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Elucidating the role of FBXW4 in osteoporosis: integrating bioinformatics and machine learning for advanced insight

BMC Pharmacology and Toxicology volume 26, Article number: 20 (2025) Cite this article

Abstract

Background

Osteoporosis (OP), often termed the “silent epidemic,” poses a substantial public health burden. Emerging insights into the molecular functions of FBXW4 have spurred interest in its potential roles across various diseases.

Methods

This study explored FBXW4 by integrating DEGs from GEO datasets GSE2208, GSE7158, GSE56815, and GSE35956 with immune-related gene compilations from the ImmPort repository. Gene selection was refined using advanced approaches, including LASSO regression and SVM-RFE. Functional enrichment of FBXW4-associated genes was assessed via GSEA and GSVA, identifying significant immune pathway involvement. Immune-related biological processes linked to FBXW4 expression were further evaluated using CIBERSORT and ESTIMATE algorithms. Validation of FBXW4 expression was performed using GSE35956.

Results

A total of 13 hub genes were selected through LASSO and SVM-RFE analyses. Functional assays implicated FBXW4 in antiviral defense, cytokine production, and immune response modulation. Notably, FBXW4 expression correlated positively with several immune cell subsets, including memory B cells, activated memory CD4+ T cells, naive B cells, gamma delta T cells, M0 macrophages, follicular helper T cells, and naive CD4+ T cells, while showing a negative association with neutrophils.

Conclusions

This study uncovers a complex interplay between FBXW4 and immune processes in osteoporosis, suggesting its potential utility as a biomarker for OP diagnosis and monitoring. These findings lay the groundwork for future investigations into the therapeutic and diagnostic potential of FBXW4 in OP.

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Introduction

Osteoporosis (OP) represents a major global health challenge, characterized by a significant reduction in bone mass and deterioration of bone microarchitecture, leading to increased bone fragility and fracture risk [1]. The disease predominantly affects older populations, particularly postmenopausal women, contributing to diminished stature, chronic pain, and an elevated risk of fractures, especially in the spine, hips, and wrists. The pathophysiology of OP is driven by an imbalance in bone remodeling, wherein bone resorption outpaces bone formation [2]. This dysregulation is influenced by a complex interplay of genetic factors, hormonal fluctuations—chiefly the decline in estrogen levels—lifestyle factors, and nutritional deficiencies [3]. Estrogen deficiency accelerates bone resorption while inhibiting bone formation, profoundly affecting osteocyte function and survival. Clinically, dual-energy X-ray absorptiometry (DEXA) remains the gold standard for OP diagnosis, providing accurate measurements of bone mineral density [4]. The assessment of specific biomarkers further enhances our ability to monitor bone metabolism and predict disease progression. Current therapeutic approaches to OP involve a combination of pharmacological treatments, lifestyle interventions, and preventive strategies [5]. Pharmacotherapies, including bisphosphonates, selective estrogen receptor modulators (SERMs), parathyroid hormone analogs, and novel monoclonal antibodies, aim to either slow bone loss or promote bone formation [6]. Concurrently, lifestyle modifications—such as increased physical activity and optimized intake of calcium and vitamin D—play a crucial role in disease management [7]. In conclusion, effective OP management requires a multifaceted approach, integrating targeted pharmacological treatments, personalized lifestyle changes, and regular monitoring. As our understanding of the molecular mechanisms underlying OP continues to expand, future therapeutic strategies are likely to become more personalized, offering enhanced precision and efficacy in the management of the disease.

FBXW4 was initially discovered through the application of genomics and proteomics technologies. Early studies indicated that FBXW4 plays a significant role in cell cycle regulation, but its specific molecular mechanisms remained unclear. With the development of molecular biology and cell biology techniques, researchers have gradually elucidated the structure and function of FBXW4, uncovering its roles in various biological processes [8]. FBXW4 is a multifunctional protein, characterized by the presence of F-box domain and WD40 repeat domain. The F-box domain enables it to form E3 ubiquitin ligase complexes with Skp1 and Cullin1, thereby mediating the ubiquitination and degradation of target proteins [9]. The WD40 repeat domain, on the other hand, is involved in the binding of FBXW4 to target proteins. Through the action of these structural domains, FBXW4 regulates a myriad of cellular processes, including cell cycle regulation, apoptosis, and cell signaling transduction [10]. FBXW4 exerts control over various cellular processes by ubiquitinating target proteins. In cell cycle regulation, FBXW4 participates in the degradation of cell cycle proteins, thereby modulating the progression of the cell cycle. In apoptosis regulation, FBXW4 regulates the occurrence of cell apoptosis by ubiquitinating apoptosis-related proteins [11]. Additionally, FBXW4 is involved in the regulation of multiple signaling pathways, influencing cell function and fate. Recent studies have demonstrated the significant roles of FBXW4 in various diseases [12]. For example, in the development of tumors, abnormal expression of FBXW4 is closely associated with the imbalance of tumor cell proliferation and apoptosis regulation. Moreover, FBXW4 is also implicated in the development of cardiovascular diseases, neurological disorders, and other conditions [13]. FBXW4 emerges as a molecule of interest due to its potential roles in immune regulation and bone homeostasis. Recent studies have linked FBXW4 expression to immune-related pathways and cell populations, highlighting its possible involvement in the pathophysiology of OP. These findings suggest FBXW4 as a promising biomarker for OP diagnosis and monitoring, offering new avenues for research into its mechanistic contributions to disease progression.

