Screening and identification of potential target genes in head and neck cancer using bioinformatics analysis
- Authors:
- Published online on: July 15, 2019 https://doi.org/10.3892/ol.2019.10616
- Pages: 2955-2966
-
Copyright: © Chen et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Head and neck cancer (HNC) is the sixth most common cancer worldwide (1). Annually, about 650,000 new cases and 350,000 deaths are reported worldwide, accounting for 6% of all cases (1,2). High-risk regions for lip and oral cavity cancers include Melanesia, South-Central Asia, and Central and Eastern Europe (2). High-risk regions for laryngeal cancer include Southern and Eastern Europe and Western Asia (2). Indonesia, Singapore, and Malaysia, which are inhabited to a large extent by Malay and Chinese individuals, have the highest incidence of nasopharyngeal carcinoma (3). Mounting evidence suggests that genetic variations or abnormal expression of keratinocyte differentiation associated protein, heme oxygenase 1 (HMOX1), Rac family small GTPase 1 (Rac1), and desmocollin 1 (DSC1) may be associated with the carcinogenesis and progression of head and neck tumors. Studies have also found that HMOX1 and keratin-associated proteins are associated with human papillomavirus (HPV)-related HNC (4,5). Inhibition of Rac1 activity may help to overcome primary or secondary chemo-radio-resistance in HNC (6). Occurrence and clinical prognosis of HNC are associated with overexpression of DSC1 (7). Early diagnosis and early treatment are keys to successful treatment of HNC, but no tumor markers with high specificity and sensitivity, or an effective therapeutic target, have been identified. Therefore, the survival rate and quality of life of patients with HNC is poor. As such, it is necessary to characterize the molecular mechanisms involved in the carcinogenesis of head and neck tumors. Better understanding of these mechanisms will allow for improved guidelines for diagnosis and treatment of head and neck tumors.
To explore the genetic alterations in head and neck tumors, identify new high-specificity and high-sensitivity tumor markers, and identify potentially effective therapeutic targets, in silico methods were used to study HNC. In the present study, GSE58911 was downloaded and analyzed from the Gene Expression Omnibus (GEO) database to obtain differentially expressed genes (DEGs) between HNC tissues and non-cancerous tissues. Subsequently, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, Gene Ontology (GO) enrichment analysis, and protein-protein interaction (PPI) network analysis was performed to characterize the molecular mechanisms underlying carcinogenesis and progression of HNC. A total of 648 differentially expressed genes and 26 hub genes were identified, which may be potential targets for clinical diagnosis and therapy of HNC.
Materials and methods
Microarray data
Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo) is a public functional genomics data repository of high throughout gene expression data, chips, and microarrays (8,9). Platform (GPL) and Series (GSE) constitute the data from GEO. The gene expression dataset (GSE58911) (10) was downloaded from GEO (Affymetrix GPL6244, Affymetrix Human Gene 1.0 ST Array) and contains 15 HNC samples and 15 normal tissues distant to the HNC sample.
Identification of DEGs
DEGs between cancerous and non-cancerous tissues were screened using GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r). To identify genes that are differentially expressed across experimental conditions, GEO2R, an interactive web tool, allows users to compare >2 groups of samples in a GEO Series. Results are presented as a table of genes ordered by significance. Log fold-change (FC) ≥1 or ≤-1 and adjusted P-value <0.05 were considered to be statistically different.
KEGG and GO enrichment analysis of DEGs
A comprehensive set of functional annotation tools were provided by the Database for Annotation, Visualization, and Integrated Discovery (DAVID; http://david.ncifcrf.gov) (version 6.8). DAVID is an online biological information database for investigators to understand biological significance underlying a large number of genes (11). KEGG (http://www.kegg.jp), an integrated database resource, is used for the biological interpretation of genome sequences and other high-throughput data (12). The GO (www.geneontology.org) project is a major bioinformatics tool and represents the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products (13). Enrichment analysis from GO and KEGG pathways for differentially expressed genes was obtained using DAVID. P<0.05 was considered to indicate a statistically significant difference.
PPI network construction and analysis
A PPI network of DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) online database (version 10.5; http://string-db.org) (14). Through the STRING database, DEGs with a combined score ≥0.4 were chosen to construct a PPI network which could be visualized using Cytoscape software (version 3.4.0; www.cytoscape.org) (15). The functional modules of the PPI network were then identified using the Molecular Complex Detection (MCODE) (version 1.4.2) plug-in of Cytoscape (16). The criteria for selection were as follows: Max depth, 100; degree cut-off, 2; k-score, 2 and node score cut-off, 0.2.
Hub gene selection and analysis
Hub genes were selected using Cytoscape software. A network of hub genes and their co-expressed genes was analyzed using the cBioPortal for Cancer Genomics (http://www.cbioportal.org) (17,18), which allows for visualization, analysis, and download of large-scale cancer genomics data sets. Hierarchical clusters of hub genes were constructed using the next generation University of California Santa Cruz (UCSC) Cancer Browser: UCSC Xena (http://xena.ucsc.edu) (19). The sample source ‘The Cancer Genome Atlas Head-Neck Squamous Cell Carcinoma (HNSC)’ was selected for these 26 hub genetic analyses and 604 samples were selected for analysis. The overall survival and disease-free survival rate analyses of hub genes was performed by constructing Kaplan-Meier curves using the cBioPortal online platform (statistical analysis performed is a log-rank test). Furthermore, the relationship between expression patterns, tumor grades, and HPV infection status was analyzed using Oncomine (https://www.oncomine.org) (20–29).
Results
Identification and PPI network construction of DEGs in HNC
After the standardization of the microarray results, 648 differentially expressed genes were identified between HNC tissues and normal tissues. The results from the GSE58911 dataset are represented as a volcano plot (Fig. 1A). The PPI network of DEGs was constructed (Fig. 1B). There were 554 nodes and 1574 edges in the PPI network, and the average node score was 5.68 (Fig. 1B).
