Evidence map›Paper›PMID 42794453›Full record

ArticleInternational journal of molecular sciences2026

Explainable Artificial Intelligence and Gene-Network Analysis Prioritise a TREM2/LGALS3/GPNMB Scar-Associated Macrophage Programme in Human Liver Cirrhosis.

Shah Faisal, Muhammad Adeel Ejaz, Piniel Alphayo Kambey, Abdul Malik, Yin-Xiong Li

Abstract read
In one paragraph

Article in International journal of molecular sciences, 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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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Shah FaisalCenter for Cell Lineage Technology and Engineering, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.ORCID 0000-0001-5474-2622
Muhammad Adeel EjazCenter for Cell Lineage Technology and Engineering, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.ORCID 0009-0006-1673-1272
Piniel Alphayo KambeyCenter for Cell Lineage Technology and Engineering, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.
Abdul MalikCenter for Cell Lineage Technology and Engineering, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.ORCID 0009-0002-5637-1241
Yin-Xiong LiCenter for Cell Lineage Technology and Engineering, Guangdong Provincial Key Laboratory of Biocomputing, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Macrophages organise the fibrotic niche of liver cirrhosis. The scar-associated macrophage state is well described, but the genes that define it have rarely been ranked in an unbiased, model-based way, and the statistical limits of such re-analyses are seldom reported. Here we set out to re-derive the scar-associated macrophage programme without supervision, to rank its constituent genes by convergent computational evidence, and to state explicitly what a cohort of this size can and cannot support. We re-analysed 58,358 single-cell transcriptomes from five uninjured and five cirrhotic human livers (GEO GSE136103; Edinburgh DataShare DS_10283_3433). After quality control, Harmony batch integration and Leiden clustering, the mononuclear-phagocyte compartment (9869 cells) was re-clustered. We applied donor-level compositional testing stratified within the CD45+/CD45- sorted fractions, donor-level pseudobulk differential expression, three classifiers with SHapley Additive exPlanations (SHAP) under donor-grouped cross-validation, transcription-factor differential expression, diffusion pseudotime, ligand-receptor modelling and CellOracle in silico perturbation. The donor, never the cell, was the unit of analysis for every condition comparison. A discrete macrophage cluster (390 cells; 60.5% cirrhotic; contributed by all 10 donors) carried a coherent lipid/scar programme comprising LGALS3, GPNMB, TREM2, CD9, FABP5, APOE, APOC1 and CTSD, and was recovered in all 22 clustering configurations tested. The integrated prioritisation was robust: seven genes remained in the top 20 in 100% of 1000 Dirichlet re-weightings. MAFB, MITF and TFEC were significantly enriched transcription factors, and pseudotime placed the state downstream of a monocyte root. Three findings temper these results. No lineage showed a significant compositional change after multiple-testing correction, and every test was underpowered for its observed effect. No gene reached significance in donor-level pseudobulk differential expression. Classifier performance of 0.99 reflects the circularity of predicting a cluster defined from the same matrix; the non-circular contrast of cirrhotic versus uninjured cells gave 0.61-0.65. Overall, a documented, fully reproducible framework re-derives and ranks a TREM2/LGALS3/GPNMB scar-associated macrophage programme and nominates galectin-3, GPNMB and the SPP1 axis as prioritised, clinically unproven candidates. The prioritisation is a hypothesis requiring independent, spatial, protein-level and functional validation.

Indexed as

Artificial IntelligenceCicatrixGalectin 3Gene Regulatory NetworksLiver CirrhosisMacrophagesMembrane GlycoproteinsReceptors, ImmunologicGene Expression ProfilingHumansTranscriptomeGalectin 3Membrane GlycoproteinsReceptors, ImmunologicTREM2 protein, humanexplainable artificial intelligencegalectin-3gene regulatory network inferencein silico perturbationosteopontinSHapley Additive exPlanationssingle-cell transcriptomicstarget prioritisation

Identifiers

PMID42794453
PMCPMC13606852

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.