Sage Journals: Discover world-class research

Abstract

Tumor interstitial fluid contains tumor-specific proteins that may be useful biomarkers for cancers. In this study, we identified proteins present in cholangiocarcinoma interstitial fluid. Proteins derived from three samples of tumor interstitial fluid and paired samples of adjacent normal interstitial fluid from cholangiocarcinoma patients were subjected to two-dimensional liquid chromatography with tandem mass spectrometry. Candidate proteins were selected based on a greater than twofold change in expression levels between tumor interstitial fluid and normal interstitial fluid. Upregulation of six proteins in tumor interstitial fluid, including S100 calcium binding protein A6 (S100A6), S100 calcium binding protein A9, aldo-keto reductase family 1 member C4, neuropilin-1, 14-3-3 zeta/delta, and triosephosphate isomerase was assessed by western blot and immunohistochemistry. Their potential as markers was evaluated in human cholangiocarcinoma tissue arrays, and in serum using enzyme-linked immunosorbent assay. Expression of S100A6 was higher in tumor interstitial fluid than in normal interstitial fluid and showed the highest positive rate (98.96%) in cholangiocarcinoma tissues. Serum levels of S100A6 did not differ between cholangitis and cholangiocarcinoma patients, but were significantly higher than in healthy individuals (p < 0.0001). In cholangiocarcinoma cases, S100A6 level was associated with vascular invasion (p = 0.007) and could distinguish cholangiocarcinoma patients from healthy individuals as effectively as the carbohydrate antigen 19-9. In addition, potential for drug treatment targeting S100A6 and other candidate proteins was also demonstrated using STITCH analysis. In conclusion, proteomics analysis of tumor interstitial fluid could be a new approach for biomarker discovery, and S100A6 is a potential risk marker for screening of cholangiocarcinoma.

Keywords

Cholangiocarcinoma tumor interstitial fluid proteomics S100 calcium binding protein A6 biomarker

Introduction

The incidence of cholangiocarcinoma (CCA), a malignant tumor of the biliary tract, is increasing worldwide. Incidence is particularly high in the Greater Mekong Sub-region¹ where infection with liver fluke, Opisthorchis viverrini, is endemic.² Infection with O. viverrini is a risk factor for CCA.¹ Because of the absence of specific symptoms and the slow progression of the disease, most CCA patients are diagnosed at an advanced stage, resulting in poor prognosis and short survival. Ultrasound has a high predictive value for CCA patients, but a high-resolution method, such as magnetic resonance imaging, is required to confirm the diagnosis and stage.³ However, these radiological methods require technical expertise and are expensive and time-consuming, limiting availability in many areas. Serum biomarkers might be an ideal tool for CCA diagnosis.

Carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) are commonly used to diagnose CCA, but have low sensitivity and/or specificity for this purpose.⁴ Thus, the search continues for a useful circulating biomarker for CCA. Several approaches are available,⁵ among which one is based on proteomics of samples such as plasma,⁶ serum,⁷ extracellular vesicles,⁸ bile,⁹ membrane protein,¹⁰ secreted protein from tumor cell lines,¹¹ tumor tissue,^12,13 and microdissected cells from tumor tissue.¹⁴ Recently, tumor interstitial fluid (TIF) has been proposed as a possible source of biomarkers when investigated using proteomic-based approaches.^15–17 TIF is believed to contain proteins secreted by cancer cells and other cellular components in the tumor microenvironment, as well as during tumorigenesis and tumor progression. In addition, pathological changes during carcinogenesis might be reflected in TIF.^18,19

Here, we performed mass spectrometry–based proteomics to identify protein profiles in TIF of CCA cases. Specific proteins in TIF and normal interstitial fluid (NIF) were investigated using western blot and immunohistochemistry. Among these, S100 calcium binding protein A6 (S100A6) was more highly expressed in TIF than in NIF and showed the highest positive rate in CCA tissues. The level of S100A6 in serum of CCA patients, as determined by enzyme-linked immunosorbent assay (ELISA), was higher than in opisthorchiasis cases or healthy individuals, suggesting it might be a potential screening marker for CCA. Possible interactions between candidate proteins and drugs used for chemotherapy of CCA were also investigated.

Materials and methods

Subjects and specimen collection

Paired tumor and adjacent non-tumor tissues were obtained from three CCA patients who underwent surgery at Department of Surgery at Srinagarind Hospital, Faculty of Medicine, Khon Kaen University. In addition, 88 blood samples were collected from 22 healthy controls, 22 O. viverrini-positive cases, 15 cholangitis cases, and 29 CCA patients. Blood samples were centrifuged at 3000g for 15 min at 4°C. Sera were collected and stored at −80°C for later use. Written informed consent was obtained from all subjects (HE521209) and the study protocol was approved by the Human Research Ethics Committee, Khon Kaen University, Thailand (HE591298).

