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Investigation of Diagnostic Biomarkers for Osteoporosis Based on Differentially Expressed Gene Profile with QCT and mDixon‐Quant Techniques

OBJECTIVE: To develop a comprehensive differential expression profile for osteoporosis based on two independent data sources. METHODS: Using a hindlimb unloading (HLU) rat model to mimic osteoporosis syndrome in humans (animal experiments), the significant differentially expressed mRNAs in osteoporo...

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Autores principales: Zhu, Shan, Tian, Aixian, Guo, Lin, Xu, Hua, Li, Xiaofeng, Wang, Zhi, He, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons Australia, Ltd 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8528986/
https://www.ncbi.nlm.nih.gov/pubmed/34516037
http://dx.doi.org/10.1111/os.13094
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author Zhu, Shan
Tian, Aixian
Guo, Lin
Xu, Hua
Li, Xiaofeng
Wang, Zhi
He, Feng
author_facet Zhu, Shan
Tian, Aixian
Guo, Lin
Xu, Hua
Li, Xiaofeng
Wang, Zhi
He, Feng
author_sort Zhu, Shan
collection PubMed
description OBJECTIVE: To develop a comprehensive differential expression profile for osteoporosis based on two independent data sources. METHODS: Using a hindlimb unloading (HLU) rat model to mimic osteoporosis syndrome in humans (animal experiments), the significant differentially expressed mRNAs in osteoporosis were analyzed using RNA‐seq. The enriched GO terms as well as KEGG signaling pathways were also deeply investigated. Using clinical specimens to verify the functions of potential hub genes (biomarkers) for osteoporosis (clinical experiments), 128 suspected cases for osteoporosis from January 2019 to December 2020 were randomly selected and analyzed by quantitative computed tomography (QCT) as well as modified Dixon quantification (mDixon‐Quant) techniques in the Tianjin hospital. Among these, 80 patients out of 128 suspected cases were finally diagnosed as the osteoporosis group. Meanwhile, 48 patients were selected for osteopenia group. There was no significant age and gender difference across participant subgroups. The protein levels of potential hub genes (FST, CCL3, and RAPGEF4) were determined by ELISA double antibody sandwich method for osteopenia and osteoporosis groups from peripheral blood. RESULT: In the RNA‐seq analysis, compared with control group, a total of 803 differentially expressed mRNAs were identified, including 288 up‐regulated and 515 down‐regulated mRNAs. Of these, FST, CCL3, CPE, RAPGEF4, IL6, MDFI, PDZD2, and GATM were primary hub genes (biomarkers) for osteoporosis. These differentially expressed genes were significantly enriched in GO terms related to extracellular matrix process and KEGG signaling pathways including osteoclast differentiation. In the functional experiments, the protein expression level of FST, CCL3, and RAPGEF4 displayed a specific expression pattern between osteoporosis patients and control group. The protein concentration of FST was 23.63 ± 6.39 ng/mL in osteoporosis patients compared as 48.36 ± 9.12 ng/mL in osteopenia group (P < 0.01). Meanwhile, CCL3 was 1.03 ± 0.64 ng/mL in osteoporosis patients vs 0.56 ± 0.24 in osteopenia group (P < 0.01) and RAPGEF4 was 53.58 ± 11.42 ng/mL in osteoporosis patients vs 66.47 ± 13.28 ng/mL in osteopenia group (P < 0.05), respectively. CONCLUSION: This study has identified potential gene biomarkers (the genes with most significantly differential expression and useful for distinguishing osteoporosis from other bone disorders) and established a differential expression profile for osteoporosis, which is a valuable reference for future clinical research.
