Open Access

Identification of gene biomarkers in patients with postmenopausal osteoporosis

  • Authors:
    • Chenggang Yang
    • Jing Ren
    • Bangling Li
    • Chuandi Jin
    • Cui Ma
    • Cheng Cheng
    • Yaolan Sun
    • Xiaofeng Shi
  • View Affiliations

  • Published online on: December 12, 2018     https://doi.org/10.3892/mmr.2018.9752
  • Pages: 1065-1073
  • Copyright: © Yang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Postmenopausal osteoporosis (PMOP) is a major public health concern worldwide. The present study aimed to provide evidence to assist in the development of specific novel biomarkers for PMOP. Differentially expressed genes (DEGs) were identified between PMOP and normal controls by integrated microarray analyses of the Gene Expression Omnibus (GEO) database, and the optimal diagnostic gene biomarkers for PMOP were identified with LASSO and Boruta algorithms. Classification models, including support vector machine (SVM), decision tree and random forests models, were established to test the diagnostic value of identified gene biomarkers for PMOP. Functional annotations and protein‑protein interaction (PPI) network constructions were also conducted. Integrated microarray analyses (GSE56815, GSE13850 and GSE7429) of the GEO database were employed, and 1,320 DEGs were identified between PMOP and normal controls. An 11‑gene combination was also identified as an optimal biomarker for PMOP by feature selection and classification methods using SVM, decision tree and random forest models. This combination was comprised of the following genes: Dehydrogenase E1 and transketolase domain containing 1 (DHTKD1), osteoclast stimulating factor 1 (OSTF1), G protein‑coupled receptor 116 (GPR116), BCL2 interacting killer, adrenoceptor β1 (ADRB1), neogenin 1 (NEO1), RB binding protein 4 (RBBP4), GPR87, cylicin 2, EF‑hand calcium binding domain 1 and DEAH‑box helicase 35. RBBP4 (degree=12) was revealed to be the hub gene of this PMOP‑specific PPI network. Among these 11 genes, three genes (OSTF1, ADRB1 and NEO1) were speculated to serve roles in PMOP by regulating the balance between bone formation and bone resorption, while two genes (GPR87 and GPR116) may be involved in PMOP by regulating the nuclear factor‑κB signaling pathway. Furthermore, DHTKD1 and RBBP4 may be involved in PMOP by regulating mitochondrial dysfunction and interacting with ESR1, respectively. In conclusion, the findings of the current study provided an insight for exploring the mechanism and developing novel biomarkers for PMOP. Further studies are required to test the diagnostic value for PMOP prior to use in a clinical setting.

Introduction

Osteoporosis is a bone metabolic disorder, characterized by low bone mineral density (BMD) and microarchitectural deterioration with increased bone fragility and subsequent susceptibility to fractures (1). It has been reported that osteoporosis is induced by an imbalance between bone resorption by osteoclasts and bone deposition by osteoblasts (2). Postmenopausal osteoporosis (PMOP) is a major public health concern worldwide that frequently presents in postmenopausal women due to the estrogen deficiency and continuous calcium loss that occurs with aging (3). A proactive approach that identifies patients at high risk of developing PMOP is recommended to prevent bone loss (4).

With the advancement of high-throughput technologies, gene microarray analysis has become an effective method for identifying differentially expressed genes (DEGs) and, therefore, potential biomarkers in various diseases. Multiple studies (57) have utilized gene microarray analysis to identify key genes in the pathogenesis of PMOP. Integrated multiple gene microarray analysis may contribute to the identification of more accurate gene biomarkers.

The present study aimed to develop accurate biomarkers and provide clues for exploring the underlying mechanism of PMOP. By integrating multiple microarray analysis in this present study, DEGs between PMOP patients and normal controls were identified. Based on these DEGs, the optimal gene combination with the greatest diagnostic value for PMOP was determined. Functional annotation and protein-protein interaction (PPI) network constructions were also performed to explore the biological functions of DEGs. These findings will help elucidate the mechanism underlying PMOP development and uncover novel diagnostic biomarkers.

