Abstract
Introduction. Cancer drug resistance is driven among others, by non-genetic mechanisms. These processes may involve transcription factors dysregulation, modifications in gene expression, as well as epigenetic mechanisms such as DNA methylation, histone modifications, and regulation by non-coding RNAs. To study these alterations, next-generation sequencing (NGS) was used which enables whole-genome analysis, transcriptome and epigenome profiling of tumor and normal tissues. NGS provides data on transcript profiles, mutations, genomic alterations, and post-transcriptional modifications, contributing to a deeper understanding of the mechanisms underlying drug resistance.
The aim. To identify differentially expressed genes in B16 melanoma cells in vivo followed by unveiling of transcription factors and signaling pathways associated with melanoma resistance to the alkylating agent dacarbazine.
Material and Methods. Melanoma model was established using female C57Bl6 mice (6–8 weeks old) by subcutaneous injection of B16 melanoma cells. On days 8, 10, and 12 after tumor transplantation the experimental group received intraperitoneal injections of dacarbazine (50 mg/kg body weight). On day 14, the tumor nodules were excised, and RNA extraction and analysis were performed. Next-generation sequencing (NGS) was performed with RNA library preparation using poly-A enrichment and fragmentation. Differentially expressed genes (DEGs) were identified using DESeq2. Gene Ontology (GO) enrichment analysis, KEGG pathway analysis, and protein-protein interaction (PPI) network analysis (STRING) were performed. Transcription factors were identified using the TRANSFAC database.
Results. In B16 melanoma cells treated with dacarbazine in vivo, 670 differentially expressed genes were identified most of them were involved in DNA damage response, apoptosis regulation, proliferation, and p53-dependent apoptosis. Protein-protein interaction network analysis highlighted 32 key genes, such as PIDD1, CCNG1, DDIAS, and EDA2R. Bioinformatics analysis revealed that the expression of these genes is regulated by transcription factors BCL6B, BEN, ZF5, the E2F family, FOXN4, GKLF, Sp1, Kaiso, and p53.
Conclusion. The results of a study on the effect of dacarbazine on B16 melanoma cells in vivo revealed a change in the expression of 670 genes, which in turn have binding sites for 73 transcription factors. This reflects their involvement in maintaining the drug resistance of tumour cells and opens up prospects for improved diagnosis, prognosis, and the development of therapeutic strategies.
Keywords
Authors and affiliations
https://elibrary.ru/asiyth
E.Z. Lapkina(1), I.S. Zinchenko(1), Е.I. Bondar(2, 3), T.G. Ruksha(1),
1-Professor V.F. Voino-Yasenetsky Krasnoyarsk State Medical University, Partizana Zheleznyaka str., 1, Krasnoyarsk, 660022,
Russian Federation; 2-Federal State Autonomous Educational Institution of Higher Education “Siberian Federal University”,
Svobodny Ave., 79, Krasnoyarsk, 660041, Russian Federation; 3-Federal Research Center “Krasnoyarsk Science Center”
of the Siberian Branch of the Russian Academy of Sciences, Akademgorodok St., 50, Krasnoyarsk, 660036, Russian Federation
References
Артюхов И.П., Гаврилюк Д.В., Дыхно Ю.А., Рукша Т.Г. Заболеваемость меланомой кожи взрослого населения Красноярского края. Сибирское медицинское обозрение. 2013; 6 (84): 37–42. [Artyukhov I.P., Gavrilyuk D.V., Dyhno Yu.A., Ruksha T.G. Zabolevaemost’ melanomoj kozhi vzroslogo naseleniya Krasnoyarskogo kraya. Sibirskoe medicinskoe obozrenie. 2013; 6 (84): 37–42 (In Russian)].
. Tian Y., Ma J., Wang H., Yi X., Wang H., Zhang H., Guo S., Yang Y., Zhang B., Du J., Shi Q., Gao T., Guo W., Li C. BCAT2 promotes melanoma progression by activating lipogenesis via the epigenetic regulation of FASN and ACLY expressions. Cell Mol Life Sci. 2023; 80: 315. DOI: 10.1007/s00018-023-04965-8.
