Identification of Shared Gene Signatures and Pathways for Diagnosing Osteoporosis with Sarcopenia through Integrated Bioinformatics Analysis and Machine Learning

Identification of Shared Gene Signatures and Pathways for Diagnosing Osteoporosis with Sarcopenia through Integrated Bioinformatics Analysis and Machine Learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of Shared Gene Signatures and Pathways for Diagnosing Osteoporosis with Sarcopenia through Integrated Bioinformatics Analysis and Machine Learning Xiaoli Zhou, Guangdong Chen, Yang Chen, Zepei Zhang, Jun Miao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3940690/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract 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. Sarcopenia Osteoporosis Hub genes Machine learning Bioinformatics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Sarcopenia is a progressive and systemic skeletal muscle disorder associated with an increased likelihood of adverse outcomes, including falls, fractures, physical disability, and mortality. Rooted in detrimental muscle changes that accumulate throughout a lifetime, sarcopenia manifests as a muscle disease or failure. While common among older adults, it can also occur earlier in life. Sarcopenia involves the loss of muscle strength and mass in older individuals, significantly contributing to an elevated risk of falls and impaired ability to perform daily activities. This often leads to disability, loss of independence, and even death. The impact of sarcopenia on morbidity, mortality, and healthcare expenditure has prompted significant research and public policy discussions, emphasizing its importance in the field[ 1] . The decrease in muscle strength, a critical factor for mobility, significantly contributes to the heightened prevalence of falls in the elderly. This condition is closely associated with self-reported physical disability in both men and women, transcending factors such as ethnicity, age, morbidity, obesity, income, or health behaviors[ 2] . The decline in muscle strength with aging not only leads to a reduction in functional capacity but also emerges as a major catalyst for disability, mortality, and other adverse health outcomes[ 3] . As the population of older individuals continues to expand, the morbidity linked to sarcopenia is anticipated to become a significant focus of healthcare resource utilization. Management options for sarcopenia encompass both non-pharmacological and pharmacological approaches. Non-pharmacological strategies include resistance exercise and proper nutrition. Among these, resistance exercise stands out as the standard non-pharmacological treatment for sarcopenia, supported by substantial positive evidence. Certain dietary measures, such as adequate protein intake, vitamin D, antioxidant nutrients, and long-chain polyunsaturated fatty acids, have demonstrated positive effects against sarcopenia. However, various pharmacological agents, such as growth hormone, anabolic or androgenic steroids, selective androgen receptor modulators, protein anabolic agents, appetite stimulants, myostatin inhibitors, activating II receptor drugs, β-receptor blockers, angiotensin-converting enzyme inhibitors, and troponin activators, are recommended and have exhibited varying efficacy. Future research endeavors should concentrate on elucidating the biological pathways of sarcopenia, refining diagnostic approaches, including the identification of biomarkers for early detection, developing consistently superior treatment methods for severe sarcopenia cases, and establishing sensitive measures for predicting the response to sarcopenia treatments[ 4] . Osteoporosis and fractures related to osteoporosis are prevalent causes of morbidity and mortality among older adults. Bisphosphonates stand as the primary therapy for numerous osteoporosis patients. Additional treatments for osteoporosis encompass denosumab, teriparatide, abaloparatide, romosozumab, and selective estrogen receptor modulators. Osteoporosis, characterized by diminished bone mass, deterioration in bone microarchitecture, and increased susceptibility to fractures, can result in debilitating pain and deformity. This ailment poses a significant health challenge, particularly for older women. Annually, approximately 1.5 million individuals in the United States experience osteoporosis-related fractures, with many facing incomplete recovery. Early identification of individuals at risk for osteoporosis and timely intervention can mitigate its deleterious effects[ 5] . The coexistence of osteoporosis and sarcopenia has recently been recognized in certain groups as a syndrome termed 'osteosarcopenia'[ 6] . Muscles and bones are intricately connected tissues that exhibit significant co-variation during development, growth, aging, and in various diseases. The term osteosarcopenia specifically refers to the dual challenge of dealing with both osteoporosis and sarcopenia. Interactions between muscles and bones at the biomechanical, cellular, paracrine, endocrine, neuronal, or nutritional levels may contribute to the pathophysiology of osteosarcopenia[ 7] . Instrumental variables at the genome-wide significance level were obtained from published summary statistics. The inverse variance weighted method, along with several other Mendelian randomization methods, was employed to assess the bi-directional causality between sarcopenia and osteoporosis. Myopia was utilized as a negative control outcome to validate the instrumental variables. The results indicated a potential significant causal effect between sarcopenia and osteoporosis, suggesting a mutual influence on each other. The study supported the notion that individuals with severe osteoporosis were more susceptible to muscle mass loss , and severe muscle mass loss might contribute to reduced lumbar spine bone mineral density[ 8] . A systematic literature review was conducted until March 10th, 2018, resulting in the identification of 1,105 papers. Of these, 1,049 and 29 were excluded based on title/abstracts and full-text assessment, respectively. Twenty-seven original papers were included in the systematic review, with 17 deemed suitable for meta-analysis. The findings emphasized the frequency of osteosarcopenia and highlighted a higher relative risk of fractures among sarcopenic patients. The study underscored the importance of a standardized and stringent classification of sarcopenia to accurately assess its true relationship and consequences[ 9] . Muscles and bones, integral components of the motor system, originate from mesodermal and ectodermal mesenchymal stem cells. Their close anatomical proximity establishes the fundamental conditions for the exchange of mechanical and chemical signals, potentially contributing to the concurrent occurrence of sarcopenia and osteoporosis. Identifying shared crosstalk genes between these two systems may offer novel insights for the prevention and treatment of their development[ 10] . Material and methods Data collection The datasets GSE1428 and GSE230665, as well as GSE56116, were curated from the GEO database (https://www.ncbi.nlm.nih.gov/geo/)[ 11] . For the microarray analysis of the GSE1428[ 12] dataset, the GPL96 platform (Affymetrix Human Genome U133A Array) was utilized. GSE230665[ 13] employed the GPL10332 platform (Agilent-026652 Whole Human Genome Microarray 4x44K v2, Feature Number version). GSE56116 [13] utilized the GPL4133 platform (Agilent-014850 Whole Human Genome Microarray 4x44K G4112F, Feature Number version). The GSE1428 dataset presents transcriptional responses related to sarcopenia, as provided by Giresi et al. On the other hand, the datasets GSE230665 and GSE56116, focusing on osteoporosis, were contributed by Ge, Li, and their respective collaborators. The GSE1428 dataset showcases the transcriptional responses associated with sarcopenia, while GSE230665 and GSE56116 pertain to datasets on osteoporosis(Figure. 