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Identification of potential targetable genes in papillary, follicular, and anaplastic thyroid carcinoma using bioinformatics analysis

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Abstract

Purpose

To perform an extensive exploratory analysis to build a deeper insight into clinically relevant molecular biomarkers in Papillary, Follicular, and Anaplastic thyroid carcinomas (PTC, FTC, ATC).

Methods

Thirteen Thyroid Cancer (THCA) datasets incorporating PTC, FTC, and ATC were derived from the Gene Expression Omnibus. Genes differentially expressed (DEGs) between THCA and normal were identified and subjected to GO and KEGG analyses. Multiple topological properties were harnessed and protein-protein interaction (PPI) networks were constructed to identify the hub genes followed by survival analysis and validation.

Results

There were 70, 87, and 377 DEGs, and 23, 27, and 53 hub genes for PTC, FTC, and ATC samples, respectively. Survival analysis detected 39 overall and 49 relapse-free survival-relevant hub genes. Six hub genes, BCL2, FN1, ITPR1, LYVE1, NTRK2, TBC1D4, were found common to more than one THCA type. The most significant hub genes found in the study were: BCL2, CD44, DCN, FN1, IRS1, ITPR1, MFAP4, MKI67, NTRK2, PCLO, TGFA. The most enriched and significant GO terms were Melanocyte differentiation for PTC, Extracellular region for FTC, and Extracellular exosome for ATC. Prostate cancer for PTC was the most significantly enriched KEGG pathway. The results were validated using TCGA data.

Conclusions

The findings unravel potential biomarkers and therapeutic targets of thyroid carcinomas.

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Data availability

Raw files of the datasets used in the study can be found in GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi) by using the corresponding accession numbers: GSE3467, GSE3678, GSE5364, GSE9115, GSE27155, GSE29265, GSE33630, GSE53072, GSE53157, GSE58545, GSE60542, GSE65144, GSE171011. The dataset corresponding to the TCGA-THCA project can be found at https://portal.gdc.cancer.gov/projects/TCGA-THCA. We accessed the datasets on 30 October 2023. The result files and the code scripts can be downloaded from https://github.com/code-rishav/bioinformatics.

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Author contributions

S.A. and S.G. contributed to the study conception and design. Material preparation, data collection, and analysis were performed by all the authors. The first draft of the manuscript was written by S.G. and R.R. and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Authors and Affiliations

  1. Department of Pathology, All India Institute of Medical Sciences, Ansari Nagar, New Delhi, India

    Shipra Agarwal

  2. Department of Computer Science, S.S. College of Business Studies, University of Delhi, New Delhi, India

    Shikha Gupta & Rishav Raj

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  1. Shipra Agarwal
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Correspondence to Shikha Gupta.

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Agarwal, S., Gupta, S. & Raj, R. Identification of potential targetable genes in papillary, follicular, and anaplastic thyroid carcinoma using bioinformatics analysis. Endocrine (2024). https://doi.org/10.1007/s12020-024-03836-x

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