Effects of silybin on triptorelin-induced bone metabolic abnormalities in prostate cancer revealed based on TMT-based proteomics.

Prostate cancer (PCa) is the most common male genitourinary malignancy [ 1 ], which seriously threatens the survival of older men [ 2 ]. Worldwide, the incidence and mortality of PCa are increasing year by year [ 3 – 5 ]. Although the molecular mechanisms of prostate cancer occurrence, development and metastasis have not been fully revealed, age, race, geographical distribution, diet and family history are important factors contributing to the development of the disease [ 6 – 8 ]. At the present stage, the common treatment for prostate cancer includes prostatectomy, radiotherapy, chemotherapy, androgen receptor (AR) antagonists, novel endocrine therapy, and immunotherapy [ 9 , 10 ]. However, there are still a considerable number of patients with poor prognosis such as recurrence and metastasis after surgery [ 11 ], which will shorten their survival and reduce their quality of life [ 12 ]. PCa bone metastasis is a common complication in advanced PCa, typically involving disruption of the bone microenvironment and abnormal bone metabolism [ 13 , 14 ]. Upon entering the bone marrow cavity, circulating cancer cells first adapt to the skeletal microenvironment, remaining dormant for an extended period. At a specific time point, these cells transition from dormancy to an active proliferative state. Ultimately, PCa invasion disrupts the osteogenic-osteolytic balance, leading to abnormal bone formation and accelerating the progression of PCa [ 15 , 16 ]. Therefore, further research on the mechanisms of prostate cancer development and the development of more effective drugs and treatments with fewer side effects are still urgent issues. Silybin (SB) is the main constituent extracted from the seeds of Asteraceae silybum marianum, which has high antioxidant and anticarcinogenic properties, as well as broad-spectrum anticancer efficacy [ 17 ]. SB also has other therapeutic effects such as hepatoprotective, antidiabetic, and cardiovascular protection [ 18 ]. The anticancer properties of SB have been demonstrated in a range of cancer models such as prostate, lung, colon, breast, bladder and hepatocellular carcinoma [ 19 ]. SB can also affect prostate cancer-related growth factors [ 20 ], cell cycle regulators and vascular endothelial growth factor [ 21 ], realizing an inhibitory effect on cell growth as well as mitosis, or inhibiting cell cycle progression [ 22 ], which is important for prostate cancer prevention as well as treatment. Some other findings suggest that SB has a positive effect on the regulation of bone metabolism, and SB can attenuate the downstream signaling cascade associated with RANKL and TNF-α to inhibit osteoclastogenesis [ 23 ], suggesting its potential for the treatment of osteoporosis and other bone diseases. Triptorelin (TRP) is a synthetic gonadotropin-releasing hormone (GnRH) agonist with a molecular formula of C 64 H 82 N 18 O 13 [ 24 ]. TRP is currently used in the treatment of prostate cancer [ 25 ], precocious puberty [ 26 ], endometriosis [ 27 ] and uterine fibroids [ 28 ]. In the process of treating prostate cancer, TRP acts on the hypothalamic-pituitary-gonadal axis, and through chronic stimulation it will down-regulate the pituitary GnRH receptor, inhibit the body’s production of luteinizing hormone and follicle-stimulating hormone, and lead to a decrease in the secretion of testosterone and estrogen [ 24 ], so as to achieve the purpose of treating locally advanced and metastatic prostate cancer, and TRP’s efficacy of its action in vitro and in vivo is higher than that of natural GnRH by a factor of 100-fold [ 29 ]. Therefore, in 2000, TRP was approved for the treatment of prostate cancer in the United States, and to date, it is still used as a first-line therapeutic agent for hormone-responsive malignancies [ 30 ]. TRP produces some side effects in the treatment of prostate cancer, including osteoporosis, hot flashes, erectile dysfunction, weight gain, and female-like breast development [ 31 – 33 ]. These side effects seriously affect the quality of life of patients. In the clinical treatment of prostate cancer, combination therapies are increasingly recognized for their ability to enhance efficacy, reduce side effects, and improve overall patient outcomes. While various drug combinations, such as docetaxel and thymoquinone, metformin and atorvastatin, and GLI-ANTagonist 61 and metformin, have shown enhanced antitumor effects and higher sensitivity compared to individual drugs [ 34 – 37 ], the rationale for combining SB with TRP lies in the unique role of SB in modulating bone metabolism. TRP, while effective in prostate cancer treatment, is associated with significant adverse effects on bone metabolism, leading to bone loss and osteopenia [ 38 – 40 ]. SB has demonstrated the ability to modulate key pathways involved in osteoclastogenesis, bone homeostasis, and inflammation [ 23 , 41 , 42 ], making it a promising candidate to mitigate TRP-induced bone metabolic abnormalities. Previous studies have shown that SB can inhibit osteoclast differentiation and activity [ 43 , 44 ], and there is growing interest in the possibility that it may help protect against bone loss associated with cancer therapies. Therefore, the combination of SB with TRP may offer a novel approach to not only enhance the efficacy of prostate cancer treatment but also reduce the bone metabolic side effects associated with TRP. In the present study, we used LNCaP cells cultured in vitro to explore the effect of SB combined with TRP on their bone metabolism, and used Tandem Mass Tag (TMT) labeling and liquid chromatography-tandem mass spectrometry (LC-MS/MS) technology to analyze the differentially expressed proteins (DEPs) of LNCaP cells cultured in the control group, the SB group, the TRP group, and the combination group under different conditions. We also performed bioinformatics analysis of the DEPs under the four conditions. The DEPs and signaling pathways identified in this study may be important in SB inhibition of TRP-induced bone metabolism abnormalities in LNCaP cells, further promoting the use of SB combined with TRP in the treatment of PCa.

