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Identification of prognostic biomarkers for papillary thyroid carcinoma by a weighted gene co‐expression network analysis

AIM: Whole transcriptome analysis was conducted to identify differentially expressed RNAs and regulatory networks associated with papillary thyroid carcinoma (PTC). METHODS: A weighted gene co‐expression network analysis based on high‐throughput sequencing data for six pairs of PTC and adjacent tiss...

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Autores principales: Meng, Kexin, Hu, Xiaotian, Zheng, Guowan, Qian, Chenhong, Xin, Ying, Guo, Haiwei, He, Ru, Ge, Minghua, Xu, Jiajie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9089218/
https://www.ncbi.nlm.nih.gov/pubmed/35152572
http://dx.doi.org/10.1002/cam4.4602
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author Meng, Kexin
Hu, Xiaotian
Zheng, Guowan
Qian, Chenhong
Xin, Ying
Guo, Haiwei
He, Ru
Ge, Minghua
Xu, Jiajie
author_facet Meng, Kexin
Hu, Xiaotian
Zheng, Guowan
Qian, Chenhong
Xin, Ying
Guo, Haiwei
He, Ru
Ge, Minghua
Xu, Jiajie
author_sort Meng, Kexin
collection PubMed
description AIM: Whole transcriptome analysis was conducted to identify differentially expressed RNAs and regulatory networks associated with papillary thyroid carcinoma (PTC). METHODS: A weighted gene co‐expression network analysis based on high‐throughput sequencing data for six pairs of PTC and adjacent tissue samples was conducted to understand the biological functions and regulatory networks involving long non‐coding RNAs (lncRNAs), circular RNAs (circRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs). RESULTS: We detected 131, 338, 31, and 556 differentially expressed circRNAs, lncRNAs, miRNAs, and mRNAs, respectively. We identified modules that were significantly positively and negatively related to cancer and lymph node metastasis. Gray and turquoise modules were positively correlated with cancer phenotypes (p < 0.05), whereas yellow, brown, and blue modules were negatively correlated with cancer (p < 0.05). Gray module was positively correlated with lateral lymph node metastasis (p = 0.02). Kaplan–Meier analyses revealed that the levels of transmembrane protein 63C (TMEM63C), lysyl oxidase‐like 1 (LOXL1), collagen type V alpha 1 chain (COL5A1), ADAM metalloproteinase with thrombospondin type I motif 2 (ADAMTS2), and LysM‐domain containing 3 (LYSMD3) were significantly associated with overall survival (p < 0.05). Significant increase in the expression of COL5A1 and LOXL1 in tumor tissues was validated by quantitative real‐time polymerase chain reaction (p < 0.05). COL5A1 and LOXL1 promoted PTC cell growth and invasion in vitro. CONCLUSIONS: We identified COL5A1 and LOXL1 as potential prognostic biomarkers, providing new insights into the occurrence and progression of PTC.
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spelling pubmed-90892182022-05-16 Identification of prognostic biomarkers for papillary thyroid carcinoma by a weighted gene co‐expression network analysis Meng, Kexin Hu, Xiaotian Zheng, Guowan Qian, Chenhong Xin, Ying Guo, Haiwei He, Ru Ge, Minghua Xu, Jiajie Cancer Med Bioinformatics AIM: Whole transcriptome analysis was conducted to identify differentially expressed RNAs and regulatory networks associated with papillary thyroid carcinoma (PTC). METHODS: A weighted gene co‐expression network analysis based on high‐throughput sequencing data for six pairs of PTC and adjacent tissue samples was conducted to understand the biological functions and regulatory networks involving long non‐coding RNAs (lncRNAs), circular RNAs (circRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs). RESULTS: We detected 131, 338, 31, and 556 differentially expressed circRNAs, lncRNAs, miRNAs, and mRNAs, respectively. We identified modules that were significantly positively and negatively related to cancer and lymph node metastasis. Gray and turquoise modules were positively correlated with cancer phenotypes (p < 0.05), whereas yellow, brown, and blue modules were negatively correlated with cancer (p < 0.05). Gray module was positively correlated with lateral lymph node metastasis (p = 0.02). Kaplan–Meier analyses revealed that the levels of transmembrane protein 63C (TMEM63C), lysyl oxidase‐like 1 (LOXL1), collagen type V alpha 1 chain (COL5A1), ADAM metalloproteinase with thrombospondin type I motif 2 (ADAMTS2), and LysM‐domain containing 3 (LYSMD3) were significantly associated with overall survival (p < 0.05). Significant increase in the expression of COL5A1 and LOXL1 in tumor tissues was validated by quantitative real‐time polymerase chain reaction (p < 0.05). COL5A1 and LOXL1 promoted PTC cell growth and invasion in vitro. CONCLUSIONS: We identified COL5A1 and LOXL1 as potential prognostic biomarkers, providing new insights into the occurrence and progression of PTC. John Wiley and Sons Inc. 2022-02-12 /pmc/articles/PMC9089218/ /pubmed/35152572 http://dx.doi.org/10.1002/cam4.4602 Text en © 2022 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Bioinformatics
Meng, Kexin
Hu, Xiaotian
Zheng, Guowan
Qian, Chenhong
Xin, Ying
Guo, Haiwei
He, Ru
Ge, Minghua
Xu, Jiajie
Identification of prognostic biomarkers for papillary thyroid carcinoma by a weighted gene co‐expression network analysis
title Identification of prognostic biomarkers for papillary thyroid carcinoma by a weighted gene co‐expression network analysis
title_full Identification of prognostic biomarkers for papillary thyroid carcinoma by a weighted gene co‐expression network analysis
title_fullStr Identification of prognostic biomarkers for papillary thyroid carcinoma by a weighted gene co‐expression network analysis
title_full_unstemmed Identification of prognostic biomarkers for papillary thyroid carcinoma by a weighted gene co‐expression network analysis
title_short Identification of prognostic biomarkers for papillary thyroid carcinoma by a weighted gene co‐expression network analysis
title_sort identification of prognostic biomarkers for papillary thyroid carcinoma by a weighted gene co‐expression network analysis
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9089218/
https://www.ncbi.nlm.nih.gov/pubmed/35152572
http://dx.doi.org/10.1002/cam4.4602
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