Gene expression profiling identifies potential molecular markers of papillary thyroid carcinoma

Abstract

BACKGROUND:

Thyroid cancer is the most common endocrine malignancy worldwide, with the predominant form papillary thyroid carcinoma (PTC) representing approximately 80% of cases.

OBJECTIVE:

This study was addressed to identify potential genes and pathways involved in the pathogenesis of PTC and potential novel biomarkers for this disease.

METHODS:

Gene expression profiling was carried out by DNA microarray technology. Validation of microarray data by qRT-PCR, western blot, and enzyme linked immunosorbent assay was also performed in a selected set of genes and gene products, with the potential to be used as diagnostic or prognostic biomarkers, such as those associated with cell adhesion, extracellular matrix (ECM) remodeling and immune/inflammatory response.

RESULTS:

In this study we found that upregulation of extracellular activities, such as proteoglycans, ECM-receptor interaction, and cell adhesion molecules, were the most prominent feature of PTC. Significantly over-expressed genes included SDC1 (syndecan 1), SDC4 (syndecan 4), KLK7 (kallikrein-related peptidase 7), KLK10 (kallikrein-related peptidase 10), SLPI (secretory leukocyte peptidase inhibitor), GDF15 (growth/differentiation factor-15), ALOX5 (arachidonate 5-lipoxygenase), SFRP2 (secreted Frizzled-related protein 2), among others. Further, elevated KLK10 levels were detected in patients with PTC. Many of these genes belong to KEGG pathway “Proteoglycans in cancer”.

CONCLUSIONS:

Using DNA microarray analysis allowed the identification of genes and pathways with known important roles in malignant transformation, and also the discovery of novel genes that may be potential biomarkers for PTC.

Keywords

Biological Markers gene expression kallikrein-related peptidase thyroid carcinoma

1. Introduction

The American Cancer Society estimates that 53,990 new cases of thyroid cancer are expected in the United States in 2018, making this cancer the most common endocrine malignancy, representing 3.1% of all new cancer cases in United States [42]. Histopathological features distinguish four main types of thyroid cancer: papillary, follicular, medullary, and anaplastic, with papillary thyroid carcinoma (PTC) accounting for 80% of cases [8]. Although PTC is characterized by slow growth and good prognosis, about 10–15% of cases exhibit aggressive behavior, including local invasion, distant metastasis and treatment resistance. In the presence of distant metastasis, considered the most important factor for aggressiveness, median survival estimates is 4.1 years, and the 10-year disease-specific survival rate is 26% [41]. Accurate classification of thyroid cancer is the primary step for assessment of prognosis and treatment selection. However, in some cases, histopathological classification is difficult to accomplish. In this regard, the Cancer Genome Atlas Research Network performed a comprehensive analysis of the PTC genomic landscape, and proposed a reclassification of thyroid cancers into molecular subtypes to better improve pathological classification and management of this disease [1]. According to that study, PTC has been classified as a Mitogen-Activated Protein Kinase (MAPK)-driven tumor with the two major signaling drivers being BRAF ${}^{\text{V600E}}$ and mutated RAS, with approximately 50% of PTC carrying the BRAF ${}^{\text{V600E}}$ mutation [44]. Despite all the advances made in recent years at the genomic and transcriptomic level increasing knowledge on the molecular pathogenesis of PTC and development of molecular-based management strategies [51], a complete understanding of its etiology and molecular basis is still lacking [54]. To date, the only FDA-approved protein tumor marker for thyroid cancer currently used in clinical practice is thyroglobulin, which is used to aid in monitoring therapy [24]. More recently, the FDA has approved four different drugs targeting MAPK signaling pathways in the treatment of advanced thyroid cancers [32]. Thus, more research is needed to find and validate markers able to distinguish PTC from benign thyroid lesions or other thyroid cancer types, and/or that can be used for therapeutic intervention, prognosis, staging and monitoring the disease. Currently, fine needle aspiration biopsy (FNAB) is the most appropriate procedure for evaluation of patients with suspected PTC, but sensitivity of this technique is insufficient and in some cases the results are difficult to interpret [36]. More recently, FNAB has been combined with molecular analysis to increase accuracy of cytological evaluation [35]. BRAF ${}^{\text{V600E}}$ has shown to be a highly specific and unique mutation in PTC, and testing for it in FNAB specimens improves diagnostic accuracy of PTC with indeterminate cytology [44]. Molecular biomarkers identified by transcriptomic techniques could also be used to improve FNAB accuracy. Previous studies have used transcriptomic techniques to identify differentially expressed genes in PTC, however, most of them have not been validated by other techniques nor have proven to be sensitive or specific enough to be clinically useful markers [14, 20, 23, 48]. In this study, DNA microarray analysis was used to identify differential expression between PTC and normal thyroid tissue, focusing on genes suitable as diagnostic and prognostic markers, such as those involved in cell adhesion, extracellular matrix (ECM) remodeling, and inflammatory response. A total of 205 genes were found differentially expressed in PTC, including some genes not previously associated with this type of cancer. A set of the overexpressed genes were further validated, including kallikrein-7 and kallikrein-10. The identification and validation of kallikrein-7 and kallikrein-10 in this study is significant because kallikrein-related peptidases have been considered as very promising biomarkers for cancer personalized medicine, especially for prediction and monitoring of patients’ response to treatment [11, 25] making them suitable as clinical biomarkers for PTC. The data discussed in this publication have been deposited in NCBIs Gene Expression Omnibus with GEO Series accession number GSE3678.

2. Methods

2.1 Patients and ethics statement

Patients diagnosed with stage II/III PTC during thyroidectomy at the New York Eye and Ear Infirmary (New York, NY, USA) and Westchester Medical Center (Valhalla, NY, USA), were considered eligible for the study. Exclusion criteria included previous chemotherapy or radiotherapy for any carcinoma. The study protocol was approved by the Institutional Review Board committee at each institution and written informed consent was obtained from each patient.