1). Identification of DEGs between Sarcopenia and Osteoporosis For the two original osteoporosis datasets, empirical Bayes methods[ 14] were applied to eliminate batch effects. The merged osteoporosis dataset and the sarcopenia dataset underwent the extraction of expression matrices, with the exclusion of genes and samples featuring missing values exceeding 50%. Subsequently, missing values were imputed using the "impute.knn" function from the R package "impute," setting the Number of neighbors to 10 for data completion. Furthermore, a log2 transformation was applied to the data. In cases where multiple probes identified the same gene, the average expression was calculated. Finally, utilizing the Limma package, criteria of |log2 Fold change (FC)| > 1.5 and P < 0.05 were set as the standards for identifying DEGs. Weighted Gene Co-expression Network Analysis and Module Gene Selection Exploring gene-gene correlations using systems biology strategy WGCNA[ 15] . Based on gene expression profiles, we computed the median absolute deviation for each gene and removed the bottom 50% of genes with the smallest median absolute deviation. We utilized the R software package WGCNA's goodSamplesGenes method to eliminate outlier genes and samples. Subsequently, we employed WGCNA to construct a scale-free co-expression network. Initially, Pearson's correlation matrices and the average linkage method were applied to all pairwise genes. Then, a weighted adjacency matrix was created using a power function A_mn=|C_mn|^β (where C_mn represents the Pearson's correlation between gene_m and gene_n, and A_mn denotes the adjacency between gene m and gene n). The parameter β was chosen to be 12 for soft-thresholding, emphasizing strong correlations and penalizing weak ones. The adjacency matrix was transformed into a topological overlap matrix, measuring the network connectivity of a gene, defined as the sum of its adjacencies with all other genes for network gene ration, and the corresponding dissimilarity (1-TOM) was computed. To classify genes with similar expression profiles into gene modules, average linkage hierarchical clustering was performed based on the TOM-based dissimilarity measure, with a minimum module size of 100 for the gene dendrogram. We set the sensitivity to 3. To further analyze the modules, we calculated the dissimilarity of module eigen genes, selected a cut line for the module dendrogram, and merged some modules. Additionally, we merged modules with a distance less than 0.25, resulting in the identification of 2 co-expression modules. Functional Enrichment Analysis The Gene Ontology (GO)[ 16] system provides structured and computable information about the functions of genes and gene products. The Kyoto Encyclopedia of Genes and Genomes (KEGG)[ 17] is a widely used database for gene system research. Using gene annotations from the R package org.Hs.eg.db (version 3.1.0), and obtaining the latest KEGG Pathway gene annotations from the KEGG rest API (https://www.kegg.jp/kegg/rest/keggapi.html) as background, genes were mapped to the background set. Functional enrichment analysis was performed using the R package clusterProfiler (version 3.14.3) to obtain results of gene set enrichment. The minimum gene set was set to 5, and the maximum gene set was set to 5000. A P value of < 0.05 and a false discovery rate of < 0.1 were considered statistically significant. Two rounds of GO and KEGG analyses were conducted based on the intersection of DEGs in sarcopenia and the most significant module genes, as well as the intersection of DEGs in osteoporosis and the most significant module genes in sarcopenia. Construction of Protein-Protein Interaction Network To explore the interactions between protein-coding genes, we utilized the String database[ 18] (version 11.5; www.string-db.org), with a minimum interaction score set to 0.400. The obtained network from String was further modified using Cytoscape software. All interacting genes within the protein-protein interaction (PPI) network were selected for subsequent analysis. Machine Learning To further screen candidate genes for diagnosing sarcopenia and osteoporosis, a machine learning algorithm was employed. LASSO[ 19] (Least Absolute Shrinkage and Selection Operator) is a regression method used for variable selection to improve prediction accuracy. It is also a regularization technique that enhances the predictive accuracy and interpretability of statistical models. Utilizing the R package glmnet, gene expression data was integrated, and regression analysis was performed using the lasso-cox method. A 3-fold cross-validation was set up to obtain the optimal model. The Lambda value was set to 0.0639847346226388. The genes obtained from this analysis were identified as candidate hub genes for diagnosing sarcopenia and osteoporosis. Construction of Column Line Plots and ROC Curve Evaluation Construction of column line plots holds certain value in diagnosing clinical sarcopenia and osteoporosis. Using the candidate genes, column line plots were constructed using the R package pROC (version 1.17.0.1). The "Score" represents the score of the candidate genes, while "Total Score" represents the sum of scores for all the aforementioned genes. ROC curves were established to evaluate the diagnostic value of the candidate genes and column line plots for sarcopenia and osteoporosis. Results Identification of DEGs Identification of DEGs using Limma method revealed a total of 821 DEGs (337 upregulated, 484 downregulated) in the sarcopenia dataset. The heatmap and volcano plot of sarcopenia DEGs are shown in Figure. 2A-B. In the combined osteoporosis dataset, a total of 2067 DEGs were identified, with 2059 upregulated and 9 downregulated genes. The heatmap and volcano plot of osteoporosis DEGs are illustrated in Figure. 3A-B. WGCNA Analysis and Identification of Key Modules We selected β = 12 (scale-free R 2 = 0.86) as the "soft" threshold based on scale independence and average connectivity (Figure. 4A-B). The dendrogram (Figure. 4C) depicts the clustering of sarcopenia and control samples. Based on this, two gene co-expression modules were generated, as indicated by different colors in Figure. 4D. Among them, the turquoise module (424 genes) exhibited the highest correlation with sarcopenia (correlation coefficient=-0.41, P =0.06) and was considered the key module for subsequent analysis. Functional Enrichment Analysis of Sarcopenia To assess whether the dataset GSE1428 reliably reflects the pathogenesis of sarcopenia, we further conducted functional enrichment analysis based on the intersection of Limma and WGCNA module genes. The intersection of 424 DEGs from the turquoise module with 821 genes yielded 16 common genes (Figure. 5A). KEGG analysis revealed that common genes were primarily enriched in "Metabolic pathways" and "Carbon metabolism" (Figure. 5B). GO analysis indicated that common genes were predominantly enriched in biological process (BP) terms, including "coenzyme metabolic process" and "purine ribonucleotide metabolic process" (Figure. 5C). Regarding cellular component (CC) ontology, CGs were mainly located in "mitochondrion," "mitochondrial part," and "mitochondrial matrix" (Figure. 5D). Molecular function (MF) analysis showed that "Ras guanyl-nucleotide exchange factor activity," "isocitrate dehydrogenase (NAD+) activity," and "L-aspartate transmembrane transporter activity" were the most significant terms within common genes (Figure. 5E). Enrichment Analysis and Node Gene Identification for Osteoporosis and Sarcopenia Based on PPI Networks To further explore whether key genes associated with sarcopenia are also related to the pathogenesis of osteoporosis, we visualized the intersection of DEGs in osteoporosis and module genes in sarcopenia through a Venn diagram, identifying 60 genes (Figure. 6A). KEGG enrichment analysis revealed that these 60 genes were mainly enriched in "Human papillomavirus infection," "mTOR signaling pathway," and "Kaposi sarcoma-associated herpesvirus infection" (Figure. 6D). GO analysis showed that these genes were enriched in "Kaposi sarcoma-associated herpesvirus infection," "organonitrogen compound biosynthetic process," and "cellular amide metabolic process" in BP; "cytosol," "cytosol," and "nuclear chromosome" in CC; and "transcription coregulator activity," "transcription coregulator activity," and "ubiquitin-like protein ligase activity" in MF (Figure. 6E-G). After confirming the filtered genes, we constructed a PPI network to identify interacting node genes for subsequent machine learning filtering. Figure. 6B displays the PPI network, where 30 genes can interact with each other. These genes are sorted by node degree in Figure. 6B. Identifying Candidate Hub Genes through Machine Learning The LASSO regression machine learning algorithm was applied to select candidate genes for column line plot construction and diagnostic value assessment. From Figure. 7A-B, it can be observed that the LASSO regression algorithm identified 7 potential candidate biomarkers for final validation. Diagnostic Value Evaluation Based on the 7 candidate hub genes, a column line plot was constructed (Figure. 