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spelling pubmed-85289862021-10-27 Investigation of Diagnostic Biomarkers for Osteoporosis Based on Differentially Expressed Gene Profile with QCT and mDixon‐Quant Techniques Zhu, Shan Tian, Aixian Guo, Lin Xu, Hua Li, Xiaofeng Wang, Zhi He, Feng Orthop Surg Research Articles OBJECTIVE: To develop a comprehensive differential expression profile for osteoporosis based on two independent data sources. METHODS: Using a hindlimb unloading (HLU) rat model to mimic osteoporosis syndrome in humans (animal experiments), the significant differentially expressed mRNAs in osteoporosis were analyzed using RNA‐seq. The enriched GO terms as well as KEGG signaling pathways were also deeply investigated. Using clinical specimens to verify the functions of potential hub genes (biomarkers) for osteoporosis (clinical experiments), 128 suspected cases for osteoporosis from January 2019 to December 2020 were randomly selected and analyzed by quantitative computed tomography (QCT) as well as modified Dixon quantification (mDixon‐Quant) techniques in the Tianjin hospital. Among these, 80 patients out of 128 suspected cases were finally diagnosed as the osteoporosis group. Meanwhile, 48 patients were selected for osteopenia group. There was no significant age and gender difference across participant subgroups. The protein levels of potential hub genes (FST, CCL3, and RAPGEF4) were determined by ELISA double antibody sandwich method for osteopenia and osteoporosis groups from peripheral blood. RESULT: In the RNA‐seq analysis, compared with control group, a total of 803 differentially expressed mRNAs were identified, including 288 up‐regulated and 515 down‐regulated mRNAs. Of these, FST, CCL3, CPE, RAPGEF4, IL6, MDFI, PDZD2, and GATM were primary hub genes (biomarkers) for osteoporosis. These differentially expressed genes were significantly enriched in GO terms related to extracellular matrix process and KEGG signaling pathways including osteoclast differentiation. In the functional experiments, the protein expression level of FST, CCL3, and RAPGEF4 displayed a specific expression pattern between osteoporosis patients and control group. The protein concentration of FST was 23.63 ± 6.39 ng/mL in osteoporosis patients compared as 48.36 ± 9.12 ng/mL in osteopenia group (P < 0.01). Meanwhile, CCL3 was 1.03 ± 0.64 ng/mL in osteoporosis patients vs 0.56 ± 0.24 in osteopenia group (P < 0.01) and RAPGEF4 was 53.58 ± 11.42 ng/mL in osteoporosis patients vs 66.47 ± 13.28 ng/mL in osteopenia group (P < 0.05), respectively. CONCLUSION: This study has identified potential gene biomarkers (the genes with most significantly differential expression and useful for distinguishing osteoporosis from other bone disorders) and established a differential expression profile for osteoporosis, which is a valuable reference for future clinical research. John Wiley & Sons Australia, Ltd 2021-09-13 /pmc/articles/PMC8528986/ /pubmed/34516037 http://dx.doi.org/10.1111/os.13094 Text en © 2021 The Authors. Orthopaedic Surgery published by Chinese Orthopaedic Association and John Wiley & Sons Australia, Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Research Articles
Zhu, Shan
Tian, Aixian
Guo, Lin
Xu, Hua
Li, Xiaofeng
Wang, Zhi
He, Feng
Investigation of Diagnostic Biomarkers for Osteoporosis Based on Differentially Expressed Gene Profile with QCT and mDixon‐Quant Techniques
title Investigation of Diagnostic Biomarkers for Osteoporosis Based on Differentially Expressed Gene Profile with QCT and mDixon‐Quant Techniques
title_full Investigation of Diagnostic Biomarkers for Osteoporosis Based on Differentially Expressed Gene Profile with QCT and mDixon‐Quant Techniques
title_fullStr Investigation of Diagnostic Biomarkers for Osteoporosis Based on Differentially Expressed Gene Profile with QCT and mDixon‐Quant Techniques
title_full_unstemmed Investigation of Diagnostic Biomarkers for Osteoporosis Based on Differentially Expressed Gene Profile with QCT and mDixon‐Quant Techniques
title_short Investigation of Diagnostic Biomarkers for Osteoporosis Based on Differentially Expressed Gene Profile with QCT and mDixon‐Quant Techniques
title_sort investigation of diagnostic biomarkers for osteoporosis based on differentially expressed gene profile with qct and mdixon‐quant techniques
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8528986/
https://www.ncbi.nlm.nih.gov/pubmed/34516037
http://dx.doi.org/10.1111/os.13094
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