Table_2_Vemurafenib inhibits immune escape biomarker BCL2A1 by targeting PI3K/AKT signaling pathway to suppress breast cancer.docx

ObjectivesTo investigate the role of immune escape encoding genes on the prognosis of BC, and to predict the novel targeting agents.MethodsHuman immune genes and immune escape encoding genes were obtained from the IMMPORT database and the previous study. Sample information and clinical data on BC were obtained from the TCGA and GTEX databases. Obtaining differentially expressed protein data from cBioportal database. To construct a risk score model by lasso analysis, and nomogram was used to predict score core. GSCA, TIMER and CELLMINER databases were used for immune and drug susceptibility correlation analyses. Cell experiments were verified by MTT, Western blotting, and RT-qPCR.ResultsWe found prognostic models consisting of eleven immune escape related protein-coding genes with ROC curves that performed well in the ontology data (AUC for TCGA is 0.672) and the external data (AUC for GSE20685 is 0.663 and for GES42568 is 0.706). Five core prognostic models are related to survival (EIF4EBP1, BCL2A1, NDRG1, ERRFI1 and BRD4) were summarized, and a nomogram was constructed to validate a C-index of 0.695, which was superior to other prognostic models. Relevant drugs targeting core genes were identified based on drug sensitivity analysis, and found that Vemurafenib downregulates the PI3K-AKT pathway and BCL2A1 protein in BC, as confirmed by external data and cellular assays.ConclusionsBriefly, our work establishes and validates an 11-immune escape risk model, and five core prognostic factors that are mined deeply from this model, and elucidates in detail that Vemurafenib suppresses breast cancer by targeting the PI3K/AKT signaling pathway to inhibit the immune escape biomarker BCL2A1, confirms the validity of the prognostic model, and provides corresponding targeted agents to guide individualized treatment of BC patients.</p

Image_2_Vemurafenib inhibits immune escape biomarker BCL2A1 by targeting PI3K/AKT signaling pathway to suppress breast cancer.tif

ObjectivesTo investigate the role of immune escape encoding genes on the prognosis of BC, and to predict the novel targeting agents.MethodsHuman immune genes and immune escape encoding genes were obtained from the IMMPORT database and the previous study. Sample information and clinical data on BC were obtained from the TCGA and GTEX databases. Obtaining differentially expressed protein data from cBioportal database. To construct a risk score model by lasso analysis, and nomogram was used to predict score core. GSCA, TIMER and CELLMINER databases were used for immune and drug susceptibility correlation analyses. Cell experiments were verified by MTT, Western blotting, and RT-qPCR.ResultsWe found prognostic models consisting of eleven immune escape related protein-coding genes with ROC curves that performed well in the ontology data (AUC for TCGA is 0.672) and the external data (AUC for GSE20685 is 0.663 and for GES42568 is 0.706). Five core prognostic models are related to survival (EIF4EBP1, BCL2A1, NDRG1, ERRFI1 and BRD4) were summarized, and a nomogram was constructed to validate a C-index of 0.695, which was superior to other prognostic models. Relevant drugs targeting core genes were identified based on drug sensitivity analysis, and found that Vemurafenib downregulates the PI3K-AKT pathway and BCL2A1 protein in BC, as confirmed by external data and cellular assays.ConclusionsBriefly, our work establishes and validates an 11-immune escape risk model, and five core prognostic factors that are mined deeply from this model, and elucidates in detail that Vemurafenib suppresses breast cancer by targeting the PI3K/AKT signaling pathway to inhibit the immune escape biomarker BCL2A1, confirms the validity of the prognostic model, and provides corresponding targeted agents to guide individualized treatment of BC patients.</p

Table_1_Vemurafenib inhibits immune escape biomarker BCL2A1 by targeting PI3K/AKT signaling pathway to suppress breast cancer.docx