Recent advancements in oncological research have revealed a distinct metabolic phenotype in neoplastic cells, which significantly influences the immune microenvironment—an intricate and heterogeneous ecosystem constrained by limited nutrient and oxygen supply due to poor vascularization [14]. This evolving understanding has shifted focus towards the role of non-neoplastic immune components in shaping tumor progression. Pioneering studies, such as those by Sharma et al., have highlighted the immunosuppressive nature of this environment, which harbors a diverse array of immune cell populations that drive extrinsic mechanisms conferring resistance to immunotherapies [15, 16]. These therapies often involve the secretion of factors that activate immune checkpoints, hindering the effector functions of cytotoxic T lymphocytes (CTLs) through interactions with CTLA4 and PD-1 inhibitors [17]. Evidence suggests that tumors exploit sophisticated immunosuppressive strategies, such as the release of immunomodulatory cytokines by regulatory T cells and the activation of inhibitory checkpoints like PD-1, CTLA4, and TIM-3 by myeloid-derived suppressor cells and stromal cells, thus facilitating immune evasion, uncontrolled tumor growth, and diminishing the efficacy of immune checkpoint inhibitors (ICIs). Despite these insights, the relationship between this immune milieu and OP remains largely unexplored, representing a critical avenue for further investigation [18]. The integration of high-throughput datasets and bioinformatics has revolutionized our ability to investigate gene function networks across diverse diseases, offering valuable insights into the molecular mechanisms at play. The wealth of transcriptomic data, combined with clinical perspectives from initiatives such as the OP Initiative, allows for a comprehensive examination of disrupted transcriptional landscapes and associated molecular cascades in OP, advancing our biological understanding of the disease [19, 20]. This study seeks to address this gap by analyzing OP-related datasets from the GEO repository, focusing on the intricate molecular terrain of OP to enhance our understanding of its underlying mechanisms (Fig. 1).

Fig. 1
figure 1

Framework

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Materials and methods

We used the approaches proposed by Zi-Xuan Wu, et al. 2024 [21].

Transcriptional profiling data and determination of differentially expressed genes (DEGs)

This investigation employed datasets GSE2208, GSE7158, GSE56815, and GSE35956, utilizing the GPL96 and GPL570 platforms from GEO database [22] (Table S1). Datasets GSE2208, GSE7158, and GSE56815 were designated for the training phase, while GSE35956 was reserved for testing purposes (Table 1). In cases where multiple probes corresponded to a single gene, the arithmetic mean of these probe values was calculated to represent the gene’s final expression level. The Sva and Limma [23] of R4.1.0 [24] were then employed exclusively for multi-chips data rectification (batch normalize). Subsequent to the standardization of the datasets, batch effect normalization was executed employing the SVA package. The efficacy of batch effect rectification was gauged through PCA (Principal Component Analysis). Differential expression analyses between OP and control groups were conducted utilizing the Linear Models for Microarray Data (limma) package. Criteria for defining DEGs were set at an absolute log fold change (|log FC|) greater than 1 and an adjusted p-value less than 0.05, with the aim of isolating immune infiltration-associated genes in OP cases.

Table 1 The clinical characteristics of patients
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Predictive modeling and computational learning

In our endeavor to construct a predictive model with unparalleled precision and reliability, we adopted the glmnet package [25] to implement Lasso regression, enhanced through rigorous cross-validation. This strategy effectively mitigated overfitting and heightened the model’s predictive accuracy across complex biological datasets. To extend our validation, we employed the advanced SVM-RFE algorithm via the e1071 package, meticulously engineering a robust machine learning model. Cross-validation was pivotal in scrutinizing the model’s error rates and precision, bolstering its robustness and dependability. Further enhancing our analysis, the Random Forest algorithm analysis was also used It generated numerous decision trees and integrated their outcomes, thereby reducing overfitting and augmenting the model’s generalization. A salient feature of this method—random selection of features and bootstrap sampling—enriched the diversity among decision trees, thereby elevating the overall model accuracy [26, 27]. Then, the DEGs obtained before were calculated by different algorithms, and then intersected by VNN package to obtain the key genes, so as to construct the model. Utilizing the randomForest and ggplot2 packages, our focus on the analysis of DEGs, pinpointing pivotal genes for OP. In the final phase, we assessed the importance of these genes through an integrated approach that amalgamated insights from Lasso regression and SVM models. The genes identified through these comprehensive methodologies are now primed for further exploratory analysis. The AUC (area under the curve) value of 1.0 denotes an ideal diagnostic test, whereas an AUC close to 0.5 indicates a lack of discriminative power, equating to random chance. This metric is particularly valuable for assessing the diagnostic accuracy of medical tests and the predictive reliability of models at different thresholds. In our analysis, we utilized the R pROC package to integrate and evaluate the dataset combining OP outcomes with pivotal genes to assess their predictive accuracy. Additionally, the dataset GSE105149 was employed to validate these findings. Through the ROC curve, we established a robust methodological framework for evaluating the diagnostic performance of these biomarkers, thus enhancing our understanding of their potential utility in clinical settings.

