BioMed Research International
Volume 2018 (2018), Article ID 5692735, 21 pages
https://doi.org/10.1155/2018/5692735

Research Article

Screening of Serum Protein Markers for Avascular Osteonecrosis of Femoral Head Differentially Expressed after Treatment with Yuanshi Shengmai Chenggu Tablets

Peng Deng,¹ Jianchun Zeng,² Jie Li,² Wenjun Feng,² Jinlun Chen,² and Yirong Zeng²

¹The First Clinical Medical College, Guangzhou University of Chinese Medicine, 16 Jichang Road, Baiyun District, Guangzhou, Guangdong Province 510405, China
²The Third Department of Orthopedics, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong Province 510405, China

Correspondence should be addressed to Yirong Zeng; zengyirong2016@yeah.net

Received 28 August 2017; Revised 4 January 2018; Accepted 22 January 2018; Published 20 March 2018

Academic Editor: Yu-Chang Tyan

Copyright © 2018 Peng Deng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Avascular necrosis of the femoral head (ANFH) is an a frequently occurring orthopaedic disease with high morbidity. Traditional Chinese Medicine (TCM) Yuanshi Shengmai Chenggu Tablet is a valid prescription for treating steroid-induced femoral head necrosis. However, there are rare investigations about the serum protein marker expression after the acting of drugs on hormone and TCM. In the present study, we aimed to systematically discover and validate the serum biomarkers expression difference in patients with steroid-induced avascular necrosis of femoral head (SANFH) after taking Yuanshi Shengmai Chenggu Tablets (SANFH-TCM), so as to reveal the action mechanism of TCM from the molecular level by using isobaric tags for relative and absolute quantification (iTRAQ) with multiple reaction monitoring quantification. Significant differences in fibrinogen alpha, fibrinogen beta, fibrinogen gamma, fibronectin, C-reactive protein, apolipoprotein A, apolipoprotein D, and apolipoprotein E were found among SANFH, SANFH-TCM, and healthy controls. Therefore, our study proposes potential biomarkers for SANFH diagnosis and for the prognosis of femoral head necrosis after Traditional Chinese Medicine treatment.

1. Introduction

Avascular necrosis of the femoral head (ANFH) is a clinical common orthopaedic disease [1–3] with very high morbidity. It is difficult to cure and is one of the major medical problems that have not yet been overcome [4, 5]. Primary osteonecrosis of the femoral head is due to gene or gene mutation of patients. Secondary osteonecrosis of the femoral head could divide into traumatic and nontraumatic osteonecrosis of femoral head [6–8], in which traumatic osteonecrosis of the femoral head is the avascular necrosis of osteocyte caused by interruption of blood flow in the blood vessels in femoral head, which was due to trauma [9, 10]. Its etiology is still unclear. It was demonstrated that long-term and large dosage usage of hormone and drinking are two important factors that cause ANFN [4, 11]. In recent years, with the wide use of corticosteroids clinically, the cases with ANFN have also increased greatly [12, 13]. However, the pathogenesis of steroid-induced ANFN is still unknown. For developing new methods to prevent and treat ANFN, study on the pathogenesis of steroid-induced ANFN is particularly urgent [12, 14, 15].

Recent reports have shown that the occurrence of ANFN could be greatly decreased by an early intervention on high-risk crowds of ANFN who use hormones such as steroid and alcohol [16]. However, the composition of the serum is very complex [17]. It contains high-abundance proteins like albumin and immunoglobulins (mainly IgG), as well as low abundance proteins that are secreted by tissue or cells [18, 19]. Some of them are key proteins involved in signal transduction and regulation [20]. Tan et al. [21] adopted two-dimensional electrophoresis technology and separated 7 differentially expressed proteins between patients with primary femoral head necrosis and normal subjects from 10 pairs serum samples. They found that four important proteins including tissue-type plasminogen activator (t-PA), plasminogen activator inhibitor type 1 (PAI-1), Crosslaps, and anti-p53 antibody were significantly changed and that all of them can be used as the diagnosis serum markers of nontraumatic femoral head necrosis. Although the pathogenesis of ANFN is still unclear and the relevance of this finding with the further clinical application was not reported, analysis of the differentially expressed proteins in the serum could provide useful information.

Traditional Chinese Medicine (TCM) Yuanshi Shengmai Chenggu Tablet is valid and specialty drug for steroid-induced ANFN treatment. Yuanshi Shengmai Chenggu Tablet has obtained the certificate of new medicine in China and has been applied in clinical [22]. Its active ingredients are mainly flavonoids such as vitexin. By clinical studies, it was demonstrated the application of Yuanshi Shengmai Chenggu Tablet can significantly relieve the patients’ pain and accelerate the absorption of dead bone and formation of new bone, showing a relatively strong osteogenetic activity [22].

Liu et al. [23] extracted proteins in bone tissue from the femur and humerus bone in rat osteonecrosis model with or without Yuanshi Shengmai Chenggu Tablet TCM treatment and performed proteomics research. They reported that anticoagulating proteins heavy chain II B, phospholipid hydroperoxide glutathione peroxidase, and ubiquitin enzymes E2 (MW: 17 kD) are closely associated with steroid-induced bone necrosis, as well as the therapeutic efficacy of TCM.

In this study we aimed to investigate the differentially expressed protein in serum between steroid-induced ANFN patients with or without TCM treatment (Yuanshi Shengmai Chenggu Tablets). For this purpose, the proteomics method isobaric tags for relative and absolute quantification (iTRAQ) with multiple reaction monitoring (MRM) quantification was adopted in this study, so as to reveal the molecular mechanism of TCM treated the SANFN in the molecular level.

2. Material and Methods

2.1. Participants

Patients diagnosed as ANFN in the First Affiliated Hospital of Traditional Chinese Medicine University of Guangzhou from February 2014 to February 2015 were included. The ANFN diagnosis was established by referring to standard of adult femoral head necrosis diagnosis expert consensus (2012 edition) and the diagnosis and treatment of avascular necrosis of the expert advice of diagnostic criteria. Patients in active period of ANFN, alcoholics who are simultaneously treated by long-term high dose of glucocorticoids (taken steroid > 10 mg/d longer than 3 years), or patients with combining chronic disease which needs prolonged treatment were excluded in present study. All participants gave written informed consent before being enrolled in the study (AE-2013012011).

2.2. Specimens and Groups

Patients with ANFN who have used long-term and high-dosage of steroid (SANFN) were further treated with TCM Yuanshi Shengmai Chenggu Tablet (6 tablets each time, 3 times per day, total 3 months; prepared by the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, China). Serum samples () from patients with or without TCM were prospectively collected after obtaining written informed consent. The study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine. Five healthy subjects were collected during the same period who were sex- and age-matched. Thus, the verification population was divided into 3 groups: steroid-induced avascular necrosis of femoral head (SANFH), SANFH-TCM treatment, and healthy controls.

All serum samples were centrifuged at 1250 for 5 min and then 13500 for 15 min at 4°C within 1 h of collection. All samples were then stored at −80°C until use.

2.3. iTRAQ Analysis of Serum Samples

iTRAQ labeling and mass spectrometry analysis were performed as previously described [24]. Then, six iTRAQ labeled sample polls were generated (steroid-induced avascular necrosis of femoral head, SANFH-TCM treatment, and health controls, each for two subgroups). Briefly, high-abundance serum proteins such as albumin, IgG, and haptoglobin were removed by using the Human 14 Multiple Affinity Removal System (Agilent Technologies, Santa Clara, CA, USA). Then, 50 μg protein of each sample was concentrated and desalted, followed by digestion using trypsin before iTRAQ labeling. Six groups were labeled, including steroid-induced avascular necrosis of femoral head, iTRAQ reagent 113, 116; SANFH-TCM treatment 114, 117; and health controls 115, 118. The six sample groups were mixed, desalted, and dried.

