Profiling of human genes afflicted with nasopharyngeal carcinoma using microarray data

被引:0
作者
Raj, Rupam [1 ]
Subhashini [2 ]
Patel, Kamalesh Kumar [3 ]
Kumar, Mukesh [4 ]
机构
[1] Banaras Hindu Univ, Dept Bioinformat, MMV, Varanasi, India
[2] Banaras Hindu Univ, Dept Zool, MMV, Varanasi, India
[3] All India Inst Med Sci, Clin Res Unit, New Delhi, India
[4] Banaras Hindu Univ, Dept Stat, MMV, Varanasi 221005, India
来源
HUMAN GENE | 2025年 / 43卷
关键词
Nasopharyngeal carcinoma (NPC); Differentially expressed genes (DEGs); Bioinformatics analysis; Gene ontology (GO); Kyoto Encyclopedia of Genes and Genomes; (KEGG) pathway; CELLS;
D O I
10.1016/j.humgen.2025.201376
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Background Nasopharyngeal carcinoma (NPC) is the most prevalent malignant carcinoma, and yet the biological mechanisms behind its pathogenesis are still unknown. Objective The objective of the research work was to apply bioinformatics tools to determine the essential expressed genes linked to NPC pathogenesis. Material and methods We retrieved three datasets (GSE12452, GSE13597, and GSE64634), from the Gene Expression Omnibus (GEO) portal. Differentially expressed genes (DEGs) determined between two groups called normal and NPC tissues. Gene ontology enrichment analysis (GO) performed through the online tool DAVID, and Kyoto Encyclopedia of Genes and Genomes (KEGG) online database used to identify pathways and progressions in which DEGs are highly involved in disease progression. Results We identified 77 commonly upregulated, 62 common downregulated in total 140 common DEGs in 3 datasets. The key cancer-causing pathways found in our study were mostly regulating cell adhesion molecules, Akt signalling pathway, cell cycle, cytochrome P450 and one carbon pool by folate. The interaction is shown between these DEGs through a protein protein interaction (PPI) network using STRING software and try to understand the effect these genes have on each other and noticed the most influential genes by studying their topological connectivity. The most influential genes, hub genes were identified by creating modules upon analysis of these modules. Conclusions We got 4 hub genes namely Aurora A (AURKA), Breast cancer susceptibility gene 1 (BRCA1), Fanconi anaemia group I protein (FANCI), and Abnormal spindle microtubule assembly (ASPM). For validation, we performed a survival analysis using GEPIA against the TCGA database, all four hub genes were upregulated in carcinoma cases compared to normal cases. These four biomarkers found can be used as potential therapeutic targets and as molecular signatures for early detection of NPC.
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