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Background: Prior studies have suggested a potential relationship between osteoporosis and sarcopenia, both of which can present symptoms of compromised mobility. Additionally, fractures among the elderly are often considered a common outcome of both conditions. There is a strong correlation between fractures in the elderly population, decreased muscle mass, weakened muscle strength, heightened risk of falls, and diminished bone density. This study aimed to pinpoint crucial diagnostic candidate genes for osteoporosis patients with concomitant sarcopenia.

Methods: Two osteoporosis datasets and one sarcopenia dataset were obtained from the Gene Expression Omnibus (GEO). Differential expression genes (DEGs) and module genes were identified using Limma and Weighted Gene Co-expression Network Analysis (WGCNA), followed by functional enrichment analysis, construction of protein-protein interaction (PPI) networks, and application of a machine learning algorithm (least absolute shrinkage and selection operator (LASSO) regression) to determine candidate hub genes for diagnosing osteoporosis combined with sarcopenia. Receiver operating characteristic (ROC) curves and column line plots were generated.

Results: The merged osteoporosis dataset comprised 2067 DEGs, with 424 module genes filtered in sarcopenia. The intersection of DEGs between osteoporosis and sarcopenia module genes consisted of 60 genes, primarily enriched in viral infection. Through construction of the PPI network, 30 node genes were filtered, and after machine learning, 7 candidate hub genes were selected for column line plot construction and diagnostic value assessment. Both the column line plots and all 7 candidate hub genes exhibited high diagnostic value (area under the curve ranging from 1.00 to 0.93).

Conclusion: We identified 7 candidate hub genes (PDP1, ALS2CL, VLDLR, PLEKHA6, PPP1CB, MOSPD2, METTL9) and constructed column line plots for osteoporosis combined with sarcopenia. This study provides reference for potential peripheral blood diagnostic candidate genes for sarcopenia in osteoporosis patients.

