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Identification of a Signature Comprising 5 Soluble Carrier Family Genes to Predict the Recurrence of Papillary Thyroid Carcinoma

RNA-sequencing data and relevant clinical data in The Cancer Genome Atlas for 502 samples of papillary thyroid cancer (PTC) were analyzed to determine the prognostic value of soluble carrier family genes in PTC. We analyzed soluble carrier family gene expression and function in the samples. Clusteri...

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Detalles Bibliográficos
Autores principales: Han, Rui, Sun, Wei, Zhang, Hao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8489750/
https://www.ncbi.nlm.nih.gov/pubmed/34590520
http://dx.doi.org/10.1177/15330338211036314
Descripción
Sumario:RNA-sequencing data and relevant clinical data in The Cancer Genome Atlas for 502 samples of papillary thyroid cancer (PTC) were analyzed to determine the prognostic value of soluble carrier family genes in PTC. We analyzed soluble carrier family gene expression and function in the samples. Clustering identified 2 clusters in the data. Risk characteristics were identified using LASSO and Univariate Cox regression analysis, which divided the patients into low and high-risk groups. The expression levels of 88 soluble carrier genes were significantly different between tumors and normal tissue. The 2 PTC clusters had different clinical outcomes and distributions of gene expression. The expression levels of SFXN1, SLC12A4, SLC35A1, SLC35E1, and SLCO1C1 were markedly different between the 2 groups. The high risk and low risk groups had significant different prognoses (P < 0.05). Significant differences were identified for disease free survival (DFS), sex and T stage between the 2 subgroups. The risk score was identified as an independent prognostic variable (P < 0.05) and as a predictor of clinicopathological variables. In patients with PTC, solute carrier gene expression showed differential associations with clinicopathological variables. The 5 genes could be used as prognostic factors for PTC, particularly to predict PTC recurrence.