Key proteins as potential effective biomarkers under Intraoperative Radiotherapy (IORT) in tumor bed of Breast Cancer patients

Key proteins as potential effective biomarkers under Intraoperative Radiotherapy (IORT) in tumor bed of Breast Cancer patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Key proteins as potential effective biomarkers under Intraoperative Radiotherapy (IORT) in tumor bed of Breast Cancer patients minoo shahani, Jafar Shakeri, Ali Tafti, Mohammad Esmaeil Akbari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-17156/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Radiotherapy (RT) is recommended to all patients undergoing Breast Conserving Surgery (BCS). Two strategies can be applied to irradiation, External Beam RT (EBRT) in addition, Intraoperative Radiation Therapy (IORT). The aim of this study was to introduce a protein biomarker panel related to molecular function under IORT. Methods Six Breast Cancer (BC) patients as a pilot study were treated by 12 Gy (Boost dose) and 21 Gy (Radical dose). Samples tissue included Margin before IORT (MB), and Margin 24 hours After IORT (MA24 h). Isobaric Tag for Relative and Absolute Quantitation (iTRAQ) was performed to study proteomic of IORT-treated tumor bed. Results We classified 110 differentially expressed proteins (DEPs) as a protein biomarker panel by mapping the annotated coding region sequences to the reference canonical pathways in the KEGG database. Conclusion Our findings indicate that the DEPs may be key proteins in IORT-treated tumor bed and may serve as potential Effective biomarkers under IORT. Oncology Radiotherapy Breast Conserving Surgery External Beam RT Intraoperative Radiation Therapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Background BC as the fifth leading cause of cancer death is recognized as the most common cancer among women [ 1 ]. Considering the advances in early diagnosis and treatment of BC, mortality has reduced by 38% from 1989 to 2014 [ 2 ]. The prevalence of BC was 9795 cases in Iran, and the age-adjusted standardized ratio (ASR) was 28.1 in 100 000 people [ 3 ]. Akbari et al reported that the five- and ten-year survival rate for BC was estimated to be 81% and 77%, respectively [ 4 ]. RT is recommended to all patients undergoing BCS and some of them after mastectomy. The main objective is to obtain more effective doses of radiation in the appropriate time and site without increasing the prevalence of complications. RT can provide curative treatment in nearly 40% of patients [ 5 ]. In a previous study, long-term outcomes in patients with BCS and RT who were timely diagnosed, were similar to or even better than patients with mastectomy [ 6 ]. Two strategies can be applied to deliver irradiation to the cancer site. The first strategy is the use EBRT which is delivered from outside the body to the tumor site via protons, photons, or charged particles [ 7 ]. The second strategy is IORT which delivers electron beams (IOERT) and low kv-x-ray (IOXRT) [ 8 ]. By applying IORT during BCS, a high single dose is delivered to the site at the highest risk to fight subclinical tumor cell contamination with high precision owing to direct visualization [ 9 ]. Tumor bed has been reported as the highest risk of breast tumor recurrence [ 10 , 11 ]. As a result, local recurrence can be significantly reduced by using an extra dose to the tumor bed [ 12 ]. EBRT can be replaced by accelerating partial breast irradiation, including an IORT session, due to approved several advantages including; lack of gap between surgery and RT, avoidance of long treatment duration, reduced radiation-induced toxicity, tumor bed delineation under direct palpation and visual assessments and sparing non-targeted tissues surrounding the tumor bed [ 12 , 13 ]. In addition to the delayed time of RT after tumor excision, EBRT has harmful effects, including cardiac attack, appearance of second tumors, or stimulation of tumor cell growth by neo angiogenesis and hypoxia condition [ 14 ]. Notably, IORT can also help to save money, time, and CO 2 emissions in some patients, inhibiting transportation [ 15 ]. Although researchers have shown great interest in a single high-dose clinical approaches for different cancers, limited research has examined the biological basis of IORT [ 16 ]. According to specific eligibility criteria, IORT may be delivered either as an anticipated Boost, followed by conventional external RT to guarantee optimal dose delivery, or as an exclusive single radiation dose of Radical, corresponding to the administration of the entire sequence of conventional adjuvant RT [ 17 , 18 ]. Proteomics analysis can provide a new insight of interactome profile of the efficacy of IORT. To the best of our knowledge, there have been only imperfect efforts to characterize genome or proteome underlying physiological and biological mechanisms of IOeRT-treated tumor beds. The present study represents iTRAQ based on the samples collected from the tumor bed before and 24 after post irradiation. Our findings highlight the involvement of specific proteins and pathways signatures related to different doses. The analyses of protein functions and signaling pathways of these DEPs reveal a molecular response to IORT, which may shed light to design a model for the individual-centered radiotherapy introduced. Results Identification of differentially expressed proteins classification We performed iTRAQ quantification after labeling of samples in three replicates (for both doses, and tissue; MB and MA24 h). In total, 1045410 spectrums were generated; also, 31572 peptides and 5860 proteins were identified with 1% FDR. DEPs for each comparisons of group/treatments analyzed. We found that there were 110 DEPs (37 up regulated, 73 down regulated) as single high dose-independent, which were common in both Boost and Radical doses (Table 2 ). Table 2 Represented common DEPs in both Boost and Radical doses. Names total elements Common DEPs under Boost and Radical 110 A2M SERPINB1 NCF4 CNN1 GCA FMNL1 PTPRC LMNB1 VASP SERPINA3 ALOX5 BASP1 SLC2A3 LBP PTX3 LTF ATP5A1 TLN1 FCGR3A LCP1 C9 MDH1 ANXA5 IDH3A PEA15 CP SUCLA2 S100A9 HSPB1 HRG RPL7A ANXA3 ELANE FGG ASS1 LDHB FGB1 BST1 HK3 PRIM2 IDH1 ALDH6A1 DLST C5 CNDP2 ACADVL ITIH3 CALB2 ITGB2 PROS1 TUFM ARHGDIB YWHAG MMP8 PYGB PADI4 APOH DEFA1 CYBB SPTA1 PLG LYZ 