Materials and methods

Microarray expression profiling

Microarray datasets of PMOP and normal controls were downloaded from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo). All datasets that contained whole-genome mRNA expression profiles between PMOP patient and control blood samples were enrolled in the current study. The datasets were scale normalized.

Identification of DEGs between PMOP patients and normal controls

MetaMA is an R package-implementing meta-analysis approach for microarray data (8). Data from multiple microarray analyses were combined by metaMA (inverse normal method), and individual P-values were obtained. In the integrated analysis performed in the present study, DEGs between PMOP patients and normal controls were identified at a P-value of <0.05. Hierarchical clustering analyses of mRNAs were conducted with ‘pheatmap’ package in R (version 3.3.3; www.r-project.org).

Identification of optimal diagnostic gene biomarkers for PMOP

The LASSO algorithm was applied with the glmnet package (https://cran.r-project.org/web/packages/glmnet/) in order to reduce the dimensions of the data (9). The Boruta algorithm (https://cran.r-project.org/web/packages/Boruta/) employs a wrapper approach, built around a random forest classifier (10). This algorithm is used to compare the relevance of the features to those of the random probes (11). The scale-standardized datasets were merged, the DEGs between PMOP patients and normal controls were retained for feature selection, and gene biomarkers for PMOP were identified with the LASSO and Boruta algorithms. Furthermore, the optimal gene biomarkers for PMOP were identified by overlapping biomarkers derived from these two algorithms. Hierarchical clustering analysis of these shared gene biomarkers, obtained by LASSO and Boruta algorithms, was conducted with the R package ‘pheatmap’ (R version 3.3.3).

Based on these optimal gene biomarkers, several classification models, including support vector machine (SVM), decision tree and random forest models, were established to further identify the diagnostic value of these biomarkers in PMOP. An SVM model was established with an ‘e1071’ package (https://cran.r-project.org/web/packages/e1071/index.html). A decision tree model was established with the ‘rpart’ package (https://cran.r-project.org/web/packages/rpart/). A random forest model was established with the ‘randomForests’ package (https://cran.r-project.org/web/packages/randomForest/). These three classification models were compared by the average misjudgment rates of their 10-fold cross validations. The diagnostic ability of the three classification models was assessed by calculating the receiver operating characteristic curve, and measuring the area under the curve (AUC), accuracy, sensitivity and specificity.

PMOP-specific PPI network

To further investigate the biological functions of these optimal gene biomarkers, a PPI network was constructed with the BioGRID (also known as Biological General Repository for Interaction Datasets; http://thebiogrid.org/) and Cytoscape (http://www.cytoscape.org). Nodes and edges in the PPI network represented the proteins and the interactions between two proteins, respectively.

Functional annotation

Based on the PMOP-specific PPI network, the proteins that integrated with proteins encoded by the optimal gene biomarkers were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of all gene biomarkers and other DEGs that encode proteins within the PMOP-specific PPI network were conducted with the online software GeneCodis (http://genecodis.cnb.csic.es/analysis). Differences (using the Benjamini and Hochberg method) were defined as statistically significant when the false discovery rate (FDR) was <0.05.

Results

DEGs between PMOP patients and normal controls

Three datasets, including GSE56815, GSE13850 and GSE7429, were downloaded from the GEO database [(Table I) (12)]. Based on these three datasets, 1,320 DEGs (710 upregulated DEGs and 613 downregulated DEGs) with FDR<0.05 were identified between PMOP patients and normal controls. Hierarchical clustering analysis of the top 100 DEGs between PMOP patients and normal controls is presented in Fig. 1.

Table I.

Datasets used in the present study.

Table I.

Datasets used in the present study.

GEO IDSampleCountryYearFirst authorPMOP to control ratio
GSE56815BloodUSA2016Liu20:20
GSE13850BloodUSA2009Xiao20:20
GSE7429BloodUSA2008Xiao10:10

[i] The platform used for all datasets was GPL96 [HG-U133A] Affymetrix Human Genome U133A Array. GEO, Gene Expression Omnibus. PMOP, postmenopausal osteoporosis.