. Zeng Z.M., Chen Y.Y., Wen X.C., Geng X.C., Zhu Y.X., Hao L.C., Dong Z.S., Yang J.F., Wang T.T., Zhang R.B., Sun Z.W., Zhang Y.H., Zheng K.B. Whole-transcriptome sequencing with ceRNA regulation network construction and verification in glioblastoma. Am. J. Transl Res. 2023; 15 (6): 4291–313.
. Vanni I., Pastorino L., Tanda E.T., Andreotti V., Dalmasso B., Solari N., Mascherini M., Cabiddu F., Guadagno A., Coco S., Allavena E., Bruno W., Pietra G., Croce M., Gangemi R., Piana M., Zoppoli G., Ferrando L., Spagnolo F., Queirolo P., Ghiorzo P. Whole-Exome Sequencing and cfDNA Analysis Uncover Genetic Determinants of Melanoma Therapy Response in a Real-World Setting. Int. J. Mol. Sci. 2023; 24 (5): 4302. DOI: 10.3390/ijms24054302.
. Baltanas F.C., Santos E. Advances in Molecular Research of Oncogenes. Int. J. Mol. Sci. 2023; 24 (8): 7222. DOI: 10.3390/ijms24087222.
. Kortylewski M., Jove R., Yu H. Targeting STAT3 affects melanoma on multiple fronts. Cancer Metastasis Rev. 2005; 24 (2): 315–27. DOI: 10.1007/s10555-005-1580-1.
. Liu Y.X., Chen Y.J., Xu B.W., Fu X.Q., Ding W.J., Li S.A., Wang X.Q., Wu J.Y., Wu Y., Dou X., Liu B., Yu Z.L. Inhibition of STAT3 signaling contributes to the anti-melanoma effects of chrysoeriol. Phytomedicine. 2023; 109: 154572. DOI: 10.1016/j.phymed.2022.154572.
. Malekan M., Ebrahimzadeh M.A., Sheida F. The role of Hypoxia-Inducible Factor-1alpha and its signaling in melanoma. Biomed Pharmacother. 2021; 141: 111873. DOI: 10.1016/j.biopha.2021.111873.
. San Juan L, Cagigal M.L., Fernandez-Flores A., Mayorga M., Gandarillas A. Protooncogene MYC drives human melanocyte melanogenesis and senescence. Cancer Gene Ther. 2022; 29 (8–9): 1160–7. DOI: 10.1038/s41417-021-00424-3.
10. Zacarias-Fluck M.F., Massó-Vallés D., Giuntini F., González-Larreategui Í., Kaur J., Casacuberta-Serra S., Jauset T., Martinez-Martin S., Martin-Fernández G., Serrano Del Pozo E., Foradada L., Grueso J., Nonell L., Beaulieu M.E., Whitfield J.R., Soucek L. Reducing MYC’s transcriptional footprint unveils a good prognostic gene signature in melanoma. Genes Dev. 2023; 37 (7–8): 303–20. DOI: 10.1101/gad.350078.122.
11. Pei H., Li Y., Liu M., Chen Y. Targeting Twist expression with small molecules. Medchemcomm. 2016; 8 (2): 268-275. DOI: 10.1039/c6md00561f.
12. Arumi-Planas M., Rodriguez-Baena F.J., Cabello-Torres F., Gracia F., Lopez-Blau C., Nieto M.A., Sanchez-Laorden B. Microenvironmental Snail1-induced immunosuppression promotes melanoma growth. Oncogene. 2023; 42 (36): 2659–72. DOI: 10.1038/s41388-023-02793-5.
13. 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.
. Andrews S. FastQC: a quality control tool for high throughput sequence data. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc. 2010.
. Kim D., Paggi J.M., Park C. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. 2019; 37: 907–15. DOI: 10.1038/s41587-019-0201-4.