The iTRAQ labeled peptides were separated by Strong Cation Exchange (SCX) chromatography (Bonna-Agela Technologies, Tianjin, China). SCX was carried out on a Polysulfoethyl 4.6 × 100 mm column (5 μm, 200 Å, PolyLC Inc., Maryland, USA). The peptides were eluted at the 45 min gradient from 100% buffer A (10 mM KH₂PO₄ pH 3.0, 25% acetonitrile) to 45% buffer B (10 mM KH₂PO₄ pH 3.0, 500 mM KCl, 25% acetonitrile) at the flow rate of 800 μL/min on Agilent 1210 LC system. All the fractions were analyzed by MALDI-TOF/TOF 5800 mass spectrometer (AB SCIEX, California, USA). Protein quantification and identification were performed with the Proteome Discoverer (version 1.3, thermos). The default bias correction was used and all quantitative variables were analyzed by the Proteome Discoverer 1.3. Peptide abundances were calculated based on the areas of the monoisotopic peaks. Protein ratios were the average ratios of all quantified peptides. Proteins with quantification value < 0.05 in at least two pairs (113 : 114, 113 : 115, 114 : 115; 116 : 117, 116 : 118, 117 : 118) and with the ratio > 1.2 (the average ratio of two repeat experiments) or ratio < 0.83 were considered as differentially expressed proteins, using a cutoff of 2 times standard deviation [25].

2.4. Bioinformatics Analysis

Biomarker candidates were then prioritized using scoring a system based on iTRAQ values from Proteome Discoverer analysis. The cellular component, molecular function, and biological process were analyzed through Gene Ontology (GO) database. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway mapping was performed by KEGG Mapper (http://www.genome.jp/kegg/mapper.html), and the enrichment analysis was performed by Blast2GO PRO software (https://www.blast2go.com/, version 2.8).

2.5. Validation of Differential Expressed Protein by Multiple Reaction Monitoring (MRM) Quantification

To validate the expression of biomarker candidates, MRM quantifications were performed as previously described [26]. Briefly, 30 μg protein of each sample was digested using trypsin before being desalted. Then, desalted peptide mixtures were loaded onto an Acclaim PePmap C18-reversed phase column (100 Ǻ, Thermo Scientific, Massachusetts, USA) and separated with reversed phase C18 column (300 Ǻ, Bonna-Agela Technologies) mounted on a Dionex ultimate 3000 nano-LC system. Peptides were eluted using a gradient of 5–80% (v/v) acetonitrile in 0.1% formic acid over 45 min at a flow rate of 300 nL/min combined with a Q Exactive mass spectrometer (Thermo Scientific, Massachusetts, USA), and then the eluates were directly entered in Q Exactive MS (Thermo Scientific, Massachusetts, USA), setting in positive ion mode and data-dependent manner with full MS scan within 350–2000 , full scan resolution at 70000, MS/MS scan resolution at 17500, and MS/MS scan with minimum signal threshold , isolation width at 2 Da. To evaluate the performance of this mass spectrometry on the iTRAQ labeled samples, two MS/MS acquisition modes and higher collision energy dissociation (HCD) were employed. And to optimize the MS/MS acquisition efficiency of HCD, normalized collision energy (NCE) was systemically examined, stepped 20%. Each MS/MS spectrum was searched against a mascot database (Uniprot_2015_human database, 20194 protein entries) and a decoy database for FDR analysis (programmed in the software). The search parameters were as follows: sample type, iTRAQ 8-plex (Peptide Labeled); cysteine modification by methyl methane-thiosulfonate; digestion, trypsin enzyme; proteins with, at least, two peptides with a high confidence score (>95%) and a low FDR (estimated local FDR of 5%) were considered positively identified.

2.6. Statistical Analysis

All studies to identify biomarkers by iTRAQ/MRM LC-MS/MS were performed on three separate occasions. Statistical analysis was performed using R (version 3.4.2, Bell Laboratories, USA). Analysis of variance (ANOVA) was performed for groups comparison. A value < 0.05 was considered as statistical significantly.

3. Results

3.1. Populations

A total of 26 patients were included in the present study. Demographic characteristics of present population were summarized in Table 1. All of them were diagnosed as Association Research Circulation Osseous (ARCO) II stage SONFH, and the time windows of being illness were from 6 to 34 months. The mean age was 39.5 years old and 11 (42.3%) of them were males, suggesting that the patients SONFH were younger. Primary cause of 50% of patients was systemic lupus erythematosus, indicating a high risk of long-term high dose of steroid in systemic lupus erythematosus.

Table 1: Demographic characteristics.

In Total
26

Male, (%)
11 (42.3)

Age, yr, mean ± SD
39.5 ± 5.3

History of SONFH, months, median (min, max)
23.1 (6, 34)

Primary disease, (%)

Systemic lupus erythematosus
13 (50.0)

Anaphylactoid purpura
5 (19.2)

Eczema
4 (15.4)

Psoriasis
2 (7.7)

Thrombocytopenic purpura
1 (3.8)

Fever of unknown origin
1 (3.8)

3.2. Protein Identification and Differentially Abundant Proteins

Serum proteins of steroid-induced ANSF (SANFH) patients, SANFH-TCM treatment patients, and health subjects were screened using iTRAQ. The experiment was repeated twice and detected 399 proteins. Among them, 61 proteins were differentially expressed between SANFH and healthy controls (Table 2), including 35 significantly upregulated proteins (>1.21-fold, ) and 26 significantly downregulated proteins (<0.83-fold, ). The top four upregulated proteins in SANFH compared to healthy controls were serum amyloid A-2 (SAA2), Ig lambda, sodium/potassium-transporting ATPase subunit alpha-3 (ATP1A3), and calcium-binding mitochondrial carrier protein Aralar1 (SLC25A12) with fold change values of 4.57, 2.64, 2.07, and 1.95. The top four downregulated proteins were properdin, keratin type I cytoskeletal 9 (KRT9), apolipoprotein (a) (LPA), and tropomyosin alpha-4 (TPM4) with fold change values of −1.77, −1.57, −1.65, and −1.61, respectively.

Table 2: Differentially expressed protein between SANFH and healthy controls.

Accession
Description
value
Ratio (mean)

Upregulated protein

P0DJI9
Serum amyloid A-2 protein

P01714
Ig lambda chain V-III region SH

P13637
Sodium/potassium-transporting ATPase subunit alpha-3

O75746
Calcium-binding mitochondrial carrier protein Aralar1

P01613
Ig kappa chain V-I region Ni

P01743
Ig heavy chain V-I region HG3

P01742
Ig heavy chain V-I region EU

P0DJI8
Serum amyloid A-1 protein

P01857
Ig gamma-1 chain C region

P01861
Ig gamma-4 chain C region

P06889
Ig lambda chain V-IV region MOL

P01860
Ig gamma-3 chain C region

P02741
C-reactive protein

Q8NBJ4
Golgi membrane protein 1

P0C0L4
Complement C4-A

P12235
ADP/ATP translocase 1

P06576
ATP synthase subunit beta, mitochondrial

P13645
Keratin, type I cytoskeletal 10

P04433
Ig kappa chain V-III region VG (Fragment)

Q08380
Galectin-3-binding protein

Q14624
Inter-alpha-trypsin inhibitor heavy chain H4

P22692
Insulin-like growth factor-binding protein 4

P01620
Ig kappa chain V-III region SIE

P02452
Collagen alpha-1(I) chain

O75636
Ficolin-3

P01717
Ig lambda chain V-IV region Hil

P14625
Endoplasmin

P01621
Ig kappa chain V-III region NG9 (Fragment)

