The nuclear factor kappaB (NF-κB) family comprises a group of evolutionarily conserved signal-activated transcription factors (TFs) that have been shown to play a central role in the control of a large number of normal and stressed cellular processes 12. NF-κB is involved in similar biological processes in cancers, as a critical modulator of genes that promote cell survival, inflammation, angiogenesis, tumor development, progression and metastasis 345. We previously showed that NF-κB is aberrantly activated and modulates the expression of gene clusters that include oncogenes that promote survival, tumorigenesis and therapeutic resistance of advanced murine and human squamous cell carcinomas 678910111213141516. In addition, NF-κB and related pathways have been identified as potential biomarkers and therapeutic targets for a variety of human cancers 34171819. However, our understanding of the regulatory mechanisms activating or affected by the NF-κB pathway still remains limited to the classical concept of linear pathway activation based on experimental observations from traditional biological approaches. Such a linear paradigm for NF-κB as well as other pathways could be problematic, as suggested by the observation that pharmacological and clinical approaches targeting individual NF-κB signal molecules alone have not yielded significant clinical efficacy in most solid tumors 202122.

Several levels of complexity contribute to our limited understanding of the function of the NF-κB pathway in health and disease. First, the NF-κB family consists of five structurally related proteins, namely RELA (p65), NFκB1 (p50/p105), cREL, RELB, and NFκB2 (p52/p100), as well as seven inhibitor kappaB (IκB) molecules 12. Constitutive activation of RELA/NFκB1 was found to be an essential factor controlling the expression of genes that affect cellular proliferation, apoptosis, angiogenesis, immune and proinflammatory responses, and therapeutic resistance in head and neck squamous cell carcinoma (HNSCC) and other cancers 345. However, nuclear activation of hetero- and homodimers composed of other NF-κB subunits has also been detected in HNSCC tissues and cell lines 23. While the function of the less studied species of NF-κB is not yet fully understood, there is evidence that formation of homo- or heterodimers from different NF-κB subunits can increase the diversity of responses through interaction with various IκBs or other regulatory factors, and by having different binding affinities for variant κB promoter binding motifs 1224. Second, multiple signals from membrane receptors and intermediate kinases converge to modulate different NF-κB subunits directly or indirectly. At present, there is evidence for signaling through a classic pathway involving a trimeric inhibitor-kappaB kinase (IKK)α/β/γ and casein kinase 2 complexes modulating NFκB1, RELA and cREL, and alternative pathways involving NF-κB inducing kinase and IKKα modulating NFκB2 and RELB 1211242526. Furthermore, there is potential for cross-talk between IKK/NF-κB and other major signal pathways, such as the mitogen-activated protein kinase (MAPK), phosphatidylinositol 3-kinase (PI3K), JAK/STAT (Janus kinase/signal transducer and transcription factor), and p53 pathways, which have been implicated in significantly affecting the cancer phenotype, including proliferation, apoptosis, angiogenesis and tumorigenesis 1427282930. These observations highlight the tremendous technical challenges and experimental limitations when studying such dynamic and complex biological and regulatory systems using a classic one molecule/one pathway approach.

Molecular and phenotypic heterogeneity represents an additional obstacle that limits our understanding of the regulatory mechanisms giving rise to differences in the malignant phenotype between different cancers of the same histological type, such as HNSCC. The identification of heterogeneous sub-populations in specific types of cancer, such as HNSCC, and selection of therapies targeting them are major hurdles for clinical diagnosis, prognosis and treatment. Such heterogeneity usually remains undetected by standard histological and pathological classification and clinical grading systems, and other biomarkers based on molecular gene expression profiles and immunohistochemistry are not yet well enough understood or validated for clinical applications. Such heterogeneity in the malignant phenotype includes differences in prognosis, therapeutic resistance, angiogenesis or metastatic potential associated with specific molecular alterations identified in HNSCC, such as overexpression or mutation of epidermal growth factor receptor (EGFR) 103132, constitutive activation of NF-κB, MAPK, AKT and STAT pathways 15313334353637, mutation or dysfunction of p53/p63/p73 family members 353638, and over-expression of proinflammatory and proangiogeneic cytokines and growth factors, including interleukin (IL)1, IL6, IL8, vascular endothelial growth factor (VEGF), platelet-derived growth factor, and hepatocyte growth factor 18343739404142.

