Septic shock in children exhibits unique characteristics relative to adults^1,27, significantly modifying the host’s immune status and resulting in an unfavorable prognosis. Although children generally demonstrate greater compensatory capacity than adults, allowing them to endure specific physiological and pathological challenges, the emergence of shock indicators denotes insufficient compensation²⁸. Hence, prompt diagnosis and intervention are essential in these instances to avert the advancement of organ dysfunction.

This study aimed to identify characteristic biomarkers of septic shock in children to facilitate early intervention for multiple organ dysfunction caused by sepsis. Three datasets from the GEO database were examined: GSE8121 and GSE13904 as the analytical sets, and GSE26378 as the validation set. The samples from the three datasets were obtained from children aged 10 years or younger in the Pediatric Intensive Care Unit (PICU) at Cincinnati Children’s Hospital. Blood samples were collected at 24 h and on the third day post-admission. Total RNA was extracted from whole blood utilizing the PaxGene Blood RNA System (PreAnalytiX, Qiagen/Becton Dickinson, Valencia, CA), and sequencing was conducted with the Affymetrix GeneChip Human Genome U133 Plus 2.0 Array (Affymetrix, Santa Clara, CA)^{2,3,12,13,29,30}. Each dataset underwent stringent quality control and standardization procedures, guaranteeing uniformity in cohort attributes, sampling techniques, and sequencing technologies. These measures mitigated potential biases arising from discrepancies in data processing, thereby improving the validity of the analysis. We recognized the potential for residual batch effects among datasets. We utilized the ComBat algorithm to execute batch effect correction during the integration of the GSE8121 and GSE13904 datasets, thereby enhancing the reliability and robustness of the combined analysis.

The research examined 12 differentially expressed genes (DEGs) between the septic shock cohort and the control group, specifically MCEMP1, CD177, MMP8, HP, IL1R2, RETN, MMP9, LTF, LCN2, OLFM4, CEACAM8, and OLAH. Following the identification of these differentially expressed genes (DEGs), they were analyzed through gene set enrichment analysis (GSEA), disease ontology (DO), gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Subsequently, LASSO³¹ and random forest analyses³² were utilized to identify characteristic genes. LASSO is an interpretable model that identifies significant features but excels primarily in linear scenarios, whereas random forest effectively addresses both linear and nonlinear challenges, offering robust predictive capabilities. However, it may exhibit suboptimal performance on high-dimensional sparse datasets and may be prone to overfitting complications. The convergence of the two methodologies produced four biomarkers: CD177, MCEMP1, MMP8, and OLAH, which partially mitigated each other’s deficiencies. The area under the receiver operating characteristic (ROC) curve (AUC) was computed to evaluate and confirm the diagnostic and predictive efficacy of the four biomarkers. An AUC value exceeding 0.9 signifies robust predictive efficacy. The ROC curve for the validation cohort exhibited elevated AUC values, further substantiating the dependability of these genes as prospective biomarkers for pediatric septic shock.

In this study, Gene Set Enrichment Analysis (GSEA) identified substantial gene set enrichment variations between the healthy control and septic shock cohorts, underscoring transitions from normal physiological conditions to a pathological state. In a healthy condition, immune system functions are oriented towards sustaining immune surveillance and tolerance mechanisms, thereby preventing excessive immune activation that could harm self-tissues. However, under pathological conditions, the expression of DEGs may excessively activate immune-related pathways, inflammation, and metabolism, resulting in the activation, migration, and proliferation of immune cells, which ultimately causes tissue and organ damage.

Moreover, the enrichment analysis of DO, GO, and KEGG revealed a significant correlation between the DEGs and multiple biological processes, such as immune cell activity, oxidative stress, neuroinflammation, and bacterial defense mechanisms. Immune response pathways emerged as a prevalent theme, underscoring the significance of immune dysregulation in the pathogenesis of septic shock. Consequently, the correlation between machine learning-validated biomarkers and immune cell infiltration in septic shock has become the focal point of our ongoing research.

To further investigate, the correlations between machine-learning-validated biomarkers and immune cell infiltration in septic shock were further examined using Pearson correlation coefficient analysis. Substantial positive correlations were identified between CD177, MCEMP1, and OLAH with neutrophils, as well as between MMP8 and M0 macrophages. Furthermore, CD177, MCEMP1, and MMP8 demonstrated substantial negative correlations with resting mast cells, whereas CD177 and OLAH revealed negative correlations with resting dendritic cells. Neutrophils, macrophages, mast cells, and dendritic cells are integral constituents of the innate immune system, functioning as the body’s primary defense against pathogens. CD177^{33,34,35,36,37,38} is a cell surface protein found on neutrophils, playing a role in chemotaxis and maturation. MCEMP1^39,40,41 is a protein expressed by mast cells that modulates the production of pro-inflammatory factors, whereas MMP8^{38,41,42,43,44,45,46,47} promotes leukocyte adhesion. These biomarkers, in conjunction with OLAH^36,48,49, are intricately linked to innate immunity. Prior research has indicated their elevated expression in sepsis, implying that suppressing their expression may offer viable therapeutic targets for sepsis management. The precise functions of these biomarkers in sepsis pathology and their interactions with developmental age are ambiguous, while their role in innate immunity is well-established.

Innate immunity offers a swift, non-specific reaction to infections, encompassing mechanisms such as dermal and mucosal barriers, inflammatory responses, and the activity of natural killer cells⁵⁰. A proposed model of sepsis progression indicates that the initial inflammatory response evolves into a compensatory anti-inflammatory response syndrome⁵¹, a notion corroborated by our findings. This study revealed a reduction in innate immune cells, including macrophages, neutrophils, and mast cells, in children experiencing septic shock relative to healthy controls. Conversely, acquired immune cells, such as T cells and B cells, demonstrated elevated proportions. This transition indicates that a compensatory anti-inflammatory mechanism modulates the primary innate immune response, curtailing excessive inflammation while simultaneously inhibiting immune reactions to pathogens, thus exacerbating septic shock in pediatric patients.

This study utilized machine learning and bioinformatics to identify four biomarkers, CD177, MCEMP1, MMP8, and OLAH, and assessed their diagnostic significance and immune characteristics in pediatric septic shock. The results enhance our comprehension of immune dysregulation in these patients and offer significant direction for present and prospective precision therapies. This study has several limitations, including a limited sample size and dependence on a single database for validation, which may affect the generalizability and robustness of the results. Further research ought to prioritize augmenting the sample size and integrating multiple independent cohorts to bolster the validity of the findings. Additionally, exploring the potential interactions between genetic determinants and age-related factors in the innate immune response would yield a more thorough comprehension of their contributions to disease progression.