P51884
Lumican

P02747
Complement C1q subcomponent subunit C

P25705
ATP synthase subunit alpha, mitochondrial

P22792
Carboxypeptidase N subunit 2

P09543
2′,3′-Cyclic-nucleotide 3′-phosphodiesterase

P18428
Lipopolysaccharide-binding protein

P01009
Alpha-1-antitrypsin

Downregulated protein

P01781
Ig heavy chain V-III region GAL

O43852
Calumenin

Q9UK55
Protein Z-dependent protease inhibitor

O43866
CD5 antigen-like

Q04756
Hepatocyte growth factor activator

P63104
14-3-3 protein zeta/delta

Q68CQ4
Digestive organ expansion factor homolog

P68366
Tubulin alpha-4A chain

P04211
Ig lambda chain V region 4A

Q6Q788
Apolipoprotein A-V

P59665
Neutrophil defensin 1

P05164
Myeloperoxidase

P02649
Apolipoprotein E

P04070
Vitamin K-dependent protein C

Q13103
Secreted phosphoprotein 24

0.76858333

P02749
beta-2-Glycoprotein 1

P01591
Immunoglobulin J chain

P36980
Complement factor H-related protein 2

P04220
Ig mu heavy chain disease protein

P01871
Ig mu chain C region

Q71U36
Tubulin alpha-1A chain

P62158
Calmodulin

P67936
Tropomyosin alpha-4 chain

P08519
Apolipoprotein(a)

P35527
Keratin, type I cytoskeletal 9

P27918
Properdin

A total of 74 proteins were differentially expressed between SANFH-TCM and healthy controls (Table 3), including 45 significantly upregulated proteins (>1.21-fold, ), and 29 significantly downregulated proteins (<0.83-fold, ). The top four upregulated proteins in SANFH-TCM compared to healthy controls were ATP-binding cassette subfamily B member 9 (ABCB9), fibrinogen alpha, fibrinogen gamma, and fibrinogen beta with fold change values of 17.47, 13.05, 12.67, and 12.11. The top four downregulated proteins were C-reactive protein (CRP), Tubulin alpha-1A (TUBA1A), fibronectin, and LPA with fold change values of −1.73, −1.73, −1.52, and −1.48, respectively.

Table 3: Differentially expressed protein between SANFH-TCM and healthy controls.

Accession
Description
value
Ratio (mean)

Upregulated protein

Q9NP78
ATP-binding cassette subfamily B member 9

P02671
Fibrinogen alpha chain

P02679
Fibrinogen gamma chain

P02675
Fibrinogen beta chain

Q86XH1
IQ and AAA domain-containing protein 1

P01019
Angiotensinogen

Q68CQ4
Digestive organ expansion factor homolog

P03950
Angiogenin

P13637
Sodium/potassium-transporting ATPase subunit alpha-3

Q8WXD2
Secretogranin-3

P06396
Gelsolin

Q8NBJ4
Golgi membrane protein 1

Q96KN2
Beta-Ala-His dipeptidase

P02775
Platelet basic protein

P06727
Apolipoprotein A-IV

P01023
Alpha-2-macroglobulin

Q14624
Inter-alpha-trypsin inhibitor heavy chain H4

P02776
Platelet factor 4

Q7Z2Y8
Interferon-induced very large GTPase 1

P29122
Proprotein convertase subtilisin/kexin type 6

P05154
Plasma serine protease inhibitor

P14618
Pyruvate kinase PKM

P06889
Ig lambda chain V-IV region MOL

P01857
Ig gamma-1 chain C region

P22792
Carboxypeptidase N subunit 2

P01742
Ig heavy chain V-I region EU

P05090
Apolipoprotein D

P51884
Lumican

P01613
Ig kappa chain V-I region Ni

P01860
Ig gamma-3 chain C region

P14625
Endoplasmin

Q04756
Hepatocyte growth factor activator

P80108
Phosphatidylinositol-glycan-specific phospholipase D

P02787
Serotransferrin

P00450
Ceruloplasmin

P10720
Platelet factor 4 variant

P80748
Ig lambda chain V-III region LOI

P01009
Alpha-1-antitrypsin

Q9NP79
Coagulation factor XII

P02671
Retinoic acid receptor responder protein 2

P02679
Ig kappa chain V-II region TEW

P02675
Kallistatin

Q86XH2
Complement C1q subcomponent subunit A

P01020
FERM and PDZ domain-containing protein 1

Q68CQ5
Retinol-binding protein 4

1.2112

Downregulated protein

Q02818
Nucleobindin-1

0.8295

Q9UK55
Protein Z-dependent protease inhibitor

0.829081

P02656
Apolipoprotein C-III

0.823015

P62258
14-3-3 protein epsilon

0.81975

P01877
Ig alpha-2 chain C region

0.817692

Q6Q788
Apolipoprotein A-V

0.81769

P36980
Complement factor H-related protein 2

0.815458

P35542
Serum amyloid A-4 protein

0.813253

P01620
Ig kappa chain V-III region SIE

0.800375

O14818
Proteasome subunit alpha type-7

0.794833

P18428
Lipopolysaccharide-binding protein

0.789371

P04438
Ig heavy chain V-II region SESS

0.78775

P49721
Proteasome subunit beta type-2

0.7765

P04196
Histidine-rich glycoprotein

0.775008

P02649
Apolipoprotein E

0.75853

Q86UD1
Out at first protein homolog

0.755267

P04211
Ig lambda chain V region 4A

0.751667

P0DJI9
Serum amyloid A-2 protein

0.744667

P67936
Tropomyosin alpha-4 chain

0.74375

Q8NDV3
Structural maintenance of chromosomes protein 1B

0.741833

P62158
Calmodulin

0.733667

Q16610
Extracellular matrix protein 1

0.731882

Q92954
Proteoglycan 4

0.728857

P59665
Neutrophil defensin 1

0.7177

P20742
Pregnancy zone protein

0.696948

P02741
C-reactive protein

0.677154

Q71U36
Tubulin alpha-1A chain

0.657333

P02751
Fibronectin

0.635439

P08519
Apolipoprotein(a)

0.56335

In addition, a total of 81 proteins were differentially expressed between SANFH-TCM and SANFH (Table 4), including significantly 44 upregulated proteins (>1.21-fold, ) and 37 significantly downregulated proteins (<0.83-fold, ). The top four upregulated proteins in SANFH-TCM compared to SANFH were ABCB9, IQ, and AAA domain-containing protein 1 (IQCA1), fibrinogen alpha, and fibrinogen beta, which showed the fold change values of 21.82, 13.86, 13.66, and 13.64, respectively. The top four downregulated proteins were serum amyloid A-2 (SAA2), Ig lambda, CRP, and collagen alpha-1 with fold change values of −2.36, −2.26, −2.24, and −2.04, respectively.

Table 4: Differentially expressed protein between SANFH-TCM and SANFH.

