DNASE1L3 (Deoxyribonuclease 1 Like 3)

Novel knowledge uncovered by this study promises to revolutionize liquid biopsy technology and lead to potential new drugs targeting certain diseases. Given that millions of cfDNA whole genome sequencing data have been generated from clinical testing, the potential of this paradigm is enormous.

Drug-gene interaction mining mapped 78 target proteins to clinically relevant compounds, including tolrestat, alcuronium, metyrosine, and 4-phenylbutyric acid...Physicochemical and pharmacokinetic profiling further prioritised tolrestat as a computationally favourable candidate (MW = 357.35, LogP = 3.64, TPSA = 81.86 Ų), exhibiting acceptable drug-likeness, high predicted gastrointestinal absorption, and low synthetic complexity (SA = 2.34), in contrast to alcuronium (MW = 666.89, SA = 7.86), which showed multiple rule violations. Collectively, this in silico study proposes a robust diagnostic gene signature for HCC and identifies tolrestat as a promising repurposing candidate that warrants experimental validation, demonstrating the utility of integrating machine learning, network biology, and molecular simulation in translational cancer research.

Our study characterizes the molecular and clinical distinctions between ecDNA-negative and ecDNA-positive HCC and establishes a clinically applicable gene signature for patient prognosis. These findings advance our understanding of ecDNA-driven tumor heterogeneity and provide potential strategies for personalized HCC management.

This study identified NET-related key biomarkers (ECM1, DNASE1L3, JUN) as reliable diagnostic tools for HCC, highlighting their diagnostic and therapeutic potential, and providing insights for HCC diagnostic tools and immunotherapy strategies.

Our results identified DNASE1L3 as a promising tissue biomarker. Loss of DNASE1L3 is indicative of advanced and aggressive LIHC, and therefore its expression may offer complementary information to current staging systems to improve prognostic assessment.

This study establishes the first migrasome-based prognostic model for LUAD, demonstrating both independent survival prediction capability and clinical utility for identifying immunotherapy beneficiaries. The MIGsig signature provides novel biological insights into migrasome-mediated tumor-immune interactions and represents a promising tool for precision oncology applications in LUAD management.

Crucially, NLRP3 agonism partially reversed DNASE1L3-deletion-induced suppression of the NLRP3-GSDMD axis and restored M1 polarization. Our findings reveal DNASE1L3 as a pivotal regulator of TAM phenotype via the NF-κB/NLRP3-GSDMD axis and highlight its potential for immunotherapy targeting macrophage reprogramming in HCC.

Our prognosis-related risk model based on DEGs between RCC and LCC may provide better prediction of clinical outcomes for patients with colon cancer.

Moreover, there was a significant correlation between risk groups and immunity. Our study established a sorafenib response-related prognostic risk prediction model in HCC based on five signature genes (DNASE1L3, ACSL6, ACAN, BRSK1 and CD68), which had high predictive precision on sorafenib response.

We validated this TLS score across multiple independent HCC cohorts, including patients receiving transarterial chemoembolization (TACE), programmed cell death protein-1 (PD-1)/ligand 1 (PD-L1) inhibitors, or lenvatinib...Our findings suggest a potential TLS-based biomarker for HCC prognosis and therapeutic response. This work offers preliminary insights into tumor immune microenvironment (TIME) heterogeneity, which may be modulated by the Treg/Tex balance, and proposes a possible tool for improving patient stratification.

This study establishes a crotonylation-based prognostic model that effectively stratifies ccRCC risk and elucidates key mechanisms linking crotonylation heterogeneity to immune evasion, mutational burden, and metabolic reprogramming. The model offers clinical utility for personalized therapy selection.

DNASE1L3 is a potential therapeutic target in MASLD-driven HCC, regulating malignant progression through dual modulation of proliferation and lipid metabolism. These findings provide mechanistic insights for future interventions.