Rab5c promotes RSV and ADV replication by autophagy in respiratory epithelial cells.

Respiratory system diseases caused by respiratory viruses are common and exert tremendous pressure on global healthcare system. In our previous studies, we found that Long non-coding RNA NRAV (Lnc NRAV) and its target molecule Rab5c plays a significant role in respiratory virus infection. However, the mechanism by which Rab5c affects virus replication remains unclear. Rab5c, a protein mainly localized on the cell membranes and in early endosomes and phagosomes, participates in endocytosis mediated by clathrin and regulates the fusion of early endosome, maturation of early phagosomes, and autophagy. Therefore, we inferred that Rab5c impacts virus replication, which might be related to endocytosis or autophagy. We selected RSV (respiratory syncytial virus) as a representative enveloped virus and ADV (Adenovirus) as a representative non-enveloped virus to explore the possible mechanism of RSV and ADV replication promoted by Rab5c in A549 cells and in Rab5c-overexpressing mice. Here, we confirmed that the activated Rab5c promotes RSV and ADV replication and the inactivated Rab5c inhibits their replication. However, Rab5c promoting RSV and ADV replication is not mediated by endocytosis rather by autophagy in respiratory epithelial cells. Our study showed that Rab5c upregulates LC3-Ⅱ (microtubule-associated protein 1 light chain 3 beta) protein expression levels by interacting with Beclin1, a key autophagy molecule, which can induce autophagy and promote replication of ADV and RSV. This study enriches the understanding of the interaction between respiratory viruses and Rab5c, providing new insights for virus prevention and treatment.

Use of machine learning-based integration to develop an immune-related signature for improving prognosis in patients with gastric cancer.

Gastric cancer is one of the most common malignancies. Although some patients benefit from immunotherapy, the majority of patients have unsatisfactory immunotherapy outcomes, and the clinical significance of immune-related genes in gastric cancer remains unknown. We used the single-sample gene set enrichment analysis (ssGSEA) method to evaluate the immune cell content of gastric cancer patients from TCGA and clustered patients based on immune cell scores. The Weighted Correlation Network Analysis (WGCNA) algorithm was used to identify immune subtype-related genes. The patients in TCGA were randomly divided into test 1 and test 2 in a 1:1 ratio, and a machine learning integration process was used to determine the best prognostic signatures in the total cohort. The signatures were then validated in the test 1 and the test 2 cohort. Based on a literature search, we selected 93 previously published prognostic signatures for gastric cancer and compared them with our prognostic signatures. At the single-cell level, the algorithms "Seurat," "SCEVAN", "scissor", and "Cellchat" were used to demonstrate the cell communication disturbance of high-risk cells. WGCNA and univariate Cox regression analysis identified 52 prognosis-related genes, which were subjected to 98 machine-learning integration processes. A prognostic signature consisting of 24 genes was identified using the StepCox[backward] and Enet[alpha = 0.7] machine learning algorithms. This signature demonstrated the best prognostic performance in the overall, test1 and test2 cohort, and outperformed 93 previously published prognostic signatures. Interaction perturbations in cellular communication of high-risk T cells were identified at the single-cell level, which may promote disease progression in patients with gastric cancer. We developed an immune-related prognostic signature with reliable validity and high accuracy for clinical use for predicting the prognosis of patients with gastric cancer.

Use of machine learning-based integration to develop a monocyte differentiation-related signature for improving prognosis in patients with sepsis.

BACKGROUND: Although significant advances have been made in intensive care medicine and antibacterial treatment, sepsis is still a common disease with high mortality. The condition of sepsis patients changes rapidly, and each hour of delay in the administration of appropriate antibiotic treatment can lead to a 4-7% increase in fatality. Therefore, early diagnosis and intervention may help improve the prognosis of patients with sepsis. METHODS: We obtained single-cell sequencing data from 12 patients. This included 14,622 cells from four patients with bacterial infectious sepsis and eight patients with sepsis admitted to the ICU for other various reasons. Monocyte differentiation trajectories were analyzed using the "monocle" software, and differentiation-related genes were identified. Based on the expression of differentiation-related genes, 99 machine-learning combinations of prognostic signatures were obtained, and risk scores were calculated for all patients. The "scissor" software was used to associate high-risk and low-risk patients with individual cells. The "cellchat" software was used to demonstrate the regulatory relationships between high-risk and low-risk cells in a cellular communication network. The diagnostic value and prognostic predictive value of Enah/Vasp-like (EVL) were determined. Clinical validation of the results was performed with 40 samples. The "CBNplot" software based on Bayesian network inference was used to construct EVL regulatory networks. RESULTS: We systematically analyzed three cell states during monocyte differentiation. The differential analysis identified 166 monocyte differentiation-related genes. Among the 99 machine-learning combinations of prognostic signatures constructed, the Lasso + CoxBoost signature with 17 genes showed the best prognostic prediction performance. The highest percentage of high-risk cells was found in state one. Cell communication analysis demonstrated regulatory networks between high-risk and low-risk cell subpopulations and other immune cells. We then determined the diagnostic and prognostic value of EVL stabilization in multiple external datasets. Experiments with clinical samples demonstrated the accuracy of this analysis. Finally, Bayesian network inference revealed potential network mechanisms of EVL regulation. CONCLUSIONS: Monocyte differentiation-related prognostic signatures based on the Lasso + CoxBoost combination were able to accurately predict the prognostic status of patients with sepsis. In addition, low EVL expression was associated with poor prognosis in sepsis.

