Identification of biomarkers associated with endoplasmic reticulum stress-related cell death in osteoporosis based on bulk and single-cell transcriptomic analyses and experimental validation

Identification of biomarkers associated with endoplasmic reticulum stress-related cell death in osteoporosis based on bulk and single-cell transcriptomic analyses and experimental validation | 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 Article Identification of biomarkers associated with endoplasmic reticulum stress-related cell death in osteoporosis based on bulk and single-cell transcriptomic analyses and experimental validation Yifeng Xia, Zhongyu Peng, Lingrui Zhao, Yuan Long, Renwei Chen, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8454279/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Background Osteoporosis (OP) is a metabolic bone disease characterized by low bone mineral density (BMD), and its pathogenesis involves endoplasmic reticulum (ER) stress-related cell death. This study aimed to identify diagnostic biomarkers associated with ER stress-related cell death in OP and explore their underlying mechanisms. Materials and Methods The training dataset (GSE56815), validation dataset (GSE56814), and single-cell RNA sequencing (scRNA-seq) dataset (GSE147287) were downloaded. Differentially expressed genes (DEGs) between OP patients and controls were identified. Candidate genes were obtained by intersecting DEGs with ER stress-related genes and programmed cell death (PCD)-related genes. Machine learning was used to screen intersection genes, and biomarkers were determined via expression level analysis. Gene set enrichment analysis (GSEA), immune cell infiltration analysis, drug prediction and molecular docking, scRNA-seq analysis, key cell screening, cell communication analysis, and pseudotime analysis were performed. Finally, reverse transcription quantitative polymerase chain reaction (RT-qPCR) were further conducted. Results A total of 28 candidate genes were obtained by intersection. CAMKK2 and DAPK3 were confirmed as biomarkers, and were consistently down-regulated in both datasets and verified by RT-qPCR. GSEA analysis revealed that biomarkers were enriched in cytokine-cytokine receptor interaction. Correlations between biomarkers and activated dendritic cells were found via immune cell infiltration analysis. Drugs like calcitriol and danazol were predicted to bind stably to biomarkers. Bone marrow-derived mesenchymal stem cells (BM-MSCs) were identified as key cells via scRNA-seq analysis. Complex interactions involving BM-MSCs, such as ANGPTL4-CDH11 mediating BM-MSC self-communication, were revealed by cell communication analysis. Dynamic expression of biomarkers during BM-MSC differentiation was shown by pseudotime analysis: CAMKK2 fluctuated with differentiation stages, while DAPK3 shifted from high to low then high expression. Conclusions CAMKK2 and DAPK3 were confirmed as diagnostic biomarkers for OP, providing insights into OP diagnosis and potential therapeutic targets. Health sciences/Biomarkers Biological sciences/Cell biology Biological sciences/Computational biology and bioinformatics Health sciences/Diseases osteoporosis endoplasmic reticulum stress-related cell death single-cell RNA sequencing machine learning bone marrow-derived mesenchymal stem cells Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1 Background Osteoporosis (OP) is a systemic skeletal disease characterized by diminished bone mass and microarchitectural degradation of bone tissue, which collectively result in enhanced bone fragility and a consequent increase in susceptibility to fractures [ 1 ]. Osteoporosis affects an estimated 200 million individuals globally and is particularly common in postmenopausal women. With the ongoing aging of the global population, both the incidence of the disease and the associated healthcare burden are steadily increasing [ 1 , 2 ]. Current therapeutic strategies include anti-resorptive agents (e.g., bisphosphonates, denosumab), bone-forming agents (e.g., teriparatide), and dual-action drugs (e.g., romosozumab), often requiring sequential regimens to sustain therapeutic efficacy [ 1 ]. Nevertheless, long-term pharmacotherapy for osteoporosis poses significant clinical challenges: bisphosphonates are associated with risks of osteonecrosis of the jaw and atypical femoral fractures; denosumab discontinuation can trigger rebound increases in bone turnover and elevated fracture risk; and bone-forming agents provide only transient anabolic effects, requiring subsequent anti-resorptive therapy to maintain skeletal benefits [ 1 , 3 , 4 ]. Furthermore, available therapies exhibit limited efficacy in improving bone quality, and a subset of patients respond inadequately to existing options [ 5 , 6 ]. These constraints underscore the need to elucidate the molecular mechanisms governing bone metabolism, which is imperative for developing novel therapeutic approaches. Growing evidence indicates that endoplasmic reticulum (ER) stress-mediated cell death plays an increasingly critical role in osteoporosis pathogenesis. ER stress, characterized as an adaptive cellular response to the accumulation of misfolded or unfolded proteins, may initiate key pathological processes when dysregulated. However, persistent or excessive ERS can trigger cell death through signaling pathways such as PERK–eIF2α–ATF4–CHOP and IRE1α–XBP1 [ 7 , 8 ]. In the context of osteoporosis (OP), cadmium exposure has been shown to induce ERS via reactive oxygen species (ROS), activating the PERK pathway while suppressing the Nrf2/NQO1 axis, ultimately leading to osteoblast apoptosis [ 7 ]. Similarly, high cholesterol levels promote osteoblast apoptosis through ERS activation [ 9 ]. Loss of COPB1 induces both ERS and ferroptosis by repressing SLC7A11 transcription via ATF6 , thereby impairing cystine uptake and exacerbating OP progression [ 10 ]. Furthermore, bisphosphonates can trigger ERS-mediated apoptosis in lymphatic endothelial cells via the NAD⁺/SIRT6/XBP1s pathway, impairing lymphatic drainage and contributing to osteonecrosis [ 11 ]. However, a systematic investigation of ER stress-related cell death in osteoporosis is still lacking, while the underlying molecular mechanisms and potential therapeutic targets remain to be fully elucidated. Single-cell RNA sequencing (scRNA-seq) enables high-resolution identification of cellular heterogeneity, allowing for the discrimination of distinct cell subtypes within complex populations. Integrating bulk transcriptomics with scRNA-seq forms a complementary strategy: bulk data reveal overall gene expression trends at the population level, while single-cell data resolve heterogeneity and uncover functional differences among cell subpopulations [ 12 , 13 ]. Therefore, this study leverages an integrative approach, combining bulk and single-cell transcriptomic analyses with machine learning and molecular docking, to unveil the core molecular mechanisms and distinct cellular subpopulations associated with endoplasmic reticulum stress-mediated cell death in osteoporosis. Our findings offer a conceptual framework for the development of innovative therapeutic interventions aimed at modulating endoplasmic reticulum stress in this disease. 2 Methods 2.1 Data collection OP-related training dataset (GSE56815), validation dataset (GSE56814), and scRNA-seq dataset (GSE147287) were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). GSE56815 comprises 40 blood monocyte samples from OP patients (with low bone mineral density (BMD)) and 40 from controls (with high BMD), which were profiled using the GPL96 platform. GSE56814 includes 42 blood monocyte samples from OP patients (with low BMD) and 31 from controls (with high BMD), analyzed with the GPL5175 platform. GSE147287 contains freshly isolated bone marrow-derived monocyte samples from the femoral head of 1 OP patient, with osteoarthritis samples excluded, and was processed using the GPL24676 platform. A total of 2,903 ERS-related genes were obtained by searching the GeneCards database (https://www.genecards.org/) for ER stress ( Additional file 1 ). A total of 1,548 programmed cell death (PCD)-related genes were downloaded from the literature [14] ( Additional file 2 ). The access time for all the above data was August 1, 2025. 2.2 Analysis of differentially expressed genes (DEGs) Differential expression analysis between the OP and control groups was performed on the training dataset gene expression matrix using the limma package (v3.58.1) [15], with an adjusted p-value < 0.05 set as the significance threshold [16]. The resulting DEGs were visualized in a volcano plot generated with ggplot2 (v3.5.1) [17]. Additionally, the top 10 up- and down-regulated DEGs, ranked by absolute log2 fold-change, were displayed in an expression heatmap created using the ComplexHeatmap package (v2.14.0) [18]. Candidate genes were identified by intersecting the DEGs with genes associated with endoplasmic reticulum (ER) stress and programmed cell death (PCD), which was conducted and visualized via the VennDiagram package (v1.7.3) [19]. 2.3 Protein-protein interaction (PPI) network construction and enrichment analysis A protein-protein interaction (PPI) network for the candidate genes was constructed using the STRING database (confidence score ≥ 0.4) to elucidate the functional interactions among the encoded proteins. The resulting network was visualized with the circlize package (v0.4.16) [20]. Furthermore, to interpret the biological roles of these genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway[21-23] enrichment analyses were performed using the clusterProfiler package (v4.8.3) [24], with terms and pathways considered significant at p < 0.05. 2.4 Machine learning To refine the candidate genes and identify those with pivotal roles in OP, we employed three distinct machine learning algorithms on the training dataset. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was implemented using the glmnet package (v4.1-8) [25] with 5-fold cross-validation, and genes retained at the optimal penalty parameter (lambda.min) were designated as LASSO genes. The Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithm, executed via the caret package (v6.0-94) [26] with 5-fold cross-validation, selected the feature set yielding the highest predictive accuracy as SVM-RFE genes. A Random Forest (RF) model was built with the randomForest package (v4.7-1.1) [27], and the top 10 genes, as ranked by the "minimum error regression trees" criterion, were defined as RF genes. Finally, the overlapping genes from these three sets were identified as key intersection genes using the VennDiagram package (v1.7.3). 2.5 Gene expression level analysis Differential expression analysis of the intersection genes between the OP and control groups was performed using the Wilcoxon rank-sum test. Genes demonstrating a consistent direction of expression change and a statistically significant difference (p < 0.05) in both the training and validation datasets were subsequently defined as final biomarkers. 2.6 Construction and evaluation of a nomogram To assess the collective predictive utility of the identified biomarkers for OP, a nomogram was developed using the rms package (v6.8.1)[28] based on the training dataset. In this model, each biomarker is assigned a points score proportional to its expression level. The summation of these individual scores yields a total points value, from which the probability of OP incidence can be directly read; a higher total score corresponds to a greater predicted risk. The model's calibration was evaluated by plotting a calibration curve (using the rms package), where a slope closer to 1 indicates superior agreement between predicted and observed outcomes. The discriminatory power of the nomogram was quantified by Receiver Operating Characteristic (ROC) analysis with the pROC package (v1.18.5)[29], reporting the Area Under the Curve (AUC). An AUC value > 0.7 and ≠ 1 was considered indicative of satisfactory model performance, with higher values denoting better prediction. Furthermore, the clinical applicability of the nomogram was appraised using Decision Curve Analysis (DCA) implemented via the rmda package (v1.6)[30], which estimates the net benefit across a range of decision thresholds. 2.7 Gene set enrichment analysis (GSEA) To delineate the signaling pathways associated with the biomarkers, we first profiled their co-expression networks. Specifically, Spearman correlation analyses between each biomarker and all other genes were performed across all training set samples using the psych package (v2.4.3)[31]. Genes were then ranked in descending order based on the derived correlation coefficients for each biomarker. For the Gene Set Enrichment Analysis (GSEA), the C2: KEGG subset from the Molecular Signatures Database (MSigDB) was retrieved as the reference gene set using the msigdbr package (v7.5.1)[32]. Enrichment pathways for each biomarker were subsequently characterized using GSEA implemented in the clusterProfiler package (v4.8.3). A signaling pathway was considered significantly enriched with a p-value 1. 2.8 Localization analysis The chromosomal locations of the identified biomarkers were mapped using the RCircos package (v1.2.2)[33]. 2.9 Molecular regulatory network construction To elucidate the upstream regulatory mechanisms of the biomarkers, putative microRNAs (miRNAs) targeting these genes were predicted using the microcosm database accessed via the multiMiR package (v1.16.0)[34]. Concurrently, potential transcription factors (TFs) were identified using the mirNet database. A comprehensive miRNA-TF-biomarker regulatory network was subsequently visualized using Cytoscape software (v3.10.2)[35]. 2.10 Analysis of immune cell infiltration In OP, alterations in immune status were induced, which gave rise to a chronic low-grade inflammatory phenotype. Immune cells were shown to interact with bone cells through direct contact or paracrine mechanisms, with various cytokines and other mediators released. These mediators affected the balance between bone formation and resorption, thereby exacerbating bone destruction and contributing to the pathogenesis of the disease [36]. The immune cell infiltration landscape was profiled in the training dataset using the single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm implemented in the GSVA package (v1.50.0)[37], which estimated the relative abundances of 28 immune cell types. The Wilcoxon rank-sum test was then applied to identify immune cell populations that were significantly dysregulated (p < 0.05) between the OP and control groups. Furthermore, Spearman correlation analysis, performed with the psych package (v2.4.3), was used to investigate the interrelationships among these differential immune cells and their associations with the biomarkers, applying thresholds of |correlation coefficient (cor)| > 0.3 and p < 0.05. 2.11 Drug prediction and molecular docking To pinpoint candidate therapeutics capable of targeting the identified biomarkers, we interrogated the Drug-Signature Database (DsigDB) and visualized the resultant drug-biomarker interactions using Cytoscape (v3.10.2). The binding affinities between the top candidate drugs and their corresponding biomarker-encoded proteins were subsequently evaluated via molecular docking using the CB-DOCK2 web server. The three-dimensional structures of the compounds were retrieved from PubChem, while protein structures for the biomarkers were obtained from the Protein Data Bank (PDB), prioritizing entries with the highest resolution. For biomarkers lacking a solved structure, homology models were generated using AlphaFold. Molecular docking was then performed with CB-DOCK2, and the resulting poses were visualized. Consistent with conventional criteria, a docking score of less than -5 kcal/mol was considered indicative of strong binding potential. 2.12 The scRNA-seq analysis To delineate the single-cell expression patterns of the biomarkers and reconstruct the developmental trajectories of key cell types in osteoporosis, we performed a comprehensive analysis of the scRNA-seq dataset using Seurat (v5.1.0). Initial quality control involved creating a Seurat object (min.cells=3, min.features=200) and filtering doublets with scDblFinder (v1.16.0). We retained cells expressing between 200 and 5,000 genes, excluded genes detected in fewer than 200 or more than 20,000 cells, and removed cells with mitochondrial gene content exceeding 20%. The top 2,000 highly variable genes were then identified using the FindVariableFeatures function (selection.method = "vst"). After dataset integration with IntegrateData, we performed principal component analysis (PCA) on these variable genes. Significant principal components (p < 0.05) were selected for downstream clustering. Cells were clustered using the FindNeighbors and FindClusters functions (resolution=0.3) and visualized via UAP. Cluster-specific marker genes were identified with FindAllMarkers (logfc.threshold=0.25, min.pct=0.1, only.pos=TRUE). Cell types were annotated by referencing both the SingleR package (v2.0.0) and the CellMarker 2.0 database, guided by established literature. 