Differential expression of genes associated with T lymphocytes function in septic patients with hypoxemia challenge

Prediction of Prognosis in Patients with Sepsis Based on Platelet-Related Genes

The study aimed to develop a risk prognostic model using platelet-related genes (PRGs) to predict sepsis patient outcomes. Sepsis patient data from the Gene Expression Omnibus (GEO) database and PRGs from the Molecular Signatures Database (MSigDB) were analyzed. Differential analysis identified 1139 differentially expressed genes (DEGs) between sepsis and control groups. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed enrichment in functions related to immune cell regulation and pathways associated with immune response and infectious diseases. A risk prognostic model was established using LASSO and Cox regression analyses, incorporating 10 PRGs selected based on their association with sepsis prognosis. The model demonstrated good stratification and prognostic effects, confirmed by survival and receiver operating characteristic (ROC) curve analyses. It served as an independent prognostic factor in sepsis patients. Further analysis using the CIBERSORT algorithm showed higher infiltration of activated natural killer (NK) cells and lower infiltration of CD8 T cells and CD4 T cells naïve in the high-risk group compared to the low-risk group. Additionally, expression levels of human leukocyte antigen (HLA) genes were significantly lower in the high-risk group. In conclusion, the 10-gene risk model based on PRGs accurately predicted sepsis patient prognosis and immune infiltration levels. This study provides valuable insights into the role of platelets in sepsis prognosis and diagnosis, offering potential implications for personalized treatment strategies.

Identification of circulating immune landscape in ischemic stroke based on bioinformatics methods

Ischemic stroke (IS) is a high-incidence disease that seriously threatens human life and health. Neuroinflammation and immune responses are key players in the pathophysiological processes of IS. However, the underlying immune mechanisms are not fully understood. In this study, we attempted to identify several immune biomarkers associated with IS. We first retrospectively collected validated human IS immune-related genes (IS-IRGs) as seed genes. Afterward, potential IS-IRGs were discovered by applying random walk with restart on the PPI network and the permutation test as a screening strategy. Doing so, the validated and potential sets of IS-IRGs were merged together as an IS-IRG catalog. Two microarray profiles were subsequently used to explore the expression patterns of the IS-IRG catalog, and only IS-IRGs that were differentially expressed between IS patients and controls in both profiles were retained for biomarker selection by the Random Forest rankings. CLEC4D and CD163 were finally identified as immune biomarkers of IS, and a classification model was constructed and verified based on the weights of two biomarkers obtained from the Neural Network algorithm. Furthermore, the CIBERSORT algorithm helped us determine the proportions of circulating immune cells. Correlation analyses between IS immune biomarkers and immune cell proportions demonstrated that CLEC4D was strongly correlated with the proportion of neutrophils (r = 0.72). These results may provide potential targets for further studies on immuno-neuroprotection therapies against reperfusion injury.

Clinical Value of Prognostic Nutritional Index and Neutrophil-to-Lymphocyte Ratio in Prediction of the Development of Sepsis-Induced Kidney Injury

Background

Sepsis-related acute kidney injury (S-AKI) is a frequent complication of hospitalized patients and is linked to increased morbidity and mortality. Early prediction and detection remain conducive to optimizing treatment strategies and limiting further insults. This study was aimed at evaluating the potential predictive value of the combined prognostic nutrition index (PNI) and neutrophil-to-lymphocyte ratio (NLR) to predict the risk of AKI in septic patients.

Methods

In this retrospective study, 1238 adult patients with sepsis who were admitted to the First Affiliated Hospital of Xi'an Jiaotong University from January 2015 to June 2021 were enrolled. Patients were divided into two groups: the non-AKI group (n = 731) and the S-AKI group (n = 507). Univariate and multivariate logistic regression analyses were performed to screen the independent predictive factors of S-AKI. A receiver operating characteristic (ROC) curve was used to evaluate the predictive value of PNI and NLR.

