Elucidating hepatocellular carcinoma progression: a novel prognostic miRNA-mRNA network and signature analysis.

There is increasing evidence that miRNAs play an important role in the prognosis of HCC. There is currently a lack of acknowledged models that accurately predict patient prognosis. The aim of this study is to create a miRNA-based model to precisely forecast a patient's prognosis and a miRNA-mRNA network to investigate the function of a targeted mRNA. TCGA miRNA dataset and survival data of HCC patients were downloaded for differential analysis. The outcomes of variance analysis were subjected to univariate and multivariate Cox regression analyses and LASSO analysis. We constructed and visualized prognosis-related models and subsequently used violin plots to probe the function of miRNAs in tumor cells. We predicted the target mRNAs added those to the String database, built PPI protein interaction networks, and screened those mRNA using Cytoscape. The hub mRNA was subjected to GO and KEGG analysis to determine its biological role. Six of them were associated with prognosis: hsa-miR-139-3p, hsa-miR-139-5p, hsa-miR-101-3p, hsa-miR-30d-5p, hsa-miR-5003-3p, and hsa-miR-6844. The prognostic model was highly predictive and consistently performs, with the C index exceeding 0.7 after 1, 3, and 5 years. The model estimated significant differences in the Kaplan-Meier plotter and the model could predict patient prognosis independently of clinical indicators. A relatively stable miRNA prognostic model for HCC patients was constructed, and the model was highly accurate in predicting patients with good stability over 5 years. The miRNA-mRNA network was constructed to explore the function of mRNA.

Development and evaluation of nomograms and risk stratification systems to predict the overall survival and cancer-specific survival of patients with hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, and patients with HCC have a poor prognosis and low survival rates. Establishing a prognostic nomogram is important for predicting the survival of patients with HCC, as it helps to improve the patient's prognosis. This study aimed to develop and evaluate nomograms and risk stratification to predict overall survival (OS) and cancer-specific survival (CSS) in HCC patients. Data from 10,302 patients with initially diagnosed HCC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2017. Patients were randomly divided into the training and validation set. Kaplan-Meier survival, LASSO regression, and Cox regression analysis were conducted to select the predictors of OS. Competing risk analysis, LASSO regression, and Cox regression analysis were conducted to select the predictors of CSS. The validation of the nomograms was performed using the concordance index (C-index), the Akaike information criterion (AIC), the Bayesian information criterion (BIC), Net Reclassification Index (NRI), Discrimination Improvement (IDI), the receiver operating characteristic (ROC) curve, calibration curves, and decision curve analyses (DCAs). The results indicated that factors including age, grade, T stage, N stage, M stage, surgery, surgery to lymph node (LN), Alpha-Fetal Protein (AFP), and tumor size were independent predictors of OS, whereas grade, T stage, surgery, AFP, tumor size, and distant lymph node metastasis were independent predictors of CSS. Based on these factors, predictive models were built and virtualized by nomograms. The C-index for predicting 1-, 3-, and 5-year OS were 0.788, 0.792, and 0.790. The C-index for predicting 1-, 3-, and 5-year CSS were 0.803, 0.808, and 0.806. AIC, BIC, NRI, and IDI suggested that nomograms had an excellent predictive performance with no significant overfitting. The calibration curves showed good consistency of OS and CSS between the actual observation and nomograms prediction, and the DCA showed great clinical usefulness of the nomograms. The risk stratification of OS and CSS was built that could perfectly classify HCC patients into three risk groups. Our study developed nomograms and a corresponding risk stratification system predicting the OS and CSS of HCC patients. These tools can assist in patient counseling and guiding treatment decision making.

CCL2 Promotes Novel Coronavirus-Mediated Inflammatory Responses in Macrophages.

PURPOSE: The hyperinflammatory response is one of the main complications associated with novel coronavirus disease 2019 (COVID-19), and there is no effective treatment for cytokine storm. Therefore, it is important to investigate the key genes associated with severity of the disease. METHODS: In this study, we used a microarray data set to analyze the key genes associated with severe illness in patients with COVID-19. The proportion of immune cells was determined using the CIBERSORT algorithm. The key genes were further verified by detecting the levels of cytokines and chemokines in the serum of patients. Additionally, macrophages were stimulated with SARS-CoV-2 spike protein and chemokine ligand (CCL) 2. The expression of cytokines, ERK1/2, and NF-κB in macrophages was detected. RESULTS: Four hub genes were identified. Among them, C-C motif chemokine receptor 2 (CCR2) was an upregulated hub gene, while killer cell lectin-like receptor subfamily K member 1 (KLRK1), macrophage colony-stimulating factor receptor (CSF1R), and CD3D human recombinant protein (CD3D) were downregulated genes. Immune cell type identification found that the proportion of monocytes was higher in patients with severe COVID-19 than that in controls. Moreover, levels of CCL2 were significantly higher in patients with COVID-19. When stimulated with SARS-CoV-2 S protein and CCL2, macrophages secreted more inflammatory cytokines. The expression level of ERK1/2 was elevated. CONCLUSIONS: These results suggested that S protein and CCL2 may mediate macrophage inflammatory responses through the ERK1/2 signaling pathway. This study provides a basis for clinical treatment and improves the prognosis of critically ill patients with COVID-19.