A combined risk model for the multi-encompassing identification of heterogeneities of prognoses, biological pathway variations and immune states for sepsis patients.

BACKGROUND: Sepsis is a highly heterogeneous syndrome with stratified severity levels and immune states. Even in patients with similar clinical appearances, the underlying signal transduction pathways are significantly different. To identify the heterogeneities of sepsis from multiple angles, we aimed to establish a combined risk model including the molecular risk score for rapid mortality prediction, pathway risk score for the identification of biological pathway variations, and immunity risk score for guidance with immune-modulation therapy. METHODS: We systematically searched and screened the mRNA expression profiles of patients with sepsis in the Gene Expression Omnibus public database. The screened datasets were divided into a training cohort and a validation cohort. In the training cohort, authentic prognostic predictor characteristics (differentially expressed mRNAs, pathway activity variations and immune cells) were screened for model construction through bioinformatics analysis and univariate Cox regression, and a P value less than 0.05 of univariate Cox regression on 28-day mortality was set as the cut-off value. The combined risk model was finally established by the decision tree algorithm. In the validation cohort, the model performance was assessed and validated by C statistics and the area under the receiver operating characteristic curve (AUC). Additionally, the current models were further compared in clinical value with traditional indicators, including procalcitonin (PCT) and interleukin-8 (IL-8). RESULTS: Datasets from two sepsis cohort studies with a total of 585 consecutive sepsis patients admitted to two intensive care units were downloaded as the training cohort (n = 479) and external validation cohort (n = 106). In the training cohort, 15 molecules, 20 pathways and 4 immune cells were eventually enrolled in model construction. These prognostic factors mainly reflected hypoxia, cellular injury, metabolic disorders and immune dysregulation in sepsis patients. In the validation cohort, the AUCs of the molecular model, pathway model, immune model, and combined model were 0.81, 0.82, 0.62 and 0.873, respectively. The AUCs of the traditional biomarkers (PCT and IL-8) were 0.565 and 0.585, respectively. The survival analysis indicated that patients in the high-risk group identified by models in the current study had a poor prognosis (P < 0.05). The above results indicated that the models in this study are all superior to the traditional biomarkers for the predicting the prognosis of sepsis patients. Furthermore, the current study provides some therapeutic recommendations for patients with high risk scores identified by the three submodels. CONCLUSIONS: In summary, the present study provides opportunities for bedside tests that could quantitatively and rapidly measure heterogeneous prognosis, underlying biological pathway variations and immune dysfunction in sepsis patients. Further therapeutic recommendations for patients with high risk scores could improve the therapeutic system for sepsis.

Reduced CENPU expression inhibits lung adenocarcinoma cell proliferation and migration through PI3K/AKT signaling.

CENPU (centromere protein U), a centromere component essential for mitosis, relates with some cancers progression. However, it is not well illustrated in lung adenocarcinoma (LAC). Here, we aimed to investigate the potential effect of CENPU on LAC progression and prognosis. In this experiment, expression level of CENPU and association between its expression and LAC patients' clinicopathological characteristics and prognosis were analyzed. The proliferation, migration and invasive abilities of LAC cells were determined by CCK-8, colony formation, transwell assays. Western blot was used to detect PI3K/AKT signaling key proteins. We found CENPU level was overexpressed in LAC tissues on comparing normal tissues. Moreover, CENPU overexpression correlated with clinicopathological variables and predicted an independent prognostic indicator in LAC patients. Functionally, CENPU downregulation significantly inhibited LAC cell proliferation, migration and invasion in, which was possibly mediated by PI3K/AKT pathway inactivation. Our findings insinuate targeting CENPU may be a potential therapeutic strategy for LAC.

Hsa-let-7c exerts an anti-tumor function by negatively regulating ANP32E in lung adenocarcinoma.

We attempted to investigate the relationship between hsa-let-7c and ANP32E, as well as their influence on the cells phenotype of lung adenocarcinoma. Expression of hsa-let-7c and prognostic values were assessed by bioinformatics analysis based on TCGA database. Quantitative real-time PCR and western blot was employed to measure relative expression of hsa-let-7c or ANP32E. The targeting relationship between let-7c and ANP32E was predicted by biological software and validated by dual luciferase reporter assay. With gene transfection technology, cell proliferation, invasion and migration were appraised by cell counting Kit-8, clone formation and Transwell assays. The results showed that hsa-let-7c was downregulated in lung adenocarcinoma. Downregulation of hsa-let-7c notably led to a poor survival. ANP32E was forecasted and confirmed as a directly target of hsa-let-7c, and was upregulated in lung adenocarcinoma. Furthermore, upregulation of ANP32E had a significant correlation with unsatisfactory survival. Meanwhile, the levels of ANP32E were negatively regulated by hsa-let-7c. Upregulation of hsa-let-7c remarkably suppressed the Calu-3 cell proliferation, invasion and migration, while ANP32E overexpression plasmids rescued the downtrend. Inversely, hsa-let-7c silencing in NCI-H209 cells presented the opposite outcomes. Collectively, hsa-let-7c shows an anti-tumor effect in lung adenocarcinoma by targeting ANP32E and is expected to be a potential therapeutic target for lung adenocarcinoma.