Author

Runzhi Wang

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Open access Jun 2026

Comprehensive multi-omics analysis reveals a fatty acid metabolism gene signature for prognostic assessment and immunotherapy in nasopharyngeal carcinoma, and identifies ABCC1 as a potential novel therapeutic target.

BACKGROUND Nasopharyngeal carcinoma (NPC) is a subtype of head and neck squamous cell carcinoma characterized by high recurrence and metastasis rates and poor prognosis. Although immune checkpoint inhibitors have emerged as a promising treatment strategy for recurrent/metastatic nasopharyngeal carcinoma (R/M NPC), only a few patients have benefitted significantly from them. Lipid metabolism reprogramming plays a crucial role in NPC progression and its interaction with the immune microenvironment. This study aims to establish a prognostic model for NPC based on lipid metabolism-related factors, further explore its association with tumor immunity, and investigate the potential for immunotherapy. METHODS Collect GEO datasets(GSE53819 and GSE102349) for differential expression analysis and least absolute shrinkage and selection operator (LASSO) regression to identify prognostic genes and construct a fatty-acid-metabolism-related prognostic model. Survival analyses and time-dependent receiver operating characteristic (ROC) curves were applied to evaluate the predictive performance of the constructed prognostic markers. Furthermore, associations between the derived risk scores and immunological characteristics were systematically evaluated. Following the identification of ABCC1 as a key gene, its expression was validated through RT-qPCR, immunoblotting, and immunohistochemistry (IHC). Its functional role was further investigated using in vitro functional assays, multiplex immunohistochemistry (mIHC), co-culture experiments with CD8⁺ T cells, and in vivo xenograft tumor models in nude mice. RESULTS Four genes (ABCC1, CD1D, CYP4B1, and DPEP2) were identified to constr uct a prognostic model associated with fatty acid metabolism. This model revealed significant distinctions in immune infiltration patterns between high-risk and low-risk groups. Specifically, the high-risk group displayed immunosuppressive characteristics, marked by reduced infiltration of CD8⁺T cells. Functional studies demonstrated that ABCC1 promoted NPC cell proliferation, migration, invasion, ROS accumulation, and lipid metabolic reprogramming. Mechanistically, ABCC1 was epigenetically upregulated by the histone acetyltransferase P300 and contributed to CD8⁺ T cell dysfunction and MEK/ERK pathway activation, thereby driving tumor progression. CONCLUSION In summary, we established a novel fatty-acid-metabolism-related prognostic model for assessing the prognosis and potential immunotherapy response of NPC patients, as well as for characterizing the immunological features of the tumor microenvironment (TME). Furthermore, ABCC1 emerged as a promising prognostic biomarker associated with immunotherapeutic responsiveness in NPC, warranting further validation. CLINICAL TRIAL NUMBER Not applicable.

Yang Xu, Liru Zhu, Qingqing Zhang et al. · 0 citations
Jul 2026

Guideline-anchored retrieval-augmented generation outperforms baseline and literature-only configurations in gynecologic oncology decision support: A pre-integration benchmark.

INTRODUCTION Large language models (LLMs) are being studied as oncology decision-support tools but can produce inaccurate outputs. We compared LLM performance in gynecologic oncology across three knowledge-integration configurations differing in retrieval strategy and underlying model, using the modified Generative Performance Score (mGPS) as the primary outcome. METHODS Fifty de-identified gynecologic oncology cases were submitted (October-November 2025) to three LLMs: baseline GPT-5, an NCCN-anchored GPT-5 retrieval-augmented generation (RAG) configuration, and OpenEvidence (a literature-anchored clinical AI without NCCN access at that time). Three gynecologic oncologists independently scored outputs using the mGPS (range - 1 to +1; Guideline Concordance plus Hallucination Penalty). Wilcoxon signed-rank tests and mixed-effects ordered logistic regression were used. RESULTS GPT-RAG produced the highest mGPS (0.83, SD 0.26), followed by OpenEvidence (0.70, SD 0.27) and baseline GPT-5 (0.65, SD 0.31). GPT-RAG exceeded baseline (W = 189.5, Z = -3.42, P < .001, r = 0.49) and OpenEvidence (W = 254.0, Z = -2.64, P = .008); OpenEvidence and baseline did not differ (P = .22). Mixed-effects modeling confirmed higher mGPS for GPT-RAG (OR 3.74; 95% CI, 1.57-8.90). Inter-rater agreement (ICC) was 0.49 for mGPS, 0.30 for Hallucination Penalty, and 0.70 for Readability and Rationality. CONCLUSION NCCN-anchored RAG outperformed both baseline GPT-5 and a literature-anchored clinical AI without direct guideline access. OpenEvidence's subsequent NCCN integration (April 27, 2026) provides external validation of guideline anchoring's operational importance. Findings reflect benchmark performance, not clinical safety or improved patient outcomes.

D. Dukes, C. Yost, Runzhi Wang et al. · 0 citations