Uncovering Overlapping Gene Networks and Potential Therapeutic Targets in Osteoporosis and COVID‐19 Through Bioinformatics Analysis
Yuwen Luo, Shizhen Liu, Xianyin Liu, Shu Zhong, Ye Wang, Zheng Wan
International Journal of Endocrinology, doi:10.1155/ije/8816596
Background: Osteoporosis is a progressive bone disease characterized by reduced bone density and deterioration of bone microarchitecture, predominantly afecting the elderly population. Te ongoing COVID-19 pandemic has introduced additional challenges in osteoporosis management, potentially due to systemic infammation and direct viral impacts on bone metabolism. Tis study aims to identify common diferentially expressed genes (DEGs) and key molecular pathways shared between osteoporosis and COVID-19, with the goal of uncovering potential therapeutic targets through bioinformatics analysis. Methods: Publicly available gene expression datasets GSE164805 (osteoporosis) and GSE230665 (COVID-19) were analyzed to identify overlapping DEGs. Functional enrichment analysis using Gene Ontology (GO), pathway analysis, protein-protein interaction (PPI) network construction, and transcription factor (TF)-hub gene regulatory network analysis were performed to explore the biological signifcance and regulatory mechanisms of these DEGs. Results: A total of 325 common DEGs were identifed between osteoporosis and COVID-19. GO enrichment analysis revealed signifcant involvement in signal transduction and plasma membrane components. Pathway analysis highlighted the "cytokine-cytokine receptor interaction" pathway as a central player. PPI network analysis identifed a module of 193 genes with 397 interactions, from which 10 key hub genes were prioritized: ACTB, CDH1, RPS8, IFNG, RPL17, UBC, RPL36, RPS4Y1, GSK3B, and FGF13. Furthermore, 76 TFs were found to regulate these hub genes, and 15 existing drugs targeting four of these hub genes were identifed. Conclusion: Tis integrative bioinformatics study reveals 15 candidate therapeutic agents that target key regulatory genes shared between osteoporosis and COVID-19, ofering promising treatment strategies for osteoporotic patients, especially those impacted by or at risk of SARS-CoV-2 infection.
Ethics Statement Te datasets analyzed in this paper are publicly available and openly accessible. Since the data are not subject to individual privacy concerns and were collected without the involvement of the current researchers, ethical approval from a local ethics committee was not necessary.
Disclosure All authors have read and agreed to the published version of the manuscript.
Conflicts of Interest Te authors declare no conficts of interest.
Author Contributions Zheng Wan and Yuwen Luo designed and coordinated the study. Yuwen Luo, Shizhen Liu, Xianyin Liu, Shu Zhong, and Ye Wang acquired and analyzed the data. All authors wrote the manuscript.
References
Amit, Beni, Biber, Grinberg, Leshem et al., Postvaccination COVID-19 Among Healthcare Workers, Israel, Emerging Infectious Diseases,
doi:10.3201/eid2704.210016Barrett, Wilhite, Ledoux, NCBI GEO: Archive for Functional Genomics Data Sets--Update, Nucleic Acids Research,
doi:10.1093/nar/gks1193Cannon, Stevenson, Stahl, DGIdb 5.0: Rebuilding the Drug-Gene Interaction Database for Precision Medicine and Drug Discovery Platforms, Nucleic Acids Research,
doi:10.1093/nar/gkad1040Fang, Chen, Liu, Age-Related GSK3β Overexpression Drives Podocyte Senescence and Glomerular Aging, Journal of Clinical Investigation,
doi:10.1172/jci141848Freshour, Kiwala, Cotto, Integration of the Drug-Gene Interaction Database (DGIdb 4.0) With Open Crowdsource Eforts, Nucleic Acids Research,
doi:10.1093/nar/gkaa1084Fu, Hu, Lan, Guan, Luo et al., Te Hippo Signalling Pathway and Its Implications in Human Health and Diseases, Signal Transduction and Targeted Terapy,
