Research Article

Proteins Which have been Found in Breast Cancer by Proteomic’s Analyzer

Haniyeh Bashi Zadeh Fakhar*, Mostafa Rezaei-Tavirani, Mohammad Esmaeil Akbari, Seyed Nahid Sajadi and Mohammad HadiZadeh

Cancer Research Centre (CRC), Shahid Beheshti University of Medical Sciences, Tehran, Iran
Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Department of biology, islamic Azad university, Ayatollah Amoli Branch, Amol, Iran


Received Date: 12/02/2022; Published Date: 20/04/2022

orresponding author: Cancer Research Centre (CRC), Shahid Beheshti University of Medical Sciences, Tehran, Iran

DOI: 10.46718/JBGSR.2022.11.000271

Cite this article: Haniyeh Bashi Zadeh Fakhar*1, Mostafa Rezaei-Tavirani², Mohammad Esmaeil Akbari¹, Seyed Nahid Sajadi³ and Mohammad HadiZadeh¹.Proteins Which have been Found in Breast Cancer by Proteomic’s Analyzer


Breast cancer is one of the most common types of invasive cancer in females worldwide. Despite major advances in early cancer detection and emerging therapeutic strategies, further improvement has to be achieved for precise diagnosis to reduce the chance of metastasis and relapses.  Recently, proteomics based analyses of breast serum and tissue lysates have resulted in the finding of a number of potential tumor biomarkers providing, therefore, a basis for a better understanding of the breast-cancer development and progression, and eventually serving as diagnostic and prognostic markers. In this review, we examined the current Proteomics techniques applied to breast cancer studies and Proteins which have been found  in this cancer.

Keywords: Proteins - breast cancer –proteomic’s- analyzer


The human genome contains approximately 35,000 genes and has the ability to encode up to 35,000 corresponding proteins. The occurrence of alternative RNA splicing and post-trans­lational modifications (PTM), including phosphorylations, acetylations, glycosylations and protein cleavages may increase the expression of proteins to 500,000-1,000,000 [1]. Providing the direct link between gene sequence and cell physiology, proteomics is expected to complement genomic analyses to evaluate disease development, prognosis and response to treatment [2]. Worldwide, it is estimated that breast cancer is by far the most frequent cancer among women; each year, around 1.5 million new breast cancer cases are diagnosed in women throughout the world. Statistically, this means that 500,000 women worldwide will die from this disease [3]. In recent years breast cancer mortality rates have declined as a result of earlier detection, more effective therapies, mainly due to detection of breast cancer at earliest stages, might allow for more favorable results [4]. Therefore, there is an important need to improve the screening and diagnosis of early invasive and noninvasive tumors [5]. At present, finding novel, pre-symptomatic screening approaches are crucial in breast cancer screening and diagnosis, and have the potential to reduce mortality caused by this disease [6]. Identifying new protein markers in screening investigations can possibly avoid many deaths caused by this type of tumor [7]. Therefore, the search for specific disease-associated biomarker signatures is of particular interest since they could be applied in a standard clinical setting. Biomarker discovery for this disease is still very much in its discovery phase [8]. Multiple approaches have been developed that hold promise for the identification of serum biomarkers. Among them, quantitative proteomics yields information that specifically recognizes the differences between samples [9]. Numerous studies have already shown that this methodology can be used to uncover proteomic expression patterns linked with cancer, and some expression patterns have shown high promise to discover new biomarkers of early-stage cancers [10].

Recently, proteomics-based analyses of breast serum and tissue lysates have resulted in the finding of a number of potential tumor biomarkers providing, therefore, a basis for a better understanding of the breast-cancer development and progression, and eventually serving as diagnostic and prognostic markers [11].  In this review, the current Proteomics techniques applied to breast cancer studies and Proteins which have been found in this cancer.

