Neutrophil extracellular trap-related genes in PTCL: identification, prognosis and drug interaction prediction via bioinformatics-machine learning

This study aimed to identify neutrophil extracellular trap-related genes (NET-RGs), explore their prognostic significance, and predict drug interactions in peripheral T-cell lymphoma (PTCL). Differentially expressed NET-RGs (DE-NRGs) between PTCL and normal tissues were screened. Functional enrichment analysis was conducted. Bioinformatics and machine learning were used to identify hub genes and assess their diagnostic value. Univariate and multivariate analyses were used to evaluate prognostic roles. Correlation and immune infiltration analyses were performed to explore relationships with the tumor microenvironment (TME). Clinical data were collected from PTCL patients who received potential agents (lenalidomide) as first-line treatment. A total of 31 DE-NRGs were identified (18 upregulated and 13 downregulated), enriched in inflammatory response, extracellular matrix organization, and infection involvement. Four hub genes (AKT2, MAPK14, IRF1, and TNF) were identified as effective PTCL diagnostic markers. Higher AKT2/MAPK14 expression correlated with poorer overall survival (OS), while elevated TNF expression associated with better OS; AKT2 and TNF independently predicted OS. These genes were implicated in modulating TME remodeling. Potential therapeutic agents (e.g. capivasertib, lenalidomide) were predicted, and lenalidomide may represent a feasible initial treatment option for PTCL, with an objective response rate (ORR) of 40.0% and a maximum survival duration exceeding 50 months. NET-RGs play crucial roles in diagnosis, prognosis, and TME regulation, and lenalidomide, a putative TNF-targeting agent, may represent a feasible initial treatment option in PTCL.