Meme Kanserinde Biyoinformatik Yaklaşım Kullanılarak TIG1 ile İlişkili Moleküler Hedeflerin Belirlenmesi

Yıl 2024, Cilt: 13 Sayı: 4, 1807 - 1817, 25.12.2024

Tuğcan Korak , Merve Gulsen Bal Albayrak , Gürler Akpınar , Murat Kasap

https://doi.org/10.37989/gumussagbil.1459020

Öz

Tazaroten-indüklü gen 1 (TIG1), α-tubulin tirosinasyon döngüsünü modüle etmek ve tümör büyümesini etkili bir şekilde inhibe etmekle ilişkilidir. Bu biyoinformatik çalışmada, TIG1’in inflamatuar meme kanserindeki (IBC) farklı olarak ifade edilen genlerinin (DEG’ler) moleküler ve immün alt tiplerle olan korelasyonlarını inceleyerek meme kanserinde yeni terapötik hedefler önermeyi amaçlamaktayız. GEO2R aracını kullanarak, GSE30543 veri setindeki DEG’ler analiz edildi ve özellikle baskılanmış TIG1 grupları SUM149 hücrelerinden kontrol örnekleriyle karşılaştırıldı. DEG’lerin fonksiyonel annotasyon analizi, SRplot aracılığıyla STRING verileri (|log2(FC)| >2 ve p<0,05) kullanılarak gerçekleştirildi. Cytoscape yazılımı, kesişen protein-protein etkileşim (PPI) ağını oluşturmak ve merkezi genleri belirlemek için kullanıldı. Ardından, moleküler ve immün alt tip analizleri, belirlenen merkezi genleri kullanılarak TISIDB'de gerçekleştirildi. IBC’de toplamda 19 yukarı regüle DEG ve 3 aşağı regüle DEG belirlendi ve bunlar yardımıyla STRING PPI ağı oluşturuldu. GO analizi, belirlenen DEG’lerin biyolojik işlevlerinin başlıca olarak hücre adezyonu ve göçünün düzenlenmesine odaklandığını ortaya koydu. KEGG yolak analizi ise DEG’lerin hücre adezyonu ile ilişkili sinyal yollarının düzenlenmesinde önemli bir rol oynadığını gösterdi. Merkezi genler STAT3, PXDNL, FN1, CTNNB1, CD44, TNF, TP53, MMP9, SRC ve INS olarak belirlendi. TISIDB analizi, tüm merkezi gen ekspresyonları ile meme kanserinin hem moleküler alt tipleri (TP53 hariç) hem de immün alt tipleri arasında anlamlı korelasyonlar olduğunu ortaya koydu (p<0,05). Bu çalışma ile TIG1 ile ilişkili DEG’lerden elde edilen merkezi genlerden yola çıkarak meme kanseri için hedefe yönelik terapötik yaklaşımlarda kullanılabilecek potansiyel prognostik biyobelirteçlerin belirlenmesini sağlandı.

Anahtar Kelimeler

Biyobelirteç, GEO, Meme kanseri, TIG1

Etik Beyan

Bu çalışma için etik kurul onayı gerekli değildir.

Identification of TIG1 Associated Molecular Targets for Breast Cancer Using Bioinformatic Approach

Öz

e-posta/e-mail: [e-mail]/[e-mail] Kabul Tarihi/Accepted:
Tazarotene-induced gene 1 (TIG1) is involved in modulating the α-tubulin modification and effectively inhibiting tumor growth. In this bioinformatics study, we aim to propose novel therapeutic targets in breast cancer by utilizing differentially expressed genes (DEGs) of TIG1 in inflammatory breast cancer (IBC) and examining their correlation with the molecular and immune subtypes. Using the GEO2R tool, we analyzed DEGs in the GSE30543 dataset, specifically comparing suppressed TIG1 groups with control samples from SUM149 cells. Functional annotation analysis of DEGs were explored using SRplot with data from STRING (|log2(FC)| >2 and p<0,05). Cytoscape software was used to construct intersected protein-protein interaction (PPI) network and define central genes. Subsequently, the molecular and immune subtype analysis were performed in TISIDB utilizing the identified hub genes. A total of 19 upregulated DEGs and 3 downregulated DEGs were identified in IBC and utilized to construct the STRING PPI network. GO analysis revealed that the biological functions of the identified DEGs primarily centered around the regulation of cell adhesion and migration. KEGG pathway analysis demonstrated their significant involvement in regulation of cell adhesion-related signaling pathways. Hub genes were identified as STAT3, PXDNL, FN1, CTNNB1, CD44, TNF, TP53, MMP9, SRC and INS. TISIDB analysis revealed significant correlations between all hub gene expressions and both the molecular subtypes (except for TP53) and immune subtypes of breast cancer (p<0,05). This study identified potential prognostic biomarkers for breast cancer based on the hub genes derived from TIG1-associated DEGs, indicating their potential use in targeted therapeutic approaches.