doi:10.1038/s41392-022-01191-9Garcia, Jeyachandran, Wang, Hippo Signaling Pathway Activation During SARS-CoV-2 Infection Contributes to Host Antiviral Response, PLoS Biology,
doi:10.1371/journal.pbio.3001851Gennari, Merlotti, Paola, Estrogen Receptor Gene Polymorphisms and the Genetics of Osteoporosis: A HuGE Review, American Journal of Epidemiology,
doi:10.1093/aje/kwi055Gong, Zhang, Dong, Integrated Bioinformatics Analysis for Identifcating the Terapeutic Targets of Aspirin in Small Cell Lung Cancer, Journal of Biomedical Informatics,
doi:10.1016/j.jbi.2018.11.001Hall, Foulkes, Insalata, Protection Against SARS-CoV-2 After Covid-19 Vaccination and Previous Infection, New England Journal of Medicine,
doi:10.1056/nejmoa2118691Lam, Duttke, Odish, Profling Transcription Initiation in Peripheral Leukocytes Reveals Severity-Associated Cis-Regulatory Elements in Critical COVID-19, bioRxiv,
doi:10.1101/2021.08.24.457187Li, Dong, Leier, Positive-Unlabeled Learning in Bioinformatics and Computational Biology: A Brief Review, Briefngs in Bioinformatics,
doi:10.1093/bib/bbab461Li, Samuel, Ul Haq, Characterizing the Oncogenic Importance and Exploring Gene-Immune Cells Correlation of ACTB in Human Cancers, American Journal of Cancer Research
Libuit, Doughty, Otieno, Accelerating Bioinformatics Implementation in Public Health, Microbial Genomics,
doi:10.1099/mgen.0.001051Liu, Verma, Garcia, Targeting the Coronavirus Nucleocapsid Protein Trough GSK-3 Inhibition, Proceedings of the National Academy of Sciences of the United States of America,
doi:10.1073/pnas.2113401118Lv, Kang, Wu, Fluorosis Increases the Risk of Postmenopausal Osteoporosis by Stimulating Interferon Γ, Biochemical and Biophysical Research Communications,
doi:10.1016/j.bbrc.2016.09.083Rudd, GSK-3 Inhibition as a Terapeutic Approach Against Sars CoV2: Dual Beneft of Inhibiting Viral Replication While Potentiating the Immune Response, Frontiers in Immunology,
doi:10.3389/fimmu.2020.01638Sherman, Hao, Qiu, DAVID: A Web Server for Functional Enrichment Analysis and Functional Annotation of Gene Lists (2021 Update), Nucleic Acids Research,
doi:10.1093/nar/gkac194Szklarczyk, Gable, Nastou, Te STRING Database in 2021: Customizable Protein-Protein Networks, and Functional Characterization of User-Uploaded Gene/ Measurement Sets, Nucleic Acids Research,
doi:10.1093/nar/gkaa1074Tu, Bao, MBL2 Genetic Polymorphisms and Severe COVID-19: A Protocol for Systematic Review and Meta-Analysis, Medicine (Baltimore),
doi:10.1097/md.0000000000029405Van Gelder, Hooft, Van Rijswijk, Bioinformatics in the Netherlands: Te Value of a Nationwide Community, Briefngs in Bioinformatics,
doi:10.1093/bib/bbx087Wooller, Benstead-Hume, Chen, Ali, Pearl, Bioinformatics in Translational Drug Discovery, Bioscience Reports,
doi:10.1042/bsr20160180Xiao, Wan, Liu, Screening of Terapeutic Targets for Pancreatic Cancer by Bioinformatics Methods, Hormone and Metabolic Research,
doi:10.1055/a-2007-2715Xu, Han, Li, Efective Treatment of Severe COVID-19 Patients With Tocilizumab, Proceedings of the National Academy of Sciences of the United States of America,
doi:10.1073/pnas.2005615117Yan, Cao, Sun, Sun, Gao, Inhibition of GSK3B Phosphorylation Improves Glucose and Lipid Metabolism Disorder, Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease,
doi:10.1016/j.bbadis.2023.166726Yan, Tang, Chen, Imperatorin Promotes Osteogenesis and Suppresses Osteoclast by Activating AKT/ GSK3 β/β-Catenin Pathways, Journal of Cellular and Molecular Medicine,
doi:10.1111/jcmm.14915Yang, Kuang, Kang, Zhao, Ma et al., Human Umbilical Cord Mesenchymal Stem Cell-Derived Exosomes Act via the miR-1263/Mob1/Hippo Signaling Pathway to Prevent Apoptosis in Disuse Osteoporosis, Biochemical and Biophysical Research Communications,
doi:10.1016/j.bbrc.2020.02.001Yu, Xia, Te Epidemiology of Osteoporosis, Associated Fragility Fractures, and Management Gap in China, Arch Osteoporos,