To investigate the effects of SB and TRP alone and in combination on the proliferation of LNCaP cells, the viability of LNCaP cells was determined by CCK-8 assay. The results showed that the viability of LNCaP cells was dose-dependent and time-dependent by SB and TRP ( Fig 1a - 1b ), and similarly dose-dependent and time-dependent by the combination of the drugs ( Fig 1c ). This indicated that SB and TRP alone and in combination were able to inhibit the proliferation of LNCaP cells, and the inhibitory effect of the combination was more significant than that of the individual drugs under the same conditions. The results showed that when 100 μmol/L SB and 200 μmol/L TRP were co-administered to LNCaP cells for 48 h, the inhibition rates of both were similar, and therefore the subsequent experiments were performed under this condition. (a) and (b) LNCaP cells were treated with different concentrations of SB (50, 100, 150, or 200 μmol/L) or TRP (50, 100, 150, or 200 μmol/L) for 24, 48, 72, and 96 h. Cell viability was determined by CCK-8 assays. (c) LNCaP cells were treated with 100 μmol/L SB and 200 μmol/L TRP for 24, 48, 72, and 96 h. Cell viability was determined by CCK-8 assays. (d) The effect of each group (Control, SB, TRP, and SB + TRP) on LNCaP cell migration was detected by a wound healing assay. (e) , (f) , and (g) The effect of each group (Control, SB, TRP, and SB + TRP) on LNCaP cell migration and invasion was detected using Transwell chambers. The data are shown as the mean ± SD values; * P  < 0.05, ** P  < 0.01, **** P  < 0.0001, vs. untreated control group. To clarify the effects of SB and TRP alone and in combination inhibit migration of LNCaP cells, we used the concentrations of 100 μmol/L SB and 200 μmol/L TRP, alone and in combination, on LNCaP cells for 0, 24, 48, and 72h. The results showed that compared with the control group, the SB group, the TRP group, and the combination group all inhibited the migration of LNCaP cells; and the inhibitory effect of the combination group on the migration of LNCaP cells was more significant than that of the group using the drug alone ( Fig 1d ). We further investigated the effects of SB, TRP alone and in combination on LNCaP cell migration and invasion. Transwell results showed that LNCaP cell migration and invasion were inhibited in the SB group, TRP group and combination group compared with the control group, and the inhibition of LNCaP cell migration and invasion was more pronounced in the combination group ( Fig 1e - 1g ). In order to determine the protein expression changes caused by SB, TRP alone and in combination when acting on LNCaP cells, we first performed principal component analysis (PCA), as shown in Fig 2a , three samples from each group were clustered together, indicating good sample reproducibility. After that, we calculated the relative standard deviation (RSD) of each group, as shown in Fig 2b , the RSD was less than 20% in each group of samples, which also indicated reliable sample reproducibility. Finally, the Pearson correlation coefficients between the two samples of each group were calculated to draw a matrix, as shown in the Fig 2c , the redder the color, the stronger the positive correlation, and the bluer the color, the stronger the negative correlation, which again indicates that the four groups of protein samples have good reproducibility. (a) Principal components analysis of proteomic data across four groups. (b) Comparison of RSD of the four groups. (c) Matrix of Pearson correlation coefficients for the four groups. Groups A, B, C, and D represent the control, SB-treated, TRP-treated, and combination-treated groups, respectively. In this experiment, a total of 478453 secondary profiles were identified by TMT quantitative proteomics analysis from LNCaP cells in control group, SB group, TRP group and combined group, of which the number of valid profiles was 119322, the number of peptides identified was 53985, and 5,270 proteins were identified, of which 4,593 proteins were quantified as shown in Fig 3 . The experimental data were further screened in order to analyze the DEPs between the different groups. DEPs were analyzed bioinformatically and showed that 153 DEPs (60 up-regulated and 93 down-regulated) were identified between the A vs. B comparison groups, 100 DEPs (36 up-regulated and 64 down-regulated) were identified between the A vs. C comparison groups,524 DEPs (303 up-regulated and 221 down-regulated) were identified between the A vs. D comparison groups, FC and p -values were screened for DEPs and represented by volcano plots ( Fig 4a - 4c ). Cluster analysis revealed significant differences in the data patterns between the comparison groups and a high degree of similarity among the three biological