Bioinformatics Identification of Immunomodulatory Genes Related to Neonatal Sepsis and Their Incorporation into a Diagnostic Model

Bioinformatics Identification of Immunomodulatory Genes Related to Neonatal Sepsis and Their Incorporation into a Diagnostic Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Bioinformatics Identification of Immunomodulatory Genes Related to Neonatal Sepsis and Their Incorporation into a Diagnostic Model Huiling Luo, Bo Bai, Zhanchao Gong, Junhua Wei, Weibin Luo, Xiaoqun Du, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8030559/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Although neonatal sepsis (NS) is a main driver of neonatal morbidity and mortality, reliable molecular biomarkers for early detection are lacking. This study identified immunomodulation-related differentially expressed genes (IMRDEGs) linked to NS through integrated bioinformatics analysis. Methods Two GEO datasets (GSE25504 and GSE69686) containing 90 NS and 122 control samples were combined via the R packages "GEOquery" and "sva". Differentially expressed genes (DEGs) were detected via "limma", and functional enrichment was determined via the GO and KEGG databases. Enriched pathways were further identified via gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). Then, we developed a diagnostic model via logistic regression (LR), SVM-RFE, and LASSO regression. Results In total, 360 DEGs were identified, including 69 IMRDEGs. Enrichment analyses highlighted significant associations with inflammatory and immune regulation pathways. Seven hub genes (HGs; ARG2 , IL18R1 , IL1RN , MERTK , RETN , STAT3 , and TSPO ) were incorporated into the diagnostic model, which displayed high accuracy (AUC > 0.9) in ROC curve analysis. Immune infiltration analysis elucidated close interconnections between the HGs and specific immune cell (IC) subsets. Conclusion These outcomes illustrate that the detected HGs represent biomarkers for early NS diagnosis and provide insights into potential therapeutic targets. Upcoming studies should concentrate on the functional validation and clinical translation of these biomarkers. Neonatal sepsis Biomarker Immune regulation Bioinformatics Diagnostic model IC infiltration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1. Introduction Neonatal sepsis (NS) represents a severe clinical syndrome causing high morbidity and mortality in infants under 28 days of age. It results from systemic infection leading to serious complications, such as circulatory shock and multiorgan failure. The main causative agents are bacteria, most prevalently Staphylococcus aureus and Escherichia coli [ 1 ] . The clinical presentation of NS varies widely, often beginning with nonspecific symptoms that hinder prompt diagnosis and treatment. Early recognition is essential, as delayed intervention can have fatal consequences, underlining the demanding requirement for reliable diagnostic biomarkers and successful treatments [ 2 ] . Current diagnosis relies predominantly on blood cultures; however, although they are the gold standard, they often fail to yield timely results. This delay can cause inappropriate or late antibiotic administration, highlighting the necessity of sensitive and specific biomarkers for early detection of sepsis [ 3 ] . Existing biomarkers show limited specificity and sensitivity, leaving a major gap in accurate NS diagnosis. A high proportion of cases remain undetected until severe clinical symptoms appear, emphasizing the importance of new diagnostic strategies to enable earlier recognition and better outcomes for affected neonates [ 4 ] . Advances in bioinformatics, including expression profiling and machine learning algorithms, provide valuable tools for determining novel biomarkers for NS [ 3 ] . Using large-scale genomic datasets, researchers can explore the molecular mechanisms underlying sepsis and detect differentially expressed genes (DEGs) that may act as diagnostic indicators. For instance, the R package "GEOquery" [ 5 ] enables the retrieval and analysis of gene expression data obtained by accessing the Gene Expression Omnibus (GEO), supporting the identification of immunomodulatory genes vital to NS pathogenesis [ 6 ] . Herein, we applied a systematic bioinformatics strategy to detect DEGs associated with NS. "Limma" was utilized to conduct differential expression analysis on combined GEO datasets, facilitating the identification of immunomodulation-related DEGs (IMRDEGs) and providing insights into immune responses during sepsis [ 3 ] . Seeking the examination of biological processes and pathways significantly connected to the determined IMRDEGs, "clusterProfiler" was utilized to apply GO and KEGG enrichment analyses [ 4 ] . Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were conducted to assess functional gene set enrichment and identify pathway variations among clinical groups. These analyses revealed the biological mechanisms underlying varying clinical outcomes in patients with NS [ 3 ] . By integrating these computational methods, we aimed to establish a robust diagnostic model to accurately stratify NS risk, promote timely clinical intervention, and enhance patient outcomes. Ultimately, this investigation enhances our understanding of the immune landscape in NS and identifies therapeutic targets to mitigate its severe effects. 2. Materials and methods 2.1. Data collection "GEOquery" (v2.70.0) from GEO [ 7 ] was utilized to download NS datasets, GSE25504 [ 8 ] and GSE69686 [ 9 ] . All samples in GSE25504 and GSE69686 were acquired from Homo sapiens blood tissue, via the chip platforms GPL6947 and GPL20292, respectively. Table 1 illustrates the detailed dataset information. Dataset GSE25504 included 26 NS samples and 37 controls, whereas dataset GSE69686 included 64 NS and 85 controls. Herein, we included all NS and control samples. The" sva" [ 10 ] (v3.50.0) was utilized to eliminate batch effects between GSE25504 and GSE69686 and generate a combined GEO dataset, which included 90 NS and 122 control samples. The "limma" (v3.58.1) was utilized to normalize this combined dataset [ 11 ] , and probe annotations were standardized. Principal component analysis (PCA) was employed to express patterns pre- and post-batch correction to verify the efficiency of the adjustment [ 12 ] . PCA, a dimensionality reduction technique, isolates key constituents from high-dimensional data and displays them in 2D or 3D space. The collection of Immunomodulation-related genes (IMRGs) was conducted from GeneCards [ 13 ] . In this database, comprehensively covering human genes, the search term "Immunomodulation" was utilized and retained only "Protein Coding" entries, yielding 981 IMRGs. The same term was searched in PubMed to identify additional IMRG sets from published studies [ 14 ] . After removing duplicates, 981 IMRGs were retained. Table S1 displays more detailed data. 2.2. DEGs related to NS-associated immune regulation Based on sample grouping in the combined datasets, we divided samples into NS and control groups, thereby analyzing differential gene expression via "limma" using thresholds of |logFC| >0.5 and p.adj 0.5 and p.adj < 0.05 were deemed overexpressed, while those with logFC < − 0.5 and p.adj < 0.05 were deemed reduced. The "ggplot2" (v3.4.4) was utilized to visualize the differential expression outcomes. For adjusting the p -value, the Benjamini-Hochberg (BH) technique was used. To identify IMRDEGs associated with NS, the intersection of DEGs meeting |logFC| >0.5 and p.adj < 0.05 with IMRGs was conducted, and a Venn diagram was created to demonstrate overlap. 2.3. GO and KEGG analyses GO analysis [ 15 ] is frequently employed for large-scale functional enrichment analysis, such as biological process (BP), cellular component (CC), and molecular function (MF) categories. KEGG [ 16 ] represents a comprehensive resource on genomes, pathways, disorders, and medications. "clusterProfiler" (v4.10.0) was utilized to carry out GO and KEGG analyses of IMRDEGs [ 17 ] . Items fulfilling p .adj < 0.05 and false discovery rate (FDR; q-value) < 0.05 indicated significance. The BH technique was applied for adjusting the p-value. 2.4. GSEA GSEA [ 18 ] was conducted via the clusterProfiler to assess the predefined gene sets' distribution, sorted by relationship with phenotype. The pooled dataset's genes were sorted using logFC values. The analysis parameters were a 2020 seed, 1,000 permutations, and a gene set size of 10–500. The dataset, including 3,050 gene sets, was acquired by accessing MSigDB [ 19 ] (c2.cp.all.v2022.1.Hs.symbols.GMT, 3,050 sets). Outcomes with p.adj < 0.05 and q-value < 0.05 after BH correction indicated significance. 2.5. GSVA GSVA [ 20 ] is a non-parametric, uncontrolled approach that ascertains gene set enrichment by transforming the gene expression matrix across samples into gene set enrichment scores. This method assesses pathway enrichment differences between samples. The h.all.v7.4.symbols.gmt gene set was acquired by accessing MSigDB, and the combined datasets were analyzed to determine functional enrichment differences between groups. P.adj < 0.05 was regarded as significant, with BH used for p-value correction. 2.6. A diagnostic model establishment for NS To create NS diagnostic models from the pooled datasets, logistic regression (LR) analysis was carried out on the IMRDEGs related to NS. When the dependent variable was binary, LR was utilized to assess the link between the independent and dependent variables (NS vs. Control). A p < 0.05 was regarded as the threshold to select IMRDEGs and establish the LR model. The levels of IMRDEGs involved in the model were visualized using a forest plot. Depending on the IMRDEGs in the LR model, "e1071" (v1.7.14) was employed for additional IMRDEG screening. The support vector machine recursive feature elimination (SVM-RFE) algorithm [ 21 ] was utilized to detect potential biomarkers. It is a feature selection algorithm within the SVM framework that iteratively removes features contributing least to classification to select the most informative genes. Subsequently, "glmnet" (v4.1.8) was deployed to conduct LASSO regression [ 22 ] with the parameters set.seed (500) and family="binomial" depending on the IMRDEGs involved in SVM-RFE. LASSO regression, following linear regression principles, reduces model overfitting by introducing a penalty term (lambda × absolute slope value) to enhance generalization. Diagnostic and variable trajectory plots were generated to observe LASSO regression outcomes. The resulting model was used as the diagnostic model for NS, and the IMRDEGs it contained were defined as hub genes (HGs). A LASSO risk score (RS) was then computed through the coefficient from LASSO regression: A nomogram [ 23 ] was constructed using "rms" (v6.7.1) based on LR results to visualize relationships among HGs. Model calibration was assessed with a calibration curve to evaluate accuracy and discrimination. Clinical utility was examined using decision curve analysis (DCA) via ggDCA (v1.1) [ 24 ] , while diagnostic performance was evaluated with receiver operating characteristic (ROC) curves and AUC values using "pROC" (v1.18.5) [ 25 ] in the combined dataset, assessing the predictive power of the LASSO-derived RS for NS. NS samples were assigned to high-risk (HRG) and low-risk groups (LRG) using the median RS from the NS diagnostic model. The "pROC" (v1.18.5) [ 25 ] was again utilized to generate ROC curves and calculate AUCs for HGs to evaluate their diagnostic accuracy for NS. The AUC ranges from 0.5 to 1.0, with values near 1 reflecting superior diagnostic precision: 0.5–0.7 low, 0.7–0.9 moderate, and > 0.9 high. To further examine HG expression variations between NS and Control in the combined dataset, comparison plots were produced. The "pheatmap" (v1.0.12) was utilized to create heatmaps, and "Rcircos" (v1.2.2) [ 26 ] was employed to create chromosome localization maps. Then, Spearman correlation analysis was utilized for the assessment of links among HGs, and pheatmap was again utilized to visualize correlation heatmaps. 2.7. GSEA of HRG and LRG Samples in the NS group from the combined datasets were assigned into HRG and LRG depending on the median LASSO RS. Limma was utilized for differential expression analysis, followed by GSEA with clusterProfiler. GSEA parameters were: seed = 2020, gene set size = 10–500. Gene sets were acquired from MSigDB (c2.cp.all.v2022.1.Hs.symbols.GMT, 3,050 sets). Outcomes with p.adj < 0.05 and q-value < 0.05 were deemed significant after BH correction. 2.8. Immune infiltration analysis: CIBERSORT CIBERSORT [ 27 ] uses linear support vector regression to deconvolute transcriptomic data and estimate immune cell (IC) composition and abundance. Using the LM22 signature matrix, samples exhibiting immune enrichment scores > 0 were maintained to produce an immune infiltration matrix for the pooled datasets. Group variations in LM22 IC expression were visualized with "ggplot2", and significantly altered ICs were selected for additional analysis. The Spearman algorithm was utilized to determine the relationships among ICs, and pheatmap was employed to create a correlation heatmap presenting IC associations. The links between HGs and ICs were computed via Spearman analysis, retaining outcomes with p < 0.05. Finally, "ggplot2" was deployed to draw correlation bubble plots and visualize these associations. 2.9. Protein-protein interaction (PPI) network A PPI network represents interactions among proteins participating in signaling, gene expression control, cell cycle control, and metabolism. Systematic interaction analysis is vital for the comprehension of protein roles, biological signal transduction, and metabolic regulation under physiological and disease conditions. The STRING database [ 28 ] was utilized to detect interactions among HGs with the lowest needed interaction score > 0.4. Medium confidence (0.4) was set as the threshold to create the PPI network linked to HGs. It is possible that chemical complexes with unique biological functions are represented by tightly linked local regions in the PPI network. Cytoscape was utilized to depict the PPI network after genes that interacted with each other were selected [ 29 ] . Gene expression was controlled via transcription factors (TFs) by interaction with HGs at the post-transcriptional level. The ChIPBase [ 30 ] was utilized to detect HG-related TFs, and Cytoscape was employed to observe the resulting mRNA–TF regulatory network. RNA-binding proteins (RBPs) [ 31 ] have main roles in gene regulation, including RNA synthesis, alternative splicing, modification, transport, and translation. Depending on the ENCORI database, the prediction of target RBPs of HGs was conducted, and Cytoscape was employed to observe the resulting mRNA–RBP regulatory network. MicroRNAs (miRNAs) are key modulators of biological evolution and development, targeting multiple genes while being regulated by other miRNAs. HG-associated miRNAs were acquired from the miRDB database, and the mRNA-miRNA regulatory network was observed via Cytoscape. Additionally, the DGIdb database was employed for the prediction of direct and indirect drug targets of HGs. Drug-associated HGs were extracted, and the mRNA-drug regulatory network was observed in Cytoscape. 