Oral squamous cell carcinoma (OSCC) is the most common malignancy form in the head and neck region, accounting for about 260,000 new cases and 124,000 OSCC-related deaths worldwide annually [1, 2]. In western countries, the etiology of some newly diagnosed primary SCCs of the head and neck has shifted from tobacco and alcohol abuse to human papillomavirus (HPV) infections, possibly as a result of changes in sexual practices [3–8]. Despite aggressive treatment modalities, 5-year survival rates for advanced head and neck cancers have remained low and relatively unchanged (about 50-60%) for several decades, partially due to early locoregional recurrences within 2 years of initial treatment [9]. There is therefore a pressing need for molecular predictors that enable earlier detection of the disease, describe tumor behavior, and improve risk assessment to identify patients at risk for recurrence and OSCC-related death.

Molecular profiling has become a common and effective method for cancer gene discovery and classification of cancer. Almost a decade ago, Chung et al. identified four intrinsic subtypes for head and neck squamous cell carcinoma with clinical and biological implications [10]. Consequently, one of the subtypes with the most unfavorable prognosis also displayed strikingly similar transcriptional patterns with the breast carcinoma basal-like phenotype and lung squamous cell carcinoma. Recently, additional evidence of shared cellular processes was found between breast carcinoma and oral squamous cell carcinoma, i.e. mechanisms for tumor lymphangiogenesis and metastasis to the regional lymph nodes as well as HER2/neu polymorphisms [11, 12]. These findings suggest that cancers derived from different sites of origin may perturb common signaling pathways and thereby display similar tumor characteristics [13]. To test this hypothesis, we evaluated the prognostic potential of 16 putative prognostic biomarkers (AZGP1, BTG2, CBX2, CNTNAP2, DNALI1, LOC389033, NME5, PIP, S100A8, SCUBE2, SERPINA11, STC2, STK32B, SUSD3, UBE2C, and WHSC1L1) previously identified in breast carcinoma using oral squamous cell carcinoma [14, 15]. Interestingly, several of the putative biomarkers have been implicated in the carcinogenesis of more than one cancer form. We, and others, have also been able to show that AZGP1, S100A8, and STK32B, as well as CNTNAP2 are associated with the basal-like phenotype and lymph node metastasis, respectively [15–17].

Here, we investigated the prognostic potential of the gene expression signature in relation to clinical outcome, disease-specific survival (DSS) and/or overall survival (OS), in two steps. First, transcriptional levels for each gene were evaluated with respect to the clinical endpoints using publicly available Affymetrix one-channel microarray (n = 168) and Illumina RNASeq datasets (n = 198) for OSCC from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) repositories, respectively. Second, because correlation between mRNA/protein levels is frequently low, Cox proportional hazards models for DSS and OS were also calculated using immunohistochemical protein expression patterns from 43 OSCC cases together with established clinicopathological features (tumor size and node status or tumor size and age, respectively).

Oral squamous cell carcinoma is a heterogeneous disease with diverse clinical, pathological, and biological behavior [10]. Nevertheless, the strongest determinants of prognosis still include tumor stage and the presence of cervical metastases at the time of diagnosis, as well as the time to locoregional recurrences [9, 24–26]. Unfortunately, up to 50% of OSCCs are diagnosed at an advanced stage with 5-year survival rates at approximately 60%, e.g. delayed diagnosis [27–30]. Therefore, many patients could benefit greatly from complementary molecular markers, which may help guide treatment decisions and be of value in the development of new therapeutic agents. Extensive efforts are currently being made to identify and validate biomarkers based on the biology of oral cancers that can complement established clinicopathological features and improve clinical management of the disease. Recent work to characterize OSCC using transcriptomic profiling has mainly focused on the identification of biomarkers for disease progression and lymph node metastasis prediction [31–38]. Surprisingly, few gene expression signatures have been developed to improve patient risk assessment [18, 34, 39, 40]. Although transcriptome analyses can give an indication of biological activity within a tissue, mRNA and protein levels may not correlate because gene expression is controlled by a multistage system. We have therefore performed immunohistochemistry using readily available FFPE samples to evaluate the clinical significance of the 16 putative biomarkers in OSCC. Here, we show the prognostic potential of the S100A8 and CNTNAP2 proteins, as well as the relationship between SCUBE2, CBX2, and STK32B protein levels and important clinicopathological features for OSCC, i.e. regional metastasis to the cervical lymph nodes, tumor inflammatory infiltration, and tumor size, respectively.

