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

Authors: Peng Deng; Jianchun Zeng; Jie Li; Wenjun Feng; Jinlun Chen; Yirong Zeng
Journal: BioMed Research International
 (2018)

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## 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.


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## Body

## 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 (n=5) 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 1250g for 5 min and then 13500g 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 (5μm, 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 P 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 m/z, full scan resolution at 70000, MS/MS scan resolution at 17500, and MS/MS scan with minimum signal threshold 1E+5, 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. AP value < 0.05 was considered as statistical significantly.


## 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 (n=5) 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 1250g for 5 min and then 13500g 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 (5μm, 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 P 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 m/z, full scan resolution at 70000, MS/MS scan resolution at 17500, and MS/MS scan with minimum signal threshold 1E+5, 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. AP 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 Table1. 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 Totaln 26 Male,n (%) 11 (42.3) Age, yr, mean ± SD 39.5 ± 5.3 History of SONFH, months, median (min, max) 23.1 (6, 34) Primary disease,n (%) 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 (Table2), including 35 significantly upregulated proteins (>1.21-fold, P<0.05) and 26 significantly downregulated proteins (<0.83-fold, P<0.05). 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 P value Ratio (mean) Upregulated protein P0DJI9 Serum amyloid A-2 protein 0.0000233 4.57402273 P01714 Ig lambda chain V-III region SH 0.000116405 2.64233333 P13637 Sodium/potassium-transporting ATPase subunit alpha-3 0.010160416 2.06683333 O75746 Calcium-binding mitochondrial carrier protein Aralar1 0.008612823 1.95325 P01613 Ig kappa chain V-I region Ni 0.005619256 1.94854167 P01743 Ig heavy chain V-I region HG3 0.00017147 1.93190909 P01742 Ig heavy chain V-I region EU 1.61 E - 18 1.8255 P0DJI8 Serum amyloid A-1 protein 0.000000019 1.72607353 P01857 Ig gamma-1 chain C region 2.12 E - 208 1.71611466 P01861 Ig gamma-4 chain C region 0.00000135 1.66788889 P06889 Ig lambda chain V-IV region MOL 0.0000383 1.59966667 P01860 Ig gamma-3 chain C region 6.59 E - 46 1.59404098 P02741 C-reactive protein 6.38 E - 146 1.58455808 Q8NBJ4 Golgi membrane protein 1 0.000564362 1.4622 P0C0L4 Complement C4-A 1.33 E - 10 1.45864286 P12235 ADP/ATP translocase 1 0.016291795 1.4491 P06576 ATP synthase subunit beta, mitochondrial 0.002154497 1.43466667 P13645 Keratin, type I cytoskeletal 10 4.63 E - 13 1.41348333 P04433 Ig kappa chain V-III region VG (Fragment) 0.000000732 1.412 Q08380 Galectin-3-binding protein 3.33 E - 45 1.38927444 Q14624 Inter-alpha-trypsin inhibitor heavy chain H4 6.37 E - 87 1.37619099 P22692 Insulin-like growth factor-binding protein 4 0.025918263 1.362 P01620 Ig kappa chain V-III region SIE 8.22 E - 09 1.3591875 P02452 Collagen alpha-1(I) chain 1.95 E - 09 1.3503 O75636 Ficolin-3 2.26 E - 53 1.341312 P01717 Ig lambda chain V-IV region Hil 0.003213417 1.3341 P14625 Endoplasmin 0.00000523 1.3105 P01621 Ig kappa chain V-III region NG9 (Fragment) 0.000190716 1.2999 P51884 Lumican 0.002279512 1.29618421 P02747 Complement C1q subcomponent subunit C 1.85 E - 11 1.290775 P25705 ATP synthase subunit alpha, mitochondrial 0.00000765 1.28091667 P22792 Carboxypeptidase N subunit 