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Identification of Prostate Cancer Risk Genetics Biomarkers Based on Intergraded Bioinformatics Analysis

BACKGROUND: Prostate cancer (PCa) is one of the most popular cancer types in men. Nevertheless, the pathogenic mechanisms of PCa are poorly understood. Hence, we aimed to identify the potential genetic biomarker of PCa in the present study. METHODS: High-throughput data set GSE46602 was obtained fro...

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Autores principales: Liang, Xiangdong, Wang, Yanchao, Pei, Long, Tan, Xiaoliang, Dong, Chunhui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8967941/
https://www.ncbi.nlm.nih.gov/pubmed/35372462
http://dx.doi.org/10.3389/fsurg.2022.856446
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author Liang, Xiangdong
Wang, Yanchao
Pei, Long
Tan, Xiaoliang
Dong, Chunhui
author_facet Liang, Xiangdong
Wang, Yanchao
Pei, Long
Tan, Xiaoliang
Dong, Chunhui
author_sort Liang, Xiangdong
collection PubMed
description BACKGROUND: Prostate cancer (PCa) is one of the most popular cancer types in men. Nevertheless, the pathogenic mechanisms of PCa are poorly understood. Hence, we aimed to identify the potential genetic biomarker of PCa in the present study. METHODS: High-throughput data set GSE46602 was obtained from the comprehensive gene expression database (GEO) for screening differentially expressed genes (DEGs). The common DEGs were further screened out using The Cancer Genome Atlas (TCGA) dataset. Functional enrichment analysis includes Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to study related mechanisms. The Cox and Lasso regression analyses were carried out to compress the target genes and construct the high-risk and low-risk gene model. Survival analyses were performed based on the gene risk signature model. The CIBERSORT algorithm was performed to clarify the correlation of the high- and low-risk gene model in risk and infiltration of immune cells in PCa. RESULTS: A total of 385 common DEGs were obtained. The results of functional enrichment analysis show that common DEGs play an important role in PCa. A three-gene signature model (KCNK3, AK5, and ARHGEF38) was established, and the model was significantly associated with cancer-related pathways, overall survival (OS), and tumor microenvironment (TME)-related immune cells in PCa. CONCLUSION: This new risk model may contribute to further investigation in the immune-related pathogenesis in progression of PCa.
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spelling pubmed-89679412022-04-01 Identification of Prostate Cancer Risk Genetics Biomarkers Based on Intergraded Bioinformatics Analysis Liang, Xiangdong Wang, Yanchao Pei, Long Tan, Xiaoliang Dong, Chunhui Front Surg Surgery BACKGROUND: Prostate cancer (PCa) is one of the most popular cancer types in men. Nevertheless, the pathogenic mechanisms of PCa are poorly understood. Hence, we aimed to identify the potential genetic biomarker of PCa in the present study. METHODS: High-throughput data set GSE46602 was obtained from the comprehensive gene expression database (GEO) for screening differentially expressed genes (DEGs). The common DEGs were further screened out using The Cancer Genome Atlas (TCGA) dataset. Functional enrichment analysis includes Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to study related mechanisms. The Cox and Lasso regression analyses were carried out to compress the target genes and construct the high-risk and low-risk gene model. Survival analyses were performed based on the gene risk signature model. The CIBERSORT algorithm was performed to clarify the correlation of the high- and low-risk gene model in risk and infiltration of immune cells in PCa. RESULTS: A total of 385 common DEGs were obtained. The results of functional enrichment analysis show that common DEGs play an important role in PCa. A three-gene signature model (KCNK3, AK5, and ARHGEF38) was established, and the model was significantly associated with cancer-related pathways, overall survival (OS), and tumor microenvironment (TME)-related immune cells in PCa. CONCLUSION: This new risk model may contribute to further investigation in the immune-related pathogenesis in progression of PCa. Frontiers Media S.A. 2022-03-17 /pmc/articles/PMC8967941/ /pubmed/35372462 http://dx.doi.org/10.3389/fsurg.2022.856446 Text en Copyright © 2022 Liang, Wang, Pei, Tan and Dong. 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 Surgery
Liang, Xiangdong
Wang, Yanchao
Pei, Long
Tan, Xiaoliang
Dong, Chunhui
Identification of Prostate Cancer Risk Genetics Biomarkers Based on Intergraded Bioinformatics Analysis
title Identification of Prostate Cancer Risk Genetics Biomarkers Based on Intergraded Bioinformatics Analysis
title_full Identification of Prostate Cancer Risk Genetics Biomarkers Based on Intergraded Bioinformatics Analysis
title_fullStr Identification of Prostate Cancer Risk Genetics Biomarkers Based on Intergraded Bioinformatics Analysis
title_full_unstemmed Identification of Prostate Cancer Risk Genetics Biomarkers Based on Intergraded Bioinformatics Analysis
title_short Identification of Prostate Cancer Risk Genetics Biomarkers Based on Intergraded Bioinformatics Analysis
title_sort identification of prostate cancer risk genetics biomarkers based on intergraded bioinformatics analysis
topic Surgery
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8967941/
https://www.ncbi.nlm.nih.gov/pubmed/35372462
http://dx.doi.org/10.3389/fsurg.2022.856446
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