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Exploration of ferroptosis-related biomarkers and regulatory mechanisms in two diseases (sarcopenia and idiopathic pulmonary fibrosis) based on transcriptome and machine learning
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DOI：10.1016/j.exger.2026.113150.png)
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Abstract
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• First identification of cross-disease shared biomarkers: Through bioinformatics and machine learning, DDIT4 and MGST1 were identified for the first time as shared ferroptosis-related biomarkers in both sarcopenia (SP) and idiopathic pulmonary fibrosis (IPF), with both significantly upregulated in the two diseases. • Construction of high-precision predictive models: The nomogram models based on these two genes demonstrated excellent predictive ability for SP and IPF (AUC of 0.91 and 0.79, respectively), providing novel molecular tools for early auxiliary diagnosis of both conditions. • Elucidation of differential regulatory mechanisms: GSEA revealed that DDIT4 is primarily enriched in protein synthesis and post-transcriptional regulatory pathways, while MGST1 is mainly involved in oxidative stress and inflammatory responses. Both genes drive ferroptosis through distinct yet partially complementary pathways in SP and IPF. • Revelation of shared immune microenvironment characteristics: Five differentially infiltrated immune cells (e.g., MDSC, Treg) were identified as common to both diseases. The biomarkers participate in disease progression by targeting specific immune cells (e.g., MGST1 positively correlated with Treg), unveiling cross-disease immune regulatory patterns. • Identification of a potential cross-disease therapeutic agent: Molecular docking predicted that Trichostatin A exhibits good binding activity with both DDIT4 and MGST1 proteins (Vina Score < -5.0), suggesting its potential as a candidate compound for simultaneously intervening in SP and IPF.
Keywords:
Sarcopenia
Idiopathic pulmonary fibrosis
Biomarker
Bioinformatics
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