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Identification and validation of an immune-related prognostic signature and key gene in papillary thyroid carcinoma

BACKGROUND: Papillary thyroid carcinoma (PTC) is the most common pathological type of thyroid cancer. The effect of traditional anti-tumor therapy is not ideal for the patients with recurrence, metastasis and radioiodine resistance. The abnormal expression of immune-related genes (IRGs) has critical...

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Autores principales: Qin, Rujia, Li, Chunyan, Wang, Xuemin, Zhong, Zhaoming, Sun, Chuanzheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8281689/
https://www.ncbi.nlm.nih.gov/pubmed/34266418
http://dx.doi.org/10.1186/s12935-021-02066-9
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author Qin, Rujia
Li, Chunyan
Wang, Xuemin
Zhong, Zhaoming
Sun, Chuanzheng
author_facet Qin, Rujia
Li, Chunyan
Wang, Xuemin
Zhong, Zhaoming
Sun, Chuanzheng
author_sort Qin, Rujia
collection PubMed
description BACKGROUND: Papillary thyroid carcinoma (PTC) is the most common pathological type of thyroid cancer. The effect of traditional anti-tumor therapy is not ideal for the patients with recurrence, metastasis and radioiodine resistance. The abnormal expression of immune-related genes (IRGs) has critical roles in the etiology of PTC. However, the effect of IRGs on PTC prognosis remains unclear. METHODS: Based on The Cancer Genome Atlas (TCGA) and ImmPort databases, we integrated IRG expression profiles and progression-free intervals (PFIs) of PTC patients. First, we identified the differentially expressed IRGs and transcription factors (TFs) in PTC. Subsequently, an IRG model that can predict the PFI was constructed by using univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analyses of the differentially expressed IRGs in the TCGA. Additionally, a protein–protein interaction (PPI) network showed the interactions between the differentially expressed genes (DEGs), and the top 30 genes with the highest degree were extracted from the network. Then, the key IRG was identified by the intersection analysis of the PPI network and univariate Cox regression, which was verified the differential expression of by western blotting and immunohistochemistry (IHC). ssGSEA was performed to understand the correlation between the key IRG expression level and immune activity. RESULTS: A total of 355 differentially expressed IRGs and 43 differentially expressed TFs were identified in PTC patients. Then, eight IRGs were finally utilized to construct an IRG model. The respective areas under the curve (AUCs) of the IRG model reached 0.948, 0.820, and 0.831 at 1, 3 and 5 years in the training set. In addition, lactotransferrin (LTF) was determined as the key IRG related to prognosis. The expression level of LTF in tumor tissues was significantly lower than that in normal tissues. And the results of ssGSEA showed the expression level of LTF is closely related to immune activity. CONCLUSIONS: These findings show that the prognostic model and key IRG may become promising molecular markers for the prognosis of PTC patients.
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spelling pubmed-82816892021-07-16 Identification and validation of an immune-related prognostic signature and key gene in papillary thyroid carcinoma Qin, Rujia Li, Chunyan Wang, Xuemin Zhong, Zhaoming Sun, Chuanzheng Cancer Cell Int Primary Research BACKGROUND: Papillary thyroid carcinoma (PTC) is the most common pathological type of thyroid cancer. The effect of traditional anti-tumor therapy is not ideal for the patients with recurrence, metastasis and radioiodine resistance. The abnormal expression of immune-related genes (IRGs) has critical roles in the etiology of PTC. However, the effect of IRGs on PTC prognosis remains unclear. METHODS: Based on The Cancer Genome Atlas (TCGA) and ImmPort databases, we integrated IRG expression profiles and progression-free intervals (PFIs) of PTC patients. First, we identified the differentially expressed IRGs and transcription factors (TFs) in PTC. Subsequently, an IRG model that can predict the PFI was constructed by using univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analyses of the differentially expressed IRGs in the TCGA. Additionally, a protein–protein interaction (PPI) network showed the interactions between the differentially expressed genes (DEGs), and the top 30 genes with the highest degree were extracted from the network. Then, the key IRG was identified by the intersection analysis of the PPI network and univariate Cox regression, which was verified the differential expression of by western blotting and immunohistochemistry (IHC). ssGSEA was performed to understand the correlation between the key IRG expression level and immune activity. RESULTS: A total of 355 differentially expressed IRGs and 43 differentially expressed TFs were identified in PTC patients. Then, eight IRGs were finally utilized to construct an IRG model. The respective areas under the curve (AUCs) of the IRG model reached 0.948, 0.820, and 0.831 at 1, 3 and 5 years in the training set. In addition, lactotransferrin (LTF) was determined as the key IRG related to prognosis. The expression level of LTF in tumor tissues was significantly lower than that in normal tissues. And the results of ssGSEA showed the expression level of LTF is closely related to immune activity. CONCLUSIONS: These findings show that the prognostic model and key IRG may become promising molecular markers for the prognosis of PTC patients. BioMed Central 2021-07-15 /pmc/articles/PMC8281689/ /pubmed/34266418 http://dx.doi.org/10.1186/s12935-021-02066-9 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Primary Research
Qin, Rujia
Li, Chunyan
Wang, Xuemin
Zhong, Zhaoming
Sun, Chuanzheng
Identification and validation of an immune-related prognostic signature and key gene in papillary thyroid carcinoma
title Identification and validation of an immune-related prognostic signature and key gene in papillary thyroid carcinoma
title_full Identification and validation of an immune-related prognostic signature and key gene in papillary thyroid carcinoma
title_fullStr Identification and validation of an immune-related prognostic signature and key gene in papillary thyroid carcinoma
title_full_unstemmed Identification and validation of an immune-related prognostic signature and key gene in papillary thyroid carcinoma
title_short Identification and validation of an immune-related prognostic signature and key gene in papillary thyroid carcinoma
title_sort identification and validation of an immune-related prognostic signature and key gene in papillary thyroid carcinoma
topic Primary Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8281689/
https://www.ncbi.nlm.nih.gov/pubmed/34266418
http://dx.doi.org/10.1186/s12935-021-02066-9
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