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Identifying GPSM Family Members as Potential Biomarkers in Breast Cancer: A Comprehensive Bioinformatics Analysis

G-protein signaling modulators (GPSMs) are a class of proteins involved in the regulation of G protein-coupled receptors, the most abundant family of cell-surface receptors that are crucial in the development of various tumors, including breast cancer. This study aims to identify the potential thera...

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Autores principales: Dang, Huy-Hoang, Ta, Hoang Dang Khoa, Nguyen, Truc T. T., Anuraga, Gangga, Wang, Chih-Yang, Lee, Kuen-Haur, Le, Nguyen Quoc Khanh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8471503/
https://www.ncbi.nlm.nih.gov/pubmed/34572330
http://dx.doi.org/10.3390/biomedicines9091144
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author Dang, Huy-Hoang
Ta, Hoang Dang Khoa
Nguyen, Truc T. T.
Anuraga, Gangga
Wang, Chih-Yang
Lee, Kuen-Haur
Le, Nguyen Quoc Khanh
author_facet Dang, Huy-Hoang
Ta, Hoang Dang Khoa
Nguyen, Truc T. T.
Anuraga, Gangga
Wang, Chih-Yang
Lee, Kuen-Haur
Le, Nguyen Quoc Khanh
author_sort Dang, Huy-Hoang
collection PubMed
description G-protein signaling modulators (GPSMs) are a class of proteins involved in the regulation of G protein-coupled receptors, the most abundant family of cell-surface receptors that are crucial in the development of various tumors, including breast cancer. This study aims to identify the potential therapeutic and prognostic roles of GPSMs in breast cancer. Oncomine and UALCAN databases were queried to determine GPSM expression levels in breast cancer tissues compared to normal samples. Survival analysis was conducted to reveal the prognostic significance of GPSMs in individuals with breast cancer. Functional enrichment analysis was performed using cBioPortal and MetaCore platforms. Finally, the association between GPSMs and immune infiltration cells in breast cancer was identified using the TIMER server. The experimental results then showed that all GPSM family members were significantly differentially expressed in breast cancer according to Oncomine and UALCAN data. Their expression levels were also associated with advanced tumor stages, and GPSM2 was found to be related to worse distant metastasis-free survival in patients with breast cancer. Functional enrichment analysis indicated that GPSMs were largely involved in cell division and cell cycle pathways. Finally, GPSM3 expression was correlated with the infiltration of several immune cells. Members of the GPSM class were differentially expressed in breast cancer. In conclusion, expression of GPSM2 was linked with worse distant metastasis-free outcomes, and hence could potentially serve as a prognostic biomarker. Furthermore, GPSM3 has potential to be a possible target for immunotherapy for breast cancer.
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spelling pubmed-84715032021-09-28 Identifying GPSM Family Members as Potential Biomarkers in Breast Cancer: A Comprehensive Bioinformatics Analysis Dang, Huy-Hoang Ta, Hoang Dang Khoa Nguyen, Truc T. T. Anuraga, Gangga Wang, Chih-Yang Lee, Kuen-Haur Le, Nguyen Quoc Khanh Biomedicines Article G-protein signaling modulators (GPSMs) are a class of proteins involved in the regulation of G protein-coupled receptors, the most abundant family of cell-surface receptors that are crucial in the development of various tumors, including breast cancer. This study aims to identify the potential therapeutic and prognostic roles of GPSMs in breast cancer. Oncomine and UALCAN databases were queried to determine GPSM expression levels in breast cancer tissues compared to normal samples. Survival analysis was conducted to reveal the prognostic significance of GPSMs in individuals with breast cancer. Functional enrichment analysis was performed using cBioPortal and MetaCore platforms. Finally, the association between GPSMs and immune infiltration cells in breast cancer was identified using the TIMER server. The experimental results then showed that all GPSM family members were significantly differentially expressed in breast cancer according to Oncomine and UALCAN data. Their expression levels were also associated with advanced tumor stages, and GPSM2 was found to be related to worse distant metastasis-free survival in patients with breast cancer. Functional enrichment analysis indicated that GPSMs were largely involved in cell division and cell cycle pathways. Finally, GPSM3 expression was correlated with the infiltration of several immune cells. Members of the GPSM class were differentially expressed in breast cancer. In conclusion, expression of GPSM2 was linked with worse distant metastasis-free outcomes, and hence could potentially serve as a prognostic biomarker. Furthermore, GPSM3 has potential to be a possible target for immunotherapy for breast cancer. MDPI 2021-09-03 /pmc/articles/PMC8471503/ /pubmed/34572330 http://dx.doi.org/10.3390/biomedicines9091144 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Dang, Huy-Hoang
Ta, Hoang Dang Khoa
Nguyen, Truc T. T.
Anuraga, Gangga
Wang, Chih-Yang
Lee, Kuen-Haur
Le, Nguyen Quoc Khanh
Identifying GPSM Family Members as Potential Biomarkers in Breast Cancer: A Comprehensive Bioinformatics Analysis
title Identifying GPSM Family Members as Potential Biomarkers in Breast Cancer: A Comprehensive Bioinformatics Analysis
title_full Identifying GPSM Family Members as Potential Biomarkers in Breast Cancer: A Comprehensive Bioinformatics Analysis
title_fullStr Identifying GPSM Family Members as Potential Biomarkers in Breast Cancer: A Comprehensive Bioinformatics Analysis
title_full_unstemmed Identifying GPSM Family Members as Potential Biomarkers in Breast Cancer: A Comprehensive Bioinformatics Analysis
title_short Identifying GPSM Family Members as Potential Biomarkers in Breast Cancer: A Comprehensive Bioinformatics Analysis
title_sort identifying gpsm family members as potential biomarkers in breast cancer: a comprehensive bioinformatics analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8471503/
https://www.ncbi.nlm.nih.gov/pubmed/34572330
http://dx.doi.org/10.3390/biomedicines9091144
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