2.2 Tissue samples and total RNA isolation

Tumor/normal paired thyroid tissue samples were obtained from PTC patients. The samples were harvested and frozen in dry ice/ethanol bath immediately after surgical removal and stored at $-$ 80 ${}^{\circ}$ C until use. PTC was diagnosed according to WHO criteria by a certified pathologist, with each tumor sample containing more than 90% tumor tissue and each normal sample containing more than 90% normal tissue. Total RNA was prepared from frozen tissue specimens by homogenization with a Brinkmann Polytron at top speed in Trizol solution (Invitrogen, Carlsbad, CA, USA) and stored as an ethanol precipitate. RNA concentration and quality were assessed spectrophotometrically by the 260/280 ratio.

2.3 Microarray procedures

RNA quality from each sample was first assessed by electrophoresis using the Agilent Bioanalyzer 2100 and spectrophotometric analysis. Then, RNA amplification, and cDNA synthesis and labelling were performed at Functional Genomics Center, University of Rochester, according to Affymetrix techniques (Santa Clara, CA, USA). One $\mu$ g of cDNA was converted to biotinylated cRNA and hybridized to Affymetrix high-density oligonucleotide GeneChip Human Genome U133 plus 2.0 arrays. Arrays were automatically washed after hybridization; stained with streptavidin-phycoerythrin conjugate in the GeneChip Fluidics Station and scanned using Hewlett-Packard GeneArray Scanner equipped with an argon ion laser.

2.4 Microarray data preprocessing and analysis

Arrays were classified into Tumor group (from PTC tumor tissue samples) and Normal group (from normal paired thyroid tissue samples). Affymetrix CEL files were subjected to quality control, pre-processing and GC-RMA (GeneChip robust multi-array average) normalization, using the automatic R pipeline AffymetrixQC (http://www.arrayanalysis.org) [10]. Statistical analysis of expression data was also performed in www.arrayanalysis.org using the statistical analysis module [2]. Probeset IDs were converted to HUGO gene symbols using BioMart data mining tool. Gene lists were generated for genes that were differentially expressed between the two groups, either over-expressed or under-expressed, with a criterion of fold change of 2.0 ( $\leqslant$ or $\geqslant$ 2.0), and FDR-corrected $p$ value $<$ 0.05 (Benjamini and Hochberg method).

2.5 Functional group and pathway enrichment analysis

To identify functional groups and biological pathways implicated in PTC, the gene lists of differentially expressed genes were subjected to Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses using the online Database for Annotation, Visualization and Integrated Discovery (DAVID) (https://david. ncifcrf.gov) [9], with the criterion of $p$ -value $<$ 0.05 and enrichment gene count $>$ 5. The online DAVID platform has been used previously for systematic and integrative analysis of large gene lists [17, 18].

2.6 Quantitative real time RT-PCR (qRT-PCR)

qRT-PCR was performed for a group of selected genes associated with ECM remodeling and the inflammatory response: kallikrein-related peptidase-7 (KLK7), kallikrein-related peptidase-10 (KLK10), arachidonate 5-lipoxygenase (ALOX5), growth differentiation factor-15 (GDF-15), and secreted frizzled-related protein-2 (SFRP2). Gene-specific primers were designed with Primer3 software (http://frodo.wi.mit.edu/ primer3/) and their sequences are shown in Table S1 (Supplementary material). One microgram of total RNA was used as template for cDNA synthesis using the Reverse Transcription System kit (Promega, CA, USA). qRT-PCR reactions and measurements were performed with LightCycler DNA Master SYBR Green system (Roche Molecular Biochemicals, Mannheim, Germany) in a LightCycler 2.0 instrument (Roche, Mannheim, Germany) with a 2 min pre-incubation step at 95 ${}^{\circ}$ C followed by 35 cycles of amplification steps. PCR products were subjected to melting curve analysis to verify absence of amplification of non-specific products. In each qRT-PCR run, a standard curve was generated using a serial dilution of a cDNA standard identical in size and sequence to the target gene fragment to be amplified. Serial dilution ranged from 0.01 to 100 pg of the standard cDNA fragment. The standard curve was calculated with LightCycler 5.32 software (LC-Run Version 5.32, Roche) by regression of the crossing points of the PCR curves from the dilution series of the standard cDNA fragment. Changes in gene expression were quantified by calculating the average value of the reactions from the PTC tumor samples group and comparing that to the average derived from the reactions from the normal thyroid tissue group. GAPDH was used as reference gene for normalization, since no change in GAPDH mRNA levels was observed across samples (tumor and normal samples). Statistical significance of changes was determined with $t$ -test.