Accession
Description
value
Ratio (mean)

Upregulated protein

Q9NP78
ATP-binding cassette sub-family B member 9

21.82029412

Q86XH1
IQ and AAA domain-containing protein 1

13.86725

P02671
Fibrinogen alpha chain

13.65918665

P02675
Fibrinogen beta chain

13.64325401

P02679
Fibrinogen gamma chain

13.23321597

P01019
Angiotensinogen

2.391730769

Q68CQ4
Digestive organ expansion factor homolog

2.26525

P03950
Angiogenin

2.0039

P35527
Keratin, type I cytoskeletal 9

1.928576923

Q8WXD2
Secretogranin-3

1.91025

P27918
Properdin

1.763869048

P14618
Pyruvate kinase PKM

1.632333333

P05154
Plasma serine protease inhibitor

1.607923077

Q04756
Hepatocyte growth factor activator

1.590333333

P02775
Platelet basic protein

1.56775

P02776
Platelet factor 4

1.559232955

P02749
Beta-2-glycoprotein 1
0.0000322
1.548

Q96KN2
Beta-Ala-His dipeptidase
0.0000169
1.533115385

P01023
Alpha-2-macroglobulin

1.519228916

P06727
Apolipoprotein A-IV

1.4910721

P06396
Gelsolin

1.485481481

P01769
Ig heavy chain V-III region GA

1.45675

P10720
Platelet factor 4 variant

1.450190476

P05090
Apolipoprotein D

1.385849624

P19823
Inter-alpha-trypsin inhibitor heavy chain H2

1.376936508

P01871
Ig mu chain C region

1.37344898

P01616
Ig kappa chain V-II region MIL

1.35375

P34096
Ribonuclease 4

1.3485

P29122
Proprotein convertase subtilisin/kexin type 6

1.3275

P80108
Phosphatidylinositol-glycan-specific phospholipase D

1.320666667

P55056
Apolipoprotein C-IV

1.3169

O95445
Apolipoprotein M

1.314954545

Q9UHG3
Prenylcysteine oxidase 1

1.305045455

P08603
Complement factor H

1.296578143

P02787
Serotransferrin

1.2947

P05164
Myeloperoxidase

1.282

Q8WWA0
Intelectin-1

1.2735

P29622
Kallistatin

1.267285714

Q9NP79
Immunoglobulin J chain

1.260973684

Q86XH2
CD5 antigen-like

1.248355422

P02671
Plasma kallikrein

1.236383333

P02675
Serum amyloid P-component

1.236112319

P02679
Inter-alpha-trypsin inhibitor heavy chain H1

1.230389423

P01019
Alpha-actinin-4

1.23

Downregulated protein

P04217
Alpha-1B-glycoprotein

0.826346154

P01613
Ig kappa chain V-I region Ni

0.825583333

Q16610
Extracellular matrix protein 1

0.821529412

Q92954
Proteoglycan 4

0.8145

Q02818
Nucleobindin-1

0.813125

P01742
Ig heavy chain V-I region EU

0.803428571

P04434
Ig kappa chain V-III region VH (Fragment)

0.802166667

P55774
C-C motif chemokine 18

0.79825

Q9HDC9
Adipocyte plasma membrane-associated protein

0.798083333

P01011
Alpha-1-antichymotrypsin

0.796217391

P01621
Ig kappa chain V-III region NG9 (Fragment)

0.795

P01717
Ig lambda chain V-IV region Hil

0.78905

P04196
Histidine-rich glycoprotein

0.783991667

P04433
Ig kappa chain V-III region VG (Fragment)

0.783357143

P35542
Serum amyloid A-4 protein

0.752563218

P0DJI8
Serum amyloid A-1 protein

0.748036765

P27824
Calnexin

0.73825

P00740
Coagulation factor IX

0.733622222

Q9UGM5
Fetuin-B

0.721833333

P0C0L4
Complement C4-A

0.71725

Q08380
Galectin-3-binding protein

0.713721805

P04438
Ig heavy chain V-II region SESS

0.70425

P20742
Pregnancy zone protein

0.698695238

P13645
Keratin, type I cytoskeletal 10

0.69635

Q86UD1
Out at first protein homolog

0.694666667

P02751
Fibronectin

0.692870958

P04208
Ig lambda chain V-I region WAH

0.6685

P01861
Ig gamma-4 chain C region

0.655777778

P18428
Lipopolysaccharide-binding protein

0.640994118

P49721
Proteasome subunit beta type-2
0.019809794
0.6185

P25789
Proteasome subunit alpha type-4

0.609

P01743
Ig heavy chain V-I region HG3

0.607136364

P01620
Ig kappa chain V-III region SIE

0.5894375

P02452
Collagen alpha-1(I) chain

0.4901

P02741
C-reactive protein

0.445641414

P01714
Ig lambda chain V-III region SH

0.442666667

P0DJI9
Serum amyloid A-2 protein

0.424595238

3.3. Biomarkers Prediction and Validation

MRM was performed to verify the results obtained from iTRAQ proteomics (Figure 1). The upregulation of fibrinogen alpha, fibrinogen beta, and fibrinogen gamma and apolipoprotein A (LPA) and apolipoprotein D (LPD) in SANFH-TCM versus healthy controls and SANFH-TCM versus SANFH was confirmed by MRM, respectively (). Meanwhile, MRM also verified the decreased expression of fibronectin and CRP in SANFH-TCM versus healthy controls and SANFH-TCM versus SANFH identified by iTRAQ, respectively () (Table 5). In SANFH versus healthy controls, CRP and LPA were confirmed to upregulate, and LPD and apolipoprotein E (LPE) were confirmed to downregulate. Using MRM, fibrinogen alpha, fibrinogen beta, and fibrinogen gamma were significantly increased in SANFH compared with healthy controls. However, iTRAQ did not detect significant changes in the expression of fibrinogen alpha, fibrinogen beta, and fibrinogen gamma between SANFH and healthy controls. Although some difference existed between iTRAQ and MRM, all these data added confidence to the results obtained from iTRAQ.

Table 5: MRM was performed to verify the results obtained from iTRAQ proteomics.

Accession number
Description
Relative protein abundance (MRM)
Relative protein abundance (iTRAQ)

SANFH versus healthy controls
SANFH-TCM versus healthy controls
SANFH-TCM versus SANFH
SANFH versus healthy controls
SANFH-TCM versus healthy controls
SANFH- TCM versus SANFH

P02671
Fibrinogen alpha
1.8493
62.3799
33.7321
0.95
7.502
7.849

P02675
Fibrinogen beta
3.2104
143.2262
44.6126
0.936
5.8
6.161

P02679
Fibrinogen gamma
1.7341
79.1554
45.6467
0.953
6.721
6.766

P02751
Fibronectin
0.8527
0.3081
0.3614
0.999
0.568
0.571

P02741
C-reactive protein
2.2723
0.522
0.2297
1.559
0.658
0.412

P06727
Apolipoprotein A
1.2723
1.8632
1.4644
1.06
1.532
1.45

P05090
Apolipoprotein D
0.9796
1.4369
1.4668
0.932
1.302
1.341

P02649
Apolipoprotein E
0.6693
0.6865
1.0257
0.759
0.713
0.934

Figure 1: MRM quantification of results obtained from iTRAQ proteomics. MRM was performed to verify the different expressions of selected proteins including fibrinogen alpha, fibrinogen beta, and fibrinogen gamma, fibronectin, apolipoprotein A (LPA), apolipoprotein D (LPD), and apolipoprotein E (LPD), and C-reaction protein in SANFH versus healthy controls, SANFH-TCM versus healthy controls, and SANFH-TCM versus SANFH.

3.4. Go Analysis of Differentially Expressed Proteins

The differentially expressed proteins (SANFH versus healthy controls, SANFH-TCM versus healthy controls, and SANFH-TCM versus SANFH) were classified by Gene Ontology (GO) based on their cellular component, molecular function, and biological process.

For SANFH versus healthy controls (Figure 2), the top five significantly enriched GO terms concerning biological process were mainly associated with purine ribonucleotide biosynthetic process, nucleoside phosphate biosynthetic process, negative regulation of endothelial cell proliferation, mitochondrial transport and immune response-regulating signaling pathway, and cellular component, in which the top listed five GO terms were proton-transporting two-sector ATPase complex, catalytic domain, proton-transporting two-sector ATPase complex, proton-transporting ATP synthase complex, pigment granule, and organelle inner membrane. With respect to molecular function, transmembrane transporter activity, substrate-specific transmembrane transporter activity, primary active transmembrane transporter activity, p-p-bond-hydrolysis-driven transmembrane transporter activity, and monovalent inorganic cation transmembrane transporter activity were the top five GO terms.

Figure 2: GO analysis of differentially expressed proteins between SANFH and healthy controls. The significantly enriched GO terms concerning biological process, cellular component, and molecular function were shown.

