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An Integrative Multi-Omics Analysis Based on Nomogram for Predicting Prostate Cancer Bone Metastasis Incidence
BACKGROUND: The most common site of prostate cancer metastasis is bone tissue with many recent studies having conducted genomic and clinical research regarding bone metastatic prostate cancer. However, further work is needed to better define those patients that are at an elevated risk of such metast...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9537037/ https://www.ncbi.nlm.nih.gov/pubmed/36245556 http://dx.doi.org/10.1155/2022/8213723 |
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author | Zhao, Jun Wang, Rui Sun, Xiaoxin Huang, Kai Jin, Jiacheng Lan, Lan Jian, Yuli Xu, Zhongyang Wu, Haotian Wang, Shujing Wang, Jianbo |
author_facet | Zhao, Jun Wang, Rui Sun, Xiaoxin Huang, Kai Jin, Jiacheng Lan, Lan Jian, Yuli Xu, Zhongyang Wu, Haotian Wang, Shujing Wang, Jianbo |
author_sort | Zhao, Jun |
collection | PubMed |
description | BACKGROUND: The most common site of prostate cancer metastasis is bone tissue with many recent studies having conducted genomic and clinical research regarding bone metastatic prostate cancer. However, further work is needed to better define those patients that are at an elevated risk of such metastasis. METHODS: SEER and TCGA databases were searched to develop a nomogram for predicting prostate cancer bone metastasis. RESULTS: Herein, we leveraged the Surveillance, Epidemiology, and End Results (SEER) database to construct a predictive nomogram capable of readily and accurately predicted the odds of bone metastasis in prostate cancer patients. This nomogram was utilized to assign patients with prostate cancer included in The Cancer Genome Atlas (TCGA) to cohorts at a high or low risk of bone metastasis (HRBM and LRBM, respectively). Comparisons of these LRBM and HRBM cohorts revealed marked differences in mutational landscapes between these patient cohorts, with increased frequencies of gene fusions, somatic copy number variations (CNVs), and single nucleotide variations (SNVs), particularly in the P53 gene, being evident in the HRBM cohort. We additionally identified lncRNAs, miRNAs, and mRNAs that were differentially expressed between these two patient cohorts and used them to construct a competing endogenous RNA (ceRNA) network. Moreover, three weighted gene co-expression network analysis (WGCNA) modules were constructed from the results of these analyses, with KIF14, MYH7, and COL10A1 being identified as hub genes within these modules. We further found immune response activity levels in the HRBM cohort to be elevated relative to that in the LRBM cohort, with single sample gene enrichment analysis (ssGSEA) scores for the immune checkpoint signature being increased in HRBM patient samples relative to those from LRBM patients. CONCLUSION: We successfully developed a nomogram capable of readily detecting patients with prostate cancer at an elevated risk of bone metastasis. |
format | Online Article Text |
id | pubmed-9537037 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-95370372022-10-13 An Integrative Multi-Omics Analysis Based on Nomogram for Predicting Prostate Cancer Bone Metastasis Incidence Zhao, Jun Wang, Rui Sun, Xiaoxin Huang, Kai Jin, Jiacheng Lan, Lan Jian, Yuli Xu, Zhongyang Wu, Haotian Wang, Shujing Wang, Jianbo Genet Res (Camb) Research Article BACKGROUND: The most common site of prostate cancer metastasis is bone tissue with many recent studies having conducted genomic and clinical research regarding bone metastatic prostate cancer. However, further work is needed to better define those patients that are at an elevated risk of such metastasis. METHODS: SEER and TCGA databases were searched to develop a nomogram for predicting prostate cancer bone metastasis. RESULTS: Herein, we leveraged the Surveillance, Epidemiology, and End Results (SEER) database to construct a predictive nomogram capable of readily and accurately predicted the odds of bone metastasis in prostate cancer patients. This nomogram was utilized to assign patients with prostate cancer included in The Cancer Genome Atlas (TCGA) to cohorts at a high or low risk of bone metastasis (HRBM and LRBM, respectively). Comparisons of these LRBM and HRBM cohorts revealed marked differences in mutational landscapes between these patient cohorts, with increased frequencies of gene fusions, somatic copy number variations (CNVs), and single nucleotide variations (SNVs), particularly in the P53 gene, being evident in the HRBM cohort. We additionally identified lncRNAs, miRNAs, and mRNAs that were differentially expressed between these two patient cohorts and used them to construct a competing endogenous RNA (ceRNA) network. Moreover, three weighted gene co-expression network analysis (WGCNA) modules were constructed from the results of these analyses, with KIF14, MYH7, and COL10A1 being identified as hub genes within these modules. We further found immune response activity levels in the HRBM cohort to be elevated relative to that in the LRBM cohort, with single sample gene enrichment analysis (ssGSEA) scores for the immune checkpoint signature being increased in HRBM patient samples relative to those from LRBM patients. CONCLUSION: We successfully developed a nomogram capable of readily detecting patients with prostate cancer at an elevated risk of bone metastasis. Hindawi 2022-09-29 /pmc/articles/PMC9537037/ /pubmed/36245556 http://dx.doi.org/10.1155/2022/8213723 Text en Copyright © 2022 Jun Zhao et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Zhao, Jun Wang, Rui Sun, Xiaoxin Huang, Kai Jin, Jiacheng Lan, Lan Jian, Yuli Xu, Zhongyang Wu, Haotian Wang, Shujing Wang, Jianbo An Integrative Multi-Omics Analysis Based on Nomogram for Predicting Prostate Cancer Bone Metastasis Incidence |
title | An Integrative Multi-Omics Analysis Based on Nomogram for Predicting Prostate Cancer Bone Metastasis Incidence |
title_full | An Integrative Multi-Omics Analysis Based on Nomogram for Predicting Prostate Cancer Bone Metastasis Incidence |
title_fullStr | An Integrative Multi-Omics Analysis Based on Nomogram for Predicting Prostate Cancer Bone Metastasis Incidence |
title_full_unstemmed | An Integrative Multi-Omics Analysis Based on Nomogram for Predicting Prostate Cancer Bone Metastasis Incidence |
title_short | An Integrative Multi-Omics Analysis Based on Nomogram for Predicting Prostate Cancer Bone Metastasis Incidence |
title_sort | integrative multi-omics analysis based on nomogram for predicting prostate cancer bone metastasis incidence |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9537037/ https://www.ncbi.nlm.nih.gov/pubmed/36245556 http://dx.doi.org/10.1155/2022/8213723 |
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