2 7.1 E - 09 1.27185227 P09543 2′,3′-Cyclic-nucleotide 3′-phosphodiesterase 0.034218978 1.2625 P18428 Lipopolysaccharide-binding protein 2.71 E - 47 1.25414706 P01009 Alpha-1-antitrypsin 6.68 E - 34 1.21782482 Downregulated protein P01781 Ig heavy chain V-III region GAL 0.00000597 0.82966667 O43852 Calumenin 0.000374747 0.82916667 Q9UK55 Protein Z-dependent protease inhibitor 2.62 E - 11 0.82908108 O43866 CD5 antigen-like 1.19 E - 23 0.82744578 Q04756 Hepatocyte growth factor activator 0.000530289 0.821 P63104 14-3-3 protein zeta/delta 0.009022339 0.8194 Q68CQ4 Digestive organ expansion factor homolog 0.000458581 0.81775 P68366 Tubulin alpha-4A chain 0.003314178 0.8143 P04211 Ig lambda chain V region 4A 0.021633191 0.812 Q6Q788 Apolipoprotein A-V 0.000000051 0.8107381 P59665 Neutrophil defensin 1 0.011081692 0.7993 P05164 Myeloperoxidase 0.001604817 0.7984 P02649 Apolipoprotein E 5.01 E - 267 0.7911827 P04070 Vitamin K-dependent protein C 1.41 E - 22 0.78128922 Q13103 Secreted phosphoprotein 24 9.56 E - 09 0.76858333 P02749 beta-2-Glycoprotein 1 0.015081278 0.7567 P01591 Immunoglobulin J chain 2.38 E - 17 0.75358772 P36980 Complement factor H-related protein 2 0.000968476 0.75325 P04220 Ig mu heavy chain disease protein 0.047631742 0.74433333 P01871 Ig mu chain C region 2.52 E - 147 0.72098342 Q71U36 Tubulin alpha-1A chain 0.005388449 0.716 P62158 Calmodulin 0.002744078 0.63166667 P67936 Tropomyosin alpha-4 chain 0.002042575 0.62 P08519 Apolipoprotein(a) 1.36 E - 09 0.606 P35527 Keratin, type I cytoskeletal 9 1.13 E - 09 0.57923077 P27918 Properdin 2.97 E - 34 0.57908333A total of 74 proteins were differentially expressed between SANFH-TCM and healthy controls (Table3), including 45 significantly upregulated proteins (>1.21-fold, P<0.05), and 29 significantly downregulated proteins (<0.83-fold, P<0.05). 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 P value Ratio (mean) Upregulated protein Q9NP78 ATP-binding cassette subfamily B member 9 8.47 E - 09 17.47338 P02671 Fibrinogen alpha chain 0 13.049 P02679 Fibrinogen gamma chain 0 12.66544 P02675 Fibrinogen beta chain 0 12.11164 Q86XH1 IQ and AAA domain-containing protein 1 0.000199 8.9745 P01019 Angiotensinogen 9.49 E - 09 2.256 Q68CQ4 Digestive organ expansion factor homolog 0.000267 1.882 P03950 Angiogenin 4.8 E - 10 1.7776 P13637 Sodium/potassium-transporting ATPase subunit alpha-3 0.017893 1.742833 Q8WXD2 Secretogranin-3 1.34 E - 07 1.71675 P06396 Gelsolin 3.3 E - 46 1.659898 Q8NBJ4 Golgi membrane protein 1 7.61 E - 08 1.6227 Q96KN2 Beta-Ala-His dipeptidase 1.18 E - 06 1.588538 P02775 Platelet basic protein 1.22 E - 17 1.544688 P06727 Apolipoprotein A-IV 0 1.529316 P01023 Alpha-2-macroglobulin 1.85 E - 68 1.506488 Q14624 Inter-alpha-trypsin inhibitor heavy chain H4 4.2 E - 152 1.503549 P02776 Platelet factor 4 1.49 E - 56 1.487591 Q7Z2Y8 Interferon-induced very large GTPase 1 0.047372 1.4735 P29122 Proprotein convertase subtilisin/kexin type 6 0.002827 1.47275 P05154 Plasma serine protease inhibitor 1.09 E - 06 1.446654 P14618 Pyruvate kinase PKM 0.005908 1.444167 P06889 Ig lambda chain V-IV region MOL 9.86 E - 05 1.4295 P01857 Ig gamma-1 chain C region 4 E - 180 1.427066 P22792 Carboxypeptidase N subunit 2 3.39 E - 15 1.348591 P01742 Ig heavy chain V-I region EU 3.44 E - 19 1.343614 P05090 Apolipoprotein D 1.43 E - 66 1.330211 P51884 Lumican 6.82 E - 07 1.323868 P01613 Ig kappa chain V-I region Ni 6.17 E - 05 1.319958 P01860 Ig gamma-3 chain C region 4.37 E - 19 1.318262 P14625 Endoplasmin 0.000738 1.3125 Q04756 Hepatocyte growth factor activator 4.34 E - 05 1.312333 P80108 Phosphatidylinositol-glycan-specific phospholipase D 6.28 E - 16 1.309714 P02787 Serotransferrin 0.03443 1.2721 P00450 Ceruloplasmin 1.43 E - 27 1.269935 P10720 Platelet factor 4 variant 1.65 E - 05 1.255714 P80748 Ig