2-sep RPL23A APOA4 C4BPA SUCLG2 HIST1H1C BPI PEBP1 ITIH4 ACOT1 C8A MSN LGALS1 MYO6 WDR1 YWHAE BCAP31 CFH CNN3 HRG MNDA MPO MYH11 VWF VTN CAMP ALDH7A1 OCIAD1 RP2 AZU1 HEBP2 HMGB2 ITGAM HIST1H1B HIBCH RNASE3 RETN HADH PRTN3 ITIH2 ANK1 CFB LCN2 SERPINC1 ctsg FLNA ALDH9A1 C6 MMP9 Network Analysis Of Deps As Single High Dose-independent We have analyzed 110 DEPs as single high dose-independent. Cytoscape network analysis represented the most important proteins based on 110 nodes, 350 edges and 6.36 as average node degree and 0.505 as avg. local clustering coefficient (Fig. 1 ). According to Fig. 2 , we identified a network of 110 DEPs as single high dose-independent. Among all, we found key proteins under Boost and Radical. Gene ontology enrichment and functional classification 24 h after IORT For the better understanding of the biological effects of IORT, we classified 110 DEPs by mapping the annotated coding region sequences to the reference canonical pathways in the KEGG pathway database (Fig. 3 ). Discussion RT is recommended to all patients undergoing BCS and some of them after mastectomy [ 22 ]. Despite the technological advances made in recent decades, RT plans still advise the same total dose per organ tumor, without taking into account the biological differences attributable to the different tumor subtypes [ 23 ]. By applying IORT during BCS, a high single dose is delivered to the site at the highest risk to fight subclinical tumor cell contamination with high precision owing to direct visualization [ 9 ]. Tumor bed has been reported as the highest risk of breast tumor recurrence [ 10 , 11 ]. As a result, local recurrence can be significantly reduced by using an extra dose to the tumor bed [ 12 ]. Although researchers have shown great interest in a single high-dose clinical approaches for different cancers, limited research has examined the biological and molecular basis of single high-dose effects, especially after IORT [ 24 ]. Studies have described that Post-surgery wound fluids (WF) induces an increase in the cell proliferation, migration in addition invasion in BC cell line [ 25 – 27 ]. However, the proteomics analysis of IORT-treated WF has shown an inhibition of cell proliferation and invasion in the BC cell line [ 28 , 29 ]. In addition, Cell and molecular traits observed in MCF-7 cells showed a typical senescent phenotype associated with cell proliferation arrest after treatment with IORT [ 30 ]. A worthy study was reported that the IORT-induced gene expression profiles and pathways appear to be BC cell-line dependent. The data suggest that some specific gene and pathway signatures seem to be linked to hormone receptor status [ 31 ]. In the context of this radiation treatment modality, this study aimed to describe the molecular response, in terms of DEPs and pathways, according to the different doses of IORT. However, this type of investigation would need to be extended to numerous panels of samples; thus, the present work should be considered as a pilot study. Here, we report substantial alterations in proteins expression levels after RT in both dose (Boost and Radical). Our proteomics approach using ITRAQ is a necessary first step for a biological study to describe the common molecular features associated with types of IORT. According to Table 2 , we detected 110 overlap DEPs between Boost and Radical doses. The identified overlapping 110 DEPs extracted from IORT 24 h post-treatment are enriched based on KEGG pathways database by using DAVID. Functional annotation revealed new insights of IORT in BC patients. Some of the most important pathways such as Complement and coagulation cascades, metabolomics pathways were enriched under IORT (Fig. 3 ). We have analyzed 110 DEPs as single high dose-independent. Cytoscape network analysis represented the most important proteins (Fig. 1 ). All DEPs were classified into 10 protein categories (Fig. 2 ), in which the most proteins related to RT are PLG, VWF and A2M (Fig. 2 ). The protein panel obtained corresponds to genes involved in cancer processes whose significantly downregulated after IORT-treated tumor bed [ 32 ]. The Plasminogen system produced by PLG (plasminogen) plays a crucial role in physiological in addition, pathological events related to tissue regeneration, wound healing, immune response, angiogenesis, invasion and metastasis. The PLG is significantly downregulated after IORT-treated tumor bed and highly enriched in Complement and coagulation cascades. In addition, Von Willebrand Factor Protein (VWF) is a major platelet ligand that has been widely used, as a biomarker in cancer growth and metastasis and associated inflammation [ 32 ], is significantly downregulated at 24 h post irradiation also highly enriched in Complement and coagulation cascades. As shown in figuere 4, reffre to complement and coagulation cascades kegg pathway data base, enrichment associated with changes in these related key pathway to RT have been illustrated which indicates that two doses of IORT (Fig. 3 ) is able to reduce cell growth and cell proliferation. It has been reported in previous stydies that 21-gene recurrence score (RS) were evaluated for prognostic and predictive benefit in IORT patients [ 33 ] in addition a value study indicated that key molecular pathways in radiotherapy (RT) are equally enriched by both Boost and Radical doses[ 34 ]. Overall, our findings indicate that the DEPs may be key proteins in IORT-treated tumor bed and may serve as potential Effective biomarkers under IORT. Eventually, results revealed new insights of IORT in BC management; all of these biological responses in tumor cells may be towards effectiveness of IORT and it shows that although IORT is a local treatment, it can have systemic effects. Conclusion As a conclusion, by using comprehensive techniques such as proteomic, we found the molecular basis of IORT-related changes that have already showed clinical and epidemiological effects in treating BC patients. In addition key biological pathways that induced by IORT indicated that, cellular microenvironment as a main causes of changes in cells through tumor development, can be affected by IORT. Due to the effectiveness of IORT, it may be recommended as a standard method for BC patients. Ultimately, proteomic study open new insights into personalized RT and this study has discovered another piece of puzzle that identifies the complexities of BC. Materials And Methods 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) size (cm) 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% H 2 O, 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% H 2 O, 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% H 2 O 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. Abbreviations Intraoperative Radiation Therapy (IORT) Breast Conserving Surgery (BCS) Breast Cancer (BC) Margin before IORT (MB) Margin 24 hours After IORT (MA24 h) Isobaric Tag for Relative and Absolute Quantitation (iTRAQ) Differentially expressed proteins (DEPs) Declarations Acknowledgements The authors thank Dr. Kui Wu; Cancer Institute, BGI-Research, China National GeneBank (CNGB) for Collaborate for great comments on this manuscript. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Availability of supporting data Further information and data sets are available from the corresponding authors on reasonable request. Authors' contributions Conception and design: Mohammad Esmaeil Akbari, Minoo Shahani, Jafar Shakeri Author responsible for figs and statistical analyses: Minoo Shahani, Ali Tafti Development of methodology: Minoo Shahani, Jafar Shakeri Acquisition of data provided: Mohammad Esmaeil Akbari Sampling and extraction: Minoo Shahani, Jafar Shakeri Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): Ali Tafti, Minoo Shahani, Jafar Shakeri Writing, review, and/or revision of the manuscript: Jafar Shakeri, Minoo Shahani, Ali Tafti Ethical Approval and Consent to participate The study was approved by the ethics committees of Shahid Beheshti university of medical sciences (Approval ID:IR.SBMU.RETECH.REC.1397.561) . Consent for publication Patient treated at the cancer research and hospital center of Shahid Beheshti University of medical sciences consented in writing for the use of their anonymous data for research. Competing interests The authors declare that they have no competing interest. Author’s details Minoo Shahani 1, 2+ ; Jafar Shakeri 1 ; Ali Tafti 1 ; Mohammad Esmaeil Akbari 1 * Cancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Department of Genetics, Islamic Azad University, Tehran Medical Sciences Branch, Tehran, Iran. [email protected] [email protected] [email protected] [email protected] References Movahedi M, Haghighat S, Khayamzadeh M, Moradi A, Ghanbari-Motlagh A, Mirzaei H, et al. Survival rate of breast cancer based on geographical variation in iran, a national study. Iran Red Crescent Med J. 2012;14(12):798-804. Siegel RL, Miller KD, Jemal A. Cancer Statistics, 2017. CA Cancer J Clin. 2017;67(1):7-30. Mohebbi E, Nahvijou A, Hadji M, Rashidian H, Seyyedsalehi MS, Nemati S, et al. Iran Cancer Statistics in 2012 and projection of cancer incidence by 2035. Basic & Clinical Cancer Research. 2018;9(3). 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-17156","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":405399,"identity":"81f68c06-ac51-4ae5-80c1-833c15759eb3","order_by":1,"name":"minoo 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sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jafar","middleName":"","lastName":"Shakeri","suffix":""},{"id":405401,"identity":"1edd01e1-82e0-4656-89a2-457a40683ff1","order_by":3,"name":"Ali Tafti","email":"","orcid":"","institution":"cancer research center shahid beheshti univercity of medical sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Tafti","suffix":""},{"id":405402,"identity":"08b95c18-96be-47b9-80fd-38c1cbf12dff","order_by":4,"name":"Mohammad Esmaeil Akbari","email":"","orcid":"","institution":"cancer research center shahid beheshti univercity of medical sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Esmaeil","lastName":"Akbari","suffix":""}],"badges":[],"createdAt":"2020-03-11 12:48:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-17156/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-17156/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":643468,"identity":"33005c4c-09d2-4c64-998e-f62d0027a97c","added_by":"auto","created_at":"2020-03-13 15:01:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":718162,"visible":true,"origin":"","legend":"Cytoscape network analysis based on degree by Cytoscape","description":"","filename":"1.PNG","url":"https://assets-eu.researchsquare.com/files/rs-17156/v1/1.PNG"},{"id":643469,"identity":"b65ae460-6a13-4776-aada-5f3be18b6bb1","added_by":"auto","created_at":"2020-03-13 15:01:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":137421,"visible":true,"origin":"","legend":"The deregulated densest protein-protein network (blue nodes: proteins, red nodes:\nproteins involved in crucial pathways) in 110 DEPs","description":"","filename":"2.PNG","url":"https://assets-eu.researchsquare.com/files/rs-17156/v1/2.PNG"},{"id":643470,"identity":"a1f89bdd-9a87-4864-96f3-6c2046f68919","added_by":"auto","created_at":"2020-03-13 15:01:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":570703,"visible":true,"origin":"","legend":"KEGG pathways on 110 DEPs 24 h after IORT","description":"","filename":"3.PNG","url":"https://assets-eu.researchsquare.com/files/rs-17156/v1/3.PNG"},{"id":643471,"identity":"69156fd3-a8c1-4277-b671-983ba65fcb2b","added_by":"auto","created_at":"2020-03-13 15:01:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":510573,"visible":true,"origin":"","legend":"Reffre to complement and coagulation cascades kegg pathway","description":"","filename":"4.PNG","url":"https://assets-eu.researchsquare.com/files/rs-17156/v1/4.PNG"},{"id":13493299,"identity":"950d9bbe-5f5c-40ef-8301-640b0fcd5b51","added_by":"auto","created_at":"2021-09-16 22:35:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1683370,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-17156/v1/fdd8f37e-0cf4-4c1a-bca7-12edfa47b44b.pdf"}],"financialInterests":"","formattedTitle":"Key proteins as potential effective biomarkers under Intraoperative Radiotherapy (IORT) in tumor bed of Breast Cancer patients","fulltext":[{"header":"Background","content":" \u003cp\u003eBC as the fifth leading cause of cancer death is recognized as the most common cancer among women [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Considering the advances in early diagnosis and treatment of BC, mortality has reduced by 38% from 1989 to 2014 