The proteomic tools for identifying molecular markers of the Breast

Different classifications of technologies for proteomic studies that are used for an analysis of tumor tissues and body fluids are known [12]. By the type of equipment used in the research, one may classify the proteomic technologies as follows: methods of gel electrophoresis (2D-PAGE, 2D-DIGE), peptide-oriented proteomics (LC combined with MS/MS: LC-MS/MS), the methods based on the use of arrays (RPPA) [13]. MS-based proteomic platforms for cancer studies and their principles of use are discussed in detail in [14]. To these platforms belong such methods as gel electrophoresis (1D-PAGE, 2D-PAGE (SDS-PAGE), 2D-DIGE), liquid chromatography (LC/MALDI or LC/MS (LC-MS/MS)), 2D-LC or multidimensional protein identification technology (MudiPuT), LC-ESI-MS, mass spectrometry (ion sources (ESI MS, MALDI MS, SELDI MS) combined with mass analyzers (Q MS, TOF MS, FT-ICR MS): MALDI-TOF MS, SELDI-TOF MS, ESI-MS/MS) [15]. By the data, LC-MS/MS is used mostly with bottom-up strategy, along with this some methodologies based on top-down strategy are already developed, too [16]. Also, for identification of new cancer biomarkers and potential therapeutic targets LC-MS/MS could be combined with quantitative methods: ICAT-LC-MS/MS, iTRAQ-LC-MS/MS, SILAC-LC-MS/MS [17].

In recent years, the combination of 2-DE and MS has been utilized extensively for proteomics research in medicine [18]. The power of the 2-DE-based technology was recognized by the research community early on, and scientists from various disciplines were attracted to the field of proteomics. The information obtained by the 2-DE-based approach is high because a number of specific protein attributes can be determined [19]. Thousands of proteins can be resolved and visualized simultaneously on a single 2-DE gel; for each protein, the isoelectric point, MW, and the relative quantity can be measured [20]. High-resolution capabilities of 2-DE allow the separation and detection of post-translationally modified proteins. In many instances, post-translationally modified proteins can be readily located in 2-DE gels because they appear as distinctive horizontal or vertical clusters of spots [21]. In addition, modified proteins can be revealed by MS analysis, when multiple spots of the same protein are identified. In terms of equipment, the 2-DE-based technology is well suited for research conducted in an academic setting [22-23].

MALDI-TOF-MS remains an important tool for protein identification because of its high throughput, sensitivity, and high mass accuracy [24]. Numerous advancements have been made in MALDI-TOF instrumentation and new-generat ion, automated MALDI-TOF mass spectrometers are commercially available [25]. These high throughput systems are run without operator intervention, and incorporate algorithms for iterative optimization of instrument parameters during data acquisition. Improved software tools for the detection of monoisotopic peaks in MALDI-TOF spectra have also been developed [26]. Another type of newly developed MS instrumentation combines electrospray ionization (ESI) with a quadrupole time-of-flight (QTOF) analyzer [27]. The QTOF analyzer can be coupled with MALDI, and MALDI-QtOF-MS was shown to be a promising new tool for proteomics [28]. The latest generation of proteomics instrumentation also includes the MALDI tandem-time-of-flight (MALDI-TOF/ TOF) mass spectrometer. The major advantages of the MALDITOF/ TOF instrument are ultra-high throughput, high sensitivity, and high-energy collision-induced dissociation capabilities that provide enhanced peptide-sequence information [29]. 2-dimensional gel electrophoresis has been used in cancer proteomics, but this technique enabled analysis of only the most abundant proteins and generally with low quantitative accuracy. Mass spectrometry–based proteomics, particularly in a high resolution and quantitative format, has developed rapidly over the last few years [30]. Hybrid mass spectrometers-such as the linear ion trap-Orbitrap-combine high resolution, high mass accuracy, and high peptide sequencing speed [31]. Together with innovations in sample preparation and computational proteomics, these technologies can enable confident peptideand protein identification and quantification at a large scale.  Examination of the signature proteins in gene expression studies of large patient cohorts identified IDH2 and CRABP2 as markers of poor prognosis and SEC14L2 as a marker of good prognosis [32].

In recent years, innovations in high-throughput proteomic profiling approaches have allowed for highly sensitive, accurate, and quantitative identification of altered proteins in multiple samples at the same time. Isobaric tags for relative and absolute quantitation (iTRAQ) has been used successfully for the characterization of protein bio indicators of diverse effects [33]. In general, modern proteomic studies often use gel electrophoresis and chromatography combined with MS. Mostly, gel electrophoresis and chromatography are used for separation of protein mixture into [34].  2-DE investigations showed elevated levels of acute phase proteins such as haptoglobin (_-chain), serum amyloid P, _1-antitrypsin, _1-antichymotrypsin and _1- acidic glycoprotein in plasma from patients diagnosed with breast cancer [35]. Other recently identified breast cancer biomarkers using SELDI include Hsp27, 14-3-3 sigma, and mammaglobin/ lipophilin B complex [36].

Table 1: The results of modern proteomic studies of BC.