lambda chain V-III region LOI 0.003 1.24625 P01009 Alpha-1-antitrypsin 6.2 E - 51 1.245105 Q9NP79 Coagulation factor XII 0.001987 1.24225 P02671 Retinoic acid receptor responder protein 2 0.008513 1.234583 P02679 Ig kappa chain V-II region TEW 0.024594 1.226038 P02675 Kallistatin 0.016269 1.223857 Q86XH2 Complement C1q subcomponent subunit A 8.72 E - 05 1.222116 P01020 FERM and PDZ domain-containing protein 1 2.36 E - 06 1.212885 Q68CQ5 Retinol-binding protein 4 4.81 E - 08 1.2112 Downregulated protein Q02818 Nucleobindin-1 0.002012 0.8295 Q9UK55 Protein Z-dependent protease inhibitor 2.62 E - 11 0.829081 P02656 Apolipoprotein C-III 3.32 E - 34 0.823015 P62258 14-3-3 protein epsilon 0.04376 0.81975 P01877 Ig alpha-2 chain C region 0.024488 0.817692 Q6Q788 Apolipoprotein A-V 5.25 E - 08 0.81769 P36980 Complement factor H-related protein 2 0.001599 0.815458 P35542 Serum amyloid A-4 protein 2.65 E - 29 0.813253 P01620 Ig kappa chain V-III region SIE 1.42 E - 05 0.800375 O14818 Proteasome subunit alpha type-7 0.024316 0.794833 P18428 Lipopolysaccharide-binding protein 4.26 E - 75 0.789371 P04438 Ig heavy chain V-II region SESS 0.044412 0.78775 P49721 Proteasome subunit beta type-2 0.026571 0.7765 P04196 Histidine-rich glycoprotein 6.01 E - 28 0.775008 P02649 Apolipoprotein E 0 0.75853 Q86UD1 Out at first protein homolog 7.29 E - 08 0.755267 P04211 Ig lambda chain V region 4A 1.19 E - 05 0.751667 P0DJI9 Serum amyloid A-2 protein 7.71 E - 05 0.744667 P67936 Tropomyosin alpha-4 chain 0.0005 0.74375 Q8NDV3 Structural maintenance of chromosomes protein 1B 0.000726 0.741833 P62158 Calmodulin 0.005327 0.733667 Q16610 Extracellular matrix protein 1 5.58 E - 10 0.731882 Q92954 Proteoglycan 4 7.15 E - 12 0.728857 P59665 Neutrophil defensin 1 0.000151 0.7177 P20742 Pregnancy zone protein 4.88 E - 71 0.696948 P02741 C-reactive protein 2.7 E - 173 0.677154 Q71U36 Tubulin alpha-1A chain 0.000833 0.657333 P02751 Fibronectin 0 0.635439 P08519 Apolipoprotein(a) 7.96 E - 09 0.56335In addition, a total of 81 proteins were differentially expressed between SANFH-TCM and SANFH (Table4), including significantly 44 upregulated proteins (>1.21-fold, P<0.05) and 37 significantly downregulated proteins (<0.83-fold, P<0.05). 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 P value Ratio (mean) Upregulated protein Q9NP78 ATP-binding cassette sub-family B member 9 7.38 E - 10 21.82029412 Q86XH1 IQ and AAA domain-containing protein 1 0.000343733 13.86725 P02671 Fibrinogen alpha chain 0 13.65918665 P02675 Fibrinogen beta chain 0 13.64325401 P02679 Fibrinogen gamma chain 0 13.23321597 P01019 Angiotensinogen 0.000000129 2.391730769 Q68CQ4 Digestive organ expansion factor homolog 0.0000351 2.26525 P03950 Angiogenin 0.00000196 2.0039 P35527 Keratin, type I cytoskeletal 9 3.95 E - 11 1.928576923 Q8WXD2 Secretogranin-3 0.000728124 1.91025 P27918 Properdin 1.39 E - 40 1.763869048 P14618 Pyruvate kinase PKM 0.002960903 1.632333333 P05154 Plasma serine protease inhibitor 2.03 E - 12 1.607923077 Q04756 Hepatocyte growth factor activator 2.81 E - 08 1.590333333 P02775 Platelet basic protein 2.34 E - 18 1.56775 P02776 Platelet factor 4 3.43 E - 84 1.559232955 P02749 Beta-2-glycoprotein 1 0.0000322 1.548 Q96KN2 Beta-Ala-His dipeptidase 0.0000169 1.533115385 P01023 Alpha-2-macroglobulin 1.1 E - 62 1.519228916 P06727 Apolipoprotein A-IV 0 1.4910721 P06396 Gelsolin 7.08 E - 33 1.485481481 P01769 Ig heavy chain V-III region GA 0.009729195 1.45675 P10720 Platelet factor 4 variant 0.000000465 1.450190476 P05090 Apolipoprotein D 5.92 E - 70 1.385849624 P19823 Inter-alpha-trypsin inhibitor heavy chain H2 1.59 E - 76 1.376936508 P01871 Ig mu chain C region 1.12 E - 123 1.37344898 P01616 Ig kappa chain V-II region MIL 0.004694041 1.35375 P34096 Ribonuclease 4 0.016807995 1.3485 P29122 Proprotein