Functional enrichment analysis

To elucidate the biological functions and signaling pathways involved in the differential expression landscape, we conducted GO and KEGG analyses. Using the R statistical environment, we explored how variations in FBXW4 expression influence BP, MF, and CC. Global gene-set enrichment analyses, including GSEA and GSVA, were employed to identify functionally coherent gene sets and signaling pathways differentially active in the studied samples. Enrichment scores and visual representations were generated to reveal dynamic activities and pathways across different risk stratifications. The R environment was used to investigate the impact of differential FBXW4 expression on BP, MF, CC, and related pathways.

Active components-targets predictive

FBXW4 were docked to verify the accuracy of principal components and prediction targets. The protein configurations of the core targets were obtained from the Uniprot database by using the minimum resolution (Resolution) and the source (Method) as X-ray as the screening condition, and the crystal structure of these protein configurations were obtained from the RCSB PDB database). 2D structures of active components of core targets were obtained from PubChen database, and these 2D structures were minimized by chem3d software. The binding strength and activity of active components and targets were evaluated by SYBYL2.0 software, and the active components of binding TotalScore greater than 3 were selected for sub-docking.

Biomarker-immune infiltrate and miRNA-lncRNA network

Spearman’s rank correlation was employed to assess the relationship between diagnostic biomarkers and immune cell infiltration within the tissue microenvironment. We used R’s limma, GSVA, GSEABase, ggpubr, reshape2 packages to match and calculate FBXW4 and immune-related data. These included removal of control samples, ssgsea analysis and correction of scores for immune-related functions. Finally, the samples will be grouped according to the FBXW4. Target gene information for the common miRNAs and lncRNAs was obtained from miRTarbase and PrognoScan databases. These databases include miRanda [28], miRDB [29], and TargetScan [30]. When the corresponding database matched the relevant miRNA, the score was marked as 1. It can be seen that when all three databases can be matched, it is 3 points. The miRNA was matched by spongeScan database [31] to obtain the corresponding lncRNA data. An integrated regulatory network, highlighting the interplay between mRNAs, miRNAs, and lncRNAs along with their shared targets in OP, was constructed and visualized using Cytoscape software. In addition, to unravel the fundamental mechanisms underlying FBXW4, we constructed a comprehensive Gene regulatory networks from GeneMANIA.

Establishing causality via mendelian randomization

In our endeavor to ascertain the non-confounded relationship between genetic predispositions and OP incidence, a Mendelian randomization study was conducted, leveraging the TwoSampleMR package within R. This analysis aimed to explore the potential causal linkage between FBXW4 gene expression—designated as the exposure variable—and OP, identified as the outcome of interest. (1) IV Selection: We pinpointed FBXW4 expressions closely linked to the exposure, employing a stringent significance cutoff of P < 5 × 10−8 to ensure the relevance of the chosen genetic instruments. (2) Ensuring Independence of IVs: To ascertain the independence of SNPs serving as risk factors, we utilized the PLINK clustering methodology to examine LD. SNPs were screened for LD, with those exhibiting an LD coefficient (r2) greater than 0.001 or a physical proximity of under 10,000 kilobases being excluded. This step was crucial to uphold the independence of SNPs and mitigate the risk of pleiotropy that could confound causal inferences. (3) Statistical Robustness of IVs: The integrity of each instrumental variable was scrutinized through the calculation of the F-statistic (F = β2/SE2), where β represents the effect size of the allele, and SE is the standard error. Instrumental variables demonstrating an F-value less than 10 were disregarded to reduce the likelihood of bias introduced by latent confounders.

Results

DEG identification and principal component analysis

We integrated GSE2208, GSE7158, GSE56815, and GSE35956 and conducted batch match evidence integration. PCA corroborated the successful demarcation of patients into risk-specific cohorts (Fig. 2a, b). Among the 4923 DEGs, some DEGs were found to be significantly different. In addition, Some genes cluster in the treat group and some in the control group. Treat: ARHGAP6, ABAT, MEIS3P1, HNMT, AMD1, BLVRA, JAK2, AHR, KIAA1598, NBN, TLE4, etc. Control: PPP2R1A, GRHPR, MLF2, CNPY3, LSM7, MRPL33, CNOT8, FYN, PRKCSH, CST3, SEPT9, etc. (Fig. 2c). Some of these DEGs were significantly up-regulated (FOS, FTH1, HIST1H2AC, ITM2B, NRGN, SRGN, etc). However, some genes were significantly down-regulated (TLR4, TMEM39B, FZD2, NUP54, MMRN1, FBXW4, SLC25A32, etc) (Fig. 2d, Table S1).

Fig. 2
figure 2

Principal component analysis. a, b Analysis of PCA. This means that our analysis can divide the samples into two groups well, and there will be no large bias in the results due to most of the data overlap. That is, our results are stable and credible. c Heatmap. Red represents high expression and blue represents low expression. That is, the darker the red, the more enriched the expression in this group. d Volcano map. logFC was used as the dividing line for calculation. Red indicates high expression and green indicates low expression. The figure shows that: Some of these DEGs were significantly up-regulated (FOS, FTH1, HIST1H2AC, ITM2B, NRGN, SRGN, etc). However, some genes were significantly down-regulated (TLR4, TMEM39B, FZD2, NUP54, MMRN1, FBXW4, SLC25A32, etc)

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Construction of the model

To construct a robust gene signature for OP, we employed LASSO and Cox regression analyses to optimize gene selection, as demonstrated in Fig. 3a and b. Subsequently, the SVM-RFE technique was utilized to develop a machine learning model, confirming the model’s high accuracy and reliability with an accuracy rate of 0.744 and an error rate of 0.256 (Fig. 3c and d). Further investigation using Random Forest analysis identified several key genes (Fig. 3e). These DEGs were then analyzed through a comprehensive approach using Lasso regression, SVM-RFE, and Random Forest algorithms. This integrated analytical strategy successfully pinpointed 13 critical hub genes, confirmed by consensus among the outputs from the three methodologies (Fig. 3f; Table S2, Table 2). Based on these results, FBXW4 was selected for in-depth analysis, underlining its potential significance in the pathophysiology of OP.

