Skip to main content
NCBI home page
ACS AuthorChoice logoLink to ACS AuthorChoice
. 2024 Sep 3;23(10):4567–4578. doi: 10.1021/acs.jproteome.4c00470

Olink Proteomics for the Identification of Biomarkers for Early Diagnosis of Postmenopausal Osteoporosis

Chunyan Li †, Xinwei Zang †,§, Heng Liu †, Shangqi Yin †, Xiang Cheng †, Wei Zhang †, Xiangyu Meng †, Liyuan Chen ‡, Shuai Lu †,*, Jun Wu †,*
PMCID: PMC11460326  PMID: 39226440

Abstract

graphic file with name pr4c00470_0009.jpg

This investigation aims to employ Olink proteomics in analyzing the distinct serum proteins associated with postmenopausal osteoporosis (PMOP) and identifying prognostic markers for early detection of PMOP via molecular mechanism research on postmenopausal osteoporosis. Postmenopausal women admitted to Beijing Jishuitan Hospital were randomly selected and categorized into three groups based on their dual-energy X-ray absorptiometry (DXA) T-scores: osteoporosis group (n = 24), osteopenia group (n = 20), and normal bone mass group (n = 16). Serum samples from all participants were collected for clinical and bone metabolism marker measurements. Olink proteomics was utilized to identify differentially expressed proteins (DEPs) that are highly associated with postmenopausal osteoporosis. The functional analysis of DEPs was performed using Gene Ontology and Kyto Encyclopedia Genes and Genomes (KEGG). The biological characteristics of these proteins and their correlation with PMOP were subsequently analyzed. ROC curve analysis was performed to identify potential biomarkers with the highest diagnostic accuracy for early stage PMOP. Through Olink proteomics, we identified five DEPs highly associated with PMOP, including two upregulated and three downregulated proteins. TWEAK and CDCP1 markers exhibited the highest area under the curve (0.8188 and 0.8031, respectively). TWEAK and CDCP1 have the potential to serve as biomarkers for early prediction of postmenopausal osteoporosis.

Keywords: Postmenopausal osteoporosis, Olink proteomics, Differentially expressed proteins, Biomarkers

Introduction

Osteoporosis is a kind of common age-related disease characterized by reduced bone mass and microstructural deterioration. The incidence rate is higher in postmenopausal women.1 It is widely accepted that the pathogenesis of osteoporosis is mainly the imbalance between osteoblast mediated osteogenesis and osteoclast mediated bone absorption.2 Postmenopausal osteoporosis is a systemic condition resulting from a significant decrease in estrogen levels after menopause, which leads to reduced bone mass, structural damage, and an increased risk of fractures.3 Estrogen plays a crucial role in promoting osteogenic differentiation and maturation of mesenchymal stem cells (MSCs) and osteoblasts, thereby enhancing bone formation. Additionally, it inhibits osteoclast formation and induces osteoclast apoptosis, thus limiting bone resorption. In the absence of sufficient levels of estrogen in women, the effects on bone synthesis and antiosteoclast activity are reduced, leading to sustained bone loss.4 In addition to direct adverse effects on the skeleton, changes in immune status among postmenopausal women can indirectly contribute to ongoing bone degradation. This is due to a chronically low-grade pro-inflammatory phenotype that occurs under conditions of estrogen deficiency5−7 accompanied by altered cytokine expression.

Bone marrow serves as both a hematopoietic and immune organ.With a deepening comprehension of the intricate interplay between the skeletal and immune systems in recent decades, Srivastava et al. introduced the concept of “immunosenescence” underscoring the mounting significance of immune cells in osteoporosis.12,13 Recent research has proposed a mechanistic association between osteoimmunology and postmenopausal osteoporosis.14 However, these mechanisms are not entirely explanatory of the pathogenesis of postmenopausal osteoporosis. Therefore, there is an urgent need for early and improved biomarkers to diagnose and monitor treatment outcomes in postmenopausal osteoporosis.

Proteomics, a branch of protein science, relies on techniques for separating proteins and the ability to identify (or analyze) them through mass spectrometry. The utilization of quantitative proteomics in osteoporosis research has facilitated the discovery of biomarkers for osteoblasts and osteoclasts, as well as the characterization of protein signaling pathways involved in bone remodeling processes.16−18 As a pioneering technology, Olink has played an indispensable role in precision proteomics research by offering a novel approach for multiomics studies and facilitating efficient and accurate detection of multiple critical biomarkers. Serum samples are simple to collect, less difficult to test and relatively inexpensive. Therefore, serum is an ideal sample for developing biomarkers for the prediction and diagnosis of diseases.

We continued to utilize Olink proteomics for screening differential proteins associated with the pathogenesis of postmenopausal osteoporosis. Through GO and KEGG analyses, we further investigated the pathways and biological functions related to these differential proteins, aiming to identify potential serum biomarkers associated with decreased bone mass and osteoporosis. Our ultimate goal is to achieve early diagnosis and intervention for postmenopausal osteoporosis based on these serum biomarkers.

Material and Methods

Study Population

Patients aged 50 years or older, who were postmenopausal females and had not received regular treatment for osteoporosis, were randomly selected from all inpatients in the Department of Orthopedics at Beijing Jishuitan Hospital. According to the diagnostic criteria for osteoporosis outlined in the 2022 edition of the “Guidelines for the Diagnosis and Treatment of Primary Osteoporosis”,20 patients were categorized into three groups: those with severe osteoporosis (T ≤ −2.5) consisting of 24 cases, those with mild osteoporosis (−2.5 < T < −1.0) consisting of 20 cases, and those with normal bone mass (T ≥ −1.0) consisting of 16 cases. The criteria for normal bone mass require bone density values to fall within one standard deviation below the peak bone mass of healthy adults of the same gender and ethnicity. All enrolled patients were Han Chinese and provided informed consent. Details on participants′ recruitment, inclusion and exclusion criteria, definition and diagnosis of osteoporosis, mild osteoporosis, collection of clinical characteristics, and measurement of plasma biochemical variables were described in Supporting Information.

ELISA Assay for TWEAK and CDCP1 Measurement

The microtiter plate in this kit has been precoated with an antibody specific to CDCP1 and TWEAK. Standards or samples are then added to the appropriate microtiter plate wells with a biotin-conjugated antibody preparation specific to CDCP1 and TWEAK. Next, avidin-conjugated to Horseradish Peroxidase (HRP) is added to each microplate well and incubated. After TMB substrate solution is added, only those wells that contain CDCP1 and TWEAK, biotin-conjugated antibody, and enzyme-conjugated avidin will exhibit a change in color. The enzyme–substrate reaction is terminated by the addition of sulfuric acid solution and the color change is measured spectrophotometrically at a wavelength of 450 nm ±10 nm. The concentration of CDCP1 and TWEAK in the samples is then determined by comparing the O.D. of the samples to the standard curve.

Olink Proteomics Analysis

Protein Quantification Analysis: Olink Proteomics employs Proximity Extension Assay (PEA), a core protein detection patent, for precise quantification of multiple proteins from each sample. PEA utilizes pairs of antibodies coupled with specific DNA oligonucleotide tags (DNA barcodes) to specifically detect target proteins. (A) Once the paired antibodies have recognized and bound to the target protein, their linked DNA barcodes undergo complementary pairing. (B) Subsequently, they are extended by DNA polymerase. (C) The newly synthesized DNA barcodes are preamplified via PCR and subsequently detected using qPCR or Next Generation Sequencing (NGS), enabling specific quantitative detection of the target proteins. The normalized protein expression (NPX) data are standardized using IPC, and the Olink data are presented as NPX, which represents the log2-transformed relative quantitative levels of protein expression by Olink. This approach aims to minimize variations within and between detections.