8A), and ROC curves were established to evaluate the diagnostic specificity and sensitivity of each gene and the column line plot. The area under the curve (AUC) and its 95% confidence interval (CI) were calculated for each item. The results are as follows: PDP1 (AUC 0.96, CI 1.00 ~ 0.88), ALS2CL (AUC 0.80, CI 1.00 ~ 0.58), VLDLR (AUC 0.83, CI 1.00 ~ 0.5), PLEKHA6 (AUC 0.93, CI 1.00 ~ 0.83), PPP1CB (AUC 0.82, CI 1.00 ~ 0.63), MOSPD2 (AUC 0.73, CI 1.00 ~ 0.44), METTL9 (AUC 0.82, CI 1.00 ~ 0.58), and the column line plot (AUC 0.98, CI 1.00 ~ 0.93) (Figure. 8B-I). All candidate genes exhibited high diagnostic value for sarcopenia combined with osteoporosis, with the column line plot demonstrating the highest diagnostic value. Discussion Osteosarcopenia, characterized by the coexistence of osteopenia/osteoporosis and sarcopenia, has emerged as a significant health concern, imposing a substantial global health burden. According to the World Health Organization, osteopenia and osteoporosis are defined by T scores equal to or less than −1 and −2.5 standard deviations, respectively, below the peak bone mass of a young, healthy cohort or in the presence of a minimal-trauma fracture. This skeletal condition results in the deterioration of bone microarchitecture and compromises bone strength[ 20] . Conversely, sarcopenia is identified by cut-off values indicating low muscle mass, strength, and/or functional capacity[ 21] and is associated with various metabolic conditions[ 22] . Both osteosarcopenia and sarcopenia share common risk factors[ 23 ] and exhibit strong associations with frailty, falls, fractures, hospitalizations, and mortality[ 23 - ] , contributing to a significant increase in healthcare expenditure. The coexistence of these conditions underscores the intricate interplay between skeletal and muscular health and emphasizes the need for comprehensive approaches to address their shared impact on overall well-being. In this study, we utilized a series of integrated bioinformatics analyses and machine learning methods to construct a nomogram and evaluate the diagnostic value of osteoporosis in sarcopenia patients. A notable finding is the identification of 7 key candidate genes (PDP1, ALS2CL, VLDLR, PLEKHA6, PPP1CB, MOSPD2, and METTL9), and the development of a nomogram for diagnosing osteoporosis in sarcopenia patients. The sarcopenia patient dataset used in this study all comes from peripheral blood samples. Therefore, we only need to collect peripheral blood samples from sarcopenia patients and evaluate the expression of the 7 identified immune-related genes to infer the probability of sarcopenia patients developing osteoporosis. This is an efficient and practical clinical approach. The use of peripheral blood testing in diagnosing various diseases is also widely accepted. Furthermore, although we confirmed that gene expression levels can serve as independent diagnostic markers, we plan to develop a more comprehensive diagnostic model by transforming them into scores and considering all 7 markers. The expression of each gene is quantified and converted into a score, with an increase in score indicating a higher linear prediction factor. When the linear prediction factor is high, we can conduct early monitoring and intervention in sarcopenia patients, which is more valuable for implementing osteoporosis diagnosis in sarcopenia. PDP1encodes a protein that is one of the three components (E1, E2, and E3) of the large pyruvate dehydrogenase complex. PDP1 plays a crucial role in protein phosphorylation and has been implicated in various diseases[ 26 -27 ]. Research has shown that miR-18a-3p improves cartilage matrix remodeling and suppresses inflammation in osteoarthritis by targeting PDP1[ 28] . In pancreatic cancer, PDP1 promotes cancer proliferation and invasion by regulating the MAPK/mTOR signaling pathway[ 29] . Additionally, PDP1 is associated with osteosarcoma progression, patient prognosis, and chemosensitivity, making it a potential biomarker for osteosarcoma[ 30] . Given its role in multiple diseases, PDP1 is considered a potential diagnostic target for osteoporosis in sarcopenic patients. ALS2CL encodes a 108-kD protein with specific but relatively weak Rab5-GEF activity and strong Rab5-binding properties. Co-expression of ALS2CL and Rab5A in HeLa cells results in a unique tubulation phenotype of endosome compartments, indicating ALS2CL's involvement in modulating Rab5-mediated endosome dynamics[ 31] . VLDLR, or Very Low Density Lipoprotein Receptor, belongs to the low-density lipoprotein receptor family, with high expression levels in the brain, heart, skeletal muscle, and adipose tissue, while its expression in the liver is very low under physiological conditions. It plays a crucial role in controlling serum triglycerides and the development of non-alcoholic fatty liver disease. Previous studies have demonstrated the involvement of VLDLR in regulating the onset of various diseases. Research has found that homozygous loss-of-function mutations in VLDLR lead to dysequilibrium syndrome, a non-progressive cerebellar ataxia syndrome associated with intellectual disability[ 32] . PLEKHA6, along with other members of the WW-PLEKHA family, plays a role in the trafficking and retention of transmembrane proteins, including nectins, Tspan33, and the copper pump ATP7A, at cell-cell junctions and lateral membranes. Its C-terminal region and coiled-coil region promote its localization at adherens junctions of epithelial cells. This suggests that PLEKHA6 is involved in maintaining cell-cell adhesion and potentially regulates signaling pathways associated with adherens junctions[ 33] . PPP1CB, located on chromosome 2p23.2, encodes a subunit of PPP1 involved in various cellular functions, including glycogen metabolism, cell division, and muscle contraction[ 34 - ]. Recent studies have identified PPP1CB as the myosin light chain phosphatase responsible for Ca2+-transient rise and enhanced cell shortening in cardiomyocytes[ 38] . MOSPD2, a member of the VAP family, facilitates contact between the endoplasmic reticulum and various cellular organelles[ 39] . Unlike other VAP family members, MOSPD2 contains an additional cytoplasmic domain called CRAL-TRIO, which may be involved in lipid transport[ 40] . Research suggests that MOSPD2 is a key regulator of inflammation-driven monocyte migration and a potential therapeutic target for CNS inflammatory diseases[ 41] . METTL9, a methyltransferase, plays a crucial role in histone methylation and is implicated as an oncogene in various cancers[ 42 -43 ]. Targeting METTL9 significantly inhibits the growth of hepatocellular carcinoma patient-derived xenografts [ 44] and correlates with increased metastatic activity in human gastric cancer[ 45] . Limitation In summary, this study identified candidate hub genes for diagnosing osteoporosis combined with sarcopenia using integrated bioinformatics and machine learning approaches. However, limitations include reliance on publicly available datasets, potential selection bias in gene identification, limited generalizability to diverse populations, cross-sectional data analysis, and the need for further experimental validation to elucidate the functional mechanisms of the identified genes. These findings provide a foundation for potential peripheral blood diagnostic markers but require additional validation and clinical translation for practical application in healthcare settings. Conclusion Our study systematically identified seven candidate hub genes (PDP1, ALS2CL, VLDLR, PLEKHA6, PPP1CB, MOSPD2, and METTL9) through a combination of various bioinformatics analyses and machine learning algorithms, and provided a nomogram for diagnosing sarcopenia associated with osteoporosis. The research offers reference for potential peripheral blood diagnostic candidate genes for sarcopenia related to osteoporosis. Abbreviations AUC:Area under the curve BP: Biological processes CC: Cellular components CI:Confidence interval DEGs:Differentially expressed genes GEO:Gene Expression Omnibus GO: Gene Ontology KEGG: Kyoto Encyclopedia of Genes and Genomes LASSO:Least Absolute Shrinkage and Selection Operator MF: Molecular functions PDP1:Pyruvate dehydrogenase phosphatase catalytic subunit 1 PPI:Protein-protein interaction ROC:Receiver operating characteristic TOM:Topological overlap matrix WGCNA:Weighted Gene Co-expression Network Analysis Declarations Author contribution statement Study Design: Xiaoli Zhou, Guangdong Chen, Zepei Zhang, Jun Miao. Data Collection: Xiaoli Zhou, Yang Chen, Zhiyong Qian, Jun Miao Statistical Analysis: Xiaoli Zhou, Guangdong Chen. Data Interpretation: Yang Chen. Manuscript Preparation: Xiaoli Zhou, Guangdong Chen, Zepei Zhang, Jun Miao. Literature Search: Xiaoli Zhou, Guangdong Chen. Funding statement This work was supported by Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-026A) and Hebei Province High Level Talent Funding Project (C20221122). Data availability statement The datasets analysed during the current study are available in Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/): GSE1428, GSE230665, GSE56116. Declaration of interest’s statement The authors declare no conflict of interest. Additional information No additional information is available for this paper. References Dhillon, R. J., and Hasni, S., 2017. Pathogenesis and Management of Sarcopenia. Clinics in geriatric medicine, 33(1), 17–26. Baumgartner, R. N., Koehler, K. M., Gallagher, D., Romero, L., Heymsfield, S. B., Ross, R. R., Garry, P. J., and Lindeman, R. D., 1998. Epidemiology of sarcopenia among the elderly in New Mexico. American journal of epidemiology, 147(8), 755–763. Roubenoff R., 2001. Origins and clinical relevance of sarcopenia. 