Fig. 3
figure 3

The development of the signature. a Regression of the OP-related genes using LASSO. b Cross-validation is used in the LASSO regression to fine-tune parameter selection. c, d Accuracy and error of this model. e Random forest analysis. f Venn

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Table 2 The characteristics of model
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DEG identification and visualization

We visualized these 13 hub genes in the OP group and the normal sample group respectively (Fig. 4). In the confirmation of 13 hub genes, we analyzed the ROC of these genes, showing that the accuracy of these genes is high. ARL4C (AUC: 0.796), ZNF212 (AUC: 0.522), ARPC5L (AUC: 0.566), CDC7 (AUC: 0.600), CYBB (AUC: 0.678), DNAJC24 (AUC: 0.592), FBXW4 (AUC: 0.594), FKBP1B (AUC: 0.702), GPRC5D (AUC: 0.554), RUNX3 (AUC: 0.727), SLC39A7 (AUC: 0.528), SMARCD1 (AUC: 0.618), SRSF5 (AUC: 0.668) (Fig. 5).

Fig. 4
figure 4

Expression of 13 hub genes in OP group and normal sample group respectively

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Fig. 5
figure 5

ROC of 13 hub genes

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Validation of hub genes

GSE35956 was used for validation to boost our model’s confidence and prediction accuracy of these hub genes. What’s interesting is that these DEGs are showed significant differences in GSE35956 analysis (Fig. 6a). In the GSE35956 analysis of 13 hub genes, we analyzed the ROC of these genes, showing that the accuracy of these genes is high. ARL4C (AUC: 1.000), ZNF212 (AUC: 0.960), ARPC5L (AUC: 0.840), CDC7 (AUC:0.960), CYBB (AUC: 1.000), DNAJC24 (AUC: 0.880), FBXW4 (AUC: 0.920), FKBP1B (AUC: 1.000), GPRC5D (AUC: 0.840), RUNX3 (AUC: 0.920), SLC39A7 (AUC: 0.920), SMARCD1 (AUC: 1.000), SRSF5 (AUC: 1.000). These results also confirmed the high reliability and accuracy of our model (Fig. 6b).

Fig. 6
figure 6

Validation of hub genes. a Expression of 13 hub genes in GSE35956 analysis. b ROC of 13 hub genes

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Differential expression and enrichment analysis analysis centered on FBXW4

FBXW4 was selected as a key investigative gene to determine its unique contributions to OP. Utilizing differential expression analysis focused on this gene, we identified 23 DEGs linked to FBXW4, as illustrated in Fig. 7. These DEGs varied significantly in expression, with certain genes predominating in distinct expression clusters. The ‘high’ expression cluster included notable genes such as PIDD1, MST1, HIP1R, GNB3, IGFBP3, DCN, TIMP3, etc. Conversely, the ‘low’ expression cluster comprised genes like SDPR, TUBB1, PRKAR2B, BCL2A1, SAT1, PTPRC, EVI2A, etc (Fig. 7a, b). Additionally, we developed a correlation matrix to further explore the relationships between FBXW4 and these DEGs, providing a detailed visualization of these associations (Fig. 7c) and catalogued in Supplementary Table S3. This analysis not only highlights the differential roles of FBXW4 in OP but also maps its potential regulatory network, offering insights into its biological and pathological roles. GO enrichment identified 8 principal targets categorized under MF and BP. The MF category was predominantly associated with spectrin binding (GO:0030507), phosphatidylinositol-3,4,5-trisphosphate binding (GO:0005547). BPs included response to response to extrinsic apoptotic signaling pathway (GO:0097191), regulation of glucose metabolic process (GO:0010906), long-term synaptic potentiation (GO:0060291). CC was predominantly associated with collagen-containing extracellular matrix (GO:0062023), dendritic spine (GO:0043197), neuron spine (GO:0044309). Additionally, KEGG pathway analysis highlighted significant involvement of overexpressed genes in pathways such as Transcriptional misregulation in cancer (hsa05202), Apoptosis (hsa04210), NF-kappa B signaling pathway (hsa04064). These findings, illustrated in Fig. 7d, e and Table S4a–b, provide insights into the molecular mechanisms through which FBXW4 influences immune and inflammatory responses, potentially impacting OP pathology.

Fig. 7
figure 7

DEG identification and enrichment analysis of FBXW4. a Heatmap. b Volcano map. c Correlation matrix diagram. d The GO circle illustrates the barplot, chord, circos, and cluster of the selected gene’s logFC. e The KEGG barplot, chord, circos, and cluster illustrates the scatter map of the logFC of the indicated gene

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GSEA of analysis

To elucidate the biological functions impacted by differential expression of FBXW4, we applied GSEA using an array of computational tools including limma for differential expression analysis, org.Hs.eg.db for gene annotation, clusterProfiler and enrichplot for visualization of enrichment results. GSEA facilitated the identification of significant functional alterations among the DEGs linked to FBXW4. In the analysis of GO categories for the high expression group, enrichment was BP animal organ morphogenesis, BP odontogenesis, CC cytosolic ribosome. Conversely, in the low expression group, functional enrichments were noted in BP neutrophil chemotaxis, BP neutrophil migration, MF immune receptor activity (Fig. 8a). KEGG pathway analysis revealed that high FBXW4 expression groups showed significant enrichment in pathways like p53 signaling pathway, purine metabolism, ribosome. Low expression groups exhibited enrichment in pathways associated with fc gamma r mediated phagocytosis, hematopoietic cell lineage, leishmania infection (Fig. 8b, Table S5).

