Evidence map›Paper›PMID 41544047›Full record

ArticlePloS one2026

Machine learning and network pharmacology identify keloid biomarkers (AMPH, TNFRSF9) and therapeutic targets (IL6, HAS2) for aloe-derived quercetin.

Congli Jia, Fu Yang, Yingchun Li

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In one paragraph

Article in PloS one, 2026. 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

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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

2 citing papers in PubMed.

  1. Article
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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.

Congli JiaDepartment of Plastic Surgery, Plastic Surgery Hospital of Shandong Second Medical University, Weifang, Shandong, China.ORCID https://orcid.org/0000-0002-7090-6364
Fu YangDepartment of Hepatobiliary Surgery, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China.
Yingchun LiWeifang Traditional Chinese Medicine Industry Development Promotion Association, Weifang, Shandong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study aimed to identify diagnostic biomarkers for keloid and explore potential therapeutic agents from traditional Chinese medicine (TCM) by integrating network pharmacology approaches. Specifically, we sought to uncover key molecular targets for Aloe vera and validate their roles in keloid pathogenesis.

methodsWe integrated keloid transcriptome datasets (GSE218007 and GSE237752) by merging GEO data, and identifying differentially expressed genes (DEGs). Functional enrichment analysis (GO, GSEA) and machine learning approaches were applied to select diagnostic biomarkers. Candidate genes were validated via Receiver Operating Characteristic (ROC) curves in training and independent cohorts (GSE44270). PPI networks and Cytohubba algorithms identified hub genes, while TCMSP-screened compounds from Aloe vera were docked with targets using molecular docking.

results91 Identified DEGs enriched in fibrosis-related pathways. Machine learning prioritized two diagnostic biomarkers: AMPH and TNFRSF9 (AUC > 0.85 in training/testing). PPI analysis revealed IL6 as a hub gene. Aloe vera-derived quercetin targeted HAS2 and IL6 (both P < 0.05 in validation), with molecular docking confirming stable binding (binding energy <-7 kcal/mol). IL6 emerged as both a key network hub and a therapeutic target, linking keloid and TCM mechanisms.

conclusionAMPH and TNFRSF9 are promising diagnostic biomarkers for keloid, while quercetin from Aloe vera targets HAS2 and IL6, offering therapeutic potential. The dual role of IL6 underscores its centrality in keloid pathogenesis, connecting bioinformatics predictions with TCM pharmacology. This study provides a foundation for clinical prediction and targeted treatment strategies.

Indexed as

AloeInterleukin-6KeloidMachine LearningNetwork PharmacologyQuercetinBiomarkersGene Expression ProfilingHumansMedicine, Chinese TraditionalMolecular Docking SimulationBiomarkersIL6 protein, humanInterleukin-6Quercetin

Identifiers

PMID41544047
PMCPMC12810848

What Socratic holds

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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 Socratic graph.