replication groups within each group, indicating that there were significant differences in protein expression levels between each of the two comparison groups, and that the DEPs were able to represent samples that were significantly affected by the biological treatments ( Fig 4d - 4f ). Table 1 summarizes all DEPs in each group. Volcano plots of DEPs identified from the A vs. B (a) , A vs. C (b) and A vs. D (c) comparisons. The red dots represent upregulated proteins, and the green dots represent downregulated proteins. Heat maps of DEPs identified from the A vs. B (d) , A vs. C (e) and A vs. D (f) comparisons. Groups A, B, C, and D represent the control, SB-treated, TRP-treated, and combination-treated groups, respectively. Based on the Gene Ontology (GO) database, we investigated the enrichment of DEPs in LNCaP cells under three different culture situations. The DEPs were associated with 1696, 1111, and 3805 functional annotations in the A vs. B, A vs. C, and A vs. D comparison groups, respectively. The vocabulary of genes and gene products involved in GO is categorized into three main groups covering three aspects of biology: biological process (BP), cellular component (CC), and molecular function (MF). Fig 5a - 5c shows the top 20 significantly enriched terms in each otology: biological processes, cell components, and molecular functions. The 3 bar charts show the GO functional classification of the DEPs identified from the A vs. B (a) , A vs. C (b) and A vs. D (c) comparisons. Enrichment of KEGG pathways with DEPs identified from the A vs. B (d) , A vs. C (e) and A vs. D (f) comparisons. Groups A, B, C, and D represent the control, SB-treated, TRP-treated, and combination-treated groups, respectively. To further understand the signaling pathways in which the differential proteins between the comparison groups may be involved, we performed pathway enrichment analysis of these differential proteins using KEGG. Analysis shows that the A vs. B comparison group achieved enrichment in pathways such as IL-17 signaling pathway, Pathways of neurodegeneration – multiple diseases, Protein processing in endoplasmic reticulum, Fluid shear stress and atherosclerosis and Estrogen signaling pathway; the A vs. C comparison group achieved enrichment in pathways such as Apelin signaling pathway, Circadian entrainment, Pathways of neurodegeneration – multiple diseases, Protein processing in endoplasmic reticulum and Pyrimidine metabolism; the A vs. D comparison group achieved enrichment in pathways such as IL-17 signaling pathway, Circadian entrainment, Apelin signaling pathway, Protein processing in endoplasmic reticulum and Rap1 signaling pathway. Fig 5d - 5f shows the top 20 KEGG pathways in the list of significance. Notably, in the A vs. B and A vs. D comparison groups, more DEPs were enriched in the same signaling pathway (IL-17 signaling pathway) ( Table 2 ). Of note, in the A vs. C and A vs. D comparison groups, some of the same DEPs were enriched for two identical signaling pathways (Circadian entrainment and Apelin signaling pathway) ( Table 3 ). These results suggest that the SB and TRP combination group may regulate the bone metabolic profile of LNCaP cells through these signaling pathways and DEPs. Protein network interactions map can visualize the correlation between proteins and proteins, based on PPI analysis Fig 6a - 6c , we found that the number of DEPs in the combination group was significantly increased, and the interactions between the proteins were more obvious and more closely linked. (a) , A vs. C (b) and A vs. D (c) comparisons. Groups A, B, C, and D represent the control, SB-treated, TRP-treated, and combination-treated groups, respectively. To validate the accuracy of the proteomics results, we selected four DEPs (p-ERK2, HSP90B1, GNAI1, and GNAI3) and detected their expression levels by Western blot. As shown in Fig 7 , compared with group A, p-ERK2 expression decreased in groups B, C, and D, with the most pronounced reduction observed in group D ( P  < 0.001). HSP90B1 expression increased in both groups B and D, with a more pronounced increase in the combination therapy group ( P   0.05). The expression levels of GNAI1 and GNAI3 showed no significant changes in groups B and C ( P  > 0.05). However, the expression levels of GNAI1 and GNAI3 significantly increased in group D ( P  < 0.0001). The Western blot results were consistent with the proteomics findings. Western blot analysis of proteins including p-ERK2 (a, e) , HSP90B1 (b, f) , GNAI1 (c, g) and GNAI3 (d, h) . The data are shown as the mean ± SD values; *** P  < 0.001, **** P  < 0.0001, vs. untreated control group. Groups A, B, C, and D represent the control, SB-treated, TRP-treated, and combination-treated groups, respectively.