Anahtar Kelimeler

Biomarker, Breast cancer, GEO, TIG1

Etik Beyan

Ethical approval is not required for this study.

Kaynakça

• 1. Eraldemir, F.C. and Korak, T. (2021). “Paraoxonases, oxidative stress, and breast cancer”. In: V. R. PREEDY and V. B. PATEL (Eds.), Cancer (Second Edition) Oxidative Stress and Dietary Antioxidants (3–14). Cambridge: Academic Press. https://doi.org/10.1016/B978-0-12-819547-5.00001-8
• 2. Masuda, H, Baggerly, K.A, Wang, Y, Iwamoto, T, Brewer, T, Pusztai, L, Kai, K, Kogawa, T, Finetti, P, Birnbaum, D, Dirix, L, Woodward, W.A, Reuben, J.M, Krishnamurthy, S, Symmans, W.F, Laere, S.J.V, Bertucci, F, Hortobagyi, G.N. and Ueno, N.T. (2013). “Comparison of molecular subtype distribution in triple-negative inflammatory and non-inflammatory breast cancers”. Breast Cancer Research, 15 (6), R112. https://doi.org/10.1186/bcr3579
• 3. Funakoshi, Y, Wang, Y, Semba, T, Masuda, H, Hout, D, Ueno, N.T. and Wang, X. (2019). “Comparison of molecular profile in triple-negative inflammatory and non-inflammatory breast cancer not of mesenchymal stem-like subtype”. PLoS ONE, 14 (9), e0222336. https://doi.org/10.1371/journal.pone.0222336
• 4. Iwamoto, T, Bianchini, G, Qi, Y, Cristofanilli, M, Lucci, A, Woodward, W.A, Reuben, J.M, Matsuoka, J, Gong, Y, Krishnamurthy, S, Valero, V, Hortobagyi, G.N, Robertson, F, Symmans, W.F, Pusztai, L. and Ueno, N.T. (2011). “Different gene expressions are associated with the different molecular subtypes of inflammatory breast cancer”. Breast Cancer Research and Treatment, 125 (3), 785–795. https://doi.org/10.1007/s10549-010-1280-6
• 5. Aussel, C, Lafaurie, M, Masseyeff, R. and Stora, C. (1981). “In vivo regulation of ovarian activity by alpha -fetoprotein”. Steroids, 38 (2), 195–204. https://doi.org/10.1071/RD10250
• 6. Shyu, R.-Y, Wang, C.-H, Wu, C.-C, Chen, M.-L, Lee, M.-C, Wang, L.-K, Jiang, S.-Y. and Tsai, F.-M. (2016). “Tazarotene-Induced Gene 1 Enhanced Cervical Cell Autophagy through Transmembrane Protein 192”. Molecules and Cells, 39 (12), 877–887. https://doi.org/10.14348/molcells.2016.0161
• 7. Wang, X, Saso, H, Iwamoto, T, Xia, W, Gong, Y, Pusztai, L, Woodward, W.A, Reuben, J.M, Warner, S. L, Bearss, D.J, Hortobagyi, G.N, Hung, M.-C. and Ueno, N.T. (2013). “TIG1 Promotes the Development and Progression of Inflammatory Breast Cancer through Activation of Axl Kinase”. Cancer Research, 73 (21), 6516–6525. https://doi.org/10.1158/0008-5472.CAN-13-0967
• 8. Jing, C, El-Ghany, M.A, Beesley, C, Foster, C.S, Rudland, P.S, Smith, P. and Ke, Y. (2002). “Tazarotene-Induced Gene 1 (TIG1) Expression in Prostate Carcinomas and Its Relationship to Tumorigenicity”. JNCI: Journal of the National Cancer Institute, 94 (7), 482–490. https://doi.org/10.1093/jnci/94.7.482
• 9. Tsai, F.-M, Wu, C.-C, Shyu, R.-Y, Wang, C.-H. and Jiang, S.-Y. (2011). “Tazarotene-induced gene 1 inhibits prostaglandin E2-stimulated HCT116 colon cancer cell growth”. Journal of Biomedical Science, 18 (1), 88. https://doi.org/10.1186/1423-0127-18-88
• 10. Takai, N, Kawamata, N, Walsh, C.S, Gery, S, Desmond, J.C, Whittaker, S, Said, J.W, Popoviciu, L.M, Jones, P.A, Miyakawa, I. and Koeffler, H.P. (2005). “Discovery of Epigenetically Masked Tumor Suppressor Genes in Endometrial Cancer”. Molecular Cancer Research, 3 (5), 261–269. https://doi.org/10.1158/1541-7786.MCR-04-0110