2.13 Screening of key cells To screen for key cells, based on the scRNA-seq dataset, the distribution of biomarkers in the annotated cells and their expression levels across each annotated cell type were first presented. Cells with high expression levels of the biomarkers were selected as key cells. Subsequently, functional enrichment analysis of all cells was performed using ReactomeGSA (v 1.02.0)[38] to explore their biological pathways (adjusted p < 0.05). The top 10 pathways with the most significant adjusted p-values were displayed. 2.14 Cells communication To systematically map intercellular signaling, we performed cell-cell communication analysis using the annotated scRNA-seq dataset. The CellChat package (v1.6.1)[39] was employed to computationally infer interactions between defined cell populations based on the expression profiles of receptor-ligand pairs. Significant interactions were identified using the following thresholds: p < 0.05 and a combined expression level (log 2 -mean [Molecule 1, Molecule 2]) ≥ 0.1. 2.15 Pseudotime analysis To understand the developmental trajectories of key cells and the expression changes of biomarkers in key cells, dimensionality reduction and clustering were first performed on key cells based on the scRNA-seq dataset (as in previous steps, details omitted here). With the resolution set to 0.3, a UMAP clustering plot was generated. Following this, cluster-specific marker genes that were highly expressed in key cell clusters were identified using the FindAllMarkers function in Seurat (v5.1.0), with the following thresholds applied: min.pct = 0.25 and only.pos = TRUE. Pseudotime analysis was then performed on the key cell subsets using Monocle 2 (v 2.28.0)[40]. All cells in the cell subsets were ordered according to their pseudotime and projected onto one root and two branches, enabling pseudotime trajectory analysis. Additionally, the expression changes of biomarkers in the cell subsets were visualized. 2.16 TF's regulatory analysis To investigate the regulated TFs among different subpopulations of key cells, Variation of Information-based Pathway Enrichment in RNA (VIPER) analysis was performed. The analysis was conducted using Dorothea (v 1.12.0)[41], which helps reveal the relationship between gene expression patterns and regulatory networks by calculating the activity of TFs. A heatmap was used to display the core regulatory TFs in each subpopulation of key cells. These TFs were ranked from high to low according to their activity, and the top 10 TFs with the highest activity were selected for display. 2.17 Experimental validation To experimentally validate the differential expression of the identified biomarkers, reverse transcription quantitative polymerase chain reaction (RT-qPCR) was performed. Clinical peripheral blood samples were obtained from the First Affiliated Hospital of Yunnan University of Chinese Medicine (Yunnan Provincial Hospital of Chinese Medicine) with approval from the Institutional Ethics Committee (Approval No. 2025-KY-018-01), and all procedures involved in this study strictly adhered to the ethical principles outlined in the Declaration of Helsinki for research involving human participants. Informed consent has been obtained from the patient.All participants were fully informed of the study's purpose, procedures, potential risks, and benefits. After thorough communication with the researchers and a complete understanding of the information, they voluntarily signed a written informed consent form. Total RNA was isolated using the TRIzol method, and its concentration was quantified with a NanoPhotometer N50. cDNA synthesis and quantitative PCR were subsequently carried out using commercial master mixes (HP All-in-one qRT Master Mix II RT203-Ver.1, Kunming Younggen Biotechnology Co., Ltd.; SwsScript All-in-One First-strand-cDNA-synthesis SuperMix for qPCR, Servare Company). The primer sequences used are provided in Table 1 . Relative gene expression levels of the biomarkers were calculated using the 2 -ΔΔCT method. Table 1. Table of Primer Sequence primer sequence CAMKK2 F GCAGGGTCAGTGAGACATCC CAMKK2 R TTGGATCCCCCAGCTGGATA DAPK3 F GCACGACATCTTCGAGAACAA DAPK3 R CTTAGAGTGCAGGTAGTGAACG GAPDH F ATGGGCAGCCGTTAGGAAAG GAPDH R AGGAAAAGCATCACCCGGAG 2.18 Statistical analysis All statistical and bioinformatic analyses were conducted using R (v 4.3.1). Data visualization was performed with GraphPad Prism 10. For group comparisons, p-values were derived from t-tests or Wilcoxon rank-sum tests, as appropriate, with a p-value < 0.05 considered statistically significant. 3 Results 3.1 Screening and enrichment analysis of candidate genes In the initial screening for candidate genes, a total of 391 differentially expressed genes (DEGs) were identified between the OP and control groups (adjusted p < 0.05). This set comprised 269 up-regulated and 122 down-regulated genes in the OP group ( Figure 1 a ). Then, the intersection of the 391 DEGs, 2,903 ER stress-related genes, and 1,548 PCD-related genes was taken, and 28 candidate genes were obtained ( Figure 1 b and Additional file 3 ). Then, a PPI network was constructed for the candidate genes (confidence score ≥ 0.4). It could be seen that genes such as EGFR and CTNNB1 had relatively close interaction relationships with other genes ( Figure 1 c ). Finally, enrichment analysis of the candidate genes (p < 0.05) identified 950 significantly enriched GO terms. These comprised 817 biological processes, primarily associated with autophagy regulation, apoptotic signaling pathway regulation, and cellular response to oxidative stress; 70 cellular components, notably enriched in vesicle lumen, secretory granule lumen, and transcription repressor complex; and 63 molecular functions, prominently featuring RAGE receptor binding, Toll-like receptor binding, and oxidoreductase activity acting on metal ions ( Figure 1 d and Additional file 4 ).Furthermore, KEGG pathway analysis revealed 64 significantly enriched signaling pathways. These encompassed several key processes, notably cellular senescence, chemical carcinogenesis - reactive oxygen species, and the FoxO signaling pathway ( Figure 1 e and Additional file 5 ). The enrichment of GO biological processes and KEGG pathways—particularly those involving autophagy regulation, apoptotic signaling, cellular response to oxidative stress, cellular senescence, ROS-related chemical carcinogenesis, and the FoxO signaling pathway—suggests a potential role for these candidate genes in modulating bone metabolic balance during osteoporosis development. This regulation likely occurs through influencing endoplasmic reticulum stress and cell death processes (e.g., apoptosis) in bone cells. These findings provide valuable insights into the involvement of ER stress and cell death-related mechanisms in OP progression. 3.2 Screening of biomarkers To refine the candidate genes and identify biomarkers closely associated with OP pathogenesis, we employed a machine learning-based screening strategy. Initially, LASSO regression analysis was applied, yielding 17 feature genes [log(lambda min) = -2.9233] ( Figure 2 a and Additional file 6 ). Through SVM-RFE analysis, 27 SVM-RFE genes were selected ( Figure 2 b and Additional file 7 ). The Random Forest (RF) model was constructed using an optimal tree threshold of 91, which corresponded to the lowest error rate, and the top 10 genes were subsequently selected as the final RF gene set ( Figure 2 c and Additional file 8 ). After taking the intersection of the three sets, 10 intersection genes were obtained, namely FOXO3 , BRSK2 , UQLN2 , RAC1 , CAMKK2 , PTEN , S100A9 , APPL1 , DAPK3 , and PHLDA3 ( Figure 2 d ). Subsequently, an analysis of the expression levels of these intersection genes was performed, and it was found that the expression levels of CAMKK2 and DAPK3 were down-regulated in OP in both the training dataset and the validation dataset ( Figures 3 a-b ).Based on these findings, CAMKK2 and DAPK3 were designated as the definitive biomarkers for all subsequent investigations. To experimentally validate the bioinformatics predictions, we assessed the expression levels of CAMKK2 and DAPK3 using RT-qPCR. The results confirmed that both genes were significantly downregulated in the OP group compared to the controls (p < 0.05; Figure 3 c ), thereby providing independent experimental corroboration for our computational findings. 3.3 Predictive accuracy of biomarkers for OP A nomogram was subsequently developed to quantify the risk of OP onset based on the biomarker profile. In this model, higher expression scores for CAMKK2 and DAPK3 contributed to an increased total points value, which corresponded to a greater predicted probability of disease. For example, a total score of 81.1 points translated to an OP probability of 29.6% ( Figure 4 a ). Evaluation of the nomogram revealed a calibration curve closely aligned with the ideal reference line, indicating strong agreement between predicted and observed outcomes. The model demonstrated robust discriminatory power, with an area under the ROC curve (AUC) of 0.786. Furthermore, decision curve analysis (DCA) confirmed the clinical utility of the nomogram, showing a superior net benefit across a wide range of risk thresholds compared to the individual biomarkers ( Figures 4 b-d ). In summary, the nomogram exhibited excellent diagnostic and predictive performance, validating its reliability and potential as a practical quantitative tool for the early screening of osteoporosis. 3.4 Enrichment analysis and construction of regulatory networks of biomarkers Subsequent Gene Set Enrichment Analysis (GSEA) of CAMKK2 and DAPK3 revealed their significant co-enrichment in multiple signaling pathways (p 1). These included cytokine-cytokine receptor interaction, long-term depression, and the vascular endothelial growth factor signaling pathway ( Figure 5 a and Additional file s 9-10 ), implicating their potential roles in these biological processes.This suggested that they might be involved in the pathological process of OP through the regulation of these pathways, and in particular, could further link ER stress status and cell death processes by influencing cytokine-mediated inflammatory responses, synapse-related signal regulation, and angiogenesis-related mechanisms, thereby affecting bone metabolic balance. Chromosomal localization analysis revealed that CAMKK2 was located on chromosome 12, and DAPK3 was located on chromosome 19 ( Figure 5 b ). For the construction of regulatory networks for the biomarkers, it was found that DAPK3 was regulated by multiple miRNAs (such as hsa-miR-149-5p), whereas fewer miRNAs (such as hsa-miR-345-5p) regulated CAMKK2 . TFs such as MLLT1, ZNF76, and HIC1 could regulate both CAMKK2 and DAPK3 simultaneously ( Figure 5 c and Additional file s 11-12 ). 3.5 Analysis of immune cells in OP We subsequently investigated alterations in the immune landscape during OP pathogenesis. Analysis revealed that Myeloid-Derived Suppressor Cells (MDSCs) exhibited the highest infiltration level among all cell types in both the OP and control groups ( Figure 6 a ). Comparative analysis identified three immune cell types with significantly altered abundance: activated dendritic cells, mast cells, and plasmacytoid dendritic cells were all substantially decreased in the OP group compared to controls (p < 0.05; Figure 6 b ). Furthermore, correlation analysis demonstrated a significant positive relationship between activated dendritic cells and mast cells (cor = 0.43, p < 0.0001). Both CAMKK2 and DAPK2 showed modest but statistically significant positive correlations with activated dendritic cells (cor = 0.24, p < 0.05; Figure 6 c and Additional file 13 ). Collectively, these results suggest that immune dysregulation, characterized by specific cellular deficiencies and their relationship with these biomarkers, may contribute to OP pathology, offering new insights into immune regulation in OP development. 3.6 Molecular docking between CAMKK2 , DAPK3 , and drugs To discover candidate drugs for OP treatment, we performed a systematic drug prediction. The results identified multiple compounds, such as captopril and cephaeline, that showed potential for interacting with CAMKK2 and DAPK3 ( Figure 7 a and Additional file 14 ). Subsequently, from a biological perspective, calcitriol, a drug that interacts with CAMKK2 and can promote calcium absorption and bone mineralization, was selected for molecular docking. For DAPK3 , danazol was chosen for molecular docking; this is a synthetic androgen derivative that has been used in some studies on postmenopausal osteoporosis to reduce bone loss (through increasing estrogen levels or decreasing bone resorption). The results of molecular docking showed that the binding ability between the drugs and the biomarkers was relatively stable, among which the binding energy of danazol and DAPK3 was -9.1 kcal/mol, indicating that their binding state was good ( Figure 7 b-c and Table 2 ). These results collectively suggest that the compounds targeting CAMKK2 and DAPK3 , identified through our drug prediction pipeline, hold significant therapeutic potential. Mechanistically, these agents may ameliorate OP progression by modulating key processes such as calcium absorption, bone mineralization, and bone loss reduction through their actions on CAMKK2 and DAPK3 . These findings thus provide a preclinical foundation for developing novel targeted therapies for OP. Table 2. Table of binding energy between biomarkers and drugs drugs genes CurPocket Vina score（kcal/mol） calcitriol CAMKK2 ChainA -7.8 danazol DAPK3 ChainA -9.1 3.7 Identification of Bone Marrow-Derived Mesenchymal Stem Cells (BM-MSCs) as a Key Cellular Player To complement the bulk transcriptomic findings and investigate the cellular mechanisms underlying OP, we performed single-cell RNA sequencing (scRNA-seq) analysis. After initial quality control of the dataset containing 9,654 cells and 20,435 genes, we retained 7,497 high-quality cells and all genes for downstream processing ( Figure 8 a ). We then identified 2,000 highly variable genes for dimensional reduction. Based on principal component analysis (PCA) results, the top 20 significant principal components (p < 0.05) were selected for subsequent analyses ( Figures 8 b-c ). UMAP clustering analysis was then performed, and 14 cell clusters were identified ( Figure 8 d ). Cell cluster annotation was performed using marker genes, resulting in the annotation of 6 cell types. These included B cells, neutrophils,BM-MSCs, monocytes, T cells, and nucleated red blood cells (NRBCs) ( Figure 8 e ). A bubble plot was employed to illustrate the distinct specificity of these marker genes, thus confirming the accuracy of the cell type annotations ( Figure 8 f ). Cell proportion analysis revealed that neutrophils, BM-MSCs, and monocytes accounted for the highest proportion ( Figure 8 g ). Analysis of biomarker expression revealed that CAMKK2 and DAPK3 were relatively highly expressed in BM-MSCs, so BM-MSCs were selected as key cells for subsequent analyses ( Figure 8 h-i ). Enrichment analysis across annotated cell populations revealed pronounced activity of the TWIK-related acid-sensitive K+ channel (TASK) in most cell types, including BM-MSCs. In contrast, although MGMT-mediated DNA damage reversal was highly active in the majority of cells, its activity was distinctly suppressed in BM-MSCs (adjusted p < 0.05; Figure 8 j and Additional file 15 ). These findings indicate that differential activity in these pathways may contribute to OP pathogenesis by disrupting core BM-MSC functions—including proliferation, differentiation, and DNA damage repair. This perspective offers valuable mechanistic insights for further investigating how cell-type-specific pathway dysregulation influences OP initiation and progression. 