For SANFH-TCM versus healthy controls (Figure 3), transport, response to organic substance, response to chemical, response to calcium ion, and regulation of triglyceride metabolic process were the top five GO terms concerning biological process; vesicle lumen, site of polarized growth, secretory granule lumen, secretory granule, and platelet alpha granule lumen were the top five GO terms concerning cellular component; and sulfur compound binding, small molecule binding, serine-type endopeptidase inhibitor activity, quaternary ammonium group binding, and peptidase regulator activity were the top five GO terms concerning molecular function.

Figure 3: GO analysis of differentially expressed proteins between SANFH-TCM and healthy controls. The significantly enriched GO terms concerning biological process, cellular component and molecular function were shown.

For SANFH-TCM versus SANFH (Figure 4), most enriched GO terms were response to metal ion, response to inorganic substance, response to calcium ion, regulation of lipoprotein oxidation, and protein polymerization in biological process, site of polarized growth, secretory granule lumen, secretory granule, ribosome, and platelet alpha granule in cellular component, and sulfur compound binding, serine-type endopeptidase inhibitor activity, scavenger receptor activity, ribonuclease activity, and ribonuclease A activity in molecular function.

Figure 4: GO analysis of differentially expressed proteins between SANFH-TCM and SANFH. The significantly enriched GO terms concerning biological process, cellular component, and molecular function were shown.

3.5. Pathway Enrichment Analysis of Differentially Expressed Proteins

The differentially expressed proteins (SANFH versus healthy controls, SANFH-TCM versus healthy controls, and SANFH-TCM versus SANFH) were mapped to the reference pathways in KEGG database to identify significantly enriched metabolic pathways or signal transduction pathways. In total, 47, 58, and 20 significantly enriched pathways were obtained in SANFH versus healthy controls (Table 6), SANFH-TCM versus healthy controls (Table 7), and SANFH-TCM versus SANFH (Table 8) (), respectively. The top listed five pathways were Alzheimer’s disease, salivary secretion, Huntington’s disease, Parkinson’s disease, and oxidative phosphorylation in SANFH versus healthy controls (Table 6); mineral absorption, PPAR signaling pathway, chemokine signaling pathway, adrenergic signaling in cardiomyocytes, and neurotrophin signaling pathway in SANFH-TCM versus healthy controls (Table 7); and chemokine signaling pathway, platelet activation, cytokine-cytokine receptor interaction, glycosylphosphatidylinositol- (GPI-) anchor biosynthesis, and beta-alanine metabolism in SANFH-TCM versus SANFH (Table 8) (), respectively. The predicted biomarker LPE was involved in the enriched pathway of Alzheimer’s disease in both SANFH versus healthy and SANFH-TCM versus healthy controls. In SANFH-TCM versus healthy controls, LPA was involved in the enriched pathway of PPAR signaling pathway; fibronectin was involved in the enriched pathway of pathways in cancer, small-cell lung cancer, and bacterial invasion of epithelial cells. Fibrinogen alpha, fibrinogen beta, and fibrinogen gamma were involved in the enriched pathway of platelet activation in both SANFH-TCM versus healthy controls and SANFH-TCM versus SANFH. In SANFH-TCM versus SANFH, fibronectin was involved in the enriched pathway of regulation of actin cytoskeleton, and fibrinogen gamma was also involved in the enriched pathway of Staphylococcus aureus infection.

Table 6: Differently enriched pathways were obtained in SANFH versus healthy controls.

Pathway_acc
Pathway_Name
value
Protein in Background
Protein in Diff Exp
Protein list

hsa05010
Alzheimer’s disease
0.015362
7
4
P25705, P02649, P62158, P06576

hsa04970
Salivary secretion
0.032411
5
3
P62158, P04220, P13637,

hsa05016
Huntington’s disease
0.057318
6
3
P25705, P12235, P06576,

hsa05012
Parkinson’s disease
0.057318
6
3
P25705, P12235, P06576,

hsa00190
Oxidative phosphorylation
0.070274
3
2
P25705, P06576,

hsa04261
Adrenergic signaling in cardiomyocytes
0.088785
7
3
P67936, P62158, P13637,

hsa04915
Estrogen signaling pathway
0.125873
4
2
P62158, P14625,

hsa04020
Calcium signaling pathway
0.125873
4
2
P12235, P62158,

hsa04974
Protein digestion and absorption
0.125873
4
2
P13637, P02452,

hsa04260
Cardiac muscle contraction
0.125873
4
2
P67936, P13637,

hsa04961
Endocrine and other 
factor-regulated calcium reabsorption
0.162907
1
1
P13637,

hsa04964
Proximal tubule bicarbonate reclamation
0.162907
1
1
P13637,

hsa04960
Aldosterone-regulated sodium reabsorption
0.162907
1
1
P13637,

hsa04070
Phosphatidylinositol signaling system
0.162907
1
1
P62158,

hsa04744
Phototransduction
0.162907
1
1
P62158,

hsa04976
Bile secretion
0.162907
1
1
P13637,

hsa05130
Pathogenic Escherichia coli infection
0.167478
9
3
Q71U36, P63104, P68366,

hsa04918
Thyroid hormone synthesis
0.188181
5
2
P14625, P13637,

hsa04971
Gastric acid secretion
0.188181
5
2
P62158, P13637,

hsa04540
Gap junction
0.253612
6
2
Q71U36, P68366,

hsa04972
Pancreatic secretion
0.299618
2
1
P13637,

hsa04720
Long-term potentiation
0.299618
2
1
P62158,

hsa04911
Insulin secretion
0.299618
2
1
P13637,

hsa05214
Glioma
0.299618
2
1
P62158,

hsa04978
Mineral absorption
0.299618
2
1
P13637,

hsa04973
Carbohydrate digestion and absorption
0.299618
2
1
P13637,

hsa04014
Ras signaling pathway
0.299618
2
1
P62158,

hsa04270
Vascular smooth muscle contraction
0.299618
2
1
P62158,

hsa04910
Insulin signaling pathway
0.299618
2
1
P62158,

hsa05031
Amphetamine addiction
0.299618
2
1
P62158,

hsa04750
Inflammatory mediator regulation of TRP channels
0.299618
2
1
P62158,

hsa04912
GnRH signaling pathway
0.299618
2
1
P62158,

hsa05152
Tuberculosis
0.319543
7
2
P18428, P62158,

hsa05150
Staphylococcus aureus infection
0.334256
18
4
P00751, P0C0L4, P13645, P02747

hsa04120
Ubiquitin mediated proteolysis
0.357215
13
3
P01742, P01781, P01743,

hsa04114
Oocyte meiosis
0.3841
8
2
P63104, P62158,

hsa04919
Thyroid hormone signaling pathway
0.41429
3
1
P13637,

hsa04916
Melanogenesis
0.41429
3
1
P62158,

hsa04064
NF-kappa B signaling pathway
0.41429
3
1
P18428,

hsa04728
Dopaminergic synapse
0.41429
3
1
P62158,

hsa04621
NOD-like receptor signaling pathway
0.41429
3
1
P14625,

hsa05215
Prostate cancer
0.41429
3
1
P14625,

hsa04740
Olfactory transduction
0.41429
3
1
P62158,

hsa04620
Toll-like receptor signaling pathway
0.41429
3
1
P18428,

hsa04713
Circadian entrainment
0.41429
3
1
P62158,

hsa03320
PPAR signaling pathway
0.445992
9
2
P08519, Q6Q788,

hsa05133
Pertussis
0.45325
15
3
P0C0L4, P62158, P02747,

Note. Italic font indicated the candidate protein.

Table 7: Differently enriched pathways were obtained in SANFH-TCM versus healthy controls.