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Yacov, N., Kafri, P., Salem, Y., Propheta-Meiran, O., Feldman, B., Breitbart, E., & Mendel, I., 2020. MOSPD2 is a therapeutic target for the treatment of CNS inflammation. Clinical and experimental immunology, 201(2), 105–120. Davydova, E., Shimazu, T., Schuhmacher, M. K., Jakobsson, M. E., Willemen, H. L. D. M., Liu, T., Moen, A., Ho, A. Y. Y., Małecki, J., Schroer, L., Pinto, R., Suzuki, T., Grønsberg, I. A., Sohtome, Y., Akakabe, M., Weirich, S., Kikuchi, M., Olsen, J. V., Dohmae, N., Umehara, T., … Falnes, P. Ø., 2021. The methyltransferase METTL9 mediates pervasive 1-methylhistidine modification in mammalian proteomes. Nature communications, 12(1), 891. Lv, M., Cao, D., Zhang, L., Hu, C., Li, S., Zhang, P., Zhu, L., Yi, X., Li, C., Yang, A., Yang, Z., Zhu, Y., Zhang, K., and Pan, W., 2021. METTL9 mediated N1-histidine methylation of zinc transporters is required for tumor growth. Protein & cell, 12(12), 965–970. Bi, F., Qiu, Y., Wu, Z., Liu, S., Zuo, D., Huang, Z., Li, B., Yuan, Y., Niu, Y., and Qiu, J., 2023. METTL9-SLC7A11 axis promotes hepatocellular carcinoma progression through ferroptosis inhibition. Cell death discovery, 9(1), 428. Hara, T., Tominaga, Y., Ueda, K., Mihara, K., Yanagihara, K., and Takei, Y., 2022. Elevated METTL9 is associated with peritoneal dissemination in human scirrhous gastric cancers. Biochemistry and biophysics reports, 30, 101255. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor invited by journal 18 Feb, 2024 Submission checks completed at journal 18 Feb, 2024 First submitted to journal 08 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3940690","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273518645,"identity":"e8a03708-9f36-48b9-a97a-97dfb5169c83","order_by":0,"name":"Xiaoli Zhou","email":"","orcid":"","institution":"Tianjin Hospital, Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoli","middleName":"","lastName":"Zhou","suffix":""},{"id":273518646,"identity":"7185dfae-8b28-4252-ad8b-30d7cda0b24e","order_by":1,"name":"Guangdong Chen","email":"","orcid":"","institution":"Cangzhou Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guangdong","middleName":"","lastName":"Chen","suffix":""},{"id":273518647,"identity":"a186cd03-0477-4899-a8bc-0eba69733d0c","order_by":2,"name":"Yang Chen","email":"","orcid":"","institution":"Tianjin Hospital, Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Chen","suffix":""},{"id":273518648,"identity":"7738c72e-ad18-4b8c-8d97-ce7783156d69","order_by":3,"name":"Zepei Zhang","email":"","orcid":"","institution":"Tianjin Hospital, Tianjin University","correspondingAuthor":false,"prefix":"","firstName":"Zepei","middleName":"","lastName":"Zhang","suffix":""},{"id":273518649,"identity":"05ac7fbe-4b17-4453-991f-f3838c36a090","order_by":4,"name":"Jun Miao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYFCCwwcffKiwYWBsALJ5iNNyLNlwxpk0CVK08KgJ87YdloCwidGg23iGjXHGmfN1zDMSGB+8bWOQNyekxezA2WNAv9yWYJyRwGw4t43BcGcDQS3n0oF+AWthk+ZtY0gwOEBQyxkzoMpzIC3sv0nRcgBsCzORWsCBnCzZ2POwWXLOOQnDDQS13ABHpR2/YXvywQ9vymzkCdrCIAFVYdgAjkwJQuqBgL8BQssToXYUjIJRMApGKAAAEyVIPXICk8YAAAAASUVORK5CYII=","orcid":"","institution":"Tianjin Hospital, Tianjin University","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"","lastName":"Miao","suffix":""}],"badges":[],"createdAt":"2024-02-08 17:29:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3940690/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3940690/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51395967,"identity":"228662d8-f88d-41ab-9e01-ef3985998de0","added_by":"auto","created_at":"2024-02-20 20:10:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":267193,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWorkflow of the whole study\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3940690/v1/683f9e95ad9f6f3db85f0635.png"},{"id":51395510,"identity":"17ce9819-dd34-4c72-b354-89c25ae0bb35","added_by":"auto","created_at":"2024-02-20 20:02:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":361069,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap and volcano plot for the DEGs identified from the sarcopenia dataset.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA Red and green plot triangles represent DEGs with upregulated and downregulated gene expression, respectively.\u003c/p\u003e\n\u003cp\u003eB Each row shows the DEGs, and each column refers to one of the samples of sarcopenia cases or controls. The red and blue represent DEGs with upregulated and downregulated gene expression, respectively.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3940690/v1/a3bd9fb8f1970373ee0611dc.png"},{"id":51396541,"identity":"6af08333-55db-4fb4-bbdf-7bcd4caddd3f","added_by":"auto","created_at":"2024-02-20 20:18:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":320930,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap and volcano plot for the DEGs identified from the osteoporosis dataset.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA Red and green plot triangles represent DEGs with upregulated and downregulated gene expression, respectively.\u003c/p\u003e\n\u003cp\u003eB Each row shows the DEGs, and each column refers to one of the samples of osteoporosis cases or controls. The red and blue represent DEGs with upregulated and downregulated gene expression, respectively.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3940690/v1/4179e3591598352957d6e674.png"},{"id":51395511,"identity":"e9af43c5-4bfd-4da6-ab5e-24c86d6c5fff","added_by":"auto","created_at":"2024-02-20 20:02:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":393258,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWGCNA of DEGs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA-B Estimation of the soft thresholding value for a scale-free co-expression network.\u003c/p\u003e\n\u003cp\u003eC Cluster dendrogram of all DEGs.\u003c/p\u003e\n\u003cp\u003eD Heatmap showing the correlation between modules and sarcopenia. The turquoise module is found to be significantly correlated with sarcopenia. The numbers in the top and bottom brackets represent the correlation coefficient and p-value, respectively.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3940690/v1/ec7ad48bada5440cc4f80b9a.png"},{"id":51395518,"identity":"a80d6e26-9c9c-4eb0-954a-308ad502267f","added_by":"auto","created_at":"2024-02-20 20:02:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":790389,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnrichment analysis of the intersection of genes in sarcopenia.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA Venn diagram shows that 16 genes are identified from the intersection of DEGs via Limma and green module genes via WGCNA.\u003c/p\u003e\n\u003cp\u003eB KEGG pathway analysis of the intersection of genes. Different colors represent various significant pathways and related enriched genes.\u003c/p\u003e\n\u003cp\u003eC-E GO analysis of the intersection of genes, including biological process, cellular component, and molecular function, respectively. The y-axis represents different GO terms, the x-axis represents gene ratio enriched in relative GO terms, the circle size refers to gene numbers, and the color represents p value.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3940690/v1/aeced01eb8e09d08fd0df8f4.png"},{"id":51395969,"identity":"14aa5589-493d-4a24-bfe8-32b46b0a7c13","added_by":"auto","created_at":"2024-02-20 20:10:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1037875,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnrichment analysis of common genes from osteoporosis with sarcopenia and the identification of node genes from PPI network.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA Venn diagram shows that 60 common genes are identified from the intersection of genes in osteoporosisusing Limma and sarcopenia using WGCNA.