Pokharel Marissa D, Feng Anlin, Liang Ying, Ma Wenli, Aggarwal Saurabh, Unwalla Hoshang, Black Stephen M, Wang Ting
Center for Translational Science, Florida International University, Port Saint Lucie, FL, United States.
Department of Cellular and Molecular Medicine, Florida International University, Miami, FL, United States.
Front Immunol. 2025 Jan 8;15:1516145. doi: 10.3389/fimmu.2024.1516145. eCollection 2024.
Sepsis is a severe and life-threatening medical syndrome that can lead to organ failure and death. Despite advances in medical treatment, current therapies are often inadequate, with high septic mortality rates. Therefore, there is a critical need for reliable prognostic markers to be used in clinical settings to improve the management and outcomes of patients with sepsis. Recent studies have suggested that mitochondrial dynamics, including the processes of mitochondrial fission and fusion, are closely related to the severity of sepsis and the status of inflammation. By monitoring transcriptomic signals related to mitochondrial dynamics, new and reliable biomarkers can be engineered to more accurately predict sepsis survival risk. Such biomarkers would be invaluable in clinical settings, aiding healthcare providers in the early identification of high-risk patients and improving treatment strategies. To achieve this goal, we utilized the major mitochondrial fission regulatory protein dynamin-related protein 1 (Drp1, gene code ) and identified Drp1-associated genes that are enriched with sepsis survival genes. A 12-gene signature (GS) was established as a differentially expressed gene (DEG)-based GS. Next, we compared genes of proteins that interact with Drp1 to sepsis survival genes and identified 7 common genes, establishing a GS we term as protein-protein interaction (PPI)-based GS. To evaluate if these GSs can predict sepsis survival, we used publicly available human blood transcriptomic datasets from sepsis patients. We confirmed that both GSs can successfully predict sepsis survival in both discovery and validation cohorts with high sensitivity and specificity, with the PPI-based GS showing enhanced prognostic performance. Together, this study successfully engineers a new and validated blood-borne biomarker (PPI-based 7-gene GS) for sepsis survival risk prediction. This biomarker holds the potential for improving the early identification of high-risk sepsis patients and optimizing personalized treatment strategies to reduce sepsis mortality.
脓毒症是一种严重且危及生命的医学综合征，可导致器官衰竭和死亡。尽管医学治疗取得了进展，但目前的治疗方法往往不足，脓毒症死亡率很高。因此，迫切需要可靠的预后标志物用于临床，以改善脓毒症患者的管理和治疗结果。最近的研究表明，线粒体动力学，包括线粒体分裂和融合过程，与脓毒症的严重程度和炎症状态密切相关。通过监测与线粒体动力学相关的转录组信号，可以设计出新的可靠生物标志物，以更准确地预测脓毒症的生存风险。这种生物标志物在临床环境中将非常宝贵，有助于医疗保健提供者早期识别高危患者并改善治疗策略。为了实现这一目标，我们利用主要的线粒体分裂调节蛋白动力相关蛋白1（Drp1，基因代码），并鉴定了富含脓毒症生存基因的Drp1相关基因。建立了一个基于差异表达基因（DEG）的12基因特征（GS）。接下来，我们将与Drp1相互作用的蛋白质基因与脓毒症生存基因进行比较，鉴定出7个共同基因，建立了一个我们称为基于蛋白质-蛋白质相互作用（PPI）的GS。为了评估这些GS是否能够预测脓毒症的生存情况，我们使用了脓毒症患者公开可用的人类血液转录组数据集。我们证实，这两种GS都能够在发现和验证队列中以高灵敏度和特异性成功预测脓毒症的生存情况，基于PPI的GS显示出更强的预后性能。总之，本研究成功设计了一种新的、经过验证的用于预测脓毒症生存风险的血液生物标志物（基于PPI的7基因GS）。这种生物标志物具有改善高危脓毒症患者的早期识别和优化个性化治疗策略以降低脓毒症死亡率的潜力。