convertase subtilisin/kexin type 6 0.007174928 1.3275 P80108 Phosphatidylinositol-glycan-specific phospholipase D 1.54 E - 12 1.320666667 P55056 Apolipoprotein C-IV 0.0000081 1.3169 O95445 Apolipoprotein M 0.00000275 1.314954545 Q9UHG3 Prenylcysteine oxidase 1 0.00000266 1.305045455 P08603 Complement factor H 8.17 E - 39 1.296578143 P02787 Serotransferrin 0.002337342 1.2947 P05164 Myeloperoxidase 0.020349711 1.282 Q8WWA0 Intelectin-1 0.0000227 1.2735 P29622 Kallistatin 0.0000069 1.267285714 Q9NP79 Immunoglobulin J chain 4.06 E - 10 1.260973684 Q86XH2 CD5 antigen-like 3 E - 31 1.248355422 P02671 Plasma kallikrein 6.95 E - 08 1.236383333 P02675 Serum amyloid P-component 1.03 E - 24 1.236112319 P02679 Inter-alpha-trypsin inhibitor heavy chain H1 1.7 E - 30 1.230389423 P01019 Alpha-actinin-4 0.045621384 1.23 Downregulated protein P04217 Alpha-1B-glycoprotein 0.0000256 0.826346154 P01613 Ig kappa chain V-I region Ni 0.010289928 0.825583333 Q16610 Extracellular matrix protein 1 0.000000945 0.821529412 Q92954 Proteoglycan 4 1.05 E - 09 0.8145 Q02818 Nucleobindin-1 0.000247875 0.813125 P01742 Ig heavy chain V-I region EU 1.22 E - 08 0.803428571 P04434 Ig kappa chain V-III region VH (Fragment) 0.030092513 0.802166667 P55774 C-C motif chemokine 18 0.003307656 0.79825 Q9HDC9 Adipocyte plasma membrane-associated protein 0.00000847 0.798083333 P01011 Alpha-1-antichymotrypsin 7.56 E - 13 0.796217391 P01621 Ig kappa chain V-III region NG9 (Fragment) 0.0000675 0.795 P01717 Ig lambda chain V-IV region Hil 0.0000608 0.78905 P04196 Histidine-rich glycoprotein 2.81 E - 26 0.783991667 P04433 Ig kappa chain V-III region VG (Fragment) 0.00000651 0.783357143 P35542 Serum amyloid A-4 protein 1.77 E - 40 0.752563218 P0DJI8 Serum amyloid A-1 protein 4.33 E - 11 0.748036765 P27824 Calnexin 0.038086684 0.73825 P00740 Coagulation factor IX 6.16 E - 23 0.733622222 Q9UGM5 Fetuin-B 0.00451302 0.721833333 P0C0L4 Complement C4-A 4.74 E - 14 0.71725 Q08380 Galectin-3-binding protein 9.69 E - 59 0.713721805 P04438 Ig heavy chain V-II region SESS 0.039314686 0.70425 P20742 Pregnancy zone protein 9.73 E - 65 0.698695238 P13645 Keratin, type I cytoskeletal 10 1.17 E - 19 0.69635 Q86UD1 Out at first protein homolog 3.02 E - 09 0.694666667 P02751 Fibronectin 0 0.692870958 P04208 Ig lambda chain V-I region WAH 0.000226793 0.6685 P01861 Ig gamma-4 chain C region 2.47 E - 08 0.655777778 P18428 Lipopolysaccharide-binding protein 5.83 E - 130 0.640994118 P49721 Proteasome subunit beta type-2 0.019809794 0.6185 P25789 Proteasome subunit alpha type-4 0.00601102 0.609 P01743 Ig heavy chain V-I region HG3 0.0000275 0.607136364 P01620 Ig kappa chain V-III region SIE 1.87 E - 11 0.5894375 P02452 Collagen alpha-1(I) chain 0.000916641 0.4901 P02741 C-reactive protein 1.19 E - 243 0.445641414 P01714 Ig lambda chain V-III region SH 1.91 E - 09 0.442666667 P0DJI9 Serum amyloid A-2 protein 1.16 E - 08 0.424595238


### 3.3. Biomarkers Prediction and Validation

MRM was performed to verify the results obtained from iTRAQ proteomics (Figure1). 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 (P<0.05). 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 (P<0.05) (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 0.934Figure 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 (Figure2), 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 (Figure3), 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 (Figure4), 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 (Table6), SANFH-TCM versus healthy controls (Table 7), and SANFH-TCM versus SANFH (Table 8) (P<0.05), 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) (P<0.03), 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 ofStaphylococcus aureus infection.Table 6
Differently enriched pathways were obtained in SANFH versus healthy controls.
