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Integrated bioinformatics analysis reveals dynamic candidate genes and signaling pathways involved in the progression and prognosis of diffuse large B-cell lymphoma
BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous malignancy with varied outcomes. However, the fundamental mechanisms remain to be fully defined. AIM: We aimed to identify core differentially co-expressed hub genes and perturbed pathways relevant to the pathogenesis and pr...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8570165/ https://www.ncbi.nlm.nih.gov/pubmed/34760386 http://dx.doi.org/10.7717/peerj.12394 |
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author | Charwudzi, Alice Meng, Ye Hu, Linhui Ding, Chen Pu, Lianfang Li, Qian Xu, Mengling Zhai, Zhimin Xiong, Shudao |
author_facet | Charwudzi, Alice Meng, Ye Hu, Linhui Ding, Chen Pu, Lianfang Li, Qian Xu, Mengling Zhai, Zhimin Xiong, Shudao |
author_sort | Charwudzi, Alice |
collection | PubMed |
description | BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous malignancy with varied outcomes. However, the fundamental mechanisms remain to be fully defined. AIM: We aimed to identify core differentially co-expressed hub genes and perturbed pathways relevant to the pathogenesis and prognosis of DLBCL. METHODS: We retrieved the raw gene expression profile and clinical information of GSE12453 from the Gene Expression Omnibus (GEO) database. We used integrated bioinformatics analysis to identify differentially co-expressed genes. The CIBERSORT analysis was also applied to predict tumor-infiltrating immune cells (TIICs) in the GSE12453 dataset. We performed survival and ssGSEA (single-sample Gene Set Enrichment Analysis) (for TIICs) analyses and validated the hub genes using GEPIA2 and an independent GSE31312 dataset. RESULTS: We identified 46 differentially co-expressed hub genes in the GSE12453 dataset. Gene expression levels and survival analysis found 15 differentially co-expressed core hub genes. The core genes prognostic values and expression levels were further validated in the GEPIA2 database and GSE31312 dataset to be reliable (p < 0.01). The core genes’ main KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichments were Ribosome and Coronavirus disease-COVID-19. High expressions of the 15 core hub genes had prognostic value in DLBCL. The core genes showed significant predictive accuracy in distinguishing DLBCL cases from non-tumor controls, with the area under the curve (AUC) ranging from 0.992 to 1.00. Finally, CIBERSORT analysis on GSE12453 revealed immune cells, including activated memory CD4(+) T cells and M0, M1, and M2-macrophages as the infiltrates in the DLBCL microenvironment. CONCLUSION: Our study found differentially co-expressed core hub genes and relevant pathways involved in ribosome and COVID-19 disease that may be potential targets for prognosis and novel therapeutic intervention in DLBCL. |
format | Online Article Text |
id | pubmed-8570165 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85701652021-11-09 Integrated bioinformatics analysis reveals dynamic candidate genes and signaling pathways involved in the progression and prognosis of diffuse large B-cell lymphoma Charwudzi, Alice Meng, Ye Hu, Linhui Ding, Chen Pu, Lianfang Li, Qian Xu, Mengling Zhai, Zhimin Xiong, Shudao PeerJ Bioinformatics BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous malignancy with varied outcomes. However, the fundamental mechanisms remain to be fully defined. AIM: We aimed to identify core differentially co-expressed hub genes and perturbed pathways relevant to the pathogenesis and prognosis of DLBCL. METHODS: We retrieved the raw gene expression profile and clinical information of GSE12453 from the Gene Expression Omnibus (GEO) database. We used integrated bioinformatics analysis to identify differentially co-expressed genes. The CIBERSORT analysis was also applied to predict tumor-infiltrating immune cells (TIICs) in the GSE12453 dataset. We performed survival and ssGSEA (single-sample Gene Set Enrichment Analysis) (for TIICs) analyses and validated the hub genes using GEPIA2 and an independent GSE31312 dataset. RESULTS: We identified 46 differentially co-expressed hub genes in the GSE12453 dataset. Gene expression levels and survival analysis found 15 differentially co-expressed core hub genes. The core genes prognostic values and expression levels were further validated in the GEPIA2 database and GSE31312 dataset to be reliable (p < 0.01). The core genes’ main KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichments were Ribosome and Coronavirus disease-COVID-19. High expressions of the 15 core hub genes had prognostic value in DLBCL. The core genes showed significant predictive accuracy in distinguishing DLBCL cases from non-tumor controls, with the area under the curve (AUC) ranging from 0.992 to 1.00. Finally, CIBERSORT analysis on GSE12453 revealed immune cells, including activated memory CD4(+) T cells and M0, M1, and M2-macrophages as the infiltrates in the DLBCL microenvironment. CONCLUSION: Our study found differentially co-expressed core hub genes and relevant pathways involved in ribosome and COVID-19 disease that may be potential targets for prognosis and novel therapeutic intervention in DLBCL. PeerJ Inc. 2021-11-02 /pmc/articles/PMC8570165/ /pubmed/34760386 http://dx.doi.org/10.7717/peerj.12394 Text en © 2021 Charwudzi et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Charwudzi, Alice Meng, Ye Hu, Linhui Ding, Chen Pu, Lianfang Li, Qian Xu, Mengling Zhai, Zhimin Xiong, Shudao Integrated bioinformatics analysis reveals dynamic candidate genes and signaling pathways involved in the progression and prognosis of diffuse large B-cell lymphoma |
title | Integrated bioinformatics analysis reveals dynamic candidate genes and signaling pathways involved in the progression and prognosis of diffuse large B-cell lymphoma |
title_full | Integrated bioinformatics analysis reveals dynamic candidate genes and signaling pathways involved in the progression and prognosis of diffuse large B-cell lymphoma |
title_fullStr | Integrated bioinformatics analysis reveals dynamic candidate genes and signaling pathways involved in the progression and prognosis of diffuse large B-cell lymphoma |
title_full_unstemmed | Integrated bioinformatics analysis reveals dynamic candidate genes and signaling pathways involved in the progression and prognosis of diffuse large B-cell lymphoma |
title_short | Integrated bioinformatics analysis reveals dynamic candidate genes and signaling pathways involved in the progression and prognosis of diffuse large B-cell lymphoma |
title_sort | integrated bioinformatics analysis reveals dynamic candidate genes and signaling pathways involved in the progression and prognosis of diffuse large b-cell lymphoma |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8570165/ https://www.ncbi.nlm.nih.gov/pubmed/34760386 http://dx.doi.org/10.7717/peerj.12394 |
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