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The prevalence of BC was 9795 cases in Iran, and the age-adjusted standardized ratio (ASR) was 28.1 in 100 000 people [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Akbari et al reported that the five- and ten-year survival rate for BC was estimated to be 81% and 77%, respectively [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRT is recommended to all patients undergoing BCS and some of them after mastectomy. The main objective is to obtain more effective doses of radiation in the appropriate time and site without increasing the prevalence of complications. RT can provide curative treatment in nearly 40% of patients [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In a previous study, long-term outcomes in patients with BCS and RT who were timely diagnosed, were similar to or even better than patients with mastectomy [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTwo strategies can be applied to deliver irradiation to the cancer site. The first strategy is the use EBRT which is delivered from outside the body to the tumor site via protons, photons, or charged particles [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe second strategy is IORT which delivers electron beams (IOERT) and low kv-x-ray (IOXRT) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. By applying IORT during BCS, a high single dose is delivered to the site at the highest risk to fight subclinical tumor cell contamination with high precision owing to direct visualization [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Tumor bed has been reported as the highest risk of breast tumor recurrence [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. As a result, local recurrence can be significantly reduced by using an extra dose to the tumor bed [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. EBRT can be replaced by accelerating partial breast irradiation, including an IORT session, due to approved several advantages including; lack of gap between surgery and RT, avoidance of long treatment duration, reduced radiation-induced toxicity, tumor bed delineation under direct palpation and visual assessments and sparing non-targeted tissues surrounding the tumor bed [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In addition to the delayed time of RT after tumor excision, EBRT has harmful effects, including cardiac attack, appearance of second tumors, or stimulation of tumor cell growth by neo angiogenesis and hypoxia condition [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Notably, IORT can also help to save money, time, and CO\u003csub\u003e2\u003c/sub\u003e emissions in some patients, inhibiting transportation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Although researchers have shown great interest in a single high-dose clinical approaches for different cancers, limited research has examined the biological basis of IORT [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to specific eligibility criteria, IORT may be delivered either as an anticipated Boost, followed by conventional external RT to guarantee optimal dose delivery, or as an exclusive single radiation dose of Radical, corresponding to the administration of the entire sequence of conventional adjuvant RT [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eProteomics analysis can provide a new insight of interactome profile of the efficacy of IORT. To the best of our knowledge, there have been only imperfect efforts to characterize genome or proteome underlying physiological and biological mechanisms of IOeRT-treated tumor beds.\u003c/p\u003e \u003cp\u003eThe present study represents iTRAQ based on the samples collected from the tumor bed before and 24 after post irradiation. Our findings highlight the involvement of specific proteins and pathways signatures related to different doses. The analyses of protein functions and signaling pathways of these DEPs reveal a molecular response to IORT, which may shed light to design a model for the individual-centered radiotherapy introduced.\u003c/p\u003e "},{"header":"Results","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003cp\u003eIdentification of differentially expressed proteins classification\u003c/p\u003e \u003cp\u003eWe performed iTRAQ quantification after labeling of samples in three replicates (for both doses, and tissue; MB and MA24 h). In total, 1045410 spectrums were generated; also, 31572 peptides and 5860 proteins were identified with 1% FDR. DEPs for each comparisons of group/treatments analyzed. We found that there were 110 DEPs (37 up regulated, 73 down regulated) as single high dose-independent, which were common in both Boost and Radical doses (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eRepresented common DEPs in both Boost and Radical doses.\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eNames\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003etotal\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eelements\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003eCommon DEPs under Boost and Radical\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e110\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eA2M SERPINB1 NCF4 CNN1 GCA FMNL1 PTPRC LMNB1 VASP SERPINA3 ALOX5 BASP1 SLC2A3 LBP PTX3 LTF ATP5A1 TLN1 FCGR3A LCP1 C9 MDH1 ANXA5 IDH3A PEA15 CP SUCLA2 S100A9 HSPB1 HRG RPL7A ANXA3 ELANE FGG ASS1 LDHB FGB1 BST1 HK3 PRIM2 IDH1 ALDH6A1 DLST C5 CNDP2 ACADVL ITIH3 CALB2 ITGB2 PROS1 TUFM ARHGDIB YWHAG MMP8 PYGB PADI4 APOH DEFA1 CYBB SPTA1 PLG LYZ 2-sep RPL23A APOA4 C4BPA SUCLG2 HIST1H1C BPI PEBP1 ITIH4 ACOT1 C8A MSN LGALS1 MYO6 WDR1 YWHAE BCAP31 CFH CNN3 HRG MNDA MPO MYH11 VWF VTN CAMP ALDH7A1 OCIAD1 RP2 AZU1 HEBP2 HMGB2 ITGAM HIST1H1B HIBCH RNASE3 RETN HADH PRTN3 ITIH2 ANK1 CFB LCN2 SERPINC1 ctsg FLNA ALDH9A1 C6 MMP9\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cp\u003e Network Analysis Of Deps As Single High Dose-independent\u003c/p\u003e \u003cp\u003eWe have analyzed 110 DEPs as single high dose-independent. Cytoscape network analysis represented the most important proteins based on 110 nodes, 350 edges and 6.36 as average node degree and 0.505 as avg. local clustering coefficient (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e, we identified a network of 110 DEPs as single high dose-independent. Among all, we found key proteins under Boost and Radical.