Evidence map›Paper›PMID 40891683›Full record

ArticleCancer medicine2025

Integrated Single-Cell RNA-Seq Reveals Immunosuppressive Mechanisms of Treg Cell Differentiation and Tumor Microenvironment Interactions in Colorectal Cancer.

Dongwei Li, Jingya Liu, Lixian Yu, Xina Luo, Xiaomei Wu, Ronghui Chen

Abstract read
In one paragraph

Article in Cancer medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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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

5 citing papers in PubMed.

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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

6 authors.

Dongwei LiDongguan Tungwah Hospital, Dongguan, Guangdong Province, China.
Jingya LiuBioMega Medical Technology Co. Ltd., Zhuhai, Guangdong Province, China.
Lixian YuThe First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, Guangdong Province, China.
Xina LuoDepartment of Anesthesiology, Puning People's Hospital, Jieyang, Guangdong Province, China.
Xiaomei WuGuangzhou Huaxia Vocational College, Guangzhou, Guangdong Province, China.
Ronghui ChenDongguan Tungwah Hospital, Dongguan, Guangdong Province, China.ORCID https://orcid.org/0009-0003-0258-7589

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundColorectal cancer (CRC) remains a major challenge in oncology, necessitating the identification of new therapeutic targets. This study aimed to explore the CRC microenvironment and uncover potential targets using single-cell RNA sequencing (scRNA-seq).

methodsSingle-cell RNA sequencing data from GEO (GSE164522, GSE132465, and GSE144735) were integrated and stratified based on CRC clinical stages and tumor grades. Cell clustering analysis identified distinct cellular subsets, while survival and functional enrichment analyses evaluated the prognostic significance and biological activity of differentially expressed genes. Additionally, transcription factor activity and pseudotime trajectory analyses elucidated cellular dynamics during CRC progression. Cell-cell communication analysis identified key ligand-receptor interactions involved in immune regulation. Finally, multiplex immunofluorescence was used to validate the expression of key markers and cells in CRC tissues from patients.

resultsIn the low differentiated colorectal adenocarcinoma (LDCA) grade of CRC, the proportion of T cells significantly increased, accompanied by the enrichment of Treg cells and the upregulation of genes associated with immunosuppression, proliferation, and differentiation. Notably, TGFβ1

conclusionCollectively, TGFβ1

Indexed as

Colorectal NeoplasmsT-Lymphocytes, RegulatoryTumor MicroenvironmentCell CommunicationCell DifferentiationGene Expression Regulation, NeoplasticHumansPrognosisRNA-SeqSingle-Cell AnalysisSingle-Cell Gene Expression Analysiscolorectal cancerimmune suppressionscRNA‐seqTregtumor microenvironment

Identifiers

PMID40891683
PMCPMC12403112

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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.