Identification and validation of salivary proteomic signatures for non-invasive detection of ovarian cancer

Accepted Manuscript Title: Identification and validation of salivary proteomic signatures for non-invasive detection of ovarian cancer Authors: Md Tajmul, Farhat Parween, Lata Singh, Sandeep R. Mathur, J.B Sharma, Sunesh Kumar, D.N Sharma, Savita Yadav PII: DOI: Reference:

S0141-8130(17)33737-6 https://doi.org/10.1016/j.ijbiomac.2017.12.014 BIOMAC 8675

To appear in:

International Journal of Biological Macromolecules

Received date: Revised date: Accepted date:

3-10-2017 20-11-2017 4-12-2017

Please cite this article as: Md Tajmul, Farhat Parween, Lata Singh, Sandeep R.Mathur, J.B Sharma, Sunesh Kumar, D.N Sharma, Savita Yadav, Identification and validation of salivary proteomic signatures for non-invasive detection of ovarian cancer, International Journal of Biological Macromolecules https://doi.org/10.1016/j.ijbiomac.2017.12.014 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Identification and validation of salivary proteomic signatures for non-invasive detection of ovarian cancer Md Tajmula, Farhat Parweenb, Lata Singhc, Sandeep R. Mathurd, J.B Sharmae, Sunesh Kumare, D.N Sharmaf and Savita Yadava,* Department of Biophysics, All India Institute of Medical Sciences, New Delhi 110029, India

b c

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Hybridoma Laboratory, National Institute of Immunology, New Delhi 110067, India

Department of Ocular Pathology, Dr. R. P. Centre for Ophthalmic Sciences, All India Institute of Medical Sciences,

New Delhi, India d e f

Department of Pathology, All India Institute of Medical Sciences, New Delhi, India

Department of Obstetrics and Gynecology, All India Institute of Medical Sciences, New Delhi 110029, India

Department of Radiotherapy, All India Institute of Medical Sciences, New Delhi 110029, India

*Corresponding author

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Dr. Savita Yadav

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Professor

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Department of Biophysics

Telephone number: 011-26546445

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Email id: [email protected]

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All India Institute of Medical Sciences, New Delhi-110029, India

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Abstract

Ovarian cancer (OC) is one of the most lethal cancers among all gynecological malignancies. An

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effective and non-invasive screening approach is needed urgently to reduce high mortality rate. The purpose of this study was to identify the salivary protein signatures (SPS) for non-invasive detection of ovarian cancer. Differentially expressed SPS were identified by fluorescence-based

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2D-DIGE coupled with MALDI/TOF-MS. The expression levels of three differential proteins (Lipocalin-2, indoleamine-2, 3-dioxygenase1 (IDO1) and S100A8) were validated using western

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blotting and ELISA. Immunohistochemistry and qRT-PCR were performed in an independent cohort of ovarian tumor tissues. 25 over expressed and 19 under expressed (p < 0.05) proteins between healthy controls and cancer patients were identified. Lipocalin-2, IDO1 and S100A8 were selected for initial verification and successfully verified by immunoassay. Diagnostic potential of the candidate biomarkers was evaluated by ROC analysis. The selected biomarkers were further validated by immunohistochemistry in an independent cohort of ovarian tissues.

The global expression of selected targets was also analyzed by microarray and validated using qRT-PCR to strengthen our hypothesis. Tumor secreted proteins identified by ‘dual-omics’ strategy, whose concentration are significantly high in ovarian cancer patients have obvious potential to be used as screening biomarker after large scale validation.

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Keywords: Ovarian cancer, Salivary proteomics, Salivary signatures, Immunohistochemistry, Non-invasive diagnosis

1. Introduction

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Ovarian cancer is one of the most lethal cancer among all gynecological malignancies worldwide

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with overall cure rate is merely 30% [1-3]. OC is a typical example of a heterogeneous disease which is mainly classified into three types on the basis of their cellular origin i.e epithelial cell

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carcinoma, germ cell carcinoma and stromal cell carcinoma, each of which has distinguishing

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biological and clinical characteristics. Epithelial ovarian carcinoma with high-grade serous (HGSC) histology alone comprises 85-90 % of ovarian malignancies [4]. 85-90% of patients are

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diagnosed at an advanced stage (With widely metastatic disease at stage III/IV) since prognosis

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is poor [5, 6]. Irrespective of advances in chemotherapy, radiation and surgery, the overall survival rate of patients suffering from OC remains very poor, with a five year survival rate of hardly 27% [7]. High mortality rate is due to delayed clinical appearance and the very high rate

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of recurrence. Although enormous efforts are being made all around the world for early detection, still detection at initial stage remains difficult. Identification of proteins, micro-RNA,

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long non-coding RNA and DNA candidate as a strategy to discover biomarker for OC has primarily focused on tissue and blood samples [8-15]. For example, a recent clinical proteomic

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study has shown that BRCA1 protein is a promising biomarker both for diagnosis as well as predicting prognosis of ovarian cancer [16]. Similarly, vascular epithelial growth factor (VEGF), human epididymis protein-4 (HE4) and cancer antigen 125 (CA125) has been identified as diagnostic biomarker for OC in blood plasma [17]. Although blood plasma level of HE4 and CA125 are being used as a diagnostic marker to detect ovarian cancer, however these biomarkers lacks desirable sensitivity and specificity and is not adequate to detect OC at early stages [18].

Various efforts are also being made to establish mi-RNA, long non-coding RNA and DNA as circulating diagnostic biomarkers to detect OC at early stages. MiR-16, long non-coding RNA HOTAIR and cell-free DNA (cfDNA) were found to be overexpressed in OC and were responsible for metastatic progression of cancer [9, 11, 19]. Tissue and blood as a source of biomarker discovery have been widely used in attempts to diagnose ovarian cancer; however,

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screening and detection of OC might be limited by availability of the samples. The use of serum or tissue based biomarkers for early diagnosis of OC relies mainly on technical expert for collection of sample and invasive procedures which cause anxiety fear and discomfort in patients. Human saliva is the mirror image of blood with large number of proteins is an attractive bio fluid for early detection of many diseases. Some of the salivary proteins have been established as a diagnostic biomarker for the detection of oral as well as systemic diseases [20-

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25]. Earlier, saliva used to be considered as a source to develop biomarkers mainly for oral

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diseases [22, 26, 27]. Recently, salivary proteomic, transcriptomic and genomic strategy to identify biomarkers have been developed beyond oral diseases to systemic diseases including

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malignancies [24, 28-30]. For example, proteomic and genomic biomarkers from human saliva

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has been developed for diagnosis of primary Sjogren's syndrome [23]. Similarly, a very recent study demonstrates about the diagnosis of gastric cancer by differential proteomic analysis of

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human saliva [28]. Another major advantage of saliva is that, it can be collected easily,

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repeatedly and non-invasively without any aid of technical staff and can be stored effortlessly. We hypothesized that proteins associated with OC present in saliva which could be exploited to differentiate OC patients from healthy controls. Saliva samples from OC patients and age

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matched healthy controls were collected and 2D-DIGE was performed to identify differentially expressed proteins by MALDI-TOF/MS. Lipocalin-2, IDO1 and S100A8 were selected for

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further evaluation on the basis of mascot score as well as their role in cancer progression and metastasis. Candidate salivary signatures were verified by immunoassay and their diagnostic

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utility for detecting OC was evaluated. In addition, IHC was performed in an independent cohort of ovarian tumor tissues to show their expression in OC tissues. Microarray analysis was done to evaluate global expression of genes in one OC tissue matched with three healthy controls and selected targets were validated using qRT-PCR in independent cohort of twelve ovarian tumor tissues. Proteome and transcriptome profile of OC reveals significant similar variations between proteomic and transcriptomic profile of OC patients. There is a possibility of non-invasive

detection of OC by using salivary signatures identified from human saliva in this study. Further transcriptomic data obtained after microarray analysis and validated by qRT-PCR supported the salivary proteomic profile and strengthen our hypothesis. The discovered and verified SPS possess significant discriminatory power for detecting ovarian cancer.