Diagnostic marker protein profiling studies

The goal of mass spectrometry-based protein profiling studies performed for breast cancer is to identify novel diag­nostic markers [37]. For genetic breast cancer classifications, the sporadic breast cancer subgroups constitute approximately 90% of cases and hereditary cases constitute approximately 10% [38]. With the improvement of MS technologies and sample preparation protocols, the size of cohorts and the quality of proteomic data significantly improved [39]. Liu et al [40] analyzed a cohort of 126 TNBC breast cancer samples using laser capture microdissection liquid chromatographers/MS approach. The total protein coverage obtained was >3500 proteins, and they identified an 11-protein signature for TNBC with 10 proteins that were up-regulated (CMPK1, AIFM1, FTH1, EML4, GANAB, CTNNA1, AP1G1AP1M1, and CAPZB), and one was down-regulated (methylenetetrahydrofolate dehydrogenase 1) in good prognosis patients. The signature presented high predictive value of patient prognosis with area under the curve of 0.83 of a receiver operating characteristics curve. With the use of the same techniques De Marchi et al [41]. obtained a four-protein signature (programmed cell death protein 4, cingulin, ovarian carcinoma immune reactive antigen domain containing protein 1, and Ras GTPase-activating protein binding protein 2), which predicts tamoxifen-susceptibility in recurrent breast cancer. The cohort consisted of 112 ER positive tumor samples with total coverage of 4000 proteins [42].

Using combined LC-MS/MS and bottom-up strategy, protein biomarkers were identified in urine of breast cancer patients with different disease stage and tumor material was studied in parallel as well [43]. Expression levels of 59 proteins was found to be different from that in control samples, in particular, 13 novel up-regulated proteins associated with breast cancer of diagnostic value have been revealed. The relation between breast cancer progression and a panel of specific protein markers has been ascertained: pervasive ductal carcinoma in-situ - leucine LRC36, protein MAST4 and uncharacterized protein CI131, early invasive breast cancer - DYH8, HBA, PEPA, MMRN2 proteins, filaggrin, and uncharacterized protein C4orf14 (CD014), and metastatic breast cancer - AGRIN, NEGR1, FIBA proteins and KIC10 keratin These data will be used for development of screening programs [44].  Predictive protein markers of different breast cancer subtypes will allow us to determine therapeutic response to particular treatment, to optimize and personalize cancer therapy [45].  the following proteins were found to be overexpressed: transketolase, transferrin, CK19, thymosin β4, and thymosin β10. The number of proteins, namely, enolase, peroxiredoxin 5, periostin precursor, cathepsin D preproprotein, vimentin, Hsp 70, annexin 1, RhoA were related to the tumor response to neoadjuvant chemotherapy. Also, two proteins for classification of these subtypes were validated (see Table 1) [44].

In addition, in other study reported an integrated cell line-based discovery based on iTRAQ approach for the identification of protein biomarkers [46]. Further filtering for secreted proteins and prioritization based on gene expression data and immunohistochemically staining from breast cancer tissues combined with iTRAQ results provided a short list of 5 proteins. then assessed their expression level in a verification cohort of 56 samples. authors confirmed the significantly higher concentrations of KLK6, FST, LIF and IGFBP2 in the breast cancer group compared to the healthy controls, whereas tPA expression showed no significant difference between both groups. When an independent validation cohort of 241 invasive breast cancer serum samples and 112 healthy control samples was used, only KLK6 and FST protein expressions were found to be significantly higher in the breast cancer group, compared to healthy controls.   Based on these findings, it  is proposed that KLK6 and FST could be considered as relevant breast cancer biomarkers that could be tested in future systematic and multi-institutional trials to investigate their clinical utility [47].  Another study data identified CRABP2 and IDH2 as markers of poor prognosis and SEC14L2 as a marker of good prognosis and suggest additional markers that require further evaluation [48].

