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Key molecules associated with thyroid carcinoma prognosis: A study based on transcriptome sequencing and GEO datasets
BACKGROUND: Thyroid carcinoma (THCA) has a low mortality rate, but its incidence has been rising over the years. We need to pay attention to its progression and prognosis. In this study, a transcriptome sequencing analysis and bioinformatics methods were used to screen key genes associated with THCA...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9428590/ https://www.ncbi.nlm.nih.gov/pubmed/36059514 http://dx.doi.org/10.3389/fimmu.2022.964891 |
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author | Bai, Miaoyu Ke, Shanjia Yu, Hongjun Xu, Yanan Yu, Yue Lu, Shounan Wang, Chaoqun Huang, Jingjing Ma, Yong Dai, Wenjie Wu, Yaohua |
author_facet | Bai, Miaoyu Ke, Shanjia Yu, Hongjun Xu, Yanan Yu, Yue Lu, Shounan Wang, Chaoqun Huang, Jingjing Ma, Yong Dai, Wenjie Wu, Yaohua |
author_sort | Bai, Miaoyu |
collection | PubMed |
description | BACKGROUND: Thyroid carcinoma (THCA) has a low mortality rate, but its incidence has been rising over the years. We need to pay attention to its progression and prognosis. In this study, a transcriptome sequencing analysis and bioinformatics methods were used to screen key genes associated with THCA development and analyse their clinical significance and diagnostic value. METHODS: We collected 10 pairs of THCA tissues and noncancerous tissues, these samples were used for transcriptome sequencing to identify disordered genes. The gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. Comprehensive analysis of thyroid clinicopathological data using The Cancer Genome Atlas (TCGA). R software was used to carry out background correction, normalization and log2 conversion. We used quantitative real-time PCR (qRT–PCR) and Western blot to determine differentially expressed genes (DEGs) expression in samples. We integrated the DEGs expression, clinical features and progression-free interval (PFI). The related functions and immune infiltration degree were established by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and single-sample Gene Set Enrichment Analysis (ssGSEA). The UALCAN database was used to analyse the methylation level. RESULTS: We evaluated DEGs between normal tissue and cancer. Three genes were identified: regulator of G protein signaling 8 (RGS8), diacylglycerol kinase iota (DGKI) and oculocutaneous albinism II (OCA2). The mRNA and protein expression levels of RGS8, DGKI and OCA2 in normal tissues were higher than those in THCA tissues. Better survival outcomes were associated with higher expression of RGS8 (HR=0.38, P=0.001), DGKI (HR=0.52, P=0.022), and OCA2 (HR=0.41, P=0.003). The GO analysis, KEGG analysis and GSEA proved that the coexpressed genes of RGS8, DGKI and OCA2 were related to thyroid hormone production and peripheral downstream signal transduction effects. The expression levels of RGS8, DGKI and OCA2 were linked to the infiltration of immune cells such as DC cells. The DNA methylation level of OCA2 in cancer tissues was higher than that in the normal samples. CONCLUSIONS: RGS8, DGKI and OCA2 might be promising prognostic molecular markers in patients with THCA and reveal the clinical significance of RGS8, DGKI and OCA2 in THCA. |
format | Online Article Text |
id | pubmed-9428590 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94285902022-09-01 Key molecules associated with thyroid carcinoma prognosis: A study based on transcriptome sequencing and GEO datasets Bai, Miaoyu Ke, Shanjia Yu, Hongjun Xu, Yanan Yu, Yue Lu, Shounan Wang, Chaoqun Huang, Jingjing Ma, Yong Dai, Wenjie Wu, Yaohua Front Immunol Immunology BACKGROUND: Thyroid carcinoma (THCA) has a low mortality rate, but its incidence has been rising over the years. We need to pay attention to its progression and prognosis. In this study, a transcriptome sequencing analysis and bioinformatics methods were used to screen key genes associated with THCA development and analyse their clinical significance and diagnostic value. METHODS: We collected 10 pairs of THCA tissues and noncancerous tissues, these samples were used for transcriptome sequencing to identify disordered genes. The gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. Comprehensive analysis of thyroid clinicopathological data using The Cancer Genome Atlas (TCGA). R software was used to carry out background correction, normalization and log2 conversion. We used quantitative real-time PCR (qRT–PCR) and Western blot to determine differentially expressed genes (DEGs) expression in samples. We integrated the DEGs expression, clinical features and progression-free interval (PFI). The related functions and immune infiltration degree were established by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and single-sample Gene Set Enrichment Analysis (ssGSEA). The UALCAN database was used to analyse the methylation level. RESULTS: We evaluated DEGs between normal tissue and cancer. Three genes were identified: regulator of G protein signaling 8 (RGS8), diacylglycerol kinase iota (DGKI) and oculocutaneous albinism II (OCA2). The mRNA and protein expression levels of RGS8, DGKI and OCA2 in normal tissues were higher than those in THCA tissues. Better survival outcomes were associated with higher expression of RGS8 (HR=0.38, P=0.001), DGKI (HR=0.52, P=0.022), and OCA2 (HR=0.41, P=0.003). The GO analysis, KEGG analysis and GSEA proved that the coexpressed genes of RGS8, DGKI and OCA2 were related to thyroid hormone production and peripheral downstream signal transduction effects. The expression levels of RGS8, DGKI and OCA2 were linked to the infiltration of immune cells such as DC cells. The DNA methylation level of OCA2 in cancer tissues was higher than that in the normal samples. CONCLUSIONS: RGS8, DGKI and OCA2 might be promising prognostic molecular markers in patients with THCA and reveal the clinical significance of RGS8, DGKI and OCA2 in THCA. Frontiers Media S.A. 2022-08-17 /pmc/articles/PMC9428590/ /pubmed/36059514 http://dx.doi.org/10.3389/fimmu.2022.964891 Text en Copyright © 2022 Bai, Ke, Yu, Xu, Yu, Lu, Wang, Huang, Ma, Dai and Wu https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Immunology Bai, Miaoyu Ke, Shanjia Yu, Hongjun Xu, Yanan Yu, Yue Lu, Shounan Wang, Chaoqun Huang, Jingjing Ma, Yong Dai, Wenjie Wu, Yaohua Key molecules associated with thyroid carcinoma prognosis: A study based on transcriptome sequencing and GEO datasets |
title | Key molecules associated with thyroid carcinoma prognosis: A study based on transcriptome sequencing and GEO datasets |
title_full | Key molecules associated with thyroid carcinoma prognosis: A study based on transcriptome sequencing and GEO datasets |
title_fullStr | Key molecules associated with thyroid carcinoma prognosis: A study based on transcriptome sequencing and GEO datasets |
title_full_unstemmed | Key molecules associated with thyroid carcinoma prognosis: A study based on transcriptome sequencing and GEO datasets |
title_short | Key molecules associated with thyroid carcinoma prognosis: A study based on transcriptome sequencing and GEO datasets |
title_sort | key molecules associated with thyroid carcinoma prognosis: a study based on transcriptome sequencing and geo datasets |
topic | Immunology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9428590/ https://www.ncbi.nlm.nih.gov/pubmed/36059514 http://dx.doi.org/10.3389/fimmu.2022.964891 |
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