We recently identified specific gene expression signatures in HNSCC cell lines (UM-SCC, University of Michigan Cell Lines Series of Head and Neck Squamous Cell Carcinoma), which were associated with differing p53 status and NF-κB regulatory activity, subsets previously associated with differences in prognosis, response to chemoradiation or metastatic phenotypes 14. Some genes in the NF-κB related expression signatures identified from our study have been identified and associated with a higher risk for HNSCC recurrence and metastasis by independent groups 4344. However, the individual genes and proteins identified from the molecular and clinical studies do not function alone, but often form dynamically complex interactions to execute their biological functions, through regulatory control mechanisms involving TFs, signal pathways and networks. The analysis of critical transcriptional modules, pathways and networks has been experimentally impractical, until the recent availability of large sets of data from different microarray and genomic platforms, as well as advances in development of bioinformatic and systems biology approaches 4546.

It remains a great challenge to systematically analyze transcriptional regulation in eukaryotes through mathematical modeling and integration of multiple large data sets from different platforms and experimental conditions, where each provides only partial information about the biological process. To address these challenges, a statistical model, COGRIM (Clustering of Gene Regulons Using Integrated Modeling) has been developed, based on a Bayesian hierarchical model with a Markov chain Monte Carlo implementation 4748. Here, this modeling has been specifically applied to novel applications in human cancer cell lines, where the successful prediction of NF-κB regulons (a set of genes under regulation of the same TF) in HNSCC cell lines has been achieved by integration of large data sets of gene expression and multiple TFs from different platforms and experimental conditions. Furthermore, the global connections of NF-κB regulons were established through networks and pathways using Ingenuity Pathway Analysis (IPA), and predicted novel NF-κB targets were confirmed with experimental validation. Our study identified distinct molecular signatures composed of NF-κB dominant signal pathways and networks specific for subsets of HNSCC cell lines differing in p53 status. Our identification of NF-κB related networks and pathways could significantly enhance our understanding of NF-κB regulatory mechanisms, lead to new concepts of molecular regulation and classification of cancer subgroups, and targeted therapeutics for HNSCC.

In this study, we used a newly developed COGRIM statistical model to systematically define NF-κB regulons of genes differentially expressed by UM-SCC cells (Figures 1 and 2). These NF-κB regulons are connected to networks and signal pathways, for which there is evidence of significant involvement in tumorigenesis (Figures 3 and 4, and Table 1). Our experimental data confirmed and validated computational and bioinformatic predictions for NF-κB regulation and binding activity on the promoter sequences of a selection of these genes (Figures 5, 6, 7), indicating that NF-κB family members function as important master controls of gene expression, coordinating action within networks and pathways that contribute to the malignant phenotype of UM-SCC. Our study revealed the power of a systems biology analysis using COGRIM modeling and IPA to identify molecular signatures at the global level that are modulated by functionally active TFs, interacting networks and signaling pathways.

This study is the first utilization of COGRIM to analyze a family of TFs in a human cancer system 4753. Previously, there have been limited genome-wide computational analyses of NF-κB binding activity and regulated genes related to malignant phenotypes and genotypes, due to the complexity of NF-κB regulatory mechanisms, heterogeneous cancer subtypes, and inherent limitations or biases in computational and experimental conditions. An important feature of the COGRIM model is the ability to computationally analyze complex transcriptional regulatory mechanisms by simultaneously integrating multiple large scaled data sources, in a principled and robust fashion without requiring a priori knowledge of the relative accuracy of each data source. This model-based strategy greatly improved the efficiency and accuracy of the elucidation of the functional and physical relationships among the TFs, pathways and networks. Although the linear model of expression used as a basis for COGRIM is an approximation of transcriptional regulation, it has proven to be effective in other investigations 545556. One potential limitation of COGRIM is that the TF activity f_jt must be approximated by a proxy measure such as the expression level of the gene that codes for that TF. The predicted functions of TFs are confirmed with experimental results even when extensive ChIP binding data were not available 47.

As described previously 47, the COGRIM method includes a probabilistic model for each data source that addresses the inherent uncertainty within each data type. COGRIM is more than a simple extension of previous linear models in that it provides a principled mechanism for integrating sequence features with expression data for the prediction of target genes and can be further extended in several interesting directions in the presence of additional data sources. It should also be noted that although we have focused on TFs, the model would work equally well with regulatory factors that are not proteins but whose levels can be measured and whose binding sites can be identified (for example, microRNAs). COGRIM represents an initial step toward solving the problem of integrating available biological information in a principled fashion. Our belief is that this goal will best be accomplished by fitting large and flexible probability models that combine data from various experimental and compiled sources in a structured or multi-level framework. We anticipate that the model will become even more valuable as the accuracy and coverage of expression and sequence feature data improve.