In one study, the differentially expressed proteins between 23 paired primary breast tumor and metastatic lymph nodes were identified by quantitative iTRAQ proteomic analysis. Immunohistochemistry was applied to locate and assess the expression of NUCB2 in paired primary breast tumor and metastatic lymph node tissues.  The results show that NUCB2 (Nucleobindin-2) expression was down-regulated in metastatic lymph node tissues compared with primary breast tumors [49]. Dowling et al. Combined metabolomics and proteomics platforms to analyze cancer and non-cancer serum samples. high mobility group protein HMG-I/HMG-Y (HMGA1) abundance level was found to be associated with breast cancer clinic pathological features [50]. Naif abdullah al-dhabi, normal and tumor tissues were collected from 20 people from a local hospital. Proteins from the diseased and normal tissues have been investigated by 2D gel electrophoresis and MALDI_TOF_MS fingerprint data were fed into various public domains like mascot, MS-fit, and pept-ident against SWISS_PROT protein database and the proteins of interest were identified. Some of the differentially expressed proteins identified were human annexin, glutathione s-transferase, vimentin, enolase-1, dihydrolipoamide dehydrogenase, glutamate dehydrogenase, cyclin A1, hormone sensitive lipase, beta catenin [51].

Based on Reverse phase protein arrays, functional protein classification, subtype differences were noted in Invasive lobular cancer. 6 proteins were statistically different between the RPPA- defined luminal A subgroups: cleaved caspase 9, 53BP1, ampka, GATA3, rad51 and p90rsk thre359/ser363 [52]. Moreover, such a comparison can be explored to find potentially new protein biomarkers for early disease detection. In one study, exosomal proteomes of MDA-MB-231, a metastatic breast cancer cell line, and MCF-10A, a non-cancerous epithelial breast cell line, were identified by nano-liquid chromatography coupled to tandem mass spectrometry. Three exosomal membrane/surface proteins, glucose transporter 1 (GLUT-1), glypican 1 (GPC-1), and disintegrin and metalloproteinase domain-containing protein 10 (ADAM10), were identified as potential breast cancer biomarkers [53]. In 2020 kosok and et al , report identification of specific proteome expression profiles pertaining to two TNBC subclasses, basal A and basal B, through in-depth proteomics analysis of breast cancer cells .they  identified kinases AXL, PEAK1, and TGFBR2 and proteases FAP, UCHL1, and MMP2/14 as specific targets for basal B subclass, which represents the more aggressive TNBC cell lines [54].


Approximately 10-15% of patients with breast cancer have an aggressive) disease and develop distant metastases within 3 years after the initial detection of the primary tumor. As it is not possible to accurately predict the risk of metastasis development in individual patients, 80% of the patients received adjuvant chemotherapy, among which 40% relapse and ultimately die of metastatic breast cancer [55]. Thus, we need to identify effective biomarkers or establish metastatic models to predict the occurrence of breast cancer metastasis to provide a better treatment for these patients. At present, many researches are focused on the different proteins of the primary tumor between breast cancer patients with or without lymph node metastases [56].  or exploring the different proteins between cell lines with different metastatic potential [57-60]. Analysis of proteins expressed by serum, plasma and tumors, using novel concepts and methods, should accelerate our quest to attain this goal and bring to light a better and more comprehensive view of the molecular heterogeneity of breast cancers [61]. In this way the proteomics approaches provide powerful tools to study pathological processes or clinically important problems at the molecular level and will have a major impact in the future. Since the introduction of proteomics, 2- DE, SERPA approach and MS have been successfully used in a large number of studies in many biological fields [22].  2-D electrophoresis coupled with MALDI-TOF/TOF is suitable tool in protein identification because of its relative simplicity and overall visualization of the proteins in the selected pH range. The combination of 2D electrophoresis with the HLPC system enables the enlargement in the number of identified proteins, their sequence coverage and unique identification of various protein isoforms [62].

The study of Breast cancer proteome is directed on profiling of various biologic materials and is aimed at the improvement of prophylaxis, screening, diagnostics, prognosis, and therapy [63]. A large pool of proteins of mammary gland tumors and Breast cancer -associated proteins from body fluids have been already identified, and in part they were validated [64]. The progress of validation methods is helpful in more efficient application of Breast cancer biomarkers in clinical practice [44]. Taken together, the results of proteomics studies demonstrate an integrated interaction of the data and “omics” sources with the systemic approach for assessment of functions of biomolecules in various pathologies and Breast cancer in particular [65].


It is clear that more research is needed.