Identification of optimal diagnostic gene biomarkers for PMOP

A total of 31 and 32 gene biomarkers were identified with the LASSO and Boruta algorithms, respectively. Furthermore, 11 shared mRNA biomarkers for PMOP were identified by overlapping the biomarkers derived from these two algorithms (Table II and Fig. 2). These 11 mRNA biomarkers included dehydrogenase E1 and transketolase domain containing 1 (DHTKD1), osteoclast stimulating factor 1 (OSTF1), G protein-coupled receptor 87 (GPR87), GPR116 (also known as adhesion G protein-coupled receptor F5), BCL2 interacting killer (BIK), adrenoceptor β1 (ADRB1), neogenin 1 (NEO1), RB binding protein 4 (RBBP4), cylicin 2 (CYLC2), EF-hand calcium binding domain 1 (EFCAB1) and DEAH-box helicase 35 (DHX35). Hierarchical clustering analysis of these 11 mRNA biomarkers was performed.

Table II.

A total of 11 shared gene biomarkers for postmenopausal osteoporosis, obtained using the LASSO and Boruta algorithms.

Table II.

A total of 11 shared gene biomarkers for postmenopausal osteoporosis, obtained using the LASSO and Boruta algorithms.

Gene IDGene symbolCombined ESP-valueFDRRegulation
79645EFCAB1−1.16 4.51×10−8 2.80×10−4Down
55526DHTKD11.07 4.61×10−7 1.45×10−3Up
26578OSTF11.05 1.99×10−6 3.38×10−3Up
221395GPR1160.98 2.58×10−6 3.38×10−3Up
638BIK0.92 9.30×10−6 8.69×10−3Up
153ADRB10.89 1.97×10−5 1.27×10−2Up
4756NEO1−0.85 5.61×10−5 2.09×10−2Down
5928RBBP40.81 7.70×10−5 2.32×10−2Up
53836GPR87−0.80 1.20×10−4 2.61×10−2Down
1539CYLC2−0.74 3.42×10−4 4.12×10−2Down
60625DHX350.73 5.64×10−4 5.08×10−2Up

[i] ES, effective size; FDR, false discovery rate; EFCAB1, EF-hand calcium binding domain 1; DHTKD1, dehydrogenase E1 and transketolase domain containing 1; OSTF1, osteoclast stimulating factor 1; GPR, G protein-coupled receptor; BIK, BCL2 interacting killer; ADRB1, adrenoceptor β1; NEO1, neogenin 1; RBBP4, RB binding protein 4; CYLC2, cylicin 2; DHX35, DEAH-box helicase 35.

The SVM, decision tree and random forest models were established with these 11 mRNA biomarkers, and the accuracy of these three models was 93, 78 and 94%, respectively. The AUC of SVM, decision tree and random forest models was 0.975, 0.799 and 0.975, respectively. In addition, the sensitivity and specificity of the SVM model were 92 and 100%, respectively (Fig. 3A). The sensitivity and specificity of the decision tree model were 70 and 88%, respectively (Fig. 3B). Finally, the sensitivity and specificity of the random forest model were 90 and 100%, respectively (Fig. 3C).

PMOP-specific PPI network

The PMOP-specific PPI network consisted of 24 nodes and 20 edges (Fig. 4). Out of the 11 genes not all interacted with other differentially expressed genes. Only the gene that interacted with other differentially expressed genes (even though one gene) in the PPI network were presented. Nodes and edges represented the proteins and the interactions between two proteins, respectively. The red and blue ellipses represented the proteins encoded by up- and downregulated DEGs between PMOP patients and normal controls, respectively. RBBP4 (degree=12) was the hub gene of this PMOP-specific PPI network.

Functional annotation

Based on the functional annotations of these 11 biomarkers and DEGs that encode proteins of the PMOP-specific PPI network, prostate epithelial cord elongation (FDR<0.05), estrogen response element binding (FDR<0.05) and nucleosome remodeling deacetylase complex (FDR<0.05) were the most significant GO terms. Furthermore, endocrine and other factor-regulated calcium reabsorption (FDR=2.23×10−5), was a significantly enriched pathway in PMOP; Estrogen receptor 1 (ESR1) was revealed to be upregulated within this pathway (Table III).