Using COGRIM in this study, 748 putative NF-κB target genes were identified, which consisted of 59% of 1,265 differentially expressed genes from microarray analysis in UM-SCC cells (Figure 1 and Additional data file 1). This ratio is slightly higher than the frequency of all predicted NF-κB binding motifs calculated in vertebrates (approximately 50%, including human, mouse and rat data from the Genomatix promoter database), but is slightly lower than the frequencies of NF-κB binding motifs predicted in the up-regulated gene clusters enriched with known NF-κB related genes published previously (approximately 65-70% in B-C gene clusters) 14. The prediction is consistent with the hypothesis and experimental data that NF-κB regulated genes are over-represented in tumor associated gene signatures, especially in the up-regulated gene clusters 14. Interestingly, the overall ratio of approximately 60% of differentially expressed genes in human UM-SCC cells is remarkably consistent with the approximate percentage of genes in murine squamous cell carcinoma restored to expression levels seen in non-malignant cells of syngeneic origin by inhibition of NF-κB using an inducible mutant IκBα 13. Inhibition of NF-κB and target genes in this murine model was accompanied by decreased proliferation, migration, cell survival, angiogenesis and tumorigenesis 13. The murine NF-κB modulated gene signature was independently associated with a gene signature associated with decreased prognosis in a large series of human HNSCC43. Together, these experimental and in silico analyses of expression profiling data in murine and human squamous cell carcinoma are consistent with involvement of NF-κB as a key regulatory factor in global alterations in gene expression in squamous cell carcinoma.

The efficiency and accuracy of COGRIM prediction are also supported by cross validation with other experimental data from published literature, as well as with our experimental results from UM-SCC cells upon TNF-α stimulation or siRNA knock down of NF-κB (Figures 5, 6, 7) 14. Among the 748 genes predicted as NF-κB target genes, 75 of them (10%; in bold in Additional data file 1) overlapped with approximately 600 NF-κB target genes published previously by the three websites described in the Materials and methods, indicating most of the predicted genes represent novel NF-κB target genes. Additionally, only 16 genes of the list of 1,265 'known NF-κB genes' based on these websites were excluded from our predicted gene list, due to low probability scores by COGRIM modeling (data not shown). Among the 16 genes, 3 were previously implicated in HNSCC and other cancers, namely AREG (amphiregulin), MMP14, and MYC. After searching the original references, we found the reference for AREG was incorrectly cited. For MMP14, a NF-κB binding motif was observed in the promoter; however, it is located at -1,165 bp from the transcriptional stating site, which is outside the proximal promoter sequence defined in this study. The reference for MYC was published in 1990, for which the consensus sequence of the NF-κB binding motif cited does not exist in the most updated human genome sequence (data not shown). However, there are several references suggesting other subunits of NF-κB could be involved in the regulation of MYC, including evidence that the RELB/p52 complex can directly bind to the MYC gene promoter 24. However, the PWM of RELB/p52 binding motifs has not been well established, and the computation in this study did not include RELB/p52. There have been reports about possible involvement of cREL in MYC gene expression but without discussing detailed mechanisms 2457. Thus, the COGRIM modeling in this study successfully predicted 82% (75/91) of known NF-κB genes identified. The few cases of failed prediction could be either due to errors in literature citations, or because the location of the NF-κB binding site is outside of the promoter sequence boundary selected for this study.