\u003c/p\u003e\n\u003cp\u003eB PPI network reveals that 30 genes interact with each other.\u003c/p\u003e\n\u003cp\u003eC The column shows the gene nodes of 26 genes in PPI network.\u003c/p\u003e\n\u003cp\u003eD KEGG analysis of 60 common genes.\u003c/p\u003e\n\u003cp\u003eE-G GO analysis (biological process, cellular component, and molecular function) of 60 common genes.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3940690/v1/873c9d71622c7f00034cfb5f.png"},{"id":51395515,"identity":"efe18efb-4968-49cb-8c5a-fa382c672c0e","added_by":"auto","created_at":"2024-02-20 20:02:44","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":332339,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMachine learning in screening candidate diagnostic biomarkers for osteoporosis with sarcopenia.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA-B Biomarkers screening in the Lasso model. The number of genes (n=7) corresponding to the lowest point of the curve is the most suitable for osteoporosis with sarcopenia diagnosis.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3940690/v1/7f4394f8ff7df26f22bbf375.png"},{"id":51395516,"identity":"0bc4c841-161c-4a14-9dc4-c3db7c6afe2b","added_by":"auto","created_at":"2024-02-20 20:02:44","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":438241,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram construction and the diagnostic value evaluation.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA The visible nomogram for diagnosing osteoporosis with sarcopenia .\u003c/p\u003e\n\u003cp\u003eB-I The ROC curve of each candidate gene (PDP1, ALS2CL, VLDLR, PLEKHA6, PPP1CB, MOSPD2, METTL9) and nomogram show the significant steoporosis with sarcopenia diagnostic value.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3940690/v1/eebf4b1cdebf419a870e1a9c.png"},{"id":51396874,"identity":"68d9744e-7d7b-4304-8383-918b04f75895","added_by":"auto","created_at":"2024-02-20 20:26:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2425471,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3940690/v1/0215671b-51eb-4f5e-8378-3119ab735b3b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of Shared Gene Signatures and Pathways for Diagnosing Osteoporosis with Sarcopenia through Integrated Bioinformatics Analysis and Machine Learning","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSarcopenia is a progressive and systemic skeletal muscle disorder associated with an increased likelihood of adverse outcomes, including falls, fractures, physical disability, and mortality. Rooted in detrimental muscle changes that accumulate throughout a lifetime, sarcopenia manifests as a muscle disease or failure. While common among older adults, it can also occur earlier in life. Sarcopenia involves the loss of muscle strength and mass in older individuals, significantly contributing to an elevated risk of falls and impaired ability to perform daily activities. This often leads to disability, loss of independence, and even death. The impact of sarcopenia on morbidity, mortality, and healthcare expenditure has prompted significant research and public policy discussions, emphasizing its importance in the field[\u003csup\u003e1]\u003c/sup\u003e. The decrease in muscle strength, a critical factor for mobility, significantly contributes to the heightened prevalence of falls in the elderly. This condition is closely associated with self-reported physical disability in both men and women, transcending factors such as ethnicity, age, morbidity, obesity, income, or health behaviors[\u003csup\u003e2]\u003c/sup\u003e. The decline in muscle strength with aging not only leads to a reduction in functional capacity but also emerges as a major catalyst for disability, mortality, and other adverse health outcomes[\u003csup\u003e3]\u003c/sup\u003e. As the population of older individuals continues to expand, the morbidity linked to sarcopenia is anticipated to become a significant focus of healthcare resource utilization.\u003c/p\u003e\n\u003cp\u003eManagement options for sarcopenia encompass both non-pharmacological and pharmacological approaches. Non-pharmacological strategies include resistance exercise and proper nutrition. Among these, resistance exercise stands out as the standard non-pharmacological treatment for sarcopenia, supported by substantial positive evidence. Certain dietary measures, such as adequate protein intake, vitamin D, antioxidant nutrients, and long-chain polyunsaturated fatty acids, have demonstrated positive effects against sarcopenia. However, various pharmacological agents, such as growth hormone, anabolic or androgenic steroids, selective androgen receptor modulators, protein anabolic agents, appetite stimulants, myostatin inhibitors, activating II receptor drugs, \u0026beta;-receptor blockers, angiotensin-converting enzyme inhibitors, and troponin activators, are recommended and have exhibited varying efficacy. Future research endeavors should concentrate on elucidating the biological pathways of sarcopenia, refining diagnostic approaches, including the identification of biomarkers for early detection, developing consistently superior treatment methods for severe sarcopenia cases, and establishing sensitive measures for predicting the response to sarcopenia treatments[\u003csup\u003e4]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eOsteoporosis and fractures related to osteoporosis are prevalent causes of morbidity and mortality among older adults. Bisphosphonates stand as the primary therapy for numerous osteoporosis patients. Additional treatments for osteoporosis encompass denosumab, teriparatide, abaloparatide, romosozumab, and selective estrogen receptor modulators.\u003c/p\u003e\n\u003cp\u003eOsteoporosis, characterized by diminished bone mass, deterioration in bone microarchitecture, and increased susceptibility to fractures, can result in debilitating pain and deformity. This ailment poses a significant health challenge, particularly for older women. Annually, approximately 1.5 million individuals in the United States experience osteoporosis-related fractures, with many facing incomplete recovery. Early identification of individuals at risk for osteoporosis and timely intervention can mitigate its deleterious effects[\u003csup\u003e5]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe coexistence of osteoporosis and sarcopenia has recently been recognized in certain groups as a syndrome termed \u0026apos;osteosarcopenia\u0026apos;[\u003csup\u003e6]\u003c/sup\u003e . Muscles and bones are intricately connected tissues that exhibit significant co-variation during development, growth, aging, and in various diseases. The term osteosarcopenia specifically refers to the dual challenge of dealing with both osteoporosis and sarcopenia. Interactions between muscles and bones at the biomechanical, cellular, paracrine, endocrine, neuronal, or nutritional levels may contribute to the pathophysiology of osteosarcopenia[\u003csup\u003e7]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eInstrumental variables at the genome-wide significance level were obtained from published summary statistics. The inverse variance weighted method, along with several other Mendelian randomization methods, was employed to assess the bi-directional causality between sarcopenia and osteoporosis. Myopia was utilized as a negative control outcome to validate the instrumental variables. The results indicated a potential significant causal effect between sarcopenia and osteoporosis, suggesting a mutual influence on each other. The study supported the notion that individuals with severe osteoporosis were more susceptible to muscle mass loss , and severe muscle mass loss might contribute to reduced lumbar spine bone mineral density[\u003csup\u003e8]\u003c/sup\u003e. A systematic literature review was conducted until March 10th, 2018, resulting in the identification of 1,105 papers. Of these, 1,049 and 29 were excluded based on title/abstracts and full-text assessment, respectively. Twenty-seven original papers were included in the systematic review, with 17 deemed suitable for meta-analysis. The findings emphasized the frequency of osteosarcopenia and highlighted a higher relative risk of fractures among sarcopenic patients. The study underscored the importance of a standardized and stringent classification of sarcopenia to accurately assess its true relationship and consequences[\u003csup\u003e9]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eMuscles and bones, integral components of the motor system, originate from mesodermal and ectodermal mesenchymal stem cells. Their close anatomical proximity establishes the fundamental conditions for the exchange of mechanical