Integrated bioinformatics analysis for the identification of key genes and signaling pathways in thyroid carcinoma

Zhang,Bo; Chen,Zuoyu; Wang,Yuyun; Fan,Guidong; He,Xianghui

doi:10.3892/etm.2021.9729

Integrated bioinformatics analysis for the identification of key genes and signaling pathways in thyroid carcinoma

Authors:
- Bo Zhang
- Zuoyu Chen
- Yuyun Wang
- Guidong Fan
- Xianghui He
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Affiliations: Department of General Surgery, Tianjin Medical University General Hospital, Tianjin 300052, P.R. China, Department of General Surgery, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia 010017, P.R. China
Published online on: January 28, 2021 https://doi.org/10.3892/etm.2021.9729
Article Number: 298

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Abstract

Thyroid carcinoma (TC) is one of the most common types of endocrine neoplasm with poor prognosis due to its aggressive behavior. Biomarkers for early diagnosis and prevention of TC are in urgent demand. By using a bioinformatics analysis, the present study aimed to identify essential genes and pathways associated with TC. First, the GSE27155 and GSE50901 expression profiles were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were obtained using the two microarray datasets and further subjected to integrated analysis. A gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed 45 common DEGs in the two datasets. GO and KEGG pathway analysis indicated that the biological functions of the DEGs included protein binding, cardiac muscle cell potential involved in contraction, aldehyde dehydrogenase activity, the TGF‑β receptor signaling pathway and the canonical Wnt signaling pathway. A protein‑protein interaction network was also constructed and visualized to display the nodes of the top 9 up‑ and 36 downregulated common DEGs. The integrated bioinformatics analysis indicated that potassium inwardly rectifying channel subfamily J member 2 (KCNJ2) was the most significantly upregulated DEG. The transcriptional levels of KCNJ2 were confirmed to be elevated in TC tissues compared with those in normal tissues using reverse transcription‑quantitative PCR analysis. Furthermore, the expression level of KCNJ2 was significantly associated with the 5‑year survival rate of patients with TC, which was determined using the Kaplan‑Meier method. In TC cell lines, KCNJ2 was also upregulated as compared with that in a normal control cell line. Finally, small interfering RNA was used to knock down the expression of KCNJ2, which was demonstrated to inhibit cell proliferation, migration and invasion, while increasing apoptosis in TC cells. In conclusion, in the present study, KCNJ2 was screened as an oncogene with a crucial role in TC development and progression and may represent a promising candidate biomarker and therapeutic target for TC.

