{"title":"Profiling of human genes afflicted with nasopharyngeal carcinoma using microarray data","authors":"Rupam Raj , Subhashini , Kamalesh Kumar Patel , Mukesh Kumar","doi":"10.1016/j.humgen.2025.201376","DOIUrl":null,"url":null,"abstract":"<div><div>Background</div><div>Nasopharyngeal carcinoma (NPC) is the most prevalent malignant carcinoma, and yet the biological mechanisms behind its pathogenesis are still unknown.</div><div>Objective</div><div>The objective of the research work was to apply bioinformatics tools to determine the essential expressed genes linked to NPC pathogenesis.</div><div>Material and methods</div><div>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.</div><div>Results</div><div>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.</div><div>Conclusions</div><div>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.</div></div>","PeriodicalId":29686,"journal":{"name":"Human Gene","volume":"43 ","pages":"Article 201376"},"PeriodicalIF":0.5000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human Gene","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2773044125000026","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0
Abstract
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.