Commentary: Tumor associated neutrophils promote prostate cancer progression by mediating neutrophil trap secretion through PSMA1- NF-κB-HIF-1α signaling axis

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What role do neutrophils play in the tumor microenvironment? A recent study by Dai and colleagues offers a compelling answer (1). This is the first study to systematically elucidate the molecular mechanism by which tumor-associated neutrophils (TANs) drive neutrophil extracellular trap (NET) formation through the PSMA1-NF-κB-HIF-1α signaling axis, thereby promoting malignant progression in prostate cancer (PCa). In parallel, the authors constructed a 6-gene prognostic model based on NET-related genes, providing a novel tool for risk stratification and individualized therapy in PCa. Integrating retrospective clinical analysis, multi-database mining, machine learning modeling, singlecell sequencing, and organoid-based functional validation, this study establishes a complete and rigorous chain of evidence. We hold great admiration for the academic contributions of the research team. At the same time, we offer several constructive suggestions regarding aspects that merit further exploration, hoping to provide reference for future investigations.NET-related gene prognostic models have been reported in various solid tumors, including breast cancer, lung cancer, and gastric cancer (2)(3)(4)(5). However, the prognostic value of NETs in prostate cancer has lacked systematic investigation. The present study fills this gap. By intersecting 16,045 differentially expressed genes with NET-related gene sets, the authors identified 314 NET-DEGs, and further screened six key genes (PLCG1, PLCB2, AGER, ALDOA, FCGR2B, and PSMA1) through univariate Cox regression and LASSO regression to construct a risk model. This model achieved AUCs of 0.78, 0.80, and 0.84 for predicting 1-, 3-, and 5-year biochemical recurrence in the training set, with robust performance maintained in the validation cohort. Notably, these findings corroborate those of Zheng et al. (6), who similarly demonstrated that NET-related molecular signatures effectively distinguish prognostic differences in prostate cancer patients. Building upon this foundation, the present study further reinforces model reliability through multi-dimensional validation, including independent cohorts, singlecell localization, and organoid-based functional experiments.Single-cell sequencing analysis represents a major highlight of this study. Through single-cell transcriptomic analysis of 32 samples (14 normal and 18 tumor), the authors identified 10 neutrophil subclusters, four of which were classified as TANs. Critically, the six key genes in the risk model were precisely localized to two TAN subclusters (Neu_c09_HLA and Neu_c07_KLK3), among which PSMA1 exhibited the highest expression in Neu_c09_HLA-a subcluster enriched for HIF-1α and NF-κB signaling pathways. This finding provides a spatial dimension for understanding the function of NET-related genes within specific cellular subsets, and identifies cellular-level intervention nodes for subsequent targeted therapy. This "risk gene → cell subcluster → signaling pathway" stepwise localization strategy carries significant methodological implications. The present study demonstrates that PCa cells induce PSMA1 upregulation in neutrophils; however, the specific molecular drivers remain to be elucidated. Which factors in the tumor microenvironment mediate this effect? Candidate molecules include tumor-secreted IL-8-which, as demonstrated by Luo and Chen ( 9), can be upregulated by NETs themselves, forming a positive feedback loop-as well as GM-CSF, TGF-β, and metabolic products such as lactate. Furthermore, does PSMA1 upregulation occur through transcriptional activation, protein stabilization, or other mechanisms? To address these questions, we propose that future studies systematically screen for key factors in PCa-conditioned medium, employ neutralizing antibodies or genetic knockdown approaches to validate their contribution to PSMA1 upregulation, and integrate transcriptomic and proteomic analyses to dissect the underlying molecular mechanisms.As a catalytic subunit of the 20S proteasome core particle, PSMA1 is theoretically involved in global protein degradation (10). However, the present study reveals its specific regulation of NET formation through the NF-κB-HIF-1α axis, and the mechanistic basis of this pathway specificity warrants in-depth investigation, which may inform future therapeutic development. Does PSMA1 activate NF-κB through degradation of specific inhibitory factors, such as IκBα? This question could be addressed in future studies by employing proteomics to identify the substrate repertoire regulated by PSMA1. Whether HIF-1α activation represents a downstream event of NF-κB signaling or constitutes an independent pathway could be addressed through ChIP-seq or CUT&Tag analysis to map the genome-wide binding sites of NF-κB and HIF-1α. Furthermore, to elucidate how the core effector molecules of NET formation, such as MPO and CitH3, are regulated at the transcriptional or post-transcriptional level, specific inhibitors could be employed to achieve stepwise blockade of pathway nodes, thereby dissecting the hierarchical regulatory relationships within the signaling cascade. To facilitate such mechanistic investigations, we have compiled a panel of pharmacological agents targeting key nodes in this signaling axis (Table 1). Systematic application of these inhibitors across distinct prostate cancer models would not only help establish the hierarchical architecture linking PSMA1, NF-κB, HIF-1α, and NET formation, but may also identify the most therapeutically accessible intervention points within this regulatory network.Neutrophils exhibit high functional plasticity in tumors, capable of assuming either an N1 anti-tumor phenotype or an N2 pro-tumor phenotype. The present study predominantly supports the pro-tumor role of NETs; however, whether NETs exert antitumor effects under specific contexts remains incompletely elucidated. Previous studies have demonstrated that neutrophil elastase (NE) can degrade the tumor stroma to inhibit metastasis (11), whereas the present study suggests that NE may promote progression through NETs. Furthermore, Harris et al. (8) showed that CAR-engineered neutrophils can directly kill tumor cells. This functional heterogeneity implies that simply categorizing "NETs" as pro-tumor factors may be overly reductive. In future investigations, we propose that NETs be classified into functional subtypes based on their compositional features (protein profiles, nucleic acid structures), inducing stimuli (hypoxia, cytokines, tumor contact), and microenvironmental context (inflammatory status, immune cell composition), in order to elucidate the differential impacts of distinct NET subtypes on tumor progression and immunotherapy response, thereby providing a deeper understanding of these complex biological processes.Most bioinformatics-based prognostic model studies stop at model construction and immune infiltration analysis. The distinctive value of this study lies in its completion of the closed loop from model to mechanism-not only statistically validating the model's efficacy (AUC, independent prognostic factors, nomogram), but also elucidating the functional mechanism of PSMA1, the core gene in the model, through single-cell localization and organoid-based experiments. This "bioinformatics-driven hypothesis → experimental mechanism validation" reverse biology paradigm parallels the approach of Jia et al. (12) in their PCP4 study-both begin with big data screening and ultimately return to molecular mechanisms and functional validation in animal and organoid models.The advantage of this paradigm is its efficiency and clear targeting; however, a potential risk is the possible neglect of molecules that, while not reaching statistical significance in the initial screening, may hold substantial biological importance-a caveat that warrants particular attention.The pro-tumor mechanism of NETs revealed in this study engages in an interesting dialogue

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.