Pathway_acc Pathway_Name P 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 otherfactor-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 P 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 P 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.


## 3.1. Populations

A total of 26 patients were included in the present study. Demographic characteristics of present population were summarized in Table1. 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 Totaln 26 Male,n (%) 11 (42.3) Age, yr, mean ± SD 39.5 ± 5.3 History of SONFH, months, median (min, max) 23.1 (6, 34) Primary disease,n (%) 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 (Table2), including 35 significantly upregulated proteins (>1.21-fold, P<0.05) and 26 significantly downregulated proteins (<0.83-fold, P<0.05). 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 P value Ratio (mean) Upregulated protein P0DJI9 Serum amyloid A-2 protein 0.0000233 4.57402273 P01714 Ig lambda chain V-III region SH 0.000116405 2.64233333 P13637 Sodium/potassium-transporting ATPase subunit alpha-3 0.010160416 2.06683333 O75746 Calcium-binding mitochondrial carrier protein Aralar1 0.008612823 1.95325 P01613 Ig kappa chain V-I region Ni 0.005619256 1.94854167 P01743 Ig heavy chain V-I region HG3 0.00017147 1.93190909 P01742 Ig heavy chain V-I region EU 1.61 E - 18 1.8255 P0DJI8 Serum amyloid A-1 protein 0.000000019 1.72607353 P01857 Ig gamma-1 chain C region 2.12 E - 208 1.71611466 P01861 Ig gamma-4 chain C region 0.00000135 1.66788889 P06889 Ig lambda chain V-IV region MOL 0.0000383 1.59966667 P01860 Ig gamma-3 chain C region 6.59 E - 46 1.59404098 P02741 C-reactive protein 6.38 E - 146 1.58455808 Q8NBJ4 Golgi membrane protein 1 0.000564362 1.4622 P0C0L4 Complement C4-A 1.33 E - 10 1.45864286 P12235 ADP/ATP translocase 1 0.016291795 1.4491 P06576 ATP synthase subunit beta, mitochondrial 0.002154497 1.43466667 P13645 Keratin, type I cytoskeletal 10 4.63 E - 13 1.41348333 P04433 Ig kappa chain V-III region VG (Fragment) 0.000000732 1.412 Q08380 Galectin-3-binding protein 3.33 E - 45 1.38927444 Q14624 Inter-alpha-trypsin inhibitor heavy chain H4 6.37 E - 87 1.37619099 P22692 Insulin-like growth factor-binding protein 4 0.025918263 1.362 P01620 Ig kappa chain V-III region SIE 8.22 E - 09 1.3591875 P02452 Collagen alpha-1(I) chain 1.95 E - 09 1.3503 O75636 Ficolin-3 2.26 E - 53 1.341312 P01717 Ig lambda chain V-IV region Hil 0.003213417 1.3341 P14625 Endoplasmin 0.00000523 1.3105 P01621 Ig kappa chain V-III region NG9 (Fragment) 0.000190716 1.2999 P51884 Lumican 0.002279512 1.29618421 P02747 Complement C1q subcomponent subunit C 1.85 E - 11 1.290775 P25705 ATP synthase subunit alpha, mitochondrial 0.00000765 1.28091667 P22792 Carboxypeptidase N subunit 2 7.1 E - 09 1.27185227 P09543 2′,3′-Cyclic-nucleotide 3′-phosphodiesterase 0.034218978 1.2625 P18428 Lipopolysaccharide-binding protein 2.71 E - 47 1.25414706 P01009 Alpha-1-antitrypsin 6.68 E - 34 1.21782482 Downregulated protein P01781 Ig heavy chain V-III region GAL 0.00000597 0.82966667 O43852 Calumenin 0.000374747 0.82916667 Q9UK55 Protein Z-dependent protease inhibitor 2.62 E - 11 0.82908108 O43866 CD5 antigen-like 1.19 E - 23 0.82744578 Q04756 Hepatocyte growth factor activator 0.000530289 0.821 P63104 14-3-3 protein zeta/delta 0.009022339 0.8194 Q68CQ4 Digestive organ expansion factor homolog 0.000458581 0.81775 P68366 Tubulin alpha-4A chain 0.003314178 0.8143 P04211 Ig lambda chain V region 4A 0.021633191 0.812 Q6Q788 Apolipoprotein A-V 0.000000051 0.8107381 P59665 Neutrophil defensin 1 0.011081692 0.7993 P05164 Myeloperoxidase 0.001604817 0.7984 P02649 Apolipoprotein E 