Exploring nasopharyngeal carcinoma genetics: Bioinformatics insights into pathways and gene associations.

Abstract

Introduction: The pathogenesis of nasopharyngeal carcinoma (NPC) is intricate, influenced by a combination of factors including host genetics, viral infection and environmental elements, resulting in genetic and epigenetic modifications. Despite a positive prognosis for early-stage patients, most NPC cases are diagnosed at an advanced stage, highlighting the pressing need for enhanced accessibility to early diagnosis and treatment. The underlying molecular pathways driving NPC progression remain elusive. This study focuses on the use of bioinformatics techniques and databases in carrying out research to gain insights into gene relevance and potential applications in NPC.

Materials and methods: Searches encompassed articles published in English from January 2017 to June 2024, utilising keywords such as 'nasopharyngeal carcinoma,' 'bioinformatics,' 'gene expression' and 'gene microarrays' across PubMed, MEDLINE and Scopus databases. The Gene Expression Omnibus (GEO) database was utilised to access NPC messenger RNA (mRNA) expression profiling studies.

Results: Most studies utilised the GEO database to identify differentially expressed genes (DEGs) between normal and NPC tissues, followed by functional analysis using gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathways. Protein protein interaction (PPI) networks of DEGs were commonly constructed using STRING and visualised with Cytoscape software. The integration of GO and KEGG pathway analysis alongside PPI network construction provides valuable insights into the dysregulated pathways and molecular mechanisms underlying NPC pathogenesis. Microarray analysis, particularly datasets such as GSE12452, GSE64634 and GSE34573, has enabled the identification of DEGs associated with NPC. PPI network analysis identifies hub genes, such as DNALI1, DNAI2 and RSPH9, implicated in NPC pathogenesis. Validation of gene expression patterns through platforms like GEPIA and Oncomine validates the clinical relevance of identified biomarkers. Furthermore, studies employing RNA sequencing and bioinformatics approaches uncover novel genes involved in NPC radio resistance and prognosis, paving the way for personalised therapeutic strategies.

Conclusion: Integration of bioinformatics analysis provides insights into the complexity of tumour biology and potential molecular pathways, enabling the development of enhanced strategies for early detection, outcome prediction, recurrence detection and therapeutic approaches for NPC.