GO and KEGG pathway enrichment analyses of DEGs
To analyze the biological classification of DEGs, GO and KEGG pathway enrichment analyses were performed using DAVID (Table I). The results of GO analysis showed that changes in biological processes of DEGs were mainly ‘enriched in muscle system process’, ‘extracellular matrix organization’, ‘muscle contraction’, ‘extracellular structure organization’, and ‘muscle filament sliding’. Molecular function DEGs included ‘actin binding’, ‘structural constituent of muscle’, ‘cytoskeletal protein binding’, ‘structural molecule activity’ and ‘actinin binding’. Cell component DEGs included ‘extracellular region part’, ‘contractile fiber’, ‘extracellular region’, ‘sarcomere’, and ‘myofibril’. KEGG pathway analysis showed that the DEGs were mainly enriched in ‘extracellular matrix (ECM)-receptor interaction’, ‘focal adhesion’, ‘amoebiasis’, ‘drug metabolism-cytochrome P450’, ‘chemical carcinogenesis’, ‘dilated cardiomyopathy’, ‘small cell lung cancer’, ‘hypertrophic cardiomyopathy’, and ‘retinol metabolism’.
Hub gene selection and analysis
Using the MCODE plug-in of Cytoscape, 26 genes were identified as hub genes. The results of GO and KEGG pathway analyses indicated that the hub genes were mainly enriched in ‘extracellular matrix organization’, ‘collagen catabolic process’, ‘extracellular structure organization’, ‘multicellular organism catabolic process’, ‘collagen metabolic process’, ‘serine-type endopeptidase activity’, ‘extracellular matrix’, ‘proteinaceous extracellular matrix’, ‘extracellular space’, ‘extracellular region part’, ‘extracellular region’, and ‘complement and coagulation cascades’ (Table II). The abbreviations, official full names, and synonyms for these hub genes are shown in Table III. A network of the hub genes and their co-expressed genes was analyzed using cBioPortal for Cancer Genomics (Fig. 2A). Hierarchical clustering revealed that the expression of hub genes could differentiate the HNC samples from normal samples (Fig. 2B). From figure 2B, it can be seen that 22 of the 26 hub genes were highly expressed in head and neck tumors compared with normal tissues, whereas expression of four genes (MMRN1/ECM1/EXCL12/CFD) was relatively high in the normal tissues. Furthermore, hierarchical clustering showed that HPV infection status determined by fluorescent in situ hybridization (FISH) testing (Fig. 2C) and P16 testing (Fig. 2D) was negatively associated with expression of the gene, although the mechanisms remains unknown. Overall survival rate analysis of the hub genes was performed using Kaplan-Meier curves in the cBioPortal online platform. Patients with HNC and high expression of interleukin (IL)8, IL1B and serpin family A member 1 (SERPINA1) had worse overall survival and worse disease-free survival (Fig. 3A and B). Oncomine analysis of cancer vs. normal tissues indicated that IL8, IL1B, and SERPINA1 were over-expressed in HNC in the different datasets (Fig. 4A, B and C). Higher mRNA expression levels of IL8 was associated with tumor grade (P=0.001). However, the mRNA expression levels of IL1B and SERPINA1 were not associated with tumor grade (P>0.05). Higher mRNA expression levels of IL8 (P=6.30×10−9) and IL1B (P=3.48×10−6) were associated with HPV infection status. The mRNA expression levels of SERPINA1 however, were not associated with HPV infection status (Fig. 5A-F).
Discussion
HNC is the sixth most common cancer worldwide and is associated with severe disease- and treatment-related morbidity, with a 5-year survival rate of <60% (1,2). The survival rate has not improved across more than two decades due to lack of early detection (30,31). There are two primary causes of HNC: Tobacco and alcohol use, and human papillomavirus infection (32). Previous studies have shown that HPV infection plays a role in the pathogenesis of head and neck tumors (33–35). The oncomine analysis of cancer vs. normal tissue for IL8, IL1B and SERPINA1 demonstrated that the expression was compared with the normal tissues (Fig. 4). mRNA expressions of IL8, IL1B and SERPINA1 were higher in the HPV-negative group compared with that in the HPV-positive group (Fig. 5D-F). These seemingly contradictory results are understandable. There are many studies suggesting that HPV-positive head and neck tumors were associated with improved disease-free and overall survival (32,36). According to the present study, IL8, IL1B and SERPINA1 are highly expressed in the HNC, and the results in Fig. 3 indicate that the overall survival rate and disease-free survival rate of patients with high expression of these 3 genes are worse. Therefore, it is understandable that IL8, IL1B and SERPINA1 have higher expressions in HPV-negative tumor patients. However, the underlying molecular mechanisms of HNC remain unclear. Abnormal expression of transglutaminase 3, regenerating islet-derived protein 3, keratin 8, and phosphatase and tensin homolog is associated with HNC (37–40). In addition, mutations within tumor protein p53, notch receptor 1, phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit α, X-ray repair cross complementing 1 and epidermal growth factor receptor have been reported to be involved in HNC (41–44). Patients with HNC that do not detect the cancer early have no effective treatments available except receiving palliative care, which leads to poor prognosis and quality of life, and a high rate of mortality (30). Therefore, there is an urgent need to identify potential target biomarkers that can be used to efficiently diagnose and treat HNC. Bioinformatics technology allows us to explore genetic differences between HNC and normal tissues, which can be used to identify potential biomarkers. Then, effective genes can be selected through screening and experimental validation for early diagnosis, clinical prognosis, and treatment of HNC.
In the present study, the dataset GSE58911 was analyzed to obtain differentially expressed genes between HNC and non-cancerous tissues. A total of 648 DEGs were identified. GO and KEGG enrichment analyses were performed to explore interactions among these genes and they were mainly enriched in ‘extracellular matrix organization’, ‘actin binding’, ‘extracellular region’, ‘ECM-receptor interaction’, ‘drug metabolism-cytochrome P450’, and ‘chemical carcinogenesis’. Previous studies reported that ‘extracellular matrix organization’, ‘actin binding’, and ‘ECM-receptor interaction’ play important roles in the carcinogenesis, progression, and metastasis of tumors (45–48). In addition, previous data indicated that focal adhesion, drug metabolism-cytochrome P450, and chemical carcinogenesis are involved in radio- and chemotherapy (49–52). Thus, the findings from the present study are consistent with results from previous studies. GO enrichment analysis indicated that changes in hub genes were mainly enriched in ‘extracellular matrix organization’, ‘collagen catabolic process’, ‘serine-type endopeptidase activity’, ‘extracellular matrix’, and ‘proteinaceous extracellular matrix’, while changes according to KEGG pathway analysis were mainly enriched in ‘complement and coagulation cascades’.