5.01 E - 267 0.7911827 P04070 Vitamin K-dependent protein C 1.41 E - 22 0.78128922 Q13103 Secreted phosphoprotein 24 9.56 E - 09 0.76858333 P02749 beta-2-Glycoprotein 1 0.015081278 0.7567 P01591 Immunoglobulin J chain 2.38 E - 17 0.75358772 P36980 Complement factor H-related protein 2 0.000968476 0.75325 P04220 Ig mu heavy chain disease protein 0.047631742 0.74433333 P01871 Ig mu chain C region 2.52 E - 147 0.72098342 Q71U36 Tubulin alpha-1A chain 0.005388449 0.716 P62158 Calmodulin 0.002744078 0.63166667 P67936 Tropomyosin alpha-4 chain 0.002042575 0.62 P08519 Apolipoprotein(a) 1.36 E - 09 0.606 P35527 Keratin, type I cytoskeletal 9 1.13 E - 09 0.57923077 P27918 Properdin 2.97 E - 34 0.57908333A total of 74 proteins were differentially expressed between SANFH-TCM and healthy controls (Table3), including 45 significantly upregulated proteins (>1.21-fold, P<0.05), and 29 significantly downregulated proteins (<0.83-fold, P<0.05). 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 P value Ratio (mean) Upregulated protein Q9NP78 ATP-binding cassette subfamily B member 9 8.47 E - 09 17.47338 P02671 Fibrinogen alpha chain 0 13.049 P02679 Fibrinogen gamma chain 0 12.66544 P02675 Fibrinogen beta chain 0 12.11164 Q86XH1 IQ and AAA domain-containing protein 1 0.000199 8.9745 P01019 Angiotensinogen 9.49 E - 09 2.256 Q68CQ4 Digestive organ expansion factor homolog 0.000267 1.882 P03950 Angiogenin 4.8 E - 10 1.7776 P13637 Sodium/potassium-transporting ATPase subunit alpha-3 0.017893 1.742833 Q8WXD2 Secretogranin-3 1.34 E - 07 1.71675 P06396 Gelsolin 3.3 E - 46 1.659898 Q8NBJ4 Golgi membrane protein 1 7.61 E - 08 1.6227 Q96KN2 Beta-Ala-His dipeptidase 1.18 E - 06 1.588538 P02775 Platelet basic protein 1.22 E - 17 1.544688 P06727 Apolipoprotein A-IV 0 1.529316 P01023 Alpha-2-macroglobulin 1.85 E - 68 1.506488 Q14624 Inter-alpha-trypsin inhibitor heavy chain H4 4.2 E - 152 1.503549 P02776 Platelet factor 4 1.49 E - 56 1.487591 Q7Z2Y8 Interferon-induced very large GTPase 1 0.047372 1.4735 P29122 Proprotein convertase subtilisin/kexin type 6 0.002827 1.47275 P05154 Plasma serine protease inhibitor 1.09 E - 06 1.446654 P14618 Pyruvate kinase PKM 0.005908 1.444167 P06889 Ig lambda chain V-IV region MOL 9.86 E - 05 1.4295 P01857 Ig gamma-1 chain C region 4 E - 180 1.427066 P22792 Carboxypeptidase N subunit 2 3.39 E - 15 1.348591 P01742 Ig heavy chain V-I region EU 3.44 E - 19 1.343614 P05090 Apolipoprotein D 1.43 E - 66 1.330211 P51884 Lumican 6.82 E - 07 1.323868 P01613 Ig kappa chain V-I region Ni 6.17 E - 05 1.319958 P01860 Ig gamma-3 chain C region 4.37 E - 19 1.318262 P14625 Endoplasmin 0.000738 1.3125 Q04756 Hepatocyte growth factor activator 4.34 E - 05 1.312333 P80108 Phosphatidylinositol-glycan-specific phospholipase D 6.28 E - 16 1.309714 P02787 Serotransferrin 0.03443 1.2721 P00450 Ceruloplasmin 1.43 E - 27 1.269935 P10720 Platelet factor 4 variant 1.65 E - 05 1.255714 P80748 Ig lambda chain V-III region LOI 0.003 1.24625 P01009 Alpha-1-antitrypsin 6.2 E - 51 1.245105 Q9NP79 Coagulation factor XII 0.001987 1.24225 P02671 Retinoic acid receptor responder protein 2 0.008513 1.234583 P02679 Ig kappa chain V-II region TEW 0.024594 1.226038 P02675 Kallistatin 0.016269 1.223857 Q86XH2 Complement C1q subcomponent subunit A 8.72 E - 05 1.222116 P01020 FERM and PDZ domain-containing protein 1 2.36 E - 06 1.212885 Q68CQ5 Retinol-binding protein 4 4.81 E - 08 1.2112 Downregulated protein Q02818 Nucleobindin-1 0.002012 0.8295 Q9UK55 Protein Z-dependent protease inhibitor 2.62 E - 11 0.829081 P02656 Apolipoprotein C-III 3.32 E - 34 0.823015 P62258 14-3-3 protein epsilon 0.04376 0.81975 P01877 Ig alpha-2 chain C