\u003c/p\u003e \u003cp\u003eGene ontology enrichment and functional classification 24\u0026nbsp;h after IORT\u003c/p\u003e \u003cp\u003eFor the better understanding of the biological effects of IORT, we classified 110 DEPs by mapping the annotated coding region sequences to the reference canonical pathways in the KEGG pathway database (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e "},{"header":"Discussion","content":" \u003cp\u003eRT is recommended to all patients undergoing BCS and some of them after mastectomy [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Despite the technological advances made in recent decades, RT plans still advise the same total dose per organ tumor, without taking into account the biological differences attributable to the different tumor subtypes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. By applying IORT during BCS, a high single dose is delivered to the site at the highest risk to fight subclinical tumor cell contamination with high precision owing to direct visualization [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Tumor bed has been reported as the highest risk of breast tumor recurrence [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. As a result, local recurrence can be significantly reduced by using an extra dose to the tumor bed [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough researchers have shown great interest in a single high-dose clinical approaches for different cancers, limited research has examined the biological and molecular basis of single high-dose effects, especially after IORT [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Studies have described that Post-surgery wound fluids (WF) induces an increase in the cell proliferation, migration in addition invasion in BC cell line [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, the proteomics analysis of IORT-treated WF has shown an inhibition of cell proliferation and invasion in the BC cell line [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In addition, Cell and molecular traits observed in MCF-7 cells showed a typical senescent phenotype associated with cell proliferation arrest after treatment with IORT [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. A worthy study was reported that the IORT-induced gene expression profiles and pathways appear to be BC cell-line dependent. The data suggest that some specific gene and pathway signatures seem to be linked to hormone receptor status [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the context of this radiation treatment modality, this study aimed to describe the molecular response, in terms of DEPs and pathways, according to the different doses of IORT. However, this type of investigation would need to be extended to numerous panels of samples; thus, the present work should be considered as a pilot study. Here, we report substantial alterations in proteins expression levels after RT in both dose (Boost and Radical). Our proteomics approach using ITRAQ is a necessary first step for a biological study to describe the common molecular features associated with types of IORT.\u003c/p\u003e \u003cp\u003eAccording to Table\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e, we detected 110 overlap DEPs between Boost and Radical doses. The identified overlapping 110 DEPs extracted from IORT 24\u0026nbsp;h post-treatment are enriched based on KEGG pathways database by using DAVID.\u003c/p\u003e \u003cp\u003eFunctional annotation revealed new insights of IORT in BC patients. Some of the most important pathways such as Complement and coagulation cascades, metabolomics pathways were enriched under IORT (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe have analyzed 110 DEPs as single high dose-independent. Cytoscape network analysis represented the most important proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All DEPs were classified into 10 protein categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e), in which the most proteins related to RT are PLG, VWF and A2M (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The protein panel obtained corresponds to genes involved in cancer processes whose significantly downregulated after IORT-treated tumor bed [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The Plasminogen system produced by PLG (plasminogen) plays a crucial role in physiological in addition, pathological events related to tissue regeneration, wound healing, immune response, angiogenesis, invasion and metastasis. The PLG is significantly downregulated after IORT-treated tumor bed and highly enriched in Complement and coagulation cascades. In addition, Von Willebrand Factor Protein (VWF) is a major platelet ligand that has been widely used, as a biomarker in cancer growth and metastasis and associated inflammation [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], is significantly downregulated at 24\u0026nbsp;h post irradiation also highly enriched in Complement and coagulation cascades. As shown in figuere 4, reffre to complement and coagulation cascades kegg pathway data base, enrichment associated with changes in these related key pathway to RT have been illustrated which indicates that two doses of IORT (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e) is able to reduce cell growth and cell proliferation.\u003c/p\u003e \u003cp\u003eIt has been reported in previous stydies that 21-gene recurrence score (RS) were evaluated for prognostic and predictive benefit in IORT patients [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] in addition a value study indicated that key molecular pathways in radiotherapy (RT) are equally enriched by both Boost and Radical doses[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOverall, our findings indicate that the DEPs may be key proteins in IORT-treated tumor bed and may serve as potential Effective biomarkers under IORT.\u003c/p\u003e \u003cp\u003eEventually, results revealed new insights of IORT in BC management; all of these biological responses in tumor cells may be towards effectiveness of IORT and it shows that although IORT is a local treatment, it can have systemic effects.