Diet influences plaque stability through its potential to modulate inflammation, a process that involves complex interactions among various lipid metabolites. This study aims to utilize metabolomics to identify key metabolites involved in this pathway and to elucidate the mechanisms by which dietary factors affect plaque stability. The Dietary Inflammatory Index (DII), derived from dietary data, was used to assess the inflammatory potential of individual diets. Propensity score matching categorized serum samples from coronary heart disease (CHD) patients into an anti-inflammatory group (n = 108) and a pro-inflammatory group (n = 108). A comprehensive analysis of lipid profiles was performed using an UPLC-MS/MS detection platform combined with the broad-targeted lipid metabolomics technique, and lipid metabolites with significant differences were screened out. Concurrently, we measured serum levels of inflammatory factors and plaque stability. A Bayesian network model was then applied to elucidate the causal relationships among DII, lipid metabolites, inflammatory factors, and plaque stability. A lipidomics analysis identified 22 differentially expressed lipid metabolites, which were associated with sphingolipid metabolism pathways in the KEGG (Kyoto Encyclopedia of Genes and Genomes) database, particularly involving nine ceramide species. The Bayesian network model exploring the impact of DII on plaque stability comprises 16 nodes and 23 directed arcs. It revealed multiple causal relationships among DII, ceramide species, inflammatory factors, and plaque stability. Specifically, six ceramide species [Cer(d16:0/20:1), Cer(d24:3/15:1), Cer(t14:1/21:0), Cer(t20:0/18:2), Cer(t22:1/16:1), Cer(t26:1/12:1)] and five inflammatory factors (IFN-γ, IL-1β, IL-8, IL-12, IL-13) were found to be involved in these associations. Ceramide species emerged as differential lipid metabolites that distinguish between the anti-inflammatory and pro-inflammatory groups, simultaneously serving as key lipid metabolic products through which diet exerts its influence on plaque stability.

RATIONALE AND OBJECTIVES:Neoadjuvant chemoimmunotherapy (NCIT) has shown promising efficacy in locally advanced esophageal squamous cell carcinoma (LA-ESCC), yet pretreatment predictors for treatment response remain to be identified. This study aimed to evaluate a pretreatment CT-based habitat radiomics model for predicting pathological response in LA-ESCC treated with NCIT. MATERIALS AND METHODS:This prospective multicenter study enrolled 215 patients with LA-ESCC receiving NCIT from three centers. Patients from Center A were randomly allocated to training (n = 110, 70%) and validation (n = 47, 30%) sets, with those from Centers B (n = 33) and C (n = 25) as an external test set. Responders and nonresponders were classified by tumor regression grades. Conventional and habitat radiomics features were extracted from intratumoral and peritumoral regions. Fourteen machine-learning (ML) classifiers were used to build intratumoral, peritumoral, and combined habitat radiomics models, along with corresponding conventional models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). Shapley Additive Explanations (SHAP) analysis was employed for model interpretation. RESULTS:The habitat radiomics models outperformed conventional radiomics models. The combined intratumoral and peritumoral habitat radiomics model achieved an AUC of 0.93 (95% CI: 0.84-0.98), accuracy of 0.83, sensitivity of 0.79, and specificity of 0.93 in the external test set. SHAP analysis revealed that both intratumoral and peritumoral habitat radiomics features contributed significantly to predictive performance. CONCLUSION:The interpretable ML model combining intratumoral and peritumoral habitat radiomics features accurately predicts the response of LA-ESCC to NCIT.

Emerging evidence implicates gut microbiota in the pathogenesis of obesity-related depression (OD); however, the underlying molecular mechanisms remain inadequately explored. This study compared the microbial and transcriptional profiles between patients with OD and healthy individuals. The results revealed an enrichment of Anaerotruncus colihominis (A. colihominis) and glutamate metabolism-related genes in the OD group. Fecal microbiota transplantation (FMT) from patients with OD induced weight gain, compromised barrier function, and intensified depression-like behaviors in high-fat diet (HFD) mice. Microbial analysis in the mice feces corroborated the clinical findings. Single-cell RNA sequencing highlighted the pivotal role of the Efnb2-Ephb2 interaction in cell communication among colon epithelial and hippocampal neuron subtypes in OD mice. Notably, A. colihominis correlated with glutamate levels in the OD mice and patients. It produced glutamate through a glutamic acid metabolism-related DNA sequence, verified in an engineered Escherichia coli MG1655 strain. Both A. colihominis and glutamate reduced barrier proteins in colon epithelial cells and modulated cognitive proteins in neurons. Finally, A. colihominis treatment induced the Efnb2-Ephb2 interaction, exacerbating depression-like behaviors in germ-free HFD mice. Collectively, these findings reveal that A. colihominis and glutamate are potential intervention targets for OD treatment.