In the present work, we evaluated the applicability of breast cancer prognostic biomarkers for OSCC, given the biological similiarities between the two cancer types [10]. In breast carcinoma, we showed the recurrent upregulation of the CBX2, CNTNAP2, S100A8, UBE2C, and WHSC1L1 genes as well as downregulation of the AZGP1, BTG2, DNALI1, LOC389033, NME5, PIP, SCUBE2, SERPINA11, STC2, STK32B, and SUSD3 genes in more aggressive tumors [14, 15, 23]. The prognostic potential of individual biomarkers in the 16-gene signature were evaluated in three OSCC patient cohorts containing clinical information, two of which (Cohorts I-II) were compiled from publicly available Affymetrix and Illumina RNASeq datasets. The Affymetrix one-channel system and Illumina RNAseq platform have recently been shown to correlate well, in particular for high abundance genes [41]. Although the majority of the markers in the signature were in agreement for both breast carcinoma and OSCC, five proteins (S100A8, STC2, STK32B, SUSD3, and WHSC1L1) were inversely regulated in aggressive OSCC samples. In addition, several of the biomarkers which showed promise at the mRNA level (CBX2, S100A8, SCUBE2, and STK32B) were also either predictive of clinical outcome or associated with clinicopathological features at the protein level. The differences in the prognostic potential of specific biomarkers at the mRNA or protein levels may possibly be the result of small sample sizes, discordant mRNA-protein expression patterns or differences in sample preparation for microarray analyses and immunohistochemistry, i.e. microarray is frequently performed using a tumor mass, which consists of both malignant and nonmalignant cells, resulting in an over- or underestimation of expression levels, whereas cell type-specific protein expression patterns can be easily interpreted using immunohistochemistry. In breast carcinoma, elevated levels of the S100A8 protein were associated with moderate/strong tumor inflammatory infiltration and significantly shorter DSS rates. In contrast, lower S100A8 protein levels were associated with significantly shorter OS rates in OSCC, but there was no association with tumor infiltration. These findings suggest differences in cell type-specific gene expression and thereby activation and/or inhibition of diverse cellular mechanisms. Furthermore, CBX2, CNTNAP2, S100A8, SCUBE2, and STK32B protein levels were significantly associated with established clinicopathological features for OSCC and/or patient clinical outcome. We propose here two potential prognostic biomarkers for OSCC (the CNTNAP2 protein for DSS and the S100A8 protein for OS). There may be several reasons why these candidate biomarkers were associated with different clinical endpoints: differences in gene function and whether the biomarkers correlated with strong determinants of OSCC-related prognosis, e.g. tumor stage, lymph node metastasis or time to locoregional recurrences.

Chen et al. identified upregulation of CNTNAP2 mRNA levels as a distinctive characteristic of OSCC in comparison with normal mucosa [32]. In a genome-wide association study for oral cancers, CNTNAP2 was found to be associated with cell migration [42]. The CNTNAP2 gene also functions as a cell adhesion molecule and has been found to be either methylated or deleted in several different cancer types, e.g. glioma, myleoid leukemia, and pancreatic adenocarcinoma [43–45]. In the present study, elevated CNTNAP2 protein levels were prevalent in tumors from pN1 patients, but this association was not statistically significant likely due to the low number of events. These findings and the known function of the CNTNAP2 protein in tumorigenesis suggest that it may be a determinant of metastatic spread to the cervical lymph nodes and thereby a more aggressive phenotype with an adverse effect on DSS. Although elevated CNTNAP2 levels were predictive of a more unfavorable prognosis in univariate Cox regression models, disease-specific survival rates were not significantly improved by including the protein in a model containing lymph node status and tumor size. These results show the profound effect node metastases have on clinical outcome. In future studies, it will be necessary to alter the study design to test the prognostic potential of CNTNAP2 further, e.g. using a larger cohort containing node-negative patients. Furthermore, the S100A8 gene, also known as MRP-8 or calgranulin A, has been studied extensively in head and neck cancer. Inclusion of S100A8 protein expression patterns in multivariate models together with tumor size, node status, and age significantly improved OS prediction. In addition, we found an association between low S100A8 levels (mRNA levels in Cohort II and protein levels in Cohort III) and tumor differentiation. S100A8 belongs to the S100 gene family, several of which form a gene cluster on chromosome 1q21 and are commonly induced by chronic and acute inflammation [46–48]. In the present study, positive S100A8 immunostaining was also observed in a peritumoral inflammatory cell population, albeit similar in both S100A8-positive and negative tumors. Overexpression of the S100A8 protein has been observed in breast, colorectal, gastric, lung, pancreatic, and prostate cancer, wheras underexpression has been shown in various squamous cell carcinomas of the head and neck [49]. Recently, the S100A8/A9 heterocomplex was identified as a regulator of cell cycle progression and cell proliferation in cancer cell lines originating from the head and neck region [50].

