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Identification of potential biomarkers for papillary thyroid carcinoma by comprehensive bioinformatics analysis

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Abstract

To perform bioinformatics analysis on the papillary thyroid carcinoma (PTC) gene chip dataset to explore new biological markers for PTC. The gene expression profiles of GSE3467 and GSE6004 chip data were collected by GEO2R, and the differentially expressed genes (DEGs) were selected for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Protein–protein interaction (PPI) relationship analysis was achieved using STRING, and the hub genes were obtained using the Cytoscape software. GEPIA was used to validate the expressions of the hub genes in the normal and tumor tissues and to conduct survival analyses. Pertinent genetic pathology results were fetched using the HPA database. Finally, the key genes were clinically verified by reverse transcription-polymerase chain reaction. 97 genes were jointly up-regulated and 107 genes were jointly down-regulated in GSE3467 and GSE6004. GO function enrichment analysis revealed that the DEGs were involved in the regulation of calcium ion transport into cytosol, integrin binding, and cell adhesion molecule binding. KEGG pathway enrichment analysis indicated that the DEGs were chiefly associated with thyroid cancer and non-small cell lung cancer. According to the PPI network, 30 key target genes were identified. Only the expressions of ANK2, TLE1, and TCF4 matched between the normal and tumor tissues, and were associated with disease prognosis. When compared with the normal thyroid tissues, the protein and mRNA expressions of ANK2, TLE1, and TCF4 were down-regulated in PTC. Significant differences exist in overall gene expression between the thyroid tissues of patients with PTC and those of healthy people. Furthermore, the differential genes ANK2, TLE1, and TCF4 are expected to be reliable molecular markers for the mechanism study and diagnosis of PTC.

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All data generated or analyzed during this study are included in this published article.

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Acknowledgements

This work was supported by the Hubei Natural Science Foundation of China under Grant No.2016CFB673. MJEditor (www.mjeditor.com) for its linguistic assistance during the preparation of this manuscript.

Funding

This study was funded by the Hubei Natural Science Foundation of China Grant No. 2016CFB673.

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Authors and Affiliations

  1. Department of Geriatrics, Renmin Hospital of Wuhan University, Wuhan, Hubei, People’s Republic of China

    Min Liao, Zhen Wang & Yarong Hao

  2. Department of Orthopedics, Renmin Hospital of Wuhan University, Wuhan, Hubei, People’s Republic of China

    Jiawei Yao, Hengte Xing & Bo Qiu

  3. Division of Metabolic Syndrome, Department of Geriatrics, Renmin Hospital of Wuhan University, 99 Zhang Zhidong Road, Wuchang District, Wuhan, Hubei, China

    Yarong Hao

  4. Division of Osteoarthritis, Department of Orthopedics, Renmin Hospital of Wuhan University, 99 Zhang Zhidong Road, Wuchang District, Wuhan, Hubei, China

    Bo Qiu

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  1. Min Liao
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  4. Hengte Xing
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Contributions

ML reviewed literature, designed and performed the experiments. ML, ZW, JY and HX collected the samples and analyzed the data. ML drafted the manuscript. ML, HY and BQ contributed to the discussion and reviewed the manuscript. All authors agreed to submit to the current journal; gave final approval of the version to be published; and agree to be accountable for all aspects of the work.

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Correspondence to Yarong Hao or Bo Qiu.

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Liao, M., Wang, Z., Yao, J. et al. Identification of potential biomarkers for papillary thyroid carcinoma by comprehensive bioinformatics analysis. Mol Cell Biochem 478, 2111–2123 (2023). https://doi.org/10.1007/s11010-022-04606-x

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  • DOI: https://doi.org/10.1007/s11010-022-04606-x

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