and chemical signals, potentially contributing to the concurrent occurrence of sarcopenia and osteoporosis. Identifying shared crosstalk genes between these two systems may offer novel insights for the prevention and treatment of their development[\u003csup\u003e10]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003e\u003cstrong\u003eData collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets GSE1428 and GSE230665, as well as GSE56116, were curated from the GEO database (https://www.ncbi.nlm.nih.gov/geo/)[\u003csup\u003e11]\u003c/sup\u003e. For the microarray analysis of the GSE1428[\u003csup\u003e12]\u003c/sup\u003e dataset, the GPL96 platform (Affymetrix Human Genome U133A Array) was utilized. GSE230665[\u003csup\u003e13]\u003c/sup\u003e employed the GPL10332 platform (Agilent-026652 Whole Human Genome Microarray 4x44K v2, Feature Number version). GSE56116\u003csup\u003e[13]\u0026nbsp;\u003c/sup\u003eutilized the GPL4133 platform (Agilent-014850 Whole Human Genome Microarray 4x44K G4112F, Feature Number version). The GSE1428 dataset presents transcriptional responses related to sarcopenia, as provided by Giresi et al. On the other hand, the datasets GSE230665 and GSE56116, focusing on osteoporosis, were contributed by Ge, Li, and their respective collaborators. The GSE1428 dataset showcases the transcriptional responses associated with sarcopenia, while GSE230665 and GSE56116 pertain to datasets on osteoporosis(Figure. 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of DEGs between Sarcopenia and Osteoporosis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the two original osteoporosis datasets, empirical Bayes methods[\u003csup\u003e14]\u003c/sup\u003e were applied to eliminate batch effects. The merged osteoporosis dataset and the sarcopenia dataset underwent the extraction of expression matrices, with the exclusion of genes and samples featuring missing values exceeding 50%. Subsequently, missing values were imputed using the \u0026quot;impute.knn\u0026quot; function from the R package \u0026quot;impute,\u0026quot; setting the Number of neighbors to 10 for data completion. Furthermore, a log2 transformation was applied to the data. In cases where multiple probes identified the same gene, the average expression was calculated. Finally, utilizing the Limma package, criteria of |log2 Fold change (FC)| \u0026gt; 1.5 and \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05 were set as the standards for identifying DEGs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWeighted Gene Co-expression Network Analysis and Module Gene Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExploring gene-gene correlations using systems biology strategy WGCNA[\u003csup\u003e15]\u003c/sup\u003e. Based on gene expression profiles, we computed the median absolute deviation for each gene and removed the bottom 50% of genes with the smallest median absolute deviation. We utilized the R software package WGCNA\u0026apos;s goodSamplesGenes method to eliminate outlier genes and samples. Subsequently, we employed WGCNA to construct a scale-free co-expression network. Initially, Pearson\u0026apos;s correlation matrices and the average linkage method were applied to all pairwise genes. Then, a weighted adjacency matrix was created using a power function A_mn=|C_mn|^\u0026beta; (where C_mn represents the Pearson\u0026apos;s correlation between gene_m and gene_n, and A_mn denotes the adjacency between gene m and gene n). The parameter \u0026beta; was chosen to be 12 for soft-thresholding, emphasizing strong correlations and penalizing weak ones. The adjacency matrix was transformed into a topological overlap matrix, measuring the network connectivity of a gene, defined as the sum of its adjacencies with all other genes for network gene ration, and the corresponding dissimilarity (1-TOM) was computed. To classify genes with similar expression profiles into gene modules, average linkage hierarchical clustering was performed based on the TOM-based dissimilarity measure, with a minimum module size of 100 for the gene dendrogram. We set the sensitivity to 3. To further analyze the modules, we calculated the dissimilarity of module eigen genes, selected a cut line for the module dendrogram, and merged some modules. Additionally, we merged modules with a distance less than 0.25, resulting in the identification of 2 co-expression modules.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional Enrichment Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Gene Ontology (GO)[\u003csup\u003e16]\u003c/sup\u003e system provides structured and computable information about the functions of genes and gene products. The Kyoto Encyclopedia of Genes and Genomes (KEGG)[\u003csup\u003e17]\u003c/sup\u003e is a widely used database for gene system research. Using gene annotations from the R package org.Hs.eg.db (version 3.1.0), and obtaining the latest KEGG Pathway gene annotations from the KEGG rest API (https://www.kegg.jp/kegg/rest/keggapi.html) as background, genes were mapped to the background set. Functional enrichment analysis was performed using the R package clusterProfiler (version 3.14.3) to obtain results of gene set enrichment. The minimum gene set was set to 5, and the maximum gene set was set to 5000. A P value of \u0026lt; 0.05 and a false discovery rate of \u0026lt; 0.1 were considered statistically significant. Two rounds of GO and KEGG analyses were conducted based on the intersection of DEGs in sarcopenia and the most significant module genes, as well as the intersection of DEGs in osteoporosis and the most significant module genes in sarcopenia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of Protein-Protein Interaction Network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the interactions between protein-coding genes, we utilized the String database[\u003csup\u003e18]\u003c/sup\u003e (version 11.5; www.string-db.org), with a minimum interaction score set to 0.400. The obtained network from String was further modified using Cytoscape software. All interacting genes within the protein-protein interaction (PPI) network were selected for subsequent analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMachine Learning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further screen candidate genes for diagnosing sarcopenia and osteoporosis, a machine learning algorithm was employed. LASSO[\u003csup\u003e19]\u003c/sup\u003e (Least Absolute Shrinkage and Selection Operator) is a regression method used for variable selection to improve prediction accuracy. It is also a regularization technique that enhances the predictive accuracy and interpretability of statistical models. Utilizing the R package glmnet, gene expression data was integrated, and regression analysis was performed using the lasso-cox method. A 3-fold cross-validation was set up to obtain the optimal model. The Lambda value was set to 0.0639847346226388. The genes obtained from this analysis were identified as candidate hub genes for diagnosing sarcopenia and osteoporosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of Column Line Plots and ROC Curve Evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConstruction of column line plots holds certain value in diagnosing clinical sarcopenia and osteoporosis. Using the candidate genes, column line plots were constructed using the R package pROC (version 1.17.0.1). The \u0026quot;Score\u0026quot; represents the score of the candidate genes, while \u0026quot;Total Score\u0026quot; represents the sum of scores for all the aforementioned genes. ROC curves were established to evaluate the diagnostic value of the candidate genes and column line plots for sarcopenia and osteoporosis.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIdentification of DEGs using Limma method revealed a total of 821 DEGs (337 upregulated, 484 downregulated) in the sarcopenia dataset. The heatmap and volcano plot of sarcopenia DEGs are shown in Figure. 2A-B. In the combined osteoporosis dataset, a total of 2067 DEGs were identified, with 2059 upregulated and 9 downregulated genes. The heatmap and volcano plot of osteoporosis DEGs are illustrated in Figure. 3A-B.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWGCNA Analysis and Identification of Key Modules\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe selected \u0026beta; = 12 (scale-free R\u003csup\u003e2\u003c/sup\u003e = 0.86) as the \u0026quot;soft\u0026quot; threshold based on scale independence and average connectivity (Figure. 4A-B). The dendrogram (Figure. 4C) depicts the clustering of sarcopenia and control samples. Based on this, two gene co-expression modules were generated, as indicated by different colors in Figure. 