ArticleMedicine2026
Potential mechanisms of Si-Wu-Tang against esophageal squamous cell carcinoma: A machine learning pharmacological study.
Article in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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Authors and funding
7 authors.
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Abstract
The purpose of this study is to explore the potential mechanism of Si-Wu-Tang (SWT) against esophageal squamous cell carcinoma (ESCC). Initially, 18 active molecules and 96 related targets of SWT obtained from publicly accessible databases. Through Genecards database queries and gene differential expression analysis combined with weighted gene correlation network analysis (WGCNA) on the GSE20347 dataset of ESCC, 3649 disease targets were identified. A subsequent analysis of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment was performed on 51 disease-drug intersection genes using the R language. Additionally, we identified 3 target hub genes (CDK1, NCOA1, and CHRM3) utilizing machine learning tools. Single-gene GSEA results suggested that hub genes may influence several signaling pathways and biological processes. Immune infiltration analysis demonstrated that SWT might impact the tumor immune microenvironment in ESCC by acting on hub targets. Molecular docking demonstrated the presence of affinity between target hub proteins and active compounds. This study revealed that SWT might exert its therapeutic effects on ESCC through multi-targets and multi-mechanisms.
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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.