A total of 26 DEGs were selected as hub genes, among which survival rates and disease-free survival rates between patients with head and neck tumors and patients without tumors were significantly associated with the expression of IL8, IL1B, and SERPINA1. IL8 is a chemotactic factor that attracts neutrophils, basophils, and T-cells, but not monocytes (53) and can be released by several cell types in response to inflammatory stimuli (53). Higher IL8 expression was observed in HNSCC tissue (54,55). Furthermore, IL8 stimulated the proliferation of HNSC cells (55,56). In addition, a previous study showed that the tumor microenvironment plays a vital role in HNC initiation, progression, and metastasis (57). Tumor-associated macrophages can promote cancer initiation and progression by releasing cytokines and may facilitate papillary thyroid carcinoma (PTC) cell metastasis through IL8 and its paracrine interaction with C-X-C chemokine receptor CXCR1 and CXCR2 (58). Thus, IL8 may be a potential therapeutic target.
IL1B is a potent pro-inflammatory cytokine. Initially discovered as the major endogenous pyrogen, IL1B induces prostaglandin synthesis, neutrophil influx and activation, cytokine production, T cell and B cell activation, antibody production, collagen production, and fibroblast proliferation (59). A recent study of IL1B has shown that it plays a major role in tumor chemotherapy resistance. Anakinra can block the IL-1 pathway and overcome erlotinib resistance in HNSCC, which may represent a novel strategy to overcome EGFR inhibitor resistance, allowing for more effective treatment of patients with HNSCC (60). Furthermore, high expression of inflammatory cytokines (IL8, IL1B) and shorter progression-free survival are significantly associated. The expression level of inflammatory cytokines may help to identify which patients with recurrent and/or metastatic squamous cell carcinoma of the head and neck are likely to benefit from dacomitinib (61).
SERPINA1, an inhibitor of serine proteases, irreversibly inhibits trypsin, chymotrypsin, and plasminogen activator (62). Its primary target is elastase, but it also has a moderate affinity for plasmin and thrombin (62). A recent study showed a higher abundance of SERPINA1 candidate biomarkers in the saliva of patients with oral squamous cell carcinoma (OSCC), demonstrating that SERPINA1 is related to OSCC development (63). Moreover, SERPINA1 may be related to PTC by responding to steroid hormone stimuli and regulating the epithelial-to-mesenchymal transition (64). Based on these associations, SERPINA1 may be an effective mRNA marker of PTC (65). Oncomine analysis indicated that higher mRNA levels of IL8, IL1B, and SERPINA1 were associated with tumor grade and HPV infection status, indicating vital roles of IL8, IL1B, and SERPINA1 in the carcinogenesis or progression of HNC.
In addition to IL1B, lL8 and SERPINA1, which were associated with the survival rate of patients with head and neck cancer, other relevant hub genes that were identified in the present study are discussed.
Tenascin C (TNC), a gene associated with tumor metastatic potential, was upregulated in the OSCC cell line LNMTca8113 (66). In addition, vascular density and higher tumor stage were associated with differences in immuno-expression of stromal TNC, demonstrating its role in the tumorigenesis of juvenile nasopharyngeal angiofibroma (67).
A previous study showed that microRNA-29a/b could regulate the expression of collagen type III alpha 1 chain to enhance migration and invasion ability of nasopharyngeal carcinoma cells (68). The markers, the combination of collagen type V alpha 1 chain (COL5A1) and hemoglobin subunit beta and COL5A1 itself can better predict the treatment response in patients with oral tongue squamous cell carcinoma (69).
Poor disease-free survival and increased progression or relapse risk were associated with high plasminogen activator, urokinase (PLAU) expression. Moreover, circulating PLAU levels were significantly higher in the plasma of patients with HNSCC compared with that in healthy individuals (70).
Extracellular matrix protein 1 (ECM1) levels gradually increased from benign laryngeal lesions to precancerous to malignant lesions, and ECM1 was expressed at lower levels in laryngeal carcinomas without metastasis (71,72). These results demonstrated that ECM1 facilitated development and metastasis of laryngeal carcinoma.
Overexpression of SERPINE1 promotes tumor migration and invasion and plays an important role in metastasis and poor prognosis of HNSCC (73). In addition, many researchers regard SERPINE1 as a prognostic marker based on its ability to stratify patients with HNSCC according to their recurrence risk (74).
Matrix metalloproteinases (MMPs) are a family of proteolytic enzymes that promote invasion and metastasis of various cancers due to their ability to degrade components of the extracellular matrix. MMP1, MMP3, MMP9, and MMP13 are predictors of poor clinical outcomes in patients with HNC (75–78). Furthermore, specific tissue inhibitors of matrix metalloproteinases (TIMPs) can regulate MMP activity. In addition to HNC, the majority of tumors are associated with alterations in MMPs and TIMPs. Imbalance between matrix metalloproteinases and their inhibitors contributes greatly to the progression and prognosis of HNC (76,79).
Compared with normal oral mucosa, secreted phosphoprotein 1 was expressed at significantly higher levels in OSCC (80). According to a previous study, secreted protein acidic and cysteine rich had significant prognostic value, especially in the stroma surrounding OSCC (81). A literature search revealed that the interaction between HNC and the hub genes procollagen-lysine 2-oxoglutarate 5-dioxygenase (PLOD)-2, collagen type XII alpha 1 chain, multimerin 1 (MMRN1), plasminogen activator urokinase receptor, collagen type X α 1 chain, collagen type VI α 3 chain, prostaglandin-endoperoxide synthase 2, PLOD1, and complement factor D (CFD) has not been widely reported.
