ArticleJournal, genetic engineering & biotechnology2026
Explainable machine learning and multimodal single cell analysis link NR4A2 to CD8
Article in Journal, genetic engineering & biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
backgroundTerminal exhaustion of CD8
methodsWe reanalysed publicly available human HCC datasets spanning four modalities: single-cell RNA sequencing (scRNA-seq; GSE149614, GSE98638, GSE151530, GSE125449), single-cell ATAC sequencing (scATAC-seq; GSE227265), imaging-based spatial transcriptomics (10× Xenium human liver-cancer section) and bulk RNA sequencing with overall-survival annotation (TCGA-LIHC). NR4A2 was profiled across cell compartments and, at higher resolution, across CD8
resultsNR4A2 was broadly expressed across HCC compartments and was highest in myeloid cells rather than being T-cell restricted; at whole-compartment and bulk-tumour resolution it was not a clean exhaustion marker. At the resolution of CD8
conclusionsA NR4A2 co-expression regulon was most active in exhausted cells; explainable modelling linked NR4A2-high status to an immediate-early/AP-1 activation programme; the programme marked immune-hot tumours; and although bulk NR4A2 was non-prognostic, a CD8-infiltration-adjusted exhaustion score was independently associated with worse overall survival (HR 1.42 per SD, p < 0.001). Across four independent data modalities, NR4A2 behaves as a resolution-dependent marker of the terminal-exhaustion programme in HCC CD8
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