Background <p>Nasopharyngeal carcinoma (NPC) continues to represent a significant therapeutic challenge. Despite the substantial success of radiotherapy, chemotherapy, and immunotherapy, their effectiveness remains limited in recurrent or advanced NPC. Hence, the urgent identification of new molecular targets and effective therapeutic agents is critically required.</p> Methods <p>Bulk and single-cell transcriptomic data were analyzed using bioinformatics approaches, and machine learning algorithms were employed to screen for hub genes in NPC. Functional validation of hub genes was performed through loss-of-function assays. Potential therapeutic agents were identified through molecular docking and subsequently evaluated for anti-NPC efficacy using both in vitro NPC cell line models and in vivo patient-derived xenograft (PDX) models.</p> Results <p>Bioinformatics analyses revealed <i>DTL</i> as a central gene with critical diagnostic and prognostic significance. Knockdown of <i>DTL</i> led to cell cycle arrest and apoptosis through stabilization of p21 and p53. Pevonedistat markedly inhibited DTL activity, recapitulating the effects of <i>DTL</i> knockdown. In vitro studies showed that Pevonedistat suppressed NPC cell proliferation, while in PDX models it significantly reduced tumor burden. Collectively, the data establish <i>DTL</i> as a key oncogenic driver in NPC and highlight Pevonedistat as a promising therapeutic candidate.</p> Conclusion <p>Our work presents an integrated framework for target identification and therapeutic development in NPC. These findings deepen our understanding of NPC biology and highlight that Pevonedistat suppresses cell proliferation and tumor growth of NPC via the CRL4-DTL-p21/p53 axis.</p> Graphical Abstract <p></p>