ArticleDiscover oncology2025
Evaluation of the molecular mechanisms of histological transformation in follicular lymphoma via bioinformatics analysis.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveTo explore the mechanism of histological transformation in follicular lymphoma (FL) at the molecular level and the potential molecular targets for the diagnosis, treatment, and prognosis of transformed follicular lymphoma (tFL).
methodsThe Gene Expression Omnibus database was searched using the following keywords: "follicular lymphoma" and "histological transformation." Then, data sets with normal controls were selected for differential expression analysis using R software with limma (biocLite "limma"). The microarray datasets were consolidated, and differential gene(DEGs) were acquired and further analyzed using bioinformatics techniques. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed using R software (version 3.6.1), respectively. The protein-protein interaction networks of DEGs were developed using data from the STRING database. Finally, CIBERSORT was used to evaluate the immune cell infiltration in tFL.
resultsGSE86613 and GSE81184 were retrieved from 44 tissue samples collected from patients with tFL. Next, seven gene expression profiles were retrieved from tissue samples (control) collected from patients with FL. Differential analysis yielded 237 differently expressed genes, including 81 significantly upregulated genes and 156 significantly downregulated genes. The GO and KEGG pathway analyses revealed that DEGs focused primarily on cytokine receptor activity and relevant signaling pathways. The 30 most firmly related genes among the DEGs were identified from the protein-protein interaction network. There was a significant difference in terms of immune infiltration between the two tissue samples.
conclusionThe screening of DEGs, pathways, and immune infiltration via integrated bioinformatics analyses could facilitate the comprehension of molecular mechanisms involved in tFL development. In addition, our study provided valuable data on DEGs, pathways, and immune infiltration in tFL and novel insights into the underlying molecular mechanisms.
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