Evidence map›Paper›PMID 34405376›Full record

ReviewProtein & cell2022

Cancer biology deciphered by single-cell transcriptomic sequencing.

Yanmeng Li, Jianshi Jin, Fan Bai

Open access · greenAbstract readReview
In one paragraph

Review in Protein & cell, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
2.6field-weighted citation impact, top 9% of its field
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

32 citing papers in PubMed, 30 citations in OpenAlex.

  1. Integrating multi-omics data for next-generation cancer research and precision medicine.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 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

3 authors at 2 institutions in 2 countries.

Yanmeng LiBiomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, 100871, China.ORCID 0000-0002-6201-9327
Jianshi JinRIKEN Center for Biosystems Dynamics Research (BDR), 6-2-3, Furuedai, Suita, Osaka, Japan.ORCID 0000-0003-4697-8760
Fan BaiBiomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, 100871, China. fbai@pku.edu.cn.ORCID 0000-0001-6119-8936
Peking University · CNRIKEN Center for Biosystems Dynamics Research · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tumors are complex ecosystems in which heterogeneous cancer cells interact with their microenvironment composed of diverse immune, endothelial, and stromal cells. Cancer biology had been studied using bulk genomic and gene expression profiling, which however mask the cellular diversity and average the variability among individual molecular programs. Recent advances in single-cell transcriptomic sequencing have enabled a detailed dissection of tumor ecosystems and promoted our understanding of tumorigenesis at single-cell resolution. In the present review, we discuss the main topics of recent cancer studies that have implemented single-cell RNA sequencing (scRNA-seq). To study cancer cells, scRNA-seq has provided novel insights into the cancer stem-cell model, treatment resistance, and cancer metastasis. To study the tumor microenvironment, scRNA-seq has portrayed the diverse cell types and complex cellular states of both immune and non-immune cells interacting with cancer cells, with the promise to discover novel targets for future immunotherapy.

Indexed as

NeoplasmsSingle-Cell AnalysisEcosystemGene Expression ProfilingGenomicsHumansSequence Analysis, RNATranscriptomeTumor Microenvironmentcancersingle-cell transcriptomic sequencingtumor microenvironment

Identifiers

PMID34405376
PMCPMC8901819
OpenAlexW3196209595

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.