Table III.

Top 10 significantly GO terms and KEGG pathways in postmenopausal osteoporosis.

Table III.

Top 10 significantly GO terms and KEGG pathways in postmenopausal osteoporosis.

A, GO terms

IDTermFDRGenes
Biological process
  GO:0060523Prostate epithelial cord elongation<0.05ESR1
  GO:0031649Heat generation<0.05ADRB1
  GO:0045986Negative regulation of smooth muscle contraction<0.05ADRB1
  GO:0002025Vasodilation by norepinephrine-epinephrine involved in regulation of systemic arterial blood pressure<0.05ADRB1
  GO:0031077Post-embryonic camera-type eye development<0.05BCL11B
  GO:0048386Positive regulation of retinoic acid receptor signaling pathway<0.05ESR1
  GO:0003382Epithelial cell morphogenesis<0.05BCL11B
  GO:0051124Synaptic growth at neuromuscular junction<0.05APP
  GO:0060750Epithelial cell proliferation involved in mammary gland duct elongation<0.05ESR1
  GO:0046878Positive regulation of saliva secretion<0.05ADRB1
Molecular function
  GO:0034056Estrogen response element binding<0.05ESR1
  GO:0051400BH domain binding<0.05BIK
  GO:0051380Norepinephrine binding<0.05ADRB1
  GO:0004535Poly(A)-specific ribonuclease activity<0.05PAN2
  GO:0051434BH3 domain binding<0.05BCL2L1
  GO:0051425PTB domain binding<0.05APP
  GO:0004939β-adrenergic receptor activity<0.05ADRB1
  GO:0004591Oxoglutarate dehydrogenase (succinyl-transferring) activity<0.05DHTKD1
  GO:0004940β1-adrenergic receptor activity<0.05ADRB1
  GO:0031798Type 1 metabotropic glutamate receptor binding<0.05ESR1
Cellular component
  GO:0016581NuRD complex<0.05APPL1, RBBP4
  GO:0097136Bcl-2 family protein complex<0.05BCL2L1
  GO:0033150Cytoskeletal calyx<0.05CYLC2
  GO:0033553rDNA heterochromatin<0.05SUV39H1
  GO:0033186CAF-1 complex<0.05RBBP4
  GO:0044429Mitochondrial part<0.05BCL2L1, ESR1
  GO:0030870Mre11 complex<0.05TERF2
  GO:0030891VCB complex<0.05VHL
  GO:0001740Barr body<0.05H3F3A
  GO:0016589NURF complex<0.05RBBP4

B, KEGG pathways

IDPathwayFDRGenes

KEGG:05200Pathways in cancer<0.05BCL2L1, VHL, APPL1
KEGG:04962 Vasopressin-regulated water reabsorption<0.05CREB3
KEGG:04961Endocrine and other factor-regulated calcium reabsorption<0.05ESR1
KEGG:00310Lysine degradation<0.05SUV39H1
KEGG:05014Amyotrophic lateral sclerosis (ALS)<0.05BCL2L1
KEGG:05210Colorectal cancer<0.05APPL1
KEGG:03018RNA degradation<0.05PAN2
KEGG:03320PPAR signaling pathway<0.05FABP4
KEGG:05211Renal cell carcinoma<0.05VHL
KEGG:05212Pancreatic cancer<0.05BCL2L1

[i] GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; FDR, false discovery rate.

Discussion

PMOP increases the risk of fragility fractures in postmenopausal women, and imposes a significant burden on patients' families and society. Previous studies have indicated that identification of patients at high risk of developing PMOP can contribute to the prevention of bone loss (4,13).

In the present study, 1,320 DEGs between PMOP patients and normal controls were identified with integrated microarray analysis. The gene biomarkers for PMOP were further identified with the LASSO and Boruta algorithms. An 11-gene combination (EFCAB1, DHTKD1, OSTF1, GPR116, BIK, ADRB1, NEO1, RBBP4, GPR87, CYLC2 and DHX35) was revealed as an optimal biomarker for PMOP with feature selection and classification procedures using SVM, decision tree and random forest models. Based on the random forest model, the 11-gene combination achieved a 94% prediction accuracy in distinguishing patients with PMOP from normal controls, with 90% sensitivity and 100% specificity. The results obtained using the other two models (SVM and decision tree) further supported this finding.