Background Diet influences plaque stability through its potential to modulate inflammation, a process that involves complex interactions among various lipid metabolites. This study aims to utilize metabolomics to identify key metabolites involved in this pathway and to elucidate the mechanisms by which dietary factors affect plaque stability. Methods The Dietary Inflammatory Index (DII), derived from dietary data, was used to assess the inflammatory potential of individual diets. Propensity score matching categorized serum samples from coronary heart disease (CHD) patients into an anti-inflammatory group (n = 108) and a pro-inflammatory group (n = 108). Broad-spectrum lipidomics technology was employed to perform a comprehensive analysis of lipid profiles and identify significantly different lipid metabolites. Concurrently, we measured serum levels of inflammatory factors and plaque stability. A Bayesian network model was then applied to elucidate the causal relationships among DII, lipid metabolites, inflammatory factors, and plaque stability. Results A lipidomics analysis identified 22 differentially expressed lipid metabolites, which were associated with sphingolipid metabolism pathways in the KEGG (Kyoto Encyclopedia of Genes and Genomes) database, particularly involving nine ceramide species. The Bayesian network model exploring the impact of DII on plaque stability comprises 16 nodes and 23 directed arcs. It revealed multiple causal relationships among DII, ceramide species, inflammatory factors, and plaque stability. Specifically, six ceramide species [Cer(d16:0/20:1), Cer(d24:3/15:1), Cer(t14:1/21:0), Cer(t20:0/18:2), Cer(t22:1/16:1), Cer(t26:1/12:1)] and five inflammatory factors (IFN-γ, IL-1β, IL-8, IL-12, IL-13) were found to be involved in these associations. Conclusions Ceramide species emerged as differential lipid metabolites that distinguish between the anti-inflammatory and pro-inflammatory groups, simultaneously serving as key lipid metabolic products through which diet exerts its influence on plaque stability.

BACKGROUND:Breast cancer remains the most prevalent malignancy among women, and patients presenting with both breast and lung cancer pose significant challenges in clinical diagnosis and treatment. Currently, comprehensive multi-omics analyses for such multiple malignancies are lacking. METHODS:An integrated multi-omics analysis was performed, incorporating quantitative proteomics and radiomics data from patients with single primary breast cancer as well as those with multiple primary tumors (breast and lung cancer). RESULTS:Quantitative proteomics analysis revealed four distinct molecular signatures (Types I-IV). Patients with single breast cancer exhibited driving pathways primarily linked to cell proliferation (e.g., HER2), whereas those with multiple breast cancers showed enrichment in ER-related and proliferative pathways. In contrast, patients with multiple lung cancers displayed pathways associated with immune response and immune escape. Additionally, immune subtyping identified three distinct immune landscapes (Types I-III). Radiomic analysis demonstrated strong correlations between these molecular/immune subtypes and imaging findings. Patients with high imaging information scores exhibited pronounced tumor heterogeneity and reduced immune infiltration. CONCLUSIONS:This study provides new insights into the molecular pathogenesis of multiple primary malignancies, particularly breast and lung cancer.

BACKGROUND:Fibrosis refers to abnormal deposition of extracellular matrix, which leads to organ dysfunction. Metabolic alterations, especially enhanced glycolysis and suppressed fatty acid oxidation, are recognized as an essential pathogenic process of fibrosis. Recently, several reports indicate that the changes in microbiota composition are associated with metabolic disorders, suggesting microbes may contribute to organ fibrosis by regulating metabolic processes. METHODS:In this study, microbial reannotation was carried out on the RNA-seq data of fibrotic organs. Then, the microbial composition differences among healthy and fibrotic organ samples were determined by alpha and beta diversity analysis. Common and specific microbial markers of fibrosis were also identified by LEfSe. After that, the correlation analysis of the characteristic microbegene- functional pathway was conducted to confirm the effects of microbes on host metabolism. RESULTS:The results showed that the microbial composition significantly differed between healthy and diseased organs. Besides, the common characteristic microbes interacted closely with each other and contributed to fibrosis through symbiosis or inhibition. The largest proportion in fibrosis organs was Proteobacteria, which was the main source of pathogenic microbes. CONCLUSION:Further study found that the metabolic alteration driven by common and special characteristic microbes in fibrotic organs focused on the processes related to glycolysis and fatty acid metabolism.

