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 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. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Shan Zhu [verfasserIn] Aixian Tian [verfasserIn] Lin Guo [verfasserIn] Hua Xu [verfasserIn] Xiaofeng Li [verfasserIn] Zhi Wang [verfasserIn] Feng He [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Differentially expressed gene HLU rat mDixon‐Quant techniques Osteoporosis QCT

Übergeordnetes Werk:	In: Orthopaedic Surgery - Wiley, 2019, 13(2021), 7, Seite 2137-2144
Übergeordnetes Werk:	volume:13 ; year:2021 ; number:7 ; pages:2137-2144

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1111/os.13094

Katalog-ID:	DOAJ012306819

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520			\|a 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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allfields	10.1111/os.13094 doi (DE-627)DOAJ012306819 (DE-599)DOAJe756d36336a14582a682266922fae3aa DE-627 ger DE-627 rakwb eng RD701-811 Shan Zhu verfasserin aut Investigation of Diagnostic Biomarkers for Osteoporosis Based on Differentially Expressed Gene Profile with QCT and mDixon‐Quant Techniques 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Differentially expressed gene HLU rat mDixon‐Quant techniques Osteoporosis QCT Orthopedic surgery Aixian Tian verfasserin aut Lin Guo verfasserin aut Hua Xu verfasserin aut Xiaofeng Li verfasserin aut Zhi Wang verfasserin aut Feng He verfasserin aut In Orthopaedic Surgery Wiley, 2019 13(2021), 7, Seite 2137-2144 (DE-627)59356393X (DE-600)2483883-4 17577861 nnns volume:13 year:2021 number:7 pages:2137-2144 https://doi.org/10.1111/os.13094 kostenfrei https://doaj.org/article/e756d36336a14582a682266922fae3aa kostenfrei https://doi.org/10.1111/os.13094 kostenfrei https://doaj.org/toc/1757-7853 Journal toc kostenfrei https://doaj.org/toc/1757-7861 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 7 2137-2144
spelling	10.1111/os.13094 doi (DE-627)DOAJ012306819 (DE-599)DOAJe756d36336a14582a682266922fae3aa DE-627 ger DE-627 rakwb eng RD701-811 Shan Zhu verfasserin aut Investigation of Diagnostic Biomarkers for Osteoporosis Based on Differentially Expressed Gene Profile with QCT and mDixon‐Quant Techniques 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Differentially expressed gene HLU rat mDixon‐Quant techniques Osteoporosis QCT Orthopedic surgery Aixian Tian verfasserin aut Lin Guo verfasserin aut Hua Xu verfasserin aut Xiaofeng Li verfasserin aut Zhi Wang verfasserin aut Feng He verfasserin aut In Orthopaedic Surgery Wiley, 2019 13(2021), 7, Seite 2137-2144 (DE-627)59356393X (DE-600)2483883-4 17577861 nnns volume:13 year:2021 number:7 pages:2137-2144 https://doi.org/10.1111/os.13094 kostenfrei https://doaj.org/article/e756d36336a14582a682266922fae3aa kostenfrei https://doi.org/10.1111/os.13094 kostenfrei https://doaj.org/toc/1757-7853 Journal toc kostenfrei https://doaj.org/toc/1757-7861 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 7 2137-2144
allfields_unstemmed	10.1111/os.13094 doi (DE-627)DOAJ012306819 (DE-599)DOAJe756d36336a14582a682266922fae3aa DE-627 ger DE-627 rakwb eng RD701-811 Shan Zhu verfasserin aut Investigation of Diagnostic Biomarkers for Osteoporosis Based on Differentially Expressed Gene Profile with QCT and mDixon‐Quant Techniques 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Differentially expressed gene HLU rat mDixon‐Quant techniques Osteoporosis QCT Orthopedic surgery Aixian Tian verfasserin aut Lin Guo verfasserin aut Hua Xu verfasserin aut Xiaofeng Li verfasserin aut Zhi Wang verfasserin aut Feng He verfasserin aut In Orthopaedic Surgery Wiley, 2019 13(2021), 7, Seite 2137-2144 (DE-627)59356393X (DE-600)2483883-4 17577861 nnns volume:13 year:2021 number:7 pages:2137-2144 https://doi.org/10.1111/os.13094 kostenfrei https://doaj.org/article/e756d36336a14582a682266922fae3aa kostenfrei https://doi.org/10.1111/os.13094 kostenfrei https://doaj.org/toc/1757-7853 Journal toc kostenfrei https://doaj.org/toc/1757-7861 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 7 2137-2144
allfieldsGer	10.1111/os.13094 doi (DE-627)DOAJ012306819 (DE-599)DOAJe756d36336a14582a682266922fae3aa DE-627 ger DE-627 rakwb eng RD701-811 Shan Zhu verfasserin aut Investigation of Diagnostic Biomarkers for Osteoporosis Based on Differentially Expressed Gene Profile with QCT and mDixon‐Quant Techniques 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Differentially expressed gene HLU rat mDixon‐Quant techniques Osteoporosis QCT Orthopedic surgery Aixian Tian verfasserin aut Lin Guo verfasserin aut Hua Xu verfasserin aut Xiaofeng Li verfasserin aut Zhi Wang verfasserin aut Feng He verfasserin aut In Orthopaedic Surgery Wiley, 2019 13(2021), 7, Seite 2137-2144 (DE-627)59356393X (DE-600)2483883-4 17577861 nnns volume:13 year:2021 number:7 pages:2137-2144 https://doi.org/10.1111/os.13094 kostenfrei https://doaj.org/article/e756d36336a14582a682266922fae3aa kostenfrei https://doi.org/10.1111/os.13094 kostenfrei https://doaj.org/toc/1757-7853 Journal toc kostenfrei https://doaj.org/toc/1757-7861 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 7 2137-2144