4D. Among them, the turquoise module (424 genes) exhibited the highest correlation with sarcopenia (correlation coefficient=-0.41, \u003cem\u003eP\u003c/em\u003e=0.06) and was considered the key module for subsequent analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional Enrichment Analysis of Sarcopenia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess whether the dataset GSE1428 reliably reflects the pathogenesis of sarcopenia, we further conducted functional enrichment analysis based on the intersection of Limma and WGCNA module genes. The intersection of 424 DEGs from the turquoise module with 821 genes yielded 16 common genes (Figure. 5A). KEGG analysis revealed that common genes were primarily enriched in \u0026quot;Metabolic pathways\u0026quot; and \u0026quot;Carbon metabolism\u0026quot; (Figure. 5B). GO analysis indicated that common genes were predominantly enriched in biological process (BP) terms, including \u0026quot;coenzyme metabolic process\u0026quot; and \u0026quot;purine ribonucleotide metabolic process\u0026quot; (Figure. 5C). Regarding cellular component (CC) ontology, CGs were mainly located in \u0026quot;mitochondrion,\u0026quot; \u0026quot;mitochondrial part,\u0026quot; and \u0026quot;mitochondrial matrix\u0026quot; (Figure. 5D). Molecular function (MF) analysis showed that \u0026quot;Ras guanyl-nucleotide exchange factor activity,\u0026quot; \u0026quot;isocitrate dehydrogenase (NAD+) activity,\u0026quot; and \u0026quot;L-aspartate transmembrane transporter activity\u0026quot; were the most significant terms within common genes (Figure. 5E).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEnrichment Analysis and Node Gene Identification for Osteoporosis and Sarcopenia Based on PPI Networks\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further explore whether key genes associated with sarcopenia are also related to the pathogenesis of osteoporosis, we visualized the intersection of DEGs in osteoporosis and module genes in sarcopenia through a Venn diagram, identifying 60 genes (Figure. 6A). KEGG enrichment analysis revealed that these 60 genes were mainly enriched in \u0026quot;Human papillomavirus infection,\u0026quot; \u0026quot;mTOR signaling pathway,\u0026quot; and \u0026quot;Kaposi sarcoma-associated herpesvirus infection\u0026quot; (Figure. 6D). GO analysis showed that these genes were enriched in \u0026quot;Kaposi sarcoma-associated herpesvirus infection,\u0026quot; \u0026quot;organonitrogen compound biosynthetic process,\u0026quot; and \u0026quot;cellular amide metabolic process\u0026quot; in BP; \u0026quot;cytosol,\u0026quot; \u0026quot;cytosol,\u0026quot; and \u0026quot;nuclear chromosome\u0026quot; in CC; and \u0026quot;transcription coregulator activity,\u0026quot; \u0026quot;transcription coregulator activity,\u0026quot; and \u0026quot;ubiquitin-like protein ligase activity\u0026quot; in MF (Figure. 6E-G).\u003c/p\u003e\n\u003cp\u003eAfter confirming the filtered genes, we constructed a PPI network to identify interacting node genes for subsequent machine learning filtering. Figure. 6B displays the PPI network, where 30 genes can interact with each other. These genes are sorted by node degree in Figure. 6B.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentifying Candidate Hub Genes through Machine Learning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe LASSO regression machine learning algorithm was applied to select candidate genes for column line plot construction and diagnostic value assessment. From Figure. 7A-B, it can be observed that the LASSO regression algorithm identified 7 potential candidate biomarkers for final validation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiagnostic Value Evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the 7 candidate hub genes, a column line plot was constructed (Figure. 8A), and ROC curves were established to evaluate the diagnostic specificity and sensitivity of each gene and the column line plot. The area under the curve (AUC) and its 95% confidence interval (CI) were calculated for each item. The results are as follows: PDP1 (AUC 0.96, CI 1.00 ~ 0.88), ALS2CL (AUC 0.80, CI 1.00 ~ 0.58), VLDLR (AUC 0.83, CI 1.00 ~ 0.5), PLEKHA6 (AUC 0.93, CI 1.00 ~ 0.83), PPP1CB (AUC 0.82, CI 1.00 ~ 0.63), MOSPD2 (AUC 0.73, CI 1.00 ~ 0.44), METTL9 (AUC 0.82, CI 1.00 ~ 0.58), and the column line plot (AUC 0.98, CI 1.00 ~ 0.93) (Figure. 8B-I). All candidate genes exhibited high diagnostic value for sarcopenia combined with osteoporosis, with the column line plot demonstrating the highest diagnostic value.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOsteosarcopenia, characterized by the coexistence of osteopenia/osteoporosis and sarcopenia, has emerged as a significant health concern, imposing a substantial global health burden. According to the World Health Organization, osteopenia and osteoporosis are defined by T scores equal to or less than \u0026minus;1 and \u0026minus;2.5 standard deviations, respectively, below the peak bone mass of a young, healthy cohort or in the presence of a minimal-trauma fracture. This skeletal condition results in the deterioration of bone microarchitecture and compromises bone strength[\u003csup\u003e20]\u003c/sup\u003e. Conversely, sarcopenia is identified by cut-off values indicating low muscle mass, strength, and/or functional capacity[\u003csup\u003e21]\u003c/sup\u003e and is associated with various metabolic conditions[\u003csup\u003e22]\u003c/sup\u003e. Both osteosarcopenia and sarcopenia share common risk factors[\u003csup\u003e23\u003c/sup\u003e]\u0026nbsp;and exhibit strong associations with frailty, falls, fractures, hospitalizations, and mortality[\u003csup\u003e23\u003c/sup\u003e\u003csup\u003e-\u003c/sup\u003e] , contributing to a significant increase in healthcare expenditure. The coexistence of these conditions underscores the intricate interplay between skeletal and muscular health and emphasizes the need for comprehensive approaches to address their shared impact on overall well-being.\u003c/p\u003e\n\u003cp\u003eIn this study, we utilized a series of integrated bioinformatics analyses and machine learning methods to construct a nomogram and evaluate the diagnostic value of osteoporosis in sarcopenia patients. A notable finding is the identification of 7 key candidate genes (PDP1, ALS2CL, VLDLR, PLEKHA6, PPP1CB, MOSPD2, and METTL9), and the development of a nomogram for diagnosing osteoporosis in sarcopenia patients.\u003c/p\u003e\n\u003cp\u003eThe sarcopenia patient dataset used in this study all comes from peripheral blood samples. Therefore, we only need to collect peripheral blood samples from sarcopenia patients and evaluate the expression of the 7 identified immune-related genes to infer the probability of sarcopenia patients developing osteoporosis. This is an efficient and practical clinical approach. The use of peripheral blood testing in diagnosing various diseases is also widely accepted. Furthermore, although we confirmed that gene expression levels can serve as independent diagnostic markers, we plan to develop a more comprehensive diagnostic model by transforming them into scores and considering all 7 markers. The expression of each gene is quantified and converted into a score, with an increase in score indicating a higher linear prediction factor. When the linear prediction factor is high, we can conduct early monitoring and intervention in sarcopenia patients, which is more valuable for implementing osteoporosis diagnosis in sarcopenia.