Among these 11 genes, CYLC2 has previously been shown to be upregulated in B cells from postmenopausal women with low BMD compared with that in postmenopausal women with high BMD (14). In addition, CYLC2 is involved in the structural component of the cytoskeleton. A previous study indicated that the structural component of the cytoskeleton is associated with PMOP, which may suggest a potential role of CYLC2 in PMOP (14).

Three DEGs identified in the present study, namely OSTF1, ADRB1 and NEO1, have previously been reported to be associated with the balance between bone formation and bone resorption (1519). OSTF1 is an intracellular protein that is highly expressed in osteoclasts, which indirectly enhances osteoclast formation and bone resorption (15). ADRB1 belongs to the family of guanine nucleotide-binding regulatory protein-coupled receptors, which regulate the physiological effects of the hormone epinephrine and the neurotransmitter norepinephrine (NE) (16). The β-adrenergic system is also involved in leptin-dependent central regulation of bone turnover (17,18). Intraosseous sympathetic nerve fibers can be activated and release NE via leptin stimulation (17). Adrenergic receptors expressed on osteoblasts bind to the released NE and result in suppression of bone formation. In addition, β-adrenergic-stimulated production of the receptor activator of nuclear factor (NF)-κB ligand by osteoblasts may contribute to a negative bone mineral balance (19). In the present study, OSTF1 and ADRB1 were upregulated in the blood of patients with PMOP compared with that of normal controls. Furthermore, NEO1 encodes a cell surface protein that belongs to the immunoglobulin superfamily, and has been speculated to serve roles in cell growth and differentiation and in cell-cell adhesion. A previous study reported abnormal chondrocyte maturation and endochondral bone growth in NEO1 knockout mice (20). Additionally, the association between NEO1 and bone mass was identified by high-throughput screening of mouse gene knockouts (21). To the best of our knowledge, the present study is the first to reveal a downregulation of NEO1 in the blood of patients with PMOP.

Estrogen deficiency is a pivotal cause of postmenopausal bone loss (22). RBBP4 is an estrogen-associated gene, which was included in the 11-gene combination described in the present study. It is also a chromatin remodeling factor that encodes a ubiquitously expressed nuclear protein that belongs to a highly conserved subfamily of WD-repeat proteins (23). RBBP4 has been reported to be involved in the chromatin remodeling and transcriptional repression associated with histone deacetylation (24). An upregulation in the expression of RBBP4 was detected in the tibia callus of estrogen-deficient rats (25). To the best of our knowledge, the present study is the first to reveal an upregulation of RBBP4 in the blood of patients with PMOP. Furthermore, as the hub protein of the PMOP-specific PPI network, RBBP4 was integrated with ESR1, a well-known PMOP-associated gene, which was revealed to be enriched in the endocrine and other factor-regulated calcium reabsorption pathway (KEGG ID: 04961). ESR1 is expressed on cells that contribute to bone formation, such as osteoblasts, osteocytes and osteoclasts. It also increases the formation and function of osteoblasts and reduces bone resorption activities (26). Therefore, the RBBP4-ESR1 interaction may serve a key role in PMOP.

