Research Paper Volume 13, Issue 16 pp 20164—20178
Identification of 4-methylation driven genes based prognostic signature in thyroid cancer: an integrative analysis based on the methylmix algorithm
- 1 Department of Gastroenterology, The Affiliated Hospital of Putian University, Putian 351100, Fujian Province, China
- 2 Department of Thyroid Surgery, The Affiliated Hospital of Putian University, Putian 351100, Fujian Province, China
- 3 Clinical Laboratory, The Affiliated Hospital of Putian University, Putian 351100, Fujian Province, China
- 4 Department of Gastrointestinal Surgery, The Affiliated Hospital of Putian University, Putian 351100, Fujian Province, China
- 5 Department of Pathology, The Affiliated Hospital of Putian University, Putian 351100, Fujian Province, China
Received: May 24, 2021 Accepted: July 1, 2021 Published: August 29, 2021https://doi.org/10.18632/aging.203338
How to Cite
Copyright: © 2021 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Thyroid cancer (TC) is known with a high rate of persistence and recurrence. We aimed to develop a prognostic signature to monitor and assess the survival of TC patients. mRNA expression and methylation data were downloaded from the TCGA database. Then, R package methylmix was applied to construct a mixed model was used to identify methylation-driven genes (MDGs) according to the methylation levels. Furthermore, an MDGs based prognostic signature and predictive nomogram were constructed according to the analysis of univariate and multivariate Cox regression. Totally 62 methylation-driven genes that were mainly enriched in substrate-dependent cell migration, cellular response to mechanical stimulus, et al. were found in TC tissues. aldolase C (AldoC), C14orf62, dishevelled 1 (DVL1), and protein tyrosine phosphatase receptor type C (PTPRC) were identified to be significantly related to patients' survival, and may serve as independent prognostic biomarkers for TC. Additionally, the prognostic methylation signature and a novel prognostic, predictive nomogram was established based on the methylation level of 4 MDGs. In this study, we developed a 4-MDGs based prognostic model, which might be the potential predictors for the survival rate of TC patients, and this findings might provide a novel sight for accurate monitoring and prognosis assessment.