There were several limitations associated with the present study. First, only one series (GSE58911) downloaded and used from the GEO database. The number of tumor and normal samples in this series were both 15. This sample size was insufficient. Second, genes were analyzed that may be related to the carcinogenesis or progression of head and neck tumors from the results of the bioinformatics analyses. The functions of these genes have not been verified in vitro and in vivo. In the present study, the expression levels of IL8 (C-X-C motif chemokine ligand 8), IL1B and SERPINA1 in tumor tissues and normal tissues of patients with head and neck tumors were not verified further. In addition, phenotypic function was also not verified in head and neck tumor cell lines. In future studies this will be investigated. Third, the number of hub genes (modes with bold black circles) in Fig. 2A is 21, and there are 5 hub genes (MMRN1, ECM1, TIMP metallopeptidase inhibitor 1, SERPINA1 and CFD) that do not appear in the network map. In particular, SERPINA1 is among one of the identified three genes following further analysis of the data. This could be due to the following reasons: i) These 5 genes may not be closely related to other genes, and have other roles and mechanisms in the occurrence and development of tumors, so they were excluded from the network map; ii) the 26 hub genes were obtained by analyzing 648 DEGs using the Cytoscape plug-in, MCODE. cBioPortal for Cancer Genomics was subsequently used to analyze these 26 hub genes to obtain a network map of hub genes and their co-expressed genes. The computer algorithms that each database performed for analysis may differ, and may also cause differences; iii) in addition, 3 hub genes (IL8, IL1B and SERPINA1) were selected for more in-depth analysis as cBioPortal for Cancer Genomics was used to analyze overall survival and disease-free survival rate for the 26 hub genes. Changes in the expression of IL8, IL1B and SERPINA1 in patients with head and neck tumors were associated with overall survival and disease-free survival rate, and were statistically significant; iv), a holistic analysis of 26 hub genes from 628 DEGs was performed in an attempt to obtain an inductive result (Fig. 2). In addition, 2 detection methods (FISH and P16 tests) of HPV infection were used to obtain more accurate results for further analysis. However, the profiles of IL8, IL1B and SERPINA1 in Fig. 2C and D are not consistent. This may be due to different detection methods or detection of HPV subtypes. This may require a more accurate method to detect the infection status of HPV for a more accurate analysis; and v), there are differences in the monitoring of overall survival and disease-free survival of patients between the gene alterations of IL8, IL1B and SERPINA1 (Fig. 3). Monitoring was performed for over 180 months (over 15 years) when there were no alteration(s) in these 3 genes while alterations in these genes was monitored for only 20 months. This may be due to the following reasons: i) These samples were obtained at different time points over a 10-year period and as such the samples may have degraded; ii) some patients cannot be contacted during follow-up or have died due to illness. An open source, free database was used therefore information pertaining to when the samples were collected and whether it was in different decades. In this regard, the lack of data beyond 20 months may be considered a limitation associated with the present study. However, using this database for analysis is reliable and credible. This database is used in many articles on bioinformatics analysis (82–84). These limitations will be addressed in future studies. Despite these limitations, research in the present study is important as it elucidated molecular mechanisms underlying development of HNC, and also provides potential target genes for clinical diagnosis and targeted therapy. In addition, this provides direction for future studies of HNC.
In conclusion, the present study identified DEGs that may be involved in the carcinogenesis or progression of HNC. A total of 648 DEGs and 26 hub genes were identified and may have potential as target biomarkers for HNC. Further studies are needed to elucidate the biological functions of these genes in HNC.
Acknowledgements
Not applicable.
Funding
This study was supported by the National Natural Science Foundation of China (grant nos. 81372880 and 81670910) and the Guidance fund of the Renmin Hospital of Wuhan University (grant no. RMYD2018Z12).
Availability of data and materials
All data are fully available without restriction.
Authors' contributions
FC and ZT conceived and designed the study. FC, AZ, FL, SW, SC and ZT processed the data. FC wrote the paper. FC, AZ, FL, SW, SC, and ZT reviewed and edited the manuscript. All authors read and approved the manuscript.
Ethics approval and consent participate
Not applicable.
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
References
Jemal A, Bray F, Center MM, Ferlay J, Ward E and Forman D: Global cancer statistics. CA Cancer J Clin. 61:69–90. 2011. View Article : Google Scholar : PubMed/NCBI | |
Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J and Jemal A: Global cancer statistics, 2012. CA Cancer J Clin. 65:87–108. 2015. View Article : Google Scholar : PubMed/NCBI | |
