves maintenance of the stem cell phenotype, regulation of the immune response, and activation of the epithelial-mesenchymal transition.

About the authors

Ekaterina Z. Lapkina

Professor V.F. Voino-Yasenetsky Krasnoyarsk State Medical University

Email: e.z.lapkina@mail.ru
ORCID iD: 0000-0002-7226-9565
SPIN-code: 7656-8584

Assistant Professor, Depart. of Pathological Physiology named after prof. V.V. Ivanov

Russian Federation, Krasnoyarsk

Ivan S. Zinchenko

Professor V.F. Voino-Yasenetsky Krasnoyarsk State Medical University

Email: zinchenko.ivan.003@gmail.com
ORCID iD: 0000-0001-7085-6304
SPIN-code: 5810-5926

Senior Lab Assistant, Depart. of Pathological Physiology named after prof. V.V. Ivanov

Russian Federation, Krasnoyarsk

Evgeniya I. Bondar

Siberian Federal University; Krasnoyarsk Science Centre of the Siberian Branch of Russian Academy of Science

Email: bondar.zhenya.iv@gmail.com
ORCID iD: 0000-0003-3762-6974
SPIN-code: 6452-2085

Cand. Sci. (Biology), Junior Research Associate, Lab. of Genomic Research and Biotechnology, Senior Lecturer, Depart. of Genomics and Bioinformatics

Russian Federation, Krasnoyarsk; Krasnoyarsk

Tatiana G. Ruksha

Professor V.F. Voino-Yasenetsky Krasnoyarsk State Medical University

Author for correspondence.
Email: tatyana_ruksha@mail.ru
ORCID iD: 0000-0001-8142-4283
SPIN-code: 5412-2148

MD, Dr. Sci. (Medicine), Professor, Head, Depart. of Pathological Physiology named after prof. V.V. Ivanov

