ArticleACS omega2025
AI-Driven Multiscale Study on the Mechanism of Polygonati Rhizoma in Regulating Immune Function in STAD.
Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- Article
- Artificial Intelligence in Traditional Chinese Medicine: Unraveling Herbal Medicine's Mechanisms.Research (Washington, D.C.) · 2026Review
- AI driven network pharmacology: Multi-scale mechanisms of traditional Chinese medicine from molecular to patient analysis.Computational and structural biotechnology journal · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Polygonati Rhizoma, a traditional Chinese medicine, has demonstrated immunomodulatory and anticancer properties, yet its precise mechanisms in stomach adenocarcinoma (STAD) remain underexplored. This study aims to uncover the multitarget mechanisms of Polygonati Rhizoma in regulating the tumor immune microenvironment in STAD using artificial intelligence (AI)-driven network pharmacology, bioinformatics, and single-cell RNA sequencing, offering new insights into its immunotherapeutic potential. This study harnessed the power of AI to unravel the molecular mechanisms underlying Polygonati Rhizoma's effects. AI-driven methodologies screened 38 putative constituents, retaining 8 based on ADME criteria. Machine Learning algorithms predicted potential targets, which were cross-referenced with 5,569 immune-related genes from GeneCards, revealing 52 immune-associated targets. Differential expression analysis of the STAD data set identified 18 overlapping DEGs with prognostic significance and immune cell infiltration correlations. Key targets (AKT1, TP53, PTGS2 and VEGFA) emerged as central nodes in the network, with AI-assisted molecular docking confirming strong binding affinities, particularly between diosgenin and these core proteins. Molecular dynamics simulations further validated these interactions. Single-cell RNA sequencing revealed distinct target-gene expression patterns across malignant, stromal, and immune cell subsets in digestive-system tumors. In vitro, Polygonati Rhizoma extract significantly inhibited HGC-27 cell viability and increased intracellular ROS levels. These findings underscore the critical role of AI in integrating multiscale analyses, unveiling a multitarget immunomodulatory and antitumor mechanism for Polygonati Rhizoma in STAD, and providing a foundation for future preclinical and clinical studies.
Identifiers
What OpenQuestion holds
Registered trials
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.