2.10. Statistical analysis Figure 1 illustrates the full study workflow. All data analyses were performed in R (v4.2.2). Continuous variables are reported as means ± standard deviations. The Wilcoxon rank-sum test was applied for two-group comparisons (unless stated otherwise), and the Kruskal–Wallis test for multiple groups. The chi-square or Fisher's exact test was deployed to compare the categorical variables. Correlations were assessed by Spearman analysis, with p < 0.05 indicating significant: p < * 0.05, ** 0.01, *** 0.001. Screening criteria: p.adj < 0.05 and FDR < 0.05 (BH correction) 3. Results 3.1. Data collection and integration of NS datasets Batch effects between the NS datasets GSE25504 and GSE69686 were eliminated via sva to generate a combined dataset. Distribution boxplots (Figs. 2a-b) compared expression before and after correction, while PCA plots (Figs. 2c-d) evaluated low-dimensional feature distributions. Both analyses confirmed effective batch effect removal in the NS datasets. 3.2. NS-related immunomodulatory DEGs Samples in the pooled dataset were categorized into control and NS. To compare gene expression between the groups, "limma" was employed for differential expression analysis. In total, 360 DEGs in the pooled dataset met the determined threshold. Among them, 280 and 80 were overexpressed and suppressed, respectively. To identify IMRDEGs related to NS, all DEGs meeting |logFC| > 0.5 and p.adj < 0.05 were overlapped with IMRGs, and a Venn diagram was plotted ( Fig. 3b ). In total, 69 IMRDEGs were identified ( Table S2 ). 3.3. GO and KEGG analysis The GO outcomes illustrate that the NS-linked 69 IMRDEGs exhibited a main enrichment in inflammatory and immune-correlated pathways. BP terms included regulating T-cell stimulation, leukocyte apoptosis, and T-cell proliferation. CC terms displayed enrichment in the external plasma membrane, secretory granule, endocytic vesicle, cytoplasmic vesicle, and vesicle lumens. MF enrichment involved NAD⁺ nucleosidase and immune receptor activities, and growth factor receptor, TNF receptor, and cytokine bindings. KEGG analysis revealed enrichment in TNF, NF-κB, T-cell receptor, and chemokine pathways, and growth hormone synthesis, secretion, and action. GO and KEGG results were observed as bubble plots (Fig. 4a) and network diagrams (Figs. 4b–e) ( Table 2). 3.4. GSEA To evaluate the biological influence of the combined dataset on NS, the GSEA (Fig. 5a; Table 3) revealed that all genes in the pooled dataset displayed a significant enrichment in functions related to RNA metabolism (Fig. 5b ), translation (Fig. 5c) , fatty acid metabolism (Fig. 5d) , and the MAPK signaling pathway (Fig. 5e) , among others. 3.5. GSVA To explore pathway differences between NS and control in the combined dataset, GSVA was conducted ( Table 4 ). Pathways with p.adj < 0.05 were ranked by logFC, and the top 10 positively and negatively enriched pathways were identified. The differential enrichment of 20 pathways between groups was visualized using group comparison plots ( Fig. 6a ) and heatmaps ( Fig. 6b ). GSVA results showed that pathways, such as Myc targets V1 and V2, DNA repair, pancreas beta cells, fatty acid metabolism, estrogen response early, UV response Dn, myogenesis, mitotic spindle, protein secretion, complement, androgen response, coagulation, TNFA signaling via NFKB, xenobiotic metabolism, angiogenesis, hypoxia, IL6/JAK/STAT3 signaling, inflammatory response, and cholesterol homeostasis, varied significantly between the NS and control. 3.6. Establishment of the NS diagnostic model To determine the diagnostic value of the 69 IMRDEGs in NS, LR models were created using data from the combined dataset. Model outcomes were observed via using a forest plot ( Fig. 7a ), showing that all 69 IMRDEGs were significant ( p < 0.05). The SVM-RFE algorithm was utilized on the 69 IMRDEGs with fivefold cross-validation to identify optimal gene subsets. Average gene ranks were calculated to ascertain the count of genes yielding the lowest error rate ( Fig. 7b ) and the highest precision ( Fig. 7c ). The SVM model reached peak accuracy when 66 genes were involved ( Table S3 ). Relying upon these 66 IMRDEGs, LASSO regression analysis was utilized to create the NS diagnostic model. The LASSO regression model ( Fig. 7d ) and variable trajectory plot ( Fig. 7e ) were generated for observation. Seven IMRDEGs, namely ARG2 , IL18R1 , IL1RN , MERTK , RETN , STAT3 , and TSPO , were identified as HGs in the last diagnostic model. To verify the diagnostic model's applicability for NS, a nomogram depending on HGs was generated to show their interrelationships in the combined dataset ( Fig. 8a ). Results revealed that IL1RN expression contributed most significantly to the NS diagnosis model, whereas IL18R1 expression contributed significantly less relative to other genes. To assess model accuracy and discrimination, a calibration curve was created to compare predictive and observed probabilities under several circumstances ( Fig. 8b ). In the calibration plot, the dotted calibration line displayed a slight deviation from, but remained near, the standard diagonal, indicating good concordance between anticipated and actual outcomes. DCA was then used to assess the clinical efficiency of the HG-based NS diagnostic model in the combined dataset ( Fig. 8c ). The DCA outcomes illustrated that the model line constantly exceeded the "all positive" and "all negative" lines across a broad range, indicating greater net clinical benefit and better overall performance. Moreover, a ROC curve was created via pROC depending on the model's RS ( Fig. 8d ). ROC outcomes demonstrated high predictive accuracy (AUC > 0.9) for NS across groups. The RS was measured as follows: Next, patients with NS were assigned to HRG and LRG as per the median RS acquired from the diagnostic model. Depending on HG levels in the NS group, pROC was utilized to plot ROC curves ( Table S4 ). ROC curve results ( Figs. 9a-b ) showed that IL18R1 , IL1RN , MERTK , RETN , and STAT3 expression levels exhibited moderate diagnostic accuracy (0.7 < AUC < 0.9), whereas ARG2 and TSPO expression levels displayed lower accuracy (0.5 < AUC < 0.7). To compare HG expression levels between NS and control in the combined dataset, a group comparison plot (Fig. 9c) was created to display variations in these levels for all seven HGs. A heatmap, created using pheatmap, was used to show these expression differences between NS and control samples (Fig. 9d) . All seven HGs showed highly significant differential expression between the groups ( p < 0.001). A correlation heatmap illustrated interrelationships among the seven HGs in the combined dataset (Fig. 9e) , showing that most genes were positively correlated. Additionally, chromosomal mapping via RCircos identified the genomic regions of the seven HGs on human chromosomes (Fig. 9f) . This mapping revealed that multiple HGs, including IL18R1 , IL1RN , and MERTK , were located on chromosome 2. 3.7. GSEA of HRG and LRG NS To further examine transcriptomic differences within NS samples, patients from the combined dataset were allocated into HRG and LRG depending on the median RS from the LASSO diagnostic model. Differential gene expression was analyzed using limma ( Table S5 ). To determine how overall gene expression patterns related to sepsis, GSEA was conducted to examine BP, CC, and MF categories linked to the gene sets ( Fig. 10a; Table 5 ). The analysis revealed significant gene enrichment in metabolism of amino acids and derivatives ( Fig. 10b ), translation ( Fig. 10c ), the adipocytokine signaling pathway ( Fig. 10d ), and the agerage pathway ( Fig. 10e ), among other biologically relevant actions and pathways. 3.8. GSVA of HRG and LRG NS To examine variations between h.all.v7.4.symbols.gmt gene sets in the HRG and LRG NS, GSVA was conducted using all NS samples from the combined datasets ( Table 6 ). Pathways with p.adj < 0.05 were identified and visualized via a group comparison plot ( Fig. 11a ) and heatmap ( Fig. 11b ). The significantly enriched pathways included cholesterol homeostasis, IL6/JAK/STAT3 signaling, androgen response, protein secretion, xenobiotic metabolism, glycolysis, bile acid metabolism, and Myc targets V2, suggesting differential activation of metabolic and inflammatory signaling between the HRG and LRG. 3.9. Immune infiltration analysis via CIBERSORT The relative abundance of 22 IC types in HRG and LRG NS was estimated via the CIBERSORT from the combined dataset. Group comparisons (Fig. 12a) identified seven IC types with significant variations ( p < 0.05): monocytes, M2 macrophages, neutrophils, central memory CD4⁺ T, CD8⁺ T, naïve CD4⁺ T, and regulatory T (Tregs) cells. Correlation analysis (Fig. 12b) illustrated a positive correlation between Tregs and neutrophils (r = 0.34) and an inverse correlation between Tregs and central memory CD4⁺ T cells (r = −0.58). HG-IC correlations (Fig. 12c) revealed that IL18R1 was negatively correlated with monocytes (r = −0.41) and positively correlated with neutrophils (r = 0.64), indicating a potential immunomodulatory role in NS pathophysiology. 3.10. Construction of the interaction network To elucidate molecular regulatory mechanisms, multiple interaction networks were constructed based on the seven HGs. First, PPI analysis using the STRING database ( Fig. 13a) identified six interrelated HGs: ARG2 , IL18R1 , IL1RN , MERTK , RETN , and STAT3 . Next, TFs targeting these HGs were acquired by accessing the ChIPBas database to generate an mRNA–TF regulatory network, which comprised 5 HGs and 27 TFs (Fig. 13b; Table S6) . HG-connected RBPs were acquired from the ENCORI database, and an mRNA–RBP regulatory network including 4 HGs and 23 RBPs was generated (Fig. 13c) (Table S7) . HG-related miRNAs were identified from miRDB to produce an mRNA-miRNA regulatory network containing 5 HGs and 57 miRNAs (Fig. 13d; Table S8) . Finally, potential drug–HG interactions were predicted via the DGIdb database to generate an mRNA–drug regulatory network comprising 5 HGs and 28 drugs (Fig. 13e) (Table S9) . Table 1. NS dataset information. GSE25504 GSE69686 Platform GPL6947 GPL20292 Species Homo sapiens Homo sapiens Tissue Blood Blood NS samples 26 64 Control samples 37 85 Reference 25120092 26052715 Table 2. Outcomes of GO and KEGG analyses. Ontology ID Description GeneRatio BgRatio p.adj q-value BP GO:0050727 Regulation of inflammatory response 21/69 394/18800 7.63E-16 4.83E-16 BP GO:0002764 Immune response-modulating pathway 19/69 482/18800 2.52E-12 1.6E-12 BP GO:0050863 Control of T-cell stimulation 16/69 342/18800 1.94E-11 1.23E-11 BP GO:2000106 Regulation of the leukocyte apoptotic process 7/69 85/18800 8.97E-07 5.68E-07 GO:0042098 T-cell proliferation 9/69 204/18800 1.76E-06 1.12E-06 CC GO:0009897 External side of the plasma membrane 13/69 455/19594 5.9E-07 4.03E-07 GO:0034774 Secretory granule lumen 11/69 322/19594 5.9E-07 4.03E-07 GO:0060205 Cytoplasmic vesicle lumen 11/69 325/19594 5.9E-07 4.03E-07 GO:0031983 Vesicle lumen 11/69 327/19594 5.9E-07 4.03E-07 GO:0030139 Endocytic vesicle 8/69 342/19594 0.000387 0.000264 MF GO:0003953 NAD + nucleosidase activity 7/68 28/18410 1.86E-09 1.31E-09 GO:0070851 Growth factor receptor binding 4/68 139/18410 0.016201 0.011415 GO:0005164 Tumor necrosis factor receptor binding 2/68 31/18410 0.033237 0.023419 GO:0140375 Immune receptor activity 9/68 148/18410 2.95E-07 2.08E-07 GO:0019955 Cytokine binding 6/68 141/18410 0.000329 0.000232 KEGG hsa04668 TNF pathway 6/57 112/8164 0.001519 0.001047 hsa04064 NF-kappa B pathway 5/57 104/8164 0.006221 0.004286 hsa04660 T-cell receptor pathway 7/57 104/8164 0.000287 0.000198 hsa04935 Growth hormone synthesis, release, and function 4/57 120/8164 0.044136 0.030409 hsa04062 Chemokine pathway 7/57 192/8164 0.003595 0.002477 Table 3. GSEA outcomes. Description Set size NES p.adj q-value WP_MAPK_SIGNALING_PATHWAY 185 1.581218 0.024634 0.020457 REACTOME_FATTY_ACID_METABOLISM 115 1.619569 0.016607 0.013792 REACTOME_TRANSLATION 234 −2.89142 1.14E-08 9.48E-09 REACTOME_METABOLISM_OF_RNA 494 −2.41236 1.14E-08 9.48E-09 Table 4. GSVA outcomes. ID logFC p-value Adjusted p-value HALLMARK_MYC_TARGETS_V2 0.286724 1.40E-12 8.75E-12 HALLMARK_MYC_TARGETS_V1 0.210348 1.87E-05 4.07E-05 HALLMARK_DNA_REPAIR 0.153172 1.38E-06 3.64E-06 HALLMARK_PANCREAS_BETA_CELLS 0.087183 0.021677 0.030968 HALLMARK_FATTY_ACID_METABOLISM −0.07554 0.013401 0.019707 HALLMARK_UV_RESPONSE_DN −0.07724 0.011539 0.01803 HALLMARK_ESTROGEN_RESPONSE_EARLY −0.08645 0.008106 0.013509 HALLMARK_MYOGENESIS −0.08767 0.031261 0.043418 HALLMARK_MITOTIC_SPINDLE −0.08846 0.013201 0.019707 HALLMARK_PROTEIN_SECRETION −0.0974 0.009228 0.014884 HALLMARK_COMPLEMENT −0.21213 6.38E-10 2.90E-09 HALLMARK_ANDROGEN_RESPONSE −0.22153 2.48E-10 1.24E-09 HALLMARK_COAGULATION −0.2401 8.16E-11 4.53E-10 HALLMARK_XENOBIOTIC_METABOLISM −0.24415 1.38E-16 1.73E-15 HALLMARK_TNFA_SIGNALING_VIA_NFKB −0.25575 1.13E-12 8.07E-12 HALLMARK_ANGIOGENESIS −0.25685 5.42E-09 2.26E-08 HALLMARK_HYPOXIA −0.25999 1.87E-18 4.68E-17 HALLMARK_INFLAMMATORY_RESPONSE -0.27214 9.73E-13 8.07E-12 HALLMARK_IL6_JAK_STAT3_SIGNALING −0.35389 3.92E-18 6.53E-17 HALLMARK_CHOLESTEROL_HOMEOSTASIS −0.37669 4.54E-27 2.27E-25 Table 5. GSEA outcomes by NS risk group. Description Set size NES p.adj q-value WP_AGERAGE_PATHWAY 55 1.811679 0.018734 0.016594 KEGG_ADIPOCYTOKINE_SIGNALING_PATHWAY 51 1.769795 0.039935 0.035373 REACTOME_TRANSLATION 234 −2.79 1.21E-08 1.07E-08 REACTOME_METABOLISM_OF_AMINO_ACIDS_AND_DERIVATIVES 250 −1.98276 2.35E-07 2.08E-07 NES, normalized enrichment score. Table 6. GSVA results by NS risk group. ID logFC p-value Adjusted p-value HALLMARK_CHOLESTEROL_HOMEOSTASIS 0.210889 5.87E-05 0.002936 HALLMARK_IL6_JAK_STAT3_SIGNALING 0.184796 0.002257 0.028217 HALLMARK_ANDROGEN_RESPONSE 0.175515 0.000566 0.014161 HALLMARK_PROTEIN_SECRETION 0.162653 0.0029 0.028995 HALLMARK_XENOBIOTIC_METABOLISM 0.139336 0.001851 0.028217 HALLMARK_GLYCOLYSIS 0.126744 0.004731 0.035826 HALLMARK_BILE_ACID_METABOLISM 0.122398 0.006966 0.043539 HALLMARK_MYC_TARGETS_V2 −0.18646 0.005016 0.035826 4. Discussion NS constitutes a common, severe infection and a major cause of neonatal mortality. Its clinical manifestations are nonspecific, and traditional inflammatory biomarkers [e.g., procalcitonin and C-reactive protein (CRP)] have