doi:10.1007/s11657-018-0549-yZheng, Vrindts, Lopez, Increase in Cytokine Production (IL-1 Beta, IL-6, TNF-Alpha but Not IFN-Gamma, GM-CSF or LIF) by Stimulated Whole Blood Cells in Postmenopausal Osteoporosis, Maturitas,
doi:10.1016/s0378-5122(96)01080-8Zhou, Ren, Zhang, Heightened Innate Immune Responses in the Respiratory Tract of COVID-19 Patients, Cell Host & Microbe,
doi:10.1016/j.chom.2020.04.017Zhou, Soufan, Ewald, Hancock, Basu et al., NetworkAnalyst 3.0: A Visual Analytics Platform for Comprehensive Gene Expression Profling and Meta-Analysis, Nucleic Acids Research,
doi:10.1093/nar/gkz240Zhu, Zhang, Xiong, Fan, Li et al., Circ-GSK3B Up-Regulates GSK3B to Suppress the Progression of Lung Adenocarcinoma, Cancer Gene Terapy,
doi:10.1038/s41417-022-00489-8DOI record:
{
"DOI": "10.1155/ije/8816596",
"ISSN": [
"1687-8337",
"1687-8345"
],
"URL": "http://dx.doi.org/10.1155/ije/8816596",
"abstract": "<jats:p><jats:bold>Background:</jats:bold> Osteoporosis is a progressive bone disease characterized by reduced bone density and deterioration of bone microarchitecture, predominantly affecting the elderly population. The ongoing COVID‐19 pandemic has introduced additional challenges in osteoporosis management, potentially due to systemic inflammation and direct viral impacts on bone metabolism. This study aims to identify common differentially expressed genes (DEGs) and key molecular pathways shared between osteoporosis and COVID‐19, with the goal of uncovering potential therapeutic targets through bioinformatics analysis.</jats:p><jats:p><jats:bold>Methods:</jats:bold> Publicly available gene expression datasets GSE164805 (osteoporosis) and GSE230665 (COVID‐19) were analyzed to identify overlapping DEGs. Functional enrichment analysis using Gene Ontology (GO), pathway analysis, protein–protein interaction (PPI) network construction, and transcription factor (TF)–hub gene regulatory network analysis were performed to explore the biological significance and regulatory mechanisms of these DEGs.</jats:p><jats:p><jats:bold>Results:</jats:bold> A total of 325 common DEGs were identified between osteoporosis and COVID‐19. GO enrichment analysis revealed significant involvement in signal transduction and plasma membrane components. Pathway analysis highlighted the “cytokine–cytokine receptor interaction” pathway as a central player. PPI network analysis identified a module of 193 genes with 397 interactions, from which 10 key hub genes were prioritized: ACTB, CDH1, RPS8, IFNG, RPL17, UBC, RPL36, RPS4Y1, GSK3B, and FGF13. Furthermore, 76 TFs were found to regulate these hub genes, and 15 existing drugs targeting four of these hub genes were identified.</jats:p><jats:p><jats:bold>Conclusion:</jats:bold> This integrative bioinformatics study reveals 15 candidate therapeutic agents that target key regulatory genes shared between osteoporosis and COVID‐19, offering promising treatment strategies for osteoporotic patients, especially those impacted by or at risk of SARS‐CoV‐2 infection.</jats:p>",
"alternative-id": [
"10.1155/ije/8816596"
],
"article-number": "8816596",
"assertion": [
{
"group": {
"label": "Publication History",
"name": "publication_history"
},
"label": "Received",
"name": "received",
"order": 0,
"value": "2024-09-14"
},
{
"group": {
"label": "Publication History",
"name": "publication_history"
},
"label": "Accepted",
"name": "accepted",
"order": 2,
"value": "2025-08-19"
},
{
"group": {
"label": "Publication History",
"name": "publication_history"
},
"label": "Published",
"name": "published",
"order": 3,
"value": "2025-08-30"
}
],
"author": [
{
"ORCID": "https://orcid.org/0009-0004-5898-9613",
"affiliation": [],
"authenticated-orcid": false,
"family": "Luo",
"given": "Yuwen",
"sequence": "first"
},
{
"ORCID": "https://orcid.org/0009-0000-1379-6173",