Bray F, Ferlay J, Laversanne M, Brewster DH, Gombe MC, Kohler B, Piñeros M, Steliarova-Foucher E, Swaminathan R, Antoni S, et al: cancer incidence in five continents: Inclusion criteria, highlights from volume X and the global status of cancer registration. Int J Cancer. 137:2060–2071. 2015. View Article : Google Scholar : PubMed/NCBI | |
Thibodeau BJ, Geddes TJ, Fortier LE, Ahmed S, Pruetz BL, Wobb J, Chen P, Wilson GD and Akervall JA: Gene expression characterization of HPV positive head and neck cancer to predict response to chemoradiation. Head Neck Pathol. 9:345–353. 2015. View Article : Google Scholar : PubMed/NCBI | |
Min SK, Lee SK, Park JS, Lee J, Paeng JY, Lee SI, Lee HJ, Kim Y, Pae HO, Lee SK and Kim EC: Endoplasmic reticulum stress is involved in hydrogen peroxide induced apoptosis in immortalized and malignant human oral keratinocytes. J Oral Pathol Med. 37:490–498. 2008. View Article : Google Scholar : PubMed/NCBI | |
Skvortsov S, Dudás J, Eichberger P, Witsch-Baumgartner M, Loeffler-Ragg J, Pritz C, Schartinger VH, Maier H, Hall J, Debbage P, et al: Rac1 as a potential therapeutic target for chemo-radioresistant head and neck squamous cell carcinomas (HNSCC). Br J Cancer. 110:2677–2687. 2014. View Article : Google Scholar : PubMed/NCBI | |
Wang Y, Chen C, Wang X, Jin F, Liu Y, Liu H, Li T and Fu J: Lower DSC1 expression is related to the poor differentiation and prognosis of head and neck squamous cell carcinoma (HNSCC). J Cancer Res Clin Oncol. 142:2461–2468. 2016. View Article : Google Scholar : PubMed/NCBI | |
Edgar R, Domrachev M and Lash AE: Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30:207–210. 2002. View Article : Google Scholar : PubMed/NCBI | |
Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, et al: NCBI GEO: Archive for functional genomics data sets-update. Nucleic Acids Res 41 (Database Issue). D991–D995. 2013. | |
Lobert S, Graichen ME, Hamilton RD, Pitman KT, Garrett MR, Hicks C and Koganti T: Prognostic biomarkers for HNSCC using quantitative real-time PCR and microarray analysis: β-tubulin isotypes and the p53 interactome. Cytoskeleton (Hoboken). 71:628–637. 2014. View Article : Google Scholar : PubMed/NCBI | |
Huang DW, Sherman BT, Tan Q, Collins JR, Alvord WG, Roayaei J, Stephens R, Baseler MW, Lane HC and Lempicki RA: The DAVID gene functional classification tool: A novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 8:R1832007. View Article : Google Scholar : PubMed/NCBI | |
Kanehisa M, Sato Y, Kawashima M, Furumichi M and Tanabe M: KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44(D1): D457–D462. 2016. View Article : Google Scholar : PubMed/NCBI | |
Pinoli P, Chicco D and Masseroli M: Computational algorithms to predict gene ontology annotations. BMC Bioinformatics. 16 (Suppl 6):S42015. View Article : Google Scholar : PubMed/NCBI | |
Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, et al: STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43 (Database issue). D447–D452. 2015. View Article : Google Scholar | |
Su G, Morris JH, Demchak B and Bader GD: Biological network exploration with Cytoscape 3. Curr Protoc Bioinformatics. 47:8.13.1–24. 2014. View Article : Google Scholar | |
Bandettini WP, Kellman P, Mancini C, Booker OJ, Vasu S, Leung SW, Wilson JR, Shanbhag SM, Chen MY and Arai AE: Multi contrast delayed enhancement (MCODE) improves detection of subendocardial myocardial infarction by late gadolinium enhancement cardiovascular magnetic resonance: A clinical validation study. J Cardiovasc Magn Reson. 14:832012. View Article : Google Scholar : PubMed/NCBI | |
Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E, et al: Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 6:pl12013. View Article : Google Scholar : PubMed/NCBI | |
Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E, et al: The cBio cancer genomics portal: An open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2:401–404. 2012. View Article : Google Scholar : PubMed/NCBI | |
Rosenbloom KR, Armstrong J, Barber GP, Casper J, Clawson H, Diekhans M, Dreszer TR, Fujita PA, Guruvadoo L, Haeussler M, et al: The UCSC Genome Browser database: 2015 update. Nucleic Acids Res 43 (Database Issue). D670–D681. 2015. View Article : Google Scholar | |
Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, Barrette T, Pandey A and Chinnaiyan AM: ONCOMINE: A cancer microarray database and integrated data-mining platform. Neoplasia. 6:1–6. 2004. View Article : Google Scholar : PubMed/NCBI | |
Cromer A, Carles A, Millon R, Ganguli G, Chalmel F, Lemaire F, Young J, Dembélé D, Thibault C, Muller D, et al: Identification of genes associated with tumorigenesis and metastatic potential of hypopharyngeal cancer by microarray analysis. Oncogene. 23:2484–2498. 2004. View Article : Google Scholar : PubMed/NCBI | |
Estilo CL, O-charoenrat P, Talbot S, Socci ND, Carlson DL, Ghossein R, Williams T, Yonekawa Y, Ramanathan Y, Boyle JO, et al: Oral tongue cancer gene expression profiling: Identification of novel potential prognosticators by oligonucleotide microarray analysis. BMC Cancer. 9:112009. View Article : Google Scholar : PubMed/NCBI | |
Ginos MA, Page GP, Michalowicz BS, Patel KJ, Volker SE, Pambuccian SE, Ondrey FG, Adams GL and Gaffney PM: Identification of a gene expression signature associated with recurrent disease in squamous cell carcinoma of the head and neck. Cancer Res. 64:55–63. 2004. View Article : Google Scholar : PubMed/NCBI | |