Preparation of tissue interstitial fluids

Preparation of TIF or NIF was slightly modified from previous methods.²⁰ In brief, approximately 0.25–0.50 g of fresh tissues was washed with 10 mL of sterile phosphate-buffered saline (PBS)-containing protease inhibitors and penicillin (100 U/mL)–streptomycin (1 µg/mL) and then cut into small pieces (approximately 1 mm³). After washing with PBS, tissues were incubated for 1 h at 37°C in a humidified CO₂ incubator and then centrifuged at 226g for 5 min at 4°C. The supernatant was collected and centrifuged again at 32,514g for 5 min at 4°C. Samples were cleaned using two-dimensional (2D) clean-up kit (GE Healthcare, USA) according to the manufacturer’s instructions. Samples were then lyophilized and kept at −80°C until used.

Two-dimensional electrophoresis and image analysis

Lyophilized samples were resuspended in PBS buffer and centrifuged at 32,514g for 30 min at 4°C. Protein concentration was measured using Bradford assay (BioRad, CA, USA). A total of 200 µg of protein was mixed with thiourea rehydration buffer containing 2% v/v immobilized pH gradient (IPG) buffer, pH 3–10 (GE healthcare). Isoelectric focusing was performed using Immobiline DryStrip (pH 3–10 nonlinear, 7 cm; GE Healthcare) and Ettan IPGphor II (GE Healthcare) followed by electrophoresis through a 12.5% sodium dodecyl sulfate–polyacrylamide gel (SDS-PAGE). The gels were then stained with Coomassie brilliant blue R-250 solution, scanned with ImageScanner™ II (Amersham Bioscience, NJ, USA) and analyzed using Image Master 2D Platinum version 7.0 software (Amersham Bioscience).

Tryptic in-gel digestion and mass spectrometry

Protein spots of interest were excised from the gel and subjected to tryptic digestion. Briefly, each protein spot was destained, reduced, and alkylated and then digested with trypsin solution and incubated at 37°C overnight. Gel pieces were incubated in extraction buffer. The mixture was vacuum-dried and kept at −80°C. Tryptic peptides were analyzed using a NanoAcquity nano-LC system (Waters Corp., MA, USA) coupled with a Synapt HDMS™ system mass spectrometer (Waters Corp.).

Protein identification and database searching

Mass spectral data files (PKL) were submitted to MASCOT (Matrix Science, London, UK) for searches of the Homo sapiens proteome database with the following parameters: trypsin as the enzyme; carbamidomethylated cysteine as a ﬁxed modiﬁcation; methionine oxidation as a variable modiﬁcation; monoisotopic mass values and unrestricted protein mass; peptide mass tolerance: ±1.2 Da; fragment mass tolerance: ±0.6 Da; and maximum of zero to one missed cleavage. Electrospray ionization–quadrupole time-of-flight (ESI-QUAD-TOF) was the instrument type. All identified proteins exhibiting a MASCOT score greater than 66 were considered as a significant match (p < 0.05) with a known protein.

Western blotting

Protein derived from TIF or NIF samples (10 µg) was separated by SDS-PAGE and transferred to polyvinylidene difluoride (PVDF) membrane (Amersham Bioscience). For immunodetection, target proteins were sequentially incubated with primary antibodies and appropriate secondary antibodies conjugated with horseradish peroxidase (HRP) (Table S1). The chemiluminescent reaction was detected using enhanced chemiluminescence (ECL) solution (GE Healthcare) and measured by ImageQuant TL software.

ELISA

Levels of S100A6 in all sera were determined using a S100A6 sandwich ELISA kit (SEB769Hu; USCN Life Sciences Inc., Hubei, China) according to the manufacturer’s instructions. The absorbance was measured at 450 nm and a standard curve generated to indicate the serum concentration.

Immunohistochemistry of CCA tissue microarrays

Immunohistochemical staining was performed to determine the expression patterns of six candidate proteins in sections of the three paired tissue samples. In addition, S100A6 was further evaluated in a CCA tissue microarray (TMA, 96 CCA cases, nine hepatocellular carcinoma (HCC) cases, and three controls including colon and gall-bladder tissues). In brief, tissue sections were incubated with 1:100 primary antibody (Table S1) in 1% fetal bovine serum (FBS) in PBS overnight at 4°C. After washing, sections were incubated with HRP-conjugated secondary antibody. Immunoreactivity was developed using 3,3′-diaminobenzidine tetrahydrochloride and then counterstained with Mayer’s hematoxylin. Immunoreactivity was scored for staining intensity (SI; negative = 0, weak = 1, moderate = 2, and strong = 3) and percentage of positive cells (PP; negative = 0, 1%–20% = 1, 21%–50% = 2, and 51%–100% = 3. The product of these two scores yielded the immunoreactivity grading score (IRS)²¹ as follows: 0 = low expression; 1–3 = weak expression; 4–6 = moderate expression; and more than 6 = strong expression. Each section was blindly evaluated by two investigators and one senior pathologist.

