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Integrated Datasets of Proteomic and Metabolomic Biomarkers to Predict Its Impacts on Comorbidities of Type 2 Diabetes Mellitus

OBJECTIVE: The objective of the current study is to accomplish a relative exploration of the biological roles of differentially dysregulated genes (DRGs) in type 2 diabetes mellitus (T2DM). The study aimed to determine the impact of these DRGs on the biological pathways and networks that are related...

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Autores principales: Cheema, Amrita K, Kaur, Prabhjit, Fadel, Amina, Younes, Noura, Zirie, Mahmoud, Rizk, Nasser M
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7354282/
https://www.ncbi.nlm.nih.gov/pubmed/32753925
http://dx.doi.org/10.2147/DMSO.S244432
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author Cheema, Amrita K
Kaur, Prabhjit
Fadel, Amina
Younes, Noura
Zirie, Mahmoud
Rizk, Nasser M
author_facet Cheema, Amrita K
Kaur, Prabhjit
Fadel, Amina
Younes, Noura
Zirie, Mahmoud
Rizk, Nasser M
author_sort Cheema, Amrita K
collection PubMed
description OBJECTIVE: The objective of the current study is to accomplish a relative exploration of the biological roles of differentially dysregulated genes (DRGs) in type 2 diabetes mellitus (T2DM). The study aimed to determine the impact of these DRGs on the biological pathways and networks that are related to the associated disorders and complications in T2DM and to predict its role as prospective biomarkers. METHODS: Datasets obtained from metabolomic and proteomic profiling were used for investigation of the differential expression of the genes. A subset of DRGs was integrated into IPA software to explore its biological pathways, related diseases, and their regulation in T2DM. Upon entry into the IPA, only 94 of the DRGs were recognizable, mapped, and matched within the database. RESULTS: The study identified networks that explore the dysregulation of several functions; cell components such as degranulation of cells; molecular transport process and metabolism of cellular proteins; and inflammatory responses. Top disorders associated with DRGs in T2DM are related to organ injuries such as renal damage, connective tissue disorders, and acute inflammatory disorders. Upstream regulator analysis predicted the role of several transcription factors of interest, such as STAT3 and HIF alpha, as well as many kinases such as JAK kinases, which affects the gene expression of the dataset in T2DM. Interleukin 6 (IL6) is the top regulator of the DRGs, followed by leptin (LEP). Monitoring the dysregulation of the coupled expression of the following biomarkers (TNF, IL6, LEP, AGT, APOE, F2, SPP1, and INS) highlights that they could be used as potential prognostic biomarkers. CONCLUSION: The integration of data obtained by advanced metabolomic and proteomic technologies has made it probable to advantage in understanding the role of these biomarkers in the identification of significant biological processes, pathways, and regulators that are associated with T2DM and its comorbidities.
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spelling pubmed-73542822020-08-03 Integrated Datasets of Proteomic and Metabolomic Biomarkers to Predict Its Impacts on Comorbidities of Type 2 Diabetes Mellitus Cheema, Amrita K Kaur, Prabhjit Fadel, Amina Younes, Noura Zirie, Mahmoud Rizk, Nasser M Diabetes Metab Syndr Obes Original Research OBJECTIVE: The objective of the current study is to accomplish a relative exploration of the biological roles of differentially dysregulated genes (DRGs) in type 2 diabetes mellitus (T2DM). The study aimed to determine the impact of these DRGs on the biological pathways and networks that are related to the associated disorders and complications in T2DM and to predict its role as prospective biomarkers. METHODS: Datasets obtained from metabolomic and proteomic profiling were used for investigation of the differential expression of the genes. A subset of DRGs was integrated into IPA software to explore its biological pathways, related diseases, and their regulation in T2DM. Upon entry into the IPA, only 94 of the DRGs were recognizable, mapped, and matched within the database. RESULTS: The study identified networks that explore the dysregulation of several functions; cell components such as degranulation of cells; molecular transport process and metabolism of cellular proteins; and inflammatory responses. Top disorders associated with DRGs in T2DM are related to organ injuries such as renal damage, connective tissue disorders, and acute inflammatory disorders. Upstream regulator analysis predicted the role of several transcription factors of interest, such as STAT3 and HIF alpha, as well as many kinases such as JAK kinases, which affects the gene expression of the dataset in T2DM. Interleukin 6 (IL6) is the top regulator of the DRGs, followed by leptin (LEP). Monitoring the dysregulation of the coupled expression of the following biomarkers (TNF, IL6, LEP, AGT, APOE, F2, SPP1, and INS) highlights that they could be used as potential prognostic biomarkers. CONCLUSION: The integration of data obtained by advanced metabolomic and proteomic technologies has made it probable to advantage in understanding the role of these biomarkers in the identification of significant biological processes, pathways, and regulators that are associated with T2DM and its comorbidities. Dove 2020-07-07 /pmc/articles/PMC7354282/ /pubmed/32753925 http://dx.doi.org/10.2147/DMSO.S244432 Text en © 2020 Cheema et al. http://creativecommons.org/licenses/by/4.0/ This work is published by Dove Medical Press Limited, and licensed under a Creative Commons Attribution License. The full terms of the License are available at http://creativecommons.org/licenses/by/4.0/. The license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Original Research
Cheema, Amrita K
Kaur, Prabhjit
Fadel, Amina
Younes, Noura
Zirie, Mahmoud
Rizk, Nasser M
Integrated Datasets of Proteomic and Metabolomic Biomarkers to Predict Its Impacts on Comorbidities of Type 2 Diabetes Mellitus
title Integrated Datasets of Proteomic and Metabolomic Biomarkers to Predict Its Impacts on Comorbidities of Type 2 Diabetes Mellitus
title_full Integrated Datasets of Proteomic and Metabolomic Biomarkers to Predict Its Impacts on Comorbidities of Type 2 Diabetes Mellitus
title_fullStr Integrated Datasets of Proteomic and Metabolomic Biomarkers to Predict Its Impacts on Comorbidities of Type 2 Diabetes Mellitus
title_full_unstemmed Integrated Datasets of Proteomic and Metabolomic Biomarkers to Predict Its Impacts on Comorbidities of Type 2 Diabetes Mellitus
title_short Integrated Datasets of Proteomic and Metabolomic Biomarkers to Predict Its Impacts on Comorbidities of Type 2 Diabetes Mellitus
title_sort integrated datasets of proteomic and metabolomic biomarkers to predict its impacts on comorbidities of type 2 diabetes mellitus
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7354282/
https://www.ncbi.nlm.nih.gov/pubmed/32753925
http://dx.doi.org/10.2147/DMSO.S244432
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