• 11. Joseph, R. J. (1989). “The differential diagnosis of disc disease”. Problems in Veterinary Medicine, 1 (3), 366–380.
• 12. Alves, L.N.R, Meira, D.D, Merigueti, L.P, Casotti, M.C, Ventorim, D. do P, Almeida, J.F.F, Sousa, V.P. de, Sant’Ana, M.C, Cruz, R. G.C.da, Louro, L.S, Santana, G.M, Louro, T.E.S, Salazar, R.E, Silva, D.R.C.da, Zetum, A.S.S, Trabach, R.S.dos R, Errera, F.I.V, Paula, F.de, Santos, E.deV.W.dos, Carvalho, E.F.de. and Louro, I.D. (2023). “Biomarkers in Breast Cancer: An Old Story with a New End”. Genes, 14 (7), 1364. https://doi.org/10.3390/genes14071364
• 13. Wu, M, Li, Q. and Wang, H. (2021). “Identification of Novel Biomarkers Associated With the Prognosis and Potential Pathogenesis of Breast Cancer via Integrated Bioinformatics Analysis”. Technology in Cancer Research & Treatment, 20, 1533033821992081. https://doi.org/10.1177/1533033821992081
• 14. Tang, D, Chen, M, Huang, X, Zhang, G, Zeng, L, Zhang, G, Wu, S. and Wang, Y. (2023). “SRplot: A free online platform for data visualization and graphing”. PLOS ONE, 18 (11), e0294236. https://doi.org/10.1371/journal.pone.0294236
• 15. Ru, B, Wong, C.N, Tong, Y, Zhong, J.Y, Zhong, S.S.W, Wu, W.C, Chu, K.C, Wong, C.Y, Lau, C.Y, Chen, I, Chan, N.W. and Zhang, J. (2019). “TISIDB: an integrated repository portal for tumor-immune system interactions”. Bioinformatics, 35 (20), 4200-4202. https://doi.org/10.1093/bioinformatics/btz210
• 16. Albayrak, M.G.B. (2023). “Biomarkers from a clinical and application point of view”. In: D. ATİK (Ed.). International Research in Health Sciences (81–89). Ankara: Platanus Publishing.
• 17. Betts, Z, Ozkan, A.D, Yuksel, B, Alimudin, J, Aydin, D, Aksoy, O. and Yanar, S. (2023). “Investigation of the combined cytotoxicity induced by sodium butyrate and a flavonoid quercetin treatment on MCF-7 breast cancer cells”. Journal of Toxicology and Environmental Health, Part A, 86 (22), 833–845. https://doi.org/10.1080/15287394.2023.2254807
• 18. Saikia, M, Bhattacharyya, D.K. and Kalita, J.K. (2023). “Identification of Potential Biomarkers Using Integrative Approach: A Case Study of ESCC”. SN Computer Science, 4 (2), 114. https://doi.org/10.1007/s42979-022-01492-4
• 19. Majumder, A, Hosseinian, S, Stroud, M, Adhikari, E, Saller, J.J, Smith, M.A, Zhang, G, Agarwal, S, Creixell, M, Meyer, B.S, Kinose, F, Bowers, K, Fang, B, Stewart, P.A, Welsh, E.A, Boyle, T.A, Meyer, A.S, Koomen, J.M. and Haura, E.B. (2021). “Integrated Proteomics-Based Physical and Functional Mapping of AXL Kinase Signaling Pathways and Inhibitors Define Its Role in Cell Migration”. Molecular Cancer Research : MCR, 20 (4), 542–555. https://doi.org/10.1007/s42979-022-01492-4
• 20. Wang, D, Bi, L, Ran, J, Zhang, L, Xiao, N. and Li, X. (2021). “Gas6/Axl signaling pathway promotes proliferation, migration and invasion and inhibits apoptosis in A549 cells”. Experimental and Therapeutic Medicine, 22 (5), 1321. https://doi.org/10.3892/etm.2021.10756