3.8 Specific communications of cell types Intercellular communication analysis revealed an extensive interaction network among annotated cell types. BM-MSCs demonstrated particularly prominent connectivity, engaging in numerous interactions with heightened communication strength to various immune populations, including monocytes, T cells, and B cells ( Figure 9 a ).Analysis of receptor-ligand interactions revealed distinct communication patterns. Within the BM-MSC population, interactions were primarily mediated by the ANGPTL4–CDH11 pair. In contrast, ligand-receptor complexes such as MIF–(CD74+CXCR4) and MIF–(CD74+CD44) served as key mediators across multiple intercellular communication axes, including monocyte-to-NRBC signaling ( Figure 9 b ). These results indicate that dysregulated intercellular communication—manifested as imbalanced signaling between BM-MSCs and immune cells, along with disrupted ligand-receptor mediated interactions—may contribute to OP pathogenesis by impairing BM-MSC function and disturbing immune microenvironment homeostasis. This insight offers a valuable direction for further elucidating how cell-cell communication networks regulate the initiation and progression of OP. 3.9 BM-MSCs differentiation trajectory Subsequently, dimensionality reduction clustering was performed on BM-MSCs, resulting in 6 cell clusters ( Figure 10 a ). Pseudotime analysis of BM-MSCs revealed that subcluster 0 was primarily localized at the initiation stage of developmental differentiation, subclusters 3 and 4 were in the middle stage of differentiation, subclusters 1 and 2 were at the terminal stage of differentiation, and subcluster 5 was distributed throughout the entire differentiation process. At the state level, states 1, 3, 6, and 7 belonged to the initial state of differentiation, state 5 was in the middle stage of differentiation, and state 4 was in the late stage of differentiation ( Figure 10 b ). Further analysis of biomarker expression patterns revealed that CAMKK2 exhibited multiphasic expression dynamics during BM-MSC differentiation: it was initially low in the early stage, subsequently increased, then declined, and finally re-increased during the late stage ( Figure 10 d ). In contrast, DAPK3 expression peaked during early differentiation, declined at the intermediate stage, and subsequently rebounded in the late phase ( Figure 10 c ). Finally, an activity analysis of TFs in different subclusters of BM-MSCs revealed that TFs such as ZNF277, GTF3A, and THAP4 exhibited strong activity in BM-MSCs subclusters 0 and 3, while their activity was suppressed in subcluster 2 ( Figure 10 d ). Taken together, these results indicated that BM-MSCs exhibited distinct stage division and subcluster specificity during the differentiation process. The expression of CAMKK2 and DAPK3 exhibited dynamic, stage-specific alterations during differentiation, concurrent with significant variations in transcription factor (TF) activity across different subclusters. These findings suggest that these TFs may orchestrate the precise progression of BM-MSC differentiation by temporally controlling the expression of key biomarkers such as CAMKK2 and DAPK3 . Dysregulation of this process may contribute to aberrant BM-MSC differentiation, thereby potentially underlying the pathogenesis and progression of osteoporosis (OP). These findings provided multi-level experimental evidence for deciphering the molecular regulatory network underlying abnormal BM-MSCs differentiation in OP. 4 Discussion Osteoporosis (OP) represents a highly prevalent skeletal disorder, yet long-term pharmacological interventions face challenges including limited efficacy and adverse effects. Growing evidence indicates that sustained endoplasmic reticulum (ER) stress promotes osteoblast apoptosis, thereby playing a critical role in disrupting bone metabolic homeostasis in OP. Consequently, in-depth investigation of ER stress and its associated cell death signaling pathways offers a promising direction for elucidating disease mechanisms and developing novel therapeutic strategies. This study successfully identified CAMKK2 and DAPK3 as key biomarkers associated with endoplasmic reticulum stress-related cell death in osteoporosis. A nomogram model constructed based on these biomarkers exhibited considerable diagnostic accuracy. Furthermore, Gene Set Enrichment Analysis (GSEA) highlighted significant involvement of these genes in critical pathways, including cytokine-cytokine receptor interaction, long-term depression, and vascular endothelial growth factor signaling. Analysis of the osteoporotic immune microenvironment indicated a weak correlation between the biomarkers and activated dendritic cells. Molecular docking suggested a strong binding affinity between danazol and DAPK3 . At the single-cell level, BM-MSCs were identified as key cells exhibiting complex communication networks with other cell types. Furthermore, the biomarker expression levels exhibited dynamic alterations during the course of BM-MSC differentiation. In this study, we innovatively integrate the critical pathway of ER stress with complementary bulk and single-cell transcriptomic analyses. This strategy provides a systematic, multi-level perspective on osteoporotic pathogenesis, from molecular mechanisms to cellular behaviors. Our work sheds new light on the dynamic expression profiles of key biomarkers throughout BM-MSC differentiation, as well as their interplay with the immune microenvironment and cell-cell communication, thereby offering a more comprehensive understanding of the disease. CAMKK2 (Calcium/calmodulin-dependent protein kinase kinase 2) is a serine/threonine kinase belonging to the Ca²⁺/calmodulin-dependent protein kinase family. Its activation is initiated by Ca²⁺/calmodulin (CaM) binding, which induces a conformational change to expose the autocatalytic site and facilitates full activation via autophosphorylation. This kinase orchestrates critical physiological processes, including cellular metabolism and immune regulation, primarily through phosphorylation of key downstream effectors such as AMPKα, CAMK1, and CAMK4[ 42 ]. Our study identified a significant downregulation of CAMKK2 in osteoporosis (OP) patients. Previous research has demonstrated that pharmacological inhibition of CAMKK2 promotes fracture healing, stimulates osteoblast formation, increases bone mass, and CAMKK2 -knockout mice exhibit enhanced bone strength[ 43 ]. Furthermore, CAMKK2 accelerates endochondral ossification by modulating Indian hedgehog signaling[ 43 ]. Collectively, this evidence suggests that the observed downregulation of CAMKK2 in OP patients may represent an endogenous compensatory protective response, attempting to counteract bone loss by reducing CAMKK2 expression to stimulate osteogenic activity. However, this compensatory mechanism appears insufficient to fully protect against OP progression, which may be attributed to the following reasons: First, the degree of compensation may be inadequate. While complete pharmacological inhibition of CAMKK2 accelerates bone healing by approximately 20%, the natural downregulation in OP patients likely represents only partial suppression, potentially failing to reach the optimal therapeutic threshold. Second, accompanying side effects might be amplified in the pathological microenvironment. CAMKK2 deficiency leads to intracellular ROS accumulation[ 44 ] and autophagy dysregulation[ 45 ]. Increased levels of reactive oxygen species (ROS) induce endoplasmic reticulum stress, thereby initiating the unfolded protein response. Within the OP pathological context—characterized by concurrent oxidative stress and inflammation—these adverse effects may be exacerbated, leading to increased osteocyte apoptosis and counteracting the pro-osteogenic effects of CAMKK2 downregulation. Third, the complex impact on the bone microenvironment must be considered. Our analysis identified significant enrichment of CAMKK2 in the cytokine-cytokine receptor interaction pathway[ 42 ] and the VEGF signaling pathway[ 43 ]. Downregulation of CAMKK2 may alter cytokine secretion profiles and potentially impair bone vascularization, which could disrupt the delicate balance of the bone immune microenvironment and nutrient supply during chronic OP progression. Fourth, spatiotemporal regulation may be disrupted. Our single-cell analysis revealed dynamic expression of CAMKK2 during BM-MSC differentiation, indicating stage-specific functions. The overall downregulation of CAMKK2 in OP may disrupt this precise spatiotemporal regulation, particularly affecting early-stage proliferation and lineage commitment of BM-MSCs. Furthermore, CAMKK2 expression demonstrated a significant positive correlation with activated dendritic cells[ 44 ], suggesting its potential role in modulating immunoregulatory pathways within the bone microenvironment. Complementing this, molecular docking confirmed a robust binding affinity between CAMKK2 and calcitriol, implying a plausible mechanism for its involvement in vitamin D-related signaling pathways[ 46 ]. This interaction suggests that variations in CAMKK2 expression could modulate responsiveness to vitamin D-based therapies. In summary, the downregulation of CAMKK2 in OP may initially represent a compensatory attempt to enhance osteogenesis. However, this protective response proves inadequate as the insufficient degree of suppression fails to counteract the amplified adverse effects within the pathological bone microenvironment, including ROS accumulation and autophagy dysregulation. The complexity is further compounded by disrupted cytokine signaling, impaired vascular regulation, and loss of spatiotemporal control over BM-MSC differentiation. These findings suggest that therapeutically targeting CAMKK2 requires precise modulation of its inhibition level, timing, and cell-type specificity, alongside concurrent interventions to improve the bone microenvironment through antioxidant or anti-inflammatory strategies. Such an integrated approach may effectively DAPK3 (death-associated protein kinase 3) is a member of the death-associated protein kinase family and functions as a serine/threonine kinase. Its structure is defined by the presence of a canonical kinase domain, a leucine zipper motif, and a nuclear localization signal. It plays important roles in apoptosis, autophagy, and immune regulation[ 47 ]. Our analysis demonstrated that DAPK3 is significantly downregulated in individuals with osteoporosis. This alteration is posited to disrupt bone metabolic homeostasis via several distinct pathways. The downregulation of DAPK3 may promote osteocyte death through endoplasmic reticulum stress (ERS)-related pathways. Studies indicate that DAPK3 deficiency activates ERS and upregulates stress-associated proteins such as ATF4 and CHOP. The elevation of these proteins has been shown to induce osteoblast apoptosis via the PERK–eIF2α–ATF4–CHOP signaling axis[ 48 ]. Furthermore, inhibition of DAPK3 can exacerbate ERS-induced apoptosis through an AMPK-mediated signaling mechanism[ 49 , 50 ]. Collectively, these observations imply that DAPK3 may function to modulate endoplasmic reticulum stress in osteoblasts under physiological contexts.Its decreased expression in OP could lead to a loss of this protection, thereby increasing cellular susceptibility to ERS. This would result in enhanced activation of the ATF4–CHOP pathway, elevated osteoblast apoptosis, and ultimately, impaired bone formation. Pathway enrichment analysis indicates that DAPK3 is associated with the cytokine-cytokine receptor interaction pathway, highlighting its potential role in OP through immunomodulation. DAPK3 regulates the infiltration and activation of immune cells such as dendritic cells via the STING–IFN-β pathway[ 51 ]. Its positive correlation with activated dendritic cells suggests that DAPK3 may influence osteoclast differentiation by modulating the secretion of cytokines such as IL-6 and TNF-α[ 52 ]. In OP, downregulation of DAPK3 may attenuate signaling in the cytokine-cytokine receptor interaction pathway, disrupt cytokine network balance, and promote excessive bone resorption. Molecular docking analysis demonstrated a robust binding affinity between DAPK3 and danazol, a synthetic androgen derivative previously studied for its efficacy in mitigating bone loss in postmenopausal osteoporosis[ 53 ]. The calculated binding energy of -9.1 kcal/mol supports the potential of DAPK3 as a therapeutic target for osteoporosis. Danazol may function by stabilizing the structure and function of DAPK3 , thereby restoring its ability to suppress endoplasmic reticulum stress and modulate cytokine networks[ 49 , 47 ]. This mechanism could contribute to reduced osteoblast apoptosis and improved immunomodulation in the bone microenvironment, ultimately promoting the restoration of bone metabolic balance. In summary, DAPK3 contributes to the pathogenesis and progression of osteoporosis by modulating endoplasmic reticulum stress-associated cell death, engaging in cytokine-mediated signaling, and regulating immune cell functions. Its downregulation is posited to compromise the homeostatic balance between osteogenesis and bone resorption. Consequently, targeting DAPK3 may represent a promising therapeutic avenue for OP intervention. In the pathogenesis of osteoporosis (OP), dysfunction of BM-MSCs is intricately linked to endoplasmic reticulum stress (ERS). The molecular basis for their aberrant differentiation involves crosstalk with the ER stress response, mediated by multiple signaling pathways. Studies have shown that in glucocorticoid-induced osteoporosis (GIOP), BM-MSCs exhibit enhanced adipogenic differentiation but impaired osteogenic differentiation. While this process is primarily linked to SENP3 -mediated deSUMOylation of PPARγ2[ 54 ], oxidative stress acts as a key driver—not only upregulating SENP3 expression but also triggering ER stress. ROS generated during oxidative stress disrupt the protein-folding environment in the ER, activating the unfolded protein response (UPR)[ 55 ]. Sustained UPR signaling can further modulate the expression and function of PPARγ2 by influencing transcription factor activity, creating a cascade of oxidative stress–ER stress–enhanced adipogenesis. Our single-cell analysis revealed high activity of TWIK-related acid-sensitive potassium (TASK) channels in BM-MSCs. Ion channels play essential roles in regulating intracellular ion homeostasis and endoplasmic reticulum (ER) calcium balance, and disruption of ER calcium equilibrium is a known trigger of ER stress. Moreover, dysfunction of ion channels can impair cellular energy metabolism and mitochondrial function[ 56 ], and mitochondrial dysfunction is closely linked with ER stress. These two pathological processes can mutually exacerbate each other and collectively promote cell death. Therefore, the high activity of TASK channels may influence BM-MSC differentiation fate by modulating ion homeostasis, energy metabolism, and ER function, though the precise underlying mechanisms require further investigation. Our single-cell RNA sequencing analysis elucidates the detailed developmental progression of bone marrow mesenchymal stem cell (BM-MSC) differentiation. Pseudotime analysis revealed a clearly staged differentiation process, during which the biomarkers CAMKK2 and DAPK3 exhibited dynamic expression patterns: CAMKK2 expression was low in the early stage, increased subsequently, then decreased, and rose again in the late differentiation phase.In contrast, DAPK3 expression peaked during the initial phase, declined through the intermediate stage, and subsequently rebounded in the terminal phase. This sophisticated temporal expression pattern suggests distinct stage-specific regulatory roles for both genes during BM-MSC differentiation. Notably, we observed significant overall downregulation of CAMKK2 and DAPK3 in osteoporosis (OP) patients. Integrated with the single-cell findings, this global downregulation may disrupt the precise spatiotemporal regulation required for orderly BM-MSC differentiation. Under physiological conditions, the dynamic expression of CAMKK2 and DAPK3 likely ensures stage-appropriate signaling and functional modulation; in OP, however, their reduced expression may deprive cells of essential regulatory signals at critical differentiation stages, ultimately compromising osteogenic efficiency. Future studies employing stage-specific knockdown or overexpression experiments will be valuable to elucidate the exact functional contributions of these genes across distinct phases of BM-MSC differentiation. In summary, BM-MSC dysfunction in OP results from the interplay of oxidative stress, ion channel regulation, ER stress, and dynamic changes in differentiation-associated biomarkers. ER stress serves as a central hub that integrates ROS signaling, ion homeostasis, and metabolic status to temporally regulate the expression of