Prediction and analysis of drug-protein interaction

The protein–chemical interactions between the six candidate proteins (S100A6, S100 calcium binding protein A9 (S100A9), 14-3-3 zeta/delta, aldo-keto reductase family 1 member C4 (AKR1C4), triosephosphate isomerase (TPI), and neuropilin-1 (NRP1)) and anticancer drugs available for CCA in the published articles database (gemcitabine, 5-fluorouracil, cisplatin, and doxorubicin) were predicted using STITCH software (http://stitch.embl.de/)²² based on the following criteria: species (Homo sapiens), confidence score (0.40), and active prediction methods (all and no more than 10).²³

Statistical analysis

SPSS and GraphPad Prism software were used to analyze data and generate graphical images. The chi-square test was used to analyze the correlation of protein expression with clinicopathological data. Diagnostic accuracy was assessed using the receiver operating characteristic (ROC) curve and area under the curve (AUC) was calculated using MedCalc software. Values of p < 0.05 were considered statistically significant.

Results

Protein identification using mass spectrometry and selection of candidate proteins

Two-dimensional electrophoresis (2DE) patterns of paired TIF and NIF samples are shown in Figure 1. A total of 27 protein spots were found to be differentially expressed (twofold or more) and were selected for further in-gel digestion and mass spectrometry (MS) analysis. Thirteen proteins from case #1, five proteins from case #2, and nine proteins from case #3 were identified. Functional properties of all these are listed in Tables S2–S4.

Figure 1.

Levels of proteins compared between tumor interstitial fluid (TIF) and normal interstitial fluid (NIF). Three pairs of tissue samples were obtained from CCA patients. TIF and NIF were extracted and subjected to 2D-PAGE. Levels of 13 spots from case #1, 5 spots from case #2, and 9 spots from case #3 were higher in TIF compared to NIF and were selected for further analyses.

Candidate proteins were selected based on the following criteria; protein fold-change, protein score, biological process, known involvement in carcinogenesis, availability of reagents for detection in serum, and lack of previous evaluation as a diagnostic biomarker in CCA. According to those criteria, S100A9, S100A6, 14-3-3 protein zeta/delta, TPI, AKR1C4, and NRP1 were selected. S100A6 (involved in immune responses), 14-3-3 protein zeta/delta (involved in signal transduction), TPI and AKR1C4 (involved in metabolic processes), and NRP1 (involved in angiogenesis) were at considerably elevated levels in CCA-TIF relative to NIF and were selected for further validation.

Verification of candidate proteins in CCA tissues

We confirmed the presence of the six candidate proteins in three sets of tumor and adjacent non-tumor tissues using immunohistochemistry. Staining was faint for all six proteins in normal tissues (Figure S1). In CCA tumor tissues, immunoreactivity was observed in nucleus (TPI), cytoplasm (NRP1 and AKR1C4), both the nucleus and cytoplasm (14-3-3 zeta/delta and S100A6), and in inflammatory cells for S100A9 (Figure 2(a)). Western blot analysis revealed that these candidate proteins were upregulated in TIF relative to NIF (Figure 2(b) and (c)).

Figure 2.

Verification of candidate proteins expression in CCA and normal tissues. (a) Immunohistochemistry staining of six candidate proteins in representative human CCA tissues (original magnification is ×200, and insert is ×400). (b) Western blot of candidate proteins in TIF and NIF, and (c) relative band intensities according to their gel images.

We further investigated the expression of candidate proteins in human TMAs. The characteristics of patients are shown in Table S5. S100A6 was upregulated in 98.96% (95 out of 96) of CCA cases (Figure 3(a)), and its expression level was significantly associated with vascular invasion (p = 0.007, Table S5). However, Kaplan–Meier survival analysis revealed that the survival of patients with low S100A6 expression (n = 2) in tumor cells was not significantly different from those in the high expression group (n = 94, p = 0.126) (data not shown).

Figure 3.