• 21. Miricescu, D, Totan, A, Stanescu-Spinu, I.-I, Badoiu, S.C, Stefani, C. and Greabu, M. (2020). “PI3K/AKT/mTOR Signaling Pathway in Breast Cancer: From Molecular Landscape to Clinical Aspects”. International Journal of Molecular Sciences, 22 (1), 173. https://doi.org/10.3390/ijms22010173
• 22. Chalhoub, N. and Baker, S.J. (2009). “PTEN and the PI3-Kinase Pathway in Cancer”. Pathology: Mechanisms of Disease, 4 (1), 127–150.https://doi.org/10.1146/annurev.pathol.4.110807.092311
• 23. Kalavska, K, Cierna, Z, Karaba, M, Minarik, G, Benca, J, Sedlackova, T, Kolekova, D, Mrvova, I, Pindak, D, Mardiak, J. and Mego, M. (2021). “Prognostic role of matrix metalloproteinase 9 in early breast cancer”. Oncology Letters, 21 (2), 78. https://doi.org/10.3892/ol.2020.12339
• 24. So, J.Y, Smolarek, A K, Salerno, D.M, Maehr, H, Uskokovic, M, Liu, F. and Suh, N. (2013). “Targeting CD44-STAT3 Signaling by Gemini Vitamin D Analog Leads to Inhibition of Invasion in Basal-Like Breast Cancer”. PLoS ONE, 8 (1), e54020. https://doi.org/10.1371/journal.pone.0054020
• 25. Abdullah, C, Korkaya, H, Iizuka, S. and Courtneidge, S.A. (2017). “SRC Increases MYC mRNA Expression in Estrogen Receptor-Positive Breast Cancer via mRNA Stabilization and Inhibition of p53 Function”. Molecular and Cellular Biology, 38 (6). https://doi.org/10.1128/MCB.00463-17
• 26. Li, Y, Jiao, Y, Luo, Z, Li, Y. and Liu, Y. (2019). “High peroxidasin-like expression is a potential and independent prognostic biomarker in breast cancer”. Medicine, 98 (44), e17703. https://doi.org/10.1097/MD.0000000000017703
• 27. Xu, X, Zhang, M, Xu, F. and Jiang, S. (2020). “Wnt signaling in breast cancer: biological mechanisms, challenges and opportunities”. Molecular Cancer, 19 (1), 165. https://doi.org/10.1186/s12943-020-01276-5
• 28. Lero, M.W. and Shaw, L.M. (2021). “Diversity of insulin and IGF signaling in breast cancer: Implications for therapy”. Molecular and Cellular Endocrinology, 527, 111213. https://doi.org/10.1016/j.mce.2021.111213
• 29. Qian, Y, Daza, J, Itzel, T, Betge, J, Zhan, T, Marmé, F. and Teufel, A. (2021). “Prognostic Cancer Gene Expression Signatures: Current Status and Challenges”. Cells, 10 (3), 648. https://doi.org/10.3390/cells10030648
• 30. Mathias, C, Muzzi, J.C.D, Antunes, B.B, Gradia, D.F, Castro, M.A.A. and Oliveira, J.C.de. (2021). “Unraveling Immune-Related lncRNAs in Breast Cancer Molecular Subtypes”. Frontiers in Oncology, 11, 692170. https://doi.org/10.3389/fonc.2021.692170
• 31. Thorsson, V, Gibbs, D.L, Brown, S.D, Wolf, D, Bortone, D.S, Yang, T.-H.O, Porta-Pardo, E, Gao, G.F, Plaisier, C.L, Eddy, J.A, Ziv, E, Culhane, A.C, Paull, E.O, Sivakumar, I.K.A, Gentles, A.J, Malhotra, R, Farshidfar, F, Colaprico, A, Parker, J.S., Mose, L.E, Vo, N.S, Liu, J, Liu, Y, Rader, J, Dhankani, V, Reynolds, S.M, Bowlby, R, Califano, A, Cherniack, A.D, Anastassiou, D, Bedognetti, D, Mokrab, Y, Newman, A.M, Rao, A, Chen, K, Krasnitz, A, Hu, H, Malta, T.M, Noushmehr, H, Pedamallu, C.S, Bullman, S, Ojesina, A.I, Lamb, A, Zhou, W, Shen, H, Choueiri, T.K, Weinstein, J.N, Guinney, J, Saltz, J, Holt, R.A, Rabkin, C.S; Lazar, A.J, Serody, J.S, Demicco, E.G, Disis, M.L, Vincent, B.G, Shmulevich, I. (2018). “The Immune Landscape of Cancer”. Immunity, 48 (4), 812-830.e14. https://doi.org/10.1016/j.immuni. 2018.03.023