region 0.024488 0.817692 Q6Q788 Apolipoprotein A-V 5.25 E - 08 0.81769 P36980 Complement factor H-related protein 2 0.001599 0.815458 P35542 Serum amyloid A-4 protein 2.65 E - 29 0.813253 P01620 Ig kappa chain V-III region SIE 1.42 E - 05 0.800375 O14818 Proteasome subunit alpha type-7 0.024316 0.794833 P18428 Lipopolysaccharide-binding protein 4.26 E - 75 0.789371 P04438 Ig heavy chain V-II region SESS 0.044412 0.78775 P49721 Proteasome subunit beta type-2 0.026571 0.7765 P04196 Histidine-rich glycoprotein 6.01 E - 28 0.775008 P02649 Apolipoprotein E 0 0.75853 Q86UD1 Out at first protein homolog 7.29 E - 08 0.755267 P04211 Ig lambda chain V region 4A 1.19 E - 05 0.751667 P0DJI9 Serum amyloid A-2 protein 7.71 E - 05 0.744667 P67936 Tropomyosin alpha-4 chain 0.0005 0.74375 Q8NDV3 Structural maintenance of chromosomes protein 1B 0.000726 0.741833 P62158 Calmodulin 0.005327 0.733667 Q16610 Extracellular matrix protein 1 5.58 E - 10 0.731882 Q92954 Proteoglycan 4 7.15 E - 12 0.728857 P59665 Neutrophil defensin 1 0.000151 0.7177 P20742 Pregnancy zone protein 4.88 E - 71 0.696948 P02741 C-reactive protein 2.7 E - 173 0.677154 Q71U36 Tubulin alpha-1A chain 0.000833 0.657333 P02751 Fibronectin 0 0.635439 P08519 Apolipoprotein(a) 7.96 E - 09 0.56335In addition, a total of 81 proteins were differentially expressed between SANFH-TCM and SANFH (Table4), including significantly 44 upregulated proteins (>1.21-fold, P<0.05) and 37 significantly downregulated proteins (<0.83-fold, P<0.05). 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 P value Ratio (mean) Upregulated protein Q9NP78 ATP-binding cassette sub-family B member 9 7.38 E - 10 21.82029412 Q86XH1 IQ and AAA domain-containing protein 1 0.000343733 13.86725 P02671 Fibrinogen alpha chain 0 13.65918665 P02675 Fibrinogen beta chain 0 13.64325401 P02679 Fibrinogen gamma chain 0 13.23321597 P01019 Angiotensinogen 0.000000129 2.391730769 Q68CQ4 Digestive organ expansion factor homolog 0.0000351 2.26525 P03950 Angiogenin 0.00000196 2.0039 P35527 Keratin, type I cytoskeletal 9 3.95 E - 11 1.928576923 Q8WXD2 Secretogranin-3 0.000728124 1.91025 P27918 Properdin 1.39 E - 40 1.763869048 P14618 Pyruvate kinase PKM 0.002960903 1.632333333 P05154 Plasma serine protease inhibitor 2.03 E - 12 1.607923077 Q04756 Hepatocyte growth factor activator 2.81 E - 08 1.590333333 P02775 Platelet basic protein 2.34 E - 18 1.56775 P02776 Platelet factor 4 3.43 E - 84 1.559232955 P02749 Beta-2-glycoprotein 1 0.0000322 1.548 Q96KN2 Beta-Ala-His dipeptidase 0.0000169 1.533115385 P01023 Alpha-2-macroglobulin 1.1 E - 62 1.519228916 P06727 Apolipoprotein A-IV 0 1.4910721 P06396 Gelsolin 7.08 E - 33 1.485481481 P01769 Ig heavy chain V-III region GA 0.009729195 1.45675 P10720 Platelet factor 4 variant 0.000000465 1.450190476 P05090 Apolipoprotein D 5.92 E - 70 1.385849624 P19823 Inter-alpha-trypsin inhibitor heavy chain H2 1.59 E - 76 1.376936508 P01871 Ig mu chain C region 1.12 E - 123 1.37344898 P01616 Ig kappa chain V-II region MIL 0.004694041 1.35375 P34096 Ribonuclease 4 0.016807995 1.3485 P29122 Proprotein convertase subtilisin/kexin type 6 0.007174928 1.3275 P80108 Phosphatidylinositol-glycan-specific phospholipase D 1.54 E - 12 1.320666667 P55056 Apolipoprotein C-IV 0.0000081 1.3169 O95445 Apolipoprotein M 0.00000275 1.314954545 Q9UHG3 Prenylcysteine oxidase 1 0.00000266 1.305045455 P08603 Complement factor H 8.17 E - 39 1.296578143 P02787 Serotransferrin 0.002337342 1.2947 P05164 Myeloperoxidase 0.020349711 1.282 Q8WWA0 Intelectin-1 0.0000227 1.2735 P29622 Kallistatin 0.0000069 1.267285714 Q9NP79 Immunoglobulin J chain 4.06 E - 10 1.260973684 Q86XH2 CD5 antigen-like 3 E - 31 