Fig. 8
figure 8

GSEA of analysis in FBXW4. a GO. b KEGG

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GSVA of analysis

For the GSVA, we utilized tools such as reshape2 for data restructuring, ggpubr for publication-quality visualizations, along with limma, GSEABase, and GSVA packages for robust analytical assessments. This analysis aimed to pinpoint functional alterations in the FBXW4 DEGs. In GO terms, the high expression group showed enrichment in processes such as BP regulation of sulfur metabolic process, BP cellular response to thyroid hormone stimulus, BP nk t cell differentiation, BP melanosome assembly, BP enucleate erythrocyte differentiation, BP platelet dense granule organization, BP n acylethanolamine metabolic process, BP pigment granule organization (Fig. 9a). KEGG analysis highlighted enrichment in pathways including in prostate cancer, pancreatic cancer, nod like receptor signaling pathway, apoptosis, regulation of actin cytoskeleton, wnt signaling pathway, mapk signaling pathway (Fig. 9b).

Fig. 9
figure 9

GSVA of analysis in FBXW4. a GO. b KEGG

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Immune landscape characterization

Figures 10 explore the immune landscape of OP, with a particular focus on FBXW4 as a pivotal gene for investigating its role within the immune contexture of the disease. This analysis provides vital insights into patterns of immune infiltration, underscoring the immunological factors critical to the initiation and progression of OP. The analysis revealed significant disparities in immune cell infiltration linked to risk profiles associated with FBXW4 expression. In the FBXW4-defined cohorts, marked differences were observed in the infiltration levels of APC co inhibition, CCR, CD8+ T cell, Inflammation-promoting, T cell co-inhibition, TIL, Type II IFN Reponse between the low and high-risk groups. These variances highlight a complex immune modulation in different risk strata. Conversely, APC co stimulation, B cells, aDCs, Check-point, Cytolytic activity, HLA, iDCs, Macrophages did not exhibit significant differences in infiltration between the risk groups, indicating a consistent involvement across the spectrum (P > 0.05) as detailed in Fig. 10a. This dichotomy in immune cell behavior underscores the nuanced role of FBXW4 in modulating the immune environment in OP. Dendritic cells resting, T cells CD4 memory activated, T cells regulatory (Tregs), Mast cells resting, Neutrophils were highly expressed in the treat group. While, T cells CD8, T cells follicular helper, Macrophages M2, Mast cells activated, Eosinophils were highly expressed in the Control group (Fig. 10b). In addition, we also constructed an immune infiltration correlation rectangle plot and heatmap (Fig. 10c, d). Through PCA analysis, immune-based patient categorization was again successfully executed (Fig. 10e). A Lollipop was created to display the expression patterns of Correlation Coefficient. B cells memory, T cells CD4 memory activated, B cells naive, T cells gamma delta, Macrophages M0, T cells follicular helper, T cells CD4 naive (Fig. 10f). Neutrophils was shown to be negatively associated with FBXW4 (Fig. 10g; Table S6).

Fig. 10
figure 10

Immune landscape characterization. a Expression of immune function. b Expression of immune cells. c Correlation rectangle plot. d Heatmap. e PCA analysis. f The expression patterns of Correlation Coefficient. g Immune infiltration analyses

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Identification of common RNAs and construction of miRNAs-LncRNAs shared genes network

Figure 11 uses FBXW4 as a hub gene to investigate its expression dynamics within OP-associated miRNAs and lncRNAs, aiming to delineate its regulatory network. Three databases were searched for 7 miRNAs and 14 lncRNAs linked with OP (Table S7a–b). The network of miRNAs-lncRNAs-genes was constructed by taking the intersection of them and shared genes (obtained by Lasso regression and SVM-RFE). Finally, the miRNAs-genes network included 14 lncRNAs (RP5-894D12.5, C10orf91, RP11-627G23.1, CTB-51J22.1, RP11-94C24.13, RP13-895J2.3, RP13-582L3.4, etc), 3 miRNAs (hsa-miR-665, hsa-miR-486-3p, hsa-miR-125a-5p) (Fig. 11; Table S7).

Fig. 11
figure 11

miRNAs-LncRNAs shared genes network. Note Red circles are mrnas, blue quadrangles are miRNAs, and green triangles are lncRNAs

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Drug enrichment analysis and molecular docking

These drugs were enriched in Ionomycin, CHEMBL212299, Kinome 192, Bisulfite Bisulfit, Kinome 248 (Fig. 12a, b). In addition, we also constructed the regulatory network of these drugs to make the results more intuitive (Fig. 12c). FBXW4 was docked to verify the accuracy of principal components and prediction targets (Fig. 12d; Table 3).

