Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhao, Jun, Wang, Rui, Sun, Xiaoxin, Huang, Kai, Jin, Jiacheng, Lan, Lan, Jian, Yuli, Xu, Zhongyang, Wu, Haotian, Wang, Shujing, Wang, Jianbo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
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
_version_ 1784803109622513664
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
work_keys_str_mv AT zhaojun anintegrativemultiomicsanalysisbasedonnomogramforpredictingprostatecancerbonemetastasisincidence
AT wangrui anintegrativemultiomicsanalysisbasedonnomogramforpredictingprostatecancerbonemetastasisincidence
AT sunxiaoxin anintegrativemultiomicsanalysisbasedonnomogramforpredictingprostatecancerbonemetastasisincidence
AT huangkai anintegrativemultiomicsanalysisbasedonnomogramforpredictingprostatecancerbonemetastasisincidence
AT jinjiacheng anintegrativemultiomicsanalysisbasedonnomogramforpredictingprostatecancerbonemetastasisincidence
AT lanlan anintegrativemultiomicsanalysisbasedonnomogramforpredictingprostatecancerbonemetastasisincidence
AT jianyuli anintegrativemultiomicsanalysisbasedonnomogramforpredictingprostatecancerbonemetastasisincidence
AT xuzhongyang anintegrativemultiomicsanalysisbasedonnomogramforpredictingprostatecancerbonemetastasisincidence
AT wuhaotian anintegrativemultiomicsanalysisbasedonnomogramforpredictingprostatecancerbonemetastasisincidence
AT wangshujing anintegrativemultiomicsanalysisbasedonnomogramforpredictingprostatecancerbonemetastasisincidence
AT wangjianbo anintegrativemultiomicsanalysisbasedonnomogramforpredictingprostatecancerbonemetastasisincidence
AT zhaojun integrativemultiomicsanalysisbasedonnomogramforpredictingprostatecancerbonemetastasisincidence
AT wangrui integrativemultiomicsanalysisbasedonnomogramforpredictingprostatecancerbonemetastasisincidence
AT sunxiaoxin integrativemultiomicsanalysisbasedonnomogramforpredictingprostatecancerbonemetastasisincidence
AT huangkai integrativemultiomicsanalysisbasedonnomogramforpredictingprostatecancerbonemetastasisincidence
AT jinjiacheng integrativemultiomicsanalysisbasedonnomogramforpredictingprostatecancerbonemetastasisincidence
AT lanlan integrativemultiomicsanalysisbasedonnomogramforpredictingprostatecancerbonemetastasisincidence
AT jianyuli integrativemultiomicsanalysisbasedonnomogramforpredictingprostatecancerbonemetastasisincidence
AT xuzhongyang integrativemultiomicsanalysisbasedonnomogramforpredictingprostatecancerbonemetastasisincidence
AT wuhaotian integrativemultiomicsanalysisbasedonnomogramforpredictingprostatecancerbonemetastasisincidence
AT wangshujing integrativemultiomicsanalysisbasedonnomogramforpredictingprostatecancerbonemetastasisincidence
AT wangjianbo integrativemultiomicsanalysisbasedonnomogramforpredictingprostatecancerbonemetastasisincidence