Analysis of Protein Expression Levels

Following the standardization of NPX data in the Olink project, protein sample signal values are linearly combined for principal component analysis. Hierarchical clustering is then performed on both samples and proteins to categorize and classify data based on similarity in protein expression quantification analysis.

Differential Analysis of Protein Expression

The Olink standard deviation analysis is utilized to compute the expression variances of standardized NPX data for proteins under distinct experimental conditions through Student’s t test. Differential protein information is acquired by filtering based on a P-value condition of <0.05 (Student’s t test).

Differential Protein Correlation Analysis

Pearson correlation coefficients are computed to assess the pairwise correlation between differentially expressed proteins, thereby quantifying the strength of association among these proteins.

Differential Protein Functional Analysis

The Blast2Go software (https://www.blast2go.com/) is utilized to conduct GO functional annotation for all identified proteins and differentially expressed proteins.

The enrichment level of each pathway protein is calculated and analyzed using Fisher’s exact test based on KEGG pathways, with all qualitative proteins considered as the background for KEGG pathway enrichment analysis. Details of recruitment are given in Supplementary Methods.

ROC Analysis

Receiver Operating Characteristic (ROC) curves are utilized to assess the predictive value of differentially expressed protein markers for the risk of developing postmenopausal osteoporosis (PMOP). LAP TGF-β-1, IL-17C, CDCP1, IFN-gamma, and TWEAK are employed as test variables while osteoporosis is considered as the state variable. ROC curves are constructed by plotting sensitivity on the y-axis and 1-specificity (false positive rate) on the x-axis to evaluate the predictive capacity of each protein marker for osteoporosis risk in patients.

Statistical Analysis

All experimental data were compiled and analyzed using Microsoft Excel 2019 and SPSS 26 statistical software, respectively. The normal distribution of continuous variables was assessed by the Shapiro–Wilk test. Mean ± standard deviation was used to describe normally distributed continuous data. Differences among the three groups were analyzed by one-way analysis of variance (ANOVA), followed by multiple comparisons between samples using the Student–Newman–Keuls (SNK-q) test. Differences were considered statistically significant at a significance level of P < 0.05 (adjusted by FDR). Receiver Operating Characteristic (ROC) analysis was utilized to assess the diagnostic performance of LAP TGF-β-1, IL-17C, CDCP1, IFN-gamma, and TWEAK in disease prediction. The results were reported as odds ratios (ORs) with corresponding 95% confidence intervals (CIs).

Results

Characteristics of the Study Subjects

We described the characteristics of postmenopausal women between osteoporosis, osteopenia, and the normal group in Table 1. There was no significant difference observed in terms of age and BMI index between these three groups.

Table 1. Comparison of Clinical Dataa.

  osteoporosis(n = 24) osteopenia(n = 20) normal bone(n = 16) F P
OC 11.35 ± 6.86 12.81 ± 7.22 13.24 ± 6.77 0.352 0.705
P1NP 43.93 ± 30.24 43.35 ± 28.37 41.22 ± 17.63 0.39 0.962
β-CTX 0.52 ± 0.27 0.41 ± 0.32 0.36 ± 0.14 1.486 0.238
25(OH)D3 12.49 ± 9.12 13.25 ± 6.08 15.02 ± 13.06 0.262 0.771
PTH 46.30 ± 14.09 32.47 ± 18.15 34.83 ± 16.92 3.468 0.04
ALP 65.17 ± 31.46 55.13 ± 40.75 59.78 ± 28.19 0.418 0.661
Ca 2.26 ± 0.18 2.35 ± 0.12 2.35 ± 0.10 2.321 0.109
P 0.91 ± 0.21 1.15 ± 0.37 1.21 ± 0.30 5.257 0.009
BMI 22.05 ± 3.65 23.93 ± 3.39 24.75 ± 3.34 2.872 0.066
Age 59.42 ± 3.24 58.05 ± 2.91 57.69 ± 2.82 1.898 0.159
T-score –4.65 ± 0.65 –1.76 ± 0.32 0.13 ± 0.81 308.317 <0.001
Open in a new tab
a

Sig. N-terminal osteocalcin (OC), N-terminal propeptide of type I collagen (P1NP), carboxy-terminal telopeptide of type I collagen (β-CTX), 25-hydroxy vitamin D (25(OH)D3), parathyroid hormone (PTH), alkaline phosphatase (ALP), calcium (Ca), and phosphorus (P).

However, a notable variation was found in bone density values among them (P = 0.0001 < 0.05), which was statistically significant. The bone formation markers P1NP and bone resorption marker β-CTX were higher in the osteoporosis group compared to the osteopenia group and normal bone group. The osteopenia group had higher values of the bone formation markers ALP, P1NP, and bone resorption marker β-CTX compared to the normal bone group, but the differences were not statistically significant. Furthermore, the bone formation marker OC was higher in the normal bone group compared to the osteopenia group and osteoporosis group, but the difference was not statistically significant.

There were no statistically significant differences in 25(OH)D3, ALP, and Ca levels among the three groups. However, PTH and P showed differences among the three groups. The pairwise SNK-q test revealed that ALP showed significant differences between the osteoporosis group and osteopenia group (P = 0.002 < 0.05), and between the osteoporosis group and normal bone group (P = 0.01 < 0.05). P showed significant differences between the osteoporosis group and osteopenia group (P = 0.004 < 0.05), and between the osteoporosis group and normal bone group (P = 0.001 < 0.05).

Olink Analysis Revealed Differential Protein Profiles in Patients with Osteoporosis

Principal component analysis (PCA) plot and Venn diagrams of the protein expression profiles showed a modest separation between severe osteoporosis, mild osteoporosis and control group (Supplementary Figure 1A-B). The clustering heatmap revealed significant differences in protein expression between the groups, with CDCP1, CXCL9, MCP-3, IL18, STAMBP, and SIRT2 showing differential expression between mild osteoporosis and normal bone (Figure 1A). In addition, IL6, MCP-3, CDCP1, CCL23, osteoprotegerin (OPG), TNFB, TWEAK, IFN-gamma, LAP TGF-β-1 and IL-17C showed differential expression between severe osteoporosis and normal bone (Figure 1B). In addition, IL8, IL10, OPG, CSF-1, CXCL1, MMP-10, ST1A1, Axinl, and CDCP1 showed differential expression between severe osteoporosis and mild osteoporosis (Figure 1C).

Figure 1.

Figure 1

Open in a new tab

Clustering heatmaps of normal bone mass group vs mild osteoporosis group (A), normal bone mass group vs severe osteoporosis group (B), mild osteoporosis group vs severe osteoporosis group (C). Heatmaps are shown with red squares representing overexpressed proteins and green representing lower expression. Volcano plots of normal bone mass group vs mild osteoporosis group (D), control osteoporosis vs severe osteoporosis (E), mild osteoporosis vs severe osteoporosis (F).