CAMKK2 and DAPK3 , ultimately leading to an adipogenic–osteogenic differentiation imbalance. These insights provide a rationale for novel OP treatment strategies aimed at improving ER function in BM-MSCs. 5 Conclusions This study utilized integrated bioinformatics approaches to explore the pathogenesis of osteoporosis (OP), leading to the identification of CAMKK2 and DAPK3 as novel biomarkers implicated in endoplasmic reticulum stress-associated cell death. Bone marrow mesenchymal stem cells (BM-MSCs) were established as a key cellular context, offering fresh perspectives on OP mechanisms and potential treatment avenues. It is important to acknowledge, however, that these findings are primarily based on computational evidence and require further experimental validation through functional assays—such as genetic manipulation studies and animal models—to definitively establish the roles of CAMKK2 and DAPK3 in OP progression. Future work will focus on elucidating the complex interplay between endoplasmic reticulum stress, bone cell differentiation, and immune regulation in OP. Additional investigations involving in vitro and in vivo models are planned to substantiate these observations, with the long-term objective of translating these insights into clinically applicable therapeutic strategies. Abbreviations Abbreviation Full Name OP Osteoporosis ER Endoplasmic Reticulum ERS Endoplasmic Reticulum Stress ROS Reactive Oxygen Species scRNA-seq Single-cell RNA sequencing GEO Gene Expression Omnibus BMD Bone Mineral Density DEGs Differentially Expressed Genes PPI Protein-Protein Interaction GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes LASSO Least Absolute Shrinkage and Selection Operator SVM-RFE Support Vector Machine-Recursive Feature Elimination RF Random Forest ROC Receiver Operating Characteristic AUC Area Under the Curve DCA Decision Curve Analysis GSEA Gene Set Enrichment Analysis MSigDB Molecular Signatures Database NES Normalized Enrichment Score miRNAs microRNAs TFs Transcription Factors ssGSEA single-sample Gene Set Enrichment Analysis DsigDB Drug-Signature Database PDB Protein Data Bank PCA Principal Component Analysis UMAP Uniform Manifold Approximation and Projection BM-MSCs Bone Marrow-Derived Mesenchymal Stem Cells NRBCs Nucleated Red Blood Cells TASK TWIK-related acid-sensitive K+ channel VIPER Variation of Information-based Pathway Enrichment in RNA RT-qPCR Reverse Transcription Quantitative Polymerase Chain Reaction cDNA Complementary DNA GIOP Glucocorticoid-induced Osteoporosis Declarations Ethics approval and consent to participate This study was approved by the Medical Ethics Committee of Yunnan Provincial Hospital of Traditional Chinese Medicine. The approval number and date of approval are as follows: 2025-KY-018-01 and November 27, 2025. All patients provided written informed consent when clinical samples were collected for RT-qPCR experiments to ensure that the research process was in accordance with ethical norms and that the patients' rights and xwishes were fully respected. All procedures involved in this study strictly adhered to the ethical principles outlined in the Declaration of Helsinki for research involving human participants.This study strictly adheres to ethical guidelines. The researchers comprehensively and clearly explain the study's purpose, procedures, risks, and benefits to potential participants to ensure full comprehension. Ample time for consideration and opportunities for questions are provided. Upon obtaining explicit and voluntary agreement, a written informed consent form is jointly signed, with a copy provided to the participant for their records. The entire process respects the participant's autonomy and safeguards their right to withdraw at any time without giving a reason. Consent for publication Not applicable Availability of data and materials The data that support the findings of this study are openly available in [Gene Expression Omnibus] at [https://www.ncbi.nlm.nih.gov/geo/], reference number [GSE56815, GSE56814, and GSE147287]. Competing interests The authors report there are no competing interests to declare. Funding This work was supported by the Joint Fund Project of Yunnan University of Traditional Chinese Medicine and Its Colleges and Departments [grant number: XYLH202347]; and the 2025 Key Clinical Specialty in Traditional Chinese Medicine - Orthopedics [grant number: Yun Cai She (2025) No. 92]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Authors' contributions Yifeng Xia: Conceptualization, Data curation, Validation, Visualization, Writing–original draft, Writing–review & editing. Zhongyu Peng: Data curation, Validation, Visualization, Writing–review & editing. Lingrui Zhao: Visualization, Writing–review & editing.Yuan Long: Validation, Writing–review & editing. Renwei Chen: Clinical blood sample collection. Jiahao Dong: Clinical blood sample collection.Meixiang Chu: Conceptualization, Writing–review & editing. Weijie Yu: Conceptualization, Supervision, Writing–review & editing. Tao Chen: Conceptualization, Project administration, Supervision, Writing–review & editing. This work currently described has not been published, is not being considered for publication elsewhere, and its publication was approved by all authors. Acknowledgements We would like to express our sincere gratitude to all individuals and organizations who supported and assisted us throughout this research. 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(\u003cstrong\u003ed\u003c/strong\u003e) Venn diagram of the intersection of LASSO genes, SVM-RFE genes, and RF genes.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8454279/v1/69ad22f681d8d0cfe9d53080.png"},{"id":100013512,"identity":"e383a6b7-9222-4595-bec3-2846b490bbc9","added_by":"auto","created_at":"2026-01-12 06:20:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":322627,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of gene expression levels of biomarkers.\u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Analysis of gene expression levels in the training dataset; (\u003cstrong\u003eb\u003c/strong\u003e) Analysis of gene expression levels in the validation dataset; (\u003cstrong\u003ec\u003c/strong\u003e) Experimental validation of the biomarkers was conducted using RT-qPCR assays.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8454279/v1/4e45400bde41490ecf4cba1a.png"},{"id":100362000,"identity":"0c25a8f3-9fe1-47c4-9477-991a127f711d","added_by":"auto","created_at":"2026-01-16 07:46:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":6351104,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and evaluation of the nomogram model.\u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Construction of the nomogram model; (\u003cstrong\u003eb\u003c/strong\u003e) Calibration curve of the nomogram model; (\u003cstrong\u003ec\u003c/strong\u003e) ROC curve of the nomogram model; (\u003cstrong\u003ed\u003c/strong\u003e) DCA analysis of the nomogram model.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8454279/v1/f15daf0c4671a74a5269f238.png"},{"id":100362345,"identity":"1b918193-5e34-4e3a-9251-0545cadeee7c","added_by":"auto","created_at":"2026-01-16 07:46:35","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":6263988,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation analysis of biomarkers.\u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e) GSEA analysis of \u003cem\u003eCAMKK2\u003c/em\u003eand \u003cem\u003eDAPK3\u003c/em\u003e; (\u003cstrong\u003eb\u003c/strong\u003e) Chromosomal localization of \u003cem\u003eCAMKK2\u003c/em\u003eand \u003cem\u003eDAPK3\u003c/em\u003e; (\u003cstrong\u003ec\u003c/strong\u003e) miRNA-biomarker-TF network.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8454279/v1/ff1b02ca18d9e70b7966cebd.png"},{"id":100362160,"identity":"8d83be89-aba0-4de5-bb49-de6d3431c29c","added_by":"auto","created_at":"2026-01-16 07:46:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":4269969,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of immune infiltration levels.\u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Enrichment analysis of 28 immune cell types; (\u003cstrong\u003eb\u003c/strong\u003e) Analysis of differential immune cells; (\u003cstrong\u003ec\u003c/strong\u003e) Correlation analysis between biomarkers and differential immune cells.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8454279/v1/7e9b07625ace3deca10b0686.png"},{"id":100013543,"identity":"db4446ba-045f-445e-a86f-7efe575c4a35","added_by":"auto","created_at":"2026-01-12 06:20:42","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":19207737,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular docking of biomarkers and drugs.\u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Drug prediction network targeting biomarkers; (\u003cstrong\u003eb-c\u003c/strong\u003e) Molecular docking of \u003cem\u003eCAMKK2\u003c/em\u003ewith calcitriol, and \u003cem\u003eDAPK3\u003c/em\u003ewith danazol.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-8454279/v1/aa71daf150d39a2a8d67f03f.png"},{"id":100361804,"identity":"a12e20e2-cfb6-4f0f-bf88-5df276c05170","added_by":"auto","created_at":"2026-01-16 07:45:44","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":15738960,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCell annotation of scRNA-seq.\u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Data quality control before and after (number of genes, total number of mRNA molecules, proportion of mitochondrial genes); (\u003cstrong\u003eb\u003c/strong\u003e) Screening of highly variable genes; (\u003cstrong\u003ec\u003c/strong\u003e) Cell distribution in PCA analysis, Jackstraw plot, and elbow plot; (\u003cstrong\u003ed\u003c/strong\u003e) UMAP cell clustering plot; (\u003cstrong\u003ee\u003c/strong\u003e) Clustering plot of cell annotation; (\u003cstrong\u003ef\u003c/strong\u003e) Bubble plot of marker genes for cell annotation; (\u003cstrong\u003eg\u003c/strong\u003e) Enrichment proportion plot of annotated cells; (\u003cstrong\u003eh-i\u003c/strong\u003e) Distribution and expression of \u003cem\u003eCAMKK2\u003c/em\u003eand \u003cem\u003eDAPK3\u003c/em\u003e in annotated cells; (\u003cstrong\u003ej\u003c/strong\u003e) Enrichment analysis of annotated cells.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-8454279/v1/2e84dfed7a280f4c1a4aab66.png"},{"id":100013539,"identity":"8e1f5f54-50f1-4371-8030-27190b20ffcf","added_by":"auto","created_at":"2026-01-12 06:20:42","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":10601041,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of intercellular communication.\u003c/strong\u003e(\u003cstrong\u003ea\u003c/strong\u003e) Network 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\u003cem\u003eCAMKK2\u003c/em\u003eand \u003cem\u003eDAPK3\u003c/em\u003e along the differentiation trajectory. (\u003cstrong\u003ed\u003c/strong\u003e) Transcription Factors Potentially Regulating Stage-specific Biomarker Expression in BM-MSC Differentiation.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-8454279/v1/8875cbeea6d7baed76b50a87.png"},{"id":100013515,"identity":"4380ef91-ac3e-4e99-8e0f-88a75a82a009","added_by":"auto","created_at":"2026-01-12 06:20:41","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":243090,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8454279/v1/103923ad8754e96479ad8de8.xlsx"},{"id":100013513,"identity":"31a6d6da-01d1-4e87-ac7f-a71b3e9e5028","added_by":"auto","created_at":"2026-01-12 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Osteoporosis affects an estimated 200\u0026nbsp;million individuals globally and is particularly common in postmenopausal women. With the ongoing aging of the global population, both the incidence of the disease and the associated healthcare burden are steadily increasing [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Current therapeutic strategies include anti-resorptive agents (e.g., bisphosphonates, denosumab), bone-forming agents (e.g., teriparatide), and dual-action drugs (e.g., romosozumab), often requiring sequential regimens to sustain therapeutic efficacy [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Nevertheless, long-term pharmacotherapy for osteoporosis poses significant clinical challenges: bisphosphonates are associated with risks of osteonecrosis of the jaw and atypical femoral fractures; denosumab discontinuation can trigger rebound increases in bone turnover and elevated fracture risk; and bone-forming agents provide only transient anabolic effects, requiring subsequent anti-resorptive therapy to maintain skeletal benefits [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Furthermore, available therapies exhibit limited efficacy in improving bone quality, and a subset of patients respond inadequately to existing options [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These constraints underscore the need to elucidate the molecular mechanisms governing bone metabolism, which is imperative for developing novel therapeutic approaches.\u003c/p\u003e \u003cp\u003eGrowing evidence indicates that endoplasmic reticulum (ER) stress-mediated cell death plays an increasingly critical role in osteoporosis pathogenesis. ER stress, characterized as an adaptive cellular response to the accumulation of misfolded or unfolded proteins, may initiate key pathological processes when dysregulated. However, persistent or excessive ERS can trigger cell death through signaling pathways such as PERK\u0026ndash;eIF2α\u0026ndash;ATF4\u0026ndash;CHOP and IRE1α\u0026ndash;XBP1 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In the context of osteoporosis (OP), cadmium exposure has been shown to induce ERS via reactive oxygen species (ROS), activating the PERK pathway while suppressing the Nrf2/NQO1 axis, ultimately leading to osteoblast apoptosis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Similarly, high cholesterol levels promote osteoblast apoptosis through ERS activation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Loss of \u003cem\u003eCOPB1\u003c/em\u003e induces both ERS and ferroptosis by repressing \u003cem\u003eSLC7A11\u003c/em\u003e transcription via \u003cem\u003eATF6\u003c/em\u003e, thereby impairing cystine uptake and exacerbating OP progression [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Furthermore, bisphosphonates can trigger ERS-mediated apoptosis in lymphatic endothelial cells via the NAD⁺/SIRT6/XBP1s pathway, impairing lymphatic drainage and contributing to osteonecrosis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, a systematic investigation of ER stress-related cell death in osteoporosis is still lacking, while the underlying molecular mechanisms and potential therapeutic targets remain to be fully elucidated.\u003c/p\u003e \u003cp\u003eSingle-cell RNA sequencing (scRNA-seq) enables high-resolution identification of cellular heterogeneity, allowing for the discrimination of distinct cell subtypes within complex populations. Integrating bulk transcriptomics with scRNA-seq forms a complementary strategy: bulk data reveal overall gene expression trends at the population level, while single-cell data resolve heterogeneity and uncover functional differences among cell subpopulations [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, this study leverages an integrative approach, combining bulk and single-cell transcriptomic analyses with machine learning and molecular docking, to unveil the core molecular mechanisms and distinct cellular subpopulations associated with endoplasmic reticulum stress-mediated cell death in osteoporosis. Our findings offer a conceptual framework for the development of innovative therapeutic interventions aimed at modulating endoplasmic reticulum stress in this disease.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003ch2\u003e2.1 Data collection\u003c/h2\u003e\n\u003cp\u003eOP-related training dataset (GSE56815), validation dataset (GSE56814), and scRNA-seq dataset (GSE147287) were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). GSE56815 comprises 40 blood monocyte samples from OP patients (with low bone mineral density (BMD)) and 40 from controls (with high BMD), which were profiled using the GPL96 platform. GSE56814 includes 42 blood monocyte samples from OP patients (with low BMD) and 31 from controls (with high BMD), analyzed with the GPL5175 platform. GSE147287 contains freshly isolated bone marrow-derived monocyte samples from the femoral head of 1 OP patient, with osteoarthritis samples excluded, and was processed using the GPL24676 platform. A total of 2,903 ERS-related genes were obtained by searching the GeneCards database (https://www.genecards.org/) for ER stress (\u003cstrong\u003eAdditional file\u003c/strong\u003e\u003cstrong\u003e 1\u003c/strong\u003e). A total of 1,548 programmed cell death (PCD)-related genes were downloaded from the literature [14] (\u003cstrong\u003eAdditional file\u003c/strong\u003e\u003cstrong\u003e 2\u003c/strong\u003e). The access time for all the above data was August 1, 2025.