Pathway_acc
Pathway_Name
value
Protein in background
Proteins in Diff Exp
Protein list

hsa04978
Mineral absorption
0.034949
2
2
P02787, P13637,

hsa03320
PPAR signaling pathway
0.068345
9
4
P02656, P08519, Q6Q788, P06727,

hsa04062
Chemokine signaling pathway
0.083136
6
3
P10720, P02776, P02775,

hsa04261
Adrenergic signaling in cardiomyocytes
0.126188
7
3
P62158, P67936, P13637,

hsa04722
Neurotrophin signaling pathway
0.161789
4
2
P62158, P62258,

hsa04915
Estrogen signaling pathway
0.161789
4
2
P62158, P14625,

hsa04260
Cardiac muscle contraction
0.161789
4
2
P67936, P13637,

hsa04114
Oocyte meiosis
0.17539
8
3
P62158, P62258, Q8NDV3,

hsa00410
beta-Alanine metabolism
0.18797
1
1
Q96KN2,

hsa04964
Proximal tubule bicarbonate reclamation
0.18797
1
1
P13637,

hsa00340
Histidine metabolism
0.18797
1
1
Q96KN2,

hsa04070
Phosphatidylinositol signaling system
0.18797
1
1
P62158,

hsa04961
Endocrine and other factor-regulated calcium reabsorption
0.18797
1
1
P13637,

hsa04976
Bile secretion
0.18797
1
1
P13637,

hsa00860
Porphyrin and chlorophyll metabolism
0.18797
1
1
P00450,

hsa00563
Glycosylphosphatidylinositol (GPI)-anchor biosynthesis
0.18797
1
1
P80108,

hsa04960
Aldosterone-regulated sodium reabsorption
0.18797
1
1
P13637,

hsa00230
Purine metabolism
0.18797
1
1
P14618,

hsa04666
Fc gamma R-mediated phagocytosis
0.18797
1
1
P06396,

hsa02010
ABC transporters
0.18797
1
1
Q9NP78,

hsa04744
Phototransduction
0.18797
1
1
P62158,

hsa04611
Platelet activation
0.228912
9
3
P02675, P02679, P02671,

hsa04060
Cytokine-cytokine receptor interaction
0.228912
9
3
P10720, P02776, P02775,

hsa04918
Thyroid hormone synthesis
0.23765
5
2
P14625, P13637,

hsa04971
Gastric acid secretion
0.23765
5
2
P62158, P13637,

hsa04970
Salivary secretion
0.23765
5
2
P62158, P13637,

hsa05200
Pathways in cancer
0.314907
6
2
P02751, P14625,

hsa04910
Insulin signaling pathway
0.340991
2
1
P62158,

hsa04912
GnRH signaling pathway
0.340991
2
1
P62158,

hsa04911
Insulin secretion
0.340991
2
1
P13637,

hsa04270
Vascular smooth muscle contraction
0.340991
2
1
P62158,

hsa05214
Glioma
0.340991
2
1
P62158,

hsa04014
Ras signaling pathway
0.340991
2
1
P62158,

hsa04614
Renin-angiotensin system
0.340991
2
1
P01019,

hsa05222
Small cell lung cancer
0.340991
2
1
P02751,

hsa05031
Amphetamine addiction
0.340991
2
1
P62158,

hsa04750
Inflammatory mediator regulation of TRP channels
0.340991
2
1
P62158,

hsa04720
Long-term potentiation
0.340991
2
1
P62158,

hsa04973
Carbohydrate digestion and absorption
0.340991
2
1
P13637,

hsa04972
Pancreatic secretion
0.340991
2
1
P13637,

hsa05203
Viral carcinogenesis
0.342177
11
3
P14618, P06396, P62258,

hsa05152
Tuberculosis
0.390394
7
2
P18428, P62158,

hsa04110
Cell cycle
0.390394
7
2
P62258, Q8NDV3,

hsa03050
Proteasome
0.390394
7
2
P49721, O14818,

hsa05010
Alzheimer’s disease
0.390394
7
2
P62158, P02649,

hsa04740
Olfactory transduction
0.465489
3
1
P62158,

hsa04713
Circadian entrainment
0.465489
3
1
P62158,

hsa04728
Dopaminergic synapse
0.465489
3
1
P62158,

hsa04064
NF-kappa B signaling pathway
0.465489
3
1
P18428,

hsa04930
Type II diabetes mellitus
0.465489
3
1
P14618,

hsa04620
Toll-like receptor signaling pathway
0.465489
3
1
P18428,

hsa04916
Melanogenesis
0.465489
3
1
P62158,

hsa05100
Bacterial invasion of epithelial cells
0.465489
3
1
P02751,

hsa00620
Pyruvate metabolism
0.465489
3
1
P14618,

hsa04919
Thyroid hormone signaling pathway
0.465489
3
1
P13637,

hsa04621
NOD-like receptor signaling pathway
0.465489
3
1
P14625,

hsa00330
Arginine and proline metabolism
0.465489
3
1
Q96KN2,

hsa05215
Prostate cancer
0.465489
3
1
P14625,

Note. Italic font indicated the candidate protein.

Table 8: Differently enriched pathways were obtained in SANFH-TCM versus SANFH.

Pathway_acc
Pathway_Name
value
Protein in background
Proteins in Diff Exp
Protein list

hsa04062
Chemokine signaling pathway
0.01871823
6
4
P10720, P55774, P02776, P02775,

hsa04611
Platelet activation
0.0942118
9
4
P02675, P02679, P02671, P02452,

hsa04060
Cytokine-cytokine receptor interaction
0.0942118
9
4
P10720, P55774, P02776, P02775,

hsa00563
Glycosylphosphatidylinositol (GPI)-anchor biosynthesis
0.2080201
1
1
P80108,

hsa00410
beta-Alanine metabolism
0.2080201
1
1
Q96KN2,

hsa02010
ABC transporters
0.2080201
1
1
Q9NP78,

hsa04666
Fc gamma R-mediated phagocytosis
0.2080201
1
1
P06396,

hsa00900
Terpenoid backbone biosynthesis
0.2080201
1
1
Q9UHG3,

hsa00230
Purine metabolism
0.2080201
1
1
P14618,

hsa00340
Histidine metabolism
0.2080201
1
1
Q96KN2,

hsa04120
Ubiquitin mediated proteolysis
0.2756015
13
4
P01742, P04438, P01769, P01743,

hsa04810
Regulation of actin cytoskeleton
0.2820364
9
3
P02751, O43707, P06396,

hsa05150
Staphylococcus aureus infection
0.3119404
18
5
P08603, P0C0L4, P02679, P06681, P13645,

hsa05146
Amoebiasis
0.3460206
10
3
P02751, O43707, P02452,

hsa05222
Small cell lung cancer
0.3731817
2
1
P02751,

hsa04614
Renin-angiotensin system
0.3731817
2
1
P01019,

hsa04978
Mineral absorption
0.3731817
2
1
P02787,

hsa05412
Arrhythmogenic right ventricular cardiomyopathy (ARVC)
0.3731817
2
1
O43707,

hsa05203
Viral carcinogenesis
0.4095525
11
3
P14618, O43707, P06396,

hsa03050
Proteasome
0.4460931
7
2
P49721, P25789,

Note. Italic font indicated the candidate protein.

4. Discussion

This is the first study to reveal proteins associated with steroid-induced avascular necrosis of the femoral head with or without Traditional Chinese Medicine treatment on the proteome level. MRM was used to add confidence to the results obtained by iTRAQ and was attempted for validating 8 proteins (fibrinogen alpha, fibrinogen beta, fibrinogen gamma, fibronectin, C-reactive protein, apolipoprotein A, apolipoprotein D, and apolipoprotein E).