2.7 Western blot (WB)

To correlate mRNA expression levels with protein levels, WB analysis was performed using standard protocols with antibodies against protein markers kallikrein-7, kallikrein-10, growth/differentiation factor-15 (GDF-15), and secretory leukocyte peptidase inhibitor (SLPI protein). Tumor/normal paired thyroid tissue samples from the PTC patients were coarsely grounded in a mortar under liquid nitrogen and washed twice in PBS containing 1 $\mu$ M complete protease inhibitor (Sigma Chemical Company, St. Louis, MO, USA) to remove excess hemoglobin. Total protein extraction was performed with RIPA buffer. Protein concentration was determined by Bio-Rad Protein Assay (Bio-Rad, Hercules, CA, USA). Twenty five micrograms of total protein were loaded and resolved on 12% SDS-PAGE gels. Gels were transferred at 200 mA for 2 h onto PVDF membranes (Millipore, Bedford, MA, USA). Membranes for kallikrein-10, growth/differentiation factor 15, and secretory leukocyte peptidase inhibitor were blocked with 4% milk, and membranes for kallikrein-7 with 4% BSA. Blocked membranes were subsequently incubated overnight with respective antibodies (R&D Systems, Minneapolis, MN, USA): anti-kallikrein-7 rabbit polyclonal (1:500), anti-kallikrein-10 goat polyclonal (1 $\mu$ g/ml), anti-GDF-15 goat polyclonal (1 $\mu$ g/ml), and anti-SLPI mouse monoclonal (2 $\mu$ g/ml). Membranes were incubated at room temperature with either secondary donkey anti-goat HRP-conjugated antibody (R&D Systems, Minneapolis, MN, USA) (1:5,000) or anti-mouse HRP-conjugated antibody (Pierce, Rockford, IL, USA) (1:10,000) for kallikrein-10, GDF-15, and SLPI; or anti-rabbit alkaline phosphatase conjugated secondary antibody (KLP, Gaithersburg, MD, USA) for kallikrein-7. To visualize bands for kallikrein-10, GDF-15 and SLPI, membranes were developed with Super Signal ${}^{\@setsize{\scriptsize}{8pt}{\viipt}{\@viipt}\textregistered}$ West Pico Chemiluminescent Substrate (Pierce, Rockford, IL, USA) and bands for kallikrein-7 were developed with Western Blue ${}^{\@setsize{\scriptsize}{8pt}{\viipt}{\@viipt}\textregistered}$ Stabilized Substrate for Alkaline Phosphatase (Promega, Madison, WI, USA). $\beta$ -actin protein was used as loading control for normalization. Band intensity was quantified by densitometry with ImageJ software (https://imagej.nih.gov/ij/index.html) and signals were normalized to $\beta$ -actin values.

2.8 Enzyme-linked immunosorbent assay (ELISA)

ELISA assay was performed to validate selected candidate serum protein biomarkers in the sera of PTC patients and healthy controls using commercial kits for kallikrein-10 (IBEX Pharmaceuticals, Montreal, Quebec, Canada) and SLPI (R&D Systems, Minneapolis, MN, USA). ELISA assay kits were run following manufacturer’s instructions. Individual samples were assayed in triplicates, and absorbance of each well was measured at 450 nm with a ELISA plate reader. Concentrations were calculated using a four parameter logistic (4-PL) curve fit of the standard curves using GraphPad Prism 5.0 (GraphPad Software, Inc., San Diego, CA, USA). Kallikrein-10 and SLPI serum levels were analyzed in 13 PTC patients and 8 age-matched healthy donors. Serum samples from PTC patients were collected pre-surgically and stored at $-$ 80 ${}^{\circ}$ C until use. Serum samples from 5 PTC patients were additionally collected one month after surgery. Patients used for ELISA assays were different from the patients used in the DNA microarray study.

2.9 Statistics

Statistics were performed using GraphPad Prism ${}^{\@setsize{\scriptsize}{8pt}{\viipt}{\@viipt}\textregistered}$ v5.00 (GraphPad Software Inc, San Diego, CA, USA). Data normality was assessed using D’Agostino/Pearson Omnibus normality tests. For qRT-PCR, statistical significance of differences in gene expression for each target gene between PTC tumor and paired normal samples was analyzed by Student’s $t$ -test; all observations were derived from at least 3 independent experiments. For WB results, differences between groups (PTC and Normal Tissue) were assessed using Mann-Whitney test (non-normally distributed data). For ELISA results, differences between groups (Healthy Controls, PTC, and PTC-PS) were assessed using a Student’s $t$ -test (normally distributed data). Statistical significance was defined as $p<$ 0.05.

3. Results

3.1 Patients and pathological findings

Age range was 23–74 years (average: 43.6); 6 females and 1 male; four were diagnosed with stage III, two with stage II, and one with stage I; four patients tested positive for BRAF ${}^{\text{V600E}}$ mutation.

Table 1
Genes validated in PTC

Gene name	Protein name	Fold change
		Microarray	qPCR	* $p$ -value	Protein level (WB)	${}^{+}p$ -value
KLK7	Kallikrein-7	78,4	19.0	0.03990	3.7	0.02390
KLK10	Kallikrein-10	17,1	31.6	0.03721	3.7	0.04926
ALOX5	Arachidonate 5-lipoxygenase	15,1	6.1	0.04905	N.P	–
GDF15	Growth/differentiation factor 15	4,9	2.5	0.03069	1.8	0.03800
SFRP2	Secreted frizzled-related protein 2	3,4	5.5	0.03124	N.P	–
SLPI	Secretory leukocyte protease inhibitor	8,9	N.P	–	2.4	0.03970

Statistical significance was determined using: * $t$ test and ${}^{+}$ Mann-Whitney test. N.P: assay not performed.

Figure 1.

Western blots of a set of proteins over-expressed in PTC. Western blot assays were carried for protein markers kallikrein-7, kallikrein-10, growth/differentiation factor 15, and antileukoproteinase/secretory leukocyte protease inhibitor in paired tumor/normal thyroid tissue samples from PTC patients. $\beta$ -actin was used as loading control. Densitometry analysis of bands was carried out to evaluate the difference in expression of protein markers between PTC and Normal samples, and showed that overall signal intensities for all the evaluated proteins were significantly higher in the PTC tumor samples compared to the normal samples, with $p<$ 0.05.

3.2 Identification of differentially expressed genes

Preprocessing and statistical analysis of DNA microarray data generated from paired tumor and normal tissue from seven papillary thyroid carcinoma patients identified 20,111 genes: 205 were differentially expressed between tumor and normal tissue, 104 over-expressed and 101 under-expressed, with $|\text{FC}|$ (absolute value of fold change) $>$ 2 and $p$ value $<$ 0.05. The highest over-expressed and under-expressed genes are shown in Tables S2 and S3 respectively (supplementary material).