\u003c/p\u003e\n\u003ch2\u003e2.2 Analysis of differentially expressed genes (DEGs)\u003c/h2\u003e\n\u003cp\u003eDifferential expression analysis between the OP and control groups was performed on the training dataset gene expression matrix using the limma package (v3.58.1) [15], with an adjusted p-value \u0026lt; 0.05 set as the significance threshold [16]. The resulting DEGs were visualized in a volcano plot generated with ggplot2 (v3.5.1) [17]. Additionally, the top 10 up- and down-regulated DEGs, ranked by absolute log2 fold-change, were displayed in an expression heatmap created using the ComplexHeatmap package (v2.14.0) [18]. Candidate genes were identified by intersecting the DEGs with genes associated with endoplasmic reticulum (ER) stress and programmed cell death (PCD), which was conducted and visualized via the VennDiagram package (v1.7.3) [19]. \u003c/p\u003e\n\u003ch2\u003e2.3 Protein-protein interaction (PPI) network construction and enrichment analysis\u003c/h2\u003e\n\u003cp\u003eA protein-protein interaction (PPI) network for the candidate genes was constructed using the STRING database (confidence score ≥ 0.4) to elucidate the functional interactions among the encoded proteins. The resulting network was visualized with the circlize package (v0.4.16) [20]. Furthermore, to interpret the biological roles of these genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway[21-23] enrichment analyses were performed using the clusterProfiler package (v4.8.3) [24], with terms and pathways considered significant at p \u0026lt; 0.05. \u003c/p\u003e\n\u003ch2\u003e2.4 Machine learning\u003c/h2\u003e\n\u003cp\u003eTo refine the candidate genes and identify those with pivotal roles in OP, we employed three distinct machine learning algorithms on the training dataset. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was implemented using the glmnet package (v4.1-8) [25] with 5-fold cross-validation, and genes retained at the optimal penalty parameter (lambda.min) were designated as LASSO genes. The Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithm, executed via the caret package (v6.0-94) [26] with 5-fold cross-validation, selected the feature set yielding the highest predictive accuracy as SVM-RFE genes. A Random Forest (RF) model was built with the randomForest package (v4.7-1.1) [27], and the top 10 genes, as ranked by the \"minimum error regression trees\" criterion, were defined as RF genes. Finally, the overlapping genes from these three sets were identified as key intersection genes using the VennDiagram package (v1.7.3). \u003c/p\u003e\n\u003ch2\u003e2.5 Gene expression level analysis\u003c/h2\u003e\n\u003cp\u003eDifferential expression analysis of the intersection genes between the OP and control groups was performed using the Wilcoxon rank-sum test. Genes demonstrating a consistent direction of expression change and a statistically significant difference (p \u0026lt; 0.05) in both the training and validation datasets were subsequently defined as final biomarkers.\u003c/p\u003e\n\u003ch2\u003e2.6 Construction and evaluation of a nomogram\u003c/h2\u003e\n\u003cp\u003eTo assess the collective predictive utility of the identified biomarkers for OP, a nomogram was developed using the rms package (v6.8.1)[28] based on the training dataset. In this model, each biomarker is assigned a points score proportional to its expression level. The summation of these individual scores yields a total points value, from which the probability of OP incidence can be directly read; a higher total score corresponds to a greater predicted risk. The model's calibration was evaluated by plotting a calibration curve (using the rms package), where a slope closer to 1 indicates superior agreement between predicted and observed outcomes. The discriminatory power of the nomogram was quantified by Receiver Operating Characteristic (ROC) analysis with the pROC package (v1.18.5)[29], reporting the Area Under the Curve (AUC). An AUC value \u0026gt; 0.7 and ≠ 1 was considered indicative of satisfactory model performance, with higher values denoting better prediction. Furthermore, the clinical applicability of the nomogram was appraised using Decision Curve Analysis (DCA) implemented via the rmda package (v1.6)[30], which estimates the net benefit across a range of decision thresholds. \u003c/p\u003e\n\u003ch2\u003e2.7 Gene set enrichment analysis (GSEA)\u003c/h2\u003e\n\u003cp\u003eTo delineate the signaling pathways associated with the biomarkers, we first profiled their co-expression networks. Specifically, Spearman correlation analyses between each biomarker and all other genes were performed across all training set samples using the psych package (v2.4.3)[31]. Genes were then ranked in descending order based on the derived correlation coefficients for each biomarker. For the Gene Set Enrichment Analysis (GSEA), the C2: KEGG subset from the Molecular Signatures Database (MSigDB) was retrieved as the reference gene set using the msigdbr package (v7.5.1)[32]. Enrichment pathways for each biomarker were subsequently characterized using GSEA implemented in the clusterProfiler package (v4.8.3). A signaling pathway was considered significantly enriched with a p-value \u0026lt; 0.05 and an absolute Normalized Enrichment Score (|NES|) \u0026gt; 1. \u003c/p\u003e\n\u003ch2\u003e2.8 Localization analysis\u003c/h2\u003e\n\u003cp\u003eThe chromosomal locations of the identified biomarkers were mapped using the RCircos package (v1.2.2)[33]. \u003c/p\u003e\n\u003ch2\u003e2.9 Molecular regulatory network construction\u003c/h2\u003e\n\u003cp\u003eTo elucidate the upstream regulatory mechanisms of the biomarkers, putative microRNAs (miRNAs) targeting these genes were predicted using the microcosm database accessed via the multiMiR package (v1.16.0)[34]. Concurrently, potential transcription factors (TFs) were identified using the mirNet database. A comprehensive miRNA-TF-biomarker regulatory network was subsequently visualized using Cytoscape software (v3.10.2)[35]. \u003c/p\u003e\n\u003ch2\u003e2.10 Analysis of immune cell infiltration\u003c/h2\u003e\n\u003cp\u003eIn OP, alterations in immune status were induced, which gave rise to a chronic low-grade inflammatory phenotype. Immune cells were shown to interact with bone cells through direct contact or paracrine mechanisms, with various cytokines and other mediators released. These mediators affected the balance between bone formation and resorption, thereby exacerbating bone destruction and contributing to the pathogenesis of the disease [36]. The immune cell infiltration landscape was profiled in the training dataset using the single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm implemented in the GSVA package (v1.50.0)[37], which estimated the relative abundances of 28 immune cell types. The Wilcoxon rank-sum test was then applied to identify immune cell populations that were significantly dysregulated (p \u0026lt; 0.05) between the OP and control groups. Furthermore, Spearman correlation analysis, performed with the psych package (v2.4.3), was used to investigate the interrelationships among these differential immune cells and their associations with the biomarkers, applying thresholds of |correlation coefficient (cor)| \u0026gt; 0.3 and p \u0026lt; 0.05.\u003c/p\u003e\n\u003ch2\u003e2.11 Drug prediction and molecular docking\u003c/h2\u003e\n\u003cp\u003eTo pinpoint candidate therapeutics capable of targeting the identified biomarkers, we interrogated the Drug-Signature Database (DsigDB) and visualized the resultant drug-biomarker interactions using Cytoscape (v3.10.2). The binding affinities between the top candidate drugs and their corresponding biomarker-encoded proteins were subsequently evaluated via molecular docking using the CB-DOCK2 web server. The three-dimensional structures of the compounds were retrieved from PubChem, while protein structures for the biomarkers were obtained from the Protein Data Bank (PDB), prioritizing entries with the highest resolution. For biomarkers lacking a solved structure, homology models were generated using AlphaFold. Molecular docking was then performed with CB-DOCK2, and the resulting poses were visualized. Consistent with conventional criteria, a docking score of less than -5 kcal/mol was considered indicative of strong binding potential. \u003c/p\u003e\n\u003ch2\u003e2.12 The scRNA-seq analysis\u003c/h2\u003e\n\u003cp\u003eTo delineate the single-cell expression patterns of the biomarkers and reconstruct the developmental trajectories of key cell types in osteoporosis, we performed a comprehensive analysis of the scRNA-seq dataset using Seurat (v5.1.0). Initial quality control involved creating a Seurat object (min.cells=3, min.features=200) and filtering doublets with scDblFinder (v1.16.0). We retained cells expressing between 200 and 5,000 genes, excluded genes detected in fewer than 200 or more than 20,000 cells, and removed cells with mitochondrial gene content exceeding 20%. The top 2,000 highly variable genes were then identified using the FindVariableFeatures function (selection.method = \"vst\"). After dataset integration with IntegrateData, we performed principal component analysis (PCA) on these variable genes. Significant principal components (p \u0026lt; 0.05) were selected for downstream clustering. Cells were clustered using the FindNeighbors and FindClusters functions (resolution=0.3) and visualized via UAP. Cluster-specific marker genes were identified with FindAllMarkers (logfc.threshold=0.25, min.pct=0.1, only.pos=TRUE). Cell types were annotated by referencing both the SingleR package (v2.0.0) and the CellMarker 2.0 database, guided by established literature. \u003c/p\u003e\n\u003ch2\u003e2.13 Screening of key cells\u003c/h2\u003e\n\u003cp\u003eTo screen for key cells, based on the scRNA-seq dataset, the distribution of biomarkers in the annotated cells and their expression levels across each annotated cell type were first presented. Cells with high expression levels of the biomarkers were selected as key cells. Subsequently, functional enrichment analysis of all cells was performed using ReactomeGSA (v 1.02.0)[38] to explore their biological pathways (adjusted p \u0026lt; 0.05). The top 10 pathways with the most significant adjusted p-values were displayed. \u003c/p\u003e\n\u003ch2\u003e2.14 Cells communication\u003c/h2\u003e\n\u003cp\u003eTo systematically map intercellular signaling, we performed cell-cell communication analysis using the annotated scRNA-seq dataset. The CellChat package (v1.6.1)[39] was employed to computationally infer interactions between defined cell populations based on the expression profiles of receptor-ligand pairs. Significant interactions were identified using the following thresholds: p \u0026lt; 0.05 and a combined expression level (log\u003csub\u003e2\u003c/sub\u003e-mean [Molecule 1, Molecule 2]) ≥ 0.1. \u003c/p\u003e\n\u003ch2\u003e2.15 Pseudotime analysis\u003c/h2\u003e\n\u003cp\u003eTo understand the developmental trajectories of key cells and the expression changes of biomarkers in key cells, dimensionality reduction and clustering were first performed on key cells based on the scRNA-seq dataset (as in previous steps, details omitted here). With the resolution set to 0.3, a UMAP clustering plot was generated. Following this, cluster-specific marker genes that were highly expressed in key cell clusters were identified using the FindAllMarkers function in Seurat (v5.1.0), with the following thresholds applied: min.pct = 0.25 and only.pos = TRUE. Pseudotime analysis was then performed on the key cell subsets using Monocle 2 (v 2.28.0)[40]. All cells in the cell subsets were ordered according to their pseudotime and projected onto one root and two branches, enabling pseudotime trajectory analysis. Additionally, the expression changes of biomarkers in the cell subsets were visualized. \u003c/p\u003e\n\u003ch2\u003e2.16 TF's regulatory analysis\u003c/h2\u003e\n\u003cp\u003eTo investigate the regulated TFs among different subpopulations of key cells, Variation of Information-based Pathway Enrichment in RNA (VIPER) analysis was performed. The analysis was conducted using Dorothea (v 1.12.0)[41], which helps reveal the relationship between gene expression patterns and regulatory networks by calculating the activity of TFs. A heatmap was used to display the core regulatory TFs in each subpopulation of key cells. These TFs were ranked from high to low according to their activity, and the top 10 TFs with the highest activity were selected for display. \u003c/p\u003e\n\u003ch2\u003e2.17 Experimental validation\u003c/h2\u003e\n\u003cp\u003eTo experimentally validate the differential expression of the identified biomarkers, reverse transcription quantitative polymerase chain reaction (RT-qPCR) was performed. Clinical peripheral blood samples were obtained from the First Affiliated Hospital of Yunnan University of Chinese Medicine (Yunnan Provincial Hospital of Chinese Medicine) with approval from the Institutional Ethics Committee (Approval No. 2025-KY-018-01), and all procedures involved in this study strictly adhered to the ethical principles outlined in the Declaration of Helsinki for research involving human participants. Informed consent has been obtained from the patient.All participants were fully informed of the study's purpose, procedures, potential risks, and benefits. After thorough communication with the researchers and a complete understanding of the information, they voluntarily signed a written informed consent form. Total RNA was isolated using the TRIzol method, and its concentration was quantified with a NanoPhotometer N50. cDNA synthesis and quantitative PCR were subsequently carried out using commercial master mixes (HP All-in-one qRT Master Mix II RT203-Ver.1, Kunming Younggen Biotechnology Co., Ltd.; SwsScript All-in-One First-strand-cDNA-synthesis SuperMix for qPCR, Servare Company). The primer sequences used are provided in \u003cstrong\u003eTable 1\u003c/strong\u003e. Relative gene expression levels of the biomarkers were calculated using the 2\u003csup\u003e-ΔΔCT\u003c/sup\u003e method. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. \u003c/strong\u003eTable of Primer Sequence\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"97%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eprimer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003esequence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eCAMKK2\u003c/em\u003e F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGCAGGGTCAGTGAGACATCC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eCAMKK2\u003c/em\u003e R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTTGGATCCCCCAGCTGGATA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eDAPK3\u003c/em\u003e F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGCACGACATCTTCGAGAACAA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eDAPK3\u003c/em\u003e R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCTTAGAGTGCAGGTAGTGAACG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eGAPDH\u003c/em\u003e F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eATGGGCAGCCGTTAGGAAAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eGAPDH\u003c/em\u003e R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAGGAAAAGCATCACCCGGAG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e2.18 Statistical analysis\u003c/h2\u003e\n\u003cp\u003eAll statistical and bioinformatic analyses were conducted using R (v 4.3.1). Data visualization was performed with GraphPad Prism 10. For group comparisons, p-values were derived from t-tests or Wilcoxon rank-sum tests, as appropriate, with a p-value \u0026lt; 0.05 considered statistically significant. \u003c/p\u003e"},{"header":"3 Results","content":"\u003ch2\u003e3.1 Screening and enrichment analysis of candidate genes\u003c/h2\u003e\n\u003cp\u003eIn the initial screening for candidate genes, a total of 391 differentially expressed genes (DEGs) were identified between the OP and control groups (adjusted p \u0026lt; 0.05). This set comprised 269 up-regulated and 122 down-regulated genes in the OP group (\u003cstrong\u003eFigure 1\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e). Then, the intersection of the 391 DEGs, 2,903 ER stress-related genes, and 1,548 PCD-related genes was taken, and 28 candidate genes were obtained (\u003cstrong\u003eFigure 1\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cstrong\u003eAdditional file\u003c/strong\u003e\u003cstrong\u003e 3\u003c/strong\u003e). Then, a PPI network was constructed for the candidate genes (confidence score ≥ 0.4). It could be seen that genes such as \u003cem\u003eEGFR\u003c/em\u003e and \u003cem\u003eCTNNB1\u003c/em\u003e had relatively close interaction relationships with other genes (\u003cstrong\u003eFigure 1\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e). Finally, enrichment analysis of the candidate genes (p \u0026lt; 0.05) identified 950 significantly enriched GO terms. These comprised 817 biological processes, primarily associated with autophagy regulation, apoptotic signaling pathway regulation, and cellular response to oxidative stress; 70 cellular components, notably enriched in vesicle lumen, secretory granule lumen, and transcription repressor complex; and 63 molecular functions, prominently featuring RAGE receptor binding, Toll-like receptor binding, and oxidoreductase activity acting on metal ions (\u003cstrong\u003eFigure 1\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cstrong\u003eAdditional file\u003c/strong\u003e\u003cstrong\u003e 4\u003c/strong\u003e).Furthermore, KEGG pathway analysis revealed 64 significantly enriched signaling pathways. These encompassed several key processes, notably cellular senescence, chemical carcinogenesis - reactive oxygen species, and the FoxO signaling pathway (\u003cstrong\u003eFigure 1\u003c/strong\u003e\u003cstrong\u003ee\u003c/strong\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cstrong\u003eAdditional file\u003c/strong\u003e\u003cstrong\u003e 5\u003c/strong\u003e). The enrichment of GO biological processes and KEGG pathways—particularly those involving autophagy regulation, apoptotic signaling, cellular response to oxidative stress, cellular senescence, ROS-related chemical carcinogenesis, and the FoxO signaling pathway—suggests a potential role for these candidate genes in modulating bone metabolic balance during osteoporosis development. This regulation likely occurs through influencing endoplasmic reticulum stress and cell death processes (e.g., apoptosis) in bone cells. These findings provide valuable insights into the involvement of ER stress and cell death-related mechanisms in OP progression.\u003c/p\u003e\n\u003ch2\u003e3.2 Screening of biomarkers\u003c/h2\u003e\n\u003cp\u003eTo refine the candidate genes and identify biomarkers closely associated with OP pathogenesis, we employed a machine learning-based screening strategy. Initially, LASSO regression analysis was applied, yielding 17 feature genes [log(lambda min) = -2.9233] (\u003cstrong\u003eFigure 2\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cstrong\u003eAdditional file\u003c/strong\u003e\u003cstrong\u003e 6\u003c/strong\u003e). Through SVM-RFE analysis, 27 SVM-RFE genes were selected (\u003cstrong\u003eFigure 2\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cstrong\u003eAdditional file\u003c/strong\u003e\u003cstrong\u003e 7\u003c/strong\u003e). The Random Forest (RF) model was constructed using an optimal tree threshold of 91, which corresponded to the lowest error rate, and the top 10 genes were subsequently selected as the final RF gene set (\u003cstrong\u003eFigure 2\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cstrong\u003eAdditional file\u003c/strong\u003e\u003cstrong\u003e 8\u003c/strong\u003e). After taking the intersection of the three sets, 10 intersection genes were obtained, namely \u003cem\u003eFOXO3\u003c/em\u003e, \u003cem\u003eBRSK2\u003c/em\u003e, \u003cem\u003eUQLN2\u003c/em\u003e, \u003cem\u003eRAC1\u003c/em\u003e, \u003cem\u003eCAMKK2\u003c/em\u003e, \u003cem\u003ePTEN\u003c/em\u003e, \u003cem\u003eS100A9\u003c/em\u003e, \u003cem\u003eAPPL1\u003c/em\u003e, \u003cem\u003eDAPK3\u003c/em\u003e, and \u003cem\u003ePHLDA3\u003c/em\u003e (\u003cstrong\u003eFigure 2\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e). Subsequently, an analysis of the expression levels of these intersection genes was performed, and it was found that the expression levels of \u003cem\u003eCAMKK2\u003c/em\u003e and \u003cem\u003eDAPK3\u003c/em\u003e were down-regulated in OP in both the training dataset and the validation dataset (\u003cstrong\u003eFigures 3\u003c/strong\u003e\u003cstrong\u003ea-b\u003c/strong\u003e).Based on these findings, \u003cem\u003eCAMKK2\u003c/em\u003e and \u003cem\u003eDAPK3\u003c/em\u003e were designated as the definitive biomarkers for all subsequent investigations. To experimentally validate the bioinformatics predictions, we assessed the expression levels of \u003cem\u003eCAMKK2\u003c/em\u003e and \u003cem\u003eDAPK3\u003c/em\u003e using RT-qPCR. The results confirmed that both genes were significantly downregulated in the OP group compared to the controls (p \u0026lt; 0.05; \u003cstrong\u003eFigure 3\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e), thereby providing independent experimental corroboration for our computational findings. \u003c/p\u003e\n\u003ch2\u003e3.3 Predictive accuracy of biomarkers for OP\u003c/h2\u003e\n\u003cp\u003eA nomogram was subsequently developed to quantify the risk of OP onset based on the biomarker profile. In this model, higher expression scores for \u003cem\u003eCAMKK2\u003c/em\u003e and \u003cem\u003eDAPK3\u003c/em\u003e contributed to an increased total points value, which corresponded to a greater predicted probability of disease. For example, a total score of 81.1 points translated to an OP probability of 29.6% (\u003cstrong\u003eFigure 4\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e). Evaluation of the nomogram revealed a calibration curve closely aligned with the ideal reference line, indicating strong agreement between predicted and observed outcomes. The model demonstrated robust discriminatory power, with an area under the ROC curve (AUC) of 0.786. Furthermore, decision curve analysis (DCA) confirmed the clinical utility of the nomogram, showing a superior net benefit across a wide range of risk thresholds compared to the individual biomarkers (\u003cstrong\u003eFigures 4\u003c/strong\u003e\u003cstrong\u003eb-d\u003c/strong\u003e). In summary, the nomogram exhibited excellent diagnostic and predictive performance, validating its reliability and potential as a practical quantitative tool for the early screening of osteoporosis.\u003c/p\u003e\n\u003ch2\u003e3.4 Enrichment analysis and construction of regulatory networks of biomarkers\u003c/h2\u003e\n\u003cp\u003eSubsequent Gene Set Enrichment Analysis (GSEA) of \u003cem\u003eCAMKK2\u003c/em\u003e and \u003cem\u003eDAPK3\u003c/em\u003e revealed their significant co-enrichment in multiple signaling pathways (p \u0026lt; 0.05, |NES| \u0026gt; 1). These included cytokine-cytokine receptor interaction, long-term depression, and the vascular endothelial growth factor signaling pathway (\u003cstrong\u003eFigure 5\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cstrong\u003eAdditional file\u003c/strong\u003e\u003cstrong\u003es 9-10\u003c/strong\u003e), implicating their potential roles in these biological processes.This suggested that they might be involved in the pathological process of OP through the regulation of these pathways, and in particular, could further link ER stress status and cell death processes by influencing cytokine-mediated inflammatory responses, synapse-related signal regulation, and angiogenesis-related mechanisms, thereby affecting bone metabolic balance. \u003c/p\u003e\n\u003cp\u003eChromosomal localization analysis revealed that \u003cem\u003eCAMKK2\u003c/em\u003e was located on chromosome 12, and \u003cem\u003eDAPK3\u003c/em\u003e was located on chromosome 19 (\u003cstrong\u003eFigure 5\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e). For the construction of regulatory networks for the biomarkers, it was found that \u003cem\u003eDAPK3\u003c/em\u003e was regulated by multiple miRNAs (such as hsa-miR-149-5p), whereas fewer miRNAs (such as hsa-miR-345-5p) regulated \u003cem\u003eCAMKK2\u003c/em\u003e. TFs such as MLLT1, ZNF76, and HIC1 could regulate both \u003cem\u003eCAMKK2\u003c/em\u003e and \u003cem\u003eDAPK3\u003c/em\u003e simultaneously (\u003cstrong\u003eFigure 5\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cstrong\u003eAdditional file\u003c/strong\u003e\u003cstrong\u003es 11-12\u003c/strong\u003e). \u003c/p\u003e\n\u003ch2\u003e3.5 Analysis of immune cells in OP\u003c/h2\u003e\n\u003cp\u003eWe subsequently investigated alterations in the immune landscape during OP pathogenesis. Analysis revealed that Myeloid-Derived Suppressor Cells (MDSCs) exhibited the highest infiltration level among all cell types in both the OP and control groups (\u003cstrong\u003eFigure 6\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e). Comparative analysis identified three immune cell types with significantly altered abundance: activated dendritic cells, mast cells, and plasmacytoid dendritic cells were all substantially decreased in the OP group compared to controls (p \u0026lt; 0.05; \u003cstrong\u003eFigure 6\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e). Furthermore, correlation analysis demonstrated a significant positive relationship between activated dendritic cells and mast cells (cor = 0.43, p \u0026lt; 0.0001). Both \u003cem\u003eCAMKK2\u003c/em\u003e and DAPK2 showed modest but statistically significant positive correlations with activated dendritic cells (cor = 0.24, p \u0026lt; 0.05; \u003cstrong\u003eFigure 6\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cstrong\u003eAdditional file\u003c/strong\u003e\u003cstrong\u003e 13\u003c/strong\u003e). Collectively, these results suggest that immune dysregulation, characterized by specific cellular deficiencies and their relationship with these biomarkers, may contribute to OP pathology, offering new insights into immune regulation in OP development. \u003c/p\u003e\n\u003ch2\u003e3.6 Molecular docking between \u003cem\u003eCAMKK2\u003c/em\u003e, \u003cem\u003eDAPK3\u003c/em\u003e, and drugs\u003c/h2\u003e\n\u003cp\u003eTo discover candidate drugs for OP treatment, we performed a systematic drug prediction. The results identified multiple compounds, such as captopril and cephaeline, that showed potential for interacting with \u003cem\u003eCAMKK2\u003c/em\u003e and \u003cem\u003eDAPK3\u003c/em\u003e (\u003cstrong\u003eFigure 7\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cstrong\u003eAdditional file\u003c/strong\u003e\u003cstrong\u003e 14\u003c/strong\u003e). Subsequently, from a biological perspective, calcitriol, a drug that interacts with \u003cem\u003eCAMKK2\u003c/em\u003e and can promote calcium absorption and bone mineralization, was selected for molecular docking. For \u003cem\u003eDAPK3\u003c/em\u003e, danazol was chosen for molecular docking; this is a synthetic androgen derivative that has been used in some studies on postmenopausal osteoporosis to reduce bone loss (through increasing estrogen levels or decreasing bone resorption). The results of molecular docking showed that the binding ability between the drugs and the biomarkers was relatively stable, among which the binding energy of danazol and \u003cem\u003eDAPK3\u003c/em\u003e was -9.1 kcal/mol, indicating that their binding state was good (\u003cstrong\u003eFigure 7\u003c/strong\u003e\u003cstrong\u003eb-c\u003c/strong\u003e\u003cstrong\u003e and Table 2\u003c/strong\u003e). These results collectively suggest that the compounds targeting \u003cem\u003eCAMKK2\u003c/em\u003e and \u003cem\u003eDAPK3\u003c/em\u003e, identified through our drug prediction pipeline, hold significant therapeutic potential. Mechanistically, these agents may ameliorate OP progression by modulating key processes such as calcium absorption, bone mineralization, and bone loss reduction through their actions on \u003cem\u003eCAMKK2\u003c/em\u003e and \u003cem\u003eDAPK3\u003c/em\u003e. These findings thus provide a preclinical foundation for developing novel targeted therapies for OP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Table of binding energy between biomarkers and drugs\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003edrugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003egenes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCurPocket\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVina score（kcal/mol）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ecalcitriol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eCAMKK2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChainA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003edanazol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eDAPK3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eChainA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e3.7 Identification of Bone Marrow-Derived Mesenchymal Stem Cells (BM-MSCs) as a Key Cellular Player\u003c/h2\u003e\n\u003cp\u003eTo complement the bulk transcriptomic findings and investigate the cellular mechanisms underlying OP, we performed single-cell RNA sequencing (scRNA-seq) analysis. After initial quality control of the dataset containing 9,654 cells and 20,435 genes, we retained 7,497 high-quality cells and all genes for downstream processing (\u003cstrong\u003eFigure 8\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e). We then identified 2,000 highly variable genes for dimensional reduction. Based on principal component analysis (PCA) results, the top 20 significant principal components (p \u0026lt; 0.05) were selected for subsequent analyses (\u003cstrong\u003eFigures 8\u003c/strong\u003e\u003cstrong\u003eb-c\u003c/strong\u003e). UMAP clustering analysis was then performed, and 14 cell clusters were identified (\u003cstrong\u003eFigure 8\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e). Cell cluster annotation was performed using marker genes, resulting in the annotation of 6 cell types. These included B cells, neutrophils,BM-MSCs, monocytes, T cells, and nucleated red blood cells (NRBCs) (\u003cstrong\u003eFigure 8\u003c/strong\u003e\u003cstrong\u003ee\u003c/strong\u003e). A bubble plot was employed to illustrate the distinct specificity of these marker genes, thus confirming the accuracy of the cell type annotations (\u003cstrong\u003eFigure 8\u003c/strong\u003e\u003cstrong\u003ef\u003c/strong\u003e). Cell proportion analysis revealed that neutrophils, BM-MSCs, and monocytes accounted for the highest proportion (\u003cstrong\u003eFigure 8\u003c/strong\u003e\u003cstrong\u003eg\u003c/strong\u003e). Analysis of biomarker expression revealed that \u003cem\u003eCAMKK2\u003c/em\u003e and \u003cem\u003eDAPK3\u003c/em\u003e were relatively highly expressed in BM-MSCs, so BM-MSCs were selected as key cells for subsequent analyses (\u003cstrong\u003eFigure 8\u003c/strong\u003e\u003cstrong\u003eh-i\u003c/strong\u003e). Enrichment analysis across annotated cell populations revealed pronounced activity of the TWIK-related acid-sensitive K+ channel (TASK) in most cell types, including BM-MSCs. In contrast, although MGMT-mediated DNA damage reversal was highly active in the majority of cells, its activity was distinctly suppressed in BM-MSCs (adjusted p \u0026lt; 0.05; \u003cstrong\u003eFigure 8\u003c/strong\u003e\u003cstrong\u003ej\u003c/strong\u003e\u003cstrong\u003e and \u003c/strong\u003e\u003cstrong\u003eAdditional file\u003c/strong\u003e\u003cstrong\u003e 15\u003c/strong\u003e). These findings indicate that differential activity in these pathways may contribute to OP pathogenesis by disrupting core BM-MSC functions—including proliferation, differentiation, and DNA damage repair. This perspective offers valuable mechanistic insights for further investigating how cell-type-specific pathway dysregulation influences OP initiation and progression.\u003c/p\u003e\n\u003ch2\u003e3.8 Specific communications of cell types\u003c/h2\u003e\n\u003cp\u003eIntercellular communication analysis revealed an extensive interaction network among annotated cell types. BM-MSCs demonstrated particularly prominent connectivity, engaging in numerous interactions with heightened communication strength to various immune populations, including monocytes, T cells, and B cells (\u003cstrong\u003eFigure 9\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e).Analysis of receptor-ligand interactions revealed distinct communication patterns. Within the BM-MSC population, interactions were primarily mediated by the ANGPTL4–CDH11 pair. In contrast, ligand-receptor complexes such as MIF–(CD74+CXCR4) and MIF–(CD74+CD44) served as key mediators across multiple intercellular communication axes, including monocyte-to-NRBC signaling (\u003cstrong\u003eFigure 9\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e). These results indicate that dysregulated intercellular communication—manifested as imbalanced signaling between BM-MSCs and immune cells, along with disrupted ligand-receptor mediated interactions—may contribute to OP pathogenesis by impairing BM-MSC function and disturbing immune microenvironment homeostasis. This insight offers a valuable direction for further elucidating how cell-cell communication networks regulate the initiation and progression of OP.