A volcano plot was generated by plotting the difference in protein expression and P value to visualize significant differences between two sample groups (Figure 1E, F, G). Six DEPs were found in Figure 1D, comparing the mild osteoporosis group with the normal bone group. Significant differences were observed in the proteins CDCP1, CXCL9, MCP-3, and SIRT2. Thirty differentially expressed proteins were identified in the severe osteoporosis group compared with the normal bone group, and proteins IL6, MCP-3, CDCP1, CCL23, OPG, TRANCE, TRAIL, TNFB, TWEAK, and DNER showed the greatest differences in expression (Figure 1E). In comparison to the mild osteoporosis group, 21 DEPs were detected in the severe osteoporosis group, significant differences were found in the proteins IL8, IL10, OPG, CSF-1, CXCL1, MMP-10, ST1A1, and AXIN1 (Figure 1F).

The difference of protein expression between the three groups of samples was analyzed by clustering heat map (as shown in Figure 2A–D). The group with normal bone mass, the group with mild osteoporosis, and the group with severe osteoporosis were analyzed. The three groups showed homogeneously decreased differential proteins (Figure 2A): SCF, uPA, DNER, LAP TGF-β-1, IFN-gamma, TRAIL, Fit3L, TWEAK, NT-3, IL7, CXCL10, CXCL5; Figure 2B shows the expression levels of decreased differential proteins. Normal bone mass group, mild osteoporosis group and severe osteoporosis group, the three groups showed homogeneously increased differential proteins (as shown in Figure 2C), including OPG, PD-L1, VEGFA, CCL23, MCP-3, ADA, IL-17C, IL-18R1, CDCP1, CD8A, 4E-BP1, TNFRSF9, CD40, and IL-10RB; Figure 2D shows the expression levels of different proteins: CXCL9, MCP-3, IL18, STAMBP, SIRT2.

Figure 2.

Figure 2

Open in a new tab

Clustering heat map of downregulated proteins (A) and their expression levels in mild osteoporosis and severe osteoporosis group (B). Clustering heat map of up-regulated proteins (C) and their expression levels in mild osteoporosis group and severe osteoporosis group (D).

Figure 3A shows the trend of changes in the normal bone mass group, the mild osteoporosis, and the severe osteoporosis group. Take 7 as an example, it is the continuously upregulated protein group of the three groups. 0 is the three continuously downregulated groups; Figure 3B and C represent continuously downregulated proteins and continuously up-regulated proteins respectively. Based on the standardization of NPX data in the Olink project, the NPX expression values of the protein were different between patients in three groups. Three downregulated proteins related to bone pine that we had been concerned about were selected, namely IFN-gamma, LAP TGF-β-1, and TWEAK. And two up-regulated proteins were IL-17C and CDCP1. As a key verification protein in future experiments. To further visualize the expression patterns of differentially expressed proteins, trend heatmaps were generated for IL-17C, CDCP1, IFN-gamma, LAP TGF-β-1, and TWEAK (Figure 3D). Box plots were also created to illustrate the expression levels of these five significantly different proteins across the three sample groups. t test analysis revealed a linear increase in NPX values for IL-17C and CDCP1 (Figure 4A, C). Conversely, IFN-gamma, LAP TGF-β-1, and TWEAK showed a linear decrease (Figure 4B, D, E). Together, these findings demonstrated a consistent upregulation of IL-17C and CDCP1, accompanied by a downregulation of IFN-gamma, LAP TGF-β-1, and TWEAK.

Figure 3.

Figure 3

Open in a new tab

(A) Trends in normal bone mass group, mild osteoporosis and severe osteoporosis group. (For example, 7 is the consistently up-regulated protein group among the three groups, while 0 is the consistently downregulated protein group among the three groups. Continuously downregulated (B) and up-regulated proteins (C) in mild and severe osteoporosis group. (D) Trend heat map of differential proteins of normal bone mass group, mild osteoporosis group and severe osteoporosis group.

Figure 4.

Figure 4

Open in a new tab

Trend heatmaps of top performing protein markers, including CDCP1 (A), LAP TGF-β-1 (B), IL-17C (C), IFN-gamma (D), and TWEAK (E). Black full circles indicate outliers.

To assess the degree of correlation among differentially expressed proteins (DEPs), we computed Pearson correlation coefficients for each pair of differential proteins and generated a clustering heatmap (Figure 5A-C). The findings unveiled the ensuing correlations, CDCP1 exhibited a negative correlation in the comparisons between severe osteoporosis and normal groups, mild osteoporosis and normal groups, as well as severe osteoporosis and mild osteoporosis groups. In the comparison between severe osteoporosis and normal groups, positive correlations were observed for IFN-gamma, LAP TGF-β-1, and TWEAK. We used protein–protein interactions (PPI) to explore associations of identified DEPs. Figure 5D-F showed the PPI networks that can distinguish differentially expressed proteins in the normal bone mass group and severe osteoporosis group, normal bone mass group and mild osteoporosis group, mild osteoporosis group, and severe osteoporosis group.

Figure 5.

Figure 5

Open in a new tab

Clustering heatmap of expression level correlations of differential proteins in normal bone mass group vs severe osteoporosis group (A), normal bone mass group vs mild osteoporosis group (B), mild osteoporosis group vs severe osteoporosis group (C). Protein–protein interactions showing associations of identified discriminating protein markers in normal bone mass group vs severe osteoporosis group (D), normal bone mass group vs mild osteoporosis group (E), mild osteoporosis group vs severe osteoporosis group (F).

DEPs Enrichment and Functional Analysis

GO and KEGG pathways analysis were performed to further investigate the functional and correlation of plasma DEPs in osteoporosis patients and the results are presented in Figure 6.

Figure 6.

Figure 6

Open in a new tab

(A) GO-term enrichment analysis of top 30 DEPs with significant changes (p < 0.05) in three groups. Circles, triangles, and squares represent the top-level GO terms: biological process (BP), cellular component (CC), and molecular function (MF), respectively. (B) The total number of enriched DEPs for KEGG pathways analysis. Colors of the stacked barplot represent KEGG pathways: environmental information processing (red), human diseases (green), organismal systems (blue).

We found that within Biological Process ontology, most annotated proteins are concentrated in biological regulation, cellular processes, developmental processes, immune system processes, localization, metabolic processes, and multicellular organismal processes. In the Molecular Function ontology, the annotated proteins are primarily associated with cellular components such as cells, extracellular regions, and organelle parts, as well as macromolecular complexes and membranes. Meanwhile, in the Cellular Component ontology, these proteins are mainly involved in molecular transducer activity, signal transduction, and binding (Figure 6A).

According to the results of functional enrichment analysis, it was observed that in terms of Biological Process, the differentially expressed proteins were primarily involved in tumor necrosis factor receptor binding, receptor binding, heparin binding, growth factor activity, cytokine activity, and chemokine activity in extracellular space. In terms of the Cellular Component, their primary association was with the extracellular matrix. In terms of Molecular Function, the differential proteins primarily participate in tumor necrosis factor-mediated signaling pathways, transcriptional regulation of DNA templates, protein phosphorylation, positive regulation of protein kinase B signaling, peptide tyrosine and serine phosphorylation, gene expression, positive regulation of cell proliferation and migration, promotion of angiogenesis, activation of MAP kinase activity including ERK1 and ERK2 cascades, facilitation of monocyte and neutrophil chemotaxis, inhibition of cell proliferation, mediation of cytokine and chemokine signaling pathways, involvement in information transmission signaling pathways such as MAPK cascades, response to lipopolysaccharide-induced cellular changes, and regulation of inflammatory response. The P-values of all 30 GO terms are significantly less than 0.05, indicating statistical significance (Figure 6A). This suggests that the differentially expressed proteins mainly participate in cytokine activity and signal transduction.