3.3 Functional and pathway enrichment analysis

According to DAVID online tool, a total of 26 GO functions belonging to the Biological Process (BP) category were enriched with a count of at least 5 genes in each GO term and $p$ value $<$ 0.05. Over-expressed genes were mainly enriched in 14 GO functions and under-expressed genes were mainly enriched in 12 GO functions. The top GO terms in each category are shown in Table S4 (supplementary material). The most significant GO function in the over-expressed genes was signal transduction, and the most significant one in the under-expressed was oxidation-reduction process. The top KEGG pathways in each category, over-expressed and under-expressed, are shown in supplementary material (Tables S5 and S6, respectively). KEGG pathways of over-expressed genes were significantly enriched in Proteoglycans in cancer ( $p=$ 0.013118572), ECM-receptor interaction ( $p=$ 0.003226258), Cell adhesion molecules (CAMs) ( $p=$ 0.0177183), and Complement and coagulation cascades ( $p=$ 0.012650834). KEGG pathways of under-expressed genes were significantly enriched in Thyroid hormone synthesis ( $p=$ 0.000124594), Insulin resistance ( $p=$ 0.006968337), Oxytocin signaling pathway ( $p=$ 0.025126328), Adipocytokine signaling pathway ( $p=$ 0.013152301), and Thyroid hormone signaling pathway ( $p=$ 0.046572382). Genes overexpressed in PTC belonging to the Proteoglycans in cancer pathway included the proteoglycan encoding genes SDC1 (syndecan-1) and SDC4 (syndecan-4); FN1 (encoding the extracellular matrix glycoprotein fibronectin-1), MET (encoding the proto-oncogene receptor tyrosine kinase MET), ERBB3 (encoding the member of the epidermal growth factor receptor family of receptor tyrosine kinases, erb-b2 receptor tyrosine kinase 3), Tiam1 (encoding the guanine nucleotide exchange factor (GEF) T cell lymphoma invasion and metastasis-1). Some of the over-expressed genes were common to several related pathways, such as SDC1, SDC4, FN1, that also belong to ECM-receptor interaction and Cell adhesion molecules pathways.

Figure 2.

Serum hK10 levels in PTC patients. Serum hK10 levels were measured by ELISA in three groups of individuals: Healthy Controls ( $n=$ 8), PTC patients ( $n=$ 13) and PTC-PS patients (PTC patients post-surgery; $n=$ 5). Box plots for each group show the minimum and maximum values. The lines in the box plots indicate the median serum hK10 levels of each group. The difference in hK10 levels between Healthy Controls and PTC or PTC and PTC-PS groups were analyzed using a Student $t$ -test. A $p$ value $<$ 0.05 was considered statistically significant.

3.4 Identification of genes with potential use as biomarkers for PTC

To identify novel genes not previously mentioned in PTC, a literature search was performed for genes with the highest over-expression in PTC compared to normal thyroid tissue (Table S2, supplementary material). Many of the genes listed have been previously identified in other published microarray expression studies [23, 43], validating our results. However, most of these genes have not been validated by other methods. Thus, we selected six of these genes and corresponding encoded proteins for validation by qPCR and/or WB: KLK7, KLK10, ALOX5, SLPI, GDF-15, and SFRP2. qRT-PCR and WB analysis confirmed the increased levels of these genes in PTC compared to normal paired thyroid tissues (Table 1; Fig. 1). Consistent with microarray results, qPCR experiments showed that expression of KLK7, KLK10, ALOX5, GDF-15, and SFRP2, were significantly increased in PTC compared to normal tissue. Densitometry analysis of bands from WB analysis showed that signal intensities for all the evaluated proteins were significantly higher in the tumor samples compared to the corresponding paired normal samples, with overall fold changes of 3.7 for kallikrein-7 ( $p=$ 0.0239); 3.7 for kallikrein-10 ( $p=$ 0.04926); 1.8 for GDF-15 ( $p=$ 0.0380); and 2.4 for SLPI ( $p=$ 0.0397). KLK-10 over-expression was found in PTC tissue samples compared to normal paired tissue, both at transcript and protein levels. Levels of two secreted protein biomarkers, kallikrein-10 and SLPI protein, were further measured in serum of PTC patients and healthy controls.

3.5 Serum levels of selected protein markers

ELISA assays were performed twice and similar results were obtained. Serum kallikrein-10 levels in healthy controls were consistent with previous reports [28]. Kallikrein-10 serum level was significantly higher in PTC patients relative to healthy controls (Fig. 2). The mean and median values of serum kallikrein-10 in healthy controls were 230 and 142 ng/lt, respectively. Pre-surgery serum kallikrein-10 levels in the PTC group was significantly higher than levels from the Healthy Control group, with mean and median of 457 and 449 ng/liter, respectively ( $p=$ 0.0465; Student $t$ -test). Post-surgery serum kallikrein-10 levels in PTC patients were lower than pre-surgical levels, although the difference was not statistically significant ( $p>$ 0.05). Higher serum kallikrein-10 levels in PTC patients (pre-surgery and post-surgery) compared to healthy controls suggests the potential of kallikrein-10 as a diagnostic or prognosis indicator. The sample size used in this study was rather small, limiting this study. Larger sample size is required to determine the real value of kallikrein-10 as biomarker for PTC. Evaluation of serum SLPI levels in PTC patients (mean $=$ 64 ng/ml; median $=$ 66 ng/ml) and normal controls (mean $=$ 70 ng/ml; median $=$ 75 ng/ml) did not find significant differences ( $p>$ 0.05) making this protein not suitable for PTC screening in serum samples.