Results

Multivariate logistic regression analysis indicated that age, chronic liver disease, cardiovascular disease, respiratory rate (RR), white blood cells (WBC), blood urea nitrogen (BUN), creatinine (CRE), international normalized ratio (INR), neutrophil-to-lymphocyte ratio (NLR), and prognostic nutrition index (PNI) were independent prognostic factors of S-AKI. In the three models, the adjusted OR of PNI for S-AKI was 0.802 (0.776-0.829), 0.801 (0.775-0.829), and 0.717 (0.666-0.772), while that of NLR was 1.094 (1.078-1.111), 1.097 (1.080-1.114), and 1.044 (1.016-1.072), respectively. In addition, the area under the ROC curve of the PNI plus NLR group was significantly greater than that of the CRE plus BUN group (0.801, 95% CI: 0.775-0.827 vs. 0.750, 95% CI: 0.722-0.778, respectively; P < 0.001).

Conclusions

PNI and NLR have been identified as readily available and independent predictors in septic patients with S-AKI. PNI, in combination with NLR, is of vital significance for early warning and efficient intervention of S-AKI and is superior to combined BUN and CRE.

Upregulated Proteasome Subunits in COVID-19 Patients: A Link with Hypoxemia, Lymphopenia and Inflammation

Severe COVID-19 disease leads to hypoxemia, inflammation and lymphopenia. Viral infection induces cellular stress and causes the activation of the innate immune response. The ubiquitin-proteasome system (UPS) is highly implicated in viral immune response regulation. The main function of the proteasome is protein degradation in its active form, which recognises and binds to ubiquitylated proteins. Some proteasome subunits have been reported to be upregulated under hypoxic and hyperinflammatory conditions. Here, we conducted a prospective cohort study of COVID-19 patients (n = 44) and age-and sex-matched controls (n = 20). In this study, we suggested that hypoxia could induce the overexpression of certain genes encoding for subunits from the α and β core of the 20S proteasome and from regulatory particles (19S and 11S) in COVID-19 patients. Furthermore, the gene expression of proteasome subunits was associated with lymphocyte count reduction and positively correlated with inflammatory molecular and clinical markers. Given the importance of the proteasome in maintaining cellular homeostasis, including the regulation of the apoptotic and pyroptotic pathways, these results provide a potential link between COVID-19 complications and proteasome gene expression.

Identification of key immune genes for sepsis-induced ARDS based on bioinformatics analysis

Regarding the extremely high mortality caused by sepsis-induced acute respiratory distress syndrome (ARDS), it is urgent to develop new biomarkers of sepsis-induced ARDS for treatment. Here, 532 differential expression genes (DEGs) related to sepsis and 433 DEGs related to sepsis-induced ARDS were screened in the GSE32707 dataset. Compared with sepsis samples, sepsis ARDS samples showed a higher infiltration of activated memory CD4 T cells and naive B cells, but a relatively lower infiltration of CD8 T cells. The pink and green modules which are significantly associated with sepsis-induced ARDS were then screened through co-expression network analysis. Differentially up-regulated GYPE and aberrantly down-regulated HSPB1, were subsequently found in the pink module of ARDS. CD81 and RPL22, two differentially low-expressed genes peculiar to ARDS, were identified in the green module. The function of CD81 was verified at the cellular level, and it was found that the up-regulation of CD81 in A549 could alleviate the LPS-induced injury of A549 cells. More importantly, the overexpressed CD81 can also increase the content of CD4⁺ CD25⁺ Foxp3⁺ Treg in Jurkat cells, and after the co-culture of overexpressed CD81 Jurkat cells with LPS treatment A549 cells, the LPS-induced lung epithelial cell damage can be improved. Overall, four new plasma biomarker candidates were found in sepsis-induced ARDS, and we verified that CD81 may play critical roles in the biological and immunological processes of sepsis-induced ARDS.

