Evidence map›Paper›PMID 41009970›Full record

ArticleGenes2025

Integrating Transcriptomics, Network Pharmacology, and Machine Learning to Reveal Transglutaminase 2 (TGM2) as a Key Target Mediating Taurocholate Efficacy in Colitis.

Junhong Zhu, Huijin Jia, Lanlan Yi, Guangyao Song, Pengfei Fu, Wenjie Cheng, Yuxiao Xie, Wenzhe Shi, Sumei Zhao

Abstract read
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. Not yet cited in PubMed.

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

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

9 authors.

Junhong ZhuYunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.ORCID 0009-0002-6431-6330
Huijin JiaYunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.
Lanlan YiYunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.
Guangyao SongYunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.
Pengfei FuYunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.
Wenjie ChengYunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.ORCID 0009-0008-9961-821X
Yuxiao XieYunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.
Wenzhe ShiYunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.
Sumei ZhaoYunnan Provincial Key Laboratory of Animal Nutrition and Feed, Faculty of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China.

Funding

Major Science and Technology Project of Yunnan Province 202102AA310054Major Science and Technology Project of Yunnan Province 202202AE090032Science and Technology Plan Project of the Department of Science and Technology of Yunnan Province 202501BD070001-038Scientifc Research Fund of Yunnan Provincial Department of Education 2025J0411State School Cooperation 2020ZXND02Technological Innovation Talent Program 2020FA011the National key research and development project 2024YFD1800404the National Natural Science Foundation of China 31060331the National Natural Science Foundation of China 31260592the National Natural Science Foundation of China 31760645the National Natural Science Foundation of China 32360808
6 · The paper itself

Abstract

backgroundUlcerative colitis (UC) is a chronic inflammatory disease of the colon with a rising global incidence. Natural conjugated taurocholic acid (TCA) possesses anti-inflammatory properties and shows potential therapeutic effects against UC, although the underlying mechanisms remain unclear.

methodsThis study employed an integrative approach-combining network pharmacology, bioinformatics, machine learning, immune infiltration analysis, and molecular docking-to investigate the therapeutic mechanisms of TCA in UC. UC-related gene expression datasets were obtained from the Gene Expression Omnibus (GEO) database, and potential TCA targets were predicted using the Comparative Toxicogenomics Database (CTD) and TargetNet platforms. Differentially expressed genes (DEGs) were identified and analyzed via GO and KEGG enrichment analyses.

resultsFour machine learning algorithms (XGBoost, RF, SVM, and NNet) were used to identify six hub genes (

conclusionsIn conclusion, this study suggests that taurocholate alleviates ulcerative colitis by targeting key genes such as

Indexed as

Colitis, UlcerativeGTP-Binding ProteinsTaurocholic AcidTranscriptomeTransglutaminasesAnimalsComputational BiologyGene Expression ProfilingGene Regulatory NetworksHumansMachine LearningMiceMolecular Docking SimulationNetwork PharmacologyProtein Glutamine gamma Glutamyltransferase 2GTP-Binding ProteinsProtein Glutamine gamma Glutamyltransferase 2Taurocholic AcidTGM2 protein, humanTransglutaminasesmachine learningmolecular dockingnetwork pharmacologytaurocholateulcerative colitis

Identifiers

PMID41009970
PMCPMC12469685

What Socratic holds

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LicenceCC BY
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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 Socratic graph.