4. Discussion

Whole transcriptome analysis has potential to identify novel biomarkers for diseases like cancer, and is beginning to impact real-time clinical decision-making [38]. In the present study, DNA microarray analysis identified genes and biological pathways differentially expressed between PTC and normal thyroid tissue. According to our results, the most apparent feature of PTC was upregulation of extracellular activities, such as proteoglycans, ECM-receptor interaction, and cell adhesion molecules, while downregulated pathways were involved with thyroid hormone biosynthesis and signaling pathways. Among over-expressed genes encoding components of the ECM were SDC1, SDC4, FN1, c-MET, erbB3, and Tiam1; upregulation of these genes has been previously reported in PTC [5, 15, 19, 34, 47], supporting the reliability of our microarray data. Syndecan-1 is mainly found on the basolateral surface of epithelial cells in adult tissues and syndecan-4 is ubiquitously expressed on most tissues [4]. Syndecan-4 binds to fibronectin leading to the activation of focal adhesion kinase (FAK), a tyrosine kinase that regulates focal adhesions [49, 50]. Binding of syndecan-1 and syndecan-4 to fibronectin has been shown to regulate tumor cell ECM attachment and growth [46]. c-MET is a receptor tyrosine kinase that regulates different cellular signaling pathways, including those involved in proliferation, motility, migration and invasion, which has been found to be aberrantly activated in many human cancers, including thyroid cancer [33, 43]. ERBB3 has been implicated in feedback regulation of MEK/ERK signaling in PTC driven by BRAF ${}^{\text{V600E}}$ mutation [13]. Tiam1 is a guanine nucleotide exchange factor (GNEF) that specifically activates Rac1, member of Rho family of GTPases [30].

Different studies have shown that high expression of Tiam1 protein is associated with progression of several cancer types [31], and it has been suggested as prognostic factor and potential therapeutic target for thyroid cancers [16, 27]. Tiam1 has also been shown to bind syndecans promoting cell-matrix adhesion and cell migration, establishing a novel link between Tiam1 and syndecans, two previously unrelated signal transduction pathways implicated in cancer [22, 39]. We found over-expression in PTC of all these genes belonging to KEGG pathway “Proteoglycans in cancer”. Our microarray data is strongly supported by the scientific literature on the involvement of these genes in thyroid carcinoma.

Functional group enrichment analysis in the context of GO_BP terms found that many of the significant genes were grouped into categories associated with cell adhesion, extracellular matrix organization, inflammatory response, activation of MAPK activity, among others. Two of the most upregulated genes in PTC were KLK7 and KLK10, two members of the tissue kallikrein (KLK) gene family of conserved serine proteases located in human chromosomal region 19q13.3–q13.4 [7], involved in cell adhesion and ECM remodeling. The encoded serine proteases have diverse expression patterns and physiological roles including activation of other proteases, degradation of extracellular matrix (ECM) components, and activation of growth and angiogenic factors [7, 53].

KLKs have been explored as promising biomarkers for different cancer types, especially for prediction and monitoring of patients’ response to chemotherapy [25]. Human kallikrein-3 (prostate-specific-antigen/PSA) and kallikrein-2 have clinical application as biomarkers for prostatic diseases [29]. Further evaluation of KLK10, both in tissue and serum, in larger cohorts of PTC patients and healthy controls are required to determine the true value of this protein in management of thyroid cancer. Another gene upregulated in PTC was SLPI (secretory leukocyte peptidase inhibitor), which encodes a protein that is a potent inhibitor of leukocyte elastase, cathepsin G, chymotrypsin, and trypsin; SLPI also has been shown to be a specific inhibitor of kallikrein-7 [12]. SLPI is produced and released into mucus by secretory cells in the parotid, bronchus, cervix, and testicular gland, playing physiological roles against proteolytic degradation of these tissues. In cancer tissues, increased expression of kallikrein-7 and decreased expression of SLPI have been found in cervical adenocarcinoma, but in ovarian tumors both proteins have been found overexpressed [40, 45].

This study identified additional genes which may represent potential markers for PTC, as they are associated with cell-extracellular matrix interaction (ECM), which is important in cell growth promotion and survival; these genes included secreted Frizzled-related protein 2 (SFRP2), proprotein convertase subtilisin/kexin type 2 (PCSK2), cathepsin-C (CTSC), and cathepsin-H (CTSH). SFRP2 gene encodes a secreted glycoprotein member of the SFRP family that function as Wnt receptors. Wnt signaling is implicated in cell proliferation, apoptosis, and onset of neoplasia. Methylation of SFRP2 gene has been associated with esophageal adenocarcinoma and colorectal cancer [52, 55].

We also found overexpression of some important genes associated with mechanisms other than epithelial-ECM interaction, such as immune/inflammatory reaction, antiapoptosis and angiogenesis, including ALOX5 and GDF-15. ALOX5 is a lipoxygenase implicated in several metabolic pathways and it has been recently associated with cell proliferation and neo-angiogenesis [37]. We previously reported ALOX5 overexpression in PTC and documented its correlation with invasive tumor histopathology [26]. GDF-15, which has been linked to prostate cancer [21], was first isolated from a differential screen for genes that were induced in macrophage activation [6]; it is member of TGF- $\beta$ superfamily associated with immune surveillance suppression, facilitation of tumor invasion, and promotion of metastasis development in tumors of epithelial origin [3].

Genes identified in this study have been shown to play important roles in malignant transformation, tumor progression and phenotype of PTC. Additional studies with larger sample size and detailed clinical characterization of patients are required to evaluate the biological role and prognostic value in PTC of some of these genes and the encoded proteins.

Footnotes

Acknowledgments

Dr. Eleftherios P. Diamandis (Mount Sinai Hospital, Toronto, Ontario) for providing the anti-hK7 antiserum. Microarray Facility of the Functional Genomics Center at the University of Rochester. Author’s contributions: Niradiz Reyes performed microarray data analysis and drafted the article. Ismael Reyes performed sample processing for microarray analysis, qRT-PCR, WB, ELISA assays and contributed to draft the article. Codrin Iacob performed diagnosis of PTC according to WHO criteria Nina Suslina was Clinical Project Manager. Jan Geliebter and Raj Tiwari designed the project. Project funded by NIH grant 1R01CA131946-01A2 and New York Medical College/New York Eye and Ear Infirmary, Department of Otolaryngology.