  1. Tyanova S (2016) Proteomic maps of breast cancer subtypes. Nat Commun 7: 10259.
  2. Johansson H (2019) Breast cancer quantitative proteome and proteogenomic landscape. Nature Communications 10: 1.
  3. Siegel RL, Miller KD, Jemal A (2020) Cancer statistics CA Cancer J Clin 70(1): 7–30.
  4. Ferlay J, Soerjomataram I, Ervik M, Dikshit R, Eser S, (2012) GLOBOCAN Estimated cancer incidence, mortality and prevalence worldwide in 2012. International Agency for Research on Cancer 25(6):105-112.
  5. Zhu Y (2019) Discovery of coding regions in the human genome by integrated proteogenomics analysis workflow. Nat. Commun 9: 93-101.
  6. Nice E (2020) The status of proteomics as we enter the 2020s: Towards personalised/precision medicine. Anal. Biochem 25: 113840.
  7. Nice E (20121) The separation sciences, the front end to proteomics: An historical perspective. Biomed. Chromatogr 35: e4995.
  8. The Financial Times (2021) Proteomics: Searching for the Real Stuff of Life.The Financial Times 14: 512-523.
  9. Omenn G, Lane L, Overall C, Cristea I, Corrales F, et al. (2020) Research on the human proteome reaches a major milestone: >90% of predicted human proteins now credibly detected, according to the hupo human proteome project.J. Proteome Res 19: 4735–4474.
  10. Diamandis E (2014) Towards identification of true cancer biomarkers, BMC Med 12: 156-162.
  11. Al-Wajeeh A, Salhimi S, Al-Mansoub M, Khalid I, Harvey T, et al. (2020) Comparative proteomic analysis ofdifferent stages of breast cancer tissues using ultra high-performance liquid chromatography tandem mass spectrometer. Plosone 15: e0227404.
  12. Chae YK, Gonzalez-Angulo AM (2014) Implications of functional proteomics in breast cancer. Oncologist 19: 328–335.
  13. Adhikari S, Nice E, Deutsch, E.; Lane L, Omenn G, et al. (2020) Ahigh-stringency blueprint of the human proteome.Nat. Commun 11: 5301-5309.
  14. Ponomarenko E, Poverennaya E, Ilgisonis E, Pyatnitskiy M, Kopylov A, et al. (2016) The size of thehuman proteome: The width and depth. Int J Anal Chem pp. 7436849.
  15. Kaur G, Poljak A, Ali S, Zhong L, Raftery M, et al. (2021) Extending the depth of human plasma proteome coverage usingsimple fractionation techniques.J. Proteome Res 20: 1261–1279.
  16. Núñez E, Domont G, Nogueira F (2017) Itraq-based shotgun proteomics approach for relative protein quantification. Methods Mol.Biol 1546: 267–274.
  17. Behboodi F, Rezaei Tavirani M, Yousefzadeh Sh, Bashi zadeh Fakhar H (2015) Studying the Proteomic Pattern of Cancerous Tissue in Patients with Breast Cancer and Its’ Comparing With Healthy Breast. Zahedan J Res Med Sci 17(11): e2197.
  18. Bashi zadeh fakhar H, Zali H, Rezaei Tavirani M (2019) Proteome profiling of low grade serous Ovarian cancer. Journal of Ovarian Research 12: 64.
  19. Gromov P, Moreira JMA, Gromova I (2014) Proteomic analysis of tissue samples in translational breast cancer research. Expert Rev Proteomics 11: 285–302.
  20. Khadir A, Tiss A (2013) Proteomics approaches towards early detection and diagnosis of cancer. J Carcinog Mutagen S14: 002.
  21. Görg A, Drews O, Lück C (2009) 2-DE with IPGs. Electrophoresis 30(1): 122-132.
  22. Hamrita B, Nassr HB, Chahed K, Chouchane L (2011) Proteomics approaches: new technologies and clinical applications in breast carcinomas. Gulf J Oncolog 1: 36-44.
  23. Yanova S (2016) Proteomic maps of breast cancer subtypes.Nat. Commun 7:10259.
  24. Ryan DJ, Spraggins JM, Caprioli RM (2019) Protein identification strategies in MALDI imaging mass spectrometry: a brief review, Curr Opin Chem Biol 48: 64-72.
  25. Long S, Yang Y, Shen C, Wang Y, Deng A, et al. (2020) Metaproteomics characterizes human gut microbiome function in colorectal cancer. NPJ Biofilms Microbiomes 6: 14.
