Evidence map›Paper›PMID 40768639›Full record

ReviewAging and disease2025

Exploration and Application of Malignant Cell Heterogeneity Analysis with Single-Cell Transcriptome Sequencing Technology.

Wen Zhang, Tianliang Liu, Ting Liu, Yu Zhang, Zhenguo Zhao, Xinxin Zhang, Xiaoyang Li, Jiasheng Zhong, Zhicheng Li, Shifu Chen and 1 more

Abstract readReview
In one paragraph

Review in Aging and disease, 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. Review
  2. Review
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

11 authors.

Wen ZhangDepartment of immunology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Tianliang LiuShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Ting LiuDepartment of Orthopedic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Yu ZhangDepartment of immunology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Zhenguo ZhaoDepartment of Orthopedic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xinxin ZhangDepartment of Orthopedic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xiaoyang LiDepartment of Orthopedic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Jiasheng ZhongShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Zhicheng LiShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Shifu ChenShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Libin XuDepartment of Orthopedic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cancer remains a formidable global health burden owing to its persistently high incidence and mortality, highlighting the need to elucidate its underlying biological mechanisms. Such insights are crucial for refining diagnostic precision and therapeutic efficacy. Notably, human tumors represent highly heterogeneous ecosystems, comprising a dynamic interplay between malignant cells and non-malignant components, such as immune infiltrates and stromal elements. This cellular heterogeneity constitutes a major obstacle in tumor research. Recent technological advancements have enabled the development and application of novel tools for investigating tumor heterogeneity. In this context, single-cell sequencing technologies emerged as transformative tools for dissecting tumor architecture at a cellular resolution, offering unprecedented insights into the cellular diversity and molecular underpinnings of cancer. In particular, single-cell RNA sequencing (scRNA-seq) has been widely used to analyze the tumor microenvironment, particularly its immune components, thereby providing a valuable framework for understanding tumor-immune system interactions. However, the intrinsic heterogeneity of tumor cells has received comparatively less attention, with limited studies focusing on these malignant populations. Comprehensive profiling of tumor cells has become increasingly feasible as scRNA-seq technologies continue to evolve, offering higher throughput, increased cell capture efficiency, and more advanced analytical pipelines. Despite these advancements, current applications of scRNA-seq remain primarily focused on immune and stromal cell populations. Therefore, a paradigm shift is warranted, redirecting the investigative focus toward tumor cells and gaining deeper insights into tumorigenesis and therapeutic vulnerabilities.

Indexed as

NeoplasmsSingle-Cell AnalysisTranscriptomeAnimalsGene Expression ProfilingGenetic HeterogeneityHumansSequence Analysis, RNASingle-Cell Gene Expression AnalysisTumor Microenvironment

Identifiers

PMID40768639
PMCPMC13437099

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.