Genome-wide association study (GWAS) analysis has exposed that genetic factors play important roles in COVID-19. Whereas a deeper understanding of the underlying mechanism of COVID-19 was hindered by the lack of expression of quantitative trait loci (eQTL) data specific for disease. To this end, we identified COVID-19-specific cis-eQTLs by integrating nucleotide sequence variations and RNA-Seq data from COVID-19 samples. These identified eQTLs have different regulatory effect on genes between patients and controls, indicating that SARS-CoV-2 infection may cause alterations in the human body's internal environment. Individuals with the TT genotype in the rs1128320 region seemed more susceptible to SARS-CoV-2 infection and developed into severe COVID-19 due to the abnormal expression of IFITM1. We subsequently discovered potential causal genes, of the result, a total of 48 genes from six tissues were identified. siRNA-mediated depletion assays in SARS-CoV-2 infection proved that 14 causal genes were directly associated with SARS-CoV-2 infection. These results enriched existing research on COVID-19 causal genes and provided a new sight in the mechanism exploration for COVID-19.

OBJECTIVE:Few symptoms persist for a long time after patients recover from COVID-19, called "long COVID". We explored the potential microbial risk factors for COVID-19 for a deeper understanding and assistance in the follow-up treatment of these sequelae.METHODS:Microbiome re-annotation was performed using whole blood RNA-Seq data collected from recovered COVID-19 patients and healthy controls at multiple time points. Subsequently, a series of downstream analyses were conducted to reveal the microbial characteristics of patients who recovered from SARS-CoV-2 infection.RESULTS:The blood microbiome at 12 weeks post-infection was most evidently disturbed, including an increasing ratio of Bacillota/Bacteroidota and a higher microbial alpha diversity. In addition, a group of pathogenic microbes at 12 weeks post-infection were identified, including Staphylococcus aureus, Klebsiella pneumoniae, Streptococcus pneumoniae, Acinetobacter baumannii, and Pseudomonas aeruginosa, which were positively associated with host genes involved in immune regulatory and olfactory transduction pathways. Several microbes, such as Streptococcus pneumoniae were associated with infiltrating immune cells, such as M2 macrophages.CONCLUSION:This study provides insights into the relationship between the blood microbiome and COVID-19 sequelae. Several pathogenic microbes were enriched in recovered COVID-19 patients and thus affected host genes participating in the immune and olfactory transduction pathways, which play critical roles in COVID-19 sequelae.

BackgroundOvarian cancer (OC) is found to be the third most common gynecologic malignancy over the world, having the highest mortality rate among such tumors. Emerging studies underscore the presence of microorganisms within tumor tissues, with certain pathogens intricately linked to disease onset and progression. Disruption of the microbiome frequently precipitates disturbances in host metabolic and immune pathways, thereby fostering the development of cancer.MethodsIn this study, we initiated the investigation by conducting microbial reannotation on the RNA sequencing data derived from ovarian cancer tissues. Subsequently, a comprehensive array of analyses on tissue microbes was executed. These analyses encompassed the assessment of intergroup variations in microbial diversity, differential microbiological analysis, exploration of the association between host gene expression and microbial abundance, as well as an enrichment analysis of functional pathways linked to host genes associated with microbes.ResultsThe analysis results revealed that Proteobacteria, Actinobacteria, Firmicutes, and Bacteroidetes were the main components at phylum level in ovarian tissue. Notably, the microbial composition of ovarian cancer tissue significantly diverged from that of normal ovarian tissue e, exhibiting markedly lower alpha diversity and distinct beta diversity. Besides, pathogenic microorganisms Achromobacter xylosoxidans and Enterobacter hormaechei were enriched in cancer tissue. Host genes associated with these pathogens were enriched in key pathways including “JAK-STAT signaling pathway”, “Transcriptional misregulation in cancer”, and “Th1 and Th2 cell differentiation”, suggesting their role in ovarian cancer progression through microbial dysbiosis and immune response interaction.ConclusionAbundance of pathogenic microorganisms in ovarian cancer tissue could modulate the expression of host genes, consequently impacting cancer-related signaling pathways and fostering cancer progression.