We experimentally validated a selected subset of predicted NF-κB genes involved in signal pathways, using TNF-α and siRNA as tools. We showed that TNF-α significantly enhanced gene expression of 15 genes selected from the prediction, where 6 are novel NF-κB targets, including IL1R2, ALDH1A3, ITGA2, ITGA5, LAMA3 and LAMB3 (Figure 5 and Additional data file 1). To date, we have experimentally tested expression of a total of 47 genes in response to TNF-α (Figure 5) 14, where 41 genes were identified as NF-κB target genes by COGRIM, of which 23 are novel. Previously, there have been several experimental studies attempting to globally investigate NF-κB binding activity and regulated gene expression, including RELA binding activity throughout human chromosome 22 58, and TNF-α-induced NF-κB target gene expression in HeLa cells 5960, U937 monocytic cells 61, lipopolysaccharide-stimulated human peripheral blood mononuclear cells 62, and THP.1 cells transfected with IKKγ 63. Under TNF-α stimulation, 767 genes (P < 0.05) or 343 genes (P < 0.01) were differentially expressed by HeLa cells 60, 348 genes exhibited NF-κB binding activity in U937 cells 61, and 79 or 72 genes were identified as NF-κB regulated and responsive to lipopolysaccharide in macrophages 6263. Among these gene lists, a total of 88 genes were confirmed as NF-κB-regulated genes and overlapped with our gene list (Additional data file 1), where 25 genes were identified as known NF-κB genes listed by the three websites previously mentioned. These experimental data also cross-validate 63 putative NF-κB target genes identified by our analysis.

We noticed different kinetics in the expression of gene subsets induced by TNF-α in UM-SCC cell lines. The responsive kinetics of many of the novel NF-κB target genes are either slowly induced, or induced and sustained without rapid decrease (Figure 5), in contrast to the typical TNF-α-induced early response gene, as observed for cytokines (IL1A, IL1B, IL1RN, CSF2 and VEGFC) and a NF-κB family member (REL). Different kinetics of gene expression in response to TNF-α treatment has been noticed previously 59, where rapid oscillatory responses could be due to TNF-α-mediated phosphorylation, degradation and re-synthesis of IκBα, in contrast to that of IκBβ and IκBε, which mediate prolonged stimulation 64. It has also been reported that the TNF-α-induced early response pattern is seen often in genes with conserved promoter regions 59. This observation supports the hypothesis that the genes with typical early response inducible patterns are those with evolutionarily conserved functions involved in the first line of defense, where a quick reaction and termination mechanism is needed. The genes with the slower induction patterns are involved in functions such as adhesion and cell structure, where slower and persistent responses are necessary.

We also observed that the predicted NF-κB regulons are not uniformly distributed in the subgroups of UM-SCC cells (Figure 2), and more genes with predicted RELA-specific regulatory motifs were observed in the up-regulated gene groups with wild-type p53-deficient status (Figure 2c). This observation is in good agreement with the general consensus and experimental data regarding the positive regulatory role of RELA in controlling oncogenic gene expression 22565, and is consistent with our observation that in UM-SCC cells with wild-type p53-deficient status a cluster of NF-κB regulated genes is over-expressed 1435. The experiments knocking down RELA or NFκB1 elucidated NF-κB-mediated specific regulatory mechanisms (Figure 6), and provided data consistent with the previous findings that the basal expression of most of the NF-κB-regulated genes depends on both RELA and NFκB1 (p65/p50 heterodimer). Negative regulation of NFκB1 compared to the basal gene expression was consistent with a repressive function associated with p50 homodimers 12. Furthermore, the binding activities of RELA and NFκB1 were confirmed in the promoter regions of a typical NF-κB target gene, namely IL8 (which served as the positive control), in the promoters of atypical NF-κB target genes, namely IGFBP3 and CDKN1A, and in the promoters of novel NF-κB target genes, namely ITGA5 and LAMB3 (Figure 6b). Interestingly, CDKN1A is also a p53 target gene with important function in the control of the cell cycle and apoptosis, and IGFBP3 is a p63 target gene involved in the IGF signaling pathway 66. Our data provide computational and experimental evidence consistent with potential cross-talk between the two important pathways, namely NF-κB and p53/p63, through which target genes could be transcriptionally regulated by either or both TFs.

The NF-κB target genes identified were connected by networks and functioned as regulons under direct interaction or close regulation by RELA (Figure 3a,b), or NFκB1 (Figure 3c,d). These gene groups included many known NF-κB target genes with confirmed NF-κB binding sites, such as CCDN1, CSF1, CSF2, ELF3, ICAM1, IL1A, IL1B, IL1RN, IL2RA, IL6, IL8, and VIM. Interestingly, most of these known NF-κB target genes appear in the networks with RELA (Figure 3a,b), and are less significantly associated with NFκB1 (Figure 3c,d). This observation is consistent with the fact that RELA contains the functional transactivation domain for mediating gene transcription 22565. In addition, even fewer NF-κB target genes were connected with NFκB1 in cells with wild-type p53-deficient status (Figure 3c); this subgroup of cells over-expressed genes with a high prevalence of RELA regulation (Figure 2c). In this subgroup of cells (Figure 3c), more genes were connected to peroxisome proliferator-activated receptor gamma (PPARG), a member of the nuclear hormone receptor subfamily. PPARG is able to form heterodimers with retinoid X receptors, affect RELA cytoplasmic distribution and negatively regulate inflammatory responses 67. Interestingly, in this network, PPARG is also linked with PPAR binding protein, a PPAR co-activator with the ability to bind to DNA and p53 protein 6869. Our and other data provide computational and experimental evidence consistent with potential cross-talk between the two important pathways, namely NF-κB and p53/p63, through which target genes could be transcriptionally regulated by either or both TFs.

