Open Access

Comparison of tear protein levels in breast cancer patients and healthy controls using a de novo proteomic approach

  • Authors:
    • Daniel Böhm
    • Ksenia Keller
    • Julia Pieter
    • Nils Boehm
    • Dominik Wolters
    • Wulf Siggelkow
    • Antje Lebrecht
    • Marcus Schmidt
    • Heinz Kölbl
    • Norbert Pfeiffer
    • Franz-Hermann Grus
  • View Affiliations

  • Published online on: June 1, 2012     https://doi.org/10.3892/or.2012.1849
  • Pages: 429-438
  • Copyright: © Böhm et al. This is an open access article distributed under the terms of Creative Commons Attribution License [CC BY_NC 3.0].

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Abstract

Noninvasive biomarkers are urgently needed for early detection of breast cancer since the risk of recurrence, morbidity and mortality are closely related to disease stage at the time of primary surgery. In the past decade, many proteomics-based approaches were developed that utilize the protein profiling of human body fluids or identification of putative biomarkers to obtain more knowledge on the effects of cancer emergence and progression. Herein, we report on an analysis of proteins in the tear fluid from breast carcinoma patients and healthy women using a de novo proteomic approach and 25 mixed samples from each group. This study included 25 patients with primary invasive breast carcinoma and 25 age-matched healthy controls. We performed a MALDI-TOF-TOF-driven semi-quantitative comparison of tear protein levels in cancer (CA) and control (CTRL) using a de novo approach in pooled samples. Over 150 proteins in the tear fluid of CTRL and CA were identified. Using an in-house-developed algorithm we found more than 20 proteins distinctly upregulated or downregulated in the CTRL and CA groups. We identified several proteins that had modified expression in breast cancer patients. These proteins are involved in host immune system pathways (e.g., C1Q1 or S100A8) and different metabolic cascades (ALDH3A or TPI). Further validation of the results in an independent population combined with individual protein profiling of participants is needed to confirm the specificity of our findings and may lead to a better understanding of the pathological mechanism of breast cancer.

Introduction

Breast cancer is still the leading cause of death in women worldwide (1). Although the detection rate of breast carcinoma has improved, many female patients die from metastatic relapse. Mammography is the best available method for detection of breast cancer after the age of 50; although, the detection rate of mammography is not as good in younger women due to their high density breast tissue (2). Early detection is beneficial in the fight against breast cancer. Currently, there are no clinical biomarkers available for early detection of breast cancer. Markers such as CA15.3 and CEA are useful, in combination with imaging and physical examination, for monitoring ongoing treatment in breast cancer patients with metastatic disease; although, they both lack the clinical specificity and sensitivity to be used routinely as a clinical diagnostic tool (3).

The development of high-throughput techniques in Proteomics expanded the search for new biomarkers and enabled the identification of proteins that may have a crucial role in emerging and progressing breast cancer. Proteome analysis of body fluids, such as sera, tear film, or urine, is a hot topic in Proteomics (46). Li et al found three differently regulated proteins in the sera of breast cancer patients and healthy subjects using surface-enhanced laser desorption/ionization time-of-flight based protein profiling in 2002 and Mathelin et al tried to validate these putative biomarkers, determining only two of them could be used for the discrimination of cancer patients (7,8) (reviewed in refs. 9,10). Some studies examined the nipple aspirate fluid of breast cancer patients and healthy patients (11). In 2005, Pawlik et al showed 17 distinctly regulated peptides; whereas, Li et al found different protein distribution patterns in the nipple aspirate fluid and ductal lavage with the use of SELDI-TOF mass spectrometry (12,13). Since then, many other protein profiling studies were published that used matrix-assisted laser desorption/ionization time-of-flight/time-of-flight mass spectrometry with differently regulated proteins (1416). The advantage of the MALDI-TOF-TOF MS is the subsequent identification of the proteins of interest. In a previous study, we reported data from MALDI-TOF-TOF-based profiling of the sera that could distinguish breast cancer patients from age-matched healthy controls, and we could classify cancer patients with a high sensitivity of 89% (17).