He H, Jazdzewski K, Li W, Liyanarachchi S, Nagy R, Volinia S, Calin GA, Liu CG, Franssila K, Suster S, et al: The role of microRNA genes in papillary thyroid carcinoma. Proc Natl Acad Sci USA. 102:19075–19080. 2005. View Article : Google Scholar : PubMed/NCBI | |
Peng CH, Liao CT, Peng SC, Chen YJ, Cheng AJ, Juang JL, Tsai CY, Chen TC, Chuang YJ, Tang CY, et al: A novel molecular signature identified by systems genetics approach predicts prognosis in oral squamous cell carcinoma. PLoS One. 6:e234522011. View Article : Google Scholar : PubMed/NCBI | |
Pyeon D, Newton MA, Lambert PF, den Boon JA, Sengupta S, Marsit CJ, Woodworth CD, Connor JP, Haugen TH, Smith EM, et al: Fundamental differences in cell cycle deregulation in human papillomavirus-positive and human papillomavirus-negative head/neck and cervical cancers. Cancer Res. 67:4605–4619. 2007. View Article : Google Scholar : PubMed/NCBI | |
Ye H, Yu T, Temam S, Ziober BL, Wang J, Schwartz JL, Mao L, Wong DT and Zhou X: Transcriptomic dissection of tongue squamous cell carcinoma. BMC Genomics. 9:692008. View Article : Google Scholar : PubMed/NCBI | |
Giordano TJ, Au AY, Kuick R, Thomas DG, Rhodes DR, Wilhelm KJ Jr, Vinco M, Misek DE, Sanders D, Zhu Z, et al: Delineation, functional validation, and bioinformatic evaluation of gene expression in thyroid follicular carcinomas with the PAX8-PPARG translocation. Clin Cancer Res. 12:1983–1993. 2006. View Article : Google Scholar : PubMed/NCBI | |
Vasko V, Espinosa AV, Scouten W, He H, Auer H, Liyanarachchi S, Larin A, Savchenko V, Francis GL, de la Chapelle A, et al: Gene expression and functional evidence of epithelial-to-mesenchymal transition in papillary thyroid carcinoma invasion. Proc Natl Acad Sci USA. 104:2803–2808. 2007. View Article : Google Scholar : PubMed/NCBI | |
Forastiere A, Koch W, Trotti A and Sidransky D: Head and neck cancer. N Engl J Med. 345:1890–900. 2001. View Article : Google Scholar : PubMed/NCBI | |
Bozec A, Ilie M, Dassonville O, Long E, Poissonnet G, Santini J, Chamorey E, Ettaiche M, Chauviere D, Peyrade F, et al: Significance of circulating tumor cell detection using the Cell search system in patients with locally advanced head and neck squamous cell carcinoma. Eur Arch Otorhinolaryngol. 270:2745–2749. 2013. View Article : Google Scholar : PubMed/NCBI | |
Rettig EM and D'Souza G: Epidemiology of head and neck cancer. Surg Oncol Clin N Am. 24:379–396. 2015. View Article : Google Scholar : PubMed/NCBI | |
Marullo R, Werner E, Zhang H, Chen GZ, Shin DM and Doetsch PW: HPV16 E6 and E7 proteins induce a chronic oxidative stress response via NOX2 that causes genomic instability and increased susceptibility to DNA damage in head and neck cancer cells. Carcinogenesis. 36:1397–1406. 2015. View Article : Google Scholar : PubMed/NCBI | |
Boscolo-Rizzo P, Zorzi M, Del Mistro A, Da Mosto MC, Tirelli G, Buzzoni C, Rugge M, Polesel J and Guzzinati S; AIRTUM Working Group, : The evolution of the epidemiological landscape of head and neck cancer in Italy: Is there evidence for an increase in the incidence of potentially HPV-related carcinomas? PLoS One. 13:e01926212018. View Article : Google Scholar : PubMed/NCBI | |
Cheraghlou S, Torabi SJ, Husain ZA, Otremba MD, Osborn HA, Mehra S, Yarbrough WG, Burtness BA and Judson BL: HPV status in unknown primary head and neck cancer: Prognosis and treatment outcomes. Laryngoscope. 129:684–691. 2019. View Article : Google Scholar : PubMed/NCBI | |
Kreimer AR, Clifford GM, Boyle P and Franceschi S: Human papillomavirus types in head and neck squamous cell carcinomas worldwide: A systematic review. Cancer Epidemiol Biomarkers Prev. 14:467–475. 2005. View Article : Google Scholar : PubMed/NCBI | |
Wu X, Cao W, Wang X, Zhang J, Lv Z, Qin X, Wu Y and Chen W: TGM3, a candidate tumor suppressor gene, contributes to human head and neck cancer. Mol Cancer. 12:1512013. View Article : Google Scholar : PubMed/NCBI | |
Masui T, Ota I, Itaya-Hironaka A, Takeda M, Kasai T, Yamauchi A, Sakuramoto-Tsuchida S, Mikami S, Yane K, Takasawa S and Hosoi H: Expression of REG III and prognosis in head and neck cancer. Oncol Rep. 30:573–578. 2013. View Article : Google Scholar : PubMed/NCBI | |
Andratschke M, Hagedorn H and Nerlich A: Expression of the epithelial cell adhesion molecule and cytokeratin 8 in head and neck squamous cell cancer: A comparative study. Anticancer Res. 35:3953–3960. 2015.PubMed/NCBI | |
Squarize CH, Castilho RM, Abrahao AC, Molinolo A, Lingen MW and Gutkind JS: PTEN deficiency contributes to the development and progression of head and neck cancer. Neoplasia. 15:461–471. 2013. View Article : Google Scholar : PubMed/NCBI | |
Gross AM, Orosco RK, Shen JP, Egloff AM, Carter H, Hofree M, Choueiri M, Coffey CS, Lippman SM, Hayes DN, et al: Multi-tiered genomic analysis of head and neck cancer ties TP53 mutation to 3p loss. Nat Genet. 46:939–943. 2014. View Article : Google Scholar : PubMed/NCBI | |
Psyrri A, Seiwert TY and Jimeno A: Molecular pathways in head and neck cancer: EGFR, PI3K, and more. Am Soc Clin Oncol Educ Book. 246–255. 2013. View Article : Google Scholar : PubMed/NCBI | |
Mahjabeen I, Baig RM, Masood N, Sabir M, Inayat U, Malik FA and Kayani MA: Genetic variations in XRCC1 gene in sporadic head and neck cancer (HNC) patients. Pathol Oncol Res. 19:183–188. 2013. View Article : Google Scholar : PubMed/NCBI | |
