Evidence map›Paper›PMID 39393173›Full record

ArticleEBioMedicine2024

Single cell atlas reveals multilayered metabolic heterogeneity across tumour types.

Zhe Zhou, Di Dong, Yuyao Yuan, Juan Luo, Xiao-Ding Liu, Long-Yun Chen, Guangxi Wang, Yuxin Yin

Abstract read
In one paragraph

Article in EBioMedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
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

17 citing papers in PubMed.

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  4. Spatial Glyco-Codes Define Human Liver Pathology and Progression.bioRxiv : the preprint server for biology · 2026
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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

8 authors.

Zhe ZhouInstitute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Peking University Health Science Centre and School of Life Sciences, Peking University, Beijing 100191, China.
Di DongInstitute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Peking University Health Science Centre and School of Life Sciences, Peking University, Beijing 100191, China.
Yuyao YuanInstitute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Peking University Health Science Centre and School of Life Sciences, Peking University, Beijing 100191, China.
Juan LuoInstitute of Precision Medicine, Peking University Shenzhen Hospital, Shenzhen 518036, China.
Xiao-Ding LiuResearch Centre for Molecular Pathology, Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100032, China.
Long-Yun ChenResearch Centre for Molecular Pathology, Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100032, China.
Guangxi WangInstitute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Peking University Health Science Centre and School of Life Sciences, Peking University, Beijing 100191, China.
Yuxin YinInstitute of Systems Biomedicine, Department of Pathology, School of Basic Medical Sciences, Peking University Health Science Centre and School of Life Sciences, Peking University, Beijing 100191, China. Electronic address: yinyuxin@hsc.pku.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMetabolic reprogramming plays a pivotal role in cancer progression, contributing to substantial intratumour heterogeneity and influencing tumour behaviour. However, a systematic characterization of metabolic heterogeneity across multiple cancer types at the single-cell level remains limited.

methodsWe integrated 296 tumour and normal samples spanning six common cancer types to construct a single-cell compendium of metabolic gene expression profiles and identify cell type-specific metabolic properties and reprogramming patterns. A computational approach based on non-negative matrix factorization (NMF) was utilised to identify metabolic meta-programs (MMPs) showing intratumour heterogeneity. In-vitro cell experiments were conducted to confirm the associations between MMPs and chemotherapy resistance, as well as the function of key metabolic regulators. Survival analyses were performed to assess clinical relevance of cellular metabolic properties.

findingsOur analysis revealed shared glycolysis upregulation and divergent regulation of citric acid cycle across different cell types. In malignant cells, we identified a colorectal cancer-specific MMP associated with resistance to the cuproptosis inducer elesclomol, validated through in-vitro cell experiments. Furthermore, our findings enabled the stratification of patients into distinct prognostic subtypes based on metabolic properties of specific cell types, such as myeloid cells.

interpretationThis study presents a nuanced understanding of multilayered metabolic heterogeneity, offering valuable insights into potential personalized therapies targeting tumour metabolism.

fundingNational Key Research and Development Program of China (2021YFA1300601). National Natural Science Foundation of China (key grants 82030081 and 81874235). The Shenzhen High-level Hospital Construction Fund and Shenzhen Basic Research Key Project (JCYJ20220818102811024). The Lam Chung Nin Foundation for Systems Biomedicine.

Indexed as

NeoplasmsSingle-Cell AnalysisBiomarkers, TumorCell Line, TumorComputational BiologyDrug Resistance, NeoplasmGene Expression ProfilingGene Expression Regulation, NeoplasticGlycolysisHumansPrognosisTranscriptomeBiomarkers, TumorCancer metabolismChemotherapy resistanceMetabolic heterogeneityPan-cancer analysisscRNA-seq

Identifiers

PMID39393173
PMCPMC11663789

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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.