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2. Materials and methods

2.1. Subject information and demographic details

This study was approved from Institute Ethics Committees (Reference No: IEC/NP-162/2013, OT-4/08.12.2016). Before saliva collection, consent was taken from all the participants who were willing to participate in this study. Figure 1 briefly illustrate about the study design. Saliva

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samples from OC patients (n=40) and age matched healthy control subjects (n=40) were

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collected from biopsy proven OC patients with stage II and stage III postoperative cases assigned for chemotherapy the Department of Obstetrics and Gynecology, All India Institute of Medical

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Sciences, New Delhi. Carcinoma patients with smoking history as well as having any other

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ovarian disease were excluded from the study. Immediately after sample collection, protease inhibitor was added and stored at -80ºC till further use. The OC tissues blocks (n=10) were

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collected from the Department of Pathology of All India Institute of Medical Sciences, New

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Delhi for immunohistochemical staining (Table 1B). In addition, tissue biopsies (Table 1B) of OC patients (n=12) and control tissue specimen (n=12) from patients with nonmalignant gynecological diseases (e.g., prolapses and menorrhagia) were collected before treatment for

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transcriptomic validation. Dissected tissue samples were dipped in RNAlater (Sigma Aldrich) with shortest possible delay to avoid degradation of RNA and were stored at -80ºC until further

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use.

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2.2. Protein preparation Proteins from 26 pooled human saliva samples of OC patients and 26 aged matched healthy control samples were precipitated using 90% acetone (v/v). After overnight incubation at –20ºC samples were centrifuged at 13,000g for 20 minutes to discard insoluble material and impurities. Further pellet was cleaned by 2-D clean-up kit (Amersham Biosciences, Buckinghamshire, UK) following manufacturers’ instructions, and quantified by Bradford method using BSA as

standard. Equal amount of protein extracts were pooled from the each group for DIGE experiments.

2.3. Sample labeling with CyDye flours The equal amount of salivary proteins from the OC patients was combined with healthy normal

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controls to minimize the individual variations. CyDye DIGE fluors (Cy2, Cy3, and Cy5) were used for minimal labeling of proteins following manufacturer’s instructions (GE Healthcare). 50µg protein extracts were taken from the healthy control and disease groups to label with Cy3 and Cy5, respectively. Dye-swapping strategy was applied to minimize dye bias by labeling controls with Cy5 and disease with Cy3 respectively in another experimental set-up. Equal amount of salivary protein extracts pooled from both the groups and internal standard was

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generated by labeling with Cy2. Four such technical replicates were made by alternate labeling

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and 12 DIGE images were generated. The labeling reaction was performed in dark condition at 4ºC for 45 minutes, and then 1µL lysine (10mM) was added in each tubes to quench labeling

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reaction. All three labeled samples were combined and used for the subsequent steps.

2.4. Two-dimensional gel electrophoresis

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The method was performed with minor modification as described previously [31]. Pooled labeled

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samples were rehydrated overnight and isoelectric focusing was performed up to total 45,000 VhT. Each isoelectric focused strip was then equilibrated first in reducing then in alkylating equilibration buffer and transferred into 14% polyacrylamide SDS gels and sealed with Agarose

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(0.8%) containing bromophenol blue (0.002%) as tracking dye. Second dimension was performed with constant running current (80V) per gel for 1 hour at 20ºC, followed by 100V at

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20ºC before the tracking dye runs off lower edge of the SDS gels. Corresponding to four different replicates, four such gels were obtained (Supplementary Data, Fig. S1). After scanning

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by Typhoon scanner (GE Healthcare), silver staining was performed using Focus-Fast SilverTM kit (G Biosciences) following manufacturers protocol.

2.5. Image acquisition and analysis Labeled protein spots were visualized at the wavelength of 488/520 nm excitation/emission for cy2, 532/580 nm for cy3 and 633/670 nm for cy5 respectively by using Typhoon 9400 scanner

(GE Healthcare). Progenesis SameSpot Software (Non Linear Dynamics, USA) was used to analyze the images obtained after performing DIGE. Fold change of the protein expression level was obtained by above mentioned software.

2.6. Spot picking and enzymatic digestion

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After analyzing DIGE results, silver staining was performed and protein spots with significant fold change (p-value ≤0.05, fold change ≥1.5) were excised manually from a preparative gel. Trypsin digestion was performed as described previously [32]. The protein spots were minced in small pieces and washed with 50 mM ammonium bicarbonate before trypsin digestion (25mM NH4HCO3, 5mM CaCl2, and 20 ng per mL trypsin, mass spectrometry grade; Promega, Madison, WI, USA) was performed at 37ºC for overnight. 20mM β-mercaptoethanol (56ºC, 30 minutes)

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and 55 mM iodoacetamide (20ºC, 20 minutes, in the dark) were used for reduction and alkylation

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respectively. Further, 25 mM NH4HCO3, 5% formic acid and acetonitrile were added to shrink

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2.7. MALDI-TOF/MS analysis

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the gel and maximize the peptide recovery.

Ultraflex III TOF-TOF tool (Bruker Daltonics, Germany) was used for MALDI-TOF/MS

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analysis. The MALDI-TOF/MS was performed as described previously [33]. Tryptic digested

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peptides were dried and desalted by using Zip-Tip C18 (Merck Millipore, USA). Sample plate (stainless steel) containing α-cyano-4-hydroxycinnamic acid matrix was used to spot zip-tipped samples. Flex Analysis 2.4 software (Bruker Daltonics) was used for resulting data processing

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and optimization of databank search after external mass calibration. All searches were accomplished by using NCBInr database with a taxonomy parameter set to Homo sapiens. The

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criteria used are as follows: peptide charge state: 1+, maximum missed Cleavages: 1, peptide mass tolerance; 0.5 Da, fixed modification: carbamidomethyl (C), variable modification:

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oxidation (M). Only greater than 95 percent probability as represented by the mascot was accepted for protein identification.

2.8. Western blot Reduced salivary protein (20µg of total proteins in each lane) was loaded into a 12 percent homogenous gel and run at 100V in SDS running buffer at room temperature. After completion

of SDS-PAGE, protein bands were transferred to PVDF membrane and blocking buffer containing 5% of nonfat dry milk (Santa Cruz, Santa Cruz, CA) was applied for one hour at ambient temperature. Primary antibodies (Mouse monoclonal to anti-lipocalin-2 (MABN481, Merck Millipore, USA), Mouse monoclonal to anti-indolamine 2, 3-Dioxygenase 1 (MAB- 05840, Merck Millipore, USA), Mouse monoclonal anti-S100A8 (MABF291, Merck

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Millipore,USA) and Mouse monoclonal to anti-GAPDH (Sigma-Aldrich)) were applied to PVDF membranes and incubated overnight at 4ºC. Following incubation with primary antibodies, membranes were washed using wash buffer (150 mM NaCl, 10 mM Tris-HCl (pH 7.6), 0.1 % Tween-20 (Sigma Aldrich)) and incubated at 4ºC with HRP conjugated secondary antibody (anti-mouse IgM from Rabbit (Sigma-Aldrich)) for one hour. After secondary incubation, the membrane was washed again and protein bands were visualized using ECL Plus detection kit

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(GE Healthcare). Image J software (National Institutes of Health, Bethesda, MD) was used for

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densitometric analyses of western blots.