KEGG analysis (Figure 6B) indicated that the identified proteins are primarily associated with signal transduction in environmental information processing, organismal systems′ immune response, specific cancers, and an overview of cancer in human diseases. The alterations in these biological functions are linked to osteoporosis and reduced bone mass. This implies that the selected proteins IL-17C, CDCP1, IFN-gamma, and TWEAK hold potential as biomarkers for postmenopausal osteoporosis.

Important Diagnostic Values of DEPs

To assess the diagnostic efficacy of LAP TGF-β-1, IL-17C, CDCP1, IFN-gamma, and TWEAK in discriminating patients with osteoporosis from healthy individuals, ROC curve analysis was conducted (Figure 7). Our findings revealed that all protein markers significantly predicted the likelihood of osteoporosis (P < 0.01). Specifically, LAP TGF-β-1 exhibited a significant differentiation between osteoporosis patients and healthy controls with an AUC of 0.6859 (Figure 7A). Additionally, CDCP1 demonstrated a significant differentiation with an AUC of 0.8031 (Figure 7B), while TWEAK exhibited an AUC of 0.8188, indicating a noteworthy distinction between osteoporosis patients and healthy controls (Figure 7C). IL-17C and IFN-gamma also displayed significant differentiations with AUC values of 0.7406 and 0.6906 (Figure 7D, E).

Figure 7.

Figure 7

Open in a new tab

Receiver operating characteristic (ROC) curves of DEPs for discriminating patients with osteoporosis from healthy individuals, including LAP TGF-β-1 (A), CDCP1 (B), TWEAK (C), IL-17C (D), and IFN (E).

Among them, TWEAK and CDCP1 exhibited the highest area under the curve (AUC), with CDCP1 being significantly upregulated and its bioinformatics functions consistent with expectations. Conversely, TWEAK was significantly downregulated and its bioinformatics functions also aligned with anticipated outcomes. These findings suggest their potential as diagnostic markers for osteoporosis, as well as their pivotal role in the pathophysiology of this condition.

Validation Analysis

To further confirm our study, the ELISA experiment test was conducted and found that the upregulation of CDCP1 in the group with mild osteoporosis and severe osteoporosis, which is consistent with our study. Similarly, TWEAK was found to be downregulated in the same group, which also aligns with experimental results (as depicted in Figure 8).

Figure 8.

Figure 8

Open in a new tab

Elisa quantifications of TWEAK-1 (A) and CCP1-1 (B) in osteoporosis patients and healthy individuals. Colors of the stacked barplot represent groups: healthy group (red) and osteoporosis group (blue).

Discussion

Osteoporosis (OP) is a skeletal disorder characterized by reduced bone strength and an elevated risk of fractures.21 Epidemiological studies have revealed that the prevalence of osteoporosis is more popular in older people, especially for postmenopausal women,22,23 and have an enormous burden on families and society.24−26

Postmenopausal osteoporosis is a metabolic disorder characterized by hormonal imbalance in women after menopause, which leads to decreased estrogen levels and an imbalance between bone resorption by osteoclasts and bone formation by osteoblasts. In postmenopausal women with osteoporosis, the risk of fracture increases 1.5 to 2 times for every one standard deviation decrease in bone density.26 Meta-analysis results indicate that low bone density can account for approximately 70% of the risk of fractures.27

Bone loss is a precursor to osteoporosis, typically progressing to osteoporosis within a few years, and in severe cases, it may even advance to osteoporosis within one year.27−29 Most patients with osteoporosis do not exhibit obvious clinical symptoms; however, as bone loss occurs, microstructural damage and decreased skeletal mechanical strength become apparent. Therefore, the prevention and treatment of osteoporosis in China are confronted with a formidable challenge due to the high incidence but low diagnosis rates.22,23 Early prediction of bone loss is crucial as most fractures occur during this period. Timely preventive measures can significantly reduce fracture incidence and mortality rates among patients.30

Traditional methods for diagnosing PMOP primarily rely on dual-energy X-ray absorptiometry (DEXA) and assessment of bone mineral density (BMD).31 However, many postmenopausal women are unaware of PMOP and often only undergo DEXA scans after experiencing adverse events related to osteoporosis, such as bone pain or fractures. Therefore, there is an urgent need to identify simple and effective biomarkers for early detection of PMOP in postmenopausal women.21

The pathogenesis of PMOP is closely associated with immune dysfunction and systemic inflammation activation.32,33 Following menopause, women lose the protective effects of endogenous estrogen, leading to an accumulation of inflammatory cytokines such as tumor necrosis factor-alpha, interleukin (IL)-6, IL-12, and IL-17. These cytokines can mediate oxidative stress damage, stimulate osteoclasts, and enhance bone resorption. Therefore, there is an urgent need to identify an immune-related biomarker that can predict primary postmenopausal osteoporosis during its early stages.

In this study, the serum protein profiles of postmenopausal women with osteoporosis, osteopenia, and normal bone mass were analyzed using Olink technology and its core technique-Proximity Extension Assay (PEA). Differential proteins were identified among the groups of serum samples. Based on the trend analysis of these differential proteins, we selected 3 significantly downregulated proteins and 2 significantly upregulated proteins. To investigate the functional pathways and roles of differentially expressed proteins, we performed enrichment analysis using GO and KEGG databases to identify key biological processes and pathways associated with these proteins. Through comprehensive functional analysis of each protein, we have identified TWEAK and CDCP1 as potential biomarkers for early detection of postmenopausal osteoporosis, supported by ROC curve analysis.

CUB Domain Containing Protein 1 (CDCP1), also known as CD318, SIMA135, gp140, and TRASK, was initially identified by Scherl-Mostageer in colon cancer cells and various other malignancies, as it is significantly overexpressed in these neoplastic tissues compared to their normal counterparts.34 In the study conducted by Iwata et al., clusters of positive cells expressing CDCP1 were detected through immunohistochemical analysis of bone marrow biopsies. These clusters were found in close proximity to bone, adipose tissue, and an active hematopoietic system, indicating that CDCP1 is expressed on the surface of bone marrow stromal cells.35 However, the role of CDCP1 in bone metabolism remains poorly understood due to limited research. Recent findings suggest that extracellular vesicle-secreted CDCP1 promotes osteoclast formation, as identified through functional siRNA screening.36 Through Olink proteomic analysis, we have discovered that the expression level of CDCP1 protein is significantly elevated in both osteoporosis and reduced bone mass groups compared to the normal bone mass group and confirmed by ELISA methodology. Correlation analyses of differential proteins revealed that CDCP1 was consistently upregulated and may be associated with low estrogen levels, immune dysregulation, and inflammation activation in postmenopausal women. This feedback may further augment the expression of CDCP1, indicating its potential as a clinical biomarker for early detection of postmenopausal osteoporosis. Further investigation is required to determine whether the expression level of CDCP1 in patients′ serum, when combined with clinical symptoms, signs, and imaging data, can be utilized as a means of assessing osteoporosis progression and predicting patient prognosis.