Conflict of interest

The authors declare that they have no competing interests.

Supplementary data

Supplementary Table S1

Primer sequences for genes validated by qRT-PCR

Gene name	Nucleotide sequences (forward/reverse)
KLK7	5’-atggcaagatcccttctcct-3’/5’-gcagcctgactttcttcacc-3’
KLK10	5’-tcctctcgtggggtgtttac-3’/5’-gagtggcagaggaagtcagg-3’
ALOX5	5’-cgcaagtactggctgaatga-3’/5’-tttctcaaagtcggcgaagt-3’
GDF-15	5’-actgctggcagaatcttcgt-3’/5’-tatgcagtggcagtctttgg-3’
SFRP2	5’-gagagttcaagcgcatctcc-3’/5’-gggccacagagaaaattgaa-3’

Supplementary Table S2

Most highly over-expressed genes in PTC

Gene stable ID	Gene name	Gene description	Fold	*adj. P.Val
			change
ENSG00000164935	DCSTAMP	dendrocyte expressed seven transmembrane protein	82.8	0.0000575
ENSG00000145864	GABRB2	gamma-aminobutyric acid type A receptor beta2 subunit	78.5	0.000186735
ENSG00000169035	KLK7	kallikrein related peptidase 7	78.4	0.00000358
ENSG00000157765	SLC34A2	solute carrier family 34 member 2	65.4	0.0000346
ENSG00000197249	SERPINA1	serpin family A member 1	57.9	0.00000809
ENSG00000163898	LIPH	lipase H	47.7	0.00000171
ENSG00000125931	CITED1	Cbp/p300 interacting transactivator with Glu/Asp rich carboxy-terminal domain 1	41.4	0.0000174
ENSG00000184574	LPAR5	lysophosphatidic acid receptor 5	32.8	0.000128406
ENSG00000168878	SFTPB	surfactant protein B	32.5	0.001260656
ENSG00000133048	CHI3L1	chitinase 3 like 1	28.9	0.00938079
ENSG00000176532	PRR15	proline rich 15	28.2	0.003127366
ENSG00000184292	TACSTD2	tumor associated calcium signal transducer 2	27.7	0.011421143
ENSG00000134569	LRP4	LDL receptor related protein 4	21.2	0.001437797
ENSG00000147883	CDKN2B	cyclin dependent kinase inhibitor 2B	20.9	0.0000276
ENSG00000115414	FN1	fibronectin 1	17.3	0.000888731
ENSG00000129451	KLK10	kallikrein related peptidase 10	17.1	0.002351774
ENSG00000009694	TENM1	teneurin transmembrane protein 1	16.9	0.001174524
ENSG00000012779	ALOX5	arachidonate 5-lipoxygenase	15.1	0.048401649
ENSG00000123700	KCNJ2	potassium voltage-gated channel subfamily J member 2	14.3	0.00095752
ENSG00000110042	DTX4	deltex E3 ubiquitin ligase 4	13.8	0.00000397
ENSG00000138061	CYP1B1	cytochrome P450 family 1 subfamily B member 1	11.7	0.035892153
ENSG00000125851	PCSK2	proprotein convertase subtilisin/kexin type 2	11.6	0.058196787
ENSG00000131435	PDLIM4	PDZ and LIM domain 4	11.4	0.004377039
ENSG00000124813	RUNX2	runt related transcription factor 2	11.3	0.000549528
ENSG00000164379	FOXQ1	forkhead box Q1	11	0.005319016
ENSG00000163347	CLDN1	claudin 1	10.8	0.002239326

Supplementary Table S2, continued
Gene stable ID	Gene name	Gene description	Fold	*adj. P.Val
			change
ENSG00000196352	CD55	CD55 molecule (Cromer blood group)	10.5	0.00530691
ENSG00000188906	LRRK2	leucine rich repeat kinase 2	10.2	0.000510028
ENSG00000159216	RUNX1	runt related transcription factor 1	10.1	0.000677037
ENSG00000091129	NRCAM	neuronal cell adhesion molecule	9.8	0.00263012
ENSG00000124107	SLPI	secretory leukocyte peptidase inhibitor	8.9	0.025152889
ENSG00000156299	TIAM1	T cell lymphoma invasion and metastasis 1	8.9	0,003420038
ENSG00000138166	DUSP5	dual specificity phosphatase 5	8.4	0.009720843
ENSG00000133110	POSTN	periostin	8.4	0.007539624
ENSG00000109861	CTSC	cathepsin C	8.2	0.000412857
ENSG00000135547	HEY2	hes related family bHLH transcription factor with YRPW motif 2	8.1	0.001020278
ENSG00000169302	STK32A	serine/threonine kinase 32A	8.1	0.000518075
ENSG00000130513	GDF-15	growth differentiation factor-15 (GDF-15)	4.9	0.018193137
ENSG00000124145	SDC4	syndecan 4	4.2	0.022380342
ENSG00000145423	SFRP2	secreted Frizzled-related protein 2	3.4	0.01292622

*Benjamini and Hochberg method.