allfieldsSound	10.1111/os.13094 doi (DE-627)DOAJ012306819 (DE-599)DOAJe756d36336a14582a682266922fae3aa DE-627 ger DE-627 rakwb eng RD701-811 Shan Zhu verfasserin aut Investigation of Diagnostic Biomarkers for Osteoporosis Based on Differentially Expressed Gene Profile with QCT and mDixon‐Quant Techniques 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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. Differentially expressed gene HLU rat mDixon‐Quant techniques Osteoporosis QCT Orthopedic surgery Aixian Tian verfasserin aut Lin Guo verfasserin aut Hua Xu verfasserin aut Xiaofeng Li verfasserin aut Zhi Wang verfasserin aut Feng He verfasserin aut In Orthopaedic Surgery Wiley, 2019 13(2021), 7, Seite 2137-2144 (DE-627)59356393X (DE-600)2483883-4 17577861 nnns volume:13 year:2021 number:7 pages:2137-2144 https://doi.org/10.1111/os.13094 kostenfrei https://doaj.org/article/e756d36336a14582a682266922fae3aa kostenfrei https://doi.org/10.1111/os.13094 kostenfrei https://doaj.org/toc/1757-7853 Journal toc kostenfrei https://doaj.org/toc/1757-7861 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2021 7 2137-2144
language	English
source	In Orthopaedic Surgery 13(2021), 7, Seite 2137-2144 volume:13 year:2021 number:7 pages:2137-2144
sourceStr	In Orthopaedic Surgery 13(2021), 7, Seite 2137-2144 volume:13 year:2021 number:7 pages:2137-2144
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Differentially expressed gene HLU rat mDixon‐Quant techniques Osteoporosis QCT Orthopedic surgery
isfreeaccess_bool	true
container_title	Orthopaedic Surgery
authorswithroles_txt_mv	Shan Zhu @@aut@@ Aixian Tian @@aut@@ Lin Guo @@aut@@ Hua Xu @@aut@@ Xiaofeng Li @@aut@@ Zhi Wang @@aut@@ Feng He @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	59356393X
id	DOAJ012306819
language_de	englisch
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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. 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callnumber-first	R - Medicine
author	Shan Zhu
spellingShingle	Shan Zhu misc RD701-811 misc Differentially expressed gene misc HLU rat misc mDixon‐Quant techniques misc Osteoporosis misc QCT misc Orthopedic surgery Investigation of Diagnostic Biomarkers for Osteoporosis Based on Differentially Expressed Gene Profile with QCT and mDixon‐Quant Techniques
authorStr	Shan Zhu
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topic_title	RD701-811 Investigation of Diagnostic Biomarkers for Osteoporosis Based on Differentially Expressed Gene Profile with QCT and mDixon‐Quant Techniques Differentially expressed gene HLU rat mDixon‐Quant techniques Osteoporosis QCT
topic	misc RD701-811 misc Differentially expressed gene misc HLU rat misc mDixon‐Quant techniques misc Osteoporosis misc QCT misc Orthopedic surgery
topic_unstemmed	misc RD701-811 misc Differentially expressed gene misc HLU rat misc mDixon‐Quant techniques misc Osteoporosis misc QCT misc Orthopedic surgery
topic_browse	misc RD701-811 misc Differentially expressed gene misc HLU rat misc mDixon‐Quant techniques misc Osteoporosis misc QCT misc Orthopedic surgery
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title	Investigation of Diagnostic Biomarkers for Osteoporosis Based on Differentially Expressed Gene Profile with QCT and mDixon‐Quant Techniques
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title_full	Investigation of Diagnostic Biomarkers for Osteoporosis Based on Differentially Expressed Gene Profile with QCT and mDixon‐Quant Techniques
author_sort	Shan Zhu
journal	Orthopaedic Surgery
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author_browse	Shan Zhu Aixian Tian Lin Guo Hua Xu Xiaofeng Li Zhi Wang Feng He
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author-letter	Shan Zhu
doi_str_mv	10.1111/os.13094
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title_sort	investigation of diagnostic biomarkers for osteoporosis based on differentially expressed gene profile with qct and mdixon‐quant techniques
callnumber	RD701-811
title_auth	Investigation of Diagnostic Biomarkers for Osteoporosis Based on Differentially Expressed Gene Profile with QCT and mDixon‐Quant Techniques
abstract	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.
abstractGer	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.
abstract_unstemmed	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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title_short	Investigation of Diagnostic Biomarkers for Osteoporosis Based on Differentially Expressed Gene Profile with QCT and mDixon‐Quant Techniques
url	https://doi.org/10.1111/os.13094 https://doaj.org/article/e756d36336a14582a682266922fae3aa https://doaj.org/toc/1757-7853 https://doaj.org/toc/1757-7861
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author2	Aixian Tian Lin Guo Hua Xu Xiaofeng Li Zhi Wang Feng He
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up_date	2024-07-04T00:33:09.394Z
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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). 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Investigation of Diagnostic Biomarkers for Osteoporosis Based on Differentially Expressed Gene Profile with QCT and mDixon‐Quant Techniques

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