Anaplastik Tiroid Karsinomu ve Papiller Tiroid Karsinomu Arasındaki Gen İfade Farklılıkları Biyobelirteç Olarak Kullanılabilir mi?

Abstract

AMAÇ: Bu çalışmanın amacı, anaplastik tiroid karsinomu (ATC) ve papiller tiroid karsinomu (PTC) arasındaki gen ekspresyon farklılıklarını karşılaştırarak, her iki tümör tipine özgü moleküler profilleri ve ilişkili biyolojik yolakları ortaya koymaktır.

GEREÇ ve YÖNTEM: GEO veri tabanından elde edilen GSE33630 mikrodizin veri seti kullanılarak 11 ATC, 49 PTC ve 45 normal tiroid dokusunun gen ekspresyon profilleri analiz edilmiştir. Diferansiyel gen ekspresyonu GEO2R ve limma tabanlı analizler ile belirlenmiş; volcano plot ve UMAP analizleri ile görselleştirilmiştir. Anlamlı genler DAVID ve KEGG yolak analizlerine tabi tutulmuştur (p<0,001).

BULGULAR: ATC örneklerinde hücre döngüsü, proliferasyon, invazyon ve diferansiyasyon kaybı ile ilişkili genlerin belirgin şekilde zenginleştiği; PTC örneklerinde ise diferansiye tiroid fonksiyonları ve erken tümörleşme ile ilişkili yolakların ön planda olduğu saptanmıştır. UMAP analizi, ATC, PTC ve normal dokular arasında belirgin transkriptomik ayrışmayı göstermiştir. ATC’de geniş çaplı gen ekspresyon düzensizlikleri gözlenirken, PTC daha sınırlı değişiklikler sergilemiştir. ATC’ye özgü olarak artmış bazı genlerin potansiyel biyobelirteç adayları olabileceği değerlendirilmiştir

SONUÇ: ATC ve PTC arasında belirgin moleküler ve biyolojik farklılıklar bulunmaktadır. ATC’nin agresif fenotipi, yaygın gen ekspresyon değişiklikleri ile ilişkilidir. Elde edilen bulgular, tiroid kanserlerinde tanısal ve prognostik biyobelirteçlerin geliştirilmesine katkı sağlayabilir.

Keywords

• Anaplastik tiroid karsinomu
• papiller tiroid karsinomu
• gen ekspresyonu
• DNA mikrodizi
• biyoinformatik analiz

Can Gene Expression Differences Between Anaplastic Thyroid Carcinoma (ATC) and Papillary Thyroid Carcinoma (PTC) be used as Biomarkers?

Abstract

OBJECTIVE: The aim of this study was to compare gene expression differences between anaplastic thyroid carcinoma (ATC) and papillary thyroid carcinoma (PTC) in order to identify tumor-specific molecular profiles and associated biological pathways.

MATERIALS and METHODS: Gene expression profiles of 11 ATC, 49 PTC, and 45 normal thyroid tissue samples were analyzed using the GSE33630 microarray dataset obtained from the GEO database. Differentially expressed genes were identified using GEO2R and limma-based analyses and visualized by volcano plot and UMAP analyses. Functional annotation and pathway enrichment were performed using DAVID and KEGG tools (p<0.001).

RESULTS: ATC samples showed significant e nrichment of g enes associated w ith cell cycle regulation, proliferation, invasion, a nd l oss of differentiation, whereas PTC samples were mainly associated with differentiated thyroid functions and early tumorigenesis-related pathways. UMAP analysis revealed a clear transcriptomic separation among ATC, PTC, and normal thyroid tissues. ATC exhibited extensive gene expression dysregulation, while PTC displayed more limited molecular alterations. Several genes uniquely upregulated in ATC were identified as potential biomarker candidates.

CONCLUSION: ATC and PTC exhibit distinct molecular and biological characteristics. The aggressive phenotype of ATC is associated with widespread transcriptional alterations. These findings may contribute t o t he development of diagnostic a nd prognostic biomarkers and targeted therapeutic strategies in thyroid cancer.

Keywords

• Anaplastic thyroid carcinoma
• papillary thyroid carcinoma
• gene expression
• DNA microarray
• bioinformatics analysis