In short, our findings suggested that SB could regulate TRP-induced bone metabolism abnormalities in LNCaP cells. The proteomics and bioinformatics analysis revealed that SB regulates bone metabolism abnormalities mainly through IL-17 signaling pathway. Five key proteins, p-ERK2, RELA, HSP90B1, GNAI1, and GNAI3, are also important regulators to ameliorate this abnormality and their specific functions need to be further studied. Our results offered novel information about the underlying molecular mechanisms involved in SB to regulate TRP-induced bone metabolism abnormality in LNCaP cells, and also provide a new direction and theoretical basis for clinical treatment of PCa.

SB and TRP were purchased from Macklin, and they were diluted with androgen-free medium (RPMI-1640 medium + 10% androgen-free serum + 1% P/S). Different concentrations of the drugs were prepared separately, four concentration gradients each for SB (50, 100, 150, 200 μmol/L) and TRP (50, 100, 150, 200 μmol/L). The human prostate cancer cell line (LNCaP) was purchased from Otwo Biotech, and the cells were cultured in normal medium (RPMI-1640 medium + 10% FBS + 1% P/S) at 37 °C in a 5% CO 2 incubator, and the medium was changed every 48h. When the cells were in logarithmic growth phase and the density was about 90%, the cells were passaged. The third generation of cells will be used for subsequent experiments. Cell viability assay kit (CCK-8) was used to detect the proliferation ability of LNCaP cells. LNCaP cells with good growth status were taken, digested with trypsin, and normal medium was diluted into single-cell suspension, and the cell density was adjusted to 1.5 × 10 4 cells per milliliter. The cell suspension of 200 μL per well was inoculated into 96-well plates with 6 replicate wells in each group, and the peripheral ring of edge wells was filled with equal amount of sterile PBS. Cells were attached to the wall 48 hours after inoculation, and the medium in the wells was discarded and replaced with androgen-free medium for another 48h. Then, the culture medium was changed into different drug-containing androgen-free medium, which were: null group (androgen-free medium without cells); control group (androgen-free medium with cells); SB group (50, 100, 150, 200 μmol/L); and TRP group (50, 100, 150, 200 μmol/L); SB and TRP combination group (SB 200 μmol/L and TRP 100 μmol/L; SB 100 μmol/L and TRP 200 μmol/L). After 48h of drug intervention, the old medium was replaced with CCK-8 solution and androgen-free medium in the ratio of 1:9, and the cells were placed in an incubator to continue incubation for 2h, and the absorbance (A) was measured at 450 nm with an enzyme meter, and the measurement was repeated three times for each group to calculate the proliferation rate. The formula for calculating proliferation rate was: proliferation rate = [(As-Ab)/ (Ac-Ab)] ×100%, As represents the test wells, Ac represents the control wells, and Ab represents the blank wells. The cells in each group were cultured until the density was fused. The medium was changed to serum-free medium and treated with 1 μg/mL of mitomycin C for 1 h before the experiment. Then, 200 μL pipette tip was utilized to scratch perpendicular to the cell plane on the cell layer of each group, and then the cell surface was washed with serum-free medium for 1 time to remove cell debris. The cells in each group were placed in an incubator at 37°C and 5% CO2, and were photographed and recorded after incubation for 0 h, 24 h and 48 h using drug-containing medium, respectively. Transwell chambers were used to assess the migration and invasion ability of LNCaP cells. LNCaP cells with good growth status and in logarithmic growth phase were taken and resuspended in serum-free medium, and then 100 μL of cell suspension was added to the upper chamber of transwell chambers. And drug-containing medium was added to the upper chamber of the transwell chamber corresponding to each group: control group (100 μL of serum-free medium); SB group (100 μL of 100 μmol/L SB serum-free medium); TRP group (100 μl of 200 μmol/L TRP serum-free medium); and the combination of SB and TRP group(100 μl of 100 μmol/L SB serum-free medium and 100 μl of 200 μmol/L TRP serum-free medium). The migratory ability of LNCaP cells was stimulated by adding 600 μL of serum-containing medium