. Anders S., Pyl P. T., Huber W. HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics (Oxford, England). 2015; 31 (2): 166–9. DOI: 10.1093/bioinformatics/btu638.
. Love M.I., Huber W., Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014; 15: 550. DOI: 10.1186/s13059-014-0550-8.
. Ge S.X., Jung D., Yao R. ShinyGO: a graphical gene-set enrichment tool for animals and plants. Bioinformatics. 2020; 36 (8): 2628–9. DOI: 10.1093/bioinformatics/btz931.
. Szklarczyk D., Franceschini A., Wyder S., Forslund K., Heller D., Huerta-Cepas J., Simonovic M., Roth A., Santos A., Tsafou K. P., Kuhn M., Bork P., Jensen L. J., and von Mering C., STRING v10: protein–protein interaction networks, integrated over the tree of life, Nucleic Acids Research. 2015; 43: 447–52. DOI: 10.1093/nar/gku1003.
. Thomas P.D., Ebert D., Muruganujan A., Mushayahama T., Albou L.P., Mi H. PANTHER: Making genome-scale phylogenetics accessible to all. Protein Sci. 2022; 31 (1): 8–22. DOI: 10.1002/pro.4218
. Kanehisa M., Furumichi M., Sato Y., Matsuura Y., Ishiguro-Watanabe M. KEGG: biological systems database as a model of the real world. Nucleic Acids Res. 2025; 53 (D1): 672–7. DOI: 10.1093/nar/gkae909.
. Matys V., Kel-Margoulis O.V., Fricke E. TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res. 2006; 34 (Database issue): 108–10. DOI: 10.1093/nar/gkj143.
. Smoot M. E., Ono K., Ruscheinski J., Wang P. L., Ideker T. Cytoscape 2.8: new features for data integration and network visualization, Bioinformatics. 2011; 27 (3): 431–2. DOI: 10.1093/bioinformatics/btq675.
. Shannon P., Markiel A., Ozier O., Baliga N. S., Wang J. T., Ramage D., Amin N., Schwikowski B., and Ideker T., Cytoscape: a software environment for integrated models of biomolecular interaction networks, Genome Research. 2003; 13 (11): 2498–504. DOI: 10.1101/gr.1239303.
. Komina A.V., Palkina N.V., Aksenenko M.B., Lavrentev S.N., Moshev A.V., Savchenko A.A., Averchuk A.S., Rybnikov Y.A, Ruksha T.G. 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): 544–8. DOI: 10.1097/CMR.0000000000000621.
. Flavahan W.A., Gaskell E., Bernstein B.E. Epigenetic plasticity and the hallmarks of cancer. Science. 2017; 357 (6348): eaal2380. DOI: 10.1126/science.aal2380.
. Reiter F, Wienerroither S, Stark A. Combinatorial function of transcription factors and cofactors. Curr Opin Genet Dev. 2017; 43: 73–81. DOI: 10.1016/j.gde.2016.12.007.
. Lu M., Muers M.R., Lu X. Introducing STRaNDs: shuttling transcriptional regulators that are non-DNA binding. Nat Rev Mol Cell Biol. 2016; 17: 523–32. DOI:10.1038/nrm.2016.41.
29. Sergeeva E, Ruksha T, Fefelova Y. Effects of obesity and calorie restriction on cancer development. Int J Mol Sci. 2023; 24 (11): 9601. DOI: 10.3390/ijms24119601.
. Gu Y., Li A., Sun H., Li X., Zha H., Zhao J., Xie J., Zeng Z., Zhou L. BCL6B suppresses proliferation and migration of colorectal carcinoma cells through inhibition of the PI3K/AKT signaling pathway. Int. J. Mol. Med. 2018; 41 (5): 2660–8. DOI: 10.3892/ijmm.2018.3451.