Single-cell Sequence Analysis Combined with Multiple Machine Learning to Identify Markers in Sepsis Patients: LILRA5.

Sepsis is a disease with a very high mortality rate, mainly involving an immune-dysregulated response due to bacterial infection. Most studies are currently limited to the whole blood transcriptome level; however, at the single cell level, there is still a great deal unknown about specific cell subsets and disease markers. We obtained 29 peripheral blood single-cell sequencing data, including 66,283 cells from 10 confirmed samples of sepsis infection and 19 healthy samples. Cells related to the sepsis phenotype were identified and characterized by the "scissor" method. The regulatory relationships of sepsis-related phenotype cells in the cellular communication network were clarified using the "cell chat" method. The least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and random forest (RF) were used to identify sepsis signature genes of diagnostic value. External validation was performed using multiple datasets from the GEO database (GSE28750, GSE185263, GSE57065) and 40 clinical samples. Bayesian algorithm was used to calculate the regulatory network of LILRA5 co-expressed genes. The stability of atenolol-targeting LILRA5 was determined by molecular docking techniques. Ultimately, action trajectory and survival analyses demonstrate the effectiveness of atenolol-targeted LILRA5 in treating patients with sepsis. We successfully identified 1215 healthy phenotypic cells and 462 sepsis phenotypic cells. We focused on 447 monocytes of the sepsis phenotype. Among the cellular communications, there were a large number of differences between these cells and other immune cells showing a significant inflammatory phenotype compared to the healthy phenotypic cells. Together, the three machine learning algorithms identified the LILRA5 marker gene in sepsis patients, and validation results from multiple external datasets as well as real-world clinical samples demonstrated the robust diagnostic performance of LILRA5. The AUC values of LILRA5 in the external datasets GSE28750, GSE185263, and GSE57065 could reach 0.875, 0.940, and 0.980, in that order. Bayesian networks identified a large number of unknown regulatory relationships for LILRA5 co-expression. Molecular docking results demonstrated the possibility of atenolol targeting LILRA5 for the treatment of sepsis. Behavioral trajectory analysis and survival analysis demonstrate that atenolol has a desirable therapeutic effect. LILRA5 is a marker gene in sepsis patients, and atenolol can stably target LILRA5.

GATR: A Road Network Traffic Violation Prediction Method Based on Graph Attention Network.

Prediction of traffic violations plays a key role in transportation safety. Combining with deep learning to predict traffic violations has become a new development trend. However, existing methods are based on regular spatial grids which leads to a fuzzy spatial expression and ignores the strong correlation between traffic violations and road network. A spatial topological graph can express the spatiotemporal correlation more accurately and then improve the accuracy of traffic violation prediction. Therefore, we propose a GATR (graph attention network based on road network) model to predict the spatiotemporal distribution of traffic violations, which adopts a graph attention network model combined with historical traffic violation features, external environmental features, and urban functional features. Experiments show that the GATR model can express the spatiotemporal distribution pattern of traffic violations more clearly and has higher prediction accuracy (RMSE = 1.7078) than Conv-LSTM (RMSE = 1.9180). The verification of the GATR model based on GNN Explainer shows the subgraph of the road network and the influence degree of features, which proves GATR is reasonable. GATR can provide an important reference for prevention and control of traffic violations and improve traffic safety.

Corrigendum: Expression of Human Epidermal Growth Factor Receptor-2 Status and Programmed Cell Death Protein-1 Ligand Is Associated With Prognosis in Gastric Cancer.

[This corrects the article DOI: 10.3389/fonc.2020.580045.].