to the lower chamber of the transwell. After 48 h of culture, cells that crossed the membrane were fixed with paraformaldehyde and stained with crystal violet for 30 min. Cells were observed under a light microscope and photographed for recording. The diluted matrix gel was added to the bottom of the upper chamber of the transwewll chambers. LNCaP cells in good growth condition and in logarithmic growth phase were taken and resuspended in serum-free medium, and then 100 μL of the cell suspension was added to the upper chamber of the transwell. The rest of the operation was performed as above to evaluate the invasive ability of LNCaP cells. Based on the results of previous experiments, LNCaP cells were divided into four groups for subsequent proteomics analysis: group A: cells were cultured with androgen-free medium; group B: cells were cultured with androgen-free medium containing 100 μmol/L SB; and group C: cells were cultured with androgen-free medium containing 200 μmol/L TRP; group D: cells were cultured with androgen-free medium containing 100 μmol/L SB and 200 μmol/L TRP. Proteins were extracted from each group of samples and processed separately. To each sample, an appropriate amount of lysis buffer was added, dissolved and mixed by vortexing, centrifuged at 14,000g for 20 min, the supernatant was collected, 10uL was taken for quantification, and the rest was frozen at −80°C. Protein concentration was determined using BCA protein assay kit. An appropriate amount of purified protein extract was taken from each sample and reduced with dithiothreitol of 5 mmol/L for 1h at 37°C and returned to room temperature. Subsequently, 10 mmol/L iodoacetamide was added for alkylation for 45 min at room temperature and protected from light, and finally the samples were diluted 4-fold with 25 mmol/L ammonium bicarbonate. Trypsin was added in the ratio of trypsin: protein = 1:50, and the digestion was carried out overnight at 37°C, and the enzyme digestion was terminated the next day by adding formic acid to adjust the pH to be less than 3. Samples were desalted with C18 Zip Tips, 100% acetonitrile activated desalting column, 0.1% formic acid equilibrated column, samples were loaded onto the column, the column was washed with 0.1% formic acid to wash out impurities, and finally eluted with 70% acetonitrile, and flow-through solution was collected and freeze-dried. According to the manufacturer’s instructions, the samples were mixed with TMT-labeled reagent (TMT10-plex) and left to react at room temperature for 1 h. Finally, the reaction was terminated by adding hydroxylamine to the samples. The differentially labeled samples were homogeneously mixed, vortexed and shaken, centrifuged to the bottom of the tube, and then vacuum freeze-dried. The TMT-labeled peptide mixture was dissolved with 100 μl of mobile phase A, followed by centrifugation at 14,000 g for 20 min, and the supernatant was extracted, and then the peptides were separated by high-pH reverse-phase HPLC using a chromatographic column (XBridge Peptide BEH 5um C18 Column) at a flow rate of 0.7 ml/min, with the temperature was 50°C. Mobile phases A (2% acetonitrile, pH adjusted to 10.0 using ammonium hydroxide) and B (98% acetonitrile, pH adjusted to 10.0 using ammonium hydroxide) were used to create a gradient elution. The gradients were set as follows: 5–8% B, 0–5 min; 8–18% B, 5–40 min; 18–32% B, 40–62 min; 32–95% B, 62–64 min; 95% B, 64–68 min; 95–5% B, 68–72 min. All fractions were freeze-dried and used for subsequent analysis. Mobile phase A was an aqueous solution containing 0.1% formic acid and 100% water, and mobile phase B was an aqueous solution containing 0.1% formic acid and 80% acetonitrile. The peptide lyophilized powder was dissolved in 10 µL of mobile phase A solution and centrifuged at 14,000 g for 20 min at 4°C, then 1 µg of the supernatant sample was taken for separation. The liquid phase gradient was set as follows: 6%B,0–8 min; 6–15%B, 8–15 min; 15–25%B, 15–42 min; 25–40%B, 42–57 min; 40–100%B, 57–58 min; 100%B, 58–68 min; 100–6%B, 68–69 min; 6%B, 69–85 min. The flow rate was maintained at 300nL/min. The peptides were separated by high performance liquid chromatography (HPLC) and exposed to a nanospray ionization (NSI) source, and then entered into an ORBITRAP ECLIPSE mass spectrometer for tandem mass