Expression of S100A6 and its potential diagnostic efficacy in CCA. (a) S100A6 staining in three representative samples of TMAs including controls ((i) colon and (ii) and (iii) gall-bladder tissues), tubular type CCA (Tub; (iv)–(vi)), papillary type CCA (Pap; (vii)–(ix)). Immunohistochemical staining for S100A6 was strong in tumor cells but weak in controls (magnification ×200). (b) The performance of S100A6 as a potential marker. Levels of S100A6 in each group are indicated: healthy individuals (n = 22), O. viverrini–positive cases (n = 22), cholangitis (n = 15), and CCA cases (n = 29). Data are illustrated as mean ± SEM. Using Student’s t-test; *p = 0.0213 and **p < 0.0001. (c) The ROC curve of S100A6 and CA19-9 in controls versus CCA patients.

In addition, we also evaluated the expression of AKR1C4, TPI, and NRP1 in TMAs. Among CCA tissues, 81.48% were positive for AKR1C4, 82.71% for TPI, and 91.35% for NRP1. However, expression levels of these proteins did not associate with any clinicopathological parameters (data not shown).

Diagnostic efficacy of S100A6 and CA19-9 in serum samples

An earlier study revealed that mRNA expression of S100A6 could distinguish between CCA and HCC.²⁴ Here, we found that S100A6 expression was significantly associated with vascular invasion (Table S5). We further determined its diagnostic ability by using ELISA to detect S100A6 levels in 88 serum samples (from 22 healthy controls, 22 O. viverrini–positive cases, 15 cholangitis, and 29 CCA cases; Table S6). Diagnostic performance of S100A6 for CCA is shown in Table 1. Levels of serum S100A6 were significantly higher in CCA patients (31.351 ± 3.341 ng/mL) than that in O. viverrini–positive cases (7.619 ± 1.399 ng/mL, p < 0.0001) and healthy controls (4.073 ± 0.488 ng/mL, p < 0.0001) (Figure 3(b)). Association of the serum level of S100A6 and clinicopathological data was also evaluated (Table S7). Serum levels of S100A6 did not differ significantly between CCA patients at early stages (I and II) and those at advanced stages (III and IV) of the disease. The routine CCA biomarker, CA19-9, was also assessed in the same sample groups and its serum levels in patients with CCA (194.10 ± 26.64 U/mL) found to be significantly higher than that in patients with O. viverrini infection (106.60 ± 15.40 U/mL, p = 0.0162) and healthy controls (47.88 ± 12.60 U/mL, p = 0.0008).

Table 1.

Diagnostic performance of S100A6 in healthy controls, Opisthorchis viverrini–infected individuals, cholangitis, and CCA groups.

Diagnosis	AUC	Cut-off (ng/mL)	Sensitivity (%)	95% CI		Specificity (%)	95% CI		p value
Diagnosis	AUC	Cut-off (ng/mL)	Sensitivity (%)	Lower	Upper	Specificity (%)	Lower	Upper	p value
Healthy vs O. viverrini	0.685	>6.460	50.00	28.2	71.8	86.36	65.1	97.1	0.0274
Healthy vs cholangitis	0.915	>9.843	80.00	51.9	95.7	100.00	84.6	100.0	<0.0001
Healthy vs CCA	0.909	>7.160	86.21	68.3	96.1	90.91	70.8	98.9	<0.0001
O. viverrini vs cholangitis	0.842	>13.343	73.33	44.9	92.2	90.91	70.8	98.9	<0.0001
O. viverrini vs CCA	0.848	>18.127	75.86	56.5	89.7	95.45	77.2	99.9	<0.0001
Cholangitis vs CCA	0.533	>46.960	31.03	15.3	50.8	86.67	59.5	98.3	0.7155

CCA: cholangiocarcinoma; AUC: area under the curve; 95% CI: 95% confidence interval.

Healthy controls: n = 22; O. viverrini–infected individuals, n = 22; cholangitis, n = 15; CCA, n = 29.

Sensitivity and specificity of S100A6 and CA19-9 for CCA diagnosis

ROC analysis revealed that the sensitivity of S100A6 for distinguishing between CCA patients and healthy controls was 86.21% (95% confidence interval (CI): 68.3–96.1) and specificity was 90.91% (95% CI: 70.8–98.9). The cut-off value was 7.160 ng/mL and the AUC was 0.909 (p < 0.0001) (Table 1). For CA19-9, predictive sensitivity was 95% (95% CI: 75.1–99.9) and specificity was 80% (95% CI: 44.4–97.5) in distinguishing CCA patients from the healthy controls (p < 0.0001) at the cut-off value of 71.278 U/mL, and AUC of 0.930 (Table S8). ROC analysis of combined S100A6 and CA19-9 to discriminate between healthy individuals and CCA patients showed a pattern similar to individual parameters (Figure 3(c)).

