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. 2020 Jan 31;40(1):BSR20193349.
doi: 10.1042/BSR20193349.

Integrated analysis of DNA methylation and mRNA expression profiles to identify key genes in head and neck squamous cell carcinoma

Yu Jin  1   2 , Xing Qin  3   2
Affiliations

Integrated analysis of DNA methylation and mRNA expression profiles to identify key genes in head and neck squamous cell carcinoma

Yu Jin et al. Biosci Rep. .

Abstract

DNA methylation has been demonstrated to play significant roles in the etiology and pathogenesis of head and neck squamous cell carcinoma (HNSCC). In the present study, methylation microarray dataset (GSE87053) and gene expression microarray dataset (GSE23558) were downloaded from GEO database and analyzed through R language. A total of 255 hypermethylated-downregulated genes and 114 hypomethylated-upregulated genes were finally identified. Functional enrichment analyses were performed and a comprehensive protein-protein interaction (PPI) network was constructed. Subsequently, the top ten hub genes selected by Cytoscape software were subjected to further analyses. It was illustrated that the expression level of CSF2, CTLA4, ETS1, PIK3CD, and CFTR was intimately associated with HNSCC. Survival analysis suggested that CTLA4 and FGFR2 could serve as effective independent prognostic biomarkers for HNSCC patients. Overall, our study lay a groundwork for further investigation into the underlying molecular mechanisms in HNSCC carcinogenesis, providing potential biomarkers and therapeutic targets for HNSCC.

Keywords: DNA methylation; GEO; PPI; biomarker; head and neck squamous cell carcinoma; prognosis.

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Conflict of interest statement

The authors declare that there are no competing interests associated with the manuscript.

Figures

Figure 1
Figure 1. Identification of aberrantly methylated-differentially expressed genes in methylation microarray dataset (GSE87053) and gene expression dataset (GSE23558)
(A) Venn diagram of both hypermethylated and down-regulated genes. (B) Venn diagram of both hypomethylated and up-regulated genes.
Figure 2
Figure 2. GO and KEGG pathway enrichment analysis of the aberrantly methylated-differentially expressed genes
(A) GO analysis of the aberrantly methylated-differentially expressed genes in biological process. (B) GO analysis of the aberrantly methylated-differentially expressed genes in cellular component. (C) GO analysis of the aberrantly methylated-differentially expressed genes in molecular function. (D) KEGG pathway analysis of the aberrantly methylated-differentially expressed genes.
Figure 3
Figure 3. Protein–protein interactions (PPI) network of aberrantly methylated-differentially expressed genes
Each node in the figure represents a protein and the edge between the nodes represents the interaction between the two proteins. The nodes with highest PPI scores were PIK3R1, CFTR, CSF2, GRM1, RUNX2, FGFR2, CTLA4, LYN, ETS1, and PIK3CD.
Figure 4
Figure 4. Gene module analysis and hub gene identification in the PPI network
(A–C) The three most significant modules were screened out by the Molecular Complex Detection (MCODE) plugin in Cytoscape software. (D) The top 10 hub genes with the most connected degrees were identified by the CytoHubba plugin in Cytoscape.
Figure 5
Figure 5. The transcription levels of the hub genes in HNSCC tissues and compared normal tissues were validated in GEPIA database
The threshold was set as P-value = 0.05 and |log2(FC)| = 1. *, P < 0.05.
See this image and copyright information in PMC

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