. Li X., Guo M., Yang L., Cheng Z., Yu X., Han Z., Liu F., Sun Q., Han X., Yu J., Liang P. BCL6B hypermethylation predicts metastasis and poor prognosis in early-stage hepatocellular carcinoma after thermal ablation. J. Cancer Res Ther. 2021; 17 (3): 644–51. DOI: 10.4103/jcrt.JCRT_1504_20.
. Katoh M., Katoh M. Characterization of human FOXN4 gene in silico. Int. J. Mol. Med. 2004; 14 (5): 949–53.
. Liu K., Zhang J., Xiao Y., Yang A., Song X., Li Y., Chen Y., Hughes T.R., Min J. Structural insights into DNA recognition by the BEN domain of the transcription factor BANP. J Biol Chem. 2023; 299 (6): 104734. DOI: 10.1016/j.jbc.2023.104734.
. Wassmann S., Wassmann K., Jung A., Velten M., Knuefermann P., Petoumenos V., Becher U., Werner C., Mueller C., Nickenig G. Induction of p53 by GKLF is essential for inhibition of proliferation of vascular smooth muscle cells. J. Mol. Cell. Cardiol. 2007; 43 (3): 301–7. DOI: 10.1016/j.yjmcc.2007.06.001.
. Dimova D. K., Dyson N. J. The E2F transcriptional network: old acquaintances with new faces. Oncogene.2005; 24: 2810–26.
. Fajas L., Landsberg R.L., Huss-Garcia Y., Sardet C., Lees J.A., Auwerx J. E2Fs regulate adipocyte differentiation. Dev Cell. 2002; 3 (1): 39–49. DOI:10.1016/s1534-5807(02)00190-9.
. Fang Z., Lin M., Chen S., Liu H., Zhu M., Hu Y., Han S., Wang Y., Sun L., Zhu F., Xu C., Gong C. E2F1 promotes cell cycle progression by stabilizing spindle fiber in colorectal cancer cells. Cell Mol Biol Lett. 2022; 27 (1): 90. DOI: 10.1186/s11658-022-00392-y.
. Li J., Bi W., Lu F., Pan B., Xiong M., Nasifu L., Nie Z., He B. Prognostic role of E2F1 gene expression in human cancer: a meta-analysis. BMC Cancer. 2023; 23 (1): 509. DOI: 10.1186/s12885-023-10865-8.
. Wang J., Dai W., Zhang M. E2F1 induced neuroblastoma cell migration and invasion via activation of CENPE/FOXM1 signaling pathway. Int J Neurosci. 2024; 134 (5): 530–42. DOI: 10.1080/00207454.2022.2126772.
. Shah Z.A., Nouroz F., Ejaz S., Tayyeb A. An Insight into the Role of E2F1 in Breast Cancer Progression, Drug Resistance, and Metastasis. Curr Mol Med. 2023; 23 (4): 365–76. DOI: 10.2174/1566524022666220308095834.
. Meng P., Bedolla R.G., Yun H., Fitzpatrick J.E., Kumar A.P., Ghosh R. Contextual role of E2F1 in suppression of melanoma cell motility and invasiveness. Mol. Carcinog. 2019; 58 (9): 1701–10. DOI: 10.1002/mc.23043.
. Hsu J., Sage J. Novel functions for the transcription factor E2F4 in development and disease. Cell Cycle. 2016; 15 (23): 3183–90. DOI: 10.1080/15384101.2016.1234551.
. Ma Y., Freeman S.N., Cress W.D. E2F4 deficiency promotes drug-induced apoptosis. Cancer Biol Ther. 2004; 3 (12): 1262–9. DOI: 10.4161/cbt.3.12.1239.
. Plesca D., Crosby M.E., Gupta D., Almasan A. E2F4 function in G2: maintaining G2-arrest to prevent mitotic entry with damaged DNA. Cell Cycle. 2007; 6 (10): 1147–52. DOI: 10.4161/cc.6.10.4259.