Targeting of candidate proteins for CCA treatment

The STITCH diagram (Figure 4) shows potential interactions between candidate proteins and drugs available for treatment of CCA. These candidate proteins likely have direct and/or indirect interactions with such drugs. For instance, NRP1 showed a directly relationship with gemcitabine and 5-fluorouracil. S100A6 and 14-3-3 zeta/delta indirectly interacted with gemcitabine, 5-fluorouracil, cisplatin, and doxorubicin via TP53. Notably, S100A9 and AKR1C4 were predicted to be novel drug-target proteins for CCA treatment.

Figure 4.

STITCH diagram of the functional interaction network representing target proteins in tumor interstitial fluid. The network shows predicted interactions of candidate proteins (S100A9, S100A6, 14-3-3 zeta/delta (YWHAZ), AKR1C4, TPI, and NRP1) with drugs available for CCA treatment (gemcitabine, 5-fluorouracil, cisplatin, and also doxorubicin) using STITCH analysis. Action types and action effects among candidate protein and drug are illustrated.

Discussion

Much effort has been devoted to discovery of effective biomarkers for diagnosis of CCA.²⁵ Although serum and tumor tissues are the usual samples of interest for biomarker discovery, quenching of target or candidate proteins by other molecules in serum or tumor remains an important obstacle for this purpose. Growing evidence indicates that the tumor microenvironment is an important player in tumor development and progression. Therefore, biomarker prospecting increasingly involves the liquid phase of tumor tissue, namely, TIF.^17,18 Protein in TIF can find its way into the blood¹⁷ and be detected in serum using highly sensitive assays such as ELISA.^26–28

In this study, we used proteomics to analyze the interstitial fluid protein derived from matched tumor and adjacent normal tissues, and 27 proteins were increased in TIF. Most of these, including S100A6, S100A9, 14-3-3 zeta/delta, AKR1C4, TPI, and NRP1, are involved in immune responses and metabolic processes. These proteins were at higher levels in TIF than in NIF. Particularly, immunohistochemical analysis of human TMA revealed that expression of S100A6, AKR1C4, TPI, and NRP1 was detected in more than 80% of CCA patients. Interestingly, S100A6 showed the highest positive rate in CCA tissues (98.96%, 95/96 cases), and detection of this protein in serum of CCA patients by ELISA supported its potential as a marker for CCA and yielded high sensitivity and specificity in distinguishing CCA from a healthy condition. Levels of S100A6 were also significantly higher in patients suffering from cholangitis and could not be used to discriminate between cholangitis and CCA. S100A6 has also been reported as a diagnostic or prognostic marker in many cancers such as pancreatic, gastric and prostate cancers, melanoma, non–small cell lung carcinoma, and HCC.²⁹ However, although elevated levels of S100A6 are not specific for CCA, its level may be useful for predicting CCA development in O. viverrini-endemic areas especially in combination with other approaches such as detection of O. viverrini antigen. Furthermore, S100A6 constitutes a promising risk marker for the screening of CCA because its level increased in cholangitis subjects and was high at all stages of CCA development (Figure S2). However, the level of S100A6 was not associated with survival of CCA patients (data not shown), indicating that S100A6 is not a good prognostic marker. This contrasts with previous studies, which have reported that other members of this protein family, such as S100A4³⁰ and S100A9,³¹ are potentially both diagnostic and prognostic markers for CCA. Our finding might be due to the small sample size used, lack of follow-up, and missing survival data for some CCA patients. The evidence we have presented, that S100A6 level can be used as an early biomarker as well as a predictive marker, but not as a prognostic marker, needs to be validated in a large-scale study.

The diagnostic efficacy of S100A6 was similar to that of CA19-9, agreeing with a previous report,³² implying that it can be used as routine marker for diagnosis of CCA. By contrast, one recent study indicated that S100A6 is not suitable for diagnosis of CCA because its serum levels did not alter in resectable European CCA cases and did not differ between CCA and healthy individuals.³² This discrepancy might be due to a difference in etiology; the CCA cases we used in this study were associated with O. viverrini infection, which is rarely the situation in Europe.² Therefore, a large-scale study is needed to confirm the diagnostic potential of S100A6 and resolve this controversy.

In addition, we discovered 14-3-3 isoform protein in TIF. This protein was reported in a previous study in an experimental opisthorchiasis model and explored in CCA patients as a potential diagnostic marker.³³ Other candidate proteins, such as TPI, that have been previously identified in media of CCA cell lines, might also merit further investigation.³⁴ We also investigated AKR1C4 and NRP1 in the serum using ELISA; however, their levels did not differ between CCA and healthy individuals, and their expression in CCA tissues did not associate with any clinicopathological parameters (data not shown). Nevertheless, a larger sample size is required for definitive evaluation.