Fig. 12
figure 12

Drug enrichment analysis and molecular docking. a, b GO. c Network pf drugs. d FBXW4

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Table 3 Molecular docking results of FBXW4
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Mendelian randomization analysis

In examining the direct linkage between the FBXW4 and OP incidence, a forest plot was utilized for visual illustration, revealing a general symmetry in the data. Through sensitivity analysis employing the “leave-one-out” technique, it was determined that the omission of any individual SNP had a minimal effect on the results of the inverse variance-weighted (IVW) analysis, indicating that the remaining SNPs closely mirrored the overall dataset’s findings. To further authenticate our outcomes, MR-Egger regression analysis was conducted, bolstering the integrity and reliability of our results and the chosen analytical framework (Fig. 13a–d).

Fig. 13
figure 13

Mendelian randomization analysis. a Correlation rectangle plot. b Heatmap. c The expression patterns of Correlation Coefficient. d SNP effect on insomnia

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Discussions

Positioned as a premier metabolic bone ailment, OP stands as the fourth most consequential chronic infirmity, wielding societal and economic ramifications surpassed solely by cardiovascular maladies, dementia, and lung carcinoma [32]. Its insidious progression culminating in incapacitating fractures accentuates the imperative for biomarkers facilitating early detection. The malady emanates from a multifaceted interplay of lifestyle predilections, comorbid ailments, environmental incursions, and hormonal vicissitudes [33]. Moreover, environmental pollutants, such as heavy metals and endocrine-disrupting agents, further imperil osseous integrity by perturbing hormonal equilibrium or directly impinging upon the architectural integrities of bones [34]. This panoply of contributory elements underscores the exigency of a holistic approach in OP’s tripartite realms of prophylaxis, diagnosis, and therapeutic intervention, with the overarching aim of attenuating its pervasive toll on public health [35]. In the context of bone physiology, FBXW4 emerges as a pivotal player modulating osteoblast and osteoclast activity, thus exerting a discernible impact on bone remodeling dynamics [36]. Osteoblasts, responsible for bone formation, undergo intricate regulatory processes mediated by FBXW4, influencing their proliferation, differentiation, and mineralization capacities [37]. Concurrently, FBXW4-mediated ubiquitination events regulate the turnover of critical factors involved in osteoclastogenesis, thereby delicately balancing bone resorption and formation processes [8]. This inquiry harnesses the potency of bioinformatics to amalgamate model construction with computational scrutiny, thereby laying the groundwork for ensuing fundamental and clinical explorations. This methodological paradigm seeks to untangle the intricate tapestry of OP and transmute these discernments into efficacious therapeutic modalities and clinically germane practices.

In the context of OP, our comprehensive integrative analysis identified 23 DEGs associated with FBXW4. Through a synergistic approach combining Lasso regression and SVM-RFE, we meticulously curated this gene set to isolate a critical subset directly implicated in OP pathology. Subsequent crossover analysis revealed 13 central hub genes—FKBP1B, SRSF5, CDC7, ARPC5L, RUNX3, SMARCD1, CYBB, ZNF212, SLC39A7, GPRC5D, DNAJC24, and FBXW4 itself. Validation across external datasets confirmed the diagnostic relevance of these genes, embedding them within the intricate molecular network governing OP. While the current findings provide compelling associations, they do not establish definitive causal relationships between these genes and the regulatory mechanisms underlying OP modulation. Nonetheless, FBXW4 stands out as a particularly significant hub gene, drawing particular attention due to its established role in inflammatory pathways and immune regulation, which may be crucial in OP pathogenesis. Several genes have been implicated in the pathogenesis of OP, offering potential biomarkers and therapeutic targets for this debilitating bone disease. FKBP1B, a co-chaperone in protein folding, regulates osteoblast differentiation and function, while SRSF5 influences splicing events crucial for osteoclastogenesis. CDC7, a cell cycle regulator, has been linked to osteoclast differentiation, and ARPC5L, involved in actin filament dynamics, plays a role in osteoclast motility and function. RUNX3, a transcription factor, modulates bone homeostasis through its effects on osteoblast and osteoclast activity, while SMARCD1, a component of chromatin remodeling complexes, regulates osteoblast differentiation. CYBB, a key gene in the NADPH oxidase complex, is involved in osteoclast activation through reactive oxygen species production. ZNF212, a zinc finger protein, may influence bone remodeling by regulating osteoclastogenesis and inflammation. SLC39A7, a zinc transporter, is essential for bone mineralization, and GPRC5D, a G-protein coupled receptor, has been linked to bone resorption. DNAJC24, a molecular chaperone, contributes to cellular stress responses in osteoblasts, potentially affecting bone formation. These genes collectively highlight the complex genetic underpinnings of OP, suggesting that targeted genetic interventions could offer novel therapeutic strategies to correct dysregulated bone remodeling. The elucidation of these central hub genes not only unveils potential avenues for prospective investigation but also underscores the imperative for deeper insights into their regulatory networks.