Currently, the pathogenesis theories on femoral head necrosis mainly include [27, 28]: theory of osteoporosis; theory of vascular wall damage or compression and theory of blood lipid disorder [29]; theory of high intraosseous pressure; theory of intravascular coagulation; theory of secondary collision; and so forth [30]. Secondary collision theory [31] considers that osteonecrosis of the femoral head is multifactor disease and it is related to genetic susceptibility factor and exposure to specific risk factors. The occurrence of femoral head necrosis is the collusion result of posterior acquired factors and genetic predisposing factor. Clinical studies also indicate that not all patients that had taken high dose hormone for a long time will suffer from femoral head necrosis and only 10% of patients will be attacked by femoral head necrosis. Though there are many clinical and basis studies about femoral head avascular necrosis, its specific pathophysiological mechanism is still not determined [10, 14]. The beginning of proteomic technology applying in femoral head necrosis is relatively late and there are rare reports. The proteomics study of femoral head necrosis will be helpful to explain the pathological physiology mechanism of femoral head necrosis.

By using meprednisone to induce chicken femoral head necrosis, Li et al. [32] found that there are adipose tissue proliferation and new bone formation through the histological examination; by two-dimensional electrophoresis, 13 protein expression differences were found. Among them, 9 kinds of proteins were downregulated 3 times after hormone treatment, which were serum amyloid P-component precursor, zinc finger protein 28, endothelial zinc finger protein 71, T-box transcription factor 3, cyclin-dependent kinase inhibitor 1, myosin 1D, dimethylaniline monooxygenase, and two kinds of unknown proteins. However, the animal species were different, the cases in the clinical study were few, and the pathogenesis was different, so they lacked comparability and the study results were also different, without representativeness, so they were not sufficient to explain the pathogenesis of femoral head necrosis.

Considering the sampling of bone tissue is an invasive operation, which will bring regional trauma for patients increasing their suffering, the sampling of serum is easier and is also easy for patients to accept. There are few studies on femoral head necrosis. Researchers [3, 33] conducted serum proteomics study on 11 patients with drinking, hormone treatment, or specific femoral head necrosis (3 female and 8 male) and they found 8 protein differential points. Comparing with the serum of healthy volunteers, the serum of patients with femoral head had higher kininogen 1 variant, complement factor C3 precursor, and complement factor H. Besides, patients with femoral head necrosis had significant lower apolipoprotein A-IV precursor, antithrombin III chain B, and gelsolin isoform α precursor.

In the present study, we further suggested that the serum amyloid A-2 (SAA2), Ig lambda, sodium/potassium-transporting ATPase subunit alpha-3 (ATP1A3), calcium-binding mitochondrial carrier protein Aralar1 (SLC25A12), properdin, KRT9, LPA, TPM4, ABCB9, fibrinogen alpha, fibrinogen gamma, fibrinogen beta, CRP, TUBA1A, fibronectin, IQCA1, SAA2, and collagen alpha-1 were potential serum marker by iTRAQ and further confirmed the changes of fibrinogen alpha, fibrinogen beta, fibrinogen gamma, fibronectin, C-reactive protein, apolipoprotein A, apolipoprotein D, and apolipoprotein E. The predicted biomarker LPE was involved in the enriched pathway of Alzheimer’s disease; LPA was involved in the enriched pathway of PPAR signaling pathway; fibronectin was involved in the enriched pathway of pathways in cancer, small-cell lung cancer, and bacterial invasion of epithelial cells; fibrinogen alpha, fibrinogen beta, and fibrinogen gamma were involved in the enriched pathway of platelet activation; fibronectin was involved in the enriched pathway of regulation of actin cytoskeleton, and fibrinogen gamma was also involved in the enriched pathway of Staphylococcus aureus infection. Consistently, it has demonstrated that apolipoprotein A1 is potential risk for femoral head necrosis [34–36]. Fibronectin related to extracellular matrix integrity and adhesion is also an identified serum marker for broiler chickens with femoral head necrosis [35]. Fibrinogen beta was candidate biomarker of infection and inflammation [37] and femoral head necrosis [35]. CRP is an acute-phase protein, negatively correlated with adiponectin level in osteonecrosis of the femoral head [38].

In conclusion, our results identified 74 differentially expressed proteins between SANTH-TCM and healthy controls, 62 proteins between SANFH and healthy controls, and 81 proteins between SANFH-TCM and SANFH. Those upregulated proteins including ABCB9, IQCA1, fibrinogen alpha, and fibrinogen beta and downregulated proteins including serum amyloid A-2 (SAA2), Ig lambda, CRP, and collagen alpha-1 are promising serum diagnosis markers of femoral head necrosis, and also the marker could be used for prognosis of femoral head necrosis after Traditional Chinese Medicine treatment. The key points of treating femoral head necrosis are early diagnosis, early treatment, and reserving femoral head of patients. Our findings on the screening of early serum diagnosis marker of femoral head necrosis are helpful for early intervention on patients with hormone risk factors and preventing femoral head necrosis.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

The authors gratefully acknowledge the financial support of the grants from the National Natural Science Foundation of China (no. 81273784) and High-Level University Construction Project of Guangzhou University of Chinese Medicine (no. A1-AFD018171Z11068).

References

C. Carulli, L. Nistri, L. Bracco, M. Giannini, and M. P. Amato, “A steroid-induced bilateral avascular necrosis of the femoral head in an underage patient affected by multiple sclerosis,” Clinical Cases in Mineral and Bone Metabolism, vol. 12, no. 3, pp. 257–259, 2015.

A. F. Kamath, M. H. McGraw, and C. L. Israelite, “Surgical management of osteonecrosis of the femoral head in patients with sickle cell disease,” World Journal of Orthopedics, vol. 6, no. 10, pp. 776–782, 2015.

Y. Chen, C. Zeng, H. Zeng et al., “Comparative serum proteome expression of the steroid-induced femoral head osteonecrosis in adults,” Experimental and Therapeutic Medicine, vol. 9, no. 1, pp. 77–83, 2015.

A. Björkman, P. J. Svensson, A. Hillarp, I. M. Burtscher, A. Rünow, and G. Benoni, “Factor V Leiden and prothrombin gene mutation: risk factors for osteonecrosis of the femoral head in adults,” Clinical Orthopaedics and Related Research, no. 425, pp. 168–172, 2004.

S. Inoue, M. Horii, T. Asano et al., “Risk factors for nontraumatic osteonecrosis of the femoral head after renal transplantation,” Journal of Orthopaedic Science, vol. 8, no. 6, pp. 751–756, 2003.

H. Serre and L. Simon, “Primary osteonecrosis of the femur head in the adult. II. Etiology and pathogenesis,” Rev Rhum Mal Osteoartic, vol. 29, pp. 536–545, 1962.

C.-G. Li, L. Shen, Y.-P. Yang, X.-J. Xu, B. Shuai, and C. Ma, “Effects of Modified Qing'e Pill () on the expression of adiponectin, bone morphogenetic protein 2 and coagulation-related factors in patients with nontraumatic osteonecrosis of the femoral head,” Chinese Journal of Integrative Medicine, vol. 23, no. 3, pp. 183–189, 2017.

M. A. Mont, J. J. Cherian, R. J. Sierra, L. C. Jones, and J. R. Lieberman, “Nontraumatic osteonecrosis of the femoral head: Where dowe stand today? A ten-year update,” Journal of Bone and Joint Surgery - American Volume, vol. 97, no. 19, pp. 1604–1627, 2014.

Z. Xu, X. Dai, Y. Yao et al., “Higher Levels of Serum Triglycerides Were Associated with Postoperative Deep Vein Thrombosis after Total Hip Arthroplasty in Patients with Nontraumatic Osteonecrosis of the Femoral Head,” The International Journal of Lower Extremity Wounds, vol. 15, no. 1, pp. 41–44, 2016.

J. Seamon, T. Keller, J. Saleh, and Q. Cui, “The pathogenesis of nontraumatic osteonecrosis,” Arthritis & Rheumatology, vol. 2012, Article ID 601763, 11 pages, 2012.