Apart from diagnostic applications, the candidate proteins identified in this study may interact directly or indirectly with drugs used for CCA treatment, as demonstrated by the STITCH analysis (Figure 4). S100A6 was indirectly involved with all the drugs via interaction with TP53,³⁵ suggesting that it may be useful as a protein target for CCA treatment. Interestingly, AKR1C4 was also predicted as a novel target protein for CCA treatment. However, further investigation is needed to assess whether these proteins can be used as targets for CCA treatment.

In conclusion, we identified tumor-associated proteins in TIF of CCA cases. These candidate proteins were highly expressed in CCA tissues and secreted to the blood circulation. S100A6 was most frequently differentially expressed in CCA tissues and its elevated level in the serum might be used as a circulating marker for CCA, like CA19-9.

Supplemental Material

R2_Table_S2 – Supplemental material for Proteomics detection of S100A6 in tumor tissue interstitial fluid and evaluation of its potential as a biomarker of cholangiocarcinoma

Supplemental material, R2_Table_S2 for Proteomics detection of S100A6 in tumor tissue interstitial fluid and evaluation of its potential as a biomarker of cholangiocarcinoma by Sudarat Onsurathum, Ornuma Haonon, Porntip Pinlaor, Chawalit Pairojkul, Narong Khuntikeo, Raynoo Thanan, Sittiruk Roytrakul and Somchai Pinlaor in Tumor Biology

Supplemental Material

R2_Table_S3 – Supplemental material for Proteomics detection of S100A6 in tumor tissue interstitial fluid and evaluation of its potential as a biomarker of cholangiocarcinoma

Supplemental material, R2_Table_S3 for Proteomics detection of S100A6 in tumor tissue interstitial fluid and evaluation of its potential as a biomarker of cholangiocarcinoma by Sudarat Onsurathum, Ornuma Haonon, Porntip Pinlaor, Chawalit Pairojkul, Narong Khuntikeo, Raynoo Thanan, Sittiruk Roytrakul and Somchai Pinlaor in Tumor Biology

Supplemental Material

R2_Table_S4 – Supplemental material for Proteomics detection of S100A6 in tumor tissue interstitial fluid and evaluation of its potential as a biomarker of cholangiocarcinoma

Supplemental material, R2_Table_S4 for Proteomics detection of S100A6 in tumor tissue interstitial fluid and evaluation of its potential as a biomarker of cholangiocarcinoma by Sudarat Onsurathum, Ornuma Haonon, Porntip Pinlaor, Chawalit Pairojkul, Narong Khuntikeo, Raynoo Thanan, Sittiruk Roytrakul and Somchai Pinlaor in Tumor Biology

Supplemental Material

R_Table_S1 – Supplemental material for Proteomics detection of S100A6 in tumor tissue interstitial fluid and evaluation of its potential as a biomarker of cholangiocarcinoma

Supplemental material, R_Table_S1 for Proteomics detection of S100A6 in tumor tissue interstitial fluid and evaluation of its potential as a biomarker of cholangiocarcinoma by Sudarat Onsurathum, Ornuma Haonon, Porntip Pinlaor, Chawalit Pairojkul, Narong Khuntikeo, Raynoo Thanan, Sittiruk Roytrakul and Somchai Pinlaor in Tumor Biology

Supplemental Material

R_Table_S5 – Supplemental material for Proteomics detection of S100A6 in tumor tissue interstitial fluid and evaluation of its potential as a biomarker of cholangiocarcinoma

Supplemental material, R_Table_S5 for Proteomics detection of S100A6 in tumor tissue interstitial fluid and evaluation of its potential as a biomarker of cholangiocarcinoma by Sudarat Onsurathum, Ornuma Haonon, Porntip Pinlaor, Chawalit Pairojkul, Narong Khuntikeo, Raynoo Thanan, Sittiruk Roytrakul and Somchai Pinlaor in Tumor Biology

Supplemental Material

R_Table_S6 – Supplemental material for Proteomics detection of S100A6 in tumor tissue interstitial fluid and evaluation of its potential as a biomarker of cholangiocarcinoma

Supplemental material, R_Table_S6 for Proteomics detection of S100A6 in tumor tissue interstitial fluid and evaluation of its potential as a biomarker of cholangiocarcinoma by Sudarat Onsurathum, Ornuma Haonon, Porntip Pinlaor, Chawalit Pairojkul, Narong Khuntikeo, Raynoo Thanan, Sittiruk Roytrakul and Somchai Pinlaor in Tumor Biology

