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Circulating microRNAs and Plasma Gelsolin as Biomarkers of Sepsis: Molecular Insights and Prospects for Precision
Mircea Stoian1, Leonard Azamfirei1, Sergio Rares Bandila2
1Department of Anesthesiology and Intensive Care Medicine, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, 540103 Târgu Mureș, Romania.
Abstract:
Sepsis is a major medical emergency, characterized by a dysfunctional immune response to infection, which often progresses to multiple organ failure and death. Early diagnosis and prognostic evaluation present significant challenges due to limitations in the specificity and sensitivity of traditional biomarkers. This narrative review summarizes recent evidence on the potential of circulating microRNAs (miRNAs) such as miR-150, miR-146a, miR-223, miR-155, miR-122, and miR-4772-5p and plasma gelsolin (pGSN) as diagnostic and prognostic markers in sepsis. We discuss mechanisms involved and their potential for integration with artificial intelligence (AI) in personalized medicine. PubMed, Embase, and Web of Science databases were searched for relevant literature. Original research, systematic reviews, and meta-analyses focused on the diagnostic or prognostic value of circulating miRNAs or pGSN in sepsis were included; opinion papers and case reports were excluded. Altered expression of certain circulating microRNAs correlates with disease severity and mortality. Among circulating microRNAs (miRNAs), miR-122 and miR-150 have become the most consistently validated biomarkers in clinical studies, associated with sepsis severity and death rates. Additionally, other miRNAs such as miR-146a, miR-155, and miR-223 play roles in modulating immune and endothelial responses, highlighting the complex regulation of sepsis pathophysiology. Low pGSN concentrations at admission are associated with severe sepsis and acute respiratory distress syndrome, and serve as an independent predictor of mortality. Preclinical studies suggest that supplementation with exogenous pGSN could increase survival. AI algorithms show promising results for early sepsis detection and optimization of therapeutic decisions. However, combining circulating miRNAs and plasma gelsolin (pGSN) into AI-based models is still an exploratory idea that needs prospective validation, assay standardization, and multicenter studies before it can be used clinically.
Insights
Circulating microRNAs (miRNAs) and plasma gelsolin (pGSN) show promise as sepsis biomarkers. miR-122 and miR-150 are validated markers, while pGSN predicts mortality, with potential AI integration for personalized medicine.
Area of Science:
- Biomarkers and Diagnostics
- Immunology and Infectious Diseases
- Computational Biology and Personalized Medicine
Background:
- Sepsis is a life-threatening emergency with challenging early diagnosis and prognosis due to limited traditional biomarker sensitivity.
- Circulating microRNAs (miRNAs) and plasma gelsolin (pGSN) are emerging as potential diagnostic and prognostic indicators in sepsis.
Purpose of the Study:
- To review current evidence on circulating miRNAs and pGSN as diagnostic and prognostic markers for sepsis.
- To explore the mechanisms involved and the potential integration of these markers with artificial intelligence (AI) for personalized sepsis management.
Main Methods:
- A narrative review of literature from PubMed, Embase, and Web of Science databases.
- Inclusion of original research, systematic reviews, and meta-analyses on circulating miRNAs or pGSN in sepsis.
- Exclusion of opinion papers and case reports.
Main Results:
- Altered expression of specific circulating miRNAs correlates with sepsis severity and mortality; miR-122 and miR-150 are consistently validated.
- Low plasma gelsolin (pGSN) levels at admission predict severe sepsis, ARDS, and mortality, with potential therapeutic benefits from supplementation.
- AI algorithms show promise for early sepsis detection and treatment optimization, though combining biomarkers with AI requires further validation.
Conclusions:
- Circulating miRNAs and pGSN represent promising biomarkers for sepsis diagnosis and prognosis, offering advantages over traditional methods.
- Integration of these biomarkers with AI holds potential for advancing personalized sepsis medicine, pending further research and standardization.