  26. Chaurand P, Norris JL, Cornett DS, Mobley JA, Caprioli RM (2006) New developments in profiling and imaging of proteins from tissue sections by MALDI mass spectrometry. J Proteome Res 5: 2889-2900.
  27. Kumar V, Ray S, Ghantasala S, Srivastava S (2020) An integrated quantitative proteomics workflow for cancer biomarker discoveryand validation in plasma. Front Oncol 10: 543997.
  28. Zhang J, Kim S, Li L, Kemp C, Jiang C, et al. (2020) Proteomic and transcriptomic profiling of pten gene-knockout mouse model ofprostate cancer. Prostate 80: 588–605.
  29. Whitman J, Lynch K (2019) Optimization and comparison of information-dependent acquisition (ida) to sequential window acquisition of all theoretical fragment ion spectra (swath) for high-resolution mass spectrometry in clinical toxicology. Clin Chem 65: 862–870.
  30. Zhang B, Whiteaker J, Hoofnagle A, Baird G, Rodland K, et al. (2019) Clinical potential of mass spectrometry-basedproteogenomics. Nat Rev Clin Oncol 16:256–268.
  31. Branca RMM (2014) HiRIEF LC-MS enables deep proteome coverage andunbiased proteogenomics.Nat. Methods 11:59–62.
  32. Wisniewski JR, Zougman A, Nagaraj N, Mann M (2009) Universal sample preparation method for proteome analysis. Nat Methods 6: 359–362.
  33. Ferreira AR, Bettencourt M, Alho I, Costa AL, Sousa AR, et al. (2017) Serum YB-1 (Y-box binding protein 1) as a biomarker of bone disease progression in patients with breast cancer and bone metastases. J Bone Oncol 6:16-21.
  34. Pendharkar N, Gajbhiye A, Taunk K (2016) Quantitative tissue proteomic investigation of invasive ductal carcinoma of breast with luminal B HER2 positive and HER2 enriched subtypes towards potential diagnostic and therapeutic biomarkers. J Proteomics 132: 112–130.
  35. TejaSwin PolePalle, SrinivaS Moogala, Shalini BoggaraPu, Divya Sai PeSala (2015) Acute Phase Proteins and Their Role in Periodontitis: A Review. ournal of Clinical and Diagnostic Research. 9(11): 01-05.
  36. Belluco (2018) Serum Proteomic Analysis Identifies a Highly Sensitive and Specific Discriminatory Pattern in Stage 1 Breast Cancer. Annals of Surgical Oncology 14: 2470–2476.
  37. Califf R (2018) Biomarker definitions and their applications. Exp Biol Med 243: 213–221.
  38. Honrado E, Benitez J, Palacios J (2006) Histopathology of BRCA1‑and BRCA2-associated breast cancer. Crit Rev Oncol Hematal 59: 27-39.
  39. Da Z, Gao L, Su G, Yao J, Fu W, et al. (2020) Bioinformatics combined with quantitative proteomics analyses and identification of potential biomarkers in cholangio carcinoma. Cancer Cell Int 20:130.
  40. Liu NQ, Stingl C, Look MP, Smid M, Braakman RB, et al. (2014) Comparative proteome analysis revealing an 11-protein signature for aggressive triple-negative breast cancer. JNatl Cancer Inst 106: 376-382.
  41. De Marchi T, Liu NQ, Stingl C, Timmermans MA, Smid M,Look MP, et al. (2016) 4-protein signature predicting tamoxifen treatment outcome inrecurrent breast cancer. Mol Oncol 10: 24e39.
  42. Pozniak Y, Balint-Lahat N, Rudolph JD, Lindskog C, Katzir R, et al. (2016) System-wideclinical proteomics of breast cancer reveals global remodeling of tissue homeostasis. Cell Syst 2:172e184.
  43. Hondermarck H, Vercoutter-Edouart AS, Révillion F, Lemoine J, El-Yazidi- Belkoura I, et al. (2001) Proteomics of breast cancer for marker discovery and signal pathway profiling. Proteomics 1: 1216-1232.
  44. Mazur MG, Pyatchanina TV (2016) The use of proteomic technologies in breast cancer research. Exp Oncol 38(3):146-157.
  45. Tkacikova S, Talian I, Petrovic M, Sabo Jan (2011) Using 2-D electrophoresis followed by nano HPLC in nuclear protein analysis of MCF-7 breast cancer cell line by MALDI-TOF/TOF. Cent Eun J Chem 10(2): 407-412.
  46. Thomas sh, Kumar R (2021) iTRAQ‑based proteome profiling revealed the role of Phytochrome A in regulating primary metabolism in tomato seedling Sherin. Scientific Reports 11: 7540-7457.