insufficient sensitivity and specificity for early diagnosis [ 32 ] . Therefore, identifying reliable molecular biomarkers and establishing diagnostic models are essential for improving early detection and prognosis. Herein, we incorporated two GEO transcriptomic datasets, identified 69 IMRDEGs, and developed a diagnostic model based on seven HGs ( ARG2 , IL18R1 , IL1RN , MERTK , RETN , STAT3 , and TSPO ). The model demonstrated excellent diagnostic precision (AUC > 0.9) in training and validation groups, providing insights into NS pathogenesis and early diagnosis. Compared with traditional inflammatory markers, our model achieved higher sensitivity and specificity (AUC > 0.9), suggesting strong clinical potential for early NS detection [ 33 ] . Among the HGs, IL1RN contributed most to the diagnostic model. IL1RN encodes IL-1 receptor antagonist (IL-1ra), competitively suppresses IL-1 activity, and limits inflammatory injury. Previous studies have shown that IL-1ra levels rise earlier than CRP levels during sepsis onset [ 34 ] and that IL1RN polymorphisms are linked to sepsis susceptibility [ 35 ] , underscoring its dual potential as a diagnostic biomarker and a therapeutic target. IL18R1 functions as a key receptor in the IL-18 signaling pathway. Elevated serum IL-18 concentrations have been detected in NS and are predictive of mortality [ 36 ] . In our study, IL18R1 expression correlated positively with neutrophil infiltration, suggesting that the IL-18– IL18R1 axis may exacerbate inflammation by modulating neutrophil function. Although less studied in NS, other HGs play recognized roles in inflammation and immune regulation. ARG2 promotes immunosuppression in adult sepsis by inhibiting T-cell activity through arginine metabolism [ 37 ] , although its neonatal role remains unclear. MERTK , encoding a phagocytic receptor, contributes to inflammation resolution and apoptotic cell clearance [ 38 ] ; impaired function may disturb immune homeostasis, although direct evidence in NS is lacking. RETN protein levels in serum have been reported to increase during sepsis, and a pediatric meta-analysis supports RETN 's biomarker potential [ 39 ] . STAT3 , encoding a central transcription factor in the JAK/STAT pathway, mediates sepsis-associated immunosuppression [ 40 ] . TSPO , encoding a mitochondrial transmembrane protein, regulates energy metabolism under inflammatory and oxidative stress; elevated plasma TSPO protein levels observed in adult sepsis cases have highlighted TSPO 's diagnostic potential [ 41 ] , although its role in neonates requires further validation. Collectively, our seven-gene diagnostic model enhances diagnostic precision and underscores immune–metabolic dysregulation as a central NS mechanism. IC infiltration is pivotal in NS pathogenesis. In the present study, seven IC types (monocytes, M2 macrophages, neutrophils, Tregs, central memory CD4 + , CD8 + , and naïve CD4 + T cells) differed significantly between NS and control samples, highlighting their roles in disease progression. Monocytes, essential for phagocytosis and antigen presentation, showed altered abundance in NS, likely reflecting pathogen invasion and inflammatory activation. Neonatal monocytes differ functionally from adult monocytes, exhibiting activation patterns that may impair pathogen clearance and elevate infection risk [ 42 ] . Neutrophils, the first defense line in neonates, are functionally immature in chemotaxis, phagocytosis, and bactericidal activity, hindering infection control [ 43 ] . We observed significant variations in neutrophil infiltration between NS and control samples, with neutrophil abundance positively correlating with IL18R1 expression, suggesting amplification of IL-18– IL18R1 -mediated inflammation [ 36 ] . T cells are developmentally immature in neonates, with reduced CD4 + memory T-cell populations, resulting in delayed adaptive immune responses [ 44 ] . The observed negative relationship between Tregs and CD4 + memory T cells supports impaired adaptive immunity. Tregs play a dual role in NS: they suppress excessive inflammation to protect tissues but induce immunosuppression when overactivated [ 45 ] . Elevated Treg frequencies in preterm infants have been associated with higher infection risk compared with term infants. A negative relationship between Tregs and CD4 + memory T cells was observed, suggesting that enhanced immunosuppression may weaken adaptive immune responses. Collectively, our outcomes illustrate that NS involves abnormalities in innate ICs (monocytes and neutrophils), deficiencies in adaptive ICs (CD8 + and CD4 + memory T cells), and Treg-driven immunosuppression, reflecting a multifaceted immune imbalance. Correlation analysis illustrated close interactions between HGs and ICs. IL18R1 correlated positively with neutrophils and negatively with monocytes, illustrating that the IL-18 –IL18R1 axis may both amplify inflammation, consistent with a prior study on IL-18 in NS [ 36 ] , and engage a negative feedback mechanism to limit inflammation. Overactivation of IL-18 has been linked to immune dysregulation in NS [ 46 ] . Additionally, STAT3 and MERTK may influence macrophage polarization and immunosuppressive states, further shaping the NS immune microenvironment. Notably, ARG2 suppresses T-cell function via arginine metabolism, contributing to sepsis-induced immunosuppression [ 36 ] . Collectively, these findings highlight gene–IC interactions as critical contributors to NS pathophysiology. Despite the insights of this investigation, several constraints should be acknowledged. Data were acquired from public databases and lack further validation in independent multicenter cohorts. Analyses were restricted to transcriptomic data and require confirmation by proteomic and metabolomic studies. Moreover, experimental validation was absent, and gene-cell interaction mechanisms warrant further investigation in vitro and in vivo . Upcoming investigations should incorporate prospective clinical samples, multiomics approaches, and functional studies to facilitate clinical translation of our diagnostic model. Integrating molecular biomarkers with immune infiltration profiles may enable early, precise diagnosis and personalized immunomodulatory therapy in NS cases. 5. Conclusion By integrating multiple transcriptomic datasets, we identified seven immune regulation–related HGs ( ARG2 , IL18R1 , IL1RN , MERTK , RETN , STAT3 , and TSPO ) and developed a novel diagnostic model for NS. The model demonstrated strong diagnostic accuracy and clinical relevance, outperforming conventional inflammatory biomarkers. Immune infiltration analysis revealed the characteristic features of NS, including impaired innate immunity, weakened adaptive responses, and heightened Treg-mediated immunosuppression. Correlation analyses further suggested that NS pathogenesis involves a complex cycle of "inflammation amplification–insufficient resolution–immunosuppression." These findings suggest innovative mechanistic visions and potential molecular targets for early NS diagnosis and intervention. Further validation in large, multicenter, prospective cohorts is warranted, along with exploration of combined clinical diagnostic strategies integrating established biomarkers (e.g., CRP and procalcitonin) and the seven-gene signature. Abbreviation AUC Area under the curve BP Biological process CC Cellular component DCA Decision curve analysis DEG Differentially expressed gene FPR False positive rate GO Gene Ontology GSEA Gene set enrichment analysis GSVA Gene set variation analysis HGs Hub genes IMRDEGs Immunomodulation-related DEGs IMRGs Immunomodulation-related gene KEGG Kyoto Encyclopedia of Genes and Genomes LASSO Least absolute shrinkage and selection operator MF Molecular function miRNA microRNA NS Neonatal sepsis PPI Protein–protein interaction RBP RNA-binding protein ROC Receiver operating characteristic SVM-RFE Support vector machine recursive feature elimination TF Transcription factor TPR True positive rate Statements and Declarations Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The authors have no relevant financial or non-financial interests to disclose.. Author Contributions Huiling Luo conceived and designed the study, conducted the bioinformatic analyses, interpreted the data, and drafted the manuscript. Bo Bai contributed to data preprocessing, statistical analyses, and interpretation of the results. All authors reviewed and approved the final version of the manuscript. Data Availability All datasets analyzed in this study are publicly available from the Gene Expression Omnibus (GEO) database. The accession numbers are GSE25504 and GSE69686. Additional processed data and analysis scripts are available from the corresponding author upon reasonable request. Ethics approval This study is based entirely on previously published, de-identified gene expression data obtained from the public GEO database. In accordance with institutional and national regulations, additional approval from a research ethics committee was not required. Consent to participate Not applicable. Consent to publish Not applicable. 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Infect Immun 84:1966–1974. https://doi.org/10.1128/iai.00111-16 Additional Declarations No competing interests reported. Supplementary Files 5SupplementaryTable.zip Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8030559","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":539974097,"identity":"511474f1-7813-4b50-99bd-24cc55054475","order_by":0,"name":"Huiling Luo","email":"","orcid":"","institution":"Huadu District People's Hospital of Guangzhou","correspondingAuthor":false,"prefix":"","firstName":"Huiling","middleName":"","lastName":"Luo","suffix":""},{"id":539974098,"identity":"4bc66997-7d58-4caf-ad01-bab3b6971dcf","order_by":1,"name":"Bo 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2","display":"","copyAsset":false,"role":"figure","size":210156,"visible":true,"origin":"","legend":"\u003cp\u003eBatch effect removal from GSE25504 and GSE69688 datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBoxplots:\u003c/strong\u003e Distribution of the combined GEO dataset \u003cstrong\u003e(a)\u003c/strong\u003epre- and \u003cstrong\u003e(b)\u003c/strong\u003e post-batch effect elimination. \u003cstrong\u003ePCA plots:\u003c/strong\u003e GEO datasets \u003cstrong\u003e(c)\u003c/strong\u003e pre- and \u003cstrong\u003e(d)\u003c/strong\u003e post-batch correction. Red and Blue: NS dataset GSE25504 and GSE69686, respectively.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8030559/v1/186e0f997f6ff34c000e1f90.png"},{"id":95795607,"identity":"e06a9574-c909-40dd-b47e-5878550096d6","added_by":"auto","created_at":"2025-11-13 07:44:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":55333,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential gene expression analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a) Volcano plot:\u003c/strong\u003e Control vs. NS samples in the pooled dataset. \u003cstrong\u003e(b) Venn diagram\u003c/strong\u003e: Intersection between DEGs in the pooled dataset and IMRGs.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8030559/v1/769920fdc12eaa3d7701f630.png"},{"id":95795619,"identity":"def10012-b168-4262-a2de-807073244c87","added_by":"auto","created_at":"2025-11-13 07:44:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":176340,"visible":true,"origin":"","legend":"\u003cp\u003eGO and KEGG analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a) Bubble plot\u003c/strong\u003e: GO and KEGG outcomes for IMRDEGs. Bubble size: Gene count; Color: \u003cem\u003ep\u003c/em\u003e.adj (red: lower; blue: higher). \u003cstrong\u003e(b–e) Network diagrams\u003c/strong\u003e: Enriched GO and KEGG terms, where terms (orange nodes), genes (blue nodes), and associations (lines).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8030559/v1/9e39f2fd70e37278bfe60197.png"},{"id":95795611,"identity":"e3912921-4509-48b6-ac07-317d006e7e43","added_by":"auto","created_at":"2025-11-13 07:44:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":92021,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA for NS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a) \u003c/strong\u003eOverview \"mountain map\" of GSEA biological roles in the pooled dataset. \u003cstrong\u003e(b–e)\u003c/strong\u003e GSEA: Significant enrichment in \u003cstrong\u003e(b)\u003c/strong\u003e RNA metabolism, \u003cstrong\u003e(c)\u003c/strong\u003e translation, \u003cstrong\u003e(d)\u003c/strong\u003e fatty acid metabolism, and \u003cstrong\u003e(e)\u003c/strong\u003e MAPK signaling.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8030559/v1/a4339486bca1960aed2a436c.png"},{"id":95818575,"identity":"06348881-ce0a-43f9-97a5-8018216fd69b","added_by":"auto","created_at":"2025-11-13 10:21:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":205623,"visible":true,"origin":"","legend":"\u003cp\u003eGSVA analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003eGroup comparison plot and \u003cstrong\u003e(b)\u003c/strong\u003e heatmap of GSVA outcomes between the NS and control in the pooled GEO dataset. ns, \u003cem\u003ep\u003c/em\u003e ≥ 0.05 (non-significant). Orange, NS; blue, control. In the heatmap: purple, low enrichment; red, high enrichment.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8030559/v1/397670bc022e0d133628b188.png"},{"id":95818651,"identity":"96f72dec-09af-4413-863d-f201416c692d","added_by":"auto","created_at":"2025-11-13 10:21:13","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":205726,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic model of NS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a) Forest plot\u003c/strong\u003e: The 69 IMRDEGs involved in the LR model. \u003cstrong\u003eSVM-RFE\u003c/strong\u003e: Number of genes with \u003cstrong\u003e(b)\u003c/strong\u003e the lowest error rate and \u003cstrong\u003e(c)\u003c/strong\u003e the highest precision. \u003cstrong\u003e(d)\u003c/strong\u003e Diagnostic model (D) and \u003cstrong\u003e(e)\u003c/strong\u003e variable trajectory plots of the LASSO regression model.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8030559/v1/d9d204ba1af44f77f26990a3.png"},{"id":95818587,"identity":"1bfa9552-bc50-4500-894e-8612f8d072a8","added_by":"auto","created_at":"2025-11-13 10:21:02","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":102017,"visible":true,"origin":"","legend":"\u003cp\u003eDiagnostic and validation analyses of NS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e Nomogram of HGs in the diagnostic model depending on the combined GEO dataset. \u003cstrong\u003e(b)\u003c/strong\u003eCalibration curve and \u003cstrong\u003e(c)\u003c/strong\u003e DCA plot: NS diagnostic model. \u003cstrong\u003e(d) ROC curve\u003c/strong\u003e: Risk score in the combined dataset. AUCs \u0026gt; 0.5: Positive association with event incidence; near 1: stronger diagnostic capability, \u0026gt; 0.9: High accuracy.