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Exploring PANoptosis Related Novel Diagnostic Biomarkers and Potential Drugs for Sarcopenia based on Machine Learning
Zhibo Deng1,2, Chao Song1,2, Rongsheng Zhang1,2
1Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China.
Background:
Sarcopenia, 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.
Methods:
According 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.
Results:
Thirty-five candidate genes were identified through WGCNA and PANRS. Machine learning and ROC curve analysis revealed three core genes: LTBP2, ETS2, and H3.3B, all of which were up-regulated in patients with sarcopenia (p<0.01). Immune infiltration analysis indicated that these three diagnostic genes were linked to the activation of NK cells and macrophages. Single-cell analysis demonstrated that LTBP2 was mainly localized in fibroblasts, while ETS2 and H3.3B exhibited a uniform distribution. Enrichment analysis indicated that the three hub genes were predominantly associated with the inhibition of energy metabolism.
Conclusion:
In this study, the hub genes LTBP2, ETS2, and H3.3B associated with PANoptosis in sarcopenia were successfully identified through a combination of bioinformatics and experimental verification methods. This establishes a foundation for new candidate diagnostic and therapeutic targets for sarcopenia.