Another proteomics-based approach for the exploration of cancer-derived differences is the highly-precise microarray platform. This approach can serve, instead of the common ELISA test, as a validation tool for the biomarkers identified from prior MALDI-TOF-TOF-based explorations of the proteome. Here, the antibodies are fixed on a highly-optimized surface. In this manner, several protein levels can be measured simultaneously due to the small required volume (nl) of the reagents. After fixation of the antibodies, the surfaces can be incubated with body fluids containing the appropriate proteins. This high-throughput technique is also very common for the profiling of carcinoma tissue or body fluids of diseased patients due to its miniaturized size, accuracy, and automated handling (1820) (reviewed in ref. 21). Several comparative studies of breast cancer and healthy sera have been published. Our study group reported the regulation of several proteins were significantly different in the sera of breast cancer patients (22). The discovery of different protein patterns in diseased cohorts and control samples and subsequent identification of these biomarkers is a promising method of obtaining knowledge about the effects of several diseases (6,23,24). A well-developed and clinically proven biomarker signature could lead to early detection of cancer, which can have great benefits for patients.

Most proteomic studies of breast carcinoma published so far concentrate on profiling the tissue or body fluids near the emergence spot. Little is known about the proteome changes of distant body fluids. Some research groups examined the protein profiles of alternative body fluids such as urine or saliva and several differently regulated proteins were reported (reviewed in refs. 25,26). Previously, we showed different protein distributions in the tear fluid of breast cancer patients and healthy controls in a SELDI-TOF-based profiling study (17,27). Another comparative MALDI-TOF-TOF-driven analysis of healthy dog’s tear fluid and dogs diagnosed with cancer has been published (28). To our knowledge, no other comparative tear fluid proteomic studies for breast cancer have been reported. Tear fluid has unique properties as retrieval is minimally invasive and it does not contain as many highly-abundant proteins as serum.

Herein, we report a MALDI-TOF-TOF-driven semi-quantitative comparison of tear protein levels in cancer (CA) and control (CTRL) using a de novo approach in pooled samples. Using a signature of biomarkers significantly decreased or increased in groups of CA and CTRL could help to discriminate diseased women from the healthy population with high specificity and sensitivity and possibly lead to the establishment of a molecular diagnostic tool for breast cancer.

Materials and methods

Comparison of tear protein levels in pooled samples from CA and CTRL

This de novo study included 50 female subjects, 25 patients were diagnosed with primary invasive breast carcinoma and treated at the University Medical Center Mainz. At the time of diagnosis, none of the patients had developed distant metastases. Patients’ characteristics are summarized in Table I. The healthy control subjects were 25 age-matched women without any known malignancies who were treated at the University of Mainz medical center. All study members gave their informed consent for voluntary participation in this study. The protocols were approved by the institutional ethics committee in accordance with the ethical standard of Declaration of Helsinki (1964).

Table I

Characteristics of breast cancer patients.

Table I

Characteristics of breast cancer patients.

CharacteristicBreast cancer patients
n=25 (%)
Healthy controls
n=25
Mean age (distribution)58 (39–85)58 (39–85)
Tumor size
 pT116 (64)
 pT29 (36)
Nodal status
 Negative18 (72)
 Positive7 (28)
Grading
 Well differentiated (G1)6 (24)
 Moderately differentiated (G2)14 (56)
 Poor/undifferentiated (G3)5 (20)
Distant metastases
 M025 (100)
 M10 (0)
Sample retrieval

Tear fluid was obtained from all participants using a Schirmer Strip. After the samples were drawn, the strips were frozen immediately at −80°C to prevent protein degradation. Tear proteins were prepared under strict and identical conditions for all patients. Prior to the experiments, the wet strip part was cut into small pieces and incubated with n-Dodecyl-β-D-maltoside overnight at 4°C with constant shaking. The next day, the eluates were briefly centrifuged and transferred into fresh tubes. All samples were stored prior to analysis at −20°C.

Sample processing

For the comparison of protein levels in CTRL and CA, each of the 25 tear eluates were pooled together accordingly to the group and precipitated with three times the volume of acetone overnight at −80°C. The next day, tear proteins were centrifuged at 14000 × g and 4°C to prevent protein degradation. The supernatant was discarded and the proteins were resuspended in PBS. Protein concentrations were measured with the BCA Protein Assay kit (Thermo Scientific, Rockford IL, USA), according to the manufacturer’s protocol.