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8030559/v1/1cfd7794a105df3beea06107.png"},{"id":95795628,"identity":"aeef0d64-c41d-42fa-8be9-07874acc298a","added_by":"auto","created_at":"2025-11-13 07:44:00","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":128114,"visible":true,"origin":"","legend":"\u003cp\u003eExpression analysis of HGs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eROC curves:\u003c/strong\u003e \u003cstrong\u003e(a)\u003c/strong\u003e IL18R1, IL1RN, TSPO, and STAT3, and \u003cstrong\u003e(b)\u003c/strong\u003e MERTK, RETN, and ARG2 in the NS group. \u003cstrong\u003e(c)\u003c/strong\u003e Group comparison plot and \u003cstrong\u003e(d)\u003c/strong\u003e heatmap of HG expression between NS and control. \u003cstrong\u003e(e) Correlation heatmap\u003c/strong\u003e: HGs in the pooled GEO dataset. \u003cstrong\u003e(f) Chromosomal mapping:\u003c/strong\u003e HGs. AUC \u0026gt; 0.5: Positive association with event occurrence; 0.5–0.7, low; 0.7–0.9, moderate accuracy. Orange: NS group; blue: control group. Correlation coefficient (r): \u0026lt;0.3, weak or none; 0.3–0.5, weak; 0.5–0.8, moderate. Red and purple: positive and negative correlation, respectively; color intensity: Correlation strength.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-8030559/v1/611294248abf44ba3aa19277.png"},{"id":95795626,"identity":"7e618090-478f-4ae9-8d4d-1070d24d2994","added_by":"auto","created_at":"2025-11-13 07:44:00","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":102023,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential gene expression analysis and GSEA of HRG and LRG NS. \u003cstrong\u003e(a)\u003c/strong\u003e Overview of GSEA results from NS samples\u003cstrong\u003e. (b–e)\u003c/strong\u003e Significantly enriched pathways in the HRG: \u003cstrong\u003e(b)\u003c/strong\u003e amino acid metabolism, \u003cstrong\u003e(c)\u003c/strong\u003e translation, \u003cstrong\u003e(d)\u003c/strong\u003eadipocytokine signaling, and \u003cstrong\u003e(e)\u003c/strong\u003e AGERAGE pathway.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-8030559/v1/782e0b8400cc8cc42cb42773.png"},{"id":95818646,"identity":"3cf853b0-4ce9-469c-91a6-ba60ba47845d","added_by":"auto","created_at":"2025-11-13 10:21:11","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":111959,"visible":true,"origin":"","legend":"\u003cp\u003eGSVA analysis of HRG and LRG NS. \u003cstrong\u003e(a)\u003c/strong\u003e Group comparison plot and \u003cstrong\u003e(b)\u003c/strong\u003e heatmap: GSVA results in the pooled GEO dataset. Green: LRG; orange: HRG. In the heatmap, purple: low enrichment, red: high enrichment.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-8030559/v1/ed73c605d0115a616b08a008.png"},{"id":95818612,"identity":"4bc33467-b4ef-4143-b7c3-ab4c3fd699c3","added_by":"auto","created_at":"2025-11-13 10:21:03","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":79138,"visible":true,"origin":"","legend":"\u003cp\u003eImmune infiltration analysis using CIBERSORT.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e Comparison of IC infiltration between HRG and LRG NS in the combined GEO dataset. \u003cstrong\u003e(b) Correlation analysis\u003c/strong\u003e: IC infiltration abundance. \u003cstrong\u003e(c)\u003c/strong\u003e \u003cstrong\u003eCorrelation bubble plot:\u003c/strong\u003e HGs and ICs. Correlation coefficient (r): \u0026lt;0.3, weak or none; 0.3–0.5, weak; 0.5–0.8, moderate. Orange: HRG; green: LRG; red: positive; purple: negative; color intensity: Correlation strength.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-8030559/v1/bbe72057f036b3d2bcece9a7.png"},{"id":95795644,"identity":"a50985d6-05a8-4929-843e-b3d0957c9c10","added_by":"auto","created_at":"2025-11-13 07:44:01","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":246028,"visible":true,"origin":"","legend":"\u003cp\u003eHG interaction networks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e PPI network of STRING database-derived HGs. Regulatory networks: \u003cstrong\u003e(b)\u003c/strong\u003e mRNA–TF. \u003cstrong\u003e(c)\u003c/strong\u003e mRNA–RBP. \u003cstrong\u003e(d)\u003c/strong\u003e mRNA–miRNA. \u003cstrong\u003e(e)\u003c/strong\u003e mRNA–drug.\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-8030559/v1/f3becb3e6536f83fd37628c7.png"},{"id":99306902,"identity":"bbce8f59-0041-44f1-9b55-2f1608e273b4","added_by":"auto","created_at":"2025-12-31 16:03:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3098563,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8030559/v1/6423327d-8619-4342-829c-744236a9241a.pdf"},{"id":96238878,"identity":"b1d1144f-97fb-4c29-b08e-e1bc481021fa","added_by":"auto","created_at":"2025-11-19 06:45:50","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":657402,"visible":true,"origin":"","legend":"","description":"","filename":"5SupplementaryTable.zip","url":"https://assets-eu.researchsquare.com/files/rs-8030559/v1/888fd59f01dbbc2196051a20.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bioinformatics Identification of Immunomodulatory Genes Related to Neonatal Sepsis and Their Incorporation into a Diagnostic Model","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNeonatal sepsis (NS) represents a severe clinical syndrome causing high morbidity and mortality in infants under 28 days of age. It results from systemic infection leading to serious complications, such as circulatory shock and multiorgan failure. The main causative agents are bacteria, most prevalently \u003cem\u003eStaphylococcus aureus\u003c/em\u003e and \u003cem\u003eEscherichia coli\u003c/em\u003e \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. The clinical presentation of NS varies widely, often beginning with nonspecific symptoms that hinder prompt diagnosis and treatment. Early recognition is essential, as delayed intervention can have fatal consequences, underlining the demanding requirement for reliable diagnostic biomarkers and successful treatments \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCurrent diagnosis relies predominantly on blood cultures; however, although they are the gold standard, they often fail to yield timely results. This delay can cause inappropriate or late antibiotic administration, highlighting the necessity of sensitive and specific biomarkers for early detection of sepsis \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Existing biomarkers show limited specificity and sensitivity, leaving a major gap in accurate NS diagnosis. A high proportion of cases remain undetected until severe clinical symptoms appear, emphasizing the importance of new diagnostic strategies to enable earlier recognition and better outcomes for affected neonates \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAdvances in bioinformatics, including expression profiling and machine learning algorithms, provide valuable tools for determining novel biomarkers for NS \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Using large-scale genomic datasets, researchers can explore the molecular mechanisms underlying sepsis and detect differentially expressed genes (DEGs) that may act as diagnostic indicators. For instance, the R package \"GEOquery\" \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e enables the retrieval and analysis of gene expression data obtained by accessing the Gene Expression Omnibus (GEO), supporting the identification of immunomodulatory genes vital to NS pathogenesis \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHerein, we applied a systematic bioinformatics strategy to detect DEGs associated with NS. \"Limma\" was utilized to conduct differential expression analysis on combined GEO datasets, facilitating the identification of immunomodulation-related DEGs (IMRDEGs) and providing insights into immune responses during sepsis \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Seeking the examination of biological processes and pathways significantly connected to the determined IMRDEGs, \"clusterProfiler\" was utilized to apply GO and KEGG enrichment analyses\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were conducted to assess functional gene set enrichment and identify pathway variations among clinical groups. These analyses revealed the biological mechanisms underlying varying clinical outcomes in patients with NS \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. By integrating these computational methods, we aimed to establish a robust diagnostic model to accurately stratify NS risk, promote timely clinical intervention, and enhance patient outcomes. Ultimately, this investigation enhances our understanding of the immune landscape in NS and identifies therapeutic targets to mitigate its severe effects.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Data collection\u003c/h2\u003e\n \u003cp\u003e\u0026quot;GEOquery\u0026quot; (v2.70.0) from GEO \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e was utilized to download NS datasets, GSE25504 \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e and GSE69686 \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. All samples in GSE25504 and GSE69686 were acquired from \u003cem\u003eHomo sapiens\u003c/em\u003e blood tissue, via the chip platforms GPL6947 and GPL20292, respectively. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the detailed dataset information. Dataset GSE25504 included 26 NS samples and 37 controls, whereas dataset GSE69686 included 64 NS and 85 controls. Herein, we included all NS and control samples.\u003c/p\u003e\n \u003cp\u003eThe\u0026quot; sva\u0026quot; \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e (v3.50.0) was utilized to eliminate batch effects between GSE25504 and GSE69686 and generate a combined GEO dataset, which included 90 NS and 122 control samples. The \u0026quot;limma\u0026quot; (v3.58.1) was utilized to normalize this combined dataset \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, and probe annotations were standardized. Principal component analysis (PCA) was employed to express patterns pre- and post-batch correction to verify the efficiency of the adjustment \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. PCA, a dimensionality reduction technique, isolates key constituents from high-dimensional data and displays them in 2D or 3D space.\u003c/p\u003e\n \u003cp\u003eThe collection of Immunomodulation-related genes (IMRGs) was conducted from GeneCards \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. In this database, comprehensively covering human genes, the search term \u0026quot;Immunomodulation\u0026quot; was utilized and retained only \u0026quot;Protein Coding\u0026quot; entries, yielding 981 IMRGs. The same term was searched in PubMed to identify additional IMRG sets from published studies \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. After removing duplicates, 981 IMRGs were retained. \u003cstrong\u003eTable \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e displays more detailed data.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. DEGs related to NS-associated immune regulation\u003c/h2\u003e\n \u003cp\u003eBased on sample grouping in the combined datasets, we divided samples into NS and control groups, thereby analyzing differential gene expression via \u0026quot;limma\u0026quot; using thresholds of |logFC| \u0026gt;0.5 and \u003cem\u003ep.adj\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for detecting DEGs. Genes displaying logFC\u0026thinsp;\u0026gt;\u0026thinsp;0.5 and \u003cem\u003ep.adj\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were deemed overexpressed, while those with logFC\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;0.5 and \u003cem\u003ep.adj\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were deemed reduced. The \u0026quot;ggplot2\u0026quot; (v3.4.4) was utilized to visualize the differential expression outcomes. For adjusting the \u003cem\u003ep\u003c/em\u003e-value, the Benjamini-Hochberg (BH) technique was used. To identify IMRDEGs associated with NS, the intersection of DEGs meeting |logFC| \u0026gt;0.5 and \u003cem\u003ep.adj\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 with IMRGs was conducted, and a Venn diagram was created to demonstrate overlap.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. GO and KEGG analyses\u003c/h2\u003e\n \u003cp\u003eGO analysis \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e is frequently employed for large-scale functional enrichment analysis, such as biological process (BP), cellular component (CC), and molecular function (MF) categories. KEGG \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e represents a comprehensive resource on genomes, pathways, disorders, and medications. \u0026quot;clusterProfiler\u0026quot; (v4.10.0) was utilized to carry out GO and KEGG analyses of IMRDEGs\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Items fulfilling \u003cem\u003ep\u003c/em\u003e.adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and false discovery rate (FDR; q-value)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated significance. The BH technique was applied for adjusting the p-value.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. GSEA\u003c/h2\u003e\n \u003cp\u003eGSEA \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e was conducted via the clusterProfiler to assess the predefined gene sets\u0026apos; distribution, sorted by relationship with phenotype. The pooled dataset\u0026apos;s genes were sorted using logFC values. The analysis parameters were a 2020 seed, 1,000 permutations, and a gene set size of 10\u0026ndash;500. The dataset, including 3,050 gene sets, was acquired by accessing MSigDB \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e (c2.cp.all.v2022.1.Hs.symbols.GMT, 3,050 sets). Outcomes with \u003cem\u003ep.adj\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 after BH correction indicated significance.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5. GSVA\u003c/h2\u003e\n \u003cp\u003eGSVA \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e is a non-parametric, uncontrolled approach that ascertains gene set enrichment by transforming the gene expression matrix across samples into gene set enrichment scores. This method assesses pathway enrichment differences between samples. The h.all.v7.4.symbols.gmt gene set was acquired by accessing MSigDB, and the combined datasets were analyzed to determine functional enrichment differences between groups. \u003cem\u003eP.adj\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was regarded as significant, with BH used for p-value correction.