In the immune factors, the major driving factors of bone remodeling are T-helper 17 (Th17) cells and regulatory T cells (Treg), which have opposing roles in maintaining bone homeostasis, especially in osteoclastogenesis.37 Estrogen not only enhances osteoblast activity but also inhibits osteoclast-mediated bone resorption. Furthermore, estrogen exerts an influence on immune cells in addition to regulating osteoblasts and osteoclasts.38 Studies have demonstrated that Treg cells exhibit enhanced inhibitory effects on osteoclast formation in the presence of estrogen,39−41 indicating their involvement in the estrogen-mediated regulation of bone metabolism and its protective effects against osteoporosis.

Inflammatory cytokines are well-known to impact the process of bone remodeling, resulting in a decrease in bone mass. T-helper 17 (Th17) cells, an inflammatory effector subset, have been shown to induce osteoclastogenesis and increase bone loss even in the absence of estrogen. Studies conducted on ovariectomized (OVX) mice have demonstrated an increase in Th17 differentiation, which leads to the secretion of interleukin-17 (IL-17) by Th17 cells. This, in turn, induces osteoclastogenesis and prompts macrophages to release other inflammatory cytokines such as IL-6, IL-1, and TNF-α that further promote osteoclastogenesis.42,43

The cytokine family of interleukin-17 (IL-17) comprises six members, ranging from IL-17A to IL-17F. In 2000, Li et al. first identified IL-17C through homology search (BLAST) as a protein similar to IL-17A.44 IL-17C is situated on chromosome 17q24, spanning a length of 1.1 kb and exhibiting approximately 27% amino acid homology with IL-17A. However, it was not until a decade later that serious investigations into the functional properties of IL-17C were initiated by two groups—Song et al. and Zhang et al., who identified the heterodimer IL-17RA/RE as its functional receptor.45 IL-17C is expressed not only in keratinocytes, colonic epithelial cells,46 resident renal cells,47,48 and respiratory epithelial cells, but also on TH17 cells. The expression of IL-17C on TH17 cells is regulated by the synergistic action of TNF-α and IL-17A.49 The receptors for IL-17C are predominantly expressed on epithelial cells and T-helper 17 (Th17) cells.50−52

Compared to other members of the IL-17 cytokine family, IL-17C is upregulated in the early stages of disease53 and serves a dual function: (a) maintaining epithelial cell barrier integrity in an autocrine manner, thereby supporting innate immune system control of infections; and (b) stimulating adaptive immune responses by binding to IL-17RE on TH17 cells for effective pathogen combat.54 The potential drawback of this mode of action lies in the possibility of immune dysregulation, which may result in the development of autoimmune diseases such as rheumatoid arthritis. Intracellular signaling of IL-17C/RE involves the activation of antiapoptotic proteins Bcl-2 and Bcl-XL, as well as the NF-κB and MAPK pathways.55 These pathways are induced in both mucosal host defense and autoimmune diseases, where the expression of pro-inflammatory cytokines, chemokines, and antimicrobial peptides plays a crucial role.56

Estrogen deficiency has been demonstrated in human studies to be associated with the expansion of T cells expressing TNF-α and RANKL.57 Excessive activity of TH17 cells is correlated with numerous autoimmune disorders. Bhadricha et al. discovered that estrogen deficiency results in an imbalance between Th17 and Treg cells, potentially leading to increased microinflammation and resorption within the bone microenvironment, resulting in excessive bone loss in postmenopausal women. This suggests that Th17 cells and IL-17 play a crucial role in the pathogenesis of postmenopausal osteoporosis. The upregulation of IL-17C expression observed in postmenopausal osteoporosis patients, consistent with previous reports, indicates its association with signaling transduction and immune factors, providing clinical evidence for the pathogenesis of osteoporosis.

Interferon-gamma (IFN-gamma), a cytokine, is widely recognized as the only member of type II interferons and plays an indispensable role in immune regulation and inflammation. In terms of bone metabolism, IFN-gamma can enhance alkaline phosphatase activity and induce the expression of Runx2, a transcription factor that promotes osteoblast differentiation, thus regulating bone metabolism. In the process of osteoclast differentiation, IFN-gamma has been reported to have a dual role. Shen Hao et al. demonstrated that the inhibitory effect of IFN-gamma on osteoclastogenesis is mediated through the suppression of RANKL-induced activation of NF-κB and MAPK signaling pathways.

Under conditions of estrogen deficiency, infection, and inflammation, the indirect pro-osteoclastogenic effects of IFN-gamma may outweigh its direct antiosteoclast activity. This provides a theoretical foundation for our clinical research. It has been discovered that IFN-gamma, particularly recombinant IFN-gamma-1b, is currently employed to decelerate the progression of severe subtypes of osteoporosis, which are genetic disorders linked with impaired osteoclast function. IFN-gamma expression was found to be significantly downregulated in the postmenopausal osteoporosis group, indicating its suppressed activity in this condition.

LAP TGF-β-1 acts as a precursor to TGF-β-1 and forms a small latent complex in conjunction with latency-associated peptide (LAP). The presence of TGF-β-LAP complexes can be detected on the surface of immune cells. In patients with ankylosing spondylitis, there was a positive correlation observed between the number of LAP-positive monocytes and the degree of bone formation in peripheral blood, as reported by a study. In addition, elevated levels of LAP TGF-β-1 have been linked to a lower incidence rate of osteoarthritis. In our study, we identified a novel finding that LAP TGF-β-1 expression is reduced in patients with PMOP compared to those with normal bone mass, indicating a potential association with the protective mechanisms of LAP TGF-β-1. However, further research is required to elucidate its role in bone-immune-related protective effects.

Tumor Necrosis Factor-Like Weak Inducer of Apoptosis (TWEAK) is encoded by the human TNFSF12 gene and belongs to the TNF ligand superfamily, is a prototypical member that shares high homology with TNF, expressed and secreted in various tissues and cell types, particularly by macrophages and monocytes during inflammatory conditions. TWEAK plays diverse biological functions by binding to Fibroblast Growth Factor-Inducible 14 (Fn14), including but not limited to cell proliferation, differentiation, migration, apoptosis/necrosis induction, as well as promotion of pro-inflammatory response and angiogenesis. So far, many studies indicate that the TWEAK/Fn14 system plays a crucial role in bone remodeling by regulating the survival and proliferation of osteoblasts and osteoclasts. Some reports suggested that lots of hen pathological conditions, including rheumatoid arthritis, skeletal muscle atrophy, and osteoporosis happen when TWEAK/Fn14 signaling is not properly regulated. Osteoblasts, a type of mesenchymal lineage progenitor cells involved in bone remodeling and regeneration after injury, express Fn14. Exposure to TWEAK enhances osteoblast proliferation by upregulating the osteoblast-specific transcription factor Osterix and downregulating Runt-related transcription factor 2 (RUNX2) levels. In contrast, TWEAK inhibits the expression of osteoblast markers, including osteocalcin (OCN), alkaline phosphatase (ALP), and osteopontin (OPN), in MC3T3-E1 cells via the MAPK-ERK pathway. Therefore, TWEAK promotes proliferation while suppressing the differentiation of osteoblasts, hindering their development, and acts as a negative regulator of both osteoblast differentiation and bone formation. The TWEAK/Fn14 signaling pathway, which resembles tumor necrosis factor (TNF), has been implicated in the regulation of bone homeostasis, particularly in osteoclast differentiation. Studies have revealed that TWEAK can activate the expression of multiple matrix metalloproteinases (MMPs) in chondrocytes, thereby promoting osteoclastogenesis. Additionally, Polek et al. demonstrated that TWEAK can induce RAW264 monocytes/macrophages to differentiate into osteoclasts independently of Fn14. Our analysis of the Olink protein group revealed significant statistical differences in TWEAK expression, with a marked downregulation observed in the osteoporosis group. This finding is consistent with changes in TWEAK levels detected in blood samples from each group and is supported by the largest area under the ROC curve (AUC), indicating its potential as an early diagnostic marker for postmenopausal osteoporosis. TWEAK/Fn14 signaling may also contribute to tumorigenesis by regulating cancer stem cells, as demonstrated in certain tumors and precancerous conditions. The interaction between TWEAK and Fn14 can activate NF-κB or induce matrix metalloproteinase 9 (MMP-9), thereby exacerbating tumor development in neuroblastoma. The TWEAK/Fn14 pathway can exert its influence on the cholangiocarcinoma microenvironment by recruiting and inducing phenotypic changes in macrophages, as well as promoting the proliferation of cancer-associated fibroblasts.