1D SDS-PAGE and sample purification

Pooled tear proteins (60 μg) from CTRL and CA were separated by molecular weight using 1D SDS-PAGE (gels, buffers, and equipment all purchased from Invitrogen, Darmstadt, Germany). After gel electrophoresis, the lanes were stained overnight and then rinsed with double-distilled water. In the next step, the lanes were subdivided into 32 bands and the proteins were digested with endopeptidase trypsin according to the modified digestion protocol from Shevchenko et al(29). For the purification and desalting of peptides, automated sample handling was preferred to reduce the fluctuations from measured proteins due to manual processing of the samples. The purification and the stepwise elution of the peptides with 10–50% acetonitrile were performed using C18 ZipTips (Millipore, Billerica, USA) on the Freedom EVO®, purification station (Tecan Group Ltd., Männedorf, Switzerland). The eluted proteins (3 μl) were directly spotted on the MALDI TOF/TOF polished steel target and coated with 3 μl crystallization matrix (20 mg cinnamic acid/50% acetonitrile/2% trifluoroacetic acid). The matrix included 0.5 μl of a Reserpine solution (1 mg/ml) dissolved in methanol for signal normalization. All samples were measured head-to-head to avoid protein degradation and measurement fluctuations in the MALDI-TOF/TOF mass spectrometer (UltraflexII, Bruker Daltonik GmbH, Bremen, Germany). The peak detection was performed with internal calibration mix (Peptide calibration standard II, Bruker Daltonik GmbH).

Data processing

After MALDI-TOF/TOF measurements, the spectra were exported into Proteomics Pipeline Mainz (P2M) software, developed in-house, and normalized according to the Reserpine peaks. Proteins were identified using the MASCOT protein search tool (30). The Swissprot database was chosen for the identification of proteins (31). The following general parameters were used: carbamidomethyl as a global modification and oxidation (M) as a variable modification with an MS tolerance of 100 ppm and MS/MS tolerance of 0.8. Only one miscleaved site was allowed and the MudPIT scoring system was used. For further analysis of the protein regulation levels, the intensities of the peptides for each protein were summed and the ratio of the intensity between both groups was calculated for each protein. Significant differences in protein expression levels were defined as at least two times higher or lower expression than the other group. STRING and Cytoscape software were used for the analysis of protein-protein interactions (32,33).

Results

In this study we conducted an explorative and comparative analysis of the tear proteome of breast carcinoma patients and age-matched healthy controls. We tried to minimize protein degradation and fluctuations of protein measurements to achieve a precise comparison of protein levels. One person performed the experimental steps for the preparation of tear samples for 1D SDS-PAGE until the transfer of digested fractions onto the sample plate for the robotic purification station. The peptide purification was performed automatically to avoid fluctuations due to the manual handling of samples. Likewise, the experimental steps from the precipitation of the tear eluates were also performed by the same person.

Semiquantitative comparison of protein levels in CA and CTRL

After destaining, a grid made of 32 bands was put under the gel for a more accurate comparison of the proteins. Each of the 32 bands from CTRL and CA were cut out and digested with trypsin overnight. Fig. 1 shows the samples after 1D SDS-PAGE separation and staining with Coomassie dye (Colloidal Blue Staining kit, Invitrogen). After digestion and automated fractionation, the peptides were measured in a MALDI-TOF-TOF mass spectrometer. Representative fractions from both groups are shown in Fig. 2. All spectra were normalized using Protein Pipeline Mainz software, which was developed in-house, and the appropriate tear proteins were identified with the MASCOT search tool.

Protein identification

After extensive comparison of the spectra obtained using the annotated proteins in the SWISSPROT Homo sapiens database under the given conditions and MOWSE score, we were able to identify over 150 proteins in the CTRL and CA. The complete merged list of identified proteins is summarized in Table II. To obtain an overview on the relevance and role of the identified proteins, we clustered the proteins in accordance to their molecular functions using the software Cytoscape 2.7.0, as shown in Fig. 3. The Cytoscape software often shows several overlapping molecular functions and distributions into several biological processes; therefore, we created an overview of one mapping possibility for a large number of the identified tear proteins.

Table II

Proteins identified from tear proteomes of CA and CTRL.

Table II

Proteins identified from tear proteomes of CA and CTRL.