FBXW4, a member of the F-box protein family, emerges as a crucial regulator intricately intertwined with the processes governing bone metabolism, offering profound insights into the etiology of OP. Initially discovered through genomic and proteomic endeavors, FBXW4’s pivotal role in cellular dynamics, particularly in cell cycle regulation and apoptosis, came to light [38]. Recent advancements in molecular and cellular biology have unraveled FBXW4’s structural intricacies, elucidating its multifaceted functionality. Comprising F-box and WD40 repeat domains, FBXW4 orchestrates the assembly of E3 ubiquitin ligase complexes, instigating the ubiquitination and subsequent degradation of target proteins pivotal for cellular homeostasis [39]. In the intricate tapestry of bone physiology, FBXW4 emerges as a pivotal regulator, exerting discernible influence on osteoblast and osteoclast function, thus profoundly impacting bone remodeling dynamics [40]. Osteoblasts, the architects of bone formation, undergo intricate regulatory processes mediated by FBXW4, modulating their proliferation, differentiation, and mineralization capacities [41]. Conversely, FBXW4-mediated ubiquitination events delicately regulate the turnover of critical factors central to osteoclastogenesis, thereby intricately balancing bone resorption and formation processes. Emerging evidence suggests that dysregulation of FBXW4 may underlie the aberrant bone remodeling dynamics observed in OP [42]. Perturbations in FBXW4 expression or function may disrupt the delicate equilibrium between bone formation and resorption, culminating in compromised bone density and heightened fracture susceptibility characteristic of osteoporotic pathology [43]. Moreover, FBXW4’s involvement in various signaling pathways implicated in bone metabolism further underscores its significance in skeletal health. By modulating the activity of key signaling molecules, FBXW4 intricately regulates cellular responses essential for maintaining bone homeostasis [44]. Our investigations highlight the significance of FBXW4, along with other differentially expressed genes, within the pathophysiological framework of OP.

FBXW4, an E3 ubiquitin ligase, has emerged as a crucial regulator in the fine-tuning of osteoclastogenesis through its effects on the RANK/RANKL/OPG signaling axis, a pivotal pathway governing bone homeostasis. The RANK/RANKL/OPG system plays a central role in the differentiation, activation, and survival of osteoclasts, thereby directly influencing bone resorption and turnover. Recent studies have revealed that FBXW4 modulates this pathway by targeting key components for ubiquitin-mediated degradation [45]. Specifically, FBXW4 has been shown to influence the expression and stability of RANK, RANKL, and OPG, all of which are critical for osteoclast differentiation and activity. Through its action on these molecules, FBXW4 contributes to the regulation of osteoclastogenesis, either by promoting the degradation of RANKL or enhancing the stabilization of OPG, thus tipping the balance toward bone protection or resorption depending on its cellular context and expression levels [10]. By modulating the RANK/RANKL/OPG signaling cascade, FBXW4 not only regulates osteoclastogenesis but also links bone metabolism with broader systemic factors, including inflammatory cytokines and hormonal signals [9]. The dysregulation of FBXW4 expression, whether through genetic mutations or altered cellular conditions, can lead to pathological bone remodeling, such as in OP and rheumatoid arthritis. Therefore, understanding the molecular mechanisms through which FBXW4 governs the RANK/RANKL/OPG axis is of profound therapeutic relevance, offering new avenues for targeted interventions in bone diseases. Data from the GSE35956 study illustrate the prognostic potential associated with FBXW4-linked traits. However, research into the genomic changes related to FBXW4 is still nascent, revealing a significant gap in our knowledge. Bridging this gap is crucial for unraveling the complex genomic interactions involving FBXW4, which could revolutionize therapeutic approaches for OP.

Bone turnover, an indispensable physiological phenomenon encompassing both osteogenesis and osteoclastogenesis, is orchestrated by a labyrinthine cytokine network at the interface of immunology and skeletal biology. This juncture serves as a conduit for intricate dialogues between the immune milieu and bone constituents, pivotal in orchestrating bone homeostasis under physiological and pathological conditions alike [46]. Notably, OP often manifests concomitantly with chronic inflammatory states, exacerbated by estrogenic decline in menopause and age-associated upsurges in osteoclastogenic inflammatory cytokine cascades [47]. Maladies such as inflammatory rheumatic disorders underscore the profound repercussions of immune perturbations on skeletal integrity, culminating typically in hyperactivation of osteoclasts and perturbed bone equilibrium. Building upon antecedent investigations, our current study delves deeper into the intricacies of FBXW4 expression within the immune microenvironment intricately linked to OP, unraveling profound insights [48]. Our analyses unveil a dichotomous immune cell comportment, elucidating the nuanced influence of FBXW4 in sculpting the immunological topography in OP [49, 50]. We observed a marked elevation of FBXW4 expression in dendritic cells, memory CD4+ T cells, regulatory T cells (Tregs), resting mast cells, and neutrophils in the treatment cohort. In contrast, CD8+ T cells, follicular helper T cells, M2 macrophages, activated mast cells, and eosinophils were predominantly enriched in the control group. A Lollipop graph illustrates the correlation coefficients among various immune cell subsets, including monocytes, resting dendritic cells, memory CD4+ T cells, M0 macrophages, resting mast cells, and gamma delta T cells. Notably, gamma delta T cells displayed a positive correlation with FBXW4 expression. This intricate immune landscape highlights FBXW4 as a critical regulator of inflammatory and immune pathways in OP, opening promising avenues for therapeutic innovation. Our findings provide a foundational framework for targeted therapeutic strategies, paving the way for novel interventions that could significantly improve clinical outcomes in managing this debilitating disease.

