Cargando…

Identification of ROCK1 as a novel biomarker for postmenopausal osteoporosis and pan-cancer analysis

Background: Postmenopausal osteoporosis (PMOP) is a prevalent bone disorder with significant global impact. The elevated risk of osteoporotic fracture in elderly women poses a substantial burden on individuals and society. Unfortunately, the current lack of dependable diagnostic markers and precise...

Descripción completa

Detalles Bibliográficos
Autores principales: Lai, Bowen, Jiang, Heng, Gao, Yuan, Zhou, Xuhui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10522383/
https://www.ncbi.nlm.nih.gov/pubmed/37683138
http://dx.doi.org/10.18632/aging.205004
_version_ 1785110343485227008
author Lai, Bowen
Jiang, Heng
Gao, Yuan
Zhou, Xuhui
author_facet Lai, Bowen
Jiang, Heng
Gao, Yuan
Zhou, Xuhui
author_sort Lai, Bowen
collection PubMed
description Background: Postmenopausal osteoporosis (PMOP) is a prevalent bone disorder with significant global impact. The elevated risk of osteoporotic fracture in elderly women poses a substantial burden on individuals and society. Unfortunately, the current lack of dependable diagnostic markers and precise therapeutic targets for PMOP remains a major challenge. Methods: PMOP-related datasets GSE7429, GSE56814, GSE56815, and GSE147287, were downloaded from the GEO database. The DEGs were identified by “limma” packages. WGCNA and Machine Learning were used to choose key module genes highly related to PMOP. GSEA, DO, GO, and KEGG enrichment analysis was performed on all DEGs and the selected key hub genes. The PPI network was constructed through the GeneMANIA database. ROC curves and AUC values validated the diagnostic values of the hub genes in both training and validation datasets. xCell immune infiltration and single-cell analysis identified the hub genes’ function on immune reaction in PMOP. Pan-cancer analysis revealed the role of the hub genes in cancers. Results: A total of 1278 DEGs were identified between PMOP patients and the healthy controls. The purple module and cyan module were selected as the key modules and 112 common genes were selected after combining the DEGs and module genes. Five Machine Learning algorithms screened three hub genes (KCNJ2, HIPK1, and ROCK1), and a PPI network was constructed for the hub genes. ROC curves validate the diagnostic values of ROCK1 in both the training (AUC = 0.73) and validation datasets of PMOP (AUC = 0.81). GSEA was performed for the low-ROCK1 patients, and the top enriched field included protein binding and immune reaction. DCs and NKT cells were highly expressed in PMOP. Pan-cancer analysis showed a correlation between low ROCK1 expression and SKCM as well as renal tumors (KIRP, KICH, and KIRC). Conclusions: ROCK1 was significantly associated with the pathogenesis and immune infiltration of PMOP, and influenced cancer development, progression, and prognosis, which provided a potential therapy target for PMOP and tumors. However, further laboratory and clinical evidence is required before the clinical application of ROCK1 as a therapeutic target.
format Online
Article
Text
id pubmed-10522383
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Impact Journals
record_format MEDLINE/PubMed
spelling pubmed-105223832023-09-27 Identification of ROCK1 as a novel biomarker for postmenopausal osteoporosis and pan-cancer analysis Lai, Bowen Jiang, Heng Gao, Yuan Zhou, Xuhui Aging (Albany NY) Research Paper Background: Postmenopausal osteoporosis (PMOP) is a prevalent bone disorder with significant global impact. The elevated risk of osteoporotic fracture in elderly women poses a substantial burden on individuals and society. Unfortunately, the current lack of dependable diagnostic markers and precise therapeutic targets for PMOP remains a major challenge. Methods: PMOP-related datasets GSE7429, GSE56814, GSE56815, and GSE147287, were downloaded from the GEO database. The DEGs were identified by “limma” packages. WGCNA and Machine Learning were used to choose key module genes highly related to PMOP. GSEA, DO, GO, and KEGG enrichment analysis was performed on all DEGs and the selected key hub genes. The PPI network was constructed through the GeneMANIA database. ROC curves and AUC values validated the diagnostic values of the hub genes in both training and validation datasets. xCell immune infiltration and single-cell analysis identified the hub genes’ function on immune reaction in PMOP. Pan-cancer analysis revealed the role of the hub genes in cancers. Results: A total of 1278 DEGs were identified between PMOP patients and the healthy controls. The purple module and cyan module were selected as the key modules and 112 common genes were selected after combining the DEGs and module genes. Five Machine Learning algorithms screened three hub genes (KCNJ2, HIPK1, and ROCK1), and a PPI network was constructed for the hub genes. ROC curves validate the diagnostic values of ROCK1 in both the training (AUC = 0.73) and validation datasets of PMOP (AUC = 0.81). GSEA was performed for the low-ROCK1 patients, and the top enriched field included protein binding and immune reaction. DCs and NKT cells were highly expressed in PMOP. Pan-cancer analysis showed a correlation between low ROCK1 expression and SKCM as well as renal tumors (KIRP, KICH, and KIRC). Conclusions: ROCK1 was significantly associated with the pathogenesis and immune infiltration of PMOP, and influenced cancer development, progression, and prognosis, which provided a potential therapy target for PMOP and tumors. However, further laboratory and clinical evidence is required before the clinical application of ROCK1 as a therapeutic target. Impact Journals 2023-09-07 /pmc/articles/PMC10522383/ /pubmed/37683138 http://dx.doi.org/10.18632/aging.205004 Text en Copyright: © 2023 Lai et al. https://creativecommons.org/licenses/by/3.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/3.0/) (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Paper
Lai, Bowen
Jiang, Heng
Gao, Yuan
Zhou, Xuhui
Identification of ROCK1 as a novel biomarker for postmenopausal osteoporosis and pan-cancer analysis
title Identification of ROCK1 as a novel biomarker for postmenopausal osteoporosis and pan-cancer analysis
title_full Identification of ROCK1 as a novel biomarker for postmenopausal osteoporosis and pan-cancer analysis
title_fullStr Identification of ROCK1 as a novel biomarker for postmenopausal osteoporosis and pan-cancer analysis
title_full_unstemmed Identification of ROCK1 as a novel biomarker for postmenopausal osteoporosis and pan-cancer analysis
title_short Identification of ROCK1 as a novel biomarker for postmenopausal osteoporosis and pan-cancer analysis
title_sort identification of rock1 as a novel biomarker for postmenopausal osteoporosis and pan-cancer analysis
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10522383/
https://www.ncbi.nlm.nih.gov/pubmed/37683138
http://dx.doi.org/10.18632/aging.205004
work_keys_str_mv AT laibowen identificationofrock1asanovelbiomarkerforpostmenopausalosteoporosisandpancanceranalysis
AT jiangheng identificationofrock1asanovelbiomarkerforpostmenopausalosteoporosisandpancanceranalysis
AT gaoyuan identificationofrock1asanovelbiomarkerforpostmenopausalosteoporosisandpancanceranalysis
AT zhouxuhui identificationofrock1asanovelbiomarkerforpostmenopausalosteoporosisandpancanceranalysis