ProteinDescriptionOrganism species (OS)Gene name (GN)
TRFL_HUMAN LactotransferrinHomo sapiensLTF
LCN1_HUMANLipocalin-1Homo sapiensLCN1
ALBU_HUMANSerum albuminHomo sapiensALB
IGKC_HUMANIg κ chain C regionHomo sapiensIGKC
SG2A1_HUMANMammaglobin-BHomo sapiensSCGB2A1
LYSC_HUMANLysozyme CHomo sapiensLYZ
PIP_HUMANProlactin-inducible proteinHomo sapiensPIP
DMBT1_HUMANDeleted in malignant brain tumors 1 proteinHomo sapiensDMBT1
IGHA1_HUMANIg α-1 chain C regionHomo sapiensIGHA1
IGHA2_HUMANIg α-2 chain C regionHomo sapiensIGHA2
GSTP1_HUMANGlutathione S-transferase PHomo sapiensGSTP1
ZA2G_HUMAN Zinc-α-2-glycoproteinHomo sapiensAZGP1
ACTB_HUMANActin, cytoplasmic 1Homo sapiensACTB
CYTN_HUMANCystatin-SNHomo sapiensCST1
LC1L1_HUMANPutative lipocalin 1-like protein 1Homo sapiensLCN1L1
PROL4_HUMANProline-rich protein 4Homo sapiensPRR4
CYTS_HUMANCystatin-SHomo sapiensCST4
ACTBL_HUMANβ-actin-like protein 2Homo sapiensACTBL2
POTEE_HUMANPOTE ankyrin domain family member EHomo sapiensPOTEE
POTEF_HUMANPOTE ankyrin domain family member FHomo sapiensPOTEF
ACTC_HUMANActin, α cardiac muscle 1Homo sapiensACTC1
LAC2_HUMANIg λ-2 chain C regionsHomo sapiensIGLC2
SG1D1_HUMANSecretoglobin family 1D member 1Homo sapiensSCGB1D1
S10A9_HUMANProtein S100-A9Homo sapiensS100A9
K1C9_HUMANKeratin, type I cytoskeletal 9Homo sapiensKRT9
TMC8_HUMANTransmembrane channel-like protein 8Homo sapiensTMC8
K2C1_HUMANKeratin, type II cytoskeletal 1Homo sapiensKRT1
LAC1_HUMANIg λ-1 chain C regionsHomo sapiensIGLC1
CYTT_HUMANCystatin-SAHomo sapiensCST2
PIGR_HUMANPolymeric immunoglobulin receptorHomo sapiensPIGR
S10A8_HUMANProtein S100-A8Homo sapiensS100A8
APOA1_HUMANApolipoprotein A-IHomo sapiensAPOA1
PROL1_HUMANProline-rich protein 1Homo sapiensPROL1
HSPB1_HUMANHeat shock protein β-1Homo sapiensHSPB1
LACRT_HUMANExtracellular glycoprotein lacritinHomo sapiensLACRT
ABCA3_HUMANATP-binding cassette sub-family A member 3Homo sapiensABCA3
IGJ_HUMANImmunoglobulin J chainHomo sapiensIGJ
ANXA2_HUMANAnnexin A2Homo sapiensANXA2
SSH2_HUMANProtein phosphatase Slingshot homolog 2Homo sapiensSSH2
KV301_HUMANIg κ chain V-III region B6Homo sapiens
KV307_HUMANIg κ chain V-III region GOLHomo sapiens
TPIS_HUMANTriosephosphate isomeraseHomo sapiensTPI1
LEG3_HUMANGalectin-3Homo sapiensLGALS3
NGAL_HUMANNeutrophil gelatinase-associated lipocalinHomo sapiensLCN2
POP1_HUMANRibonucleases P/MRP protein subunit POP1Homo sapiensPOP1
ZC3H1_HUMANZinc finger C3H1 domain-containing proteinHomo sapiensZFC3H1
CLIC1_HUMANChloride intracellular channel protein 1Homo sapiensCLIC1
LIME1_HUMANLck-interacting transmembrane adapter 1Homo sapiensLIME1
HV307_HUMANIg heavy chain V-III region CAMHomo sapiens
GNL3_HUMANGuanine nucleotide-binding protein-like 3Homo sapiensGNL3
POTEI_HUMANPOTE ankyrin domain family member IHomo sapiensPOTEI
ENOA_HUMANα-enolaseHomo sapiensENO1
PRDX1_HUMAN Peroxiredoxin-1Homo sapiensPRDX1
MECP2_HUMANMethyl-CpG-binding protein 2Homo sapiensMECP2
K2C78_HUMANKeratin, type II cytoskeletal 78Homo sapiensKRT78