"affiliation": [],
"authenticated-orcid": false,
"family": "Liu",
"given": "Shizhen",
"sequence": "additional"
},
{
"ORCID": "https://orcid.org/0009-0002-4404-4825",
"affiliation": [],
"authenticated-orcid": false,
"family": "Liu",
"given": "Xianyin",
"sequence": "additional"
},
{
"ORCID": "https://orcid.org/0009-0003-5566-5608",
"affiliation": [],
"authenticated-orcid": false,
"family": "Zhong",
"given": "Shu",
"sequence": "additional"
},
{
"ORCID": "https://orcid.org/0009-0006-7109-6144",
"affiliation": [],
"authenticated-orcid": false,
"family": "Wang",
"given": "Ye",
"sequence": "additional"
},
{
"ORCID": "https://orcid.org/0000-0001-8455-3478",
"affiliation": [],
"authenticated-orcid": false,
"family": "Wan",
"given": "Zheng",
"sequence": "additional"
}
],
"container-title": "International Journal of Endocrinology",
"container-title-short": "International Journal of Endocrinology",
"content-domain": {
"crossmark-restriction": true,
"domain": [
"onlinelibrary.wiley.com"
]
},
"created": {
"date-parts": [
[
2025,
8,
30
]
],
"date-time": "2025-08-30T10:04:46Z",
"timestamp": 1756548286000
},
"deposited": {
"date-parts": [
[
2025,
8,
30
]
],
"date-time": "2025-08-30T10:04:48Z",
"timestamp": 1756548288000
},
"editor": [
{
"affiliation": [],
"family": "Saleem",
"given": "Suraiya",
"sequence": "additional"
}
],
"funder": [
{
"DOI": "10.13039/501100003392",
"award": [
"2022J05298"
],
"doi-asserted-by": "publisher",
"id": [
{
"asserted-by": "publisher",
"id": "10.13039/501100003392",
"id-type": "DOI"
}
],
"name": "Natural Science Foundation of Fujian Province"
}
],
"indexed": {
"date-parts": [
[
2025,
8,
30
]
],
"date-time": "2025-08-30T10:40:08Z",
"timestamp": 1756550408019,
"version": "3.44.0"
},
"is-referenced-by-count": 0,
"issue": "1",
"issued": {
"date-parts": [
[
2025,
1
]
]
},
"journal-issue": {
"issue": "1",
"published-print": {
"date-parts": [
[
2025,
1
]
]
}
},
"language": "en",
"license": [
{
"URL": "http://creativecommons.org/licenses/by/4.0/",
"content-version": "vor",
"delay-in-days": 241,
"start": {
"date-parts": [
[
2025,
8,
30
]
],
"date-time": "2025-08-30T00:00:00Z",
"timestamp": 1756512000000
}
}
],
"link": [
{
"URL": "https://onlinelibrary.wiley.com/doi/pdf/10.1155/ije/8816596",
"content-type": "unspecified",
"content-version": "vor",
"intended-application": "similarity-checking"
}
],
"member": "311",
"original-title": [],
"prefix": "10.1155",
"published": {
"date-parts": [
[
2025,
1
]
]
},
"published-online": {
"date-parts": [
[
2025,
8,
30
]
]
},
"published-print": {
"date-parts": [
[
2025,
1
]
]
},
"publisher": "Wiley",
"reference": [
{
"DOI": "10.3803/enm.2021.301",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_1_2"
},
{
"DOI": "10.7861/clinmedicine.14-2-187",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_2_2"
},
{
"DOI": "10.1007/s11657-018-0549-y",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_3_2"
},
{
"DOI": "10.1056/nejmoa2118691",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_4_2"
},
{
"DOI": "10.1208/s12248-020-00532-2",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_5_2"
},
{
"DOI": "10.1136/postgradmedj-2020-138234",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_6_2"
},
{
"DOI": "10.3201/eid2704.210016",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_7_2"
},
{
"DOI": "10.1093/bib/bbx087",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_8_2"
},
{
"DOI": "10.1042/bsr20160180",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_9_2"
},
{
"DOI": "10.1093/bib/bbab461",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_10_2"
},
{
"DOI": "10.1099/mgen.0.001051",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_11_2"
},
{
"DOI": "10.1016/j.jbi.2018.11.001",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_12_2"
},
{
"DOI": "10.1093/nar/gks1193",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_13_2"
},
