Potential Novel Modules and Hub Genes as Prognostic Candidates of Thyroid Cancer by Weighted Gene Co-Expression Network Analysis

Abstract

Background

Although thyroid cancer (THCA) is one of the most common type of endocrine malignancy, its highly complex molecular mechanisms of carcinogenesis are not completely known.

Materials and Methods

In this study, weighted gene co-expression network analysis (WGCNA) was utilized to construct gene co-expression networks and evaluate the relations between modules and clinical traits to identify potential prognostic biomarkers for THCA patients. RNA-seq data and clinical data were downloaded from The Cancer Genome Atlas (TCGA). Other independent datasets from the Gene Expression Omnibus (GEO) database and the Human Protein Atlas database were performed to validate findings.

Results

Finally, 11 co-expression modules were constructed and four hub genes, CCDC146, SLC4A4, TDRD9 and MUM1L1, were identified and validated statistically, which were considerably interrelated to worse survival of THCA patients.

Conclusion

This research study revealed four hub genes may be considered candidate prognostic biomarkers and potential therapeutic targets for THCA patients in the future.

Keywords:

• thyroid cancer
• THCA
• weighted gene co-expression network analysis
• WGCNA
• hub gene
• prognosis

Abbreviations

THCA, thyroid cancer; WGCNA, weighted gene co-expression network analysis; TCGA, The Cancer Genome Atlas; GEI, Gene Expression Omnibus; PTC, papillary thyroid carcinoma; FNAB, fine needle aspiration biopsy; FMA, robust multi-array average; FDR, false discovery rate; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; CAMs, cell adhesion molecules.

Ethics Approval and Consent to Participate

This study was approved in accordance with the Ethical Standards of the Institutional Ethics Committee of University of Chinese Academy of Sciences - Shenzhen Hospital and with the 1964 Helsinki declaration and its later amendments or comparable Ethical Standards.

Acknowledgments

The results of this study are based on the data from TCGA (https://www.cancer.gov/tcga) and GEO database (http://www.ncbi.nlm.nih.gov/geo/). We thank all the authors who provided the data for this study.

Disclosure

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Additional information

Funding

This work was supported by the Startup Fund for scientific research, University of Chinese Academy of Sciences–Shenzhen Hospital (Grant No. HRF-2020012); and Guangming District Soft Science Research Project (Grant No. 2021R01063).