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. 2021 Sep 17;100(37):e27257.
doi: 10.1097/MD.0000000000027257.

Identifying key genes and small molecule compounds for nasopharyngeal carcinoma by various bioinformatic analysis

Lucheng Fang  1 , Licai Shi  1 , Wen Wang  1 , Qinjuan Chen  2 , Xingwang Rao  1
Affiliations

Identifying key genes and small molecule compounds for nasopharyngeal carcinoma by various bioinformatic analysis

Lucheng Fang et al. Medicine (Baltimore). .

Abstract

Nasopharyngeal carcinoma (NPC) is one of the most prevalent head and neck cancer in southeast Asia. It is necessary to proceed further studies on the mechanism of occurrence and development of NPC.In this study, we employed the microarray dataset GSE12452 and GSE53819 including 28 normal samples and 49 nasopharyngeal carcinoma samples downloaded from the Gene Expression Omnibus(GEO) to analysis. R software, STRING, CMap, and various databases were used to screen differentially expressed genes (DEGs), construct the protein-protein interaction (PPI) network, and proceed small molecule compounds analysis, among others.Totally, 424 DEGs were selected from the dataset. DEGs were mainly enriched in extracellular matrix organization, cilium organization, PI3K-Akt signaling pathway, collagen-containing extracellular matrix, and extracellular matrix-receptor interaction, among others. Top 10 upregulated and top 10 downregulated hub genes were identified as hub DEGs. Piperlongumine, apigenin, menadione, 1,4-chrysenequinone, and chrysin were identified as potential drugs to prevent and treat NPC. Besides, the effect of genes CDK1, CDC45, RSPH4A, and ZMYND10 on survival of NPC was validated in GEPIA database.The data revealed novel aberrantly expressed genes and pathways in NPC by bioinformatics analysis, potentially providing novel insights for the molecular mechanisms governing NPC progression. Although further studies needed, the results demonstrated that the expression levels of CDK1, CDC45, RSPH4A, and ZMYND10 probably affected survival of NPC patients.

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Conflict of interest statement

The authors report no conflicts of interest.

Figures

Figure 1
Figure 1
Flowchart of the study procedures.
Figure 2
Figure 2
(A, B) Volcano plots of DEGs in GSE12452 and GSE53819. Black dots represent genes equally represented between NPC and normal samples. Red dots represent upregulated and downregulated DEGs. Volcano plots showing all DEGs. |log2FC| ≥ 1 and P < .05 were set as cut-off criteria. (C) High expression DEGs. (D) Low expression DEGs. DEGs = differentially expressed genes.
Figure 3
Figure 3
The gene ontology annotation and pathway enrichment analysis. The high count means that the number of genes were found in the particular ontology or pathway. (A, C, E) GO of upregulated DEGs. (B, D, F) GO of downregulated DEGs. (G) KEGG of DEGs, orange represented upregulated DEGs and blue represented downregulated DEGs. DEG = differentially expressed genes, GO = Gene Ontology, KEGG = Kyoto Encyclopedia of Genes and Genomes.
Figure 4
Figure 4
(A) The most significant module of high-expression genes. (B) The most significant module of low-expression genes.
Figure 5
Figure 5
(A) The top 10 upregulation hub genes. (B) The top 10 downregulation hub genes.
Figure 6
Figure 6
Prognostic value of genes detected in GEPIA database. The survival curve comparing the patients with high (red) and low (blue) expression in NPC. (A) RSPH4A, (B) ZMYD10, (C) CDK1, (D) CDC45.
Figure 7
Figure 7
The 2D structures of the 5 compounds. (A) piperlongumine, (B) apigenin, (C) menadione, (D) 1,4-chrysenequinone, (E) chrysin.
See this image and copyright information in PMC

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