ArticleTranslational cancer research2024
Identifying key pathogenic mechanisms and potential intervention targets for recurrence after laryngeal cancer treatment through bioinformatics screening.
Article in Translational cancer research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Abstract
Background: Laryngeal cancer (LC), a prevalent malignant tumor of the head and neck, is characterized by a high rate of postoperative recurrence and significant treatment challenges upon recurrence, severely impacting patients' quality of life. There is a pressing need for effective biomarkers in clinical practice to predict the risk of LC recurrence and guide the development of personalized treatment plans. This study uses bioinformatics methods to explore potential biomarkers for LC recurrence, focusing on key genes and exploring their functions and mechanisms of action in LC recurrence. The aim is to provide new perspectives and evidence for clinical diagnosis, prognostic evaluation, and targeted treatment of LC. Methods: Gene expression profiles from the GSE25727 data set in the Gene Expression Omnibus database were analyzed to detect the differentially expressed genes (DEGs) between the tumor tissues of postoperative recurrent and non-recurrent early stage LC patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were also conducted. A protein-protein interaction (PPI) network and transcription factor (TF)-DEG-microRNA (miRNA) network were developed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, with key genes selected using the Molecular Complex Detection (MCODE) plugin. A Gene Set Enrichment Analysis (GSEA) was carried out to investigate the possible mechanisms of the key genes. A retrospective analysis was conducted using the clinical data of 83 LC patients. Immunohistochemical staining was used to examine the transcription level of the key genes in the LC tumor tissues and the factors affecting postoperative recurrence. Results: A total of 248 upregulated and 34 downregulated DEGs were identified in the GSE25727 data set. The PPI network analysis identified a significant module and five candidate genes (i.e., Conclusions: The key genes and signaling pathways identified through the bioinformatics screening provide insights into the potential mechanisms of the pathogenesis of LC.
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