{
"DOI": "10.1093/nar/gkac194",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_14_2"
},
{
"DOI": "10.1093/nar/gkaa1074",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_15_2"
},
{
"DOI": "10.1093/nar/gkz240",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_16_2"
},
{
"DOI": "10.1093/nar/gkaa1084",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_17_2"
},
{
"DOI": "10.1146/annurev-biochem-013118-111829",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_18_2"
},
{
"DOI": "10.1038/s41392-022-01191-9",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_19_2"
},
{
"DOI": "10.1016/j.bbrc.2020.02.001",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_20_2"
},
{
"DOI": "10.1371/journal.pbio.3001851",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_21_2"
},
{
"DOI": "10.1016/j.cca.2012.12.012",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_22_2"
},
{
"article-title": "Characterizing the Oncogenic Importance and Exploring Gene-Immune Cells Correlation of ACTB in Human Cancers",
"author": "Li G.",
"first-page": "758",
"journal-title": "American Journal of Cancer Research",
"key": "e_1_2_12_23_2",
"volume": "13",
"year": "2023"
},
{
"DOI": "10.1016/s0140-6736(10)62349-5",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_24_2"
},
{
"DOI": "10.1073/pnas.2005615117",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_25_2"
},
{
"DOI": "10.1001/jamaoncol.2014.168",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_26_2"
},
{
"DOI": "10.1001/jamasurg.2020.6155",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_27_2"
},
{
"DOI": "10.1016/j.molcel.2011.09.024",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_28_2"
},
{
"DOI": "10.1038/s41586-020-2600-6",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_29_2"
},
{
"DOI": "10.1172/jci141848",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_30_2"
},
{
"DOI": "10.1016/j.bbadis.2023.166726",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_31_2"
},
{
"DOI": "10.1038/s41417-022-00489-8",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_32_2"
},
{
"DOI": "10.1111/jcmm.14915",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_33_2"
},
{
"DOI": "10.1073/pnas.2113401118",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_34_2"
},
{
"DOI": "10.3389/fimmu.2020.01638",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_35_2"
},
{
"DOI": "10.1136/gutjnl-2019-320441",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_36_2"
},
{
"DOI": "10.1016/j.bbrc.2016.09.083",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_37_2"
},
{
"DOI": "10.1016/s0378-5122(96)01080-8",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_38_2"
},
{
"DOI": "10.1016/j.chom.2020.04.017",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_39_2"
},
{
"DOI": "10.1097/md.0000000000029405",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_40_2"
},
{
"DOI": "10.1093/aje/kwi055",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_41_2"
},
{
"DOI": "10.1016/j.micpath.2021.104836",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_42_2"
},
{
"DOI": "10.1101/2021.08.24.457187",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_43_2"
},
{
"DOI": "10.1093/nar/gkad1040",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_44_2"
},
{
"DOI": "10.1055/a-2007-2715",
"doi-asserted-by": "publisher",
"key": "e_1_2_12_45_2"
}
],
"reference-count": 45,
"references-count": 45,
"relation": {},
"resource": {
"primary": {
"URL": "https://onlinelibrary.wiley.com/doi/10.1155/ije/8816596"
}
},
"score": 1,
"short-title": [],
"source": "Crossref",
"subject": [],
"subtitle": [],
"title": "Uncovering Overlapping Gene Networks and Potential Therapeutic Targets in Osteoporosis and COVID‐19 Through Bioinformatics Analysis",
"type": "journal-article",
"update-policy": "https://doi.org/10.1002/crossmark_policy",
"volume": "2025"
}