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1	Siegel RL, Miller KD and Jemal A: Cancer statistics, 2019. CA Cancer J Clin. 69:7–34. 2019.PubMed/NCBI View Article : Google Scholar
2	Pacini F: Observation for newly diagnosed micro-papillary thyroid cancer: Is now the time? J Endocrinol Invest. 38:101–102. 2015.PubMed/NCBI View Article : Google Scholar
3	Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, Jemal A, Yu XQ and He J: Cancer statistics in China, 2015. CA Cancer J Clin. 66:115–132. 2016.PubMed/NCBI View Article : Google Scholar
4	Lodewijk L, Prins AM, Kist JW, Valk GD, Kranenburg O, Rinkes IH and Vriens MR: The value of miRNA in diagnosing thyroid cancer: A systematic review. Cancer Biomark. 11:229–238. 2012.PubMed/NCBI View Article : Google Scholar
5	Yu J, Mai W, Cui Y and Kong L: Key genes and pathways predicted in papillary thyroid carcinoma based on bioinformatics analysis. J Endocrinol Invest. 39:1285–1293. 2016.PubMed/NCBI View Article : Google Scholar
6	Smallridge RC, Marlow LA and Copland JA: Anaplastic thyroid cancer: Molecular pathogenesis and emerging therapies. Endocr Relat Cancer. 16:17–44. 2009.PubMed/NCBI View Article : Google Scholar
7	Geraldo MV and Kimura ET: Integrated analysis of thyroid cancer public datasets reveals role of post-transcriptional regulation on tumor progression by targeting of immune system mediators. PLoS One. 10(e0141726)2015.PubMed/NCBI View Article : Google Scholar
8	Hu S, Liao Y and Chen L: Identification of key pathways and genes in anaplastic thyroid carcinoma via integrated bioinformatics analysis. Med Sci Monit. 24:6438–6448. 2018.PubMed/NCBI View Article : Google Scholar
9	Fluge Ø, Bruland O, Akslen LA, Lillehaug JR and Varhaug JE: Gene expression in poorly differentiated papillary thyroid carcinomas. Thyroid. 16:161–175. 2006.PubMed/NCBI View Article : Google Scholar
10	Garg M, Kanojia D, Okamoto R, Jain S, Madan V, Chien W, Sampath Ding LW, Xuan M, Said JW, et al: Laminin-5γ-2 (LAMC2) is highly expressed in anaplastic thyroid carcinoma and is associated with tumor progression, migration, and invasion by modulating signaling of EGFR. J Clin Endocrinol Metab. 99:E62–E72. 2014.PubMed/NCBI View Article : Google Scholar
11	Kulasingam V and Diamandis EP: Strategies for discovering novel cancer biomarkers through utilization of emerging technologies. Nat Clin Pract Oncol. 5:588–599. 2008.PubMed/NCBI View Article : Google Scholar
12	Liu YJ, Zhang S, Hou K, Li YT, Liu Z, Ren HL, Luo D and Li SH: Analysis of key genes and pathways associated with colorectal cancer with microarray technology. Asian Pac J Cancer Prev. 14:1819–1823. 2013.PubMed/NCBI View Article : Google Scholar
13	Wang J, Wei B, Cao S, Xu F, Chen W, Lin H, Du C and Sun Z: Identification by microarray technology of key genes involved in the progression of carotid atherosclerotic plaque. Genes Genet Syst. 89:253–258. 2014.PubMed/NCBI View Article : Google Scholar
14	Wang Y and Zheng T: Screening of hub genes and pathways in colorectal cancer with microarray technology. Pathol Oncol Res. 20:611–618. 2014.PubMed/NCBI View Article : Google Scholar
15	Goujon M, McWilliam H, Li W, Valentin F, Squizzato S, Paern J and Lopez R: A new bioinformatics analysis tools framework at EMBL-EBI. Nucleic Acids Res. 38:W695–9. 2010.PubMed/NCBI View Article : Google Scholar
16	Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, Barrette T, Pandey A and Chinnaiyan AM: ONCOMINE: A cancer microarray database and integrated data-mining platform. Neoplasia. 6:1–6. 2004.PubMed/NCBI View Article : Google Scholar
17	Giordano TJ, Kuick R, Thomas DG, Misek DE, Vinco M, Sanders D, Zhu Z, Ciampi R, Roh M, Shedden K, et al: Molecular classification of papillary thyroid carcinoma: Distinct BRAF, RAS, and RET/PTC mutation-specific gene expression profiles discovered by DNA microarray analysis. Oncogene. 24:6646–6656. 2005.PubMed/NCBI View Article : Google Scholar
18	Barros-Filho MC, Marchi FA, Pinto CA, Rogatto SR and Kowalski LP: High diagnostic accuracy based on CLDN10, HMGA2, and LAMB3 transcripts in papillary thyroid carcinoma. J Clin Endocrinol Metab. 100:E890–E899. 2015.PubMed/NCBI View Article : Google Scholar
19	Kenneth L and Thomas S: Analysis of relative gene expression data using real-time quantitative PCR and the 2^-ΔΔCT method. Methods. 25:402–408. 2005.
20	Kubo Y, Baldwin TJ, Jan YN and Jan LY: Primary structure and functional expression of a mouse inward rectifier potassium channel. Nature. 362:127–133. 1993.PubMed/NCBI View Article : Google Scholar
21	Liu H, Huang J, Peng J, Wu X, Zhang Y, Zhu W and Guo L: Upregulation of the inwardly rectifying potassium channel Kir2.1 (KCNJ2) modulates multidrug resistance of small-cell lung cancer under the regulation of miR-7 and the Ras/MAPK pathway. Mol Cancer. 14(59)2015.PubMed/NCBI View Article : Google Scholar
22	Li M, Kanda Y, Ashihara T, Sasano T, Nakai Y, Kodama M, Hayashi E, Sekino Y, Furukawa T and Kurokawa J: Overexpression of KCNJ2 in induced pluripotent stem cell-derived cardiomyocytes for the assessment of QT-prolonging drugs. J Pharmacol Sci. 134:75–85. 2017.PubMed/NCBI View Article : Google Scholar
23	Giovannardi S, Forlani G, Balestrini M, Bossi E, Tonini R, Sturani E, Peres A and Zippel R: Modulation of the inward rectifier potassium channel IRK1 by the Ras signaling pathway. J Biol Chem. 277:12158–12163. 2002.PubMed/NCBI View Article : Google Scholar