Supplementary Table S3

Most highly under-expressed genes in PTC

Gene stable ID	Gene name	Gene description	Fold	adj.P.Val
			change
ENSG00000153246	PLA2R1	phospholipase A2 receptor 1	$-$ 40.1	0.00002860
ENSG00000066382	MPPED2	metallophosphoesterase domain containing 2	$-$ 40.1	0.00000524
ENSG00000115705	TPO	thyroid peroxidase	$-$ 39.5	0.00003920
ENSG00000166426	CRABP1	cellular retinoic acid binding protein 1	$-$ 33.8	0.00051808
ENSG00000160180	TFF3	trefoil factor 3	$-$ 30.2	0.00002760
ENSG00000233705	SLC26A4-AS1	SLC26A4 antisense RNA 1	$-$ 26.8	0.00012965
ENSG00000211452	DIO1	iodothyronine deiodinase 1	$-$ 26.6	0.00059553
ENSG00000074706	IPCEF1	interaction protein for cytohesin exchange factors 1	$-$ 25.7	0.00000268
ENSG00000091137	SLC26A4	solute carrier family 26 member 4	$-$ 15.6	0.00511303
ENSG00000132561	MATN2	matrilin 2	$-$ 15.5	0.00000969
ENSG00000184905	TCEAL2	transcription elongation factor A like 2	$-$ 14.8	0.00048673
ENSG00000184908	CLCNKB	chloride voltage-gated channel Kb	$-$ 14.6	0.00002590
ENSG00000170323	FABP4	fatty acid binding protein 4	$-$ 14.5	0.00325702
ENSG00000109819	PPARGC1A	PPARG coactivator 1 alpha	$-$ 14.4	0.00695480
ENSG00000147606	SLC26A7	solute carrier family 26 member 7	$-$ 12.9	0.00778682
ENSG00000022267	FHL1	four and a half LIM domains 1	$-$ 12.0	0.00331415
ENSG00000149294	NCAM1	neural cell adhesion molecule 1	$-$ 11.5	0.00979842
ENSG00000166148	AVPR1A	arginine vasopressin receptor 1A	$-$ 10.7	0.00098430
ENSG00000009765	IYD	iodotyrosine deiodinase	$-$ 10.7	0.03486764
ENSG00000166415	WDR72	WD repeat domain 72	$-$ 10.6	0.00051920
ENSG00000169282	KCNAB1	potassium voltage-gated channel subfamily A member regulatory beta subunit 1	$-$ 10.5	0.00191092
ENSG00000125845	BMP2	bone morphogenetic protein 2	$-$ 10.2	0.00563634
ENSG00000198682	PAPSS2	3’-phosphoadenosine 5’-phosphosulfate synthase 2	$-$ 10.1	0.00078994
ENSG00000164761	TNFRSF11B	TNF receptor superfamily member 11b	$-$ 9.4	0.00468003
ENSG00000168079	SCARA5	scavenger receptor class A member 5	$-$ 9.2	0.00331415
ENSG00000101938	CHRDL1	chordin like 1	$-$ 9.0	0.00186596
ENSG00000141338	ABCA8	ATP binding cassette subfamily A member 8	$-$ 8.8	0.00051916
ENSG00000150995	ITPR1	inositol 1,4,5-trisphosphate receptor type 1	$-$ 8.2	0.00000589
ENSG00000151136	BTBD11	BTB domain containing 11	$-$ 7.8	0.00115643
ENSG00000116194	ANGPTL1	angiopoietin like 1	$-$ 7.8	0.00206053
ENSG00000154556	SORBS2	sorbin and SH3 domain containing 2	$-$ 7.7	0.00040446
ENSG00000135218	CD36	CD36 molecule	$-$ 7.7	0.01309059
ENSG00000154175	ABI3BP	ABI family member 3 binding protein	$-$ 7.5	0.00454651
ENSG00000155792	DEPTOR	DEP domain containing MTOR interacting protein	$-$ 7.5	0.00586608
ENSG00000153823	PID1	phosphotyrosine interaction domain containing 1	$-$ 7.1	0.00118254
ENSG00000169047	IRS1	insulin receptor substrate 1	$-$ 7.1	0.00942050
ENSG00000178568	ERBB4	erb-b2 receptor tyrosine kinase 4	$-$ 6.9	0.00002760
ENSG00000125740	FOSB	FosB proto-oncogene, AP-1 transcription factor subunit	$-$ 6.9	0.00332647
ENSG00000076864	RAP1GAP	RAP1 GTPase activating protein	$-$ 6.8	0.00029231
ENSG00000164442	CITED2	Cbp/p300 interacting transactivator with Glu/Asp rich carboxy-terminal domain 2	$-$ 6.7	0.00136737

Supplementary Table S4

GO_BP functional enrichment of differentially expressed genes

Category	GO term/description	Gene count	$p$ -value
Over-expressed	GO:0007165 $\sim$ signal transduction	16	0.001715599
	GO:0006915 $\sim$ apoptotic process	10	0.004154717
	GO:0006508 $\sim$ proteolysis	9	0.006517584
	GO:0007155 $\sim$ cell adhesion	8	0.013743966
	GO:0030198 $\sim$ extracellular matrix organization	7	0.00076932
	GO:0010628 $\sim$ positive regulation of gene expression	7	0.003347854
	GO:0006954 $\sim$ inflammatory response	7	0.018726822
	GO:0042060 $\sim$ wound healing	6	0.00008099
	GO:0010951 $\sim$ negative regulation of endopeptidase activity	6	0.00056046
	GO:0007596 $\sim$ blood coagulation	6	0.003572519
	GO:0000187 $\sim$ activation of MAPK activity	5	0.002945726
	GO:0071356 $\sim$ cellular response to tumor necrosis factor	5	0.003254238
	GO:0045766 $\sim$ positive regulation of angiogenesis	5	0.003816204
	GO:0006888 $\sim$ ER to Golgi vesicle-mediated transport	5	0.012031361
Under-expressed	GO:0055114 $\sim$ oxidation-reduction process	11	0.001247293
	GO:0045944 $\sim$ positive regulation of transcription from RNA pol II promoter	11	0.036881265
	GO:0007155 $\sim$ cell adhesion	8	0.011614032
	GO:0006898 $\sim$ receptor-mediated endocytosis	7	0.000490878
	GO:0042493 $\sim$ response to drug	7	0.005851721
	GO:0070374 $\sim$ positive regulation of ERK1 and ERK2 cascade	6	0.002498736
	GO:0010628 $\sim$ positive regulation of gene expression	6	0.013351468
	GO:0007399 $\sim$ nervous system development	6	0.019058266
	GO:0008283 $\sim$ cell proliferation	6	0.046843321
	GO:0098869 $\sim$ cellular oxidant detoxification	5	0.000540099

Gene count: the number of differentially expressed genes. The $p$ -value is a modified Fisher Exact $p$ -Value for gene-enrichment analysis.