ZG16B_HUMANZymogen granule protein 16 homolog BHomo sapiensZG16B
YM012_HUMANUncharacterized protein DKFZp434B061Homo sapiens
YV021_HUMANUncharacterized protein LOC284861Homo sapiens
ILEU_HUMANLeukocyte elastase inhibitorHomo sapiensSERPINB1
ANXA1_HUMANAnnexin A1Homo sapiensANXA1
POTEJ_HUMANPOTE ankyrin domain family member JHomo sapiensPOTEJ
PLSL_HUMANPlastin-2Homo sapiensLCP1
NCOA5_HUMANNuclear receptor coactivator 5, protein existence (PE), 1; sequence version (SV), 2Homo sapiensNCOA5
B2MG_HUMAN β-2-microglobulinHomo sapiensB2M
KLH34_HUMANKelch-like protein 34Homo sapiensKLHL34
ANX13_HUMANAnnexin A13Homo sapiensANXA13
MDHC_HUMANMalate dehydrogenase, cytoplasmicHomo sapiensMDH1
AIFM2_HUMANApoptosis-inducing factor 2Homo sapiensAIFM2
STAG3_HUMANCohesin subunit SA-3Homo sapiensSTAG3
SMCA4_HUMANTranscription activator BRG1Homo sapiensSMARCA4
DDB1_HUMANDNA damage-binding protein 1Homo sapiensDDB1
RM18_HUMAN39S ribosomal protein L18, mitochondrialHomo sapiensMRPL18
KRIT1_HUMANKrev interaction trapped protein 1Homo sapiensKRIT1
PERT_HUMANThyroid peroxidaseHomo sapiensTPO
HPT_HUMANHaptoglobinHomo sapiensHP
F184A_HUMANProtein FAM184AHomo sapiensFAM184A
AAKG2_HUMAN5′-AMP-activated protein kinase subunit γ-2Homo sapiensPRKAG2
AAKG3_HUMAN5′-AMP-activated protein kinase subunit γ-3Homo sapiensPRKAG3
EIF2A_HUMANEukaryotic translation initiation factor 2AHomo sapiensEIF2A
RGPA2_HUMANRal GTPase-activating protein subunit α-2Homo sapiensRALGAPA2
TUT4_HUMANTerminal uridylyltransferase 4Homo sapiensZCCHC11
ATP4A_HUMAN Potassium-transporting ATPase α chain 1Homo sapiensATP4A
YJ017_HUMANPutative uncharacterized protein LOC439951Homo sapiens
AINX_HUMANα-internexinHomo sapiensINA
TTBK2_HUMANTau-tubulin kinase 2Homo sapiensTTBK2
SPTN2_HUMANSpectrin β chain, brain 2Homo sapiensSPTBN2
MDGA1_HUMANMAM domain-containing glycosylphosphatidylinositol anchor protein 1Homo sapiensMDGA1
FREM3_HUMANFRAS1-related extracellular matrix protein 3Homo sapiensFREM3
PDE4C_HUMANcAMP-specific 3′,5′-cyclic phosphodiesterase 4CHomo sapiensPDE4C
SULF1_HUMANExtracellular sulfatase Sulf-1Homo sapiensSULF1
LRC4C_HUMANLeucine-rich repeat-containing protein 4CHomo sapiensLRRC4C
S10A4_HUMANProtein S100-A4Homo sapiensS100A4
LRFN6_HUMANLeucine-rich repeat and fibronectin type-III domain-containing protein 6Homo sapiensELFN2
IGHG3_HUMANIg γ-3 chain C regionHomo sapiensIGHG3
IGHG2_HUMANIg γ-2 chain C regionHomo sapiensIGHG2
ELOA1_HUMANTranscription elongation factor B polypeptide 3Homo sapiensTCEB3
DLG3_HUMANDisks large homolog 3Homo sapiensDLG3
PDZD7_HUMANPDZ domain-containing protein 7Homo sapiensPDZD7
HV315_HUMANIg heavy chain V-III region WASHomo sapiens
HV304_HUMANIg heavy chain V-III region TILHomo sapiens
WBS23_HUMANWilliams-Beuren syndrome chromosomal region 23 proteinHomo sapiensWBSCR23
PKHH3_HUMANPleckstrin homology domain-containing family H member 3Homo sapiensPLEKHH3
DMXL2_HUMANDmX-like protein 2Homo sapiensDMXL2
CBR3_HUMANCarbonyl reductase [NADPH] 3Homo sapiensCBR3
CE164_HUMANCentrosomal protein of 164 kDaHomo sapiensCEP164
