Evidence map›Paper›PMID 42907887›Full record

ArticleMolecular cancer2026

Single-cell profiling defines cellular ecosystems and malignant cell state coupling across pan-cancer tumor microenvironments.

Xiaoxiao Yang, Yang Tong, Yuting Wang, Peng Xie, Xin Li, Jiapei Yuan, Yang Yang

Abstract read
In one paragraph

Article in Molecular cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

7 authors.

Xiaoxiao YangThe Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, State Key Laboratory of Experimental Hematology, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China.
Yang TongState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, 300020, China.
Yuting WangThe Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, State Key Laboratory of Experimental Hematology, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China.
Peng XieState Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China.
Xin LiDepartment of Geriatrics, The Second Hospital of Tianjin Medical University, Tianjin Interdisciplinary Innovation Centre for Health and Meteorology, Tianjin, 300211, China. lixinsci@126.com.
Jiapei YuanState Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, 300020, China. yuanjiapei@ihcams.ac.cn.
Yang YangThe Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, State Key Laboratory of Experimental Hematology, The Second Hospital of Tianjin Medical University, Tianjin Key Laboratory of Inflammatory Biology, Department of Bioinformatics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China. yy@tmu.edu.cn.ORCID https://orcid.org/0000-0002-0273-9242

Funding

National Natural Science Foundation of China 32200514National Natural Science Foundation of China 32470721the Chinese Academy of Medical Sciences (CAMS) Innovation Fund for Medical Sciences 2023-I2M-3-004Tianjin Natural Science Foundation Project 24JCYBJC00960Tianjin Youth Science and Technology Talent Project QN20230324
6 · The paper itself

Abstract

Tumors are embedded within complex microenvironments composed of malignant, immune and stromal cells, yet how coordinated cellular organization shapes tumor biology across cancers remains incompletely understood. Here we integrate single-cell transcriptomic profiles from over two million cells spanning 19 human epithelial cancers to construct a comprehensive pan-cancer atlas of tumor microenvironment (TME) organization. We identify cell subsets selectively enriched in tumors compared with normal tissues and show that their relative abundance is associated with clinical outcomes. By clustering tumors according to their cellular composition, we define eight recurrent cellular ecosystem subtypes that capture coordinated variation in immune and stromal architectures across cancer types. Comparative analyses reveal that these ecosystem states are associated with distinct tumor-intrinsic molecular programs, including differences in signaling pathway activity, transcriptional regulation and copy number alterations. We further uncover subtype-specific patterns of ligand-receptor interactions linking malignant cells with immune and stromal compartments and provide independent spatial support for these interactions using spatial transcriptomic data. Together, this work provides a unified framework for tumor ecosystem stratification and highlights cellular organization as a key dimension of tumor heterogeneity with potential implications for precision cancer therapy.

Indexed as

NeoplasmsSingle-Cell AnalysisTumor MicroenvironmentBiomarkers, TumorGene Expression ProfilingGene Expression Regulation, NeoplasticHumansSignal TransductionSingle-Cell Gene Expression AnalysisStromal CellsTranscriptomeBiomarkers, TumorCellular ecosystemsPan-cancer atlasPatient stratificationSingle-cell RNA sequencingTumor microenvironment

Identifiers

PMID42907887
PMCPMC13688759

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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.