OSCC outcome prediction may also be improved by identifying biomarkers that can predict which tumors will inevitabily spread to the cervical nodes. When we initiated this study, we believed that the CNTNAP2 gene would be a strong candidate for lymph node status because of its association with metastatic spread to the axillary lymph nodes in breast carcinoma [23]. However in the present study, loss of SCUBE2 protein expression in the tumor epithelial component was the only marker significantly associated with lymph node status and borderline significant for DSS. Interestingly, all 12 patients with SCUBE2-positive tumors were long-term disease-specific survivors and only 1/12 tumors had metastasized to the cervical lymph nodes.These findings are consistent with reports in breast carcinoma where elevated levels of the SCUBE2 protein not only inhibited proliferation of breast cancer cell lines, but was indicative of a more favorable prognosis [23, 51]. SCUBE2 is a multidomain, secreted glycoprotein that was first implicated in functioning upstream of hedgehog signaling in zebrafish, but has since been shown to also play a pivotal role in human tumorigenesis [52, 53]. Furthermore, SCUBE2 serves two different functions by using either the CUB domain at the C-terminal end or the EGF-like repeats at the N-terminus, i.e. control of cell proliferation by antagonizing bone morphogenetic protein (BMP) activity and regulation of cell-cell contacts, and possibly tumor metastasis, by forming complexes with E-cadherin in adherens junctions, respectively [54].

The role CBX2 and STK32B play in OSCC tumorigenesis is largely unknown. In a correlation analysis between protein expression patterns and established clinicopathological features, a clear link was found between CBX2-positivity and tumor inflammatory infiltration as well as STK32B-positivity and small tumor size (T1-T2). CBX2 plays a role in epigenetic regulation and hematopoietic stem cell differentiation, whereas the STK32B gene is one of several serine/threonine kinases (STK32B, STK36, and STK39) with similar gene expression patterns in basal-like breast cancers [16, 55, 56]. Consequently, STK36 is a component of the hedgehog signaling pathway. The function of the STK32B gene is not known, but single nucleotide polymorphisms in the STK32B gene have been associated with oral clefts [57].

Hedgehog signaling has also been implicated in OSCC and basal cell carcinoma, a skin cancer frequently found in the head and neck region [58, 59]. Leovic and colleagues illustrated the clinical relevance and aberrant expression of key hedgehog signaling components in SCCs of the oral cavity and oropharynx, i.e. PTCH1, GLI1, SMO, and SHH[58]. In Cohort II, several genes which play a role in hedgehog signaling were also found to be associated with high histological grade (the KIF7, TCTN1, and TCTN2 genes) and metastatic spread to the cervical lymph nodes (the FGF4 and PTCHD1 genes). These findings further emphasize the importance of hedgehog signaling in OSCC.

In summary, our results illustrate that cancer is a complex, heterogeneous disease. However, certain clinically relevant biomarkers may be useful therapeutic targets for several different cancer types. Despite the limited number of patients included in the study, the integrative analysis identified at least five of the putative biomarkers originally developed in breast carcinoma (CBX2, CNTNAP2, S100A8, SCUBE2, and STK32B) that also improve risk assessment for OSCC patients and may play a crucial role in cancer-related processes, e.g. hedgehog signaling, regulation of cell cycle progression, cell proliferation, metastasis. These targets should be studied in a larger cohort to further evaluate their clinical significance in OSCC.