Editorial: Advancements in Sepsis Diagnosis Utilizing Next-Generation Sequencing Approaches for Personalized Medicine

JK Jitendra Kumar Tripathi †

doi:10.3389/fcimb.2026.1839007

Editorial: Advancements in Sepsis Diagnosis Utilizing Next-Generation Sequencing Approaches for Personalized Medicine

2026-04-28

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JK Jitendra Kumar Tripathi †

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KG Kuldeep Gupta †

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Anupam Jyoti *

DOI：10.3389/fcimb.2026.1839007

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medicine Sepsis is one of the leading causes of morbidity and mortality worldwide (Grey et al.) [1]. It is a complex clinical syndrome resulting from host immune dysregulation in response to infection. It is characterized by multiple manifestations; which results in dysfunction or failure of one or more organs and even death. Early diagnosis of sepsis is key to initiating the timely intervention as each hour of delayed treatment led to increased patient mortality up to 7% (Kumar et al.) [2]. However; the diagnosis of sepsis is complicated by its heterogeneity; nonspecific clinical symptomatology; a high-false negative rate; overlapping clinical features with sterile inflammation. Additionally; the conventional methods have limited sensitivity; and prolonged turnaround time further delays in initiation of appropriate treatment. The advent of next-generation sequencing (NGS) technologies offers a promising solution to these diagnostic challenges it can analyze genetic material quickly and thoroughly.The present research topic aims to explore the recent developments in NGS and related advanced molecular techniques in diagnosis of sepsis. By fostering interdisciplinary approach; the goal is to integrate studies focusing on the development of diagnostic biomarkers; molecular signatures; OMICS as well as machine learning approaches; and ensemble classifiers for diagnosis; severity; and predicting mortality with an aim of individualized treatment for sepsis patients. Additionally; the goal is to learn how OMICS-based biomarkers and machine learning utilizing large datasets help to develop new diagnostic models by fostering diagnostics and personalized medicine.Metagenomic next-generation sequencing (mNGS) is preferred over body fluid culture as it provides information at species level without need to culture. In a meta-analysis; Zhang et al. [3] demonstrated that mNGS exhibits better sensitivity and specificity in detecting spinal infections as compared with routine microbial culture; highlighting it as a complementary diagnostic tool. Furthermore; mNGS outperform the conventional methods in diagnosing central nervous system infections by offering rapid and accurate pathogen detection; better sensitivity with less turnaround time (Cao et al.) [4]. Zhang et al. [5] reported the improved performance of plasma cell free DNA in pathogen detection in pediatric patients with hematologic diseases; enabling clinicians to initiate appropriate antimicrobial therapy improving the patient outcomes. Moreover; Pinzauti et al. Identification of gene signatures and prognostic classifiers utilizing transcriptomics and machine learning are instrumental in understanding and deciphering host reponse heterogeneity. Liu et al. [11] identified three leptin-associated sepsis subtypes using non-negative matrix factorization. TFRC and PILRA were identified as potential biomarkers and further validated using qPCR and Western blotting. Shi et al. [12] utilized an integrated machine learning approach by constructing XGBoost algorithm for predicting the detection of Staphylococcus aureus bloodstream infections involving five genes (DRAM1; UPP1; IL18RAP; CLEC4A; and PGLYRP1).Analysis of bulk transcriptomic and single cell data decipher immune dysregulation pathways.Wang et al. [13] identified key necroptosis genes using an integrated bioinformatics approach and concluded their role in pathophysiology of sepsis and related immune responses. In another study conducted by Lu et al. [14] where two ferroptosis-associated biomarkers named DPP4 and TXN were identified using machine learning models and validated using RT-qPCR in sepsis samples. Jiang et al.[15] utilized omics followed by machine learning approach have identified CX3CR1; PID1; and PTGDS and validated by RT-qPCR as key molecular signatures for diagnosis of sepsis and acute respiratory distress syndrome. Furthermore; in a retrospective study; CRISP3 was found to be upregulated in adult sepsis; hence bears the potential as latent biomarker (Zhang et al.) [16].Biomarker help in early diagnosis; disease severity and prediction of mortality in critically ill patients with sepsis Lee et al. [17] have compared the two plasma proteins EphA2 and Del-1 for their levels in sepsis patients. EphA2 levels increased and correlated with sepsis severity; suggesting a better biomarker as compared with Del-1. Zhou et al. [18] identified mitochondrial-encoded NADH dehydrogenase 6 (MT-ND6) and Annexin A1 as mortality-associated biomarkers mixed inflammation phenotypes. Pu et al. [19] have reported that the prediction of pediatric sepsis improves when nucleated red blood cell counts combined with C-reactive protein and pro-calcitonin.In a retrospective analysis consisting of 490 patients over 10 years of data; a nomogram having 4 clinical features including white blood cell count; international normalized ratio; gas formation; and SOFA score has been developed to predict sepsis risk in pyogenic liver abscess (Zhang et al.) [20].Gao et al. [21] concluded the specific combinations of pathogens influence survival in post-sepsis persistent respiratory dysfunction.OMICS-based biomarkers have substantially deepened our understanding of sepsis by enabling comprehensive; system-level insights into host responses. Despite this promise; their integration into routine clinical practice remains limited compared with established molecular techniques; such as PCR and immunoassays. Key challenges include longer turnaround times; high costs; and the need for specialized infrastructure and bioinformatics expertise; all of which limit their utility in acute; timesensitive settings. In addition; the complex and high-dimensional nature of OMICS data poses significant difficulties in interpretation; standardization; and reproducibility across studies. Patient heterogeneity further complicates the identification of robust and universally applicable biomarkers.In contrast; conventional molecular diagnostics offer faster; more accessible; and clinically validated solutions with clearer interpretability; making them more suitable for immediate clinical decisionmaking. Thus; while OMICS technologies hold significant potential for advancing precision medicine in sepsis; further efforts are required to improve their scalability; standardization; and clinical applicability.Overall; these nineteen submissions in this special issue highlight a paradigm shift from conventional techniques towards modern techniques; including omics-based approaches. These studies also highlight the importance of machine learning; ensemble classifiers; and other computational approaches for the diagnosis of sepsis; its risk stratification; and predicting mortality with an aim of personalized medicine or precision therapy for sepsis.

Keywords:

diagnosis

machine learning

sepsis

personalized medicine

omics

next-generation sequencing

Editorial: Advancements in Sepsis Diagnosis Utilizing Next-Generation Sequencing Approaches for Personalized Medicine

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