Evidence map›Paper›PMID 42051493›Full record

ArticleFrontiers in immunology2026

The inhibitory effect of hepatic cancer energy metabolism on immune checkpoint therapy: perspectives from single-cell multi-omics analysis.

Xin Li, Ling Tang, Ju Cao, Yao Liu

Abstract read
In one paragraph

Article in Frontiers in immunology, 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

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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

4 authors.

Xin Li *Department of Laboratory Medicine, First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Ling Tang *Department of Laboratory Medicine, First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Ju CaoDepartment of Laboratory Medicine, First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Yao LiuDepartment of Laboratory Medicine, First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To investigate metabolic reprogramming-especially pyruvate metabolism-in hepatocellular carcinoma (HCC) before and after immune checkpoint inhibitor (ICI) therapy, construct a metabolism-related prognostic model, and evaluate the therapeutic potential of targeting LDHA. Methods: Integrated single-cell RNA-seq data (GEO, Mendeley) were analyzed using Seurat, AUCell, pySCENIC, CellChat, and Monocle. A prognostic model was developed from TCGA data by Cox and Lasso regression. Functional validation included Results: Post-ICI, HCC cells upregulated pyruvate metabolism genes (LDHA, LDHB, LDHD) but showed decreased glycolysis, lactate buildup, reduced acetylation, and suppressed TCA cycle with AMPK activation. Key transcription factors (MYC, SP5, HLF, SREBF1) were identified. CellChat revealed enhanced SPP1-CD44 and APOA1-ABCA1 signaling. Pseudotime analysis indicated terminal hepatocyte differentiation. The pyruvate metabolism-based signature predicted prognosis effectively. Combination therapy with (R)-GNE-140 and PD-1 blockade inhibited Huh7 proliferation, migration, and xenograft tumor growth without liver or renal toxicity. Conclusion: ICI therapy induces metabolic remodeling in HCC, marked by pyruvate metabolism dysregulation and lactate accumulation, contributing to resistance. Dual targeting of LDHA and PD-1 enhances antitumor efficacy, and the identified metabolic signature may serve as a prognostic biomarker and therapeutic target.

Indexed as

Carcinoma, HepatocellularEnergy MetabolismImmune Checkpoint InhibitorsLiver NeoplasmsAnimalsCell Line, TumorGene Expression Regulation, NeoplasticHumansL-Lactate DehydrogenaseMetabolic ReprogrammingMiceMultiomicsPrognosisPyruvic AcidSingle-Cell AnalysisXenograft Model Antitumor AssaysImmune Checkpoint InhibitorsL-Lactate DehydrogenasePyruvic Aciddrug resistanceenergy metabolismhepatocellular carcinomaimmune checkpoint inhibitorsprognosispyruvate metabolismsingle-cell sequencing

Identifiers

PMID42051493
PMCPMC13111318

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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.