Utilizing Bioinformatics Approaches to Conduct a Comparative Analysis of the Thyroid Transcriptome in Thyroid Disorders


Thyroid disease
Gene expression profiling
Differentially expressed genes
Molecular mechanisms

How to Cite

de Oliveira Andrade, L. J., Matos de Oliveira , L., Vinhaes Bittencourt , A. M., Matos de Oliveira , L. C. ., & Matos de Oliveira , G. C. (2024). Utilizing Bioinformatics Approaches to Conduct a Comparative Analysis of the Thyroid Transcriptome in Thyroid Disorders. Revista Colombiana De Endocrinología, Diabetes &Amp; Metabolismo, 11(1). https://doi.org/10.53853/encr.11.1.847


Introduction: This study aims to identify common gene expression patterns and dysregulated pathways in various thyroid disorders by leveraging publicly available transcriptomic datasets. The integration of other omics data, when possible, will allow us to uncover potential molecular drivers and biomarkers associated with specific thyroid dysfunctions. However, there are still gaps in the analysis of the transcriptomes of the various thyroid disorders.

Objective: To conduct a comparative analysis of the thyroid transcriptome in thyroid disorders using bioinformatics approaches.

Methods: We retrieved publicly available gene expression datasets related to the thyroid from the European Nucleotide Archive. Data preprocessing involved conducting quality control, trimming reads, and aligning them to a reference genome. Differential expression analysis was performed using bioinformatics packages, and finally, a functional enrichment analysis was conducted to gain insights into the biological processes. Network analysis was conducted to explore interactions and regulatory relationships among differentially expressed genes (DEGs).

Results: Our analysis included a total of 18 gene expression datasets, of which 15 were selected based on inclusion criteria and quality assessment. Numerous genes exhibiting differential expression (P < 0.01) were discerned, and their significance was systematically ranked. Functional enrichment analysis revealed numerous biological processes associated with the differentially expressed genes, providing insights into the molecular mechanisms of thyroid disorders. Network analysis using Cytoscape software revealed potential interactions among differentially expressed genes and identified key hub genes and potential therapeutic targets.

Conclusion: This study demonstrates an accessible methodology for conducting a comparative analysis of the thyroid transcriptome in different disorders without the need for thyroid tissue samples. The integration of bioinformatics approaches provides a comprehensive understanding of the molecular mechanisms underlying thyroid diseases.



Ilnytskyy S, Bilichak A. Bioinformatics Analysis of Small RNA Transcriptomes: The Detailed Workflow. Methods Mol Biol. 2017;1456:197-224. https://doi.org/10.1007/978-1-4899-7708-3_16

Skowron P, Ramaswamy V, Taylor MD. Genetic and molecular alterations across medulloblastoma subgroups. J Mol Med (Berl). 2015;93(10):1075-84. https://doi.org/10.1007/s00109-015-1333-8

Vitale L, Piovesan A, Antonaros F, Strippoli P, Pelleri MC, Caracausi M. Dataset of differential gene expression between total normal human thyroid and histologically normal thyroid adjacent to papillary thyroid carcinoma. Data Brief. 2019;24:103835. https://doi.org/10.1016/j.dib.2019.103835

Cai LL, Liu GY, Tzeng CM. Genome-wide DNA methylation profiling and its involved molecular pathways from one individual with thyroid malignant/benign tumor and hyperplasia: A case report. Medicine (Baltimore). 2016;95(35):e4695. https://doi.org/10.1097/MD.0000000000004695

Massolt ET, Meima ME, Swagemakers SMA, Leeuwenburgh S, van den Hout-van Vroonhoven MCGM, Brigante G, et al. Thyroid State Regulates Gene Expression in Human Whole Blood. J Clin Endocrinol Metab. 2018;103(1):169-178. https://doi.org/10.1210/jc.2017-01144

Marabita F, James T, Karhu A, Virtanen H, Kettunen K, Stenlund H, et al. Multiomics and digital monitoring during lifestyle changes reveal independent dimensions of human biology and health. Cell Syst. 2022;13(3):241-255.e7. https://doi.org/10.1016/j.cels.2021.11.001

Cho BA, Yoo SK, Song YS, Kim SJ, Lee KE, Shong M, et al. Transcriptome Network Analysis Reveals Aging-Related Mitochondrial and Proteasomal Dysfunction and Immune Activation in Human Thyroid. Thyroid. 2018;28(5):656-666. https://doi.org/10.1089/thy.2017.0359

Liu C, Pan Y, Li Q, Zhang Y. Bioinformatics analysis identified shared differentially expressed genes as potential biomarkers for Hashimoto's thyroiditis-related papillary thyroid cancer. Int J Med Sci. 2021;18(15):3478-3487. https://doi.org/10.7150/ijms.63402