KEGG enrichment analysis revealed that the differentially expressed proteins in the three groups are implicated in signaling pathways, immune responses, and cancer-related processes. Considering the molecular mechanisms and biological functions reported in existing literature, it is suggested that differentially expressed proteins including LAP TGF-β-1, IL-17C, CDCP1, IFN-gamma, and TWEAK may be associated with the pathogenesis of PMOP in women by promoting osteoclast activity, inhibiting osteoblast differentiation, and inducing inflammation. Therefore, CDCP1 and TWEAK exhibit potential clinical significance as biomarkers for early PMOP diagnosis and may contribute to the understanding of molecular mechanisms and biological functions of PMOP, thus warranting further investigation.

However, there are certain limitations to this study. First, the sample size is relatively small and therefore requires replication in larger-scale studies across diverse ethnicities and regions. Additionally, in order to determine whether CDCP1 and TWEAK can serve as diagnostic biomarkers for PMOP, it is necessary to assess their discriminative ability compared to senile osteoporosis. Secondary, osteoporosis, rheumatoid arthritis, ankylosing spondylitis, and osteoarthritis are all orthopedic diseases characterized by joint damage. Further investigation of other orthopedic diseases is necessary to strengthen the argument for CDCP1 and TWEAK as diagnostic biomarkers for PMOP. Validation of the results by incorporating a new independent cohort is also required. Additionally, we did not analyze CDCP1 and TWEAK in extracellular vesicles. However, studies have shown that these biomarkers are more stable in extracellular vesicles than in serum or plasma. In the future, we anticipate that conducting clinical studies on serum samples from a substantial population of osteoporosis patients and validating them in bone tissue will enhance the precision of this study.

Conclusion

TWEAK and CDCP1 have the potential to serve as biomarkers for early prediction of postmenopausal osteoporosis.

Acknowledgments

This work was supported by the Beijing Hospitals Authority Youth Program QMS20220405, funded by the Beijing Jishuitan Hospital Nova Program XKXX202212. We are grateful to our clinical laboratory staff at Beijing Jishuitan Hospital of Capital Medical University for their assistance in recruiting patients.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jproteome.4c00470.

  • Supplementary methods and supplementary Figure 1 (PDF)

Author Contributions

# Chunyan Li and Xinwei Zang contributed to the work equally and should be regarded as cofirst authors.

Ethics approval and consent to participate. This study was approved by the Medical Ethics Committee of Beijing Jishuitan Hospital, Capital Medical University (No. 202004-89). Informed consent was obtained from patients of pregnant women or guardians of children.

The authors declare no competing financial interest.

Supplementary Material

pr4c00470_si_001.pdf (144KB, pdf)