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2.9. ELISA

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ELISA for human Lipocalin-2 (ICL, China), Indolamine, 2, 3-Dioxygenase 1 and S100A8 (CUSABIO, China) was performed following manufacturer’s protocol. Sample diluents provided

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by the manufacturer were used to dilute all the saliva samples.

2.10. Immunohistochemistry

Formalin fixed paraffin embedded tissues were collected from Department of Pathology, All

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India Institute of Medical Sciences, New Delhi. Immunohistochemical staining using the avidinbiotin indirect method was performed in 4 µm thicknesses of tissue blocks fix on poly-L-lysine-

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coated slides. Briefly, xylene and graded ethanol solutions were used to dewax and rehydration of 10 paraffin embedded tissue sections. Following deparaffinization, heat induced antigen

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retrieval was done in citrate buffer solution (pH 6.0) for 20 min at 100 ºC. To inactivate endogenous peroxidase, sections were cooled at room temperature and blocked subsequently in blocking buffer containing 1.5% H2O2 in methanol for 30 min. Slides were washed and incubated with corresponding primary antibodies (anti-Lipocalin-2, anti-IDO-1 and anti-S-100 A8) at a dilution of 1:100 for each antibody overnight at 4ºC. After further washing, slides were incubated with the HRP-conjugated secondary antibody (Ultravision Quanto Detection system,

Thermo Fisher Scientific, Fremont, CA, USA) for 20 min at 4ºC. 3’,3’-diaminobenzidene(DAB) peroxidase substrate was applied for 2-3 min to visualize immunoreactivity and counterstained using haematoxylin. Finally, the slides were mounted in distyrene-plasticizer-xylene (DPX) and examined under light microscopy. Corresponding positive controls and negative controls without

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primary antibodies were run for each complex.

2.11. Immunohistochemical staining

Tumor slides were assessed by an expert pathologist for the staining intensity for evaluation of Lipocalin-2, IDO-1 and S-100 A8. An immunoreactivity score (IRS) system which combines data of staining intensity and proportion of staining was applied to show the results. The staining intensity was scored from 0 to 3 as follows: unstained scored as 0; weakly stained scored as 1;

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moderately stained scored as 2; and strongly stained scored as 3. The proportion of staining was

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scored on a scale from 0 to 3 as follows: 0=negative; 1= 1-9%; 2= 10-49 % and 3= ≥50 %. The intensity score was multiplied by proportion of staining to obtain immunoreactive score and the

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total score greater than 3 was considered as positive expression.

2.12. RNA isolation and microarray platform

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Illumina HumanHT-12 V4 Bead Chip consisting of 12 equally spaced strips of beads was used

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as a platform to perform microarray. Frozen tissue specimens were removed from RNAlater (Sigma) and homogenized using trizol. The total RNA samples are checked for quality and quantity by Bioanalyzer and Qubit respectively. Samples which have RIN number > 8 and

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concentration > 50ng/ uL are selected for further amplification process. After amplification, the quality and quantity of aRNA (amplified RNA) was checked in Bioanalyzer and Qubit. Each

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arrays present on HumanHT-12v4 Bead chip were loaded with 1.5 μg of aRNA material and hybridized for 16 hours (overnight). After hybridization the Bead Chip is scanned using Illumina

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BeadArray Reader (Scanner) with default scan settings of laser wavelength 532nm (Green) at a resolution of 0.3 microns. Each gene among the array was normalized between the range 95th percentile and 5th percentile signal. In this experiment average normalization was being used due to good quality of the data, where signal intensity is transformed to average scale or 50th percentile scale. Illumina Custom error model was used for performing significance analysis.

2.13. RNA isolation and quantitative RT-PCR Total RNA was extracted from frozen ovarian tumor tissues and control samples using TRIzol reagent (Invitrogen, USA). The messenger RNA levels of Lipocalin-2, IDO and S100A8 were determined by qPCR using the SYBR Green (Eurogentec) on ABI 7300 instrument (Applied Biosystems, USA). Primers were designed using Primer3 software (Supplementary Data:

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Supplemental Table S1) and synthesized from Sigma Aldrich (USA). Complementary DNA (cDNA) was generated using qPCR RT Kit (Eurogentec) following manufacturer’s instructions. PCR was performed at 95°C for 1 minute, 95°C for 15 seconds, 58°C for 15 seconds and 72°C for 45 seconds for 40 cycles. Data were normalized using 18S-rRNA as reference gene and calculated according to the mathematical model R = 2−ΔΔCT, where ΔCT= CT of selected genes–

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triplicate, and the data are presented as the mean ± SD.

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CT of 18S-rRNA, and ΔΔCT=ΔCT test−ΔCT control. All RT-PCR reactions were performed in

2.14. Data analysis

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SigmaPlot (Version 10.0) and Graphpad Prism software was used to perform all statistical

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analysis. p-value was calculated for the number of proteins quantified by ELISA in the 40 saliva samples. p-value < 0.05 was used as the cut-off value for significance. The confirmed proteins

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were fitted for logistic regression models. The AUC value and ROC curve were constructed by

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numerical regression of the ROC curve.

2.15. Pathway analysis

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Functional annotations of differentially expressed genes were performed on the basis of annotation. GOEAST and DAVID software were used for predicting gene ontology while

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protein classification was performed by Pathway miner and DAVID classification System (Database version 6.1) on the basis of their molecular function, biological process, cellular

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component, protein class and related pathway.

3. Results

3.1. Identification of differentially expressed salivary proteins by proteomic analysis For the discovery phase of salivary protein signature identification, four set of DIGE experiments were performed and total of twelve images were generated showing uniform and similar of

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consistently

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gels

(Fig.

2).

Progenesis

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distribution

SameSpots (Nonlinear Dynamics) software was used to analyze differences between two groups. In total, 44 differential protein spots (25 over-expressed and 19 under- expressed) having at least 1.5-fold discrepancies were identified by MALDI-TOF/MS (Table 2). On the basis of fold change and biological functions related to cancer development and metastasis of identified

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3.2. Confirmation of candidate SPS by western blot

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proteins; lipocalin-2, IDO1 and human S100A8 were selected for further verification.

Distribution of Lipocalin-2, IDO1 and human S100A8 was detected by western blot in three

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representative individual saliva samples from OC and healthy matched controls. The results were

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consistent with the proteomic finding and Lipocalin-2, IDO1 and human S100A8 were found to be consistently up regulated in saliva of cancerous patients compared with normal controls.

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Further, pooled saliva samples of OC and matched controls also showed the similar pattern (Fig.

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3A-C).