Supplemental Material

R_Table_S7 – Supplemental material for Proteomics detection of S100A6 in tumor tissue interstitial fluid and evaluation of its potential as a biomarker of cholangiocarcinoma

Supplemental material, R_Table_S7 for Proteomics detection of S100A6 in tumor tissue interstitial fluid and evaluation of its potential as a biomarker of cholangiocarcinoma by Sudarat Onsurathum, Ornuma Haonon, Porntip Pinlaor, Chawalit Pairojkul, Narong Khuntikeo, Raynoo Thanan, Sittiruk Roytrakul and Somchai Pinlaor in Tumor Biology

Supplemental Material

R_Table_S8 – Supplemental material for Proteomics detection of S100A6 in tumor tissue interstitial fluid and evaluation of its potential as a biomarker of cholangiocarcinoma

Supplemental material, R_Table_S8 for Proteomics detection of S100A6 in tumor tissue interstitial fluid and evaluation of its potential as a biomarker of cholangiocarcinoma by Sudarat Onsurathum, Ornuma Haonon, Porntip Pinlaor, Chawalit Pairojkul, Narong Khuntikeo, Raynoo Thanan, Sittiruk Roytrakul and Somchai Pinlaor in Tumor Biology

Footnotes

Acknowledgements

The authors thank the research assistants at the Faculty of Medicine, Khon Kaen University, for technical support and Prof. David Blair at the publication clinic for his advice, suggestion, and also English presentation.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Khon Kaen University Research Fund research funding (grant no. KKU580805) and Invitation Research Fund from the Faculty of Medicine, Khon Kaen University (grant no. IN59159). Sudarat Onsurathum and Somchai Pinlaor were supported by the Thailand Research Fund through the Royal Golden Jubilee PhD Program (PHD/0167/2556).

References

Sripa

Pairojkul

Cholangiocarcinoma: lessons from Thailand. Curr Opin Gastroenterol 2008; 24: 349–356.

Banales

Cardinale

Carpino

et al . Expert consensus document: cholangiocarcinoma: current knowledge and future perspectives consensus statement from the European Network for the Study of Cholangiocarcinoma (ENS-CCA). Nat Rev Gastroenterol Hepatol 2016; 13: 261–280.

Patel

Cholangiocarcinoma —controversies and challenges. Nat Rev Gastroenterol Hepatol 2011; 8: 189–200.

Briggs

Neal

Mann

et al . Prognostic molecular markers in cholangiocarcinoma: a systematic review. Eur J Cancer 2009; 45: 33–47.

Bonney

Craven

Prasad

et al . Circulating markers of biliary malignancy: opportunities in proteomics? Lancet Oncol 2008; 9: 149–158.

Khoontawad

Hongsrichan

Chamgramol

et al . Increase of exostosin 1 in plasma as a potential biomarker for opisthorchiasis-associated cholangiocarcinoma. Tumour Biol 2014; 35: 1029–1039.

Wang

Dai

Zhang

et al . Characterization of apolipoprotein A-I as a potential biomarker for cholangiocarcinoma. Eur J Cancer Care 2009; 18: 625–635.

Arbelaiz

Azkargorta

Krawczyk

et al . Serum extracellular vesicles contain protein biomarkers for primary sclerosing cholangitis and cholangiocarcinoma. Hepatology 2017; 66: 1125–1143.

Laohaviroj

Potriquet

Jia

et al . A comparative proteomic analysis of bile for biomarkers of cholangiocarcinoma. Tumour Biol 2017; 39: 1–9.

10.

Kristiansen

Harsha

Gronborg

et al . Differential membrane proteomics using 18O-labeling to identify biomarkers for cholangiocarcinoma. J Proteome Res 2008; 7: 4670–4677.

11.

Srisomsap

Sawangareetrakul

Subhasitanont

et al . Proteomic studies of cholangiocarcinoma and hepatocellular carcinoma cell secretomes. J Biomed Biotechnol 2010; 2010: 437143.

12.

Kawase

Fujii

Miyamoto

et al . Differential LC-MS-based proteomics of surgical human cholangiocarcinoma tissues. J Proteome Res 2009; 8: 4092–4103.

13.

Padden

Megger

Bracht

et al . Identification of novel biomarker candidates for the immunohistochemical diagnosis of cholangiocellular carcinoma. Mol Cell Proteomics 2014; 13: 2661–2672.

14.

Padden

Ahrens

Kalsch

et al . Immunohistochemical Markers Distinguishing Cholangiocellular Carcinoma (CCC) from Pancreatic Ductal Adenocarcinoma (PDAC) Discovered by Proteomic Analysis of Microdissected Cells. Mol Cell Proteomics 2016; 15: 1072–1082.

