5892 Original Article Comprehensive analysis reveals novel gene signature in head and neck squamous cell carcinoma: predicting is associated with poor prognosis in patients Yixin Sun1,2#, Quan Zhang1,2#, Lanlin Yao2#, Shuai Wang3, Zhiming Zhang1,2 1Department of Breast Surgery, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China; 2School of Medicine, Xiamen University, Xiamen, China; 3State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China Contributions: (I) Conception and design: Y Sun, Q Zhang; (II) Administrative support: Z Zhang; (III) Provision of study materials or patients: Y Sun, Q Zhang; (IV) Collection and assembly of data: Y Sun, L Yao; (V) Data analysis and interpretation: Y Sun, S Wang; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. #These authors contributed equally to this work. Correspondence to: Zhiming Zhang. Department of Surgery, The First Affiliated Hospital of Xiamen University, Xiamen, China. Email: [email protected]. Background: Head and neck squamous cell carcinoma (HNSC) remains an important public health problem, with classic risk factors being smoking and excessive alcohol consumption and usually has a poor prognosis. Therefore, it is important to explore the underlying mechanisms of tumorigenesis and screen the genes and pathways identified from such studies and their role in pathogenesis. The purpose of this study was to identify genes or signal pathways associated with the development of HNSC. Methods: In this study, we downloaded gene expression profiles of GSE53819 from the Gene Expression Omnibus (GEO) database, including 18 HNSC tissues and 18 normal tissues. The differentially expressed genes (DEGs) were identified using the Linear Models for Microarray Data R package. Adjusted P values <0.01 and |log2 fold change (FC)| ≥2 was regarded as the filter condition. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of these DEGs were performed on the Database for Annotation, Visualization, and Integrated Discovery (DAVID) online website. Protein-protein interaction (PPI) network was built to visualize the interactions between these DEGs using the STRING online website. Finally, hub genes were identified by The Cancer Genome Atlas (TCGA) database. Results: A total of 604 DEGs consisting of 159 upregulated genes and 445 downregulated genes were selected. From these DEGs, prognostic related genes could serve as potential biomarkers for the molecular diagnosis and therapeutic intervention of HNSC were identified. Including the known genes, GPR18, CNR2, RSPH4A, ULBP2, TEX101, and STC2. And the novel genes, CCR8, CCDC39, CNTN5, MSLN, and CHGB were strongly implicated in HNSC. Conclusions: In summary, we indicated genes associated with prognostic in patients, which improve our understanding of HNSC and could be used as new therapeutic targets for HNSC. Keywords: Bioinformatics analysis; head and neck squamous cell carcinoma (HNSC); microarray data; differentially expressed genes (DEGs); biomarkers Submitted Feb 03, 2020. Accepted for publication Aug 28, 2020. doi: 10.21037/tcr-20-805 View this article at: http://dx.doi.org/10.21037/tcr-20-805 © Translational Cancer Research. All rights reserved. Transl Cancer Res 2020;9(10):5882-5892 | http://dx.doi.org/10.21037/tcr-20-805 Translational Cancer Research, Vol 9, No 10 October 2020 5883 Introduction on the GPL6480 platform (8). GSE53819 contains specimens from 18 patients with HNSC and paired healthy Head and neck squamous cell carcinoma (HNSC) consists of head and neck tissues. All the specimens were collected a group of tumors caused by the squamous epithelium in the before any chemotherapy. oral epithelium, oropharynx, larynx, and hypopharynx (1). HNSC is one of the most common malignancies and the sixth most common in the world (2). The United States Data preprocessing recorded about 50,000 new HNSCC cases and 10,000 We downloaded GSE53819, and then the probe deaths in 2017. Rates are rising about 1 percent a year in identification numbers were converted into Ensembl gene whites, and more than twice as high in men as in women. ID, the ensemble gene ID was converted into gene symbol, Early diagnosis of cancer is one of the most important and then the probe identification numbers correspond factors leading to less widespread and more successful to the gene symbol. For multiple probes identification treatment and better outcomes for patients (3). numbers corresponding to one gene symbol, the significant Tumorigenesis is a complex pathological process that expression value was taken as the gene expression value (9). involves multiple genetic changes, including overexpression of oncogenes and/or inactivation of tumor suppressor genes (4). Despite