3.3. Pre-validation of potential biomarkers by ELISA

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To further test the utility of these 3 candidates, ELISA was performed using 40 OC and 40 control saliva samples. The ELISA results demonstrated that selected SPSs show significant

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difference in saliva of OC patients and normal control (p < 0.01). Fig. 4A-C depicts the corresponding dot plot diagram of the three SPS (Lipocalin-2, IDO and human S100A8) in the

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40 saliva samples.

3.4. Lipocalin-2, IDO1 and S100A8 expression in human ovarian tumors by IHC Cytoplasmic expression of lipocalin-2, IDO1 and S100A8 in OC were observed in 10 representative cases of OC tissues by immunohistochemistry to confirm our proteomics findings (Fig. 5). Tumor secreted lipocalin-2, IDO1 and S100A8 proteins were significantly expressed in

>70% of the cases. Furthermore, expression of lipocalin-2 was detected in 100% (10/10) of OC tissues. Similarly, IDO1 and S-100 proteins were also showing the similar trend of staining in viable tumor cells. The intensity of all the selected proteins was found to be much stronger in OC tissues.

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3.5. Identification of differentially expressed genes in cancer tissue by microarray

Microarray-based RNA profiling identified 5378 genes which were ≥2 fold up-regulated and 3198 genes >2 fold down-regulated in the tissues of OC patients, compared to matched controls (Supplementary Data, Supplementary Fig. S2). Most of the proteins identified by salivary proteomic analysis are consistent with microarray data and these results demonstrate that the

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changes in expression level of the identified proteins are regulated at the genomic level.

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3.6. Validation of differential mRNA expression by Real-Time PCR qPCR was done to verify the up-regulation of selected SPS at mRNA level (n= 12). The qRT-

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PCR results confirmed that the relative mRNA expression of all three up-regulated transcripts

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(Lipocalin-2, IDO1 and S100A8) was in concurrence with the salivary proteomic profile (Fig.

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6A-C).

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3.7. Receiver Operating Characteristic (ROC) curve The receiver operating characteristic (ROC) curve of selected SPS were generated and corresponding area under curve (AUC) values for Lipocalin-2, IDO1 and S100A8 are found to

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be 0.96, 0.95, and 0.95 respectively (Fig. 7A-C ). The corresponding cutoff values were chosen on the basis of maximum sum of sensitivity as well as specificity. By combining the three SPS

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through logistic regression, the SPS panel could reach area under curve (AUC) value of 0.93

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with 87.5% sensitivity and 86.7% specificity at cutoff value of 11.17ng/µL (Fig. 7D).

3.8. Gene Ontology analysis by DAVID and PANTHER Protein classification was finished by Panther classification system on the basis of cellular component, molecular function, related biological process, protein class and related pathway. Pathway analysis and protein class of these identified proteins are shown in (Supplementary Data, Supplementary Fig. S3). Proteins are grouped into biological process, cellular component

and molecular function groups (Supplementary Data, Supplementary Fig. S4). Interestingly most of the identified proteins were somewhere related with the events responsible with cancer progression and metastasis.

4. Discussion

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Early diagnosis of ovarian cancer might offer an opportunity for easier treatment and improved survival. Traditional screening for OC detection (serum level of CA125 and transvaginal ultrasound) has a lesser sensitivity and specificity [34-40]. Therefore an effective non-invasive approach is required to discover candidate biomarkers for early detection of ovarian cancer. From past decades, saliva becomes the preferable biological source for development of markers for different systemic diseases including cancer because of its easy, non-invasive and cost-

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effective accessibility. Although various technologies for molecular profiling have been

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developed recently with intention to identify and quantify genes in biological samples of ovarian carcinoma patients. For example, a recent study demonstrates the ability of multi-platform

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molecular profiling based on quantitative PCR and in situ hybridization to identify collagen-

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remodeling genes regulated by TGF-β1 signaling which promotes metastasis and contribute to poor overall survival of OC patients [41]. Similarly in another study, genome-wide high-

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resolution copy number analysis shows that there are recurrent oncogenic mutations and copy

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number aberrations (CNAs) were present in the serous borderline ovarian tumors (SBTs) [42]. Nevertheless, differential splicing and post-translational modifications are some of the challenges associated with the molecular profiling of the genes, however, mRNA levels in

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biological specimen are only a partial reflection of the functional state of an individual [43]. Therefore, analyzing quantitative differences on a proteome-wide scale is necessary for

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comprehensive understanding of the genomic information [44]. In recent years, salivary proteomics as a non-invasive approach alternative to serum and tissue based profiling is rapidly

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advancing [45]. There is anxiety and fear in patients, particularly if sample is required repeatedly. Saliva can be collected by patients themselves without requiring any technical person. In the present study, we combined DIGE-based salivary proteomics and tissue based transcriptomic approach to show the presence of tumor specific differentially expressed proteins in saliva of OC patients compared to healthy controls. Most of the identified proteins were shown to be associated with cancer progression and metastasis. Similarly, the 8576 genes (≥2

fold up/down-regulated, p-0.01) were identified by DNA microarray were shown to be involved in various metabolic pathways related to biological regulation, metabolic processes and regulation of cellular processes. Of note it that the selected candidate salivary protein signatures were up regulated in the saliva of ovarian cancer patients. According to our preliminary work on messenger RNA profiling all the three identified candidates (which (p-value ≤0.01, and >2-fold))

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were also up regulated in cancer patients. Remotely located ovarian cancer might regulate the systemic changes in the human body as there is a uniform consistency were present among the protein and mRNA expression which fulfil the perspective of system biology. Therefore, salivary diagnostics could be a better approach for detection of systemic diseases including ovarian cancer. Additionally, positive expression of lipocalin-2, IDO1 and S100A8 was observed in independent cohort of ovarian tumor tissues by immunohistochemistry. The results collectively

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demonstrate that 10 of 10 (100 %) OC tissues slides were stained positive with anti-Lipocalin-2.

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Similar pattern were also observed in case of IDO1 and S100A8.

The up-regulation of salivary protein signatures in ovarian cancer

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Lipocalin-2: Lipocalin-2 plays a significant role in generating innate immune response and protecting against bacterial infections by sequestering iron at normal physiological condition

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[46]. Lipocalin-2 binds with metalloprotein-9 (MMP-9) with high affinity and facilitates

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remodeling of extracellular matrix by degrading E-cadherin resulting into induction of epithelial to mesenchymal transition (EMT) which is essential for enhanced tumorogenesis and metastasis

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[47]. The levels of Lipocalin-2 have been shown to correlate with tumor presence and stages in many other studies. Recently, urinary Lipocalin-2 was identified as a candidate biomarker for breast cancer [48]. Lipocalin-2 is shown to be over expressed and led to the poor prognosis of

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colorectal cancer patients [49]. Lipocalin-2 is also established as tissue and serum based biomarker which is involved in recurrence and disease progression of pancreatic cancer [50].

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Indoleamine-2,3- Dioxygenase 1: Surprisingly, immune cells present at the tumor site are not only to eliminate transformed cells , but also conversely plays a critical role to help the tumor cell in escaping from immune destruction [51, 52]. Various growth factors and cytokines acts synergistically to increase IDO expression in tumor cells which can be used by tumor cell to avoid elimination by the host immune response [53]. High concentration of IDO1 in draining lymph nodes of tumor cells was observed [54]. It is well studied that IDO was found to be up-

regulated in paclitaxel-resistant advanced stages of OC patient with poor clinical outcomes [55]. A recent immunohistochemical study in tissue cohort of advanced OC shows that high level of IDO was present in more than 70% of OC cases and responsible for impaired patient survival [56]. These results indicate that high level of IDO exists in OC patients and associated with disease progression.