\u003c/p\u003e\n\u003ch2\u003e3.9 BM-MSCs differentiation trajectory\u003c/h2\u003e\n\u003cp\u003eSubsequently, dimensionality reduction clustering was performed on BM-MSCs, resulting in 6 cell clusters (\u003cstrong\u003eFigure 10\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e). Pseudotime analysis of BM-MSCs revealed that subcluster 0 was primarily localized at the initiation stage of developmental differentiation, subclusters 3 and 4 were in the middle stage of differentiation, subclusters 1 and 2 were at the terminal stage of differentiation, and subcluster 5 was distributed throughout the entire differentiation process. At the state level, states 1, 3, 6, and 7 belonged to the initial state of differentiation, state 5 was in the middle stage of differentiation, and state 4 was in the late stage of differentiation (\u003cstrong\u003eFigure 10\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e). Further analysis of biomarker expression patterns revealed that \u003cem\u003eCAMKK2\u003c/em\u003e exhibited multiphasic expression dynamics during BM-MSC differentiation: it was initially low in the early stage, subsequently increased, then declined, and finally re-increased during the late stage (\u003cstrong\u003eFigure 10\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e). In contrast, \u003cem\u003eDAPK3\u003c/em\u003e expression peaked during early differentiation, declined at the intermediate stage, and subsequently rebounded in the late phase (\u003cstrong\u003eFigure 10\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e). Finally, an activity analysis of TFs in different subclusters of BM-MSCs revealed that TFs such as ZNF277, GTF3A, and THAP4 exhibited strong activity in BM-MSCs subclusters 0 and 3, while their activity was suppressed in subcluster 2 (\u003cstrong\u003eFigure 10\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e). Taken together, these results indicated that BM-MSCs exhibited distinct stage division and subcluster specificity during the differentiation process. The expression of \u003cem\u003eCAMKK2\u003c/em\u003e and \u003cem\u003eDAPK3\u003c/em\u003e exhibited dynamic, stage-specific alterations during differentiation, concurrent with significant variations in transcription factor (TF) activity across different subclusters. These findings suggest that these TFs may orchestrate the precise progression of BM-MSC differentiation by temporally controlling the expression of key biomarkers such as \u003cem\u003eCAMKK2\u003c/em\u003e and \u003cem\u003eDAPK3\u003c/em\u003e. Dysregulation of this process may contribute to aberrant BM-MSC differentiation, thereby potentially underlying the pathogenesis and progression of osteoporosis (OP). These findings provided multi-level experimental evidence for deciphering the molecular regulatory network underlying abnormal BM-MSCs differentiation in OP. \u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eOsteoporosis (OP) represents a highly prevalent skeletal disorder, yet long-term pharmacological interventions face challenges including limited efficacy and adverse effects. Growing evidence indicates that sustained endoplasmic reticulum (ER) stress promotes osteoblast apoptosis, thereby playing a critical role in disrupting bone metabolic homeostasis in OP. Consequently, in-depth investigation of ER stress and its associated cell death signaling pathways offers a promising direction for elucidating disease mechanisms and developing novel therapeutic strategies.\u003c/p\u003e \u003cp\u003eThis study successfully identified \u003cem\u003eCAMKK2\u003c/em\u003e and \u003cem\u003eDAPK3\u003c/em\u003e as key biomarkers associated with endoplasmic reticulum stress-related cell death in osteoporosis. A nomogram model constructed based on these biomarkers exhibited considerable diagnostic accuracy. Furthermore, Gene Set Enrichment Analysis (GSEA) highlighted significant involvement of these genes in critical pathways, including cytokine-cytokine receptor interaction, long-term depression, and vascular endothelial growth factor signaling. Analysis of the osteoporotic immune microenvironment indicated a weak correlation between the biomarkers and activated dendritic cells. Molecular docking suggested a strong binding affinity between danazol and \u003cem\u003eDAPK3\u003c/em\u003e. At the single-cell level, BM-MSCs were identified as key cells exhibiting complex communication networks with other cell types. Furthermore, the biomarker expression levels exhibited dynamic alterations during the course of BM-MSC differentiation. In this study, we innovatively integrate the critical pathway of ER stress with complementary bulk and single-cell transcriptomic analyses. This strategy provides a systematic, multi-level perspective on osteoporotic pathogenesis, from molecular mechanisms to cellular behaviors. Our work sheds new light on the dynamic expression profiles of key biomarkers throughout BM-MSC differentiation, as well as their interplay with the immune microenvironment and cell-cell communication, thereby offering a more comprehensive understanding of the disease.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCAMKK2\u003c/em\u003e (Calcium/calmodulin-dependent protein kinase kinase 2) is a serine/threonine kinase belonging to the Ca\u0026sup2;⁺/calmodulin-dependent protein kinase family. Its activation is initiated by Ca\u0026sup2;⁺/calmodulin (CaM) binding, which induces a conformational change to expose the autocatalytic site and facilitates full activation via autophosphorylation. This kinase orchestrates critical physiological processes, including cellular metabolism and immune regulation, primarily through phosphorylation of key downstream effectors such as AMPKα, CAMK1, and CAMK4[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Our study identified a significant downregulation of \u003cem\u003eCAMKK2\u003c/em\u003e in osteoporosis (OP) patients. Previous research has demonstrated that pharmacological inhibition of \u003cem\u003eCAMKK2\u003c/em\u003e promotes fracture healing, stimulates osteoblast formation, increases bone mass, and \u003cem\u003eCAMKK2\u003c/em\u003e-knockout mice exhibit enhanced bone strength[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Furthermore, \u003cem\u003eCAMKK2\u003c/em\u003e accelerates endochondral ossification by modulating Indian hedgehog signaling[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Collectively, this evidence suggests that the observed downregulation of \u003cem\u003eCAMKK2\u003c/em\u003e in OP patients may represent an endogenous compensatory protective response, attempting to counteract bone loss by reducing \u003cem\u003eCAMKK2\u003c/em\u003e expression to stimulate osteogenic activity. However, this compensatory mechanism appears insufficient to fully protect against OP progression, which may be attributed to the following reasons: First, the degree of compensation may be inadequate. While complete pharmacological inhibition of \u003cem\u003eCAMKK2\u003c/em\u003e accelerates bone healing by approximately 20%, the natural downregulation in OP patients likely represents only partial suppression, potentially failing to reach the optimal therapeutic threshold. Second, accompanying side effects might be amplified in the pathological microenvironment. \u003cem\u003eCAMKK2\u003c/em\u003e deficiency leads to intracellular ROS accumulation[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] and autophagy dysregulation[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Increased levels of reactive oxygen species (ROS) induce endoplasmic reticulum stress, thereby initiating the unfolded protein response. Within the OP pathological context\u0026mdash;characterized by concurrent oxidative stress and inflammation\u0026mdash;these adverse effects may be exacerbated, leading to increased osteocyte apoptosis and counteracting the pro-osteogenic effects of \u003cem\u003eCAMKK2\u003c/em\u003e downregulation. Third, the complex impact on the bone microenvironment must be considered. Our analysis identified significant enrichment of \u003cem\u003eCAMKK2\u003c/em\u003e in the cytokine-cytokine receptor interaction pathway[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and the VEGF signaling pathway[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Downregulation of \u003cem\u003eCAMKK2\u003c/em\u003e may alter cytokine secretion profiles and potentially impair bone vascularization, which could disrupt the delicate balance of the bone immune microenvironment and nutrient supply during chronic OP progression. Fourth, spatiotemporal regulation may be disrupted. Our single-cell analysis revealed dynamic expression of \u003cem\u003eCAMKK2\u003c/em\u003e during BM-MSC differentiation, indicating stage-specific functions. The overall downregulation of \u003cem\u003eCAMKK2\u003c/em\u003e in OP may disrupt this precise spatiotemporal regulation, particularly affecting early-stage proliferation and lineage commitment of BM-MSCs.\u003c/p\u003e \u003cp\u003eFurthermore, \u003cem\u003eCAMKK2\u003c/em\u003e expression demonstrated a significant positive correlation with activated dendritic cells[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], suggesting its potential role in modulating immunoregulatory pathways within the bone microenvironment. Complementing this, molecular docking confirmed a robust binding affinity between \u003cem\u003eCAMKK2\u003c/em\u003e and calcitriol, implying a plausible mechanism for its involvement in vitamin D-related signaling pathways[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. This interaction suggests that variations in \u003cem\u003eCAMKK2\u003c/em\u003e expression could modulate responsiveness to vitamin D-based therapies. In summary, the downregulation of \u003cem\u003eCAMKK2\u003c/em\u003e in OP may initially represent a compensatory attempt to enhance osteogenesis. However, this protective response proves inadequate as the insufficient degree of suppression fails to counteract the amplified adverse effects within the pathological bone microenvironment, including ROS accumulation and autophagy dysregulation. The complexity is further compounded by disrupted cytokine signaling, impaired vascular regulation, and loss of spatiotemporal control over BM-MSC differentiation. These findings suggest that therapeutically targeting \u003cem\u003eCAMKK2\u003c/em\u003e requires precise modulation of its inhibition level, timing, and cell-type specificity, alongside concurrent interventions to improve the bone microenvironment through antioxidant or anti-inflammatory strategies. Such an integrated approach may effectively\u003c/p\u003e \u003cp\u003e \u003cem\u003eDAPK3\u003c/em\u003e (death-associated protein kinase 3) is a member of the death-associated protein kinase family and functions as a serine/threonine kinase. Its structure is defined by the presence of a canonical kinase domain, a leucine zipper motif, and a nuclear localization signal. It plays important roles in apoptosis, autophagy, and immune regulation[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Our analysis demonstrated that \u003cem\u003eDAPK3\u003c/em\u003e is significantly downregulated in individuals with osteoporosis. This alteration is posited to disrupt bone metabolic homeostasis via several distinct pathways. The downregulation of \u003cem\u003eDAPK3\u003c/em\u003e may promote osteocyte death through endoplasmic reticulum stress (ERS)-related pathways. Studies indicate that \u003cem\u003eDAPK3\u003c/em\u003e deficiency activates ERS and upregulates stress-associated proteins such as ATF4 and CHOP. The elevation of these proteins has been shown to induce osteoblast apoptosis via the PERK\u0026ndash;eIF2α\u0026ndash;ATF4\u0026ndash;CHOP signaling axis[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Furthermore, inhibition of \u003cem\u003eDAPK3\u003c/em\u003e can exacerbate ERS-induced apoptosis through an AMPK-mediated signaling mechanism[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Collectively, these observations imply that \u003cem\u003eDAPK3\u003c/em\u003e may function to modulate endoplasmic reticulum stress in osteoblasts under physiological contexts.Its decreased expression in OP could lead to a loss of this protection, thereby increasing cellular susceptibility to ERS. This would result in enhanced activation of the ATF4\u0026ndash;CHOP pathway, elevated osteoblast apoptosis, and ultimately, impaired bone formation. Pathway enrichment analysis indicates that \u003cem\u003eDAPK3\u003c/em\u003e is associated with the cytokine-cytokine receptor interaction pathway, highlighting its potential role in OP through immunomodulation. \u003cem\u003eDAPK3\u003c/em\u003e regulates the infiltration and activation of immune cells such as dendritic cells via the STING\u0026ndash;IFN-β pathway[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Its positive correlation with activated dendritic cells suggests that \u003cem\u003eDAPK3\u003c/em\u003e may influence osteoclast differentiation by modulating the secretion of cytokines such as IL-6 and TNF-α[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. In OP, downregulation of \u003cem\u003eDAPK3\u003c/em\u003e may attenuate signaling in the cytokine-cytokine receptor interaction pathway, disrupt cytokine network balance, and promote excessive bone resorption. Molecular docking analysis demonstrated a robust binding affinity between \u003cem\u003eDAPK3\u003c/em\u003e and danazol, a synthetic androgen derivative previously studied for its efficacy in mitigating bone loss in postmenopausal osteoporosis[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The calculated binding energy of -9.1 kcal/mol supports the potential of \u003cem\u003eDAPK3\u003c/em\u003e as a therapeutic target for osteoporosis. Danazol may function by stabilizing the structure and function of \u003cem\u003eDAPK3\u003c/em\u003e, thereby restoring its ability to suppress endoplasmic reticulum stress and modulate cytokine networks[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. This mechanism could contribute to reduced osteoblast apoptosis and improved immunomodulation in the bone microenvironment, ultimately promoting the restoration of bone metabolic balance. In summary, \u003cem\u003eDAPK3\u003c/em\u003e contributes to the pathogenesis and progression of osteoporosis by modulating endoplasmic reticulum stress-associated cell death, engaging in cytokine-mediated signaling, and regulating immune cell functions. Its downregulation is posited to compromise the homeostatic balance between osteogenesis and bone resorption. Consequently, targeting \u003cem\u003eDAPK3\u003c/em\u003e may represent a promising therapeutic avenue for OP intervention.