FBXW4 has emerged as a pivotal regulator of bone metabolism and immune response in OP, demonstrating its multilayered role in the pathophysiology of the disease. As a member of the F-box protein family within the SCF E3 ubiquitin ligase complex, FBXW4 is integral to the ubiquitination and subsequent degradation of target proteins [43]. Dysregulation of FBXW4 in OP can disrupt bone homeostasis by modulating the intricate signaling network between bone and immune cells, ultimately skewing the balance between bone resorption and formation. Studies indicate that FBXW4 influences osteoblast and osteoclast activity by mediating the degradation of specific transcription factors, such as RUNX2 and NFATc1. RUNX2 serves as a crucial transcription factor in osteoblast differentiation, while NFATc1 is a principal regulator of osteoclastogenesis [8]. Dysregulated FBXW4 expression leads to aberrant levels of RUNX2 and NFATc1, potentially upregulating or downregulating bone formation, respectively. This dysregulation results in the metabolic imbalance characteristic of OP, particularly exacerbating bone resorption. In addition to its effects on bone cells, FBXW4 impacts immune responses in OP by modulating “osteoimmunology” interactions between immune and bone cells [51]. FBXW4 regulates key signaling pathways in immune cells, including NF-κB and MAPK, which govern the release of pro-inflammatory cytokines. These cytokines, such as IL-6 and TNF-α, can directly target bone cells, further accelerating bone degradation. Consequently, abnormal FBXW4 expression or function in OP may intensify inflammatory responses, promoting bone metabolic imbalance through the osteoimmune axis. In summary, FBXW4 significantly impacts the onset and progression of OP through its dual roles in bone metabolism and the osteoimmune axis. Its regulatory influence underscores FBXW4’s potential as a promising therapeutic target, holding substantial clinical significance in addressing OP.

Specifically, FBXW4 promotes the ubiquitination and degradation of RANKL, while stabilizing OPG, thus balancing osteoclast activation and inhibition. In the context of OP, excessive osteoclast activity leads to increased bone resorption, and impaired FBXW4-mediated regulation exacerbates this imbalance. Therapeutic modulation of FBXW4 activity could therefore restore equilibrium, preventing excessive osteoclastogenesis and halting bone degradation. Beyond OP, targeting FBXW4 may have broader implications for skeletal diseases such as rheumatoid arthritis, where abnormal osteoclast activation contributes to joint destruction. The quest for biomarkers indicative of OP has traditionally relied on conventional scientific approaches. However, recent advancements in bioinformatics have shed light on the complex interactions between metabolic pathways and OP, unveiling critical biomolecular dynamics [52,53,54]. Notably, Mo et al. identified a suite of potential biomarkers—COL1A1, IBSP, CTSP, CTSD, RAC2, MAF, and THBS1—through a sophisticated predictive model. Concurrently, Liu et al. highlighted key genes including PRKCB, GSDMD, ARMCX3, and CASP3, emphasizing their crucial roles in dictating the prognosis and therapeutic strategies for OP. Despite these scientific advancements, a significant knowledge gap persists regarding the involvement of FBXW4 in OP. Our research aims to bridge this gap, contributing essential insights to the field. While acknowledging the limitations of our study, particularly in delineating the complete mechanistic pathways, we employ both in vivo and in vitro methodologies to provide valuable data. These approaches, though not yet fully exploited, set the stage for more exhaustive future research essential for fully understanding the role of FBXW4 in OP. Such investigations are critical for integrating these new findings into the development of future therapeutic strategies, potentially leading to more precise and efficacious treatments.

Conclusions

Our study elucidates the complex mechanisms underlying disease, highlighting FBXW4’s pivotal role in tissue repair and inflammation, alongside its critical prognostic value in OP. Through advanced predictive modeling, we rigorously analyzed FBXW4’s transcriptional profile, uncovering significant expression differences between OP-affected and healthy tissues. These findings lay a robust foundation for future research, with the potential to inform targeted therapeutic strategies for OP.

Data availability

The datasets generated during and/or analyzed during the current study are available in the appendix. The datasets generated and/or analysed during the current study are available in the [GEO] repository. https://www.ncbi.nlm.nih.gov/geo/. Thanks to the KEGG database for the great support [55]. The citation guidelines: http://www.kegg.jp/kegg/kegg1.html.

Abbreviations

OP:

Osteoporosis

GO:

Gene Ontology

TCM:

Traditional Chinese medicine

MF:

Molecular functions

KEGG:

Kyoto Encyclopedia of Genes and Genomes

GEO:

Gene Expression Omnibus

BP:

Biological processes

CC:

Cellular components

DEGs:

Differentially Expressed Genes

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  1. Jinxiao Li and Jing Li contributed equally to this article as a co-first author.

Authors and Affiliations

  1. First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, 250014, China

    Jinxiao Li & Jinxing Liu

  2. Dongying Traditional Chinese Medicine Hospital, Dongying, Shandong, China

    Jing Li

  3. Yanzhou District People’s Hospital, Jining, Shandong, China

    Xinyou Zhao

  4. Dongying People’s Hospital (Dongying Hospital of Shandong Provincial Hospital Group), Dongying, Shandong, 257091, China

    Man Zheng

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JL and JL drafted and revised the manuscript. JL and MZ were in charge of data collection. JL and MZ was in charge of design of frame. XZ and JL conceived and designed this article, in charge of syntax modification and revised of the manuscript. All the authors have read and agreed to the final version manuscript.

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Li, J., Li, J., Zheng, M. et al. Elucidating the role of FBXW4 in osteoporosis: integrating bioinformatics and machine learning for advanced insight. BMC Pharmacol Toxicol 26, 20 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40360-025-00844-z

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BMC Pharmacology and Toxicology

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