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Identification of key genes of papillary thyroid cancer using integrated bioinformatics analysis

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Abstract

Objective

To identify novel clinically relevant genes in papillary thyroid carcinoma from public databases.

Methods

Four original microarray datasets, GSE3678, GSE3467, GSE33630 and GSE58545, were downloaded. Differentially expressed genes (DEGs) were filtered from integrated data. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, followed by protein–protein interaction (PPI) network construction. The CentiScape pug-in was performed to scale degree. The genes at the top of the degree distribution (≥ 95% percentile) in the significantly perturbed networks were defined as central genes. UALCAN and The Cancer Genome Atlas Clinical Explorer were used to verify clinically relevant genes and perform survival analysis.

Result

225 commonly changed DEGs (111 up-regulated and 114 down-regulated) were identified. The DEGs were classified into three groups by GO terms. KEGG pathway enrichment analysis showed DEGs mainly enriched in the PI3K–Akt signaling pathway, pathways in cancer, focal adhesion and proteoglycans in cancer. DEGs’ protein–protein interaction (PPI) network complex was developed; six central genes (BCL2, CCND1, FN1, IRS1, COL1A1, CXCL12) were identified. Among them, BCL2, CCND1 and COL1A1 were identified as clinically relevant genes.

Conclusion

BCL2, CCND1 and COL1A1 may be key genes for papillary thyroid carcinoma. Further molecular biological experiments are required to confirm the function of the identified genes.

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Authors and Affiliations

  1. Department of Endocrinology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310000, People’s Republic of China

    W. Liang

  2. Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, The Second Affiliated Hospital, Cancer Institute, Zhejiang University School of Medicine, Hangzhou, 310009, People’s Republic of China

    F. Sun

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Correspondence to W. Liang.

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On behalf of all authors, the corresponding author states that there is no conflict of interest.

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This research did not involve human participants or animals. Data were collected from public databases.

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Liang, W., Sun, F. Identification of key genes of papillary thyroid cancer using integrated bioinformatics analysis. J Endocrinol Invest 41, 1237–1245 (2018). https://doi.org/10.1007/s40618-018-0859-3

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  • DOI: https://doi.org/10.1007/s40618-018-0859-3

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