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6. A diagnostic model establishment for NS\u003c/h2\u003e\n \u003cp\u003eTo create NS diagnostic models from the pooled datasets, logistic regression (LR) analysis was carried out on the IMRDEGs related to NS. When the dependent variable was binary, LR was utilized to assess the link between the independent and dependent variables (NS vs. Control). A \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was regarded as the threshold to select IMRDEGs and establish the LR model. The levels of IMRDEGs involved in the model were visualized using a forest plot.\u003c/p\u003e\n \u003cp\u003eDepending on the IMRDEGs in the LR model, \u0026quot;e1071\u0026quot; (v1.7.14) was employed for additional IMRDEG screening. The support vector machine recursive feature elimination (SVM-RFE) algorithm \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e was utilized to detect potential biomarkers. It is a feature selection algorithm within the SVM framework that iteratively removes features contributing least to classification to select the most informative genes.\u003c/p\u003e\n \u003cp\u003eSubsequently, \u0026quot;glmnet\u0026quot; (v4.1.8) was deployed to conduct LASSO regression\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e with the parameters set.seed (500) and family=\u0026quot;binomial\u0026quot; depending on the IMRDEGs involved in SVM-RFE. LASSO regression, following linear regression principles, reduces model overfitting by introducing a penalty term (lambda \u0026times; absolute slope value) to enhance generalization. Diagnostic and variable trajectory plots were generated to observe LASSO regression outcomes. The resulting model was used as the diagnostic model for NS, and the IMRDEGs it contained were defined as hub genes (HGs). A LASSO risk score (RS) was then computed through the coefficient from LASSO regression:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eA nomogram \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e was constructed using \u0026quot;rms\u0026quot; (v6.7.1) based on LR results to visualize relationships among HGs. Model calibration was assessed with a calibration curve to evaluate accuracy and discrimination. Clinical utility was examined using decision curve analysis (DCA) via ggDCA (v1.1)\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, while diagnostic performance was evaluated with receiver operating characteristic (ROC) curves and AUC values using \u0026quot;pROC\u0026quot; (v1.18.5) \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e in the combined dataset, assessing the predictive power of the LASSO-derived RS for NS.\u003c/p\u003e\n \u003cp\u003eNS samples were assigned to high-risk (HRG) and low-risk groups (LRG) using the median RS from the NS diagnostic model. The \u0026quot;pROC\u0026quot; (v1.18.5) \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e was again utilized to generate ROC curves and calculate AUCs for HGs to evaluate their diagnostic accuracy for NS. The AUC ranges from 0.5 to 1.0, with values near 1 reflecting superior diagnostic precision: 0.5\u0026ndash;0.7 low, 0.7\u0026ndash;0.9 moderate, and \u0026gt;\u0026thinsp;0.9 high.\u003c/p\u003e\n \u003cp\u003eTo further examine HG expression variations between NS and Control in the combined dataset, comparison plots were produced. The \u0026quot;pheatmap\u0026quot; (v1.0.12) was utilized to create heatmaps, and \u0026quot;Rcircos\u0026quot; (v1.2.2) \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e was employed to create chromosome localization maps. Then, Spearman correlation analysis was utilized for the assessment of links among HGs, and pheatmap was again utilized to visualize correlation heatmaps.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.7. GSEA of HRG and LRG\u003c/h2\u003e\n \u003cp\u003eSamples in the NS group from the combined datasets were assigned into HRG and LRG depending on the median LASSO RS. Limma was utilized for differential expression analysis, followed by GSEA with clusterProfiler. GSEA parameters were: seed\u0026thinsp;=\u0026thinsp;2020, gene set size\u0026thinsp;=\u0026thinsp;10\u0026ndash;500. Gene sets were acquired from MSigDB (c2.cp.all.v2022.1.Hs.symbols.GMT, 3,050 sets). Outcomes with \u003cem\u003ep.adj\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were deemed significant after BH correction.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e2.8. Immune infiltration analysis: CIBERSORT\u003c/h2\u003e\n \u003cp\u003eCIBERSORT \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e uses linear support vector regression to deconvolute transcriptomic data and estimate immune cell (IC) composition and abundance. Using the LM22 signature matrix, samples exhibiting immune enrichment scores\u0026thinsp;\u0026gt;\u0026thinsp;0 were maintained to produce an immune infiltration matrix for the pooled datasets. Group variations in LM22 IC expression were visualized with \u0026quot;ggplot2\u0026quot;, and significantly altered ICs were selected for additional analysis.\u003c/p\u003e\n \u003cp\u003eThe Spearman algorithm was utilized to determine the relationships among ICs, and pheatmap was employed to create a correlation heatmap presenting IC associations. The links between HGs and ICs were computed via Spearman analysis, retaining outcomes with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Finally, \u0026quot;ggplot2\u0026quot; was deployed to draw correlation bubble plots and visualize these associations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e2.9. Protein-protein interaction (PPI) network\u003c/h2\u003e\n \u003cp\u003eA PPI network represents interactions among proteins participating in signaling, gene expression control, cell cycle control, and metabolism. Systematic interaction analysis is vital for the comprehension of protein roles, biological signal transduction, and metabolic regulation under physiological and disease conditions. The STRING database \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e was utilized to detect interactions among HGs with the lowest needed interaction score\u0026thinsp;\u0026gt;\u0026thinsp;0.4. Medium confidence (0.4) was set as the threshold to create the PPI network linked to HGs. It is possible that chemical complexes with unique biological functions are represented by tightly linked local regions in the PPI network. Cytoscape was utilized to depict the PPI network after genes that interacted with each other were selected \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eGene expression was controlled via transcription factors (TFs) by interaction with HGs at the post-transcriptional level. The ChIPBase \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e was utilized to detect HG-related TFs, and Cytoscape was employed to observe the resulting mRNA\u0026ndash;TF regulatory network.\u003c/p\u003e\n \u003cp\u003eRNA-binding proteins (RBPs) \u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e have main roles in gene regulation, including RNA synthesis, alternative splicing, modification, transport, and translation. Depending on the ENCORI database, the prediction of target RBPs of HGs was conducted, and Cytoscape was employed to observe the resulting mRNA\u0026ndash;RBP regulatory network.\u003c/p\u003e\n \u003cp\u003eMicroRNAs (miRNAs) are key modulators of biological evolution and development, targeting multiple genes while being regulated by other miRNAs. HG-associated miRNAs were acquired from the miRDB database, and the mRNA-miRNA regulatory network was observed via Cytoscape.\u003c/p\u003e\n \u003cp\u003eAdditionally, the DGIdb database was employed for the prediction of direct and indirect drug targets of HGs. Drug-associated HGs were extracted, and the mRNA-drug regulatory network was observed in Cytoscape.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e2.10. Statistical analysis\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the full study workflow. All data analyses were performed in R (v4.2.2). Continuous variables are reported as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations. The Wilcoxon rank-sum test was applied for two-group comparisons (unless stated otherwise), and the Kruskal\u0026ndash;Wallis test for multiple groups. The chi-square or Fisher\u0026apos;s exact test was deployed to compare the categorical variables. Correlations were assessed by Spearman analysis, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating significant: \u003cem\u003ep\u003c/em\u003e \u0026lt; * 0.05, ** 0.01, *** 0.001. Screening criteria: \u003cem\u003ep.adj\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (BH correction)\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003ch3\u003e3.1. Data collection and integration of NS datasets\u003c/h3\u003e\n\u003cp\u003eBatch effects between the NS datasets GSE25504 and GSE69686 were eliminated via sva to generate a combined dataset. Distribution boxplots \u003cstrong\u003e(Figs. 2a-b)\u003c/strong\u003e compared expression before and after correction, while PCA plots \u003cstrong\u003e(Figs. 2c-d)\u003c/strong\u003e evaluated low-dimensional feature distributions. Both analyses confirmed effective batch effect removal in the NS datasets.\u003c/p\u003e\n\u003ch3\u003e3.2. NS-related immunomodulatory DEGs\u003c/h3\u003e\n\u003cp\u003eSamples in the pooled dataset were categorized into control and NS. To compare gene expression between the groups, \u0026quot;limma\u0026quot; was employed for differential expression analysis. In total, 360 DEGs in the pooled dataset met the determined threshold. Among them, 280 and 80 were overexpressed and suppressed, respectively. To identify IMRDEGs related to NS, all DEGs meeting |logFC| \u0026gt; 0.5 and \u003cem\u003ep.adj\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05 were overlapped with IMRGs, and a Venn diagram was plotted (\u003cstrong\u003eFig. 3b\u003c/strong\u003e). In total, 69 IMRDEGs were identified (\u003cstrong\u003eTable S2\u003c/strong\u003e).\u003c/p\u003e\n\u003ch3\u003e3.3. GO and KEGG analysis\u003c/h3\u003e\n\u003cp\u003eThe GO outcomes illustrate that the NS-linked 69 IMRDEGs exhibited a main enrichment in inflammatory and immune-correlated pathways. BP terms included regulating T-cell stimulation, leukocyte apoptosis, and T-cell proliferation. CC terms displayed enrichment in the external plasma membrane, secretory granule, endocytic vesicle, cytoplasmic vesicle, and vesicle lumens. MF enrichment involved NAD⁺ nucleosidase and immune receptor activities, and growth factor receptor, TNF receptor, and cytokine bindings. KEGG analysis revealed enrichment in TNF, NF-\u0026kappa;B, T-cell receptor, and chemokine pathways, and growth hormone synthesis, secretion, and action. GO and KEGG results were observed as bubble plots \u003cstrong\u003e(Fig. 4a)\u003c/strong\u003e and network diagrams \u003cstrong\u003e(Figs. 4b\u0026ndash;e)\u003c/strong\u003e (\u003cstrong\u003eTable 2).\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003e3.4. GSEA\u003c/h3\u003e\n\u003cp\u003eTo evaluate the biological influence of the combined dataset on NS, the GSEA \u003cstrong\u003e(Fig. 5a; Table 3)\u003c/strong\u003e revealed that all genes in the pooled dataset displayed a significant enrichment in functions related to RNA metabolism \u003cstrong\u003e(Fig. 5b\u003c/strong\u003e), translation \u003cstrong\u003e(Fig. 5c)\u003c/strong\u003e, fatty acid metabolism \u003cstrong\u003e(Fig. 5d)\u003c/strong\u003e, and the MAPK signaling pathway \u003cstrong\u003e(Fig. 5e)\u003c/strong\u003e, among others.\u003c/p\u003e\n\u003ch3\u003e3.5. GSVA\u003c/h3\u003e\n\u003cp\u003eTo explore pathway differences between NS and control in the combined dataset, GSVA was conducted (\u003cstrong\u003eTable 4\u003c/strong\u003e). Pathways with \u003cem\u003ep.adj\u003c/em\u003e \u0026lt; 0.05 were ranked by logFC, and the top 10 positively and negatively enriched pathways were identified. The differential enrichment of 20 pathways between groups was visualized using group comparison plots (\u003cstrong\u003eFig. 6a\u003c/strong\u003e) and heatmaps (\u003cstrong\u003eFig. 6b\u003c/strong\u003e). GSVA results showed that pathways, such as Myc targets V1 and V2, DNA repair, pancreas beta cells, fatty acid metabolism, estrogen response early, UV response Dn, myogenesis, mitotic spindle, protein secretion, complement, androgen response, coagulation, TNFA signaling via NFKB, xenobiotic metabolism, angiogenesis, hypoxia, IL6/JAK/STAT3 signaling, inflammatory response, and cholesterol homeostasis, varied significantly between the NS and control.\u003c/p\u003e\n\u003ch3\u003e3.6. Establishment of the NS diagnostic model\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eTo determine the diagnostic value of the 69 IMRDEGs in NS, LR models were created using data from the combined dataset. Model outcomes were observed via using a forest plot (\u003cstrong\u003eFig. 7a\u003c/strong\u003e), showing that all 69 IMRDEGs were significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). The SVM-RFE algorithm was utilized on the 69 IMRDEGs with fivefold cross-validation to identify optimal gene subsets. Average gene ranks were calculated to ascertain the count of genes yielding the lowest error rate (\u003cstrong\u003eFig. 7b\u003c/strong\u003e) and the highest precision (\u003cstrong\u003eFig. 7c\u003c/strong\u003e). The SVM model reached peak accuracy when 66 genes were involved (\u003cstrong\u003eTable S3\u003c/strong\u003e). Relying upon these 66 IMRDEGs, LASSO regression analysis was utilized to create the NS diagnostic model. The LASSO regression model (\u003cstrong\u003eFig. 7d\u003c/strong\u003e) and variable trajectory plot (\u003cstrong\u003eFig. 7e\u003c/strong\u003e) were generated for observation. Seven IMRDEGs, namely \u003cem\u003eARG2\u003c/em\u003e, \u003cem\u003eIL18R1\u003c/em\u003e, \u003cem\u003eIL1RN\u003c/em\u003e, \u003cem\u003eMERTK\u003c/em\u003e, \u003cem\u003eRETN\u003c/em\u003e, \u003cem\u003eSTAT3\u003c/em\u003e, and \u003cem\u003eTSPO\u003c/em\u003e, were identified as HGs in the last diagnostic model.