He H, Liyanarachchi S, Li W, Comiskey DF Jr, Yan P, Bundschuh R, et al. Transcriptome analysis discloses dysregulated genes in normal appearing tumor-adjacent thyroid tissues from patients with papillary thyroid carcinoma. Sci Rep. 2021;11(1):14126. https://doi.org/10.1038/s41598-021-93526-9

Shih ML, Lawal B, Cheng SY, Olugbodi JO, Babalghith AO, Ho CL, et al. Large-scale transcriptomic analysis of coding and non-coding pathological biomarkers, associated with the tumor immune microenvironment of thyroid cancer and potential target therapy exploration. Front Cell Dev Biol. 2022;10:923503. https://doi.org/10.3389/fcell.2022.923503

Payne K, Brooks J, Spruce R, Batis N, Taylor G, Nankivell P, et al. Circulating Tumour Cell Biomarkers in Head and Neck Cancer: Current Progress and Future Prospects. Cancers (Basel). 2019;11(8):1115. https://doi.org/10.3390/cancers11081115

Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003;4(5):P3. https://doi.org/10.1002/jbt.20304

Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016;44(W1):W90-7. https://doi.org/10.1093/nar/gkw377

Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation (Camb). 2021;2(3):100141. https://doi.org/10.1016/j.xinn.2021.100141

Simmonds MJ, Gough SC. Unravelling the genetic complexity of autoimmune thyroid disease: HLA, CTLA-4 and beyond. Clin Exp Immunol. 2004;136(1):1-10. https://doi.org/10.1111/j.1365-2249.2004.02424.x

Cummins C, Ahamed A, Aslam R, Burgin J, Devraj R, Edbali O, et al. The European Nucleotide Archive in 2021. Nucleic Acids Res. 2022;50(D1):D106-D110. https://doi.org/10.1093/nar/gkab1051

Kapushesky M, Adamusiak T, Burdett T, Culhane A, Farne A, Filippov A, et al. Gene Expression Atlas update--a value-added database of microarray and sequencing-based functional genomics experiments. Nucleic Acids Res. 2012;40(Database issue):D1077-81. https://doi.org/10.1093/nar/gkr913

Lau SF, Cao H, Fu AKY, Ip NY. Single-nucleus transcriptome analysis reveals dysregulation of angiogenic endothelial cells and neuroprotective glia in Alzheimer's disease. Proc Natl Acad Sci U S A. 2020;117(41):25800-25809. https://doi.org/10.1073/pnas.2008762117

Zhang S, Wang Q, Han Q, Han H, Lu P. Identification and analysis of genes associated with papillary thyroid carcinoma by bioinformatics methods. Biosci Rep. 2019;39(4):BSR20190083. https://doi.org/10.1042/BSR20190083

Hossain MA, Asa TA, Rahman MM, Uddin S, Moustafa AA, Quinn JMW, et al. Network-Based Genetic Profiling Reveals Cellular Pathway Differences Between Follicular Thyroid Carcinoma and Follicular Thyroid Adenoma. Int J Environ Res Public Health. 2020;17(4):1373. https://doi.org/10.3390/ijerph17041373

Andalib KMS, Rahman MH, Habib A. Bioinformatics and cheminformatics approaches to identify pathways, molecular mechanisms and drug substances related to genetic basis of cervical cancer. J Biomol Struct Dyn. 2023:1-16. https://doi.org/10.1080/07391102.2023.2179542

Doncheva NT, Morris JH, Gorodkin J, Jensen LJ. Cytoscape StringApp: Network Analysis and Visualization of Proteomics Data. J Proteome Res. 2019;18(2):623-632. https://doi.org/10.1021/acs.jproteome.8b00702

Wan Y, Zhang X, Leng H, Yin W, Zeng W, Zhang C. Identifying hub genes of papillary thyroid carcinoma in the TCGA and GEO database using bioinformatics analysis. PeerJ. 2020;8:e9120. https://doi.org/10.7717/peerj.9120

Qiu K, Li K, Zeng T, Liao Y, Min J, Zhang N, et al. Integrative Analyses of Genes Associated with Hashimoto's Thyroiditis. J Immunol Res. 2021;2021:8263829. https://doi.org/10.1155/2021/8263829

Guo Q, Qiu P, Yao Q, Chen J, Lin J. Integrated Bioinformatics Analysis for the Screening of Hub Genes and Therapeutic Drugs in Androgen Receptor-Positive TNBC. Dis Markers. 2022;2022:4964793. https://doi.org/10.1155/2022/4964793

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