Latest therapeutic target for gastric cancer: Anthrax toxin receptor 1.

Anthrax toxin receptor 1 (ANTXR1), also known as tumor endothelial marker 8, is a highly conserved cell surface protein overexpressed in tumor-infiltrating vessels. It was first found in vascular endothelial cells of human colorectal cancer. Although our understanding of its physiological function is limited, it has been found that ANTXR1 binds collagen and promotes migration of endothelial cells in vitro. ANTXR1 is upregulated in vessels of different tumor types in mice and humans, and is also expressed by tumor cells themselves in some tumors, such as gastric, lung, intestinal and breast cancer. Developmental angiogenesis and wound healing were not disturbed in ANTXR1 knockout mice, but compared with wild-type mice, growth of melanoma was impaired after ANTXR1 knockout, indicating that host-derived ANTXR1 can promote tumor growth on the basis of immune activity. Previous studies have shown that ANTXR1 vaccines or sublethal doses of anthrax toxin can inhibit angiogenesis, slow tumor growth and prolong survival. These studies suggest that ANTXR1 is necessary for tumor rather than physiological angiogenesis. It has been found that ANTXR1 plays an important role in tumor angiogenesisas well as in the growth and metastasis of many kinds of tumors. This article reviews the physiological function of ANTXR1 and its role in different kinds of cancer.

EBV-Positive Gastric Cancer: Current Knowledge and Future Perspectives.

Gastric cancer is the fifth most common malignant tumor and second leading cause of cancer-related deaths worldwide. With the improved understanding of gastric cancer, a subset of gastric cancer patients infected with Epstein-Barr virus (EBV) has been identified. EBV-positive gastric cancer is a type of tumor with unique genomic aberrations, significant clinicopathological features, and a good prognosis. After EBV infects the human body, it first enters an incubation period in which the virus integrates its DNA into the host and expresses the latent protein and then affects DNA methylation through miRNA under the action of the latent protein, which leads to the occurrence of EBV-positive gastric cancer. With recent developments in immunotherapy, better treatment of EBV-positive gastric cancer patients appears achievable. Moreover, studies show that treatment with immunotherapy has a high effective rate in patients with EBV-positive gastric cancer. This review summarizes the research status of EBV-positive gastric cancer in recent years and indicates areas for improvement of clinical practice.

Expression of Human Epidermal Growth Factor Receptor-2 Status and Programmed Cell Death Protein-1 Ligand Is Associated With Prognosis in Gastric Cancer.

BACKGROUND: PD-L1 and HER-2 are routine biomarkers for gastric cancer (GC). However, little research has been done to investigate the correlation among PD-L1, HER-2, immune microenvironment, and clinical features in GC. METHODS: Between January 2013 and May 2020, a total of 120 GC patients treated with chemotherapy were admitted to Henan Tumor Hospital. We retrospectively identified PD-L1, HER-2 level before chemotherapy and abstracted clinicopathologic features and treatment outcomes. Univariate and multivariate survival analyses were performed to assess the relationship between PD-L1/HER-2 levels and progression-free survival (PFS). The mRNA and tumor microenvironment of 343 patients with GC from The Cancer Genome Atlas (TCGA) were used to explore the underlying mechanism. RESULTS: We retrospectively analyzed 120 patients with gastric cancer, including 17 patients with HER-2 positive and 103 patients with HER-2 negative GC. The results showed that the expression of PD-L1 was closely correlated with HER-2 (P = 0.015). Patients with PD-L1/HER-2 positive obtained lower PFS compared to PD-L1/HER-2 negative (mPFS: 6.4 vs. 11.1 months, P = 0.014, mPFS: 5.3 vs. 11.1 months, P = 0.002, respectively), and the PD-L1 negative and HER-2 negative had the best PFS than other groups (P = 0.0008). In a multivariate model, PD-L1 status, HER-2 status, tumor location, and tumor differentiation remained independent prognostic indicators for PFS (P < 0.05). The results of database further analysis showed that the proportion of PD-L1+/CD8A+ in HER-2 negative patients was higher than that in HER-2 positive patients (37.6 vs 20.3%), while PD-L1-/CD8A- was significantly higher in HER-2 positive patients than HER-2 negative patients (57.8 vs. 28.8%). In addition, it showed that not only CD4+T cells, macrophages, and CD8+T cells, but also the associated inflammatory pathways such as IFN-γ/STAT1 were associated with HER-2. CONCLUSION: HER-2 status could predict the efficacy of immune checkpoint inhibitors, and HER-2 status combined with PD-L1 level could predict the prognosis of GC patients.