Titel:	Identification of shared gene signatures and pathways for diagnosing osteoporosis with sarcopenia through integrated bioinformatics analysis and machine learning.
Autor/in / Beteiligte Person:	Zhou, X ; Zhao, L ; Zhang, Z ; Chen, Y ; Chen, G ; Miao, J ; Li, X
Link:	Full Text Finder (Volltext)
Zeitschrift:	BMC musculoskeletal disorders, Jg. 25 (2024-06-03), Heft 1, S. 435
Veröffentlichung:	London : BioMed Central, [2000-, 2024
Medientyp:	academicJournal
ISSN:	1471-2474 (electronic)
DOI:	10.1186/s12891-024-07555-2
Schlagwort:	Humans Gene Expression Profiling Protein Interaction Maps genetics Gene Regulatory Networks Aged Transcriptome Databases, Genetic Female Sarcopenia genetics Sarcopenia diagnosis Machine Learning Osteoporosis genetics Osteoporosis diagnosis Computational Biology
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [BMC Musculoskelet Disord] 2024 Jun 03; Vol. 25 (1), pp. 435. <i>Date of Electronic Publication: </i>2024 Jun 03. MeSH Terms: Sarcopenia* / genetics ; Sarcopenia* / diagnosis ; Machine Learning* ; Osteoporosis* / genetics ; Osteoporosis* / diagnosis ; Computational Biology* ; Humans ; Gene Expression Profiling ; Protein Interaction Maps / genetics ; Gene Regulatory Networks ; Aged ; Transcriptome ; Databases, Genetic ; Female References: J Am Med Dir Assoc. 2020 Feb;21(2):220-225. (PMID: 31669290) ; Can J Appl Physiol. 2001 Feb;26(1):78-89. (PMID: 11291626) ; Cardiovasc Res. 2011 Jan 1;89(1):79-88. (PMID: 20675715) ; Science. 1997 Mar 7;275(5305):1475-8. (PMID: 9045612) ; Nucleic Acids Res. 2013 Jan;41(Database issue):D991-5. (PMID: 23193258) ; Prog Lipid Res. 2016 Jan;61:30-9. (PMID: 26658141) ; Geriatr Nurs. 2001 Jan-Feb;22(1):17-21; quiz 22-3. (PMID: 11223792) ; Front Immunol. 2023 Mar 09;14:1126647. (PMID: 36969166) ; Nucleic Acids Res. 2021 Jan 8;49(D1):D605-D612. (PMID: 33237311) ; Biochem Biophys Res Commun. 2015 Nov 13;467(2):211-7. (PMID: 26449462) ; Biomed Eng Online. 2018 Nov 20;17(Suppl 1):131. (PMID: 30458798) ; Metabolism. 2023 Aug;145:155638. (PMID: 37348597) ; FEBS Lett. 2004 Sep 24;575(1-3):64-70. (PMID: 15388334) ; Front Genet. 2023 Jul 05;14:1163162. (PMID: 37476411) ; Mol Cell. 2014 Feb 20;53(4):534-48. (PMID: 24486017) ; Nat Commun. 2021 Feb 9;12(1):891. (PMID: 33563959) ; J Biol Chem. 2010 Apr 30;285(18):13918-29. (PMID: 20110354) ; Biostatistics. 2007 Jan;8(1):118-27. (PMID: 16632515) ; J Korean Med Sci. 2018 Jan 22;33(4):e27. (PMID: 29318794) ; Protein Cell. 2021 Dec;12(12):965-970. (PMID: 34218407) ; Cell Death Discov. 2023 Nov 28;9(1):428. (PMID: 38017014) ; Physiol Genomics. 2005 Apr 14;21(2):253-63. (PMID: 15687482) ; Am J Hum Genet. 2005 Sep;77(3):477-83. (PMID: 16080122) ; Clin Exp Immunol. 2020 Aug;201(2):105-120. (PMID: 32353176) ; Am J Epidemiol. 1998 Apr 15;147(8):755-63. (PMID: 9554417) ; Biochem Biophys Rep. 2022 Apr 02;30:101255. (PMID: 35402738) ; Age Ageing. 2019 Jul 1;48(4):601. (PMID: 31081853) ; Front Endocrinol (Lausanne). 2019 Apr 24;10:255. (PMID: 31068903) ; Transl Res. 2014 Nov;164(5):366-91. (PMID: 25090308) ; Front Cell Dev Biol. 2021 Sep 09;9:729444. (PMID: 34568338) ; Genes (Basel). 2019 May 29;10(6):. (PMID: 31146489) ; EMBO Rep. 2018 Jul;19(7):. (PMID: 29858488) ; Cell Biosci. 2020 Aug 06;10:95. (PMID: 32782783) ; BMC Bioinformatics. 2008 Dec 29;9:559. (PMID: 19114008) ; J Physiol Sci. 2022 Feb 11;72(1):3. (PMID: 35148687) ; Eur Geriatr Med. 2018 Aug;9(4):419-434. (PMID: 34674498) ; Medicine (Baltimore). 2023 May 19;102(20):e33829. (PMID: 37335694) ; Nucleic Acids Res. 2019 Jan 8;47(D1):D330-D338. (PMID: 30395331) ; Nucleic Acids Res. 2000 Jan 1;28(1):27-30. (PMID: 10592173) ; Comput Math Methods Med. 2022 Oct 14;2022:7397250. (PMID: 36276992) ; Aging Med (Milton). 2019 Sep 08;2(3):147-156. (PMID: 31942528) ; Clin Geriatr Med. 2017 Feb;33(1):17-26. (PMID: 27886695) ; Eur J Biochem. 1992 Dec 15;210(3):1023-35. (PMID: 1336455) ; J Korean Med Sci. 2022 May 09;37(18):e146. (PMID: 35535373) Contributed Indexing: Keywords: Bioinformatics; Hub genes; Machine learning; Osteoporosis; Sarcopenia Entry Date(s): Date Created: 20240603 Date Completed: 20240604 Latest Revision: 20240607 Update Code: 20240607 PubMed Central ID: PMC11149362

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Identification of shared gene signatures and pathways for diagnosing osteoporosis with sarcopenia through integrated bioinformatics analysis and machine learning.