Russian Federation, Krasnoyarsk

References

  1. Artyukhov IP, Gavrilyuk DV, Dyhno YuA, Ruksha TG. Incidence of melanoma of the skin in the adult population of the Krasnoyarsk Territory. Sibirskoe medicinskoe obozrenie. 2013;6(84):37–42. EDN: RTJCPR
  2. Komina AV, Palkina NV, Aksenenko MB, et al. Semaphorin-5A downregulation is associated with enhanced migration and invasion of BRAF-positive melanoma cells under vemurafenib treatment in melanomas with heterogeneous BRAF status. Melanoma Res. 2019;29(5):544–548. doi: 10.1097/CMR.0000000000000621 EDN: JFNRFC
  3. De Craene B, Berx G. Regulatory networks defining EMT during cancer initiation and progression. Nat Rev Cancer. 2013;13:97–110. doi: 10.1038/nrc3447 EDN: RJOJMD
  4. Schatton T, Murphy GF, Frank NY, et al. Identification of cells initiating human melanomas. Nature. 2008;451:345–349. doi: 10.1038/nature06489
  5. Scheel C, Weinberg RA. Cancer stem cells and epithelial-mesenchymal transition: concepts and molecular links. Semin Cancer Biol. 2012;22:396–403. doi: 10.1016/j.semcancer.2012.04.001
  6. Hsiao JJ, Fisher DE. The roles of microphthalmia-associated transcription factor and pigmentation in melanoma. Arch Biochem Biophys. 2014;563C:28–34. doi: 10.1016/j.abb.2014.07.019
  7. Motorina AV, Palkina NV, Komina AV, et al. Genetic analysis of melanocortin 1 receptor red hair color variants in a Russian population of Eastern Siberia. Eur J Cancer Prev. 2018;27(2):192–196. doi: 10.1097/CEJ.0000000000000317 EDN: ATQNER
  8. Carreira S, Goodall J, Denat L, et al. Mitf regulation of Dia1 controls melanoma proliferation and invasiveness. Genes Dev. 2006;20:3426–3439. doi: 10.1101/gad.406406 EDN: MHRQVB
  9. Fu YC, Liang SB, Luo M, et al. Intratumoral heterogeneity and drug resistance in cancer. Cancer Cell Int. 2025;25:103. doi: 10.1186/s12935-025-03734-w EDN: MRNQGG
  10. Roesch A, Vultur A, Bogeski I, et al. Overcoming intrinsic multidrug resistance in melanoma by blocking the mitochondrial respiratory chain of slow-cycling JARID1B(high) cells. Cancer Cell. 2013;23(6):811–825. doi: 10.1016/j.ccr.2013.05.003
  11. Hossain SM, Eccles MR. Phenotype Switching and the Melanoma Microenvironment; Impact on Immunotherapy and Drug Resistance. Int J Mol Sci. 2023;24(2):1601. doi: 10.3390/ijms24021601 EDN: FPUICF
  12. Smith MP, Brunton H, Rowling EJ, et al. Inhibiting Drivers of Non-mutational Drug Tolerance Is a Salvage Strategy for Targeted Melanoma Therapy. Cancer Cell. 2016;29(3):270–284. doi: 10.1016/j.ccell.2016.02.003 EDN: WPIAOZ
  13. Van Allen EM, Wagle N, Sucker A, et al. The genetic landscape of clinical resistance to RAF inhibition in metastatic melanoma. Cancer Discov. 2014;4(1):94–109. doi: 10.1158/2159-8290.CD-13-0617 EDN: SQDRZZ
  14. Hartman ML, Sztiller-Sikorska M, Gajos-Michniewicz A, Czyz M. Dissecting Mechanisms of Melanoma Resistance to BRAF and MEK Inhibitors Revealed Genetic and Non-Genetic Patient- and Drug-Specific Alterations and Remarkable Phenotypic Plasticity. Cells. 2020;9(1):142. doi: 10.3390/cells9010142 EDN: IRVJPE
  15. Wang J, Huang SK, Marzese DM, et al. Epigenetic changes of EGFR have an important role in BRAF inhibitor-resistant cutaneous melanomas. J Invest Dermatol. 2015;135(2):532–541. doi: 10.1038/jid.2014.418 EDN: UUMQWT
  16. Karwaciak I, Sałkowska A, Karaś K, et al. Targeting SIRT2 Sensitizes Melanoma Cells to Cisplatin via an EGFR-Dependent Mechanism. Int J Mol Sci. 2021;22(9):5034. doi: 10.3390/ijms22095034 EDN: QMAOUP
  17. Konieczkowski DJ, Johannessen CM, Abudayyeh O, et al. A melanoma cell state distinction influences sensitivity to MAPK pathway inhibitors. Cancer Discov. 2014;4(7):816–827. doi: 10.1158/2159-8290.CD-13-0424
  18. National Research Council (US) Committee for the Update of the Guide for the Care and Use of Laboratory Animals. Guide for the Care and Use of Laboratory Animals. 8th edition. Washington (DC): National Academies Press (US); 2011. doi: 10.17226/12910
  19. Smoot ME, Ono K, Ruscheinski J, et al. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 2011;27(3):431–432. doi: 10.1093/bioinformatics/btq675 EDN: OAEBEB
  20. Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Research. 2003;13(11):2498–2504. doi: 10.1101/gr.1239303
  21. Grant CE, Bailey TL, Noble WS. FIMO: scanning for occurrences of a given motif. Bioinformatics. 2011;27(7):1017–1018. doi: 10.1093/bioinformatics/btr064