spectrometry (MS/MS). The applied electrospray voltage was 2.0 kV, the compensation voltage was set to switch between −45 V and −65 V every 1 S, the ion transfer tube temperature was set to 320 °C, and the data acquisition mode used the data-dependent acquisition (DDA) program. The full scan range was set to 350–1500 m/z, the primary MS resolution was set to 120,000 (200 m/z), the automatic gain control (AGC) was set to 4E5, and the maximum injection time (MIT) was set to 50 ms. After the parent ions were fragmented using the higherenergy collision dissociation (HCD) method, a secondary scan was performed with the resolution set to 30,000 (200 m/z), AGC set to 5E4, MIT set to 54 ms, and the peptide fragmentation collision energy set to 36%. The ion dynamic exclusion time was set to 30 s to avoid repeated scanning of the parent ion. MS raw data were generated. MS raw data were quantified and explored using Proteome Discoverer 2.4 (Thermo Fisher Scientific, Waltham, MA, USA) and UniProt Homo Sapiens database ( http://www.uniprot.org ). Various search parameters were set as follows: the enzyme digestion method was set to Trypsin; the maximum number of missed cleavages was set to 2; the minimum length of the peptide was set to 7 amino acid residues; and the mass tolerance was set to 15 ppm for primary parent ions and 0.02 Da for secondary fragment ions. Carbamidomethyl (C) was set as a fixed modification and oxidation of methionine, Acetyl (Protein N-terminal), TMT-10plex (K, N-terminal) was set as a variable modification. The quantification method was set to TMT-10plex. the False discovery rate (FDR) for protein, peptide, and spectra identification were all set to 1%. DEPs were screened by Student’s t -test p -value 1.2 or FC  1.2 and down-regulated when FC < 0.83. The quality of proteomics data was assessed by heat map analysis, principal component analysis (PCA) analysis, relative standard deviation analysis and normalized expression analysis. Functional enrichment analysis of differentially expressed proteins was performed using the Gene Ontology (GO) database, and the identified proteins were categorized into the following three groups: biological process (BP), cellular component (CC), and molecular function (MF). Signal-passage enrichment analysis of DEPs was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database ( https://www.genome.jp/kegg/ ). Protein-protein interaction (PPI) networks of DEPs were obtained through the STRING database ( http://www.string-db.org/ ). Groups of samples were lysed with lysis solution, centrifuged and the supernatant was taken. Protein concentration was determined using BCA protein assay kit. In addition, 40 μg of each sample was separated by 10% SDS-PAGE and the proteins were transferred to PVDF membranes. Then, the membranes were blocked with 5% skimmed milk in TBST for 1 h. The membranes were then incubated with the following primary antibodies: p-ERK1/2 antibody (1:1000), HSP90B1 antibody (1:500), GNAI1 antibody (1:500), GNAI3 antibody (1:500) at 4°C overnight. After washing with TBST 4 times for 5 min each, the gel was incubated with secondary antibody: sheep anti-rabbit IgG-HRP (1:5000) for 45 min at 37°C. Finally, the reaction was left to react with ECL luminescent solution for 5 min. and covered with a plastic sealing film, and exposure was performed in a dark room. The optical density values of the target bands were analyzed with Gel-Pro-Analyzer software. Statistical analysis was performed with Statistical Program for Social Sciences (SPSS) (SPSSInc., version 20.0, United States). All charts were generated using GraphPad Prism 8.0.2. Each experiment was repeated more than three times. The quantitative data were reported as the means ± Standard Deviation (SD), and the significant difference was analyzed with Student’s t -test between two groups, one-way analysis of variance (ANOVA) was used for comparisons among multiple groups, p -value <0.05 was considered statistically significant. In TMT proteins with p -value 1.2 or < 0.83 were considered as DEPs. GO and KEGG analyses were carried out using Fisher’s exact test, using the entire quantified protein annotations as the background dataset. Categories and pathways with p -value <0.05 were considered statistically significant.

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