Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Mohammad, Taj, Singh, Prithvi, Jairajpuri, Deeba Shamim, Al-Keridis, Lamya Ahmed, Alshammari, Nawaf, Adnan, Mohd., Dohare, Ravins, Hassan, Md Imtaiyaz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
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
_version_ 1784727582758928384
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
work_keys_str_mv AT mohammadtaj differentialgeneexpressionandweightedcorrelationnetworkdynamicsinhighthroughputdatasetsofprostatecancer
AT singhprithvi differentialgeneexpressionandweightedcorrelationnetworkdynamicsinhighthroughputdatasetsofprostatecancer
AT jairajpurideebashamim differentialgeneexpressionandweightedcorrelationnetworkdynamicsinhighthroughputdatasetsofprostatecancer
AT alkeridislamyaahmed differentialgeneexpressionandweightedcorrelationnetworkdynamicsinhighthroughputdatasetsofprostatecancer
AT alshammarinawaf differentialgeneexpressionandweightedcorrelationnetworkdynamicsinhighthroughputdatasetsofprostatecancer
AT adnanmohd differentialgeneexpressionandweightedcorrelationnetworkdynamicsinhighthroughputdatasetsofprostatecancer
AT dohareravins differentialgeneexpressionandweightedcorrelationnetworkdynamicsinhighthroughputdatasetsofprostatecancer
AT hassanmdimtaiyaz differentialgeneexpressionandweightedcorrelationnetworkdynamicsinhighthroughputdatasetsofprostatecancer