Evidence map›Paper›PMID 40725477›Full record

ArticleGenes2025

Construction of Gene Regulatory Networks Based on Spatial Multi-Omics Data and Application in Tumor-Boundary Analysis.

Yiwen Du, Kun Xu, Siwen Zhang, Lanming Chen, Zhenhao Liu, Lu Xie

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. 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

6 authors.

Yiwen DuCollege of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China.
Kun XuShanghai-MOST Key Laboratory of Health and Disease Genomics, The Department of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Fudan University, Shanghai 200237, China.
Siwen ZhangShanghai-MOST Key Laboratory of Health and Disease Genomics, The Department of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Fudan University, Shanghai 200237, China.
Lanming ChenKey Laboratory of Quality and Safety Risk Assessment for Aquatic Products on Storage and Preservation (Shanghai), China Ministry of Agriculture, College of Food Science, Shanghai Ocean University, Shanghai 201306, China.ORCID 0000-0002-4393-6250
Zhenhao LiuShanghai-MOST Key Laboratory of Health and Disease Genomics, The Department of Genome and Bioinformatics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Fudan University, Shanghai 200237, China.ORCID 0000-0003-4465-5925
Lu XieCollege of Food Science and Technology, Shanghai Ocean University, Shanghai 201306, China.

Funding

2024 STCSM "Science and Technology Innovation Action Plan" Computational Biology Program 24JS2840300Innovation Promotion Program of NHC and Shanghai Key Labs, STBPT Q2025-02Open Project Fund from Shanghai-MOST Key Laboratory of Health and Disease Genomics KF2025-02
6 · The paper itself

Abstract

BACKGROUND/

objectivesCell-cell communication (CCC) is a critical process within the tumor microenvironment, governing regulatory interactions between cancer cells and other cellular subpopulations. Aiming to improve the accuracy and completeness of intercellular gene-regulatory network inference, we constructed a novel spatial-resolved gene-regulatory network framework (spGRN).

methodsFirstly, the spatial multi-omics data of colorectal cancer (CRC) patients were analyzed. We precisely located the tumor boundaries and then systematically constructed the spGRN framework to study the network regulation. Subsequently, the key signaling molecules obtained by the spGRN were identified and further validated by the spatial-proteomics dataset.

resultsThrough the constructed spatial gene regulatory network, we found that in the communication with malignant cells, the highly expressed ligands

conclusionthe spGRN was proven to be a useful tool to select signal molecules as potential biomarkers or valuable therapeutic targets.

Indexed as

Colorectal NeoplasmsGene Regulatory NetworksBiomarkers, TumorCell CommunicationGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMultiomicsProteomicsSignal TransductionTranscriptomeTumor MicroenvironmentBiomarkers, Tumorsingle-cell transcriptomicsspatial proteomicsspatial-resolved gene regulatory networkspatial transcriptomicstumor boundary

Identifiers

PMID40725477
PMCPMC12295195

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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.