The up-regulated NF-κB target genes are enriched in important signal pathways implicated in most cancers, including leukocyte extravasation, inositol phosphate metabolism, and xenobiotic metabolism pathways (Table 1 and Figure 4a). The pathways identified are consistent with previous evidence from studies by us and others that NF-κB promotes proinflammation, pro-angiogenesis, cell adhesion and migration through up-regulation of genes involved in these pathways 46789101112131415167071. The inositol phosphate metabolism pathway consists of molecular components of PI3K and protein kinase C pathways, both important signal pathways implicated in promoting tumorigenesis, especially in epidermis and epithelia 72737475. The involvement of NF-κB with the down-regulated genes has been less studied; in this study, genes in Wnt/β-catenin and TGF-β pathways were down-regulated in all tumor cells through regulation in association with all NF-κB family members (Figure 4b). The Wnt/β-catenin signaling pathway includes many negative regulators of cell growth and survival, and the down-regulation of these genes has been shown to be the critical step in tumorigenesis in epidermis and epithelia 76. Interestingly, the involvement of RELA and NFκB1 in the Wnt/β-catenin pathway was not significant, suggesting other NF-κB family members or NF-κB-independent intermediates could be involved. The TGF-β signaling pathway is another negative regulatory pathway and the resulting deficiency has been demonstrated in HNSCC. Lu et al. 77 identified a defect of TGF-β receptor 2 (TGFβR2) and the related pathway that significantly contributes to HNSCC carcinogenesis and metastasis. Other signal pathways identified are more specific to the phenotypic and genotypic differences in UM-SCC cells resulting from different p53 statuses (Figure 4). The pathways related to IGF, integrins, receptor and intermediate signals (Ephrin receptor, NF-κB, p38 MAPK, PPAR and PTEN, and cytokines (VEGF and GM-CSF are dominant in cells with mutant p53 status, which is consistent with either the loss of the negative regulation (PTEN and PPAR), or the suppression of NF-κB and other signal pathways and genes by gaining or retaining p53 functions in cells with mutant p53 status 1435. For cells with wild-type p53-deficient status, down-regulated genes were only involved in the cell cycle:G2/M DNA damage checkpoint pathway, where RELA showed dominant effects (Figure 4b).

This study provides a strong link between NF-κB regulons and related pathways identified by the systems biology approaches, consistent with many conclusions previously drawn from individual and classic biological experiments. The data from both computational and experimental strategies support the hypothesis that the malignant progression of HNSCC is due to, or leads to, multiple genetic and phenotypic defects, such as p53 mutation or underexpression 3878, and aberrant activation of several major growth factor and cytokine receptor pathways, including the TNF receptor 16, IL1R 939, IL6R 31, EGFR 10, hepatocyte growth factor receptor/cMet 41, TGF-β receptor 77, and platelet-derived growth factor receptor 79 pathways. These receptors modulate multiple signal pathways, including aberrant activation of NF-κB 6719, AP1 69, JAK/STAT 31, early growth response-1(EGR1) 37, casein kinase 2 11, MAPK 15, PI3K 1041, and BCL-XL/IAP associated apoptosis pathways 8. Our previous report showed that the five major TFs - NF-κB, STAT3, AP1, EGR1, and p53 - are specifically implicated in the unique gene signatures of UM-SCC cells 14, adding supporting evidence to current work that multiple transcriptional mechanisms and signal pathways control specific gene and pathway signatures that determine the malignant phenotypes and the heterogeneous characteristics in UM-SCC cells.

