Six random selected BC patients entered into our study (Table 1). These eligible patients had neither comorbidity / history of drug nor significant mental health disorder. Informed consent is attached to a separate file. IORT delivered electron beams (LIAC linear accelerators, Sordina IOeRT Technologies S.p.A, Italy), and an Intraoperative accelerator was employed for cell irradiation [19]. Patients were treated with irradiation following two separate strategies, including Boost and Radical dose treatment. The machine dose rate was adjusted to 1 cGy/MU during the irradiation. Approximately 100 mg of tissue was extracted from tumor bed under Boost and Radical doses; MB, MA24 h.
Table 1
Clinical and pathological data of patients by study groups.
| Pathologic data | IOeRT data |
Pt Number | Age | Tumor history type | Tumor size (cm) | Node status | Tumor grade | ER/PR status | HER-2 Expression | Ki67 | P53 | Tumor Necrosis | LVI | Delivered dose (Gy) | Irradiation time (second) | | Flap depth (cm) |
1 | 53 | IDC | 4 | N0 | 3 | -/- | Neg | %50 | - | Present | Not seen | 12 | 63 | 7/9 | 1 |
2 | 42 | IDC | 1 | N0 | 1 | + | Neg | 30% | + | Negative | Not seen | 12 | 55 | 5/6 | 1.6 |
3 | 39 | IDC | 2.5 | N0 | 2 | +/+ | Neg | %25 | + | Not seen | Not seen | 12 | 58 | 6/9 | 1.8 |
4 | 63 | ILC | 1.3 | N0 | 2 | +/_ | Neg | %15 | -_ | Not seen | Not seen | 21 | 65 | 5/6 | 2.3 |
5 | 48 | IDC | 2 | N0 | 2 | +/+ | Neg | %30 | + | Not seen | Not seen | 21 | 78 | 7/9 | 2.2 |
6 | 55 | IDC | 3 | N0 | 2 | +/+ | Neg | %30 | + | Not seen | Not seen | 21 | 85 | 8/9 | 2.5 |
a |
Protein Extraction
First total proteins were extracted from samples with the TRIzol reagent used as supplied by Invitrogen Life Technologies. Then Proteins of more than 200ug qualified likewise were labeled to be used for iTRAQ.
Proteomics Assays
Peptide Labeling
The iTRAQ labeling reagents were recovered to ambient temperature, and then transferred into and combined with proper samples. Peptide labeling was performed by iTRAQ Reagent 8-plex Kit according to the manufacturer's protocol. The labeled peptides with different reagents were combined and desalted.
Peptide 1st Dimensional Fractionation
The peptides were reconstituted with buffer A (5% ACN, 95% H2O, adjusted pH to 9.8 with ammonia) and separated by a Shimadzu LC-20AB HPLC system coupled with a high pH RP column (5-µm particles, Phenomenex). The peptides were separated at a flow rate of 1 ml/min with a 60 min gradient: 5% buffer B (5% H2O, 95% ACN, adjusted pH to 9.8 with ammonia) for 10 min, 5–35% buffer B for 40 min, 35–95% buffer B for 1 min, and 95% buffer B for 3 min. The gradient was then decreased to 5% B within 1 min before re-equilibrating with 5% buffer B for 5 min. Elution was monitored by measuring absorbance at 214 nm and the eluted peptides were pooled as 20 fractions in a concatenation mode and vacuum dried.
Peptide 2nd Dimensional Fractionation
Each fraction was resuspended in buffer A (2% ACN and 0.1% FA in water) and loaded onto a C18 trap column using an LC-20AD nano-HPLC instrument (Shimadzu, Kyoto, Japan) by the Autosampler. Then, the peptides were eluted from the trap column and separated by an analytical C18 column (inner diameter 75 µm⋅20 cm, 3 µm) packed in-house. The gradient was run at 300 nL/min starting from 8 to 35% of buffer B (2% H2O and 0.1% FA in ACN) for 35 minutes, increasing to 60% for 5 minutes, then maintaining at 80% B for 5 minutes, and finally returning to 5% in 0.1 min and keeping for 10 min.
Mass Spectrometer Detection
Data acquisition was performed with a TripleTOF 5600 System (SCIEX, Framingham, MA, USA) equipped with a Nanospray III source (SCIEX, Framingham, MA, USA), a pulled quartz tip as the emitter (New Objectives, Woburn, MA) and controlled with software Analyst 1.6 (AB SCIEX, Concord, ON). Data was acquired with the following MS conditions: ion spray voltage of 2,300 V, curtain gas of 30, nebulizer gas of 15, and interface heater temperature of 150 °C. High sensitivity mode was used for the whole data acquisition. The mass ranges for MS1 were from 350 to 1500 Da. Based on the intensity in MS1 survey, as many as 30 product ion scans were collected if exceeding a threshold of 120 counts per second (counts/s) and with charge-state 2 + to 5+, dynamic exclusion was set for 1/2 of the peak width (12 s). For iTRAQ data acquisition, the collision energy was adjusted to all precursor ions for collision-induced dissociation and the Q2 transmission window for 100 Da was 100%.
Protein identification and quantification.
The raw MS/MS data were converted into MGF format by ProteoWizard tool msConvert, and the exported MGF files were searched using Mascot version 2.3.02 (Matrix Science, London, UK). In this project against the human Uniprot database .To reduce the probability of false peptide identification, only those peptides with significant scores (≥ 20) with 99% confidence were counted as identified. The IQuant software was used to quantitatively analyze the labeled peptides with isobaric tags[20]. It integrates Mascot Percolator, a well performing machine learning method for re-scoring database search results, to provide reliable significance measures. In order to assess the confidence of peptides, the PSMs were pre-filtered at a PSM-level FDR of 1%. Then, based on the "simple principle" (The parsimony principle), identified peptide sequences were assembled into a set of confident proteins. In order to control the rate of false-positive at the protein level, a protein FDR at 1%, which is based on Picked protein FDR strategy will also be estimated after protein inference (Protein-level FDR < = 0.01) [21]. The protein quantification process includes the following steps: Protein identification, Tag impurity correction, Data normalization, Missing value imputation, Protein ratio calculation, Statistical analysis, and Results presentation. Proteins with 1.2-fold change and Q-value less than 0.05 were determined as differentially expressed protein. For quantification repeat analysis, we used CV to evaluate the reproducibility. CV is defined as the ratio of the standard deviation (SD) to the mean.
Gene functional enrichment analysis
Gene functional enrichment analysis was applied on DEPs. In this study, we used KEGG pathway database by using DAVID (http://david.abcc.ncifcrf.gov/) for functional enrichment analysis.