Evaluation of autophagy-related genes and lncRNAs signature for prognositic prediction in thyroid carcinoma via bioinformatics analysis
Journal Title: TMR Clinical Research - Year 2021, Vol 4, Issue 2
Abstract
Autophagy plays a significant role in the pathogenesis and prognosis of thyroid carcinoma. The role of autophagy-related genes and long non-coding RNAs, as well as the risk model of thyroid carcinoma patients were investigated to predict clinical outcome of thyroid carcinoma. Different expression of autophagy-related genes and long non-coding RNAs in thyroid carcinoma patients was identified in The Cancer Genome Atlas database. Functional enrichment analysis and gene set enrichment analysis was used to hint the mechanism that autophagy might act in thyroid carcinoma. Univariate and multivariate Cox regression analyses were performed for screening the prognostic autophagy-related genes and long non-coding RNAs to construct prognostic related risk model. thyroid carcinoma patients were divided into the low-risk and high-risk groups. The overall survival time was both shorter in the high-risk groups than that in the low-risk groups. As for autophagy-related genes prognostic risk model, age and autophagy-related genes risk score are independent prognostic factors that affect the survival of thyroid carcinoma. ATIC and CDKN2A expression was closely related to pathological stage and T status, DNAJB1 expression was closely related to M status, age and gender. While autophagy-associated long non-coding RNA related prognostic risk model consequently demonstrated that the long non-coding RNA risk score could significantly predict the survival rate of thyroid carcinoma patients with areas under the curve of 0.972. gene set enrichment analysis presented that a total of 16 gene sets including 10 up-regulated and 6 down-regulated gene sets were significantly enriched. The autophagy-related genes and long non-coding RNAs based prognostic risk models are a reliable forecasting tool for thyroid carcinoma patients.
Authors and Affiliations
Shan-Qi Guo1, Ying-Jie Jia1, Deng Hao2*, Xiao-Jiang Li1*
Keywords
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- EP ID EP695491
- DOI 10.12032/TMRCR20210516008
- Views 105
- Downloads 0
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