Boeckx C, Weyn C, Vanden Bempt I, Deschoolmeester V, Wouters A, Specenier P, Van Laer C, Van den Weyngaert D, Kockx M, Vermorken JB, et al: Mutation analysis of genes in the EGFR pathway in Head and Neck cancer patients: Implications for anti-EGFR treatment response. BMC Res Notes. 7:3372014. View Article : Google Scholar : PubMed/NCBI | |
Gilkes DM, Semenza GL and Wirtz D: Hypoxia and the extracellular matrix: Drivers of tumour metastasis. Nat Rev Cancer. 14:430–439. 2014. View Article : Google Scholar : PubMed/NCBI | |
Malik R, Lelkes PI and Cukierman E: Biomechanical and biochemical remodeling of stromal extracellular matrix in cancer. Trends Biotechnol. 33:230–236. 2015. View Article : Google Scholar : PubMed/NCBI | |
Trulsson M, Yu H, Gisselsson L, Chao Y, Urbano A, Aits S, Mossberg AK and Svanborg C: HAMLET binding to α-actinin facilitates tumor cell detachment. PLoS One. 6:e171792011. View Article : Google Scholar : PubMed/NCBI | |
Zhang HJ, Tao J, Sheng L, Hu X, Rong RM, Xu M and Zhu TY: Twist2 promotes kidney cancer cell proliferation and invasion by regulating ITGA6 and CD44 expression in the ECM-receptor interaction pathway. Onco Targets Ther. 9:1801–1812. 2016.PubMed/NCBI | |
Eke I and Cordes N: Focal adhesion signaling and therapy resistance in cancer. Semin Cancer Biol. 31:65–75. 2015. View Article : Google Scholar : PubMed/NCBI | |
Blackstone BN, Li R, Ackerman WT, Ghadiali SN, Powell HM and Kniss DA: Myoferlin depletion elevates focal adhesion kinase and paxillin phosphorylation and enhances cell-matrix adhesion in breast cancer cells. Am J Physiol Cell Physiol. 308:C642–C649. 2015. View Article : Google Scholar : PubMed/NCBI | |
Johnson AL, Edson KZ, Totah RA and Rettie AE: Cytochrome P450 ω-hydroxylases in inflammation and cancer. Adv Pharmacol. 74:223–262. 2015. View Article : Google Scholar : PubMed/NCBI | |
Ravegnini G, Sammarini G, Hrelia P and Angelini S: Key genetic and epigenetic mechanisms in chemical carcinogenesis. Toxicol Sci. 148:2–13. 2015. View Article : Google Scholar : PubMed/NCBI | |
Van Damme J, Rampart M, Conings R, Decock B, Van Osselaer N, Willems J and Billiau A: The neutrophil-activating proteins interleukin 8 and beta-thromboglobulin: In vitro and in vivo comparison of NH2-terminally processed forms. Eur J Immunol. 20:2113–2118. 1990. View Article : Google Scholar : PubMed/NCBI | |
Lo MC, Yip TC, Ngan KC, Cheng WW, Law CK, Chan PS, Chan KC, Wong CK, Wong RN, Lo KW, et al: Role of MIF/CXCL8/CXCR2 signaling in the growth of nasopharyngeal carcinoma tumor spheres. Cancer Lett. 335:81–92. 2013. View Article : Google Scholar : PubMed/NCBI | |
Chan LP, Wang LF, Chiang FY, Lee KW, Kuo PL and Liang CH: IL-8 promotes HNSCC progression on CXCR1/2-meidated NOD1/RIP2 signaling pathway. Oncotarget. 7:61820–61831. 2016. View Article : Google Scholar : PubMed/NCBI | |
Christofakis EP, Miyazaki H, Rubink DS and Yeudall WA: Roles of CXCL8 in squamous cell carcinoma proliferation and migration. Oral Oncol. 44:920–926. 2008. View Article : Google Scholar : PubMed/NCBI | |
Curry JM, Sprandio J, Cognetti D, Luginbuhl A, Bar-ad V, Pribitkin E and Tuluc M: Tumor microenvironment in head and neck squamous cell carcinoma. Semin Oncol. 41:217–234. 2014. View Article : Google Scholar : PubMed/NCBI | |
Fang W, Ye L, Shen L, Cai J, Huang F, Wei Q, Fei X, Chen X, Guan H, Wang W, et al: Tumor-associated macrophages promote the metastatic potential of thyroid papillary cancer by releasing CXCL8. Carcinogenesis. 35:1780–1787. 2014. View Article : Google Scholar : PubMed/NCBI | |
Tominaga K, Yoshimoto T, Torigoe K, Kurimoto M, Matsui K, Hada T, Okamura H and Nakanishi K: IL-12 synergizes with IL-18 or IL-1beta for IFN-gamma production from human T cells. Int Immunol. 12:151–160. 2000. View Article : Google Scholar : PubMed/NCBI | |
Stanam A, Gibson-Corley KN, Love-Homan L, Ihejirika N and Simons AL: Interleukin-1 blockade overcomes erlotinib resistance in head and neck squamous cell carcinoma. Oncotarget. 7:76087–76100. 2016. View Article : Google Scholar : PubMed/NCBI | |
Kim HS, Kwon HJ, Jung I, Yun MR, Ahn MJ, Kang BW, Sun JM, Kim SB, Yoon DH, Park KU, et al: Phase II clinical and exploratory biomarker study of dacomitinib in patients with recurrent and/or metastatic squamous cell carcinoma of head and neck. Clin Cancer Res. 21:544–552. 2015. View Article : Google Scholar : PubMed/NCBI | |
Kwon CH, Park HJ, Choi JH, Lee JR, Kim HK, Jo HJ, Kim HS, Oh N, Song GA and Park DY: Snail and serpinA1 promote tumor progression and predict prognosis in colorectal cancer. Oncotarget. 6:20312–20326. 2015. View Article : Google Scholar : PubMed/NCBI | |
Kawahara R, Bollinger JG, Rivera C, Ribeiro AC, Brandão TB, Paes Leme AF and MacCoss MJ: A targeted proteomic strategy for the measurement of oral cancer candidate biomarkers in human saliva. Proteomics. 16:159–173. 2016. View Article : Google Scholar : PubMed/NCBI | |
Qu T, Li YP, Li XH and Chen Y: Identification of potential biomarkers and drugs for papillary thyroid cancer based on gene expression profile analysis. Mol Med Rep. 14:5041–5048. 2016. View Article : Google Scholar : PubMed/NCBI | |
Vierlinger K, Mansfeld MH, Koperek O, Nöhammer C, Kaserer K and Leisch F: Identification of SERPINA1 as single marker for papillary thyroid carcinoma through microarray meta analysis and quantification of its discriminatory power in independent validation. BMC Med Genomics. 4:302011. View Article : Google Scholar : PubMed/NCBI | |
