ArticleMedicine2026
Analysis of differentially expressed genes related to acute lung injury and their role in metabolic pathways: An integrative study using GEO data.
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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Abstract
Acute lung injury (ALI) is a life-threatening pulmonary disorder with high morbidity and mortality, and current treatments remain limited. Mitochondrial energy metabolism plays a key role in ALI pathophysiology. This research aims to systematically explore the relationship between mitochondrial energy metabolism and ALI pathogenesis, thereby advancing our understanding of the condition and informing the establishment of improved treatment strategies. In this study, we systematically investigated its involvement through comprehensive bioinformatics analyses of publicly available Gene Expression Omnibus datasets, including differential expression analysis, functional enrichment, immune infiltration profiling, protein-protein interaction network construction, and regulatory network prediction, with the aim of elucidating disease mechanisms and identifying potential therapeutic targets. Differential expression analysis identified 575 differentially expressed genes (DEGs), comprising 431 and 144 upregulated and downregulated genes, respectively. Subsequent pathway analysis revealed that mitochondrial energy metabolism-related DEGs were significantly enriched in fatty acid oxidation and other key metabolic processes, highlighting the crucial role of mitochondrial dysfunction in ALI pathogenesis. Additionally, immune cell infiltration analysis indicated obvious differences in the composition of 11 immune cell types between ALI and control samples (P < .05), suggesting potential avenues for immunotherapeutic interventions. The protein-protein interaction network identified 12 mitochondrial energy metabolism-related DEGs with significant connectivity, from which 9 hub genes were prioritized as promising therapeutic targets. Furthermore, regulatory network analyses elucidated interactions among these hub genes, transcription factors, and miRNAs. Despite limitations, such as the absence of experimental validation and the potential influence of batch effects, this study provides new insights into the molecular mechanisms of ALI and establishes a foundation for future research on metabolic modulation and personalized therapeutic strategies to improve patient outcomes.
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