Supplementary Table S5

KEGG pathway enrichment of over-expressed genes

KEGG code/description/genes	Gene count	$p$ -value	Fold change
hsa05205: Proteoglycans in cancer	6	0.013118572
Fibronectin 1(FN1)		0.00000271	16.3
Syndecan 4 (SDC4)		0.000427846	4.2
MET proto-oncogene, receptor tyrosine kinase (cMET)		0.00000303	5.1
Erb-b2 receptor tyrosine kinase 3 (ERBB3)		0.000000015	7.7
Syndecan 1 (SDC1)		0.001565398	4.8
T cell lymphoma invasion and metastasis 1 (Tiam1)		0.0000236	8.9
hsa04512: ECM-receptor interaction	5	0.003226258
Fibronectin 1(FN1)		0.00000271	16.3
Syndecan 4 (SDC4)		0.000427846	4.2
Syndecan 1 (SDC1)		0.001565398	4.8
Collagen type I alpha 2 chain (COL1A2)		0.003648144	4.5
Collagen type I alpha 1 chain (COL1A1)		0.000841148	4.9
hsa04514: Cell adhesion molecules (CAMs)	5	0.0177183
Neuronal cell adhesion molecule (NRCAM)		0.0000149	9.8
Syndecan 4 (SDC4)		0.000427846	4.2
Claudin 1 (CLDN1)		0.0000113	10.8
Versican (VCAN)		0.006636398	4.9
Syndecan 1 (SDC1)		0.001565398	4.8
hsa04610: Complement and coagulation cascades	4	0.012650834
Protein S (PROS1)		0.00000000039	7.4
Serpin family A member 1 (SERPINA1)		0.00000000186	57.8
CD55 molecule/Cromer blood group (CD55)		0.000046	10.5
Complement C3 (C3)		0.025288406	4.2

Gene count: the number of differentially expressed genes. The $p$ -value is a modified Fisher Exact $p$ -Value. for gene-enrichment analysis.

Supplementary Table S6

KEGG pathway enrichment of under-expressed genes

KEGG code/description/genes	Gene count	$p$ -value	Fold change
hsa04918: Thyroid hormone synthesis	6	0.000124594
Solute carrier family 26 member 4		0.005113032	$-$ 15.6
Heat shock protein family A (Hsp70) member 5		0.003082101	$-$ 5.1
LDL receptor related protein 2		0.014633499	$-$ 6.1
Inositol 1,4,5-trisphosphate receptor type 1		0.00000589	$-$ 8.2
Thyroid peroxidase		0.0000392	$-$ 39.5
Iodotyrosine deiodinase		0.034867638	$-$ 10.7
hsa04931: Insulin resistance	5	0.006968337
TBC1 domain family member 4		0.003314146	$-$ 6.1
CD36 molecule		0.013090592	$-$ 7.6
Insulin receptor substrate 1		0.009420503	$-$ 7.1
PPARG coactivator 1 alpha		0.006954803	$-$ 14.3
Acetyl-CoA carboxylase beta		0.009982905	$-$ 4.4
hsa04921: Oxytocin signaling pathway	5	0.025126328
Regulator of calcineurin 1		0.009836101	$-$ 4.1
Jun proto-oncogene, AP-1 TF subunit		0.000292306	$-$ 4.1
Inositol 1,4,5-trisphosphate receptor type 1		0.00000589	$-$ 8.2
Fos proto-oncogene, AP-1 TF subunit		0.037532991	$-$ 4.6
Calcium/calmodulin dependent protein kinase ID		0.021868732	$-$ 4
hsa04920: Adipocytokine signaling pathway	4	0.013152301
CD36 molecule		0.013090592	$-$ 7.6
Insulin receptor substrate 1		0.009420503	$-$ 7.1
PPARG coactivator 1 alpha		0.006954803	$-$ 14.3
Acetyl-CoA carboxylase beta		0.009982905	$-$ 4.4
hsa04919: Thyroid hormone signaling pathway	4	0.046572382
Regulator of calcineurin 1		0.009836101	$-$ 4.1
TBC1 domain family member 4		0.003314146	$-$ 6.1
Regulator of calcineurin 2		0.016633138	$-$ 4.3
Iodothyronine deiodinase 1		0.000595533	$-$ 26.6

Gene count: the number of differentially expressed genes. The $p$ -value is a modified Fisher Exact $p$ -Value, for gene-enrichment analysis.

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Gene expression profiling identifies potential molecular markers of papillary thyroid carcinoma

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1. Introduction

2. Methods

2.1 Patients and ethics statement

2.2 Tissue samples and total RNA isolation

2.3 Microarray procedures

2.4 Microarray data preprocessing and analysis

2.5 Functional group and pathway enrichment analysis

2.6 Quantitative real time RT-PCR (qRT-PCR)

2.7 Western blot (WB)

2.8 Enzyme-linked immunosorbent assay (ELISA)

2.9 Statistics

3. Results

3.1 Patients and pathological findings

Table 1 Genes validated in PTC

3.3 Functional and pathway enrichment analysis

3.5 Serum levels of selected protein markers

4. Discussion

Footnotes

Acknowledgments

Conflict of interest

Supplementary data

References

Table 1
Genes validated in PTC