ArticleCurrent medicinal chemistry2025
Exploring PANoptosis Related Novel Diagnostic Biomarkers and Potential Drugs for Sarcopenia based on Machine Learning and Experimental Validation.
Article in Current medicinal chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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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Who cites it
1 citing paper in PubMed.
- Integrative transcriptomic and experimental analyses prioritize TPT1 as a PANoptosis-associated candidate molecular marker in sarcopenia.Frontiers in cell and developmental biology · 2026Article
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8 authors.
Funding
Abstract
backgroundSarcopenia, an aseptic chronic inflammatory disease, is a complex and debilitating disease characterized by the progressive degeneration of skeletal muscle. PANoptosis, a novel proinflammatory programmed cell death pathway, has been linked to various diseases. However, the precise role of PANoptosis-related features in sarcopenia remains uncertain.
methodsAccording to the intersection of differentially expressed genes (DEGs) in the sarcopenia dataset GSE167186 and the PANoptosis gene set, we classified patients into PANoptosis-related subtypes (PANRS) using consensus clustering. The DEGs of PANRS were intersected with weighted gene co-expression network analysis (WGCNA). Protein- protein interaction network and cytoHubba algorithms were employed to further identify potential genes related to PANoptosis. The most characteristic genes were selected using LASSO regression and validated by ROC curve analysis, followed by relevant immune infiltration analysis. Additionally, small-molecule drug screening was performed using Cmap. The relative expression levels of hub genes in sarcopenia were confirmed by PCR. Finally, single-cell analysis and GSEA were used to examine the distribution and function of hub genes.
resultsThirty-five candidate genes were identified through WGCNA and PANRS. Machine learning and ROC curve analysis revealed three core genes:
conclusionIn this study, the hub genes
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Registered trials
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