To the best of our knowledge, no previous study has reported the association between PMOP and the six other genes described in the current study, including DHTKD1, GPR87, GPR116, BIK, EFCAB1 and DHX35. DHTKD1 is a nuclear gene that is involved in mitochondrial lysine metabolism and adenosine triphosphate production (27,28). DHTKD1 has also been demonstrated to link mitochondrial dysfunction and eosinophilic esophagitis (29). Kim and Lee (30) indicated that mitochondrial dysfunction may be a potential pathophysiological mechanism of PMOP, which suggested that DHTKD1 may regulate mitochondrial dysfunction in PMOP. In addition, GPR87 is a cell surface G protein-coupled receptor that has been reported to be overexpressed in various types of cancer (31,32), and it serves a critical oncogenic role in pancreatic cancer progression by activating the NF-κB signaling pathway (33). GPR116 is a member of the G protein-coupled receptor family predominantly expressed in the alveolar type II epithelial cells of the lung. Since NF-κB signaling pathway is an important mediator in osteoblast differentiation, it can be speculated that both GPR87 and GPR116 may serve a role in PMOP by regulating the NF-κB signaling pathway. Another identified gene, BIK, is a member of the BH3-only Bcl-2 family of pro-apoptotic proteins, which is suppressed in various types of cancer (34). Methylated BIK was identified in the bone marrow of patients with multiple myeloma, and dysregulated BIK expression was observed in hematopoietic cell fractions of patients with myelodysplastic syndrome, highlighting the importance of BIK in bone disease (34,35). Furthermore, the gene DHX35 is a putative RNA helicase, and its variants have been reported to be involved with facial morphology, thyroid cancer and colorectal cancer (3638) Finally, DNA methylation of EFCAB1 was demonstrated to be involved in multi-organ carcinogenesis (39). However, further research is required to explore the roles of DHX35 and EFCAB1 in PMOP.

In conclusion, the present study identified 11 genes that were significantly associated with PMOP and provided clues for exploring the molecular mechanism of PMOP. Three of the identified genes (OSTF1, ADRB1 and NEO1) were speculated to be involved in PMOP by regulating the balance between bone formation and bone resorption, while two genes (GPR87 and GPR116) may regulate the NF-κB signaling pathway. RBBP4 and DHTKD1 may also be potential regulators of PMOP via interacting with ESR1 and regulating mitochondrial dysfunction, respectively. Furthermore, the constituents of this 11-gene combination may serve as potential biomarkers for PMOP. However, biological investigations and validation with a larger sample size are lacking, and are considered to be limitations of the present study. Further investigations are required to validate the diagnostic abilities of this gene combination for PMOP prior to its clinical application.

Acknowledgements

Not applicable.

Funding

No funding was received.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Authors' contributions

CY and XS made substantial contributions to the study conception and design. CY, JR, BL, CJ, CM, CC and YS collected and analyzed the data. CY, JR, BL and CJ interpreted the data. All authors were involved in drafting the manuscript and provided final approval of this manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Glossary

Abbreviations

Abbreviations:

BMD

bone mineral density

DEGs

differentially expressed genes

ESR1

estrogen receptor 1

GEO

Gene Expression Omnibus

GO

Gene Ontology

KEGG

Kyoto Encyclopedia of Genes and Genomes

NE

norepinephrine

PMOP

postmenopausal osteoporosis

PPI

protein-protein interaction

RANKL

receptor activator of NF-κB ligand

SVM

support vector machine

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February-2019
Volume 19 Issue 2

Print ISSN: 1791-2997
Online ISSN:1791-3004

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Spandidos Publications style
Yang C, Ren J, Li B, Jin C, Ma C, Cheng C, Sun Y and Shi X: Identification of gene biomarkers in patients with postmenopausal osteoporosis. Mol Med Rep 19: 1065-1073, 2019.
APA
Yang, C., Ren, J., Li, B., Jin, C., Ma, C., Cheng, C. ... Shi, X. (2019). Identification of gene biomarkers in patients with postmenopausal osteoporosis. Molecular Medicine Reports, 19, 1065-1073. https://doi.org/10.3892/mmr.2018.9752
MLA
Yang, C., Ren, J., Li, B., Jin, C., Ma, C., Cheng, C., Sun, Y., Shi, X."Identification of gene biomarkers in patients with postmenopausal osteoporosis". Molecular Medicine Reports 19.2 (2019): 1065-1073.
Chicago
Yang, C., Ren, J., Li, B., Jin, C., Ma, C., Cheng, C., Sun, Y., Shi, X."Identification of gene biomarkers in patients with postmenopausal osteoporosis". Molecular Medicine Reports 19, no. 2 (2019): 1065-1073. https://doi.org/10.3892/mmr.2018.9752