Rosacea is a chronic inflammatory skin disorder that significantly impairs quality of life, however, its pathophysiology is still unclear. Previous studies have suspected that the bacterial -microbiome plays a causative role in the disease. To investigate whether there are differences in the abundance and diversity of facial bacterial microbiomes between rosacea patients and healthy controls. Samples of facial microorganisms from subjects were collected with sterile swabs, and the V3 and V4 regions of bacterial 16S rRNA were amplified and sequenced using the MiSeq platform of the Illumina system. A total of 44 samples qualified (including 17 in the case group and 27 in the control group), comprising 2,048 operational taxonomic units belonging to 40 phyla and 1,312 species that were clustered. The alpha diversity in patients with rosacea was higher than that in healthy controls, but this difference was not statistically significant. In addition, compared with healthy individuals, the mean relative abundance of Cutibacterium acens was significantly lower (61.79% vs 79.69%, p=0.014) and that of Staphylococcus epidermidis was higher (19.64% vs 6.48%, p=0.036) in rosacea patients. Changes in microbial abundance and diversity correlate with the pathogenesis of rosacea.

Gut microbiota plays a significant role in maintaining host health, and conversely, disorders potentially lead to dysbiosis, an imbalance in the composition of the gut microbial community. Intervention approaches, such as medications, diets, and several others, also alter the gut microbiota in either a beneficial or harmful direction. In 2020, the gutMDisorder was developed to facilitate researchers in the investigation of dysbiosis of gut microbes as occurs in various disorders as well as with therapeutic interventions. The database has been updated this year, following revision of previous publications and newly published reports to manually integrate confirmed associations under multitudinous conditions. Additionally, the microbial contents of downloaded gut microbial raw sequencing data were annotated, the metadata of the corresponding hosts were manually curated, and the interactive charts were developed to enhance visualization. The improvements have assembled into gutMDisorder v2.0, a more advanced search engine and an upgraded web interface, which can be freely accessed via http://bio-annotation.cn/gutMDisorder/.

After infection with SARS-CoV-2, the microbiome inside the human body changes dramatically. By re-annotating microbial sequences in bulk RNA-seq and scRNA-seq data of COVID-19 patients, we described the cellular microbial landscape of COVID-19 patients and identified characteristic microorganisms in various tissues. We found that Acinetobacter lwoffii was highly correlated with COVID-19 symptoms and might disrupt some pathways of patients by interacting with the host and other microbes, such as Klebsiella pneumoniae. We further identified characteristic microorganisms specific to cell type, indicating the enrichment preference of some microbes. We also revealed the co-infection of SARS-CoV-2 with hMPV, which may cause the development of COVID-19. Overall, we demonstrated that the presence of intracellular microorganisms in COVID-19 patients and the synergies between microorganisms were strongly correlated with disease progression, providing a theoretical basis for COVID-19 treatment in a certain extent.

Achieving complete response (CR) after first-line chemotherapy in gastric DLBCL patients often results in longer disease-free survival. We explored whether a model based on imaging features combined with clinicopathological factors could assess the CR to chemotherapy in patients with gastric DLBCL. Univariate (P < 0.10) and multivariate (P < 0.05) analyses were used to identify factors associated with a CR to treatment. As a result, a system was developed to evaluate whether gastric DLBCL patients had a CR to chemotherapy. Evidence was found to support the model's ability to predict outcomes and demonstrate clinical value. We retrospectively analysed 108 people who had been diagnosed gastric DLBCL; 53 were in CR. Patients were divided at random into a 5:4 training/testing dataset split. β2 microglobulin before and after chemotherapy and lesion length after chemotherapy were independent predictors of the CR of gastric DLBCL patients after chemotherapy. These factors were used in the predictive model construction. In the training dataset, the area under the curve (AUC) of the model was 0.929, the specificity was 0.806, and the sensitivity was 0.862. In the testing dataset, the model had an AUC of 0.957, specificity of 0.792, and sensitivity of 0.958. The AUC did not differ significantly between the training and testing dates (P > 0.05). A model constructed using imaging features combined with clinicopathological factors could effectively evaluate the CR to chemotherapy in gastric DLBCL patients. The predictive model can facilitate the monitoring of patients and be used to adjust individualised treatment plans.

