Potential biomarkers screening to predict side effects of dexamethasone in different cancers

Abstract Background Excessive or prolonged usage of dexamethasone can cause serious side effects, but few studies reveal the related mechanism. Dexamethasone work differently in blood tumors and solid tumors, and the cause is still obscure. The aims of this study was to identify potential biomarkers...

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Main Authors: Da Jiang, Hui Jin, Jing Zuo, Yan Kong, Xue Zhang, Qian Dong, Zhihong Xu, Ying Li
Format: Article
Language:English
Published: Wiley 2020-04-01
Series:Molecular Genetics & Genomic Medicine
Subjects:
Online Access:https://doi.org/10.1002/mgg3.1160
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author Da Jiang
Hui Jin
Jing Zuo
Yan Kong
Xue Zhang
Qian Dong
Zhihong Xu
Ying Li
author_facet Da Jiang
Hui Jin
Jing Zuo
Yan Kong
Xue Zhang
Qian Dong
Zhihong Xu
Ying Li
author_sort Da Jiang
collection DOAJ
description Abstract Background Excessive or prolonged usage of dexamethasone can cause serious side effects, but few studies reveal the related mechanism. Dexamethasone work differently in blood tumors and solid tumors, and the cause is still obscure. The aims of this study was to identify potential biomarkers associated with the side effects of dexamethasone in different tumors. Methods Gene Expression Omnibus database (GEO) datasets of blood tumors and solid tumors were retrieval to selected microarray data. The differentially expressed genes (DEGs) were identified. Gene ontology (GO) and pathway enrichment analyses, and protein–protein interaction (PPI) network analysis were performed. Results One hundred and eighty dexamethasone‐specific DEGs (92 up and 88 downregulated) were obtained in lymphoma cell samples (named as DEGs‐lymph), including APOD, TP53INP1, CLIC3, SERPINA9, and C3orf52. One hundred and four specific DEGs (100 up and 4 downregulated) were identified in prostate cancer cell samples (named as DEGs‐prostate), including COL6A2, OSBPL5, OLAH, OGFRL1, and SLC39A14. The significantly enriched GO terms of DEGs‐lymph contained cellular amino acid metabolic process and cell cycle. The most significantly enriched pathway of DEGs‐lymph was cytosolic tRNA aminoacylation. The DEGs‐prostate was enriched in 39 GO terms and two pathways, and the pathways were PPARA activates gene expression Homo sapiens, and insulin resistance. The PPI network of DEGs‐lymph gathered into two major clusters, WARS1 and CDC25A were representatives for them, respectively. One cluster was mainly involved in cytosolic tRNA aminoacylation, aminoacyl‐tRNA biosynthesis and the function of amino acid metabolism; another was associated with cell cycle and cell apoptosis. As for the PPI network of DEGs‐prostate, HELZ2 was the top nodes involved in the most protein–protein pairs, which was related to the pathway of “PPARA activates gene expression Homo sapiens.” Conclusions WARS1 and CDC25A might be potential biomarkers for side effects of dexamethasone in lymphoma, and HELZ2 in prostate cancer.
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spelling doaj.art-b3afc1812eea4f778053d24623c028312024-02-21T10:29:43ZengWileyMolecular Genetics & Genomic Medicine2324-92692020-04-0184n/an/a10.1002/mgg3.1160Potential biomarkers screening to predict side effects of dexamethasone in different cancersDa Jiang0Hui Jin1Jing Zuo2Yan Kong3Xue Zhang4Qian Dong5Zhihong Xu6Ying Li7Department of Medical Oncology the Fourth Hospital of Hebei Medical University Shijiazhuang ChinaDepartment of Medical Oncology the Fourth Hospital of Hebei Medical University Shijiazhuang ChinaDepartment of Medical Oncology the Fourth Hospital of Hebei Medical University Shijiazhuang ChinaDepartment of Medical Oncology the Fourth Hospital of Hebei Medical University Shijiazhuang ChinaDepartment of Medical Oncology the Fourth Hospital of Hebei Medical University Shijiazhuang ChinaDepartment of Medical Oncology the Fourth Hospital of Hebei Medical University Shijiazhuang ChinaDepartment of Medical Oncology the Fourth Hospital of Hebei Medical University Shijiazhuang ChinaDepartment of Medical Oncology the Fourth Hospital of Hebei Medical University Shijiazhuang ChinaAbstract Background Excessive or prolonged usage of dexamethasone can cause serious side effects, but few studies reveal the related mechanism. Dexamethasone work differently in blood tumors and solid tumors, and the cause is still obscure. The aims of this study was to identify potential biomarkers associated with the side effects of dexamethasone in different tumors. Methods Gene Expression Omnibus database (GEO) datasets of blood tumors and solid tumors were retrieval to selected microarray data. The differentially expressed genes (DEGs) were identified. Gene ontology (GO) and pathway enrichment analyses, and protein–protein interaction (PPI) network analysis were performed. Results One hundred and eighty dexamethasone‐specific DEGs (92 up and 88 downregulated) were obtained in lymphoma cell samples (named as DEGs‐lymph), including APOD, TP53INP1, CLIC3, SERPINA9, and C3orf52. One hundred and four specific DEGs (100 up and 4 downregulated) were identified in prostate cancer cell samples (named as DEGs‐prostate), including COL6A2, OSBPL5, OLAH, OGFRL1, and SLC39A14. The significantly enriched GO terms of DEGs‐lymph contained cellular amino acid metabolic process and cell cycle. The most significantly enriched pathway of DEGs‐lymph was cytosolic tRNA aminoacylation. The DEGs‐prostate was enriched in 39 GO terms and two pathways, and the pathways were PPARA activates gene expression Homo sapiens, and insulin resistance. The PPI network of DEGs‐lymph gathered into two major clusters, WARS1 and CDC25A were representatives for them, respectively. One cluster was mainly involved in cytosolic tRNA aminoacylation, aminoacyl‐tRNA biosynthesis and the function of amino acid metabolism; another was associated with cell cycle and cell apoptosis. As for the PPI network of DEGs‐prostate, HELZ2 was the top nodes involved in the most protein–protein pairs, which was related to the pathway of “PPARA activates gene expression Homo sapiens.” Conclusions WARS1 and CDC25A might be potential biomarkers for side effects of dexamethasone in lymphoma, and HELZ2 in prostate cancer.https://doi.org/10.1002/mgg3.1160biomarkersdexamethasonegene expressionglucocorticoid receptorside effects
spellingShingle Da Jiang
Hui Jin
Jing Zuo
Yan Kong
Xue Zhang
Qian Dong
Zhihong Xu
Ying Li
Potential biomarkers screening to predict side effects of dexamethasone in different cancers
Molecular Genetics & Genomic Medicine
biomarkers
dexamethasone
gene expression
glucocorticoid receptor
side effects
title Potential biomarkers screening to predict side effects of dexamethasone in different cancers
title_full Potential biomarkers screening to predict side effects of dexamethasone in different cancers
title_fullStr Potential biomarkers screening to predict side effects of dexamethasone in different cancers
title_full_unstemmed Potential biomarkers screening to predict side effects of dexamethasone in different cancers
title_short Potential biomarkers screening to predict side effects of dexamethasone in different cancers
title_sort potential biomarkers screening to predict side effects of dexamethasone in different cancers
topic biomarkers
dexamethasone
gene expression
glucocorticoid receptor
side effects
url https://doi.org/10.1002/mgg3.1160
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