References

  1. Cheung A. M.; Papaioannou A.; Morin S. Postmenopausal Osteoporosis. N Engl J. Med. 2016, 374 (21), 2096. 10.1056/NEJMc1602599. [DOI] [PubMed] [Google Scholar]
  2. Yang T. L.; Shen H.; Liu A.; Dong S. S.; Zhang L.; Deng F. Y.; Zhao Q.; Deng H. W. A road map for understanding molecular and genetic determinants of osteoporosis. Nat. Rev. Endocrinol 2020, 16 (2), 91–103. 10.1038/s41574-019-0282-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Avioli L. V. Senile and postmenopausal osteoporosis. Adv. Intern Med. 1976, 21, 391–415. [PubMed] [Google Scholar]
  4. Börjesson A. E.; Lagerquist M. K.; Windahl S. H.; Ohlsson C. The role of estrogen receptor α in the regulation of bone and growth plate cartilage. Cell. Mol. Life Sci. 2013, 70 (21), 4023–37. 10.1007/s00018-013-1317-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Inada M.; Miyaura C. [Cytokines in bone diseases. Cytokine and postmenopausal osteoporosis]. Clin Calcium 2010, 20 (10), 1467–1472. [PubMed] [Google Scholar]
  6. Pacifici R. Estrogen, cytokines, and pathogenesis of postmenopausal osteoporosis. J. Bone Miner Res. 1996, 11 (8), 1043–51. 10.1002/jbmr.5650110802. [DOI] [PubMed] [Google Scholar]
  7. Ralston S. H. Analysis of gene expression in human bone biopsies by polymerase chain reaction: evidence for enhanced cytokine expression in postmenopausal osteoporosis. J. Bone Miner Res. 1994, 9 (6), 883–90. 10.1002/jbmr.5650090614. [DOI] [PubMed] [Google Scholar]
  8. Srivastava R. K.; Dar H. Y.; Mishra P. K. Immunoporosis: Immunology of Osteoporosis-Role of T Cells. Front Immunol 2018, 9, 657. 10.3389/fimmu.2018.00657. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Srivastava R. K.; Sapra L. The Rising Era of “Immunoporosis”: Role of Immune System in the Pathophysiology of Osteoporosis. J. Inflamm Res. 2022, 15, 1667–1698. 10.2147/JIR.S351918. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Breuil V.; Ticchioni M.; Testa J.; Roux C. H.; Ferrari P.; Breittmayer J. P.; Albert-Sabonnadière C.; Durant J.; De Perreti F.; Bernard A.; Euller-Ziegler L.; Carle G. F. Immune changes in post-menopausal osteoporosis: the Immunos study. Osteoporos Int. 2010, 21 (5), 805–14. 10.1007/s00198-009-1018-7. [DOI] [PubMed] [Google Scholar]
  11. Hong D.; Chen H. X.; Yu H. Q.; Liang Y.; Wang C.; Lian Q. Q.; Deng H. T.; Ge R. S. Morphological and proteomic analysis of early stage of osteoblast differentiation in osteoblastic progenitor cells. Exp. Cell Res. 2010, 316 (14), 2291–300. 10.1016/j.yexcr.2010.05.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Kim W. K.; Bae K. H.; Choi H. R.; Kim D. H.; Choi K. S.; Cho Y. S.; Kim H. D.; Park S. G.; Park B. C.; Ko Y.; Lee S. C. Leukocyte common antigen-related (LAR) tyrosine phosphatase positively regulates osteoblast differentiation by modulating extracellular signal-regulated kinase (ERK) activation. Mol. Cells 2010, 30 (4), 335–40. 10.1007/s10059-010-0123-y. [DOI] [PubMed] [Google Scholar]
  13. Lee J. H.; Cho J. Y. Proteomics approaches for the studies of bone metabolism. BMB Rep 2014, 47 (3), 141–8. 10.5483/BMBRep.2014.47.3.270. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. NIH Consensus Development Panel on Osteoporosis Prevention, Diagnosis, and Therapy, March 7–29, 2000: Highlights of the Conference. South Med. J. 2001, 94 (6), 569–573. [PubMed] [Google Scholar]
  15. Wang L.; Yu W.; Yin X.; Cui L.; Tang S.; Jiang N.; Cui L.; Zhao N.; Lin Q.; Chen L.; Lin H.; Jin X.; Dong Z.; Ren Z.; Hou Z.; Zhang Y.; Zhong J.; Cai S.; Liu Y.; Meng R.; Deng Y.; Ding X.; Ma J.; Xie Z.; Shen L.; Wu W.; Zhang M.; Ying Q.; Zeng Y.; Dong J.; Cummings S. R.; Li Z.; Xia W. Prevalence of Osteoporosis and Fracture in China: The China Osteoporosis Prevalence Study. JAMA Netw Open 2021, 4 (8), e2121106 10.1001/jamanetworkopen.2021.21106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Keene G. S.; Parker M. J.; Pryor G. A. Mortality and morbidity after hip fractures. Bmj 1993, 307 (6914), 1248–50. 10.1136/bmj.307.6914.1248. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Wang O.; Hu Y.; Gong S.; Xue Q.; Deng Z.; Wang L.; Liu H.; Tang H.; Guo X.; Chen J.; Jia X.; Xu Y.; Lan L.; Lei C.; Dong H.; Yuan G.; Fu Q.; Wei Y.; Xia W.; Xu L. A survey of outcomes and management of patients post fragility fractures in China. Osteoporos Int. 2015, 26 (11), 2631–40. 10.1007/s00198-015-3162-6. [DOI] [PubMed] [Google Scholar]
  18. Kung A. W.; Lee K. K.; Ho A. Y.; Tang G.; Luk K. D. Ten-year risk of osteoporotic fractures in postmenopausal Chinese women according to clinical risk factors and BMD T-scores: a prospective study. J. Bone Miner Res. 2007, 22 (7), 1080–7. 10.1359/jbmr.070320. [DOI] [PubMed] [Google Scholar]
  19. Black D. M.; Bauer D. C.; Vittinghoff E.; Lui L. Y.; Grauer A.; Marin F.; Khosla S.; de Papp A.; Mitlak B.; Cauley J. A.; McCulloch C. E.; Eastell R.; Bouxsein M. L. Treatment-related changes in bone mineral density as a surrogate biomarker for fracture risk reduction: meta-regression analyses of individual patient data from multiple randomised controlled trials. Lancet Diabetes Endocrinol 2020, 8 (8), 672–682. 10.1016/S2213-8587(20)30159-5. [DOI] [PubMed] [Google Scholar]
  20. Benzvi L.; Gershon A.; Lavi I.; Wollstein R. Secondary prevention of osteoporosis following fragility fractures of the distal radius in a large health maintenance organization. Arch Osteoporos 2016, 11, 20. 10.1007/s11657-016-0275-2. [DOI] [PubMed] [Google Scholar]
  21. Liu X.; Zhang R.; Zhou Y.; Yang Y.; Si H.; Li X.; Liu L. The effect of Astragalus extractive on alveolar bone rebuilding progress of tooth extracted socket of ovariectomied rats. Afr J. Tradit Complement Altern Med. 2014, 11 (5), 91–8. 10.4314/ajtcam.v11i5.15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Eastell R.; Szulc P. Use of bone turnover markers in postmenopausal osteoporosis. Lancet Diabetes Endocrinol 2017, 5 (11), 908–923. 10.1016/S2213-8587(17)30184-5. [DOI] [PubMed] [Google Scholar]
  23. Wang L.; Hu Y. Q.; Zhao Z. J.; Zhang H. Y.; Gao B.; Lu W. G.; Xu X. L.; Lin X. S.; Wang J. P.; Jie Q.; Luo Z. J.; Yang L. Screening and validation of serum protein biomarkers for early postmenopausal osteoporosis diagnosis. Mol. Med. Rep 2017, 16 (6), 8427–8433. 10.3892/mmr.2017.7620. [DOI] [PubMed] [Google Scholar]
  24. Lerner U. H. Inflammation-induced bone remodeling in periodontal disease and the influence of post-menopausal osteoporosis. J. Dent Res. 2006, 85 (7), 596–607. 10.1177/154405910608500704. [DOI] [PubMed] [Google Scholar]
  25. Al-Daghri N. M.; Aziz I.; Yakout S.; Aljohani N. J.; Al-Saleh Y.; Amer O. E.; Sheshah E.; Younis G. Z.; Al-Badr F. B. M. Inflammation as a contributing factor among postmenopausal Saudi women with osteoporosis. Medicine (Baltimore) 2017, 96 (4), e5780 10.1097/MD.0000000000005780. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Azizieh F.; Raghupathy R.; Shehab D.; Al-Jarallah K.; Gupta R. Cytokine profiles in osteoporosis suggest a proresorptive bias. Menopause 2017, 24 (9), 1057–1064. 10.1097/GME.0000000000000885. [DOI] [PubMed] [Google Scholar]
  27. Dar H. Y.; Azam Z.; Anupam R.; Mondal R. K.; Srivastava R. K. Osteoimmunology: The Nexus between bone and immune system. Front Biosci (Landmark Ed) 2018, 23 (3), 464–492. 10.2741/4600. [DOI] [PubMed] [Google Scholar]
