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Identifying Key Lysosome-Related Genes Associated with Drug-Resistant Breast Cancer Using Computational and Systems Biology Approach

BACKGROUND: Drug resistance in breast cancer is an unsolved problem in treating patients. It has been recently discussed that lysosomes contribute to the invasion and angiogenesis of cancer cells. There is evidence that lysosomes can also cause multi-drug resistance. We analyzed this emerging concep...

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Autores principales: Shiralipour, Aref, Khorsand, Babak, Jafari, Leila, Salehi, Mohammad, Kazemi, Mahsa, Zahiri, Javad, Jajarmi, Vahid, Kazemi, Bahram
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Brieflands 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10007991/
https://www.ncbi.nlm.nih.gov/pubmed/36915401
http://dx.doi.org/10.5812/ijpr-130342
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author Shiralipour, Aref
Khorsand, Babak
Jafari, Leila
Salehi, Mohammad
Kazemi, Mahsa
Zahiri, Javad
Jajarmi, Vahid
Kazemi, Bahram
author_facet Shiralipour, Aref
Khorsand, Babak
Jafari, Leila
Salehi, Mohammad
Kazemi, Mahsa
Zahiri, Javad
Jajarmi, Vahid
Kazemi, Bahram
author_sort Shiralipour, Aref
collection PubMed
description BACKGROUND: Drug resistance in breast cancer is an unsolved problem in treating patients. It has been recently discussed that lysosomes contribute to the invasion and angiogenesis of cancer cells. There is evidence that lysosomes can also cause multi-drug resistance. We analyzed this emerging concept in breast cancer through computational and systems biology approaches. OBJECTIVES: We aimed to identify the key lysosome-related genes associated with drug-resistant breast cancer. METHODS: All genes contributing to the structure and function of lysosomes were inquired through the Human Lysosome Gene Database. The prioritized top 51 genes from the provided lists of Endeavour, ToppGene, and GPSy as prioritization tools were selected. All lysosomal genes and 12 breast cancer-related genes aligned to identify the most similar genes to breast cancer-related genes. Different centralities were applied to score each human protein to calculate the most central lysosomal genes in the human protein-protein interaction (PPI) network. Common genes were extracted from the results of the mentioned methods as a selected gene set. For Gene Ontology enrichment, the selected gene set was analyzed by WebGestalt, DAVID, and KOBAS. The PPI network was constructed via the STRING database. The PPI network was analyzed utilizing Cytoscape for topology network interaction and CytoHubba to extract hub genes. RESULTS: Based on biological studies, literature reviews, and comparing all mentioned analyzing methods, six genes were introduced as essential in breast cancer. This computational approach to all lysosome-related genes suggested that candidate genes include PRF1, TLR9, CLTC, GJA1, AP3B1, and RPTOR. The analyses of these six genes suggest that they may have a crucial role in breast cancer development, which has rarely been evaluated. These genes have a potential therapeutic implication for new drug discovery for chemo-resistant breast cancer. CONCLUSIONS: The present work focused on all the functional and structural lysosome-related genes associated with breast cancer. It revealed the top six lysosome hub genes that might serve as therapeutic targets in drug-resistant breast cancer. Since these genes play a pivotal role in the structure and function of lysosomes, targeting them can effectively overcome drug resistance.
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spelling pubmed-100079912023-03-12 Identifying Key Lysosome-Related Genes Associated with Drug-Resistant Breast Cancer Using Computational and Systems Biology Approach Shiralipour, Aref Khorsand, Babak Jafari, Leila Salehi, Mohammad Kazemi, Mahsa Zahiri, Javad Jajarmi, Vahid Kazemi, Bahram Iran J Pharm Res Research Article BACKGROUND: Drug resistance in breast cancer is an unsolved problem in treating patients. It has been recently discussed that lysosomes contribute to the invasion and angiogenesis of cancer cells. There is evidence that lysosomes can also cause multi-drug resistance. We analyzed this emerging concept in breast cancer through computational and systems biology approaches. OBJECTIVES: We aimed to identify the key lysosome-related genes associated with drug-resistant breast cancer. METHODS: All genes contributing to the structure and function of lysosomes were inquired through the Human Lysosome Gene Database. The prioritized top 51 genes from the provided lists of Endeavour, ToppGene, and GPSy as prioritization tools were selected. All lysosomal genes and 12 breast cancer-related genes aligned to identify the most similar genes to breast cancer-related genes. Different centralities were applied to score each human protein to calculate the most central lysosomal genes in the human protein-protein interaction (PPI) network. Common genes were extracted from the results of the mentioned methods as a selected gene set. For Gene Ontology enrichment, the selected gene set was analyzed by WebGestalt, DAVID, and KOBAS. The PPI network was constructed via the STRING database. The PPI network was analyzed utilizing Cytoscape for topology network interaction and CytoHubba to extract hub genes. RESULTS: Based on biological studies, literature reviews, and comparing all mentioned analyzing methods, six genes were introduced as essential in breast cancer. This computational approach to all lysosome-related genes suggested that candidate genes include PRF1, TLR9, CLTC, GJA1, AP3B1, and RPTOR. The analyses of these six genes suggest that they may have a crucial role in breast cancer development, which has rarely been evaluated. These genes have a potential therapeutic implication for new drug discovery for chemo-resistant breast cancer. CONCLUSIONS: The present work focused on all the functional and structural lysosome-related genes associated with breast cancer. It revealed the top six lysosome hub genes that might serve as therapeutic targets in drug-resistant breast cancer. Since these genes play a pivotal role in the structure and function of lysosomes, targeting them can effectively overcome drug resistance. Brieflands 2022-10-15 /pmc/articles/PMC10007991/ /pubmed/36915401 http://dx.doi.org/10.5812/ijpr-130342 Text en Copyright © 2022, Author(s) 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 International License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.
spellingShingle Research Article
Shiralipour, Aref
Khorsand, Babak
Jafari, Leila
Salehi, Mohammad
Kazemi, Mahsa
Zahiri, Javad
Jajarmi, Vahid
Kazemi, Bahram
Identifying Key Lysosome-Related Genes Associated with Drug-Resistant Breast Cancer Using Computational and Systems Biology Approach
title Identifying Key Lysosome-Related Genes Associated with Drug-Resistant Breast Cancer Using Computational and Systems Biology Approach
title_full Identifying Key Lysosome-Related Genes Associated with Drug-Resistant Breast Cancer Using Computational and Systems Biology Approach
title_fullStr Identifying Key Lysosome-Related Genes Associated with Drug-Resistant Breast Cancer Using Computational and Systems Biology Approach
title_full_unstemmed Identifying Key Lysosome-Related Genes Associated with Drug-Resistant Breast Cancer Using Computational and Systems Biology Approach
title_short Identifying Key Lysosome-Related Genes Associated with Drug-Resistant Breast Cancer Using Computational and Systems Biology Approach
title_sort identifying key lysosome-related genes associated with drug-resistant breast cancer using computational and systems biology approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10007991/
https://www.ncbi.nlm.nih.gov/pubmed/36915401
http://dx.doi.org/10.5812/ijpr-130342
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