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S100A8: Genetic deletion of S100 genes in mouse model suggests that it has minimal effects on normal physiology of mouse and hence an S100 family member has the potential to be an excellent target for cancer diagnosis and treatment. Abnormal expressions of S100 family members, including S100A8 were well established in variety of different type of cancers, such as breast, lung, gastric, pancreatic, squamous esophageal and prostate carcinomas [57-61].

The diagnostic significance of these identified and verified SPS for detection of ovarian cancer

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were also studied. The results collectively demonstrate that salivary proteomic signatures might

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be exploited as a diagnostic marker for the detection and screening of ovarian cancer after large

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scale validation.

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5. Conclusions

This work is first proof of concept on de novo salivary protein signatures discovery for diagnosis

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of ovarian cancer. 44 differentially expressed proteins were identified by 2D-DIGE and MALDI-

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TOF/MS. Lipocalin-2, IDO1 and S100A8 were selected on the basis of fold change, mascot score and their role in different malignancies. Western blot and ELISA further confirmed the expression of three candidates in the saliva of OC patients. Transcriptomic profiling confirms the

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up-regulation of these candidates in OC tissues. Positive expression of three candidates validated from saliva was also observed in ovarian tumor tissues by immunohistochemistry. Their

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performance for the detection of OC was evaluated, which is very encouraging, however, larger sample size is needed for definitive rationalization. These salivary protein signatures have the

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potential to be used as biomarker for early detection and screening of ovarian cancer.

Acknowledgments

Financial grant from Indian Council of Medical Research, New Delhi is gratefully acknowledged. Md Tajmul thanks the Indian Council of Medical Research (ICMR), New Delhi, India for providing a research fellowship. We thank all patients at All India Institute of Medical Sciences, New Delhi, who enrolled in this study which has made this work possible.

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References

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[1] G. Chornokur, E.K. Amankwah, J.M. Schildkraut, C.M. Phelan, Global ovarian cancer health disparities, Gynecol Oncol 129(1) (2013) 258-64. [2] G.C. Jayson, E.C. Kohn, H.C. Kitchener, J.A. Ledermann, Ovarian cancer, Lancet 384(9951) (2014) 1376-88. [3] L.A. Torre, F. Bray, R.L. Siegel, J. Ferlay, J. Lortet-Tieulent, A. Jemal, Global cancer statistics, 2012, CA Cancer J Clin 65(2) (2015) 87-108. [4] D.D. Bowtell, The genesis and evolution of high-grade serous ovarian cancer, Nat Rev Cancer 10(11) (2010) 803-8. [5] T.W. Liu, J.M. Stewart, T.D. Macdonald, J. Chen, B. Clarke, J. Shi, B.C. Wilson, B.G. Neel, G. Zheng, Biologically-targeted detection of primary and micro-metastatic ovarian cancer, Theranostics 3(6) (2013) 420-7. [6] I. Vergote, C.G. Trope, F. Amant, G.B. Kristensen, T. Ehlen, N. Johnson, R.H. Verheijen, M.E. van der Burg, A.J. Lacave, P.B. Panici, G.G. Kenter, A. Casado, C. Mendiola, C. Coens, L. Verleye, G.C. Stuart, S. Pecorelli, N.S. Reed, Neoadjuvant chemotherapy or primary surgery in stage IIIC or IV ovarian cancer, N Engl J Med 363(10) (2010) 943-53. [7] R. Siegel, J. Ma, Z. Zou, A. Jemal, Cancer statistics, 2014, CA Cancer J Clin 64(1) (2014) 9-29. [8] A. Kurosaki, K. Hasegawa, T. Kato, K. Abe, T. Hanaoka, A. Miyara, D.J. O'Shannessy, E.B. Somers, M. Yasuda, T. Sekino, K. Fujiwara, Serum folate receptor alpha as a biomarker for ovarian cancer: Implications for diagnosis, prognosis and predicting its local tumor expression, Int J Cancer 138(8) (2016) 1994-2002. [9] I. Shapira, M. Oswald, J. Lovecchio, H. Khalili, A. Menzin, J. Whyte, L. Dos Santos, S. Liang, T. Bhuiya, M. Keogh, C. Mason, K. Sultan, D. Budman, P.K. Gregersen, A.T. Lee, Circulating biomarkers for detection of ovarian cancer and predicting cancer outcomes, Br J Cancer 110(4) (2014) 976-83. [10] B.L. Seagle, K.H. Eng, J.Y. Yeh, M. Dandapani, E. Schiller, R. Samuelson, K. Odunsi, S. Shahabi, Discovery of candidate tumor biomarkers for treatment with intraperitoneal chemotherapy for ovarian cancer, Sci Rep 6 (2016) 21591. [11] J.J. Qiu, Y.Y. Lin, L.C. Ye, J.X. Ding, W.W. Feng, H.Y. Jin, Y. Zhang, Q. Li, K.Q. Hua, Overexpression of long non-coding RNA HOTAIR predicts poor patient prognosis and promotes tumor metastasis in epithelial ovarian cancer, Gynecol Oncol 134(1) (2014) 121-8. [12] N. Luo, J. Guo, L. Chen, W. Yang, X. Qu, Z. Cheng, ARHGAP10, downregulated in ovarian cancer, suppresses tumorigenicity of ovarian cancer cells, Cell Death Dis 7 (2016) e2157. [13] F. Trillsch, S. Kuerti, C. Eulenburg, E. Burandt, L. Woelber, K. Prieske, K. Eylmann, L. Oliveira-Ferrer, K. Milde-Langosch, S. Mahner, E-Cadherin fragments as potential mediators for peritoneal metastasis in advanced epithelial ovarian cancer, Br J Cancer 114(2) (2016) 213-20. [14] N. Ahmed, G. Barker, K.T. Oliva, P. Hoffmann, C. Riley, S. Reeve, A.I. Smith, B.E. Kemp, M.A. Quinn, G.E. Rice, Proteomic-based identification of haptoglobin-1 precursor as a novel circulating biomarker of ovarian cancer, Br J Cancer 91(1) (2004) 129-40.

