Cargando…

Identifying hub genes and common biological pathways between COVID-19 and benign prostatic hyperplasia by machine learning algorithms

BACKGROUND: COVID-19, a serious respiratory disease that has the potential to affect numerous organs, is a serious threat to the health of people around the world. The objective of this article is to investigate the potential biological targets and mechanisms by which SARS-CoV-2 affects benign prost...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhou, Hang, Xu, Mingming, Hu, Ping, Li, Yuezheng, Ren, Congzhe, Li, Muwei, Pan, Yang, Wang, Shangren, Liu, Xiaoqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10328422/
https://www.ncbi.nlm.nih.gov/pubmed/37426635
http://dx.doi.org/10.3389/fimmu.2023.1172724
_version_ 1785069796465836032
author Zhou, Hang
Xu, Mingming
Hu, Ping
Li, Yuezheng
Ren, Congzhe
Li, Muwei
Pan, Yang
Wang, Shangren
Liu, Xiaoqiang
author_facet Zhou, Hang
Xu, Mingming
Hu, Ping
Li, Yuezheng
Ren, Congzhe
Li, Muwei
Pan, Yang
Wang, Shangren
Liu, Xiaoqiang
author_sort Zhou, Hang
collection PubMed
description BACKGROUND: COVID-19, a serious respiratory disease that has the potential to affect numerous organs, is a serious threat to the health of people around the world. The objective of this article is to investigate the potential biological targets and mechanisms by which SARS-CoV-2 affects benign prostatic hyperplasia (BPH) and related symptoms. METHODS: We downloaded the COVID-19 datasets (GSE157103 and GSE166253) and the BPH datasets (GSE7307 and GSE132714) from the Gene Expression Omnibus (GEO) database. In GSE157103 and GSE7307, differentially expressed genes (DEGs) were found using the “Limma” package, and the intersection was utilized to obtain common DEGs. Further analyses followed, including those using Protein-Protein Interaction (PPI), Gene Ontology (GO) function enrichment analysis, and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Potential hub genes were screened using three machine learning methods, and they were later verified using GSE132714 and GSE166253. The CIBERSORT analysis and the identification of transcription factors, miRNAs, and drugs as candidates were among the subsequent analyses. RESULTS: We identified 97 common DEGs from GSE157103 and GSE7307. According to the GO and KEGG analyses, the primary gene enrichment pathways were immune-related pathways. Machine learning methods were used to identify five hub genes (BIRC5, DNAJC4, DTL, LILRB2, and NDC80). They had good diagnostic properties in the training sets and were validated in the validation sets. According to CIBERSORT analysis, hub genes were closely related to CD4 memory activated of T cells, T cells regulatory and NK cells activated. The top 10 drug candidates (lucanthone, phytoestrogens, etoposide, dasatinib, piroxicam, pyrvinium, rapamycin, niclosamide, genistein, and testosterone) will also be evaluated by the P value, which is expected to be helpful for the treatment of COVID-19-infected patients with BPH. CONCLUSION: Our findings reveal common signaling pathways, possible biological targets, and promising small molecule drugs for BPH and COVID-19. This is crucial to understand the potential common pathogenic and susceptibility pathways between them.
