A study published on March 12, 2025, explores the significant role of NAD(P)H dehydrogenase quinone 1 (NQO1) in hepatocellular carcinoma (HCC) and its potential as both a prognostic biomarker and therapeutic target. HCC, accounting for 75-85% of primary liver cancers and the third leading cause of cancer-related deaths worldwide, presents challenges such as late diagnosis and resistance to treatment options. The researchers utilized multi-omics analysis and machine learning to investigate the expression of NQO1, which was found to be overexpressed in HCC cells, correlationally linked to poor prognosis, and involved in the polarization of tumor-associated macrophages (TAMs) to the M2 subtype.
The study identified core NQO1-related genes (NRGs) and developed the NRGs-related risk scores for patients with hepatocellular carcinoma (NRSHC). By integrating this model with factors like age and pathological tumor-node-metastasis (pTNM) staging, the researchers created a nomogram achieving over 0.7 area under the curve (AUC) for survival outcome predictions, marking its accuracy for guiding clinical decisions.
Notably, the study revealed high-risk patients—those exhibiting marked risk scores—showed worse prognoses but greater sensitivity to immunotherapy, providing insights for personalized treatment strategies.
The research utilized various analytical techniques, including multiplex immunofluorescence, single-cell transcriptome analysis, and spatial quantification to ascertain the levels of NQO1 within tumor tissues. Their findings suggest NQO1's accumulation within HCC cells contributes to the tumor microenvironment's immunosuppressive properties by facilitating M2 macrophage polarization, effectively promoting tumor progression.
Given the increasing complexity of HCC and the limitations of traditional therapies, this study emphasizes the urgent need for integrating novel biomarkers such as NQO1 to facilitate improvements not only in patient stratification for treatment but also for enhancing response rates to immunotherapies, particularly immune checkpoint inhibitors (ICIs).
The authors have also developed the NRSHC TOOL, a web-based application aimed at enhancing the utility of their findings. Clinicians can input patient-specific data, utilizing the available model to predict not only survival probabilities but also potential drug sensitivity, which can decisively improve treatment outcomes.
Through confirming these correlations and embeddings within treatment protocols, the study presents NQO1 as not merely a prognostic marker but as a viable target for therapeutic interventions against HCC. The integration of these tools positions the research within the broader spectrum of precision medicine, ready to tackle one of the most aggressive forms of cancer threatening patients globally.
Through rigorous multi-omics analysis combined with machine learning methodologies, this research opens avenues for future investigation, primarily to validate the role of NQO1 across varied populations and explore the mechanistic details of its influence on TAM polarization and tumor progression.