  22. Timothy LB. STREME: accurate and versatile sequence motif discovery. Bioinformatics. 2021;37(18):2834–2840. doi: 10.1093/bioinformatics/btab203 EDN: VKMHST
  23. Gupta S, Stamatoyannopoulos JA, Bailey TL, Noble WS. Quantifying similarity between motifs. Genome Biol. 2007;8(2):R24. doi: 10.1186/gb-2007-8-2-r24
  24. Bailey TL, Boden M, Buske FA, et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 2009;37(Web Server issue):W202–W208. doi: 10.1093/nar/gkp335
  25. Yanai H, Negishi H, Taniguchi T. The IRF family of transcription factors: Inception, impact and implications in oncogenesis. Oncoimmunology. 2012;1(8):1376–1386. doi: 10.4161/onci.22475
  26. Roberts BK, Collado G, Barnes BJ. Role of interferon regulatory factor 5 (IRF5) in tumor progression: Prognostic and therapeutic potential. Biochim Biophys Acta Rev Cancer. 2024;1879(1):189061. doi: 10.1016/j.bbcan.2023.189061 EDN: QHGDFG
  27. Roberts BK, Li DI, Somerville C. IRF5 suppresses metastasis through the regulation of tumor-derived extracellular vesicles and pre-metastatic niche formation. Sci Rep. 2024;14(1):15557. doi: 10.1038/s41598-024-66168-w EDN: PWYBCB
  28. Uccellini L, De Giorgi V, Zhao Y, et al. IRF5 gene polymorphisms in melanoma. J Transl Med. 2012;10:170. doi: 10.1186/1479-5876-10-170 EDN: NLKEML
  29. Bidwell BN, Slaney CY, Withana NP, et al. Silencing of Irf7 pathways in breast cancer cells promotes bone metastasis through immune escape. Nat Med. 2012;18(8):1224–1231. doi: 10.1038/nm.2830
  30. Yan H, Dai Y, Zhang X, et al. The transcription factor IRF4 determines the anti-tumor immunity of CD8+ T cells. iScience. 2023;26(11):108087. doi: 10.1016/j.isci.2023.108087 EDN: KFWLUK
  31. Lu J, Liang T, Li P, Yin Q. Regulatory effects of IRF4 on immune cells in the tumor microenvironment. Front Immunol. 2023;14:1086803. doi: 10.3389/fimmu.2023.1086803 EDN: HAEPDV
  32. Yokoyama S, Takahashi A, Kikuchi R, et al. SOX10 Regulates Melanoma Immunogenicity through an IRF4-IRF1 Axis. Cancer Res. 2021;81(24):6131–6141. doi: 10.1158/0008-5472.CAN-21-2078 EDN: YUSQWG
  33. Jia M, Sayed K, Kapetanaki MG, et al. LEF1 isoforms regulate cellular senescence and aging. Aging Cell. 2023;22(12):e14024. doi: 10.1111/acel.14024 EDN: CHWKCZ
  34. Ngai H, Barragan GA, Tian G, et al. LEF1 Drives a Central Memory Program and Supports Antitumor Activity of Natural Killer T Cells. Cancer Immunol Res. 2023;11(2):171–183. doi: 10.1158/2326-6066.CIR-22-0333 EDN: JXBHAW
  35. Santiago L, Daniels G, Wang D, et al. Wnt signaling pathway protein LEF1 in cancer, as a biomarker for prognosis and a target for treatment. Am J Cancer Res. 2017;7(6):1389–1406. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC5489786/
  36. Wu J, Yu X, Zhu H, et al. RelB is a potential molecular biomarker for immunotherapy in human pan-cancer. Front Mol Biosci. 2023;10:1178446. doi: 10.3389/fmolb.2023.1178446 EDN: HEKPDY
  37. Zeng F, Wang K, Huang R, et al. RELB: A novel prognostic marker for glioblastoma as identified by population-based analysis. Oncol Lett. 2019;18(1):386–394. doi: 10.3892/ol.2019.10296
  38. Wang M, Zhang Y, Xu Z, et al. RelB sustains endocrine resistant malignancy: an insight of noncanonical NF-κB pathway into breast Cancer progression. Cell Commun Signal. 2020;18(1):128. doi: 10.1186/s12964-020-00613-x EDN: BRQQZM
  39. Deng G, Zeng F, Su J, et al. BET inhibitor suppresses melanoma progression via the noncanonical NF-κB/SPP1 pathway. Theranostics. 2020;10(25):11428–11443. doi: 10.7150/thno.47432 EDN: KUMYZZ
  40. Zhang J, Ma F, Li Z, et al. NFKB2 mediates colorectal cancer cell immune escape and metastasis in a STAT2/PD-L1-dependent manner. MedComm (2020). 2024;5(5):e521. doi: 10.1002/mco2.521 EDN: XBGLJH
  41. Najafzadeh B, Asadzadeh Z, Motafakker Azad R, et al. The oncogenic potential of NANOG: An important cancer induction mediator. J Cell Physiol. 2021;236(4):2443–2458. doi: 10.1002/jcp.30063 EDN: VZXHTW
  42. Vasefifar P, Motafakkerazad R, Maleki LA, et al. Nanog, as a key cancer stem cell marker in tumor progression. Gene. 2022;827:146448. doi: 10.1016/j.gene.2022.146448 EDN: KFMVLM
  43. Grubelnik G, Boštjančič E, Pavlič A, et al. NANOG expression in human development and cancerogenesis. Exp Biol Med. 2020;245(5):456–464. doi: 10.1177/1535370220905560 EDN: HMQAWU

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

© 2025 Eco-Vector