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Differential Gene Expression and Weighted Correlation Network Dynamics in High-Throughput Datasets of Prostate Cancer
Precision oncology is an absolute need today due to the emergence of treatment resistance and heterogeneity among cancerous profiles. Target-propelled cancer therapy is one of the treasures of precision oncology which has come together with substantial medical accomplishment. Prostate cancer is one...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9198298/ https://www.ncbi.nlm.nih.gov/pubmed/35719950 http://dx.doi.org/10.3389/fonc.2022.881246 |
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author | Mohammad, Taj Singh, Prithvi Jairajpuri, Deeba Shamim Al-Keridis, Lamya Ahmed Alshammari, Nawaf Adnan, Mohd. Dohare, Ravins Hassan, Md Imtaiyaz |
author_facet | Mohammad, Taj Singh, Prithvi Jairajpuri, Deeba Shamim Al-Keridis, Lamya Ahmed Alshammari, Nawaf Adnan, Mohd. Dohare, Ravins Hassan, Md Imtaiyaz |
author_sort | Mohammad, Taj |
collection | PubMed |
description | Precision oncology is an absolute need today due to the emergence of treatment resistance and heterogeneity among cancerous profiles. Target-propelled cancer therapy is one of the treasures of precision oncology which has come together with substantial medical accomplishment. Prostate cancer is one of the most common cancers in males, with tremendous biological heterogeneity in molecular and clinical behavior. The spectrum of molecular abnormalities and varying clinical patterns in prostate cancer suggest substantial heterogeneity among different profiles. To identify novel therapeutic targets and precise biomarkers implicated with prostate cancer, we performed a state-of-the-art bioinformatics study, beginning with analyzing high-throughput genomic datasets from The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA) suggests a set of five dysregulated hub genes (MAF, STAT6, SOX2, FOXO1, and WNT3A) that played crucial roles in biological pathways associated with prostate cancer progression. We found overexpressed STAT6 and SOX2 and proposed them as candidate biomarkers and potential targets in prostate cancer. Furthermore, the alteration frequencies in STAT6 and SOX2 and their impact on the patients’ survival were explored through the cBioPortal platform. The Kaplan-Meier survival analysis suggested that the alterations in the candidate genes were linked to the decreased overall survival of the patients. Altogether, the results signify that STAT6 and SOX2 and their genomic alterations can be explored in therapeutic interventions of prostate cancer for precision oncology, utilizing early diagnosis and target-propelled therapy. |
format | Online Article Text |
id | pubmed-9198298 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91982982022-06-16 Differential Gene Expression and Weighted Correlation Network Dynamics in High-Throughput Datasets of Prostate Cancer Mohammad, Taj Singh, Prithvi Jairajpuri, Deeba Shamim Al-Keridis, Lamya Ahmed Alshammari, Nawaf Adnan, Mohd. Dohare, Ravins Hassan, Md Imtaiyaz Front Oncol Oncology Precision oncology is an absolute need today due to the emergence of treatment resistance and heterogeneity among cancerous profiles. Target-propelled cancer therapy is one of the treasures of precision oncology which has come together with substantial medical accomplishment. Prostate cancer is one of the most common cancers in males, with tremendous biological heterogeneity in molecular and clinical behavior. The spectrum of molecular abnormalities and varying clinical patterns in prostate cancer suggest substantial heterogeneity among different profiles. To identify novel therapeutic targets and precise biomarkers implicated with prostate cancer, we performed a state-of-the-art bioinformatics study, beginning with analyzing high-throughput genomic datasets from The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA) suggests a set of five dysregulated hub genes (MAF, STAT6, SOX2, FOXO1, and WNT3A) that played crucial roles in biological pathways associated with prostate cancer progression. We found overexpressed STAT6 and SOX2 and proposed them as candidate biomarkers and potential targets in prostate cancer. Furthermore, the alteration frequencies in STAT6 and SOX2 and their impact on the patients’ survival were explored through the cBioPortal platform. The Kaplan-Meier survival analysis suggested that the alterations in the candidate genes were linked to the decreased overall survival of the patients. Altogether, the results signify that STAT6 and SOX2 and their genomic alterations can be explored in therapeutic interventions of prostate cancer for precision oncology, utilizing early diagnosis and target-propelled therapy. Frontiers Media S.A. 2022-06-01 /pmc/articles/PMC9198298/ /pubmed/35719950 http://dx.doi.org/10.3389/fonc.2022.881246 Text en Copyright © 2022 Mohammad, Singh, Jairajpuri, Al-Keridis, Alshammari, Adnan, Dohare and Hassan 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 | Oncology Mohammad, Taj Singh, Prithvi Jairajpuri, Deeba Shamim Al-Keridis, Lamya Ahmed Alshammari, Nawaf Adnan, Mohd. Dohare, Ravins Hassan, Md Imtaiyaz Differential Gene Expression and Weighted Correlation Network Dynamics in High-Throughput Datasets of Prostate Cancer |
title | Differential Gene Expression and Weighted Correlation Network Dynamics in High-Throughput Datasets of Prostate Cancer |
title_full | Differential Gene Expression and Weighted Correlation Network Dynamics in High-Throughput Datasets of Prostate Cancer |
title_fullStr | Differential Gene Expression and Weighted Correlation Network Dynamics in High-Throughput Datasets of Prostate Cancer |
title_full_unstemmed | Differential Gene Expression and Weighted Correlation Network Dynamics in High-Throughput Datasets of Prostate Cancer |
title_short | Differential Gene Expression and Weighted Correlation Network Dynamics in High-Throughput Datasets of Prostate Cancer |
title_sort | differential gene expression and weighted correlation network dynamics in high-throughput datasets of prostate cancer |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9198298/ https://www.ncbi.nlm.nih.gov/pubmed/35719950 http://dx.doi.org/10.3389/fonc.2022.881246 |
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