A

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[15] I. Visintin, Z. Feng, G. Longton, D.C. Ward, A.B. Alvero, Y. Lai, J. Tenthorey, A. Leiser, R. Flores-Saaib, H. Yu, M. Azori, T. Rutherford, P.E. Schwartz, G. Mor, Diagnostic markers for early detection of ovarian cancer, Clin Cancer Res 14(4) (2008) 1065-72. [16] J.E. Carser, J.E. Quinn, C.O. Michie, E.J. O'Brien, W.G. McCluggage, P. Maxwell, E. Lamers, T.F. Lioe, A.R. Williams, R.D. Kennedy, C. Gourley, D.P. Harkin, BRCA1 is both a prognostic and predictive biomarker of response to chemotherapy in sporadic epithelial ovarian cancer, Gynecol Oncol 123(3) (2011) 492-8. [17] S. Lawicki, G.E. Bedkowska, E. Gacuta-Szumarska, M. Szmitkowski, The plasma concentration of VEGF, HE4 and CA125 as a new biomarkers panel in different stages and sub-types of epithelial ovarian tumors, J Ovarian Res 6(1) (2013) 45. [18] T. Van Gorp, I. Cadron, E. Despierre, A. Daemen, K. Leunen, F. Amant, D. Timmerman, B. De Moor, I. Vergote, HE4 and CA125 as a diagnostic test in ovarian cancer: prospective validation of the Risk of Ovarian Malignancy Algorithm, Br J Cancer 104(5) (2011) 863-70. [19] A.A. Kamat, M. Baldwin, D. Urbauer, D. Dang, L.Y. Han, A. Godwin, B.Y. Karlan, J.L. Simpson, D.M. Gershenson, R.L. Coleman, F.Z. Bischoff, A.K. Sood, Plasma cell-free DNA in ovarian cancer: an independent prognostic biomarker, Cancer 116(8) (2010) 1918-25. [20] C.F. Streckfus, L.R. Bigler, Saliva as a diagnostic fluid, Oral Dis 8(2) (2002) 69-76. [21] S. Hu, J.A. Loo, D.T. Wong, Human saliva proteome analysis and disease biomarker discovery, Expert Rev Proteomics 4(4) (2007) 531-8. [22] S. Hu, M. Arellano, P. Boontheung, J. Wang, H. Zhou, J. Jiang, D. Elashoff, R. Wei, J.A. Loo, D.T. Wong, Salivary proteomics for oral cancer biomarker discovery, Clin Cancer Res 14(19) (2008) 6246-52. [23] S. Hu, J. Wang, J. Meijer, S. Ieong, Y. Xie, T. Yu, H. Zhou, S. Henry, A. Vissink, J. Pijpe, C. Kallenberg, D. Elashoff, J.A. Loo, D.T. Wong, Salivary proteomic and genomic biomarkers for primary Sjogren's syndrome, Arthritis Rheum 56(11) (2007) 3588-600. [24] L. Zhang, H. Xiao, S. Karlan, H. Zhou, J. Gross, D. Elashoff, D. Akin, X. Yan, D. Chia, B. Karlan, D.T. Wong, Discovery and preclinical validation of salivary transcriptomic and proteomic biomarkers for the non-invasive detection of breast cancer, PLoS One 5(12) (2010) e15573. [25] L. Zhang, J.J. Farrell, H. Zhou, D. Elashoff, D. Akin, N.H. Park, D. Chia, D.T. Wong, Salivary transcriptomic biomarkers for detection of resectable pancreatic cancer, Gastroenterology 138(3) (2010) 949-57 e1-7. [26] A.Z. Reznick, O. Hershkovich, R.M. Nagler, Saliva--a pivotal player in the pathogenesis of oropharyngeal cancer, Br J Cancer 91(1) (2004) 111-8. [27] N.J. Park, H. Zhou, D. Elashoff, B.S. Henson, D.A. Kastratovic, E. Abemayor, D.T. Wong, Salivary microRNA: discovery, characterization, and clinical utility for oral cancer detection, Clin Cancer Res 15(17) (2009) 5473-7. [28] H. Xiao, Y. Zhang, Y. Kim, S. Kim, J.J. Kim, K.M. Kim, J. Yoshizawa, L.Y. Fan, C.X. Cao, D.T. Wong, Differential Proteomic Analysis of Human Saliva using Tandem Mass Tags Quantification for Gastric Cancer Detection, Sci Rep 6 (2016) 22165. [29] H. Xiao, L. Zhang, H. Zhou, J.M. Lee, E.B. Garon, D.T. Wong, Proteomic analysis of human saliva from lung cancer patients using two-dimensional difference gel electrophoresis and mass spectrometry, Mol Cell Proteomics 11(2) (2012) M111 012112. [30] S.A. Khuder, I. Al-Hashimi, A.B. Mutgi, N. Altorok, Identification of potential genomic biomarkers for Sjogren's syndrome using data pooling of gene expression microarrays, Rheumatol Int 35(5) (2015) 82936. [31] S. Kumar, V.K. Sharma, S. Singh, G.R. Hariprasad, G. Mal, A. Srinivasan, S. Yadav, Proteomic identification of camel seminal plasma: purification of beta-nerve growth factor, Anim Reprod Sci 136(4) (2013) 289-95.

A

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[32] M.K. Aslam, A. Kumaresan, V.K. Sharma, M. Tajmul, S. Chhillar, A.K. Chakravarty, A. Manimaran, T.K. Mohanty, A. Srinivasan, S. Yadav, Identification of putative fertility markers in seminal plasma of crossbred bulls through differential proteomics, Theriogenology 82(9) (2014) 1254-62 e1. [33] A.K. Tomar, B.S. Sooch, I. Raj, S. Singh, S. Yadav, Interaction analysis identifies semenogelin I fragments as new binding partners of PIP in human seminal plasma, Int J Biol Macromol 52 (2013) 296-9. [34] I. Jacobs, R.C. Bast, Jr., The CA 125 tumour-associated antigen: a review of the literature, Hum Reprod 4(1) (1989) 1-12. [35] R.P. Woolas, F.J. Xu, I.J. Jacobs, Y.H. Yu, L. Daly, A. Berchuck, J.T. Soper, D.L. Clarke-Pearson, D.H. Oram, R.C. Bast, Jr., Elevation of multiple serum markers in patients with stage I ovarian cancer, J Natl Cancer Inst 85(21) (1993) 1748-51. [36] H.A. Fritsche, R.C. Bast, CA 125 in ovarian cancer: advances and controversy, Clin Chem 44(7) (1998) 1379-80. [37] M. Ozguroglu, H. Turna, G. Demir, A. Doventas, F. Demirelli, N.M. Mandel, E. Buyukunal, S. Serdengecti, B. Berkarda, Usefulness of the epithelial tumor marker CA-125 in non-Hodgkin's lymphoma, Am J Clin Oncol 22(6) (1999) 615-8. [38] H. Meden, A. Fattahi-Meibodi, CA 125 in benign gynecological conditions, Int J Biol Markers 13(4) (1998) 231-7. [39] J.R. van Nagell, Jr., P.D. DePriest, M.B. Reedy, H.H. Gallion, F.R. Ueland, E.J. Pavlik, R.J. Kryscio, The efficacy of transvaginal sonographic screening in asymptomatic women at risk for ovarian cancer, Gynecol Oncol 77(3) (2000) 350-6. [40] H. Hayashi, Y. Yaginuma, S. Kitamura, Y. Saitou, T. Miyamoto, H. Komori, K. Wada, M. Ishikawa, Bilateral oophorectomy in asymptomatic women over 50 years old selected by ovarian cancer screening, Gynecol Obstet Invest 47(1) (1999) 58-64. [41] D.J. Cheon, Y. Tong, M.S. Sim, J. Dering, D. Berel, X. Cui, J. Lester, J.A. Beach, M. Tighiouart, A.E. Walts, B.Y. Karlan, S. Orsulic, A collagen-remodeling gene signature regulated by TGF-beta signaling is associated with metastasis and poor survival in serous ovarian cancer, Clin Cancer Res 20(3) (2014) 71123. [42] S.M. Hunter, M.S. Anglesio, G.L. Ryland, R. Sharma, Y.E. Chiew, S.M. Rowley, M.A. Doyle, J. Li, C.B. Gilks, P. Moss, P.E. Allan, A.N. Stephens, D.G. Huntsman, A. deFazio, D.D. Bowtell, K.L. Gorringe, I.G. Campbell, Molecular profiling of low grade serous ovarian tumours identifies novel candidate driver genes, Oncotarget 6(35) (2015) 37663-77. [43] P.S. Hegde, I.R. White, C. Debouck, Interplay of transcriptomics and proteomics, Curr Opin Biotechnol 14(6) (2003) 647-51. [44] K.J. Dechering, The transcriptome's drugable frequenters, Drug Discov Today 10(12) (2005) 857-64. [45] T. Pfaffe, J. Cooper-White, P. Beyerlein, K. Kostner, C. Punyadeera, Diagnostic potential of saliva: current state and future applications, Clin Chem 57(5) (2011) 675-87. [46] J. Yang, D. Goetz, J.Y. Li, W. Wang, K. Mori, D. Setlik, T. Du, H. Erdjument-Bromage, P. Tempst, R. Strong, J. Barasch, An iron delivery pathway mediated by a lipocalin, Mol Cell 10(5) (2002) 1045-56. [47] P.M. Rudd, T.S. Mattu, S. Masure, T. Bratt, P.E. Van den Steen, M.R. Wormald, B. Kuster, D.J. Harvey, N. Borregaard, J. Van Damme, R.A. Dwek, G. Opdenakker, Glycosylation of natural human neutrophil gelatinase B and neutrophil gelatinase B-associated lipocalin, Biochemistry 38(42) (1999) 13937-50. [48] A.S. Wenners, K. Mehta, S. Loibl, H. Park, B. Mueller, N. Arnold, S. Hamann, J. Weimer, B. Ataseven, S. Darb-Esfahani, C. Schem, C. Mundhenke, F. Khandan, C. Thomssen, W. Jonat, H.J. Holzhausen, G. von Minckwitz, C. Denkert, M. Bauer, Neutrophil gelatinase-associated lipocalin (NGAL) predicts response to neoadjuvant chemotherapy and clinical outcome in primary human breast cancer, PLoS One 7(10) (2012) e45826.