. Qi L., Ren Z., Li W. E2F4 transcription factor is a prognostic biomarker related to immune infiltration of head and neck squamous cell carcinoma. Sci Rep. 2022; 12 (1): 12132. DOI: 10.1038/s41598-022-16541-4.
. Jusino S., Rivera-Rivera Y., Chardón-Colón C., Ruiz-Justiz A.J., Vélez-Velázquez J., Isidro A., Cruz-Robles M.E., Bonilla-Claudio M., Armaiz-Pena G.N., Saavedra H.I. E2F3 drives the epithelial-to-mesenchymal transition, cell invasion, and metastasis in breast cancer. Exp. Biol. Med. (Maywood). 2021; 246 (19): 2057–71. DOI: 10.1177/15353702211035693.
. Humbert P.O., Verona R., Trimarchi J.M., Rogers C., Dandapani S., Lees J.A. E2f3 is critical for normal cellular proliferation. Genes Dev. 2000; 14 (6): 690–703.
. Wei L., Bai Y., Na L., Sun Y., Zhao C., Wang W. E2F3 induces DNA damage repair, stem-like properties and therapy resistance in breast cancer. Biochim Biophys Acta Mol Basis Dis. 2023; 1869 (8): 166816. DOI: 10.1016/j.bbadis.2023.166816.
. Trikha P., Sharma N., Pena C, Reyes A., Pécot T., Khurshid S., Rawahneh M., Moffitt J., Stephens J.A, Fernandez S.A., Ostrowski M.C., Leone G. E2f3 in tumor macrophages promotes lung metastasis. Oncogene. 2016; 35 (28): 3636–46. DOI: 10.1038/onc.2015.429.
. Wu L., Wan S., Li J., Xu Y., Lou X., Sun M., Wang S. Expression and prognostic value of E2F3 transcription factor in non-small cell lung cancer. Oncol Lett. 2021; 21 (5): 411. DOI: 10.3892/ol.2021.12672.
. Donaldson N.S., Pierre C.C., Anstey M.I., Robinson S.C., Weerawardane S.M., Daniel J.M. Kaiso represses the cell cycle gene cyclin D1 via sequence-specific and methyl-CpG-dependent mechanisms. PLoS One. 2012; 7 (11): e50398. DOI: 10.1371/journal.pone.0050398.
. Dai S.D., Wang Y., Miao Y., Zhao Y., Zhang Y., Jiang G.Y., Zhang P.X., Yang Z.Q., Wang E.H. Cytoplasmic Kaiso is associated with poor prognosis in non-small cell lung cancer. BMC Cancer. 2009; 9: 178. DOI: 10.1186/1471-2407-9-178.
. Bocian A., Kędzierawski P., Kopczyński J., Wabik O., Wawruszak A., Kiełbus M., Miziak P., Stepulak A. Kaiso Protein Expression Correlates with Overall Survival in TNBC Patients. J. Clin. Med. 2023; 12 (1): 370. DOI: 10.3390/jcm12010370.
. Tian W., Yuan H., Qin S., Liu W., Zhang B., Gu L., Zhou J., Deng D. Kaiso phosphorylation at threonine 606 leads to its accumulation in the cytoplasm, reducing its transcriptional repression of the tumour suppressor CDH1. Mol Oncol. 2022; 16 (17): 3192–209. DOI: 10.1002/1878-0261.13292.
. Engeland K. Cell cycle regulation: p53-p21-RB signaling. Cell Death Differ. 2022; 29 (5): 946–60. DOI: 10.1038/s41418-022-00988-z.
. Pandya P., Kublo L., Stewart-Ornstein J. p53 Promotes Cytokine Expression in Melanoma to Regulate Drug Resistance and Migration. Cells. 2022; 11 (3): 405. DOI: 10.3390/cells11030405.
. Hientz K., Mohr A., Bhakta-Guha D., Efferth, T. The role of p53 in cancer drug resistance and targeted chemotherapy. Oncotarget. 2017; 8 (5): 8921–46. DOI: 10.18632/oncotarget.13475.