  28. Harrington B. S.; He Y.; Davies C. M.; Wallace S. J.; Adams M. N.; Beaven E. A.; Roche D. K.; Kennedy C.; Chetty N. P.; Crandon A. J.; Flatley C.; Oliveira N. B.; Shannon C. M.; deFazio A.; Tinker A. V.; Gilks C. B.; Gabrielli B.; Brennan D. J.; Coward J. I.; Armes J. E.; Perrin L. C.; Hooper J. D. Cell line and patient-derived xenograft models reveal elevated CDCP1 as a target in high-grade serous ovarian cancer. Br. J. Cancer 2016, 114 (4), 417–26. 10.1038/bjc.2015.471. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Magina S.; Filipe P. Pathophysiology of moderate to severe plaque psoriasis: anti-IL-17 towards disease modification. Drugs Today (Barc) 2021, 57 (5), 347–357. 10.1358/dot.2021.57.5.3266244. [DOI] [PubMed] [Google Scholar]
  30. Zhao Y.; Cai L.; Liu X. Y.; Zhang H.; Zhang J. Z. Efficacy and safety of secukinumab in Chinese patients with moderate-to-severe plaque psoriasis: a real-life cohort study. Chin Med. J. (Engl) 2021, 134 (11), 1324–1328. 10.1097/CM9.0000000000001510. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Rendon A.; Schäkel K. Psoriasis Pathogenesis and Treatment. Int. J. Mol. Sci. 2019, 20 (6), 1475. 10.3390/ijms20061475. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Hawkes J. E.; Chan T. C.; Krueger J. G. Psoriasis pathogenesis and the development of novel targeted immune therapies. J. Allergy Clin Immunol 2017, 140 (3), 645–653. 10.1016/j.jaci.2017.07.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Khan T.; Lyons N. J.; Gough M.; Kwah K. K. X.; Cuda T. J.; Snell C. E.; Tse B. W.; Sokolowski K. A.; Pearce L. A.; Adams T. E.; Rose S. E.; Puttick S.; Pajic M.; Adams M. N.; He Y.; Hooper J. D.; Kryza T. CUB Domain-Containing Protein 1 (CDCP1) is a rational target for the development of imaging tracers and antibody-drug conjugates for cancer detection and therapy. Theranostics 2022, 12 (16), 6915–6930. 10.7150/thno.78171. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Iwata M.; Torok-Storb B.; Wayner E. A.; Carter W. G. CDCP1 identifies a CD146 negative subset of marrow fibroblasts involved with cytokine production. PLoS One 2014, 9 (10), e109304 10.1371/journal.pone.0109304. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Urabe F.; Kosaka N.; Yamamoto Y.; Ito K.; Otsuka K.; Soekmadji C.; Egawa S.; Kimura T.; Ochiya T. Metastatic prostate cancer-derived extracellular vesicles facilitate osteoclastogenesis by transferring the CDCP1 protein. J. Extracell Vesicles 2023, 12 (3), e12312 10.1002/jev2.12312. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Sato K.; Suematsu A.; Okamoto K.; Yamaguchi A.; Morishita Y.; Kadono Y.; Tanaka S.; Kodama T.; Akira S.; Iwakura Y.; Cua D. J.; Takayanagi H. Th17 functions as an osteoclastogenic helper T cell subset that links T cell activation and bone destruction. J. Exp Med. 2006, 203 (12), 2673–82. 10.1084/jem.20061775. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Kramer J. M.; Gaffen S. L. Interleukin-17: a new paradigm in inflammation, autoimmunity, and therapy. J. Periodontol 2007, 78 (6), 1083–93. 10.1902/jop.2007.060392. [DOI] [PubMed] [Google Scholar]
  38. Khosla S.; Oursler M. J.; Monroe D. G. Estrogen and the skeleton. Trends Endocrinol Metab 2012, 23 (11), 576–81. 10.1016/j.tem.2012.03.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Tai P.; Wang J.; Jin H.; Song X.; Yan J.; Kang Y.; Zhao L.; An X.; Du X.; Chen X.; Wang S.; Xia G.; Wang B. Induction of regulatory T cells by physiological level estrogen. J. Cell Physiol 2008, 214 (2), 456–64. 10.1002/jcp.21221. [DOI] [PubMed] [Google Scholar]
  40. Raphael I.; Nalawade S.; Eagar T. N.; Forsthuber T. G. T cell subsets and their signature cytokines in autoimmune and inflammatory diseases. Cytokine 2015, 74 (1), 5–17. 10.1016/j.cyto.2014.09.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Tyagi A. M.; Srivastava K.; Mansoori M. N.; Trivedi R.; Chattopadhyay N.; Singh D. Estrogen deficiency induces the differentiation of IL-17 secreting Th17 cells: a new candidate in the pathogenesis of osteoporosis. PLoS One 2012, 7 (9), e44552 10.1371/journal.pone.0044552. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Yago T.; Nanke Y.; Ichikawa N.; Kobashigawa T.; Mogi M.; Kamatani N.; Kotake S. IL-17 induces osteoclastogenesis from human monocytes alone in the absence of osteoblasts, which is potently inhibited by anti-TNF-alpha antibody: a novel mechanism of osteoclastogenesis by IL-17. J. Cell Biochem 2009, 108 (4), 947–55. 10.1002/jcb.22326. [DOI] [PubMed] [Google Scholar]
  43. Dar H. Y.; Shukla P.; Mishra P. K.; Anupam R.; Mondal R. K.; Tomar G. B.; Sharma V.; Srivastava R. K. Lactobacillus acidophilus inhibits bone loss and increases bone heterogeneity in osteoporotic mice via modulating Treg-Th17 cell balance. Bone Rep 2018, 8, 46–56. 10.1016/j.bonr.2018.02.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Sapra L.; Dar H. Y.; Bhardwaj A.; Pandey A.; Kumari S.; Azam Z.; Upmanyu V.; Anwar A.; Shukla P.; Mishra P. K.; Saini C.; Verma B.; Srivastava R. K. Lactobacillus rhamnosus attenuates bone loss and maintains bone health by skewing Treg-Th17 cell balance in Ovx mice. Sci. Rep 2021, 11 (1), 1807. 10.1038/s41598-020-80536-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Li H.; Chen J.; Huang A.; Stinson J.; Heldens S.; Foster J.; Dowd P.; Gurney A. L.; Wood W. I. Cloning and characterization of IL-17B and IL-17C, two new members of the IL-17 cytokine family. Proc. Natl. Acad. Sci. U. S. A. 2000, 97 (2), 773–8. 10.1073/pnas.97.2.773. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Chang S. H.; Reynolds J. M.; Pappu B. P.; Chen G.; Martinez G. J.; Dong C. Interleukin-17C promotes Th17 cell responses and autoimmune disease via interleukin-17 receptor E. Immunity 2011, 35 (4), 611–21. 10.1016/j.immuni.2011.09.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Krohn S.; Nies J. F.; Kapffer S.; Schmidt T.; Riedel J. H.; Kaffke A.; Peters A.; Borchers A.; Steinmetz O. M.; Krebs C. F.; Turner J. E.; Brix S. R.; Paust H. J.; Stahl R. A. K.; Panzer U. IL-17C/IL-17 Receptor E Signaling in CD4(+) T Cells Promotes T(H)17 Cell-Driven Glomerular Inflammation. J. Am. Soc. Nephrol 2018, 29 (4), 1210–1222. 10.1681/ASN.2017090949. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Huang J.; Meng S.; Hong S.; Lin X.; Jin W.; Dong C. IL-17C is required for lethal inflammation during systemic fungal infection. Cell Mol. Immunol 2016, 13 (4), 474–83. 10.1038/cmi.2015.56. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Dharmapatni A. A.; Smith M. D.; Crotti T. N.; Holding C. A.; Vincent C.; Weedon H. M.; Zannettino A. C.; Zheng T. S.; Findlay D. M.; Atkins G. J.; Haynes D. R. TWEAK and Fn14 expression in the pathogenesis of joint inflammation and bone erosion in rheumatoid arthritis. Arthritis Res. Ther 2011, 13 (2), R51. 10.1186/ar3294. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Trebing J.; Arana J. A.; Salzmann S.; Wajant H. Analyzing the signaling capabilities of soluble and membrane TWEAK. Methods Mol. Biol. 2014, 1155, 31–45. 10.1007/978-1-4939-0669-7_4. [DOI] [PubMed] [Google Scholar]
  51. Wang X.; Xiao S.; Xia Y. Tumor Necrosis Factor Receptor Mediates Fibroblast Growth Factor-Inducible 14 Signaling. Cell Physiol Biochem 2017, 43 (2), 579–588. 10.1159/000480530. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

pr4c00470_si_001.pdf (144KB, pdf)

Articles from Journal of Proteome Research are provided here courtesy of American Chemical Society

RESOURCES