\u003c/p\u003e\n\u003cp\u003eTo verify the diagnostic model\u0026apos;s applicability for NS, a nomogram depending on HGs was generated to show their interrelationships in the combined dataset (\u003cstrong\u003eFig. 8a\u003c/strong\u003e). Results revealed that \u003cem\u003eIL1RN\u003c/em\u003e expression contributed most significantly to the NS diagnosis model, whereas \u003cem\u003eIL18R1\u003c/em\u003e expression contributed significantly less relative to other genes. To assess model accuracy and discrimination, a calibration curve was created to compare predictive and observed probabilities under several circumstances (\u003cstrong\u003eFig. 8b\u003c/strong\u003e). In the calibration plot, the dotted calibration line displayed a slight deviation from, but remained near, the standard diagonal, indicating good concordance between anticipated and actual outcomes. DCA was then used to assess the clinical efficiency of the HG-based NS diagnostic model in the combined dataset (\u003cstrong\u003eFig. 8c\u003c/strong\u003e). The DCA outcomes illustrated that the model line constantly exceeded the \u0026quot;all positive\u0026quot; and \u0026quot;all negative\u0026quot; lines across a broad range, indicating greater net clinical benefit and better overall performance. Moreover, a ROC curve was created via pROC depending on the model\u0026apos;s RS (\u003cstrong\u003eFig. 8d\u003c/strong\u003e). ROC outcomes demonstrated high predictive accuracy (AUC \u0026gt; 0.9) for NS across groups. The RS was measured as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eNext, patients with NS were assigned to HRG and LRG as per the median RS acquired from the diagnostic model. Depending on HG levels in the NS group, pROC was utilized to plot ROC curves (\u003cstrong\u003eTable S4\u003c/strong\u003e). ROC curve results (\u003cstrong\u003eFigs. 9a-b\u003c/strong\u003e) showed that \u003cem\u003eIL18R1\u003c/em\u003e, \u003cem\u003eIL1RN\u003c/em\u003e, \u003cem\u003eMERTK\u003c/em\u003e, \u003cem\u003eRETN\u003c/em\u003e, and\u0026nbsp;\u003cem\u003eSTAT3\u0026nbsp;\u003c/em\u003eexpression levels exhibited moderate diagnostic accuracy (0.7 \u0026lt; AUC \u0026lt; 0.9), whereas \u003cem\u003eARG2\u003c/em\u003e and \u003cem\u003eTSPO\u003c/em\u003e expression levels displayed lower accuracy (0.5 \u0026lt; AUC \u0026lt; 0.7).\u003c/p\u003e\n\u003cp\u003eTo compare HG expression levels between NS and control in the combined dataset, a group comparison plot \u003cstrong\u003e(Fig. 9c)\u003c/strong\u003e was created to display variations in these levels for all seven HGs. A heatmap, created using pheatmap, was used to show these expression differences between NS and control samples \u003cstrong\u003e(Fig. 9d)\u003c/strong\u003e. All seven HGs showed highly significant differential expression between the groups (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). A correlation heatmap illustrated interrelationships among the seven HGs in the combined dataset \u003cstrong\u003e(Fig. 9e)\u003c/strong\u003e, showing that most genes were positively correlated. Additionally, chromosomal mapping via RCircos identified the genomic regions of the seven HGs on human chromosomes \u003cstrong\u003e(Fig. 9f)\u003c/strong\u003e. This mapping revealed that multiple HGs, including\u003cem\u003e\u0026nbsp;IL18R1\u003c/em\u003e, \u003cem\u003eIL1RN\u003c/em\u003e, and \u003cem\u003eMERTK\u003c/em\u003e, were located on chromosome 2.\u003c/p\u003e\n\u003ch3\u003e3.7. GSEA of HRG and LRG NS\u003c/h3\u003e\n\u003cp\u003eTo further examine transcriptomic differences within NS samples, patients from the combined dataset were allocated into HRG and LRG depending on the median RS from the LASSO diagnostic model. Differential gene expression was analyzed using limma (\u003cstrong\u003eTable S5\u003c/strong\u003e). To determine how overall gene expression patterns related to sepsis, GSEA was conducted to examine BP, CC, and MF categories linked to the gene sets (\u003cstrong\u003eFig. 10a; Table 5\u003c/strong\u003e). The analysis revealed significant gene enrichment in metabolism of amino acids and derivatives (\u003cstrong\u003eFig. 10b\u003c/strong\u003e), translation (\u003cstrong\u003eFig. 10c\u003c/strong\u003e), the adipocytokine signaling pathway (\u003cstrong\u003eFig. 10d\u003c/strong\u003e), and the agerage pathway (\u003cstrong\u003eFig. 10e\u003c/strong\u003e), among other biologically relevant actions and pathways.\u003c/p\u003e\n\u003ch3\u003e3.8. GSVA of HRG and LRG NS\u003c/h3\u003e\n\u003cp\u003eTo examine variations between h.all.v7.4.symbols.gmt gene sets in the HRG and LRG NS, GSVA was conducted using all NS samples from the combined datasets (\u003cstrong\u003eTable 6\u003c/strong\u003e). Pathways with \u003cem\u003ep.adj\u003c/em\u003e \u0026lt; 0.05 were identified and visualized via a group comparison plot (\u003cstrong\u003eFig. 11a\u003c/strong\u003e) and heatmap (\u003cstrong\u003eFig. 11b\u003c/strong\u003e). The significantly enriched pathways included cholesterol homeostasis, IL6/JAK/STAT3 signaling, androgen response, protein secretion, xenobiotic metabolism, glycolysis, bile acid metabolism, and Myc targets V2, suggesting differential activation of metabolic and inflammatory signaling between the HRG and LRG.\u003c/p\u003e\n\u003ch3\u003e3.9. Immune infiltration analysis via CIBERSORT\u003c/h3\u003e\n\u003cp\u003eThe relative abundance of 22 IC types in HRG and LRG NS was estimated via the CIBERSORT from the combined dataset. Group comparisons \u003cstrong\u003e(Fig. 12a)\u003c/strong\u003e identified seven IC types with significant variations (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05): monocytes, M2 macrophages, neutrophils, central memory CD4⁺ T, CD8⁺ T, na\u0026iuml;ve CD4⁺ T, and regulatory T (Tregs) cells. Correlation analysis \u003cstrong\u003e(Fig. 12b)\u003c/strong\u003e illustrated a positive correlation between Tregs and neutrophils (r = 0.34) and an inverse correlation between Tregs and central memory CD4⁺ T cells (r = \u0026minus;0.58). HG-IC correlations \u003cstrong\u003e(Fig. 12c)\u003c/strong\u003e revealed that IL18R1 was negatively correlated with monocytes (r = \u0026minus;0.41) and positively correlated with neutrophils (r = 0.64), indicating a potential immunomodulatory role in NS pathophysiology.\u003c/p\u003e\n\u003ch3\u003e3.10. Construction of the interaction network\u003c/h3\u003e\n\u003cp\u003eTo elucidate molecular regulatory mechanisms, multiple interaction networks were constructed based on the seven HGs. First, PPI analysis using the STRING database (\u003cstrong\u003eFig. 13a)\u003c/strong\u003e identified six interrelated HGs: \u003cem\u003eARG2\u003c/em\u003e,\u003cem\u003e\u0026nbsp;IL18R1\u003c/em\u003e, \u003cem\u003eIL1RN\u003c/em\u003e,\u003cem\u003e\u0026nbsp;MERTK\u003c/em\u003e, \u003cem\u003eRETN\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eSTAT3\u003c/em\u003e. Next, TFs targeting these HGs were acquired by accessing the ChIPBas database to generate an mRNA\u0026ndash;TF regulatory network, which comprised 5 HGs and 27 TFs \u003cstrong\u003e(Fig. 13b; Table S6)\u003c/strong\u003e. HG-connected RBPs were acquired from the ENCORI database, and an mRNA\u0026ndash;RBP regulatory network including 4 HGs and 23 RBPs was generated \u003cstrong\u003e(Fig. 13c) (Table S7)\u003c/strong\u003e. HG-related miRNAs were identified from miRDB to produce an mRNA-miRNA regulatory network containing 5 HGs and 57 miRNAs \u003cstrong\u003e(Fig. 13d; Table S8)\u003c/strong\u003e. Finally, potential drug\u0026ndash;HG interactions were predicted via the DGIdb database to generate an mRNA\u0026ndash;drug regulatory network comprising 5 HGs and 28 drugs \u003cstrong\u003e(Fig. 13e) (Table S9)\u003c/strong\u003e.\u003c/p\u003e\n\u003ch3\u003eTable 1.\u0026nbsp;NS dataset information.\u003c/h3\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"61%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGSE25504\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGSE69686\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003ePlatform\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eGPL6947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eGPL20292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eSpecies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cem\u003eHomo sapiens\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e\u003cem\u003eHomo sapiens\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eTissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eBlood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003eBlood\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eNS\u0026nbsp;samples\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eControl samples\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e25120092\u003c/td\u003e\n \u003ctd style=\"width: 36px;\"\u003e26052715\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eTable 2.\u0026nbsp;Outcomes of GO and KEGG analyses.\u003c/h3\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"566\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eOntology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eGeneRatio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003eBgRatio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003ep.adj\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003eq-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eGO:0050727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eRegulation of inflammatory response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e21/69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e394/18800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e7.63E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4.83E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eGO:0002764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eImmune response-modulating pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e19/69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e482/18800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e2.52E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.6E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eGO:0050863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eControl of T-cell stimulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e16/69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e342/18800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e1.94E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.23E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eGO:2000106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eRegulation of the leukocyte apoptotic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e7/69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e85/18800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e8.97E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e5.68E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eGO:0042098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eT-cell proliferation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e9/69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e204/18800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e1.76E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.12E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 54px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eGO:0009897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eExternal side of the plasma membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e13/69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e455/19594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e5.9E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4.03E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eGO:0034774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eSecretory granule lumen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e11/69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e322/19594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e5.9E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4.03E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eGO:0060205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eCytoplasmic vesicle lumen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e11/69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e325/19594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e5.9E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4.03E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eGO:0031983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eVesicle lumen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e11/69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e327/19594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e5.9E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4.03E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eGO:0030139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eEndocytic vesicle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e8/69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e342/19594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.000387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.000264\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 54px;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eGO:0003953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eNAD\u003csup\u003e+\u003c/sup\u003e nucleosidase activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e7/68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e28/18410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e1.86E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.31E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eGO:0070851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eGrowth factor receptor binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4/68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e139/18410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.016201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.011415\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eGO:0005164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eTumor necrosis factor receptor binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2/68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e31/18410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.033237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.023419\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eGO:0140375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eImmune receptor activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e9/68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e148/18410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e2.95E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2.08E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003eGO:0019955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eCytokine binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6/68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e141/18410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.000329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.000232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 54px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003ehsa04668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eTNF pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e6/57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e112/8164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.001519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.001047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003ehsa04064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eNF-kappa B pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e5/57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e104/8164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.006221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.004286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003ehsa04660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eT-cell receptor pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e7/57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e104/8164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.000287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.000198\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003ehsa04935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eGrowth hormone synthesis, release, and function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e4/57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e120/8164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.044136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.030409\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 114px;\"\u003e\n \u003cp\u003ehsa04062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003eChemokine pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e7/57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e192/8164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.003595\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e0.002477\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eTable 3.