A

CC

EP

TE

D

M

A

N

U

SC RI PT

[49] F.A. Haggar, R.P. Boushey, Colorectal cancer epidemiology: incidence, mortality, survival, and risk factors, Clin Colon Rectal Surg 22(4) (2009) 191-7. [50] N. Moniaux, S. Chakraborty, M. Yalniz, J. Gonzalez, V.K. Shostrom, J. Standop, S.M. Lele, M. Ouellette, P.M. Pour, A.R. Sasson, R.E. Brand, M.A. Hollingsworth, M. Jain, S.K. Batra, Early diagnosis of pancreatic cancer: neutrophil gelatinase-associated lipocalin as a marker of pancreatic intraepithelial neoplasia, Br J Cancer 98(9) (2008) 1540-7. [51] A.J. Muller, J.B. DuHadaway, M.Y. Chang, A. Ramalingam, E. Sutanto-Ward, J. Boulden, A.P. Soler, L. Mandik-Nayak, S.K. Gilmour, G.C. Prendergast, Non-hematopoietic expression of IDO is integrally required for inflammatory tumor promotion, Cancer Immunol Immunother 59(11) (2010) 1655-63. [52] F. Li, J. Huang, S. Li, H. Li, J. Yu, X. Ren, J. Liu, The subsets of dendritic cells and memory T cells correspond to indoleamine 2,3-dioxygenase in stomach tumor microenvironment, Tumour Biol 35(9) (2014) 8691-8. [53] M.W. Taylor, G.S. Feng, Relationship between interferon-gamma, indoleamine 2,3-dioxygenase, and tryptophan catabolism, Faseb J 5(11) (1991) 2516-22. [54] J.R. Moffett, M.A. Namboodiri, Tryptophan and the immune response, Immunol Cell Biol 81(4) (2003) 247-65. [55] A. Okamoto, T. Nikaido, K. Ochiai, S. Takakura, M. Saito, Y. Aoki, N. Ishii, N. Yanaihara, K. Yamada, O. Takikawa, R. Kawaguchi, S. Isonishi, T. Tanaka, M. Urashima, Indoleamine 2,3-dioxygenase serves as a marker of poor prognosis in gene expression profiles of serous ovarian cancer cells, Clin Cancer Res 11(16) (2005) 6030-9. [56] T. Inaba, K. Ino, H. Kajiyama, E. Yamamoto, K. Shibata, A. Nawa, T. Nagasaka, H. Akimoto, O. Takikawa, F. Kikkawa, Role of the immunosuppressive enzyme indoleamine 2,3-dioxygenase in the progression of ovarian carcinoma, Gynecol Oncol 115(2) (2009) 185-92. [57] W. El-Rifai, C.A. Moskaluk, M.K. Abdrabbo, J. Harper, C. Yoshida, G.J. Riggins, H.F. Frierson, Jr., S.M. Powell, Gastric cancers overexpress S100A calcium-binding proteins, Cancer Res 62(23) (2002) 6823-6. [58] S.S. Cross, F.C. Hamdy, J.C. Deloulme, I. Rehman, Expression of S100 proteins in normal human tissues and common cancers using tissue microarrays: S100A6, S100A8, S100A9 and S100A11 are all overexpressed in common cancers, Histopathology 46(3) (2005) 256-69. [59] A. Hermani, J. Hess, B. De Servi, S. Medunjanin, R. Grobholz, L. Trojan, P. Angel, D. Mayer, Calciumbinding proteins S100A8 and S100A9 as novel diagnostic markers in human prostate cancer, Clin Cancer Res 11(14) (2005) 5146-52. [60] J. Shen, M.D. Person, J. Zhu, J.L. Abbruzzese, D. Li, Protein expression profiles in pancreatic adenocarcinoma compared with normal pancreatic tissue and tissue affected by pancreatitis as detected by two-dimensional gel electrophoresis and mass spectrometry, Cancer Res 64(24) (2004) 9018-26. [61] A. Luo, J. Kong, G. Hu, C.C. Liew, M. Xiong, X. Wang, J. Ji, T. Wang, H. Zhi, M. Wu, Z. Liu, Discovery of Ca2+-relevant and differentiation-associated genes downregulated in esophageal squamous cell carcinoma using cDNA microarray, Oncogene 23(6) (2004) 1291-9.

Figures Fig. 1 Study design for ovarian cancer salivary proteomic signature discovery. Fig. 2 Salivary proteomic signature discovery. Proteins extracted from the saliva of two groups were labeled with Cy3 (green, control) and Cy5 (red, cancer) in experiment 1 and 3. In

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experiment 2 and 4, two groups were labeled with Cy5 (red, controls) and Cy3 (green, cancer) respectively. An internal standard (a mixture of control and ovarian) was labeled with Cy2 (blue). Fig. 3 Salivary proteomic signature confirmation by western blot. Representative western blots of (a) Lipocalin-2, IDO1 and S100A8 in the saliva of three ovarian cancer patients and matched healthy controls. (b) Western blot analysis demonstrated higher levels of Lipocalin-2, IDO1 and S100A8 in cancer compared with controls. (c) Blots of Lipocalin-2, IDO1 and S100A8 in pooled saliva samples from ovarian cancer patients and matched controls. GAPDH were used as an internal loading control. Fig. 4 Salivary proteomic signature evaluation and pre-validation by ELISA. The dot plots of (a) Lipocalin-2 (b) IDO1 and (c) S100A8 in the saliva of ovarian cancer patients and healthy controls of pre-validation sample set. Fig. 5 IHC analysis of Lipocalin-2, IDO1 and S100A8 in ovarian cancer tissues (stage III), which were graded as weak staining, moderate staining and strong staining. Bone marrow tissue, Breast tissue and Schwan cells were used as positive control for Lipocalin-2, IDO1 and S100A8 respectively. Lipocalin-2, IDO1 and S100A8 expression was primarily localized to the cytoplasm of cancer cells. Fig. 6 Validation of differential mRNA expression by Real-Time PCR. Total RNA was isolated and processed for qPCR analysis as described in the Methods section. Relative expression of (a) Lipocalin-2, (b) IDO1 and (c) S100A8 at mRNA level in ovarian tissues were evaluated using relative Ct method. 18S-rRNA expression levels were used as internal control. Fig. 7 Diagnostic utility of salivary proteomic signatures. ROC curves of (a) Lipocalin-2 (b) IDO and (c) S100A8, as markers to differentiate ovarian cancer from healthy controls. (d) ROC curve for the combined three biomarkers.