Background We previously identified the tumor suppressor gene TOB1 as related to gastric cancer. The purpose of this study was to explore whether TOB1 induces autophagy through the AKT/mTOR signaling pathway in gastric cancer. Methods Western blotting was used to detect the protein levels of TOB1, LC3, AKT, mTOR, phosphorylated (p) AKT, and p-mTOR. A double fluorescent GFP-RFP-LC3 fusion protein was used to trace autophagy by laser confocal microscopy. Autophagosomes were observed by transmission electron microscopy. Results The conversion of LC3-I to LC3-II and the LC3-II/LC3-I ratio were significantly increased in AGS cells overexpressing TOB1 compared with control cells. Fluorescence imaging showed LC3 puncta at 48 h, and these puncta increased significantly at 72 h after TOB1 transfection compared with control tumor cells. The presence of autophagosomes in AGS cells was observed at 72 h after TOB1 transfection by transmission electron microscopy, and no autophagosomes were found in the control cells. Moreover, the levels of p-AKT and p -mTOR were lower in AGS cells than in control cancer cells. Conclusion Our results provide novel insight that TOB1 might suppress gastric cancer by inducing autophagy, possibly through decreasing phosphorylation and the subsequent activation of the AKT/mTOR signaling pathway.

PurposeThis study aimed to assess the diagnostic performance and the added value to radiologists of different levels of a computer-aided diagnosis (CAD) system for the detection of pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in patients with breast cancer. Besides, to investigate whether tumor molecular typing is associated with the efficiency of diagnosis of the CAD systems.Methods470 patients were identified with breast cancers who underwent NAC and post MR imaging between January 2016 and March 2019. The diagnostic performance of radiologists of different levels and the CAD system were compared. The added value of the CAD system was assessed and subgroup analyses were performed according to the tumor molecular typing.ResultsAmong 470 patients, 123 (26%) underwent pCR. The CAD system showed a comparable specificity as the senior radiologist (83.29% vs. 84.15%, p=0.488) and comparable area under the curve (AUC) (0.839 vs. 0.835, p =0.452). The performance of all radiologists significantly improved when aided by the CAD system (P<0.05), And there were no statistical differences in terms of sensitivity, specificity and accuracy between the two groups with CAD assistance(p>0.05).The AUC values for identifying pCR in TN patients were significant (0.883, 95%CI: 0.801-0.964, p < 0.001).ConclusionThe CAD system assessed in this study improves the performance of all radiologists, regardless of experience. The molecular typing of breast cancer is potential influencer of CAD diagnostic performance.

Since the first report of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in December 2019, over 100 million people have been infected by COVID-19, millions of whom have died. In the latest year, a large number of omics data have sprung up and helped researchers broadly study the sequence, chemical structure and function of SARS-CoV-2, as well as molecular abnormal mechanisms of COVID-19 patients. Though some successes have been achieved in these areas, it is necessary to analyze and mine omics data for comprehensively understanding SARS-CoV-2 and COVID-19. Hence, we reviewed the current advantages and limitations of the integration of omics data herein. Firstly, we sorted out the sequence resources and database resources of SARS-CoV-2, including protein chemical structure, potential drug information and research literature resources. Next, we collected omics data of the COVID-19 hosts, including genomics, transcriptomics, microbiology and potential drug information data. And subsequently, based on the integration of omics data, we summarized the existing data analysis methods and the related research results of COVID-19 multi-omics data in recent years. Finally, we put forward SARS-CoV-2 (COVID-19) multi-omics data integration research direction and gave a case study to mine deeper for the disease mechanisms of COVID-19.

Ping Wang Harbin Medical University Cancer Hospital Kaige Chen Harbin Medical University Cancer Hospital Ying Han Harbin Medical University Cancer Hospital Min Zhao GE Healthcare Nanding Abiyasi Harbin Medical University Cancer Hospital Jiming Shang Harbin Medical University Cancer Hospital Shaolei Yan Harbin Medical University Cancer Hospital Haiyong Peng Harbin Medical University Cancer Hospital Naijian Shang Harbin Medical University Cancer Hospital Wei Meng (  articlemengwei@163.com ) Harbin Medical University Cancer Hospital