1.248355422 P02671 Plasma kallikrein 6.95 E - 08 1.236383333 P02675 Serum amyloid P-component 1.03 E - 24 1.236112319 P02679 Inter-alpha-trypsin inhibitor heavy chain H1 1.7 E - 30 1.230389423 P01019 Alpha-actinin-4 0.045621384 1.23 Downregulated protein P04217 Alpha-1B-glycoprotein 0.0000256 0.826346154 P01613 Ig kappa chain V-I region Ni 0.010289928 0.825583333 Q16610 Extracellular matrix protein 1 0.000000945 0.821529412 Q92954 Proteoglycan 4 1.05 E - 09 0.8145 Q02818 Nucleobindin-1 0.000247875 0.813125 P01742 Ig heavy chain V-I region EU 1.22 E - 08 0.803428571 P04434 Ig kappa chain V-III region VH (Fragment) 0.030092513 0.802166667 P55774 C-C motif chemokine 18 0.003307656 0.79825 Q9HDC9 Adipocyte plasma membrane-associated protein 0.00000847 0.798083333 P01011 Alpha-1-antichymotrypsin 7.56 E - 13 0.796217391 P01621 Ig kappa chain V-III region NG9 (Fragment) 0.0000675 0.795 P01717 Ig lambda chain V-IV region Hil 0.0000608 0.78905 P04196 Histidine-rich glycoprotein 2.81 E - 26 0.783991667 P04433 Ig kappa chain V-III region VG (Fragment) 0.00000651 0.783357143 P35542 Serum amyloid A-4 protein 1.77 E - 40 0.752563218 P0DJI8 Serum amyloid A-1 protein 4.33 E - 11 0.748036765 P27824 Calnexin 0.038086684 0.73825 P00740 Coagulation factor IX 6.16 E - 23 0.733622222 Q9UGM5 Fetuin-B 0.00451302 0.721833333 P0C0L4 Complement C4-A 4.74 E - 14 0.71725 Q08380 Galectin-3-binding protein 9.69 E - 59 0.713721805 P04438 Ig heavy chain V-II region SESS 0.039314686 0.70425 P20742 Pregnancy zone protein 9.73 E - 65 0.698695238 P13645 Keratin, type I cytoskeletal 10 1.17 E - 19 0.69635 Q86UD1 Out at first protein homolog 3.02 E - 09 0.694666667 P02751 Fibronectin 0 0.692870958 P04208 Ig lambda chain V-I region WAH 0.000226793 0.6685 P01861 Ig gamma-4 chain C region 2.47 E - 08 0.655777778 P18428 Lipopolysaccharide-binding protein 5.83 E - 130 0.640994118 P49721 Proteasome subunit beta type-2 0.019809794 0.6185 P25789 Proteasome subunit alpha type-4 0.00601102 0.609 P01743 Ig heavy chain V-I region HG3 0.0000275 0.607136364 P01620 Ig kappa chain V-III region SIE 1.87 E - 11 0.5894375 P02452 Collagen alpha-1(I) chain 0.000916641 0.4901 P02741 C-reactive protein 1.19 E - 243 0.445641414 P01714 Ig lambda chain V-III region SH 1.91 E - 09 0.442666667 P0DJI9 Serum amyloid A-2 protein 1.16 E - 08 0.424595238


## 3.3. Biomarkers Prediction and Validation

MRM was performed to verify the results obtained from iTRAQ proteomics (Figure1). 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 (P<0.05). 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 (P<0.05) (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 0.934Figure 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 (Figure2), 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 (Figure3), 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 (Figure4), 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 (Table6), SANFH-TCM versus healthy controls (Table 7), and SANFH-TCM versus SANFH (Table 8) (P<0.05), 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) (P<0.03), 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 ofStaphylococcus aureus infection.Table 6
Differently enriched pathways were obtained in SANFH versus healthy controls.
Pathway_acc Pathway_Name P 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 otherfactor-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 P 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 P 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 ofStaphylococcus 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.



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*Source: 5692735-2018-03-20.xml*