In interpreting our current study, we recognize that there are differences between cell lines and human tissues. However, many of our previous studies using these cell lines have led to the demonstration and confirmation of important molecular findings made with them in tumor tissue and serum specimens. These include the demonstration of alterations and the biological and clinical significance of NF-κB activation and of multiple NF-κB-regulated genes and cytokines expressed in HNSCC tumor specimens and serum 183233428081, and the demonstration of an inverse relationship between NF-κB and p53 nuclear localization and associated protein expression in tumors 35. As a result, and to further examine the validity of the results of the bioinformatic analysis of the present study, we have recently undertaken a meta-analysis of 34 microarray datasets of HNSCC (approximately 80% from tissue specimens). Preliminary analyses are consistent with key observations from this study using UM-SCC cell lines, including that the molecules in, and/or regulated by, the NF-κB and p53 signaling pathways are significantly enriched and related to HNSCC malignancy (B Yan et al., manuscript in preparation). Since there are many important differences between the tumor cell lines in culture and human tumor specimens, where the paracrine effects from fibroblasts and other host cells are missing, it will be important in future studies to integrate stromal cell gene and protein expression data with functional studies of potential networks involving these interactions.

We successfully predicted NF-κB regulons through COGRIM modeling and connected them into organized NF-κB regulatory modules. This analysis revealed the concerted activation of NF-κB target genes or gene products, many of them previously identified as unrelated molecules. The analysis of NF-κB regulons established a complex interaction comprising novel or previously identified pathways and networks, where the molecular signatures were particularly associated with cells differing in p53 status. Our study identified pathway signatures related to UM-SCC cells in general for over-activated proinflammation, self-defense and inositol phosphate metabolism, as well as down-regulated Wnt/β-catenin, TGF-β and neuregulin pathways. RELA-controlled over-activation of IL6 signaling and down-regulated cell cycle:G2M checkpoint was specific for tumor cells with wild-type p53-deficient status. Up-regulated IGF signaling and multiple down-regulated pathways comprise the molecular signature for cells with mutant p53 status. Such molecular signatures composed of multiple pathways established the foundation for further global identification of biomarkers and therapeutic targets in HNSCC and other cancers with phenotypic and genetic heterogeneity related to p53 status or other abnormalities.

AKT, v-akt murine thymoma viral oncogene homolog 1; AP1, activating protein-1; ChIP, chromatin immunoprecipitation; COGRIM, Clustering Of Gene Regulons using Integrated Modeling; EGFR, epidermal growth factor receptor; EGR1, early growth response-1; ETS, v-ets erythroblastosis virus E26 oncogene homolog (avian); GM-CSF, granulocyte-macrophage colony-stimulating-factor; HNSCC, head and neck squamous cell carcinoma; IGF, insulin-like growth factor; IκB, inhibitor kappaB; IKK, inhibitor-kappaB kinase; IL, interleukin; IL-R, interleukin receptor; IPA, Ingenuity Pathway Analysis; IPKB, Ingenuity Pathways Knowledge Base; MAPK, mitogen-activated protein kinase; NF-κB, nuclear factor kappaB; PI3K, phosphatidylinositol 3-kinase; PPAR, peroxisome proliferator-activated receptor; PTEN, phosphatase and tensin homolog; PWM, position weighted matrix; siRNA, small interfering RNA; STAT, signal transducer and transcription factor; STAT3, signal transducer and transcription factor 3; TF, transcription factor; TGF, transforming growth factor; TNF, tumor necrosis factor; UM-SCC, University of Michigan Cell Lines Series of Head and Neck Squamous Cell Carcinoma; VEGF, vascular endothelial growth factor.

BY, GC and ZC conceived and designed the bioninformatic and analytic strategies and experiments. GC, STJ and CJS originally developed the COGRIM model. BY prepared micorarray and related promoter data for caculation through COGRIM modeling. GC performed mathematical caculation through COGRIM modeling. BY performed data analysis after mathematical caculation using IPA and other bioinformatic tools. BY, KS, XY and ZC contributed to the experimental designs. BY, KS and XY wrote experimental protocols, and performed experiments and data analyses. BY prepared the final figures and tables. ZC, BY, CVW, GC, STJ and CJS contributed to the writing and discussion of the paper.

The following additional data are available with the online version for this paper. Additional data file 1 lists NF-κB target genes differentially expressed in UM-SCC cell lines. Additional data file 2 lists Gene Ontology annotations for NF-κB target genes in wild-type p53-deficient, mutant p53 and wild-type plus mutant p53 subsets of UM-SCC cells. Additional data file 3 shows network lists generated by IPA based on gene sets regulated by NF-κB subunits RELA/p65 and NFκB1/p50.