\u003c/p\u003e \u003cp\u003eIn the pathogenesis of osteoporosis (OP), dysfunction of BM-MSCs is intricately linked to endoplasmic reticulum stress (ERS). The molecular basis for their aberrant differentiation involves crosstalk with the ER stress response, mediated by multiple signaling pathways. Studies have shown that in glucocorticoid-induced osteoporosis (GIOP), BM-MSCs exhibit enhanced adipogenic differentiation but impaired osteogenic differentiation. While this process is primarily linked to \u003cem\u003eSENP3\u003c/em\u003e-mediated deSUMOylation of PPARγ2[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], oxidative stress acts as a key driver\u0026mdash;not only upregulating \u003cem\u003eSENP3\u003c/em\u003e expression but also triggering ER stress. ROS generated during oxidative stress disrupt the protein-folding environment in the ER, activating the unfolded protein response (UPR)[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Sustained UPR signaling can further modulate the expression and function of \u003cem\u003ePPARγ2\u003c/em\u003eby influencing transcription factor activity, creating a cascade of oxidative stress\u0026ndash;ER stress\u0026ndash;enhanced adipogenesis. Our single-cell analysis revealed high activity of TWIK-related acid-sensitive potassium (TASK) channels in BM-MSCs. Ion channels play essential roles in regulating intracellular ion homeostasis and endoplasmic reticulum (ER) calcium balance, and disruption of ER calcium equilibrium is a known trigger of ER stress. Moreover, dysfunction of ion channels can impair cellular energy metabolism and mitochondrial function[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], and mitochondrial dysfunction is closely linked with ER stress. These two pathological processes can mutually exacerbate each other and collectively promote cell death. Therefore, the high activity of TASK channels may influence BM-MSC differentiation fate by modulating ion homeostasis, energy metabolism, and ER function, though the precise underlying mechanisms require further investigation. Our single-cell RNA sequencing analysis elucidates the detailed developmental progression of bone marrow mesenchymal stem cell (BM-MSC) differentiation. Pseudotime analysis revealed a clearly staged differentiation process, during which the biomarkers \u003cem\u003eCAMKK2\u003c/em\u003e and \u003cem\u003eDAPK3\u003c/em\u003e exhibited dynamic expression patterns: \u003cem\u003eCAMKK2\u003c/em\u003e expression was low in the early stage, increased subsequently, then decreased, and rose again in the late differentiation phase.In contrast, \u003cem\u003eDAPK3\u003c/em\u003e expression peaked during the initial phase, declined through the intermediate stage, and subsequently rebounded in the terminal phase. This sophisticated temporal expression pattern suggests distinct stage-specific regulatory roles for both genes during BM-MSC differentiation. Notably, we observed significant overall downregulation of \u003cem\u003eCAMKK2\u003c/em\u003e and \u003cem\u003eDAPK3\u003c/em\u003e in osteoporosis (OP) patients. Integrated with the single-cell findings, this global downregulation may disrupt the precise spatiotemporal regulation required for orderly BM-MSC differentiation. Under physiological conditions, the dynamic expression of \u003cem\u003eCAMKK2\u003c/em\u003e and \u003cem\u003eDAPK3\u003c/em\u003e likely ensures stage-appropriate signaling and functional modulation; in OP, however, their reduced expression may deprive cells of essential regulatory signals at critical differentiation stages, ultimately compromising osteogenic efficiency. Future studies employing stage-specific knockdown or overexpression experiments will be valuable to elucidate the exact functional contributions of these genes across distinct phases of BM-MSC differentiation. In summary, BM-MSC dysfunction in OP results from the interplay of oxidative stress, ion channel regulation, ER stress, and dynamic changes in differentiation-associated biomarkers. ER stress serves as a central hub that integrates ROS signaling, ion homeostasis, and metabolic status to temporally regulate the expression of \u003cem\u003eCAMKK2\u003c/em\u003e and \u003cem\u003eDAPK3\u003c/em\u003e, ultimately leading to an adipogenic\u0026ndash;osteogenic differentiation imbalance. These insights provide a rationale for novel OP treatment strategies aimed at improving ER function in BM-MSCs.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eThis study utilized integrated bioinformatics approaches to explore the pathogenesis of osteoporosis (OP), leading to the identification of \u003cem\u003eCAMKK2\u003c/em\u003e and \u003cem\u003eDAPK3\u003c/em\u003e as novel biomarkers implicated in endoplasmic reticulum stress-associated cell death. Bone marrow mesenchymal stem cells (BM-MSCs) were established as a key cellular context, offering fresh perspectives on OP mechanisms and potential treatment avenues. It is important to acknowledge, however, that these findings are primarily based on computational evidence and require further experimental validation through functional assays\u0026mdash;such as genetic manipulation studies and animal models\u0026mdash;to definitively establish the roles of \u003cem\u003eCAMKK2\u003c/em\u003e and \u003cem\u003eDAPK3\u003c/em\u003e in OP progression. Future work will focus on elucidating the complex interplay between endoplasmic reticulum stress, bone cell differentiation, and immune regulation in OP. Additional investigations involving in vitro and in vivo models are planned to substantiate these observations, with the long-term objective of translating these insights into clinically applicable therapeutic strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFull Name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eOsteoporosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEndoplasmic Reticulum\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eERS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEndoplasmic Reticulum Stress\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eROS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReactive Oxygen Species\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003escRNA-seq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSingle-cell RNA sequencing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGEO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGene Expression Omnibus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBone Mineral Density\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDEGs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDifferentially Expressed Genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eProtein-Protein Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGene Ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLASSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLeast Absolute Shrinkage and Selection Operator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSVM-RFE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSupport Vector Machine-Recursive Feature Elimination\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRandom Forest\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eArea Under the Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDecision Curve Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGene Set Enrichment Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMSigDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMolecular Signatures Database\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNormalized Enrichment Score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003emiRNAs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003emicroRNAs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTFs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTranscription Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003essGSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003esingle-sample Gene Set Enrichment Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDsigDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDrug-Signature Database\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eProtein Data Bank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePrincipal Component Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUMAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUniform Manifold Approximation and Projection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBM-MSCs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBone Marrow-Derived Mesenchymal Stem Cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNRBCs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNucleated Red Blood Cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTASK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTWIK-related acid-sensitive K+ channel\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVIPER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eVariation of Information-based Pathway Enrichment in RNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRT-qPCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReverse Transcription Quantitative Polymerase Chain Reaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ecDNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eComplementary DNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGIOP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGlucocorticoid-induced Osteoporosis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Medical Ethics Committee of Yunnan Provincial Hospital of Traditional Chinese Medicine. The approval number and date of approval are as follows: 2025-KY-018-01 and November 27, 2025. All patients provided written informed consent when clinical samples were collected for RT-qPCR experiments to ensure that the research process was in accordance with ethical norms and that the patients' rights and xwishes were fully respected. All procedures involved in this study strictly adhered to the ethical principles outlined in the Declaration of Helsinki for research involving human participants.This study strictly adheres to ethical guidelines. The researchers comprehensively and clearly explain the study's purpose, procedures, risks, and benefits to potential participants to ensure full comprehension. Ample time for consideration and opportunities for questions are provided. Upon obtaining explicit and voluntary agreement, a written informed consent form is jointly signed, with a copy provided to the participant for their records. The entire process respects the participant's autonomy and safeguards their right to withdraw at any time without giving a reason.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are openly available in [Gene Expression Omnibus] at [https://www.ncbi.nlm.nih.gov/geo/], reference number [GSE56815, GSE56814, and GSE147287].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report there are no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Joint Fund Project of Yunnan University of Traditional Chinese Medicine and Its Colleges and Departments [grant number: XYLH202347]; and the 2025 Key Clinical Specialty in Traditional Chinese Medicine - Orthopedics [grant number: Yun Cai She (2025) No. 92]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYifeng Xia: Conceptualization, Data curation, Validation, Visualization, Writing–original draft, Writing–review \u0026amp; editing. Zhongyu Peng: Data curation, Validation, Visualization, Writing–review \u0026amp; editing. Lingrui Zhao: Visualization, Writing–review \u0026amp; editing.Yuan Long: Validation, Writing–review \u0026amp; editing. Renwei Chen: Clinical blood sample collection. Jiahao Dong: Clinical blood sample collection.Meixiang Chu: Conceptualization, Writing–review \u0026amp; editing. Weijie Yu: Conceptualization, Supervision, Writing–review \u0026amp; editing. Tao Chen: Conceptualization, Project administration, Supervision, Writing–review \u0026amp; editing. This work currently described has not been published, is not being considered for publication elsewhere, and its publication was approved by all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere gratitude to all individuals and organizations who supported and assisted us throughout this research. In conclusion, we extend our thanks to everyone who has supported and assisted us along the way. Without your support, this research would not have been possible.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRamchand, S. K. \u0026amp; Leder, B. Z. Sequential Therapy for the Long-Term Treatment of Postmenopausal Osteoporosis. \u003cem\u003eJ. Clin. Endocrinol. 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TWIK-related acid-sensitive K(+) channel 2 promotes renal fibrosis by inducing cell-cycle arrest. \u003cem\u003eiScience\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 105620 (2022).\u003c/span\u003e\u003c/li\u003e\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":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"osteoporosis, endoplasmic reticulum stress-related cell death, single-cell RNA sequencing, machine learning, bone marrow-derived mesenchymal stem cells","lastPublishedDoi":"10.21203/rs.3.rs-8454279/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8454279/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eOsteoporosis (OP) is a metabolic bone disease characterized by low bone mineral density (BMD), and its pathogenesis involves endoplasmic reticulum (ER) stress-related cell death. This study aimed to identify diagnostic biomarkers associated with ER stress-related cell death in OP and explore their underlying mechanisms.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eThe training dataset (GSE56815), validation dataset (GSE56814), and single-cell RNA sequencing (scRNA-seq) dataset (GSE147287) were downloaded. Differentially expressed genes (DEGs) between OP patients and controls were identified. Candidate genes were obtained by intersecting DEGs with ER stress-related genes and programmed cell death (PCD)-related genes. Machine learning was used to screen intersection genes, and biomarkers were determined via expression level analysis. Gene set enrichment analysis (GSEA), immune cell infiltration analysis, drug prediction and molecular docking, scRNA-seq analysis, key cell screening, cell communication analysis, and pseudotime analysis were performed. Finally, reverse transcription quantitative polymerase chain reaction (RT-qPCR) were further conducted.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 28 candidate genes were obtained by intersection. \u003cem\u003eCAMKK2\u003c/em\u003e and \u003cem\u003eDAPK3\u003c/em\u003e were confirmed as biomarkers, and were consistently down-regulated in both datasets and verified by RT-qPCR. GSEA analysis revealed that biomarkers were enriched in cytokine-cytokine receptor interaction. Correlations between biomarkers and activated dendritic cells were found via immune cell infiltration analysis. Drugs like calcitriol and danazol were predicted to bind stably to biomarkers. Bone marrow-derived mesenchymal stem cells (BM-MSCs) were identified as key cells via scRNA-seq analysis. Complex interactions involving BM-MSCs, such as ANGPTL4-CDH11 mediating BM-MSC self-communication, were revealed by cell communication analysis. Dynamic expression of biomarkers during BM-MSC differentiation was shown by pseudotime analysis: \u003cem\u003eCAMKK2\u003c/em\u003e fluctuated with differentiation stages, while \u003cem\u003eDAPK3\u003c/em\u003e shifted from high to low then high expression.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003e \u003cem\u003eCAMKK2\u003c/em\u003e and \u003cem\u003eDAPK3\u003c/em\u003e were confirmed as diagnostic biomarkers for OP, providing insights into OP diagnosis and potential therapeutic targets.\u003c/p\u003e","manuscriptTitle":"Identification of biomarkers associated with endoplasmic reticulum stress-related cell death in osteoporosis based on bulk and single-cell transcriptomic analyses and experimental validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 06:20:36","doi":"10.21203/rs.3.rs-8454279/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-14T05:24:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-11T06:41:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"331177547163986021872924773274935316187","date":"2026-01-11T06:26:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-09T21:41:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316082850509672022868206495011325887748","date":"2026-01-09T15:26:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-08T05:35:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"185341017985033312528502478690359861091","date":"2026-01-07T22:32:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-07T15:13:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-07T15:10:02+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-07T13:42:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-05T07:44:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-05T07:08:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c25ef206-2931-4137-8ead-c02e35ff8d47","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":60844018,"name":"Health sciences/Biomarkers"},{"id":60844019,"name":"Biological sciences/Cell biology"},{"id":60844020,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":60844021,"name":"Health sciences/Diseases"}],"tags":[],"updatedAt":"2026-04-07T16:05:04+00:00","versionOfRecord":{"articleIdentity":"rs-8454279","link":"https://doi.org/10.1038/s41598-026-43744-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-03-30 15:58:52","publishedOnDateReadable":"March 30th, 2026"},"versionCreatedAt":"2026-01-12 06:20:36","video":"","vorDoi":"10.1038/s41598-026-43744-w","vorDoiUrl":"https://doi.org/10.1038/s41598-026-43744-w","workflowStages":[]},"version":"v1","identity":"rs-8454279","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8454279","identity":"rs-8454279","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}