This study offers new insights into the mechanisms underlying sepsis progression and highlights the importance of continuous monitoring of ELL2 expression during early diagnosis and treatment. (IMAGE)
Caption
We analyzed bulk transcriptome sequencing data from 10 cohorts of sepsis patients. Using unbiased patient clustering, we identified three subtypes with significantly different prognoses, which were consistently reproduced across all 10 cohorts. Through comprehensive multi-angle analyses, we revealed distinct differences among the subtypes in terms of inflammation, immune responses, and functional pathways. By integrating multiple machine learning algorithms with single-cell transcriptomic analysis, we identified ELL2 as an effective diagnostic and prognostic biomarker for sepsis. This study offers new insights into the mechanisms underlying sepsis progression and highlights the importance of continuous monitoring of ELL2 expression during early diagnosis and treatment.
Credit
Jingyuan Ning
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Credit must be given to the creator.
License
CC BY