Fialka F, Gruber RM, Hitt R, Opitz L, Brunner E, Schliephake H and Kramer FJ: CPA6, FMO2, LGI1, SIAT1 and TNC are differentially expressed in early- and late-stage oral squamous cell carcinoma-a pilot study. Oral Oncol. 44:941–948. 2008. View Article : Google Scholar : PubMed/NCBI | |
Renkonen S, Heikkilä P, Haglund C, Mäkitie AA and Hagström J: Tenascin-C, GLUT-1, and syndecan-2 expression in juvenile nasopharyngeal angiofibroma: Correlations to vessel density and tumor stage. Head Neck. 35:1036–1042. 2013. View Article : Google Scholar : PubMed/NCBI | |
Qiu F, Sun R, Deng N, Guo T, Cao Y, Yu Y, Wang X, Zou B, Zhang S, Jing T, et al: miR-29a/b enhances cell migration and invasion in nasopharyngeal carcinoma progression by regulating SPARC and COL3A1 gene expression. PLoS One. 10:e01209692015. View Article : Google Scholar : PubMed/NCBI | |
Suresh A, Vannan M, Kumaran D, Gümüs ZH, Sivadas P, Murugaian EE, Kekatpure V, Iyer S, Thangaraj K and Kuriakose MA: Resistance/response molecular signature for oral tongue squamous cell carcinoma. Dis Markers. 32:51–64. 2012. View Article : Google Scholar : PubMed/NCBI | |
Sepiashvili L, Hui A, Ignatchenko V, Shi W, Su S, Xu W, Huang SH, O'Sullivan B, Waldron J, Irish JC, et al: Potentially novel candidate biomarkers for head and neck squamous cell carcinoma identified using an integrated cell line-based discovery strategy. Mol Cell Proteomics. 11:1404–1415. 2012. View Article : Google Scholar : PubMed/NCBI | |
Gu M, Guan J, Zhao L, Ni K, Li X and Han Z: Correlation of ECM1 expression level with the pathogenesis and metastasis of laryngeal carcinoma. Int J Clin Exp Pathol. 6:1132–1137. 2013.PubMed/NCBI | |
Meng XY, Liu J, Lv F, Liu MQ and Wan JM: Study on the correlation between extracellular matrix protein-1 and the growth, metastasis and angiogenesis of laryngeal carcinoma. Asian Pac J Cancer Prev. 16:2313–2316. 2015. View Article : Google Scholar : PubMed/NCBI | |
Pavón MA, Arroyo-Solera I, Céspedes MV, Casanova I, León X and Mangues R: uPA/uPAR and SERPINE1 in head and neck cancer: Role in tumor resistance, metastasis, prognosis and therapy. Oncotarget. 7:57351–57366. 2016. View Article : Google Scholar : PubMed/NCBI | |
Pavón MA, Arroyo-Solera I, Téllez-Gabriel M, León X, Virós D, López M, Gallardo A, Céspedes MV, Casanova I, López-Pousa A, et al: Enhanced cell migration and apoptosis resistance may underlie the association between high SERPINE1 expression and poor outcome in head and neck carcinoma patients. Oncotarget. 6:29016–29033. 2015. View Article : Google Scholar : PubMed/NCBI | |
Van Tubergen EA, Banerjee R, Liu M, Vander Broek R, Light E, Kuo S, Feinberg SE, Willis AL, Wolf G, Carey T, et al: Inactivation or loss of TTP promotes invasion in head and neck cancer via transcript stabilization and secretion of MMP9, MMP2, and IL-6. Clin Cancer Res. 19:1169–1179. 2013. View Article : Google Scholar : PubMed/NCBI | |
Pietruszewska W, Bojanowska-Poźniak K and Kobos J: Matrix metalloproteinases MMP1, MMP2, MMP9 and their tissue inhibitors TIMP1, TIMP2, TIMP3 in head and neck cancer: An immunohistochemical study. Otolaryngol Pol. 70:32–43. 2016. View Article : Google Scholar : PubMed/NCBI | |
Zhang C, Li C, Zhu M, Zhang Q, Xie Z, Niu G, Song X, Jin L, Li G and Zheng H: Meta-analysis of MMP2, MMP3, and MMP9 promoter polymorphisms and head and neck cancer risk. PLoS One. 8:e620232013. View Article : Google Scholar : PubMed/NCBI | |
Vincent-Chong VK, Salahshourifar I, Karen-Ng LP, Siow MY, Kallarakkal TG, Ramanathan A, Yang YH, Khor GH, Rahman ZA, Ismail SM, et al: Overexpression of MMP13 is associated with clinical outcomes and poor prognosis in oral squamous cell carcinoma. ScientificWorldJournal. 2014:8975232014. View Article : Google Scholar : PubMed/NCBI | |
Pradhan-Palikhe P, Vesterinen T, Tarkkanen J, Leivo I, Sorsa T, Salo T and Mattila PS: Plasma level of tissue inhibitor of matrix metalloproteinase-1 but not that of matrix metalloproteinase-8 predicts survival in head and neck squamous cell cancer. Oral Oncol. 46:514–518. 2010. View Article : Google Scholar : PubMed/NCBI | |
Huang CF, Yu GT, Wang WM, Liu B and Sun ZJ: Prognostic and predictive values of SPP1, PAI and caveolin-1 in patients with oral squamous cell carcinoma. Int J Clin Exp Pathol. 7:6032–6039. 2014.PubMed/NCBI | |
Aquino G, Sabatino R, Cantile M, Aversa C, Ionna F, Botti G, La Mantia E, Collina F, Malzone G, Pannone G, et al: Expression analysis of SPARC/osteonectin in oral squamous cell carcinoma patients: From saliva to surgical specimen. Biomed Res Int. 2013:7364382013. View Article : Google Scholar : PubMed/NCBI | |
Li L, Lei Q, Zhang S, Kong L and Qin B: Screening and identification of key biomarkers in hepatocellular carcinoma: Evidence from bioinformatic analysis. Oncol Rep. 38:2607–2618. 2017. View Article : Google Scholar : PubMed/NCBI | |
Saha SK, Jeong Y, Cho S and Cho SG: Systematic expression alteration analysis of master reprogramming factor OCT4 and its three pseudogenes in human cancer and their prognostic outcomes. Sci Rep. 8:148062018. View Article : Google Scholar : PubMed/NCBI | |
Yang Y, Dong X, Xie B, Ding N, Chen J, Li Y, Zhang Q, Qu H and Fang X: Databases and web tools for cancer genomics study. Genomics Proteomics Bioinformatics. 13:46–50. 2015. View Article : Google Scholar : PubMed/NCBI |