A pathology-to-single-cell framework links SPP1+ M2 macrophages with NETs-prone high-risk niches in HCC

生物 计算生物学 推论 巨噬细胞极化 间质细胞 可药性 计算机科学 中性粒细胞胞外陷阱 优先次序 拉布 癌症研究 生态位 生物信息学 转录组 肝细胞癌 免疫疗法 判别式
作者
Rouxin Li,Yuheng Gu,Shi Huang,Shiya Zeng,Xuanchen Zhou,Weiqi Luo,Zhuo Wen,W Chen,Cheng Chen,Lantian Cui,Hongyu Duan,Mengfan Zhao
出处
期刊:Journal of Translational Medicine [BioMed Central]
卷期号:24 (1) 被引量:1
标识
DOI:10.1186/s12967-026-07892-x
摘要

Neutrophil extracellular traps (NETs) facilitate hepatocellular carcinoma (HCC) progression, but the upstream cellular organizers and the histopathological correlates of NETosis-prone niches remain poorly defined. We aimed to build a pathology-to-single-cell framework to (i) quantify NETs-associated risk from routine whole-slide images (WSI) and (ii) nominate candidate organizer cells and mechanisms, focusing on SPP1+ M2 macrophages. We integrated WSI, bulk transcriptomics, single-cell RNA-seq, spatial transcriptomics, and germline association data. NETs activity was quantified using gene-set scoring and aligned with WSI-derived morphology features (e.g. texture, stromal boundary disruption, and tumor–stroma interface complexity) to train and validate a prognostic model using regularized feature selection and survival machine learning. A 26,928-cell single-cell atlas was constructed to annotate macrophage states and neutrophil subsets, followed by trajectory inference to model macrophage polarization dynamics. Cell–cell communication was evaluated by ligand–receptor co-expression networks, and pathway/metabolic programs were inferred using multi-method enrichment and flux estimation. Spatial co-localization and neighborhood effects were assessed by deconvolution and spatial interaction modeling. Finally, network-based target prioritization was performed to highlight druggable nodes and candidate intervention pathways. The WSI-derived NETs risk score stratified overall survival in training and validation cohorts (HR ≈ 8.48 and ≈6.44; AUC > 0.78). Multi-omics integration prioritized SPP1 as a central hub. SPP1+ M2 macrophages and NETs+ neutrophils preferentially localized at the tumor–stroma interface, where inferred communication converged on OPN(SPP1)–CD44/integrins and ICAM1–β2-integrin axes with downstream FAK–PI3K–Akt and NF-κB/MAPK signaling. Metabolic inference suggested a shared hypoxia/glycolysis–lactate–glutathione/antioxidant program in SPP1+ M2 macrophages mirrored by NETs+ neutrophils, consistent with a NETosis-permissive niche. Germline mapping indicated an antagonistic association pattern for the SPP1+ M2 program. Network prioritization highlighted SRC, AKT1, and CEBPB and pathways including ECM–receptor interaction, FAK/PI3K–Akt, MAPK, and TNF/IL-17. Our framework links routine pathology morphology to NETs activity and nominates SPP1+ M2 macrophages as candidate organizers of NETosis-prone high-risk niches in HCC. Importantly, the proposed macrophage–NETs axis is supported by convergent multi-omics/spatial evidence but remains primarily associative and inference-based, not definitive causation. Future work should include functional validation (e.g. macrophage–neutrophil co-culture NETosis assays, SPP1/CD44/integrin or ICAM1–ITGB2 perturbation, and in vivo depletion/blockade studies), prospective multi-center evaluation of the WSI risk score, and testing whether this axis generalizes beyond HCC or is liver-specific. These results provide a quantitative pathology-based risk stratification approach and a set of mechanistically plausible, targetable signaling–metabolic nodes for therapeutic exploration.
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