format Online
Article
Text
id pubmed-10328422
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-103284222023-07-08 Identifying hub genes and common biological pathways between COVID-19 and benign prostatic hyperplasia by machine learning algorithms Zhou, Hang Xu, Mingming Hu, Ping Li, Yuezheng Ren, Congzhe Li, Muwei Pan, Yang Wang, Shangren Liu, Xiaoqiang Front Immunol Immunology BACKGROUND: COVID-19, a serious respiratory disease that has the potential to affect numerous organs, is a serious threat to the health of people around the world. The objective of this article is to investigate the potential biological targets and mechanisms by which SARS-CoV-2 affects benign prostatic hyperplasia (BPH) and related symptoms. METHODS: We downloaded the COVID-19 datasets (GSE157103 and GSE166253) and the BPH datasets (GSE7307 and GSE132714) from the Gene Expression Omnibus (GEO) database. In GSE157103 and GSE7307, differentially expressed genes (DEGs) were found using the “Limma” package, and the intersection was utilized to obtain common DEGs. Further analyses followed, including those using Protein-Protein Interaction (PPI), Gene Ontology (GO) function enrichment analysis, and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Potential hub genes were screened using three machine learning methods, and they were later verified using GSE132714 and GSE166253. The CIBERSORT analysis and the identification of transcription factors, miRNAs, and drugs as candidates were among the subsequent analyses. RESULTS: We identified 97 common DEGs from GSE157103 and GSE7307. According to the GO and KEGG analyses, the primary gene enrichment pathways were immune-related pathways. Machine learning methods were used to identify five hub genes (BIRC5, DNAJC4, DTL, LILRB2, and NDC80). They had good diagnostic properties in the training sets and were validated in the validation sets. According to CIBERSORT analysis, hub genes were closely related to CD4 memory activated of T cells, T cells regulatory and NK cells activated. The top 10 drug candidates (lucanthone, phytoestrogens, etoposide, dasatinib, piroxicam, pyrvinium, rapamycin, niclosamide, genistein, and testosterone) will also be evaluated by the P value, which is expected to be helpful for the treatment of COVID-19-infected patients with BPH. CONCLUSION: Our findings reveal common signaling pathways, possible biological targets, and promising small molecule drugs for BPH and COVID-19. This is crucial to understand the potential common pathogenic and susceptibility pathways between them. Frontiers Media S.A. 2023-06-23 /pmc/articles/PMC10328422/ /pubmed/37426635 http://dx.doi.org/10.3389/fimmu.2023.1172724 Text en Copyright © 2023 Zhou, Xu, Hu, Li, Ren, Li, Pan, Wang and Liu 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
Zhou, Hang
Xu, Mingming
Hu, Ping
Li, Yuezheng
Ren, Congzhe
Li, Muwei
Pan, Yang
Wang, Shangren
Liu, Xiaoqiang
Identifying hub genes and common biological pathways between COVID-19 and benign prostatic hyperplasia by machine learning algorithms
title Identifying hub genes and common biological pathways between COVID-19 and benign prostatic hyperplasia by machine learning algorithms
title_full Identifying hub genes and common biological pathways between COVID-19 and benign prostatic hyperplasia by machine learning algorithms
title_fullStr Identifying hub genes and common biological pathways between COVID-19 and benign prostatic hyperplasia by machine learning algorithms
title_full_unstemmed Identifying hub genes and common biological pathways between COVID-19 and benign prostatic hyperplasia by machine learning algorithms
title_short Identifying hub genes and common biological pathways between COVID-19 and benign prostatic hyperplasia by machine learning algorithms
title_sort identifying hub genes and common biological pathways between covid-19 and benign prostatic hyperplasia by machine learning algorithms
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10328422/
https://www.ncbi.nlm.nih.gov/pubmed/37426635
http://dx.doi.org/10.3389/fimmu.2023.1172724
work_keys_str_mv AT zhouhang identifyinghubgenesandcommonbiologicalpathwaysbetweencovid19andbenignprostatichyperplasiabymachinelearningalgorithms
AT xumingming identifyinghubgenesandcommonbiologicalpathwaysbetweencovid19andbenignprostatichyperplasiabymachinelearningalgorithms
AT huping identifyinghubgenesandcommonbiologicalpathwaysbetweencovid19andbenignprostatichyperplasiabymachinelearningalgorithms
AT liyuezheng identifyinghubgenesandcommonbiologicalpathwaysbetweencovid19andbenignprostatichyperplasiabymachinelearningalgorithms
AT rencongzhe identifyinghubgenesandcommonbiologicalpathwaysbetweencovid19andbenignprostatichyperplasiabymachinelearningalgorithms
AT limuwei identifyinghubgenesandcommonbiologicalpathwaysbetweencovid19andbenignprostatichyperplasiabymachinelearningalgorithms
AT panyang identifyinghubgenesandcommonbiologicalpathwaysbetweencovid19andbenignprostatichyperplasiabymachinelearningalgorithms
AT wangshangren identifyinghubgenesandcommonbiologicalpathwaysbetweencovid19andbenignprostatichyperplasiabymachinelearningalgorithms
AT liuxiaoqiang identifyinghubgenesandcommonbiologicalpathwaysbetweencovid19andbenignprostatichyperplasiabymachinelearningalgorithms