\u0026nbsp;GSEA outcomes.\u003c/h3\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eSet size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eNES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003ep.adj\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eq-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eWP_MAPK_SIGNALING_PATHWAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.581218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.024634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.020457\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eREACTOME_FATTY_ACID_METABOLISM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.619569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.016607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.013792\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eREACTOME_TRANSLATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026minus;2.89142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.14E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e9.48E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eREACTOME_METABOLISM_OF_RNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026minus;2.41236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.14E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e9.48E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eTable 4. GSVA outcomes.\u003c/h3\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003elogFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eAdjusted p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eHALLMARK_MYC_TARGETS_V2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.286724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.40E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e8.75E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eHALLMARK_MYC_TARGETS_V1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.210348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.87E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e4.07E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eHALLMARK_DNA_REPAIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.153172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.38E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e3.64E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eHALLMARK_PANCREAS_BETA_CELLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.087183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.021677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.030968\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eHALLMARK_FATTY_ACID_METABOLISM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026minus;0.07554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.013401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.019707\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eHALLMARK_UV_RESPONSE_DN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026minus;0.07724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.011539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.01803\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eHALLMARK_ESTROGEN_RESPONSE_EARLY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026minus;0.08645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.008106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.013509\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eHALLMARK_MYOGENESIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026minus;0.08767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.031261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.043418\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eHALLMARK_MITOTIC_SPINDLE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026minus;0.08846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.013201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.019707\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eHALLMARK_PROTEIN_SECRETION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026minus;0.0974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.009228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.014884\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eHALLMARK_COMPLEMENT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026minus;0.21213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e6.38E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2.90E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eHALLMARK_ANDROGEN_RESPONSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026minus;0.22153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e2.48E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1.24E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eHALLMARK_COAGULATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026minus;0.2401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e8.16E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e4.53E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eHALLMARK_XENOBIOTIC_METABOLISM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026minus;0.24415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.38E-16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1.73E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eHALLMARK_TNFA_SIGNALING_VIA_NFKB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026minus;0.25575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.13E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e8.07E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eHALLMARK_ANGIOGENESIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026minus;0.25685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e5.42E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2.26E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eHALLMARK_HYPOXIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026minus;0.25999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.87E-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e4.68E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eHALLMARK_INFLAMMATORY_RESPONSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e-0.27214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e9.73E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e8.07E-12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eHALLMARK_IL6_JAK_STAT3_SIGNALING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026minus;0.35389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3.92E-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e6.53E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eHALLMARK_CHOLESTEROL_HOMEOSTASIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026minus;0.37669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4.54E-27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2.27E-25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003eTable 5.\u0026nbsp;GSEA outcomes by NS risk group.\u003c/h3\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eSet size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eNES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;p.adj\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eq-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eWP_AGERAGE_PATHWAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.811679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.018734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.016594\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eKEGG_ADIPOCYTOKINE_SIGNALING_PATHWAY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.769795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.039935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.035373\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eREACTOME_TRANSLATION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026minus;2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e1.21E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e1.07E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eREACTOME_METABOLISM_OF_AMINO_ACIDS_AND_DERIVATIVES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026minus;1.98276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e2.35E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2.08E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNES, normalized enrichment score.\u003c/p\u003e\n\u003ch3\u003eTable 6.\u0026nbsp;GSVA results by NS risk group.\u003c/h3\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003elogFC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAdjusted p-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHALLMARK_CHOLESTEROL_HOMEOSTASIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.210889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.87E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002936\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHALLMARK_IL6_JAK_STAT3_SIGNALING\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.184796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.002257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.028217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHALLMARK_ANDROGEN_RESPONSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.175515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.000566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.014161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHALLMARK_PROTEIN_SECRETION\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.162653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.0029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.028995\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHALLMARK_XENOBIOTIC_METABOLISM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.139336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.001851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.028217\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHALLMARK_GLYCOLYSIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.126744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.004731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.035826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHALLMARK_BILE_ACID_METABOLISM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.122398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.006966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.043539\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHALLMARK_MYC_TARGETS_V2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026minus;0.18646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.005016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.035826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eNS constitutes a common, severe infection and a major cause of neonatal mortality. Its clinical manifestations are nonspecific, and traditional inflammatory biomarkers [e.g., procalcitonin and C-reactive protein (CRP)] have insufficient sensitivity and specificity for early diagnosis \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Therefore, identifying reliable molecular biomarkers and establishing diagnostic models are essential for improving early detection and prognosis. Herein, we incorporated two GEO transcriptomic datasets, identified 69 IMRDEGs, and developed a diagnostic model based on seven HGs (\u003cem\u003eARG2\u003c/em\u003e, \u003cem\u003eIL18R1\u003c/em\u003e, \u003cem\u003eIL1RN\u003c/em\u003e, \u003cem\u003eMERTK\u003c/em\u003e, \u003cem\u003eRETN\u003c/em\u003e, \u003cem\u003eSTAT3\u003c/em\u003e, and \u003cem\u003eTSPO\u003c/em\u003e). The model demonstrated excellent diagnostic precision (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.9) in training and validation groups, providing insights into NS pathogenesis and early diagnosis.\u003c/p\u003e\u003cp\u003eCompared with traditional inflammatory markers, our model achieved higher sensitivity and specificity (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.9), suggesting strong clinical potential for early NS detection \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Among the HGs, \u003cem\u003eIL1RN\u003c/em\u003e contributed most to the diagnostic model. \u003cem\u003eIL1RN\u003c/em\u003e encodes IL-1 receptor antagonist (IL-1ra), competitively suppresses IL-1 activity, and limits inflammatory injury. Previous studies have shown that IL-1ra levels rise earlier than CRP levels during sepsis onset \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e and that \u003cem\u003eIL1RN\u003c/em\u003e polymorphisms are linked to sepsis susceptibility \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e, underscoring its dual potential as a diagnostic biomarker and a therapeutic target.\u003c/p\u003e\u003cp\u003e\u003cem\u003eIL18R1\u003c/em\u003e functions as a key receptor in the IL-18 signaling pathway. Elevated serum IL-18 concentrations have been detected in NS and are predictive of mortality \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. In our study, \u003cem\u003eIL18R1\u003c/em\u003e expression correlated positively with neutrophil infiltration, suggesting that the IL-18\u0026ndash;\u003cem\u003eIL18R1\u003c/em\u003e axis may exacerbate inflammation by modulating neutrophil function.