USPL1_HUMANUbiquitin-specific peptidase-like protein 1Homo sapiensUSPL1
TRFE_HUMAN SerotransferrinHomo sapiensTF
MPPA_HUMAN Mitochondrial-processing peptidase subunit αHomo sapiensPMPCA
CABP1_HUMANCalcium-binding protein 1Homo sapiensCABP1
TFR1_HUMANTransferrin receptor protein 1Homo sapiensTFRC
ZN446_HUMANZinc finger protein 446Homo sapiensZNF446
MTDC_HUMANBifunctional methylenetetrahydrofolate dehydrogenase/cyclohydrolase, mitochondrialHomo sapiensMTHFD2
CT151_HUMANUncharacterized protein C20orf151Homo sapiensC20orf151
LIPB2_HUMANLiprin-β-2Homo sapiensPPFIBP2
ZSWM5_HUMANZinc finger SWIM domain-containing protein 5Homo sapiensZSWIM5
WDR60_HUMANWD repeat-containing protein 60Homo sapiensWDR60
C1QC_HUMANComplement C1q subcomponent subunit CHomo sapiensC1QC
CNOT1_HUMANCCR4-NOT transcription complex subunit 1Homo sapiensCNOT1
CDK13_HUMANCyclin-dependent kinase 13Homo sapiensCDK13
GLE1_HUMANNucleoporin GLE1Homo sapiensGLE1
RFIP4_HUMANRab11 family-interacting protein 4Homo sapiensRAB11FIP4
AL3A1_HUMANAldehyde dehydrogenase, dimeric NADP-preferringHomo sapiensALDH3A1
FRMD7_HUMANFERM domain-containing protein 7Homo sapiensFRMD7
SEM4C_HUMANSemaphorin-4CHomo sapiensSEMA4C
PRTG_HUMANProtogeninHomo sapiensPRTG
PTPRR_HUMANReceptor-type tyrosine-protein phosphatase RHomo sapiensPTPRR
HV305_HUMANIg heavy chain V-III region BROHomo sapiens
TGS1_HUMANTrimethylguanosine synthaseHomo sapiensTGS1
LRRK2_HUMANLeucine-rich repeat serine/threonine-protein kinase 2Homo sapiensLRRK2
BMPR2_HUMANBone morphogenetic protein receptor type-2Homo sapiensBMPR2
F178A_HUMANProtein FAM178AHomo sapiensFAM178A
MOV10_HUMANPutative helicase MOV-10Homo sapiensMOV10
K0556_HUMANUncharacterized protein KIAA0556Homo sapiensKIAA0556
KAT2A_HUMANHistone acetyltransferase KAT2AHomo sapiensKAT2A
EAP1_HUMANEnhanced at puberty protein 1Homo sapiensEAP1
CA175_HUMANUncharacterized protein C1orf175Homo sapiensC1orf175
ENOG_HUMANγ-enolaseHomo sapiensENO2
ENOB_HUMANβ-enolaseHomo sapiensENO3
LOX5_HUMANArachidonate 5-lipoxygenaseHomo sapiensALOX5
MTMR4_HUMAN Myotubularin-related protein 4Homo sapiensMTMR4
YQ050_HUMANPutative uncharacterized protein FLJ45831Homo sapiens
TRI75_HUMANTripartite motif-containing protein 75Homo sapiensTRIM75
LRIG3_HUMANLeucine-rich repeats and immunoglobulin-like domains protein 3Homo sapiensLRIG3
DSCL1_HUMANDown syndrome cell adhesion molecule-like protein 1Homo sapiensDSCAML1
CD20_HUMANB-lymphocyte antigen CD20Homo sapiensMS4A1
IGHG4_HUMANIg γ-4 chain C regionHomo sapiensIGHG4
MIDA_HUMANProtein midA homolog, mitochondrialHomo sapiensC2orf56
SI1L3_HUMANSignal-induced proliferation-associated 1-like protein 3Homo sapiensSIPA1L3
TLE2_HUMANTransducin-like enhancer protein 2Homo sapiensTLE2
KLH17_HUMANKelch-like protein 17Homo sapiensKLHL17
CO7A1_HUMANCollagen α-1(VII) chainHomo sapiensCOL7A1
MRGRD_HUMANMas-related G-protein coupled receptor member DHomo sapiensMRGPRD
MCF2L_HUMANGuanine nucleotide exchange factor DBSHomo sapiensMCF2L
MTUS1_HUMAN Microtubule-associated tumor suppressor 1Homo sapiensMTUS1