\u003c/p\u003e "},{"header":"Conclusion","content":" \u003cp\u003eAs a conclusion, by using comprehensive techniques such as proteomic, we found the molecular basis of IORT-related changes that have already showed clinical and epidemiological effects in treating BC patients. In addition key biological pathways that induced by IORT indicated that, cellular microenvironment as a main causes of changes in cells through tumor development, can be affected by IORT. Due to the effectiveness of IORT, it may be recommended as a standard method for BC patients.\u003c/p\u003e \u003cp\u003eUltimately, proteomic study open new insights into personalized RT and this study has discovered another piece of puzzle that identifies the complexities of BC.\u003c/p\u003e "},{"header":"Materials And Methods","content":" \u003cp\u003eSix random selected BC patients entered into our study (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). 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 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Patients were treated with irradiation following two separate strategies, including Boost and Radical dose treatment. The machine dose rate was adjusted to 1\u0026nbsp;cGy/MU during the irradiation. Approximately 100\u0026nbsp;mg of tissue was extracted from tumor bed under Boost and Radical doses; MB, MA24 h.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cdiv class=\"SimplePara\"\u003eClinical and pathological data of patients by study groups.\u003c/div\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"16\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"10\" nameend=\"c12\" namest=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003ePathologic data\u003c/div\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c16\" namest=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003eIOeRT data\u003c/div\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003ePt Number\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003eAge\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eTumor history type\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003eTumor size (cm)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eNode status\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003eTumor grade\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003eER/PR status\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003eHER-2 Expression\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003eKi67\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003eP53\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003eTumor Necrosis\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003eLVI\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003eDelivered dose (Gy)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cdiv class=\"SimplePara\"\u003eIrradiation time (second)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cdiv class=\"SimplePara\"\u003e\u003cdiv id=\"IEq1\" class=\"InlineEquation\"\u003e\u003cdiv format=\"TEX\" class=\"mathinline\" id=\"FileID_IEq1\" name=\"EquationSource\"\u003e\u003c/div\u003e\u003c/div\u003e size (cm)\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cdiv class=\"SimplePara\"\u003eFlap depth (cm)\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e53\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eIDC\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eN0\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e-/-\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003eNeg\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e%50\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e-\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003ePresent\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003eNot seen\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003e12\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cdiv class=\"SimplePara\"\u003e63\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cdiv class=\"SimplePara\"\u003e7/9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e42\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eIDC\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eN0\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e1\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e+\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003eNeg\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e30%\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e+\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003eNegative\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003eNot seen\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003e12\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cdiv class=\"SimplePara\"\u003e55\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cdiv class=\"SimplePara\"\u003e5/6\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.6\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e39\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eIDC\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.5\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eN0\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e+/+\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003eNeg\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e%25\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e+\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003eNot seen\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003eNot seen\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003e12\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cdiv class=\"SimplePara\"\u003e58\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cdiv class=\"SimplePara\"\u003e6/9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.8\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e4\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e63\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eILC\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e1.3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eN0\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e+/_\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003eNeg\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e%15\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e-_\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003eNot seen\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003eNot