A Hypoxia-Associated Prognostic Gene Signature Risk Model and Prognosis Predictors in Gliomas

Most solid tumours are hypoxic. Tumour cell proliferation and metabolism accelerate oxygen consumption. The low oxygen supply due to vascular abnormalisation and the high oxygen demand of tumour cells give rise to an imbalance, resulting in tumour hypoxia. Hypoxia alters cellular behaviour and is associated with extracellular matrix remodelling, enhanced tumour migration, and metastatic behaviour. In light of the foregoing, more research on the progressive and prognostic impacts of hypoxia on gliomas are crucial. In this study, we analysed the expression levels of 75 hypoxia-related genes in gliomas and found that a total of 26 genes were differentially expressed in The Cancer Genome Atlas (TCGA) database samples. We also constructed protein–protein interaction networks using the STRING database and Cytoscape. We obtained a total of 10 Hub genes using the MCC algorithm screening in the cytoHubba plugin. A prognostic risk model with seven gene signatures (PSMB6, PSMD9, UBB, PSMD12, PSMB10, PSMA5, and PSMD14) was constructed based on the 10 Hub genes using LASSO–Cox regression analysis. The model was verified to be highly accurate using subject work characteristic curves. The seven-gene signatures were then analysed by univariate and multivariate Cox. Notably, PSMB10, PSMD12, UBB, PSMA5, and PSMB6 were found to be independent prognostic predictive markers for glioma. In addition, PSMB6, PSMA5, UBB, and PSMD12 were lowly expressed, while PSMB10 was highly expressed, in the TCGA and GTEx integrated glioma samples and normal samples, which were verified through protein expression levels in the Human Protein Atlas database. This study found the prognostic predictive values of the hypoxia-related genes PSMB10, PSMD12, UBB, PSMA5, and PSMB6 for glioma and provided ideas and entry points for the progress of hypoxia-related glioma.

Predictive value of immune cell counts and neutrophil-to-lymphocyte ratio for 28-day mortality in patients with sepsis caused by intra-abdominal infection

Background

The current study aimed to evaluate the value of immune cell counts and neutrophil-to-lymphocyte ratio (NLR) when attempting to predict 28-day mortality.

Methods

We conducted an observational retrospective study that included consecutive septic patients. Severity scores on the first day and peripheral circulating immune cell counts (at day 1, day 3, day 5 and day 7 of admission) were collected during each patient’s emergency intensive care unit stay. We assessed the associations of peripheral circulating immune cell counts and NLR with the severity of illness. The relationships between 28-day mortality and peripheral circulating immune cell counts and NLR with were evaluated using Cox proportional cause-specific hazards models.

Results

A total of 216 patients diagnosed with sepsis caused by IAI were enrolled. The lymphocyte counts (days 1, 3, 5 and 7) and monocyte counts (days 3, 5 and 7) were significantly lower in non-survivors (n = 72) than survivors (n = 144). The NLR values at each time point were significantly higher in non-survivors. The day 1 lymphocyte counts, as well as the monocyte counts, were significantly lower in the highest-scoring group, when stratified by the Acute Physiology and Chronic Health Evaluation II and Sequential Organ Failure Assessment scores, than in the other groups (p < 0.05). The day 1 NLR was significantly higher in the highest-scoring group than in the other groups (p < 0.05). The day 5 and day 7 lymphocyte counts, day 3 and day 7 monocyte counts and day 7 NLR were significant predictors of 28-day mortality in the Cox proportional hazards models (day 5 lymphocyte count: hazard ratio, 0.123 (95% CI, 0.055–0.279), p < 0.001; day 7 lymphocyte count: hazard ratio, 0.115 (95% CI, 0.052–0.254), p < 0.001; day 3 monocyte count: hazard ratio, 0.067 (95% CI, 0.005–0.861), p = 0.038; day 7 monocyte count: hazard ratio, 0.015 (95% CI, 0.001–0.158), p < 0.001; day 7 NLR: hazard ratio, 0.773 (95% CI, 0.659–0.905), p = 0.001).

Conclusions

The results showed that circulating lymphocytes and monocytes were dramatically decreased within 7 days in non-survivors following sepsis from an IAI. Lymphocyte counts, monocyte counts and NLR appeared to be associated with the severity of illness, and they may serve as independent predictors of 28-day mortality in septic patients with IAIs.