\u003c/p\u003e\n\u003cp\u003ePDP1encodes a protein that is one of the three components (E1, E2, and E3) of the large pyruvate dehydrogenase complex. PDP1 plays a crucial role in protein phosphorylation and has been implicated in various diseases[\u003csup\u003e26\u003c/sup\u003e\u003csup\u003e-27\u003c/sup\u003e]. Research has shown that miR-18a-3p improves cartilage matrix remodeling and suppresses inflammation in osteoarthritis by targeting PDP1[\u003csup\u003e28]\u003c/sup\u003e. In pancreatic cancer, PDP1 promotes cancer proliferation and invasion by regulating the MAPK/mTOR signaling pathway[\u003csup\u003e29]\u003c/sup\u003e. Additionally, PDP1 is associated with osteosarcoma progression, patient prognosis, and chemosensitivity, making it a potential biomarker for osteosarcoma[\u003csup\u003e30]\u003c/sup\u003e. Given its role in multiple diseases, PDP1 is considered a potential diagnostic target for osteoporosis in sarcopenic patients. ALS2CL encodes a 108-kD protein with specific but relatively weak Rab5-GEF activity and strong Rab5-binding properties. Co-expression of ALS2CL and Rab5A in HeLa cells results in a unique tubulation phenotype of endosome compartments, indicating ALS2CL\u0026apos;s involvement in modulating Rab5-mediated endosome dynamics[\u003csup\u003e31]\u003c/sup\u003e. VLDLR, or Very Low Density Lipoprotein Receptor, belongs to the low-density lipoprotein receptor family, with high expression levels in the brain, heart, skeletal muscle, and adipose tissue, while its expression in the liver is very low under physiological conditions. It plays a crucial role in controlling serum triglycerides and the development of non-alcoholic fatty liver disease. Previous studies have demonstrated the involvement of VLDLR in regulating the onset of various diseases. Research has found that homozygous loss-of-function mutations in VLDLR lead to dysequilibrium syndrome, a non-progressive cerebellar ataxia syndrome associated with intellectual disability[\u003csup\u003e32]\u003c/sup\u003e. PLEKHA6, along with other members of the WW-PLEKHA family, plays a role in the trafficking and retention of transmembrane proteins, including nectins, Tspan33, and the copper pump ATP7A, at cell-cell junctions and lateral membranes. Its C-terminal region and coiled-coil region promote its localization at adherens junctions of epithelial cells. This suggests that PLEKHA6 is involved in maintaining cell-cell adhesion and potentially regulates signaling pathways associated with adherens junctions[\u003csup\u003e33]\u003c/sup\u003e. PPP1CB, located on chromosome 2p23.2, encodes a subunit of PPP1 involved in various cellular functions, including glycogen metabolism, cell division, and muscle contraction[\u003csup\u003e34\u003c/sup\u003e\u003csup\u003e-\u003c/sup\u003e]. Recent studies have identified PPP1CB as the myosin light chain phosphatase responsible for Ca2+-transient rise and enhanced cell shortening in cardiomyocytes[\u003csup\u003e38]\u003c/sup\u003e. MOSPD2, a member of the VAP family, facilitates contact between the endoplasmic reticulum and various cellular organelles[\u003csup\u003e39]\u003c/sup\u003e. Unlike other VAP family members, MOSPD2 contains an additional cytoplasmic domain called CRAL-TRIO, which may be involved in lipid transport[\u003csup\u003e40]\u003c/sup\u003e. Research suggests that MOSPD2 is a key regulator of inflammation-driven monocyte migration and a potential therapeutic target for CNS inflammatory diseases[\u003csup\u003e41]\u003c/sup\u003e. METTL9, a methyltransferase, plays a crucial role in histone methylation and is implicated as an oncogene in various cancers[\u003csup\u003e42\u003c/sup\u003e\u003csup\u003e-43\u003c/sup\u003e]. Targeting METTL9 significantly inhibits the growth of hepatocellular carcinoma patient-derived xenografts\u0026nbsp;[\u003csup\u003e44]\u003c/sup\u003e and correlates with increased metastatic activity in human gastric cancer[\u003csup\u003e45]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn summary, this study identified candidate hub genes for diagnosing osteoporosis combined with sarcopenia using integrated bioinformatics and machine learning approaches. However, limitations include reliance on publicly available datasets, potential selection bias in gene identification, limited generalizability to diverse populations, cross-sectional data analysis, and the need for further experimental validation to elucidate the functional mechanisms of the identified genes. These findings provide a foundation for potential peripheral blood diagnostic markers but require additional validation and clinical translation for practical application in healthcare settings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study systematically identified seven candidate hub genes (PDP1, ALS2CL, VLDLR, PLEKHA6, PPP1CB, MOSPD2, and METTL9) through a combination of various bioinformatics analyses and machine learning algorithms, and provided a nomogram for diagnosing sarcopenia associated with osteoporosis. The research offers reference for potential peripheral blood diagnostic candidate genes for sarcopenia related to osteoporosis.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUC:Area under the curve\u003c/p\u003e\n\u003cp\u003eBP: Biological processes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCC: Cellular components\u003c/p\u003e\n\u003cp\u003eCI:Confidence interval\u003c/p\u003e\n\u003cp\u003eDEGs:Differentially expressed genes\u003c/p\u003e\n\u003cp\u003eGEO:Gene Expression Omnibus\u003c/p\u003e\n\u003cp\u003eGO: Gene Ontology\u003c/p\u003e\n\u003cp\u003eKEGG: Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eLASSO:Least Absolute Shrinkage and Selection Operator\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMF: Molecular functions\u003c/p\u003e\n\u003cp\u003ePDP1:Pyruvate dehydrogenase phosphatase catalytic subunit 1\u003c/p\u003e\n\u003cp\u003ePPI:Protein-protein interaction\u003c/p\u003e\n\u003cp\u003eROC:Receiver operating characteristic\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTOM:Topological overlap matrix\u003c/p\u003e\n\u003cp\u003eWGCNA:Weighted Gene Co-expression Network Analysis\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy Design:\u0026nbsp;Xiaoli Zhou,\u0026nbsp;Guangdong Chen,\u0026nbsp;Zepei Zhang, Jun Miao.\u003c/p\u003e\n\u003cp\u003eData Collection:\u0026nbsp;Xiaoli Zhou,\u0026nbsp;Yang Chen, Zhiyong Qian, Jun Miao\u003c/p\u003e\n\u003cp\u003eStatistical Analysis:\u0026nbsp;Xiaoli Zhou, Guangdong Chen.\u003c/p\u003e\n\u003cp\u003eData Interpretation:\u0026nbsp;Yang Chen.\u003c/p\u003e\n\u003cp\u003eManuscript Preparation:\u0026nbsp;Xiaoli Zhou,\u0026nbsp;Guangdong Chen,\u0026nbsp;Zepei Zhang, Jun Miao.\u003c/p\u003e\n\u003cp\u003eLiterature Search: Xiaoli Zhou, Guangdong Chen.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Tianjin\u0026nbsp;Key\u0026nbsp;Medical\u0026nbsp;Discipline (Specialty)\u0026nbsp;Construction\u0026nbsp;Project (TJYXZDXK-026A) and Hebei Province High Level Talent Funding Project (C20221122).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are available in Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/): GSE1428, GSE230665, GSE56116.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interest\u0026rsquo;s statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo additional information is available for this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eDhillon, R. 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Biochemistry and biophysics reports, 30, 101255. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-musculoskeletal-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmsd","sideBox":"Learn more about [BMC Musculoskeletal Disorders](http://bmcmusculoskeletdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12891","title":"BMC Musculoskeletal Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sarcopenia, Osteoporosis, Hub genes, Machine learning, Bioinformatics","lastPublishedDoi":"10.21203/rs.3.rs-3940690/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3940690/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePrior 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.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTwo 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.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe 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).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWe 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.\u003c/p\u003e","manuscriptTitle":"Identification of Shared Gene Signatures and Pathways for Diagnosing Osteoporosis with Sarcopenia through Integrated Bioinformatics Analysis and Machine Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-20 20:02:39","doi":"10.21203/rs.3.rs-3940690/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvited","content":"","date":"2024-02-18T05:59:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-18T05:53:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Musculoskeletal Disorders","date":"2024-02-08T17:27:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-musculoskeletal-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmsd","sideBox":"Learn more about [BMC Musculoskeletal Disorders](http://bmcmusculoskeletdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://author-welcome.nature.com/12891","title":"BMC Musculoskeletal Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cd720a18-2f29-4cfb-80f3-635a62e343ad","owner":[],"postedDate":"February 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-05-28T13:19:01+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-20 20:02:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3940690","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3940690","identity":"rs-3940690","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}