seen\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003e21\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cdiv class=\"SimplePara\"\u003e65\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cdiv class=\"SimplePara\"\u003e5/6\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.3\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e5\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e48\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eIDC\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eN0\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e+/+\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003eNeg\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e%30\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e+\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003eNot seen\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003eNot seen\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003e21\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cdiv class=\"SimplePara\"\u003e78\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cdiv class=\"SimplePara\"\u003e7/9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.2\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cdiv class=\"SimplePara\"\u003e6\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cdiv class=\"SimplePara\"\u003e55\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cdiv class=\"SimplePara\"\u003eIDC\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cdiv class=\"SimplePara\"\u003e3\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cdiv class=\"SimplePara\"\u003eN0\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cdiv class=\"SimplePara\"\u003e2\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cdiv class=\"SimplePara\"\u003e+/+\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cdiv class=\"SimplePara\"\u003eNeg\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cdiv class=\"SimplePara\"\u003e%30\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cdiv class=\"SimplePara\"\u003e+\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cdiv class=\"SimplePara\"\u003eNot seen\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cdiv class=\"SimplePara\"\u003eNot seen\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cdiv class=\"SimplePara\"\u003e21\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cdiv class=\"SimplePara\"\u003e85\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cdiv class=\"SimplePara\"\u003e8/9\u003c/div\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cdiv class=\"SimplePara\"\u003e2.5\u003c/div\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"16\"\u003ea\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eProtein Extraction\u003c/p\u003e \u003cp\u003eFirst 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.\u003c/p\u003e \u003cp\u003eProteomics Assays\u003c/p\u003e \u003cp\u003ePeptide Labeling\u003c/p\u003e \u003cp\u003eThe 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.\u003c/p\u003e \u003cp\u003ePeptide 1st Dimensional Fractionation\u003c/p\u003e \u003cp\u003eThe peptides were reconstituted with buffer A (5% ACN, 95% H\u003csub\u003e2\u003c/sub\u003eO, adjusted pH to 9.8 with ammonia) and separated by a Shimadzu LC-20AB HPLC system coupled with a high pH RP column (5-\u0026micro;m particles, Phenomenex). The peptides were separated at a flow rate of 1\u0026nbsp;ml/min with a 60\u0026nbsp;min gradient: 5% buffer B (5% H\u003csub\u003e2\u003c/sub\u003eO, 95% ACN, adjusted pH to 9.8 with ammonia) for 10\u0026nbsp;min, 5\u0026ndash;35% buffer B for 40\u0026nbsp;min, 35\u0026ndash;95% buffer B for 1\u0026nbsp;min, and 95% buffer B for 3\u0026nbsp;min. The gradient was then decreased to 5% B within 1\u0026nbsp;min before re-equilibrating with 5% buffer B for 5\u0026nbsp;min. Elution was monitored by measuring absorbance at 214\u0026nbsp;nm and the eluted peptides were pooled as 20 fractions in a concatenation mode and vacuum dried.\u003c/p\u003e \u003cp\u003ePeptide 2nd Dimensional Fractionation\u003c/p\u003e \u003cp\u003eEach 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 \u0026micro;m\u0026sdot;20 cm, 3\u0026nbsp;\u0026micro;m) packed in-house. The gradient was run at 300\u0026nbsp;nL/min starting from 8 to 35% of buffer B (2% H\u003csub\u003e2\u003c/sub\u003eO 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\u0026nbsp;min and keeping for 10\u0026nbsp;min.\u003c/p\u003e \u003cp\u003eMass Spectrometer Detection\u003c/p\u003e \u003cp\u003eData 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\u0026nbsp;V, curtain gas of 30, nebulizer gas of 15, and interface heater temperature of 150\u0026nbsp;\u0026deg;C. High sensitivity mode was used for the whole data acquisition. The mass ranges for MS1 were from 350 to 1500\u0026nbsp;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\u0026thinsp;+\u0026thinsp;to 5+, dynamic exclusion was set for 1/2 of the peak width (12\u0026nbsp;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\u0026nbsp;Da was 100%.\u003c/p\u003e \u003cp\u003eProtein identification and quantification.\u003c/p\u003e \u003cp\u003eThe 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 (\u0026ge;\u0026thinsp;20) with 99% confidence were counted as identified. The IQuant software was used to quantitatively analyze the labeled peptides with isobaric tags[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. 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\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;0.01) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eGene functional enrichment analysis\u003c/p\u003e \u003cp\u003eGene functional enrichment analysis was applied on DEPs. In this study, we used KEGG pathway database by using DAVID (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://david.abcc.ncifcrf.gov/\u003c/span\u003e\u003c/span\u003e) for functional enrichment analysis.