surgery, radiation, and chemotherapy, about half Identification of DEGs of all patients die from the disease. The risk stratification Bioconductor provides tools for the analysis and of HNSCC depends on the anatomical site, staging, and comprehension of high-throughput genomic data. histological characteristics of the tumor. In addition to the Bioconductor uses the R statistical programming language status of HPV, many molecular and clinical risk factors have and is open source and open development. RStudio is an been studied that limit its clinical usefulness (5). integrated development environment (IDE) for R (10). It In this study, we download the original dataset includes a console, syntax-highlighting editor that supports GSE53819 from Gene Expression Omnibus (GEO) (http:// direct code execution, as well as tools for plotting, history, www.ncbi.nlm.nih.gov/geo/) website, the database is used debugging and workspace management. We downloaded the to archive and store a public database (6). By querying limma package from the Bioconductor and imported it into microarray data, gene expression profiles of HNSC patients Rstudio. We used the Benjamini and Hochberg methods, were compared with normal healthy controls to determine genes with |log2-fold change (FC)| ≥2 and adjusted P values the differentially expressed genes (DEGs). Gene ontology <0.01 were used in the next analysis stage (11). (GO) and pathway enrichment analysis was then performed on the online website DAVID 6.8 (https://david.ncifcrf. gov/). We then constructed a protein-protein interaction GO and pathway enrichment analysis (PPI) network for DEGs and conducted a module analysis DAVID now provides a comprehensive set of functional of this network (7). Finally, we analyzed the survival of the annotation tools for investigators to understand the hub genes. Novel genes related to patient prognosis were biological meaning behind large list of genes. We used screened from hub genes, which had not been previously the DAVID online website to perform GO and Kyoto reported. For example: CCR8, CCDC39, CNTN5, MSLN, Encyclopedia of Genes and Genomes (KEGG) analysis and CHGB. Our study provides new information on the of DEGs. GO analysis includes three major categories: molecular etiology and pathogenesis of HNSC and provides biological process (BP), cellular component (CC), and new potential molecular targets for treatment. molecular function (MF) (12). The KEGG analysis is to find out the signal pathways for these differential expression Methods genes enrichment. P<0.05 as the cutoff criterion was considered statistically significant. Data source GSE53819 is a gene expression profile of HNSC and Integration of PPI network and module analysis belongs to the Agilent-014850 Whole Human Genome Microarray 4x44K G4112F (Probe Name version), which STRING is part of the ELIXIR infrastructure: it is one can be downloaded from the GEO database and executed of ELIXIR’s Core Data (version 11.0; https://string-db. © Translational Cancer Research. All rights reserved. Transl Cancer Res 2020;9(10):5882-5892 | http://dx.doi.org/10.21037/tcr-20-805 5884 Sun and Zhang. Gene signature is associated with prognostic in patients org/). We uploaded the differential expressed genes to the Results STRING website for analysis with a minimum required Identification of DEGs interaction score of 0.7000. We found that 572 genes and 547 edges were present in the PPI network, then we output In this study, we selected a microarray dataset related to the analysis results as a TSV file (13). Cytoscape, a free human HNSC from the GEO database. GSE53819 contains visualization software, is a bioinformatics software platform 18 HNSC tissues and 18 normal tissues. Using both |log2- for integrated models of biomolecular interaction networks fold change (FC)| ≥2 and adjusted P value <0.01 criteria, (version 3.7.2; https://cytoscape.org/). To further analyze a total of 604 genes were identified from GSE53819, the PPI network, we uploaded the TSV file to Cytoscape, including 159 upregulated genes and 445 downregulated and the whole PPI network consisted of 255 genes and 547 genes (Table 1). Whereafter, the volcano map and heatmap edges. Molecular complex detection (MCODE), a plug- were displayed (Figure 1A,B). in of Cytoscape software was used to conducted module analysis of the PPI network. We set K-score =6 and other GO enrichment and pathway analysis parameters were set by default (14). Module genes are considered as hub genes. To further analyze the biological functions of these DEGs, we conducted GO of these DEGs (Table 2). As shown in Figure 2, it respectively demonstrates the top six meaningful Overall survival of