A. Björkman, I. M. Burtscher, P. J. Svensson, A. Hillarp, J. Besjakov, and G. Benoni, “Factor V Leiden and the prothrombin 20210A gene mutation and osteonecrosis of the knee,” Archives of Orthopaedic and Trauma Surgery, vol. 125, no. 1, pp. 51–55, 2005.

I. R. Reid and J. Cornish, “Epidemiology and pathogenesis of osteonecrosis of the jaw,” Nature Reviews Rheumatology, vol. 8, no. 2, pp. 90–96, 2012.

K. Ikeuchi, Y. Hasegawa, T. Seki, Y. Takegami, T. Amano, and N. Ishiguro, “Epidemiology of nontraumatic osteonecrosis of the femoral head in Japan,” Modern Rheumatology, vol. 25, no. 2, pp. 278–281, 2015.

L. Wang, L. Zhang, H. Pan, S. Peng, X. Zhao, and W. W. Lu, “Abnormal subchondral bone microstructure following steroid administration is involved in the early pathogenesis of steroid-induced osteonecrosis,” Osteoporosis International, vol. 27, no. 1, pp. 153–159, 2016.

K. Tateda, S. Okazaki, S. Nagoya et al., “The suppression of TRIM21 and the accumulation of IFN-α play crucial roles in the pathogenesis of osteonecrosis of the femoral head,” Laboratory Investigation, vol. 92, no. 9, pp. 1318–1329, 2012.

D. Zhao, M. Yu, K. Hu et al., “Prevalence of nontraumatic osteonecrosis of the femoral head and its associated risk factors in the Chinese population: results from a nationally representative survey,” Chinese Medical Journal, vol. 128, no. 21, pp. 2843–2850, 2015.

J. Zeng, P. Deng, J. Li, W. Feng, J. Chen, and Y. Zeng, “Increased serum protein levels by Yuanshi Shengmai Chenggu Tablet in treatment of avascular osteonecrosis of the femoral head,” Molecular Medicine Reports, 2017.

A. Cave and P. Garlick, “Re: Preconditioning with ischemia: a delay of lethal cell injury in ischemic myocardium,” Journal of Molecular and Cellular Cardiology, vol. 32, pp. 1759-1760, 2000.

C. E. Murry, R. B. Jennings, and K. A. Reimer, “Preconditioning with ischemia: a delay of lethal cell injury in ischemic myocardium,” Circulation, vol. 74, no. 5, pp. 1124–1136, 1986.

Z.-Q. Zhao, J. S. Corvera, M. E. Halkos et al., “Inhibition of myocardial injury by ischemic postconditioning during reperfusion: comparison with ischemic preconditioning,” American Journal of Physiology-Heart and Circulatory Physiology, vol. 285, pp. H579–H588, 2003.

X. Tan, D. Cai, Y. Wu et al., “Comparative analysis of serum proteomes: discovery of proteins associated with osteonecrosis of the femoral head,” Translational Research, vol. 148, no. 3, pp. 114–119, 2006.

F. Shi, G. Li, H. Wang, and H. Yuan, “Antagonism of yuanshi shengmai chenggu capsule on steroid-induceds adipogenesis in rat marrow stromal cells,” Journal of Guangzhou University of Traditional Chinese Medicine, vol. 20, pp. 302–304, 2003.

J. Liu, Y. Fan, H. Wang et al., “Bone proteomic analysis about chinese medicine action on rat glucocorticoid - induced model of osteonecrsis,” Chinese Journal of Traditional Medical Traumatology & Orthopedics, vol. 13, pp. 5–9, 2005.

A. Mangé, L. Dimitrakopoulos, A. Soosaipillai, P. Coopman, E. P. Diamandis, and J. Solassol, “An integrated cell line-based discovery strategy identified follistatin and kallikrein 6 as serum biomarker candidates of breast carcinoma,” Journal of Proteomics, vol. 142, pp. 114–121, 2016.

Y. Zhang, Y. Li, Y. Zhang et al., “Quantitative Proteomics Reveals Membrane Protein-Mediated Hypersaline Sensitivity and Adaptation in Halophilic Nocardiopsis xinjiangensis,” Journal of Proteome Research, vol. 15, no. 1, pp. 68–85, 2016.

R. Narumi and T. Tomonaga, “Quantitative analysis of tissue samples by combining itraq isobaric labeling with selected/multiple reaction monitoring (SRM/MRM),” Methods in Molecular Biology, vol. 1355, pp. 85–101, 2016.

C. Powell, C. Chang, and M. E. Gershwin, “Current concepts on the pathogenesis and natural history of steroid-induced osteonecrosis,” Clinical Reviews in Allergy &; Immunology, vol. 41, no. 1, pp. 102–113, 2011.

Y. Assouline-Dayan, C. Chang, A. Greenspan, Y. Shoenfeld, and M. E. Gershwin, “Pathogenesis and natural history of osteonecrosis,” Seminars in Arthritis and Rheumatism, vol. 32, no. 2, pp. 94–124, 2002.

K. Kawai, A. Tamaki, and K. Hirohata, “Steroid-induced accumulation of lipid in the osteocytes of the rabbit femoral head,” The Journal of Bone & Joint Surgery, vol. 67, no. 5, pp. 755–763, 1985.

W. Yin, Z. Xu, J. Sheng, C. Zhang, and Z. Zhu, “Logistic regression analysis of risk factors for femoral head osteonecrosis after healed intertrochanteric fractures,” Hip International, vol. 26, no. 3, pp. 215–219, 2016.

K. N. Malizos, A. H. Karantanas, S. E. Varitimidis, Z. H. Dailiana, K. Bargiotas, and T. Maris, “Osteonecrosis of the femoral head: etiology, imaging and treatment,” European Journal of Radiology, vol. 63, no. 1, pp. 16–28, 2007.

S. C. Li, C. Y. Lin, T. F. Kuo et al., “Chicken model of steroid-induced bone marrow adipogenesis using proteome analysis: A preliminary study,” Proteome Science, vol. 8, article no. 47, 2010.

R. W. Wu, F. S. Wang, J. Y. Ko, C. J. Wang, and S. Wu, “Comparative serum proteome expression of osteonecrosis of the femoral head in adults,” Bone, vol. 43, no. 3, pp. 561–566, 2008.

X.-Y. Wang, X.-H. Niu, W.-H. Chen et al., “Effects of apolipoprotein A1 and B gene polymorphism on avascular necrosis of the femoral head in Chinese population,” Zhongguo gu shang = China journal of orthopaedics and traumatology, vol. 21, no. 2, pp. 99–102, 2008.

B. Packialakshmi, R. Liyanage, J. O. Lay, R. Okimoto, and N. C. Rath, “Proteomic changes in the plasma of broiler chickens with femoral head necrosis,” Biomarker Insights, vol. 11, pp. 55–62, 2016.

K. Miyanishi, T. Yamamoto, T. Irisa, Y. Noguchi, Y. Sugioka, and Y. Iwamoto, “Increased level of apolipoprotein B/apolipoprotein A1 ratio as a potential risk for osteonecrosis,” Annals of the Rheumatic Diseases, vol. 58, no. 8, pp. 514–516, 1999.

B. Packialakshmi, R. Liyanage, J. O. Lay, S. K. Makkar, and N. C. Rath, “Proteomic changes in chicken plasma induced by Salmonella typhimurium lipopolysaccharides,” Proteomics Insights, vol. 7, no. 1, 2016.

B. Shuai, L. Shen, Y. P. Yang, J. Xie, Z. X. Shou, and B. Wei, “Low plasma adiponectin as a potential biomarker for osteonecrosis of the femoral head,” The Journal of Rheumatology, vol. 37, no. 10, pp. 2151–2155, 2010.