Background: Pyroptosis is a novel inflammatory form of programmed cell death and a prospective target for cancer therapy. Nevertheless, little is known about the association between pyroptosis-related genes (PRGs) and acute myeloid leukemia (AML) prognosis. Herein, we systematically investigated the specific functions and clinical prognostic value of multiple PRGs in AML.Methods: Univariate and LASSO Cox regression analyses based on TCGA and GTEx databases were used to generate the PRG signature, whose predictive efficacy of survival was evaluated using survival analysis, ROC, univariate and multivariate Cox analyses as well as subgroup analysis. The BeatAML cohort was used for data validation. The association between risk score and immune cell infiltration, HLA, immune checkpoints, cancer stem cell (CSC), tumor mutation burden (TMB), and therapeutic drug sensitivity were also analyzed.Results: Six -PRG signatures, namely, CASP3, ELANE, GSDMA, NOD1, PYCARD, and VDR were generated. The high-risk score represented a poorer prognosis and the PRG risk score was also validated as an independent predictor of prognosis. A nomogram including the cytogenetic risk, age, and risk score was constructed for accurate prediction of 1-, 3-, and 5-year survival probabilities. Meanwhile, this risk score was significantly associated with the tumor immune microenvironment (TIME). A high-risk score is characterized by high immune cell infiltration, HLA, and immune checkpoints, as well as low CSC and TMB. In addition, patients with low-risk scores presented significantly lower IC50 values for ATRA, cytarabine, midostaurin, doxorubicin, and etoposide.Conclusion: Our findings might contribute to further understanding of PRGs in the prognosis and development of AML and provide novel and reliable biomarkers for its precise prevention and treatment.

Coronavirus disease 2019 or COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a significant threat to the health of human beings. While wearing mask, maintaining social distance and performing self-quarantine can reduce virus spreading passively, vaccination actively enhances immune defense against COVID-19. However, mutations of SARS-CoV-2 and presence of asymptomatic carriers frustrate the effort of completely conquering COVID-19. A strategy that can reduce the susceptibility and thus prevent COVID-19 while blocking viral invasion and pathogenesis independent of viral antigen stability is highly desirable. In the pathogenesis of COVID-19, endocrine disorders have been implicated. Correspondingly, many hormones have been identified to possess therapeutic potential of treating COVID-19, such as estrogen, melatonin, corticosteroids, thyroid hormone and oxytocin. Among them, oxytocin has the potential of both treatment and prevention of COVID-19. This is based on oxytocin promotion of immune-metabolic homeostasis, suppression of inflammation and pre-existing comorbidities, acceleration of damage repair, and reduction of individuals’ susceptibility to pathogen infection. Oxytocin may specifically inactivate SARS-COV-2 spike protein and block viral entry into cells via angiotensin-converting enzyme 2 by suppressing serine protease and increasing interferon levels and number of T-lymphocytes. In addition, oxytocin can promote parasympathetic outflow and the secretion of body fluids that could dilute and even inactivate SARS-CoV-2 on the surface of cornea, oral cavity and gastrointestinal tract. What we need to do now is clinical trials. Such trials should fully balance the advantages and disadvantages of oxytocin application, consider the time- and dose-dependency of oxytocin effects, optimize the dosage form and administration approach, combine oxytocin with inhibitors of SARS-CoV-2 replication, apply specific passive immunization, and timely utilize efficient vaccines. Meanwhile, blocking COVID-19 transmission chain and developing other efficient anti-SARS-CoV-2 drugs are also important. In addition, relative to the complex issues with drug applications over a long term, oxytocin can be mobilized through many physiological stimuli, and thus used as a general prevention measure. In this review, we explore the potential of oxytocin for treatment and prevention of COVID-19 and perhaps other similar pathogens.

Sepsis has high fatality rates. Early diagnosis could increase its curating rates. There were no reliable molecular biomarkers to distinguish between infected and uninfected patients currently, which limit the treatment of sepsis. To this end, we analyzed gene expression datasets from the GEO database to identify its mRNA signature. First, two gene expression datasets (GSE154918 and GSE131761) were downloaded to identify the differentially expressed genes (DEGs) using Limma package. Totally 384 common DEGs were found in three contrast groups. We found that as the condition worsens, more genes were under disorder condition. Then, random forest model was performed with expression matrix of all genes as feature and disease state as label. After which 279 genes were left. We further analyzed the functions of 279 important DEGs, and their potential biological roles mainly focused on neutrophil threshing, neutrophil activation involved in immune response, neutrophil-mediated immunity, RAGE receptor binding, long-chain fatty acid binding, specific granule, tertiary granule, and secretory granule lumen. Finally, the top nine mRNAs (MCEMP1, PSTPIP2, CD177, GCA, NDUFAF1, CLIC1, UFD1, SEPT9, and UBE2A) associated with sepsis were considered as signatures for distinguishing between sepsis and healthy controls. Based on 5-fold cross-validation and leave-one-out cross-validation, the nine mRNA signature showed very high AUC.