\u003c/p\u003e "},{"header":"Abbreviations ","content":"\u003cp\u003eIntraoperative Radiation Therapy (IORT)\u003c/p\u003e \u003cp\u003eBreast Conserving Surgery (BCS)\u003c/p\u003e \u003cp\u003eBreast Cancer (BC)\u003c/p\u003e \u003cp\u003eMargin before IORT (MB)\u003c/p\u003e \u003cp\u003eMargin 24 hours After IORT (MA24 h)\u003c/p\u003e \u003cp\u003eIsobaric Tag for Relative and Absolute Quantitation (iTRAQ)\u003c/p\u003e \u003cp\u003eDifferentially expressed proteins (DEPs)\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Dr. Kui Wu; Cancer Institute, BGI-Research, China National GeneBank (CNGB) for Collaborate for great comments on this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of supporting data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurther information and data sets are available from the corresponding authors on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design: Mohammad Esmaeil Akbari, Minoo Shahani, Jafar Shakeri\u003c/p\u003e\n\u003cp\u003eAuthor responsible for figs and statistical analyses: Minoo Shahani, Ali Tafti\u003c/p\u003e\n\u003cp\u003eDevelopment of methodology: Minoo Shahani, Jafar Shakeri\u003c/p\u003e\n\u003cp\u003eAcquisition of data provided: Mohammad Esmaeil Akbari\u003c/p\u003e\n\u003cp\u003eSampling and extraction: Minoo Shahani, Jafar Shakeri\u003c/p\u003e\n\u003cp\u003eAnalysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): Ali Tafti, Minoo Shahani, Jafar Shakeri\u003c/p\u003e\n\u003cp\u003eWriting, review, and/or revision of the manuscript: Jafar Shakeri, Minoo Shahani, Ali Tafti\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the ethics committees of Shahid Beheshti university of medical sciences (Approval ID:IR.SBMU.RETECH.REC.1397.561) .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatient treated at the cancer research and hospital center of Shahid Beheshti University of medical sciences consented in writing for the use of their anonymous data for research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s details \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMinoo Shahani\u003csup\u003e1, 2+\u003c/sup\u003e; Jafar Shakeri\u003csup\u003e1\u003c/sup\u003e; Ali Tafti\u003csup\u003e1\u003c/sup\u003e; Mohammad Esmaeil Akbari\u003csup\u003e1\u003c/sup\u003e*\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCancer Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.\u003c/li\u003e\n\u003cli\u003eDepartment of Genetics, Islamic Azad University, Tehran Medical Sciences Branch, Tehran, Iran.\u003c/li\u003e\n\u003cli\[email protected]\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"mailto:[email protected]\"\[email protected]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"mailto:[email protected]\"\[email protected]\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"mailto:[email protected]\"\[email protected]\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMovahedi M, Haghighat S, Khayamzadeh M, Moradi A, Ghanbari-Motlagh A, Mirzaei H, et al. 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International journal of molecular sciences 2018, 19 (4).\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=Luo%20T%5BAuthor%5D\u0026amp;cauthor=true\u0026amp;cauthor_uid=30008862\"\u003eLuo T\u003c/a\u003e, \u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=Chen%20X%5BAuthor%5D\u0026amp;cauthor=true\u0026amp;cauthor_uid=30008862\"\u003eChen X\u003c/a\u003e, \u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=Zeng%20S%5BAuthor%5D\u0026amp;cauthor=true\u0026amp;cauthor_uid=30008862\"\u003eZeng S\u003c/a\u003e, \u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=Guan%20B%5BAuthor%5D\u0026amp;cauthor=true\u0026amp;cauthor_uid=30008862\"\u003eGuan B\u003c/a\u003e, \u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=Hu%20B%5BAuthor%5D\u0026amp;cauthor=true\u0026amp;cauthor_uid=30008862\"\u003eHu B\u003c/a\u003e, \u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=Meng%20Y%5BAuthor%5D\u0026amp;cauthor=true\u0026amp;cauthor_uid=30008862\"\u003eMeng Y\u003c/a\u003e, \u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=Liu%20F%5BAuthor%5D\u0026amp;cauthor=true\u0026amp;cauthor_uid=30008862\"\u003eLiu F\u003c/a\u003e, \u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=Wong%20T%5BAuthor%5D\u0026amp;cauthor=true\u0026amp;cauthor_uid=30008862\"\u003eWong T\u003c/a\u003e, \u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=Lu%20Y%5BAuthor%5D\u0026amp;cauthor=true\u0026amp;cauthor_uid=30008862\"\u003eLu Y\u003c/a\u003e, \u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=Yun%20C%5BAuthor%5D\u0026amp;cauthor=true\u0026amp;cauthor_uid=30008862\"\u003eYun C\u003c/a\u003e, \u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=Hocher%20B%5BAuthor%5D\u0026amp;cauthor=true\u0026amp;cauthor_uid=30008862\"\u003eHocher B\u003c/a\u003e, \u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=Yin%20L%5BAuthor%5D\u0026amp;cauthor=true\u0026amp;cauthor_uid=30008862\"\u003eYin L\u003c/a\u003e. 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Two strategies can be applied to irradiation, External Beam RT (EBRT) in addition, Intraoperative Radiation Therapy (IORT). The aim of this study was to introduce a protein biomarker panel related to molecular function under IORT. \u003c/p\u003e\u003cp\u003eMethods Six Breast Cancer (BC) patients as a pilot study were treated by 12 Gy (Boost dose) and 21 Gy (Radical dose). Samples tissue included Margin before IORT (MB), and Margin 24 hours After IORT (MA24 h). Isobaric Tag for Relative and Absolute Quantitation (iTRAQ) was performed to study proteomic of IORT-treated tumor bed. \u003c/p\u003e\u003cp\u003eResults We classified 110 differentially expressed proteins (DEPs) as a protein biomarker panel by mapping the annotated coding region sequences to the reference canonical pathways in the KEGG database. \u003c/p\u003e\u003cp\u003eConclusion Our findings indicate that the DEPs may be key proteins in IORT-treated tumor bed and may serve as potential Effective biomarkers under IORT.\u003c/p\u003e","manuscriptTitle":"Key proteins as potential effective biomarkers under Intraoperative Radiotherapy (IORT) in tumor bed of Breast Cancer patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-03-13 15:01:44","doi":"10.21203/rs.3.rs-17156/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8dfdf4e5-7478-40e2-8ce3-b286bbd33019","owner":[],"postedDate":"March 13th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":69777,"name":"Oncology"}],"tags":[],"updatedAt":"2020-03-24T13:10:49+00:00","versionOfRecord":[],"versionCreatedAt":"2020-03-13 15:01:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-17156","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-17156","identity":"rs-17156","version":["v1"]},"buildId":"oE6Zbj460LM0Up2FdVbMZ","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}