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Table 1. Demographic and clinical characteristics of ovarian cancer patients A Demographic Training cohort Validation cohort-1 parameters (Proteomic analysis) (ELISA) Ovarian Cancer 26 40 Age (in years) Range 34-68 34-68 Mean 52.96± 9.02 53.27 ± 8.13 Gender Female 26 40

0 Indian Nil

0 Indian Nil

1 12 13 Nil

2 19 19 Nil

Validation cohort-2 (Real Time-PCR) 12

Validation cohort-3 (IHC) 10

54-68 57.16 ± 5.96

38-64 50.2 ± 8.9

12 0 Indian Nil

10 0 Indian Nil

SC RI PT

Male Ethnicity Smoke History Clinical Stage (FIGO Stage) I II III IV

N A M

Nil 2 8 Nil

D

Nil 3 9 Nil

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Demographic parameters Ovarian Cancer Age (in years) Range Mean Gender Female Male Ethnicity Smoke History Clinical Stage (FIGO Stage) I II III IV Tumour subtype High Grade Serous Endometrioid

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Table 2. Identified human salivary proteins with significant changes between ovarian cancer patients and healthy control subjects Description

Accession no

MW (kDa)

Mascot score

#Peptides

Fold change

p-value

SPS1/STE20-related protein kinase YSK4 carbonate dehydratase

YSK4_HUMAN

152

55

7

+1.5

0.006

gi|1070519

35.5

74

11

+1.9

6.248e-004

YN004_HUMAN

97.8

56

21

+2.6

0.011

121

Putative uncharacterized protein ENST00000281581 Keratin, type II cytoskeletal 1

77

19

-1.8

0.001

122

AMY1A protein

72

10

+2.7

4.587e-005

A

Master no 93 94 100

K2C1_HUMAN gi|47124258

56.9

126 127 130

20.2

72

38

-1.9

0.028

gi|270052567

13.3

64

4

-2.9

0.002

gi|55652965

23.0

68

9

+2.2

5.411e-004

COPA1_HUMAN

65.1

54

11

+1.6

0.007

RSRC2_HUMAN

50.6

68

21

-2.2

0.009

gi|121944684

12.2

66

5

+3.5

3.036e-006

68

8

+1.5

0.046

57

13

-1.9

0.013

59

65

-3.2

0.002

65

12

+1.8

0.011

72

15

+2.1

0.003

20.6

CABP1_HUMAN

40.0

188

Zinc finger protein 268

ZN268_HUMAN

111.7

194

SREBF1 protein

gi|40226180

75.3

198

interferon, gamma-inducible protein 16

gi|55959315

56.8

201

Keratin 1

gi|11935049

66.2

77

13

+2.3

0.002

204

keratin, type II cytoskeletal 1

gi|119395750

66.2

83

14

+7.1

0.014

209

Indoleamine 2,3-dioxygenase 1

I23O1_HUMAN

45.8

56

9

+1.9

0.014

211

Fibroblast growth factor 8

FGF8_HUMAN

26.7

40

11

-4.2

2.777e-004

213

Keratin, type I cytoskeletal 9

K1C9_HUMAN

62.3

57

10

-2.4

3.037e-004

215

MTU1_HUMAN

48.2

48

12

+1.6

0.043

216

Mitochondrial tRNA-specific 2thiouridylase 1 Sentrin-specific protease 1

SENP1_HUMAN

74.0

57

14

-2.0

0.017

219

Unconventional myosin-Ic

MYO1C_HUMAN

122.5

59

91

-2.2

7.714e-004

227

RNMT-activating mini protein

RAM_HUMAN

14.4

42

6

-2.0

2.542e-004

232

PREDICTED: chromodomainhelicase-DNA-binding protein 9 isoform X9 cystatin-S precursor

gi|530424378

271.4

76

188

-4.1

0.046

gi|4503109

16.5

131

13

-1.7

0.011

gi|2982014

16.0

112

12

-2.8

0.007

MMRN1_HUMAN ARPC2_HUMAN

139.2 34.4

56 40

24 7

-2.5 +3.9

0.024 0.010

HBA_HUMAN

15.3

46

4

-2.1

7.215e-005

242

U

A

M

D

TE

178

EP

143

SC RI PT

gi|60593959

187

Arginine/serine-rich coiled-coil protein 2 immunoglobulin A heavy chain variable region Chain A, Crystal Structure Of Siderocalin (Ngal, Lipocalin 2) Complex Calcium-binding protein 1

CC

136

RS10L_HUMAN

N

131

Putative 40S ribosomal protein S10like immunoglobulin heavy chain variable region PREDICTED: docking protein 5 isoform 4 Collagen alpha-1(XXV) chain

Table 2.continued 244

250

Multimerin-1 Actin-related protein 2/3 complex subunit 2 Hemoglobin subunit alpha

253

beta-globin

gi|256028940

16.1

84

8

-2.0

9.066e-004

254

cystatin SA

gi|359513

14.0

93

7

+1.6

0.020

A

246 248

Chain B, Cyanomet Rhb1.1

13.5

131

8

+3

0.002

260

unknown

gi|62630113

61.0

66

18

+1.7

0.011

272

gi|119622403

19.3

66

7

+2.1

0.007

S10A8_HUMAN

10.9

60

8

+3.3

7.769e-004

277

mitochondrial ribosomal protein L46, isoform CRA_d Protein S100-A8 (S100 calciumbinding protein A8) (CalgranulinA) Uncharacterized protein C9orf64

CI064_HUMAN

39.5

54

10

+1.9

0.009

278

Protein S100-A8

S10A8_HUMAN

10.9

279

Zinc finger protein 525

ZN525_HUMAN

24.1

280

Potassium voltage-gated channel subfamily V member 1 Suppressor of tumorigenicity 20 protein This CDS feature is included to show the translation of the corresponding V_region Small integral membrane protein 18

KCNV1_HUMAN

57.1

ST20_HUMAN

9.2

gi|619775

11.3

SIM18_HUMAN

11.4

276

291 296

A

CC

EP

TE

D

M

A

299

SC RI PT

gi|352334

87

6

+3.2

0.002

60

6

+3.6

0.006

56

10

+1.9

0.016

69

4

+1.8

0.018

68

5

+1.9

0.027

4

+2.4

0.047

U

protein SAP1

N

257

59