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Meta-Analysis of EGF-Stimulated Normal and Cancer Cell Lines to Discover EGF-Associated Oncogenic Signaling Pathways and Prognostic Biomarkers
BACKGROUND: Although epidermal growth factor (EGF) controls many crucial processes in the human body, it can increase the risk of developing cancer when overexpresses. OBJECTIVES: This study focused on detecting cancer-associated genes that are dysregulated by EGF overexpression. MATERIALS AND METHO...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Institute of Genetic Engineering and Biotechnology
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9618017/ https://www.ncbi.nlm.nih.gov/pubmed/36381277 http://dx.doi.org/10.30498/ijb.2022.323464.3245 |
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author | Garousi, Shahrokh Jahanbakhsh Godehkahriz, Sodabeh Esfahani, Kasra Lohrasebi, Tahmineh Mousavi, Amir Hatef Salmanian, Ali Rezvani, Mahsa Moein, Maryam |
author_facet | Garousi, Shahrokh Jahanbakhsh Godehkahriz, Sodabeh Esfahani, Kasra Lohrasebi, Tahmineh Mousavi, Amir Hatef Salmanian, Ali Rezvani, Mahsa Moein, Maryam |
author_sort | Garousi, Shahrokh |
collection | PubMed |
description | BACKGROUND: Although epidermal growth factor (EGF) controls many crucial processes in the human body, it can increase the risk of developing cancer when overexpresses. OBJECTIVES: This study focused on detecting cancer-associated genes that are dysregulated by EGF overexpression. MATERIALS AND METHODS: To identify differentially expressed genes (DEGs), two independent meta-analyses with normal and cancer RNA-Seq samples treated by EGF were conducted. The new DEGs detected only via two meta-analyses were used in all downstream analyses. To reach count data, the tools of FastQC, Trimmomatic, HISAT2, SAMtools, and HTSeq-count were employed. DEGs in each individual RNA-Seq study and the meta-analysis of RNA-Seq studies were identified using DESeq2 and metaSeq R package, respectively. MCODE detected densely interconnected top clusters in the protein-protein interaction (PPI) network of DEGs obtained from normal and cancer datasets. The DEGs were then introduced to Enrichr and ClueGO/CluePedia, and terms, pathways, and hub genes enriched in Gene Ontology (GO) and KEGG and Reactome were detected. RESULTS: The meta-analysis of normal and cancer datasets revealed 990 and 541 new DEGs, all upregulated. A number of DEGs were enriched in protein K48-linked deubiquitination, ncRNA processing, ribosomal large subunit binding, and protein processing in endoplasmic reticulum. Hub genes overexpression (DHX33, INTS8, NMD3, OTUD4, P4HB, RPS3A, SEC13, SKP1, USP34, USP9X, and YOD1) in tumor samples were validated by TCGA and GTEx databases. Overall survival and disease-free survival analysis also confirmed worse survival in patients with hub genes overexpression. CONCLUSIONS: The detected hub genes could be used as cancer biomarkers when EGF overexpresses. |
format | Online Article Text |
id | pubmed-9618017 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | National Institute of Genetic Engineering and Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-96180172022-11-14 Meta-Analysis of EGF-Stimulated Normal and Cancer Cell Lines to Discover EGF-Associated Oncogenic Signaling Pathways and Prognostic Biomarkers Garousi, Shahrokh Jahanbakhsh Godehkahriz, Sodabeh Esfahani, Kasra Lohrasebi, Tahmineh Mousavi, Amir Hatef Salmanian, Ali Rezvani, Mahsa Moein, Maryam Iran J Biotechnol Research Article BACKGROUND: Although epidermal growth factor (EGF) controls many crucial processes in the human body, it can increase the risk of developing cancer when overexpresses. OBJECTIVES: This study focused on detecting cancer-associated genes that are dysregulated by EGF overexpression. MATERIALS AND METHODS: To identify differentially expressed genes (DEGs), two independent meta-analyses with normal and cancer RNA-Seq samples treated by EGF were conducted. The new DEGs detected only via two meta-analyses were used in all downstream analyses. To reach count data, the tools of FastQC, Trimmomatic, HISAT2, SAMtools, and HTSeq-count were employed. DEGs in each individual RNA-Seq study and the meta-analysis of RNA-Seq studies were identified using DESeq2 and metaSeq R package, respectively. MCODE detected densely interconnected top clusters in the protein-protein interaction (PPI) network of DEGs obtained from normal and cancer datasets. The DEGs were then introduced to Enrichr and ClueGO/CluePedia, and terms, pathways, and hub genes enriched in Gene Ontology (GO) and KEGG and Reactome were detected. RESULTS: The meta-analysis of normal and cancer datasets revealed 990 and 541 new DEGs, all upregulated. A number of DEGs were enriched in protein K48-linked deubiquitination, ncRNA processing, ribosomal large subunit binding, and protein processing in endoplasmic reticulum. Hub genes overexpression (DHX33, INTS8, NMD3, OTUD4, P4HB, RPS3A, SEC13, SKP1, USP34, USP9X, and YOD1) in tumor samples were validated by TCGA and GTEx databases. Overall survival and disease-free survival analysis also confirmed worse survival in patients with hub genes overexpression. CONCLUSIONS: The detected hub genes could be used as cancer biomarkers when EGF overexpresses. National Institute of Genetic Engineering and Biotechnology 2022-07-01 /pmc/articles/PMC9618017/ /pubmed/36381277 http://dx.doi.org/10.30498/ijb.2022.323464.3245 Text en Copyright: © 2021 The Author(s); Published by Iranian Journal of Biotechnology https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 Unported License, ( http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Garousi, Shahrokh Jahanbakhsh Godehkahriz, Sodabeh Esfahani, Kasra Lohrasebi, Tahmineh Mousavi, Amir Hatef Salmanian, Ali Rezvani, Mahsa Moein, Maryam Meta-Analysis of EGF-Stimulated Normal and Cancer Cell Lines to Discover EGF-Associated Oncogenic Signaling Pathways and Prognostic Biomarkers |
title | Meta-Analysis of EGF-Stimulated Normal and Cancer Cell Lines to Discover EGF-Associated Oncogenic Signaling Pathways and Prognostic Biomarkers |
title_full | Meta-Analysis of EGF-Stimulated Normal and Cancer Cell Lines to Discover EGF-Associated Oncogenic Signaling Pathways and Prognostic Biomarkers |
title_fullStr | Meta-Analysis of EGF-Stimulated Normal and Cancer Cell Lines to Discover EGF-Associated Oncogenic Signaling Pathways and Prognostic Biomarkers |
title_full_unstemmed | Meta-Analysis of EGF-Stimulated Normal and Cancer Cell Lines to Discover EGF-Associated Oncogenic Signaling Pathways and Prognostic Biomarkers |
title_short | Meta-Analysis of EGF-Stimulated Normal and Cancer Cell Lines to Discover EGF-Associated Oncogenic Signaling Pathways and Prognostic Biomarkers |
title_sort | meta-analysis of egf-stimulated normal and cancer cell lines to discover egf-associated oncogenic signaling pathways and prognostic biomarkers |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9618017/ https://www.ncbi.nlm.nih.gov/pubmed/36381277 http://dx.doi.org/10.30498/ijb.2022.323464.3245 |
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