References

1. 1. Özdemir A, Dilli UD, Özdemir D, Neselioğlu S, Erel Ö. Levels of thiol–disulfide in colorectal cancer. Anadolu Klin. 2019; 24: 137-141. doi: 10.21673/anadoluklin.469201
2. 2. Özdemir A, Erel Ö. Thiol-disulphide balance: could be a new marker for thyroid cancer? J Cancer Ther. 2018; 9: 598- 604. doi: 10.4236/jct.2018.98051
3. 3. He L, Zeng T, Pu L, Pan S, Xia W, Chen L. Thyroid hormones, autoantibodies, ultrasonography, and clinical parameters for predicting thyroid cancer. Int J Endocrinol. 2016; 2016: 8215834. doi: 10.1155/2016/8215834
4. 4. Chen DW, Lang BHH, McLeod DSA, Newbold K, Haymart MR. Thyroid cancer. Lancet. 2023; 401: 1531-1544. doi: 10.1016/S0140-6736(23)00020-X
5. 5. Kusumadewi RC, Pratiwi RS, Exhasna ATI, Azizaturrahmah GA, Ilham MAR. Understanding thyroid carcinoma: clinical manifestations, diagnosis, and management. J Biol Trop. 2024; 24: 645-656. doi: 10.29303/jbt.v24i4.7664
6. 6. Erdem H. Tiroid Papiller Karsinomlarında E-CADHERİN, VEGF, COX-2 Ekspresyonunun Prognostik Parametrelerle İlişkisi [Tez]. Erzurum: Atatürk Üniversitesi Tıp Fakültesi Patoloji Anabilim Dalı; 2008.
7. 7. Granjeaud S, Bertucci F, Jordan BR. Expression profiling. Bioessays. 1999; 21: 781-790. doi: 10.1002/(SICI)1521-1878(199909)21:9<781::AID-BIES10>3.0.CO;2-2
8. 8. Geiger A, Lester A, Kleiber J, Ørum H. PNA array technology in molecular diagnostics. Nucleosides Nucleotides. 1998; 17: 1717-1724. doi: 10.1080/07328319808004515

9. 9. Stoughton RB. Applications of DNA microarrays in biology. Annu Rev Biochem. 2005; 74: 53-82. doi: 10.1146/annurev.biochem.74.082803.133228
10. 10. Katagiri F, Glazebrook J. Overview of mRNA expression profiling using DNA microarrays. Curr Protoc Mol Biol. 2009; 85: 22.4.1-22.4.13. doi: 10.1002/0471142727. mb2204s85
11. 11. Dom G, Tarabichi M, Unger K, Thomas G, Oczko-Wojciechowska M, Bogdanova T, et al. Gene expression signature distinguishes normal tissues of sporadic and radiation-induced papillary thyroid carcinomas. Br J Cancer. 2012; 107: 994-1000. doi: 10.1038/bjc.2012.302
12. 12. Maenhaut C, Detours V, Dom G, Handkiewicz-Junak D, Oczko-Wojciechowska M, Jarzab B. Gene expression profiles for radiation-induced thyroid cancer. Clin Oncol (R Coll Radiol). 2011; 23: 282-288. doi: 10.1016/j. clon.2011.01.509
13. 13. Malone J, Unger J, Delange F, Lagasse R, Dumont JE. Thyroid consequences of Chernobyl accident in the countries of the European community. J Endocrinol Invest. 1991; 14: 701-717. doi: 10.1007/BF03348639
14. 14. Meyer D. Support vector machines: the interface to libsvm in package e1071[Internet]. R package vignette; 2001 [cited 2026 Apr 29]. Available from: https://cran.r-project. org/web/packages/e1071/vignettes/svmdoc.pdf
15. 15. Port M, Boltze C, Wang Y, Roper B, Meineke V, Abend M. Radiation-induced gene signature distinguishes post-Chernobyl from sporadic papillary thyroid cancers. Radiat Res. 2007; 168: 639-649. doi: 10.1667/RR1050.1
16. 16. Powell N, Jeremiah S, Morishita M, Dudley E, Bethel J, Bogdanova T, et al. Frequency of BRAF T1796A mutation in papillary thyroid carcinoma relates to age of patient at diagnosis and not to radiation exposure. J Pathol. 2005; 205: 558-564. doi: 10.1002/path.1738
17. 17. Dom G, Tarabichi M, Unger K, Thomas G, Oczko-Wojciechowska M, Bogdanova T, et al. Gene expression signature distinguishes normal tissues of sporadic and radiation-induced papillary thyroid carcinomas. Br J Cancer. 2012; 107: 994-1000. doi: 10.1038/bjc.2012.302 KEGG PATHWAY Database [homepage on the Internet]. Kyoto: Kyoto University;[updated 2026 Oct 18; cited 2026 Apr 29]. Available from: https://www.genome.jp/kegg/pathway.html
18. 18. DAVID Knowledgebase [homepage on the Internet]. Bethesda: National Institutes of Health;[updated 2026 Oct 18; cited 2026 Apr 29]. Available from: https://david. ncifcrf.gov/list.jsp
19. 19. Yano Y, Uematsu N, Yashiro T, Hara H, Ueno E, Miwa M, et al. Gene expression profiling identifies platelet-derived growth factor as a diagnostic molecular marker for papillary thyroid carcinoma. Clin Cancer Res. 2004; 10: 2035-2043. doi: 10.1158/1078-0432.CCR-03-0306
20. 20. Sheils O. Molecular classification and biomarker discovery in papillary thyroid carcinoma. Expert Rev Mol Diagn. 2005; 5: 927-946. doi: 10.1586/14737159.5.6.927