Using the in-house-developed algorithm, we compared the protein levels in both groups. More than 20 proteins were distinctly upregulated or downregulated in the CTRL and CA groups and were involved in many biological processes such as metabolism (ALDH3A or TPI) or immune response (e.g., C1Q1 or S100A8). Table III shows a detailed list of the increased or decreased proteins in the tear fluid of breast cancer patients. Of note, the findings include inflammation proteins or complement factors for pathologic processes such as cancer that have already been described (3436). Moreover, several proteins show at least four-fold higher (Extracellular sulfatase Sulf-1, Cystatin SA, cst2; 5-AMP-activated protein kinase subunit gamma-3, prkag3; Triosephosphate isomerase, tpi1; Microtubule-associated tumor suppressor 1, mtus1; Transferrin receptor protein 1, trfc; and Putative lipocalin 1-like protein 1, lcn1l1) or lower levels (DNA damage-binding protein 1, ddb1; Protein S100-A9, s100a9; and GTP-binding protein Di-Ras2, diras2) in CA. An overview of the proteins differently regulated in the CA group was constructed according to their regulation using the STRING tool and is shown in Fig. 4(32).

Table III

Proteins increased or decreased at least 2-fold in CA.

Table III

Proteins increased or decreased at least 2-fold in CA.

A, Increased proteins in CA with fold increase

Protein IDFold decreaseNumber of compared peptides
Extracellular sulfatase Sulf-1441
Cystatin-SA92
5-AMP-activated protein kinase subunit γ-361
Triosephosphate isomerase5.55
Microtubule-associated tumor suppressor 14.713
Transferrin receptor protein 14.56
Keratin, type I cytoskeletal 94.417
Putative lipocalin 1-like protein 14.11
Malate dehydrogenase, cytoplasmic45
Ig α-2 chain C region3.22
Ig heavy chain V-III region BRO3.26
Protein S100-A43.21
Keratin, type II cytoskeletal 13.136
Pericentrin2.849
Ig heavy chain V-III region WEA2.72
Complement C1q subcomponent subunit C2.61

B, Decreased proteins in CA with fold increase

Protein IDFold decreaseNumber of compared peptides

Aldehyde dehydrogenase, dimeric NADP-preferring2.16
Immunoglobulin J chain2.414
Ig γ-3 chain C region2.412
POTE ankyrin domain family member F2.56
Protein S100-A82.518
Uncharacterized protein C20orf1512.99
Ig γ-4 chain C region31
WD repeat-containing protein 6033
DNA damage-binding protein 13.33
Protein S100-A93.311
GTP-binding protein Di-Ras2101

Discussion

Data from high-throughput proteomic technologies, such as SELDI-TOF MS, MALDI-TOF-TOF MS, and microarray platforms, have recently increased. These techniques allow simultaneous protein profiling and subsequent identification of proteins and their subunits (5,37,38). A huge number of proteome studies have been published for proteome comparison of cancer patients and controls. Likewise, different proteomic studies reported significant differences in protein levels in the body fluids of breast cancer patients and healthy subjects (38,39). In our study, we concentrated on the tear proteome for several reasons. First, the sample retrieval is minimally invasive for the participants and tear fluid is easy to obtain with a simple Schirmer test. Second, the tear proteome contains no high-abundant proteins, such as albumin and immunoglobulins that are found in serum; therefore, it is not necessary to perform additional depletion steps that may cause distortion of potentially important proteins. In addition, we find it very intriguing to explore the tear proteome for potential biomarkers of breast cancer as it is an uncommon approach.

