Evidence map›Paper›PMID 41420243›Full record

ArticleGenome medicine2025

Reference-guided computational framework identifies microenvironment metabolic subtypes and targets using pan-cancer single-cell datasets.

Ke Tang, Ya Han, Dongqing Sun, Xin Dong, Tong Han, Hailin Wei, Wenwen Shao, Junjie Hu, Zhaoyang Liu, Lele Zhang and 4 more

Abstract read
In one paragraph

Article in Genome medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

14 authors.

Ke Tang *Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, School of Life Science and Technology, Tongji Hospital, Tongji University, Shanghai, 200092, China.
Ya Han *Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, School of Life Science and Technology, Tongji Hospital, Tongji University, Shanghai, 200092, China.
Dongqing SunKey Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, School of Life Science and Technology, Tongji Hospital, Tongji University, Shanghai, 200092, China.
Xin DongKey Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, School of Life Science and Technology, Tongji Hospital, Tongji University, Shanghai, 200092, China.
Tong HanKey Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, School of Life Science and Technology, Tongji Hospital, Tongji University, Shanghai, 200092, China.
Hailin WeiKey Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, School of Life Science and Technology, Tongji Hospital, Tongji University, Shanghai, 200092, China.
Wenwen ShaoKey Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, School of Life Science and Technology, Tongji Hospital, Tongji University, Shanghai, 200092, China.
Junjie HuDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China.
Zhaoyang LiuKey Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, School of Life Science and Technology, Tongji Hospital, Tongji University, Shanghai, 200092, China.
Lele ZhangCentral Laboratory, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China.
Taiwen LiState Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Research Unit of Oral Carcinogenesis and Management, Chinese Academy of Medical Sciences, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, China.
Peng ZhangDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China.
Qiu WuKey Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, School of Life Science and Technology, Tongji Hospital, Tongji University, Shanghai, 200092, China. qiu_wu@tongji.edu.cn.
Chenfei WangKey Laboratory of Spine and Spinal Cord Injury Repair and Regeneration of Ministry of Education, Department of Orthopedics, School of Life Science and Technology, Tongji Hospital, Tongji University, Shanghai, 200092, China. 08chenfeiwang@tongji.edu.cn.

Funding

National Natural Science Foundation of China 32222026
6 · The paper itself

Abstract

backgroundMetabolic reprogramming is a hallmark of cancer; however, the mechanisms driving metabolic heterogeneity across diverse cell types in the tumor microenvironment remain poorly understood. Most existing methods predict metabolic states at the pathway level but rarely map reaction-level alterations to their upstream regulators, thereby constraining both interpretability and translational relevance.

methodsWe developed MetroSCREEN, a reference-guided computational framework that infers reaction-level metabolic flux propensity and nominates upstream regulators from bulk and single-cell transcriptomes. MetroSCREEN uses a fast enrichment-based procedure to quantify reaction-level metabolic activity. To characterize metabolic regulons, it integrates intrinsic gene-regulatory signals with extrinsic cell-cell interaction cues, then applies a robust multi-evidence ranking scheme to combine these information sources, and finally employs a constraint-based causal discovery module to infer regulatory directionality.

resultsMetroSCREEN accurately predicts reaction-level metabolic activities and their upstream regulators, as demonstrated using paired transcriptomic-metabolomic datasets from the cancer cell lines. We further validated predicted regulators with in-house single-cell CRISPR screens in PC9 cells targeting metabolic regulators. Applying MetroSCREEN to a pan-cancer single-cell atlas of more than 700,000 fibroblasts and myeloid cells across 36 cancer types, we identified ZNF281 and STAT1 as key regulators of collagen metabolism, which is elevated in extracellular-matrix-associated fibroblasts and macrophages at tumor margins. By contrast, APOE and KLF7 regulate sphingolipid metabolism and antigen presentation in macrophages. Leveraging extensive tumor profiles, MetroSCREEN also delineates metabolic subtypes and regulators associated with patient survival and response to immunotherapy.

conclusionsMetroSCREEN is a robust and scalable approach for characterizing metabolic heterogeneity and pinpointing metabolic regulators at single-cell resolution, unveiling novel antitumor targets for future metabolic interventions. The source codes of MetroSCREEN is available at the Github site https://github.com/wanglabtongji/MetroSCREEN .

Indexed as

Computational BiologyNeoplasmsSingle-Cell AnalysisTumor MicroenvironmentCell Line, TumorHumansTranscriptomeImmunotherapy responseLarge-scale data integrationMetabolic regulationSingle-cell transcriptomicsTumor microenvironment

Identifiers

PMID41420243
PMCPMC12717757

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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 Socratic graph.