\u003c/p\u003e\u003cp\u003eAlthough less studied in NS, other HGs play recognized roles in inflammation and immune regulation. \u003cem\u003eARG2\u003c/em\u003e promotes immunosuppression in adult sepsis by inhibiting T-cell activity through arginine metabolism \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e, although its neonatal role remains unclear. \u003cem\u003eMERTK\u003c/em\u003e, encoding a phagocytic receptor, contributes to inflammation resolution and apoptotic cell clearance \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e; impaired function may disturb immune homeostasis, although direct evidence in NS is lacking. RETN protein levels in serum have been reported to increase during sepsis, and a pediatric meta-analysis supports \u003cem\u003eRETN\u003c/em\u003e's biomarker potential \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eSTAT3\u003c/em\u003e, encoding a central transcription factor in the JAK/STAT pathway, mediates sepsis-associated immunosuppression \u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eTSPO\u003c/em\u003e, encoding a mitochondrial transmembrane protein, regulates energy metabolism under inflammatory and oxidative stress; elevated plasma TSPO protein levels observed in adult sepsis cases have highlighted \u003cem\u003eTSPO\u003c/em\u003e's diagnostic potential \u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e, although its role in neonates requires further validation. Collectively, our seven-gene diagnostic model enhances diagnostic precision and underscores immune\u0026ndash;metabolic dysregulation as a central NS mechanism.\u003c/p\u003e\u003cp\u003eIC infiltration is pivotal in NS pathogenesis. In the present study, seven IC types (monocytes, M2 macrophages, neutrophils, Tregs, central memory CD4\u003csup\u003e+\u003c/sup\u003e, CD8\u003csup\u003e+\u003c/sup\u003e, and na\u0026iuml;ve CD4\u003csup\u003e+\u003c/sup\u003e T cells) differed significantly between NS and control samples, highlighting their roles in disease progression. Monocytes, essential for phagocytosis and antigen presentation, showed altered abundance in NS, likely reflecting pathogen invasion and inflammatory activation. Neonatal monocytes differ functionally from adult monocytes, exhibiting activation patterns that may impair pathogen clearance and elevate infection risk \u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Neutrophils, the first defense line in neonates, are functionally immature in chemotaxis, phagocytosis, and bactericidal activity, hindering infection control \u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. We observed significant variations in neutrophil infiltration between NS and control samples, with neutrophil abundance positively correlating with \u003cem\u003eIL18R1\u003c/em\u003e expression, suggesting amplification of IL-18\u0026ndash;\u003cem\u003eIL18R1\u003c/em\u003e-mediated inflammation \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. T cells are developmentally immature in neonates, with reduced CD4\u003csup\u003e+\u003c/sup\u003e memory T-cell populations, resulting in delayed adaptive immune responses \u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. The observed negative relationship between Tregs and CD4\u003csup\u003e+\u003c/sup\u003e memory T cells supports impaired adaptive immunity. Tregs play a dual role in NS: they suppress excessive inflammation to protect tissues but induce immunosuppression when overactivated \u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. Elevated Treg frequencies in preterm infants have been associated with higher infection risk compared with term infants. A negative relationship between Tregs and CD4\u003csup\u003e+\u003c/sup\u003e memory T cells was observed, suggesting that enhanced immunosuppression may weaken adaptive immune responses. Collectively, our outcomes illustrate that NS involves abnormalities in innate ICs (monocytes and neutrophils), deficiencies in adaptive ICs (CD8\u003csup\u003e+\u003c/sup\u003e and CD4\u003csup\u003e+\u003c/sup\u003e memory T cells), and Treg-driven immunosuppression, reflecting a multifaceted immune imbalance.\u003c/p\u003e\u003cp\u003eCorrelation analysis illustrated close interactions between HGs and ICs. \u003cem\u003eIL18R1\u003c/em\u003e correlated positively with neutrophils and negatively with monocytes, illustrating that the IL-18\u003cem\u003e\u0026ndash;IL18R1\u003c/em\u003e axis may both amplify inflammation, consistent with a prior study on IL-18 in NS \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e, and engage a negative feedback mechanism to limit inflammation. Overactivation of IL-18 has been linked to immune dysregulation in NS \u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. Additionally, \u003cem\u003eSTAT3\u003c/em\u003e and \u003cem\u003eMERTK\u003c/em\u003e may influence macrophage polarization and immunosuppressive states, further shaping the NS immune microenvironment. Notably, \u003cem\u003eARG2\u003c/em\u003e suppresses T-cell function via arginine metabolism, contributing to sepsis-induced immunosuppression \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Collectively, these findings highlight gene\u0026ndash;IC interactions as critical contributors to NS pathophysiology.\u003c/p\u003e\u003cp\u003eDespite the insights of this investigation, several constraints should be acknowledged. Data were acquired from public databases and lack further validation in independent multicenter cohorts. Analyses were restricted to transcriptomic data and require confirmation by proteomic and metabolomic studies. Moreover, experimental validation was absent, and gene-cell interaction mechanisms warrant further investigation \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e. Upcoming investigations should incorporate prospective clinical samples, multiomics approaches, and functional studies to facilitate clinical translation of our diagnostic model. Integrating molecular biomarkers with immune infiltration profiles may enable early, precise diagnosis and personalized immunomodulatory therapy in NS cases.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eBy integrating multiple transcriptomic datasets, we identified seven immune regulation\u0026ndash;related HGs (\u003cem\u003eARG2\u003c/em\u003e, \u003cem\u003eIL18R1\u003c/em\u003e, \u003cem\u003eIL1RN\u003c/em\u003e, \u003cem\u003eMERTK\u003c/em\u003e, \u003cem\u003eRETN\u003c/em\u003e, \u003cem\u003eSTAT3\u003c/em\u003e, and \u003cem\u003eTSPO\u003c/em\u003e) and developed a novel diagnostic model for NS. The model demonstrated strong diagnostic accuracy and clinical relevance, outperforming conventional inflammatory biomarkers. Immune infiltration analysis revealed the characteristic features of NS, including impaired innate immunity, weakened adaptive responses, and heightened Treg-mediated immunosuppression. Correlation analyses further suggested that NS pathogenesis involves a complex cycle of \"inflammation amplification\u0026ndash;insufficient resolution\u0026ndash;immunosuppression.\" These findings suggest innovative mechanistic visions and potential molecular targets for early NS diagnosis and intervention. Further validation in large, multicenter, prospective cohorts is warranted, along with exploration of combined clinical diagnostic strategies integrating established biomarkers (e.g., CRP and procalcitonin) and the seven-gene signature.\u003c/p\u003e"},{"header":"Abbreviation","content":"\u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eArea under the curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eBP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBiological process\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCellular component\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDCA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDecision curve analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDEG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDifferentially expressed gene\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFalse positive rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGene Ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGSEA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGene set enrichment analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGSVA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGene set variation analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eHGs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHub genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIMRDEGs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eImmunomodulation-related DEGs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIMRGs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eImmunomodulation-related gene\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eKEGG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLASSO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLeast absolute shrinkage and selection operator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMolecular function\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003emiRNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003emicroRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNeonatal sepsis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePPI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eProtein–protein interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eRBP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRNA-binding protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eROC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReceiver operating characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSVM-RFE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSupport vector machine recursive feature elimination\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTranscription factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTPR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTrue positive rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Statements and Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuiling Luo conceived and designed the study, conducted the bioinformatic analyses, interpreted the data, and drafted the manuscript. Bo Bai contributed to data preprocessing, statistical analyses, and interpretation of the results. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets analyzed in this study are publicly available from the Gene Expression Omnibus (GEO) database. The accession numbers are GSE25504 and GSE69686. Additional processed data and analysis scripts are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is based entirely on previously published, de-identified gene expression data obtained from the public GEO database. In accordance with institutional and national regulations, additional approval from a research ethics committee was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors sincerely thank the investigators who generated and shared the GSE25504 and GSE69686 datasets in the GEO repository, which made this study possible. In addition, we thank the Home for Researchers editorial team (www.home-for-researchers.com) for providing language editing services.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLi Y, Zu X, Hu X, Zhao C, Mo M, Fan B (2021) Competing endogenous RNA network analysis reveals pivotal ceRNAs in bladder urothelial carcinoma. Transl Androl Urol 10:797\u0026ndash;808. https://doi.org/10.21037/tau-20-1167 \u003c/li\u003e\n\u003cli\u003eZhao M, Feng J, Tang L (2021) Competing endogenous RNAs in lung cancer. Cancer Biol Med 18:1\u0026ndash;20. https://doi.org/10.20892/j.issn.2095-3941.2020.0203 \u003c/li\u003e\n\u003cli\u003eCruz M, et al (1979) Neonatal sepsis. Pediatrician 8:4\u0026ndash;16.\u003c/li\u003e\n\u003cli\u003eShane AL, S\u0026aacute;nchez PJ, Stoll BJ (2017) Neonatal sepsis. 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Infect Immun 84:1966\u0026ndash;1974. https://doi.org/10.1128/iai.00111-16\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Neonatal sepsis, Biomarker, Immune regulation, Bioinformatics, Diagnostic model, IC infiltration","lastPublishedDoi":"10.21203/rs.3.rs-8030559/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8030559/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eAlthough neonatal sepsis (NS) is a main driver of neonatal morbidity and mortality, reliable molecular biomarkers for early detection are lacking. This study identified immunomodulation-related differentially expressed genes (IMRDEGs) linked to NS through integrated bioinformatics analysis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eTwo GEO datasets (GSE25504 and GSE69686) containing 90 NS and 122 control samples were combined via the R packages \"GEOquery\" and \"sva\". Differentially expressed genes (DEGs) were detected via \"limma\", and functional enrichment was determined via the GO and KEGG databases. Enriched pathways were further identified via gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). Then, we developed a diagnostic model via logistic regression (LR), SVM-RFE, and LASSO regression.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIn total, 360 DEGs were identified, including 69 IMRDEGs. Enrichment analyses highlighted significant associations with inflammatory and immune regulation pathways. Seven hub genes (HGs; \u003cem\u003eARG2\u003c/em\u003e, \u003cem\u003eIL18R1\u003c/em\u003e, \u003cem\u003eIL1RN\u003c/em\u003e, \u003cem\u003eMERTK\u003c/em\u003e, \u003cem\u003eRETN\u003c/em\u003e, \u003cem\u003eSTAT3\u003c/em\u003e, and \u003cem\u003eTSPO\u003c/em\u003e) were incorporated into the diagnostic model, which displayed high accuracy (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.9) in ROC curve analysis. Immune infiltration analysis elucidated close interconnections between the HGs and specific immune cell (IC) subsets.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThese outcomes illustrate that the detected HGs represent biomarkers for early NS diagnosis and provide insights into potential therapeutic targets. Upcoming studies should concentrate on the functional validation and clinical translation of these biomarkers.\u003c/p\u003e","manuscriptTitle":"Bioinformatics Identification of Immunomodulatory Genes Related to Neonatal Sepsis and Their Incorporation into a Diagnostic Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 07:43:55","doi":"10.21203/rs.3.rs-8030559/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2b4a4a48-61ae-4199-a735-5e6ceb51dba2","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T14:39:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 07:43:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8030559","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8030559","identity":"rs-8030559","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}