Some of the differently regulated proteins in our de novo pooled experiment have been reported to be altered in the tear fluid of patients with ophthalmic disease. Zhou et al reported S100A8 and S100A9 are increased in patients with dry eyes and Grus et al reported an increase in protein S100A8 (34,40). Both proteins belong to the family of S100 calcium-binding proteins, whose members seem to be involved in pro-inflammatory pathways as previously reported by Nacken et al(35). Some of the proteins may be of high interest, e.g., Mitochondrial tumor suppressor 1, MTUS1 and DNA damage binding protein, DDB1. MTUS1 regulates the cell cycle by acting as a tumor suppressor and DDB1 is involved in nucleotide excision repair. In addition, many of the differently regulated proteins are involved in metabolic processes, e.g., TPI or MDH1 in glycolysis and the citric acid cycle, which are both increased in the tear fluid of cancer patients. However, higher levels of autoantibodies against TPI1 have been reported in the sera of breast cancer patients (36). In our previous studies, we found several alterations in protein expression in the sera and tear fluid of breast cancer patients (22,41). Further analysis of the SELDI-TOF-based tear proteome profiling identified the protein S100A4 to be increased in the tears of breast cancer patients (data not shown). This result was confirmed in this study. The protein S100A4 was also previously found to be upregulated in patients with dry eye syndrome (40). Noteworthy, we observed several alterations in the level of proteins involved in immune response, such as complement factor C1Q1 or fragments of immunoglobulins (Table II). Also, several complement factors have been reported to be differentially regulated in the sera of cancer patients (42,43). Although, some of the results were controversial and may have resulted from different storage and handling conditions (44). Thus, members of the complement system may have additional roles. Markiewski et al reported tumor growth was promoted by C5a in their experiments with a cervical cancer mouse model (45,46).

To our knowledge, little is known about protein expression in the tear fluid of breast cancer patients. Only a very small number of tear proteome studies concerning proteome changes during breast cancer or cancer in general have been published. Further subsequent analyses and validation of our results in a tear protein study with an independent population and a higher number of participants will follow that also includes individual profiling. The findings from this study are intriguing as they may deepen the understanding of the impact of cancer and several cancer-driven pathways. Our study demonstrates that different biological processes are altered not only in prominent and broadly investigated body fluids such as serum and plasma, but also in discrete fluids such as tears that are located far away from the cancer site. As we already mentioned, several proteins have been reported to be modified in various types of body fluids, such as nipple aspirate fluid or urine. Our pilot study adds to these findings and shows again the complexity and multiple impacts of breast cancer while emerging and developing in the host, affecting biological processes and signal cascades. Moreover, we propose that a biomarker panel consisting of different proteins could accurately discriminate cancer patients from healthy controls. Therefore, it is important to examine the protein levels in an independent study population using individual protein profiling to validate our results. Further de novo approaches and validation of our results could lead to a better understanding of the pathological mechanism of breast cancer.

Acknowledgements

This study was partially supported by funding from the Mainz research program (MAIFOR NR. 122), University Medical Centre Mainz. Some of the data were developed within the doctoral thesis of Julia Pieter.

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August 2012
Volume 28 Issue 2

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Copy and paste a formatted citation
APA
Böhm, D., Keller, K., Pieter, J., Boehm, N., Wolters, D., Siggelkow, W. ... Grus, F. (2012). Comparison of tear protein levels in breast cancer patients and healthy controls using a de novo proteomic approach. Oncology Reports, 28, 429-438. https://doi.org/10.3892/or.2012.1849
MLA
Böhm, D., Keller, K., Pieter, J., Boehm, N., Wolters, D., Siggelkow, W., Lebrecht, A., Schmidt, M., Kölbl, H., Pfeiffer, N., Grus, F."Comparison of tear protein levels in breast cancer patients and healthy controls using a de novo proteomic approach". Oncology Reports 28.2 (2012): 429-438.
Chicago
Böhm, D., Keller, K., Pieter, J., Boehm, N., Wolters, D., Siggelkow, W., Lebrecht, A., Schmidt, M., Kölbl, H., Pfeiffer, N., Grus, F."Comparison of tear protein levels in breast cancer patients and healthy controls using a de novo proteomic approach". Oncology Reports 28, no. 2 (2012): 429-438. https://doi.org/10.3892/or.2012.1849