  47. Mange A, Dimitrakopoulos L, Soosaipillai A, Coopman P, Diamandis E (2016) An integrated cell line-based discovery strategy identified follistatin and kallikrein 6 as serum biomarker candidates of breast carcinoma. Journal of Proteomics 142(3): 114-121.
  48. Geiger T, Madden SF, Gallagher WM, Cox J, Mann M (2012) Proteomic Portrait of Human Breast Cancer Progression Identifies Novel Prognostic Markers. Cancer Res 72(9): 2428-2439.
  49. Zeng L, Zhong J, He G, Li F, Li J, et al. (2017) Identification of Nucleobindin-2 as a Potential Biomarker for Breast Cancer Metastasis Using iTRAQ-based Quantitative Proteomic Analysis. Journal of Cancer 8(15):3062-3069.
  50. Dowling P (2014) Metabolomic and proteomic analysis of breast cancer patient samples suggests that glutamate and 12-HETE in combination with CA15-3 may be useful biomarkers reflecting tumour burden. Metabolomics 11(3):723-750.
  51. Al-dhabi N, srigopalram S, soundharrajan I (2016) Proteomic analysis of stage-ii breast cancer from formalin-fixed paraffin-embedded tissues. Biomed research international.25(2):102-108.
  52. Mueller C, Haymond A, Davis J, Williams A, Espina V (2018) Protein biomarkers for subtyping breast cancer and implications for future research. Expert rev proteomics. 15(2): 131–152.
  53. Yousef Risha, ZoranMinic, Shahrokh MGhobadloo, Maxim V Berezovskithe (2020) proteomic analysis ofbreast cell line exosomes reveal disease patterns andpotential biomarkers. Scientific Reports. 10:135720.
  54. Kosok M, Alli-Shaik A, Huat Bay B, Gunaratn J (2020) Comprehensive Proteomic Characterization Reveals Subclass-Specific Molecular Aberrations within Triple-negative Breast Cancer. iScience 21:111-121.
  55. Weigelt B, Peterse JL, van 't Veer LJ (2005) Breast cancer metastasis: markers and models. Nat Rev Cancer. 5: 591-602.
  56. Hoon Tan P, Ellis I, Allison K, Brogi E, Fox SB, et al. (2020) The 2019 WHO classification of tumours of the breast. Histopathology 77:181–185.
  57. Bouchal P, Roumeliotis T, Hrstka R, Nenutil R, Vojtesek B, (2009) Biomarker discovery in low-grade breast cancer using isobaric stable isotope tags and two-dimensional liquid chromatography-tandem mass spectrometry (iTRAQ-2DLC-MS/MS) based quantitative proteomic analysis. J Proteome Res 8:362-373.
  58. Boutte AM, McDonald WH, Shyr Y, Yang L, Lin PC (2011) Characterization of the MDSC proteome associated with metastatic murine mammary tumors using label-free mass spectrometry and shotgun proteomics. Plosone. 6: e22446.
  59. Jin L, Zhang Y, Li H, Yao L, Fu D, et al. (2012) Differential secretome analysis reveals CST6 as a suppressor of breast cancer bone metastasis. Cell Res 22:1356-1373.
  60. Naba A, Clauser KR, Lamar JM, Carr SA, Hynes RO (2014) Extracellular matrix signatures of human mammary carcinoma identify novel metastasis promoters. Elife 3: e01308.
  61. Liu JF, Lee CW, Tsai MH, Tang CH, Chen PC, et al. (2018) Thrombospondin 2 promotes tumor metastasis by inducing matrix metalloproteinase-13 production in lung cancer cells. Biochem Pharmacol 155: 537–546.
  62. Tyanova S, Temu T, Sinitcyn P, Carlson A, Hein MY, et al. (2016) The Perseus computational platform for comprehensive analysis of (prote)omics data. Nature Methods 13: 731-740.
  63. Heitzer E, Haque IS, Roberts CES, Speicher MR (2019) Current and future perspectives of liquid biopsies in genomics-driven oncology. Nat Rev Genet 20: 71–88.
  64. Poulos R, Hains P, Shah R, Lucas N, Xavier D, et al. (2020) Strategies to
    enable large-scale proteomics for reproducible research. Nat Commun 11: 3793.
  65. Baruch E, Youngster I, Ben-Betzalel G, Ortenberg R, Lahat A, S.; Bloch, N.; et al.
    Fecal microbiota transplant promotes response in immunotherapy-refractory melanoma patients. Science .2021; 371: 602–609.