ObjectivesTo investigate the role of immune escape encoding genes on the prognosis of BC, and to predict the novel targeting agents.MethodsHuman immune genes and immune escape encoding genes were obtained from the IMMPORT database and the previous study. Sample information and clinical data on BC were obtained from the TCGA and GTEX databases. Obtaining differentially expressed protein data from cBioportal database. To construct a risk score model by lasso analysis, and nomogram was used to predict score core. GSCA, TIMER and CELLMINER databases were used for immune and drug susceptibility correlation analyses. Cell experiments were verified by MTT, Western blotting, and RT-qPCR.ResultsWe found prognostic models consisting of eleven immune escape related protein-coding genes with ROC curves that performed well in the ontology data (AUC for TCGA is 0.672) and the external data (AUC for GSE20685 is 0.663 and for GES42568 is 0.706). Five core prognostic models are related to survival (EIF4EBP1, BCL2A1, NDRG1, ERRFI1 and BRD4) were summarized, and a nomogram was constructed to validate a C-index of 0.695, which was superior to other prognostic models. Relevant drugs targeting core genes were identified based on drug sensitivity analysis, and found that Vemurafenib downregulates the PI3K-AKT pathway and BCL2A1 protein in BC, as confirmed by external data and cellular assays.ConclusionsBriefly, our work establishes and validates an 11-immune escape risk model, and five core prognostic factors that are mined deeply from this model, and elucidates in detail that Vemurafenib suppresses breast cancer by targeting the PI3K/AKT signaling pathway to inhibit the immune escape biomarker BCL2A1, confirms the validity of the prognostic model, and provides corresponding targeted agents to guide individualized treatment of BC patients.</p

Image_1_Vemurafenib inhibits immune escape biomarker BCL2A1 by targeting PI3K/AKT signaling pathway to suppress breast cancer.tif

ObjectivesTo investigate the role of immune escape encoding genes on the prognosis of BC, and to predict the novel targeting agents.MethodsHuman immune genes and immune escape encoding genes were obtained from the IMMPORT database and the previous study. Sample information and clinical data on BC were obtained from the TCGA and GTEX databases. Obtaining differentially expressed protein data from cBioportal database. To construct a risk score model by lasso analysis, and nomogram was used to predict score core. GSCA, TIMER and CELLMINER databases were used for immune and drug susceptibility correlation analyses. Cell experiments were verified by MTT, Western blotting, and RT-qPCR.ResultsWe found prognostic models consisting of eleven immune escape related protein-coding genes with ROC curves that performed well in the ontology data (AUC for TCGA is 0.672) and the external data (AUC for GSE20685 is 0.663 and for GES42568 is 0.706). Five core prognostic models are related to survival (EIF4EBP1, BCL2A1, NDRG1, ERRFI1 and BRD4) were summarized, and a nomogram was constructed to validate a C-index of 0.695, which was superior to other prognostic models. Relevant drugs targeting core genes were identified based on drug sensitivity analysis, and found that Vemurafenib downregulates the PI3K-AKT pathway and BCL2A1 protein in BC, as confirmed by external data and cellular assays.ConclusionsBriefly, our work establishes and validates an 11-immune escape risk model, and five core prognostic factors that are mined deeply from this model, and elucidates in detail that Vemurafenib suppresses breast cancer by targeting the PI3K/AKT signaling pathway to inhibit the immune escape biomarker BCL2A1, confirms the validity of the prognostic model, and provides corresponding targeted agents to guide individualized treatment of BC patients.</p

DataSheet_1_Therapeutic prospects of ceRNAs in COVID-19.zip

Since the end of 2019, COVID-19 caused by SARS-CoV-2 has spread worldwide, and the understanding of the new coronavirus is in a preliminary stage. Currently, immunotherapy, cell therapy, antiviral therapy, and Chinese herbal medicine have been applied in the clinical treatment of the new coronavirus; however, more efficient and safe drugs to control the progress of the new coronavirus are needed. Long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and circular RNAs (circRNAs) may provide new therapeutic targets for novel coronavirus treatments. The first aim of this paper is to review research progress on COVID-19 in the respiratory, immune, digestive, circulatory, urinary, reproductive, and nervous systems. The second aim is to review the body systems and potential therapeutic targets of lncRNAs, miRNAs, and circRNAs in patients with COVID-19. The current research on competing endogenous RNA (ceRNA) (lncRNA-miRNA-mRNA and circRNA-miRNA-mRNA) in SARS-CoV-2 is summarized. Finally, we predict the possible therapeutic targets of four lncRNAs, MALAT1, NEAT1, TUG1, and GAS5, in COVID-19. Importantly, the role of PTEN gene in the ceRNA network predicted by lncRNA MALAT1 and lncRNA TUG1 may help in the discovery and clinical treatment of effective drugs for COVID-19.</p