15.

Baronzio

Parmar

Baronzio

et al . Tumor interstitial fluid: proteomic determination as a possible source of biomarkers. Cancer Genomics Proteomics 2014; 11: 225–237.

16.

Celis

Gromov

Cabezon

et al . Proteomic characterization of the interstitial fluid perfusing the breast tumor microenvironment: a novel resource for biomarker and therapeutic target discovery. Mol Cell Proteomics 2004; 3: 327–344.

17.

Gromov

Gromova

Olsen

et al . Tumor interstitial fluid—a treasure trove of cancer biomarkers. Biochim Biophys Acta 2013; 1834: 2259–2270.

18.

Wiig

Swartz

MA.

Interstitial fluid and lymph formation and transport: physiological regulation and roles in inflammation and cancer. Physiol Rev 2012; 92: 1005–1060.

19.

Haslene-Hox

Tenstad

Wiig

Interstitial fluid-a reflection of the tumor cell microenvironment and secretome. Biochim Biophys Acta 2013; 1834: 2336–2346.

20.

Teng

Rungruang

Hood

et al . Assessment of buffer systems for harvesting proteins from tissue interstitial fluid for proteomic analysis. J Proteome Res 2010; 9: 4161–4169.

21.

Chui

Egami

Yamashita

et al . Immunohistochemical expression of the c-kit proto-oncogene product in human malignant and non-malignant breast tissues. Br J Cancer 1996; 73: 1233–1236.

22.

Kuhn

Szklarczyk

Pletscher-Frankild

et al . STITCH 4: integration of protein-chemical interactions with user data. Nucleic Acids Res 2014; 42: D401–407.

23.

Sriwanitchrak

Paemanee

Roytrakul

et al . Glycoproteomics analysis of plasma proteins associated with Opisthorchis viverrini infection-induced cholangiocarcinoma in hamster model. Asian Pac J Trop Med 2016; 9: 1165–1171.

24.

Kim

Yoon

et al . S100A6 protein as a marker for differential diagnosis of cholangiocarcinoma from hepatocellular carcinoma. Hepatol Res 2002; 23: 274–286.

25.

Silsirivanit

Sawanyawisuth

Riggins

et al . Cancer biomarker discovery for cholangiocarcinoma: the high-throughput approaches. J Hepatobiliary Pancreat Sci 2014; 21: 388–396.

26.

Wang

Chao

Tsai

et al . Stress-induced phosphoprotein 1 as a secreted biomarker for human ovarian cancer promotes cancer cell proliferation. Mol Cell Proteomics 2010; 9: 1873–1884.

27.

Lee

Chen

et al . Fascin is a circulating tumor marker for head and neck cancer as determined by a proteomic analysis of interstitial fluid from the tumor microenvironment. Clin Chem Lab Med 2015; 53: 1631–1641.

28.

Sun

Xing

Guo

et al . Quantitative proteomics analysis of tissue interstitial fluid for identification of novel serum candidate diagnostic marker for hepatocellular carcinoma. Sci Rep 2016; 6: 26499.

29.

Donato

Sorci

Giambanco

S100A6 protein: functional roles. Cell Mol Life Sci 2017; 74: 2749–2760.

30.

Tian

Wang

et al . The expression of S100A4 protein in human intrahepatic cholangiocarcinoma: clinicopathologic significance and prognostic value. Pathol Oncol Res 2015; 21: 195–201.

31.

Shi

Deng

Zhan

et al . A prospective proteomic-based study for identifying potential biomarkers for the diagnosis of cholangiocarcinoma. J Gastrointest Surg 2013; 17: 1584–1591.

32.

Loosen

Benz

Niedeggen

et al . Serum levels of S100A6 are unaltered in patients with resectable cholangiocarcinoma. Clin Transl Med 2016; 5: 39.

33.

Haonon

Rucksaken

Pinlaor

et al . Upregulation of 14-3-3 eta in chronic liver fluke infection is a potential diagnostic marker of cholangiocarcinoma. Proteomics Clin Appl 2016; 10: 248–256.

34.

Tit- Oon

Chokchaichamnankit

Khongmanee

et al . Comparative secretome analysis of cholangiocarcinoma cell line in three-dimensional culture. Int J Oncol 2014; 45: 2108–2116.

35.

Kroliczak

Pietrzak

Puzianowska-Kuznicka

P53-dependent suppression of the human calcyclin gene (S100A6): the role of Sp1 and of NFkappaB. Acta Biochim Pol 2008; 55: 559–570.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.22 MB

0.13 MB

0.14 MB

0.09 MB

0.16 MB

0.08 MB

0.13 MB

0.09 MB