Network Pharmacology to Reveal the Mechanism of Fufang Banmao Capsule for Treating Unresectable Primary Liver Cancer and Clinical Data Validation.

INTRODUCTION: Fufang Banmao capsules (FFBM), a traditional Chinese medicine, has been used to treat primary liver cancer (PLC) for several years. However, the bioactive ingredients, and mechanism of FFBM for treating PLC remains unclear. Our objective is to utilize network pharmacology to investigate these aspects and subsequently validate their effectiveness through clinical data. MATERIALS AND METHODS: The FFBM ingredients were obtained from the HERB database and screened for bioactive ingredients using the SwissTargetPrediction database. The PharmMapper and GEO database were used to acquire targets and differentially expressed genes (DEGs) for FFBM and PLC, respectively. Common targets were identified using Venn diagrams, followed by enrichment and protein-protein interaction (PPI) analysis. Furthermore, the Cytoscape software was utilized to identify Hub genes and construct the ingredienttarget- pathway network. Subsequently, patients diagnosed with unresectable PLC who underwent transcatheter arterial chemoembolization (TACE) at our hospital between January 2008 and December 2019 were retrospectively collected. Finally, Cox analysis was conducted to reveal the role of FFBM in the treatment of unresectable PLC. RESULTS: FFBM had 232 targets, and PLC had 1582 DEGs. HSP90AA1 and SRC were identified as crucial targets. Alpha-santalol, glycyrrhizin, and morroniside were identified as the top three bioactive ingredients. Enrichment analysis revealed a significant connection between FFBM utilization for treating PLC and multiple pathways, such as chemical carcinogenesis, PI3K-AKT, Rap1, FoxO, MAPK, and VEGF pathway. Clinic data revealed that consuming FFBM significantly improved the prognosis of unresectable PLC with a hazard ratio of 0.69. CONCLUSION: Our study identified the bioactive ingredients of FFBM and its potential mechanisms for treating PLC. Additionally, we validated the effectiveness through clinical data.

Single-Cell RNA Sequencing Identifies Crucial Genes Influencing the Polarization of Tumor-Associated Macrophages in Liver Cancer.

Background: In the context of hepatocellular carcinoma (HCC), tumor-associated macrophages (TAMs) are pivotal for the immunosuppressive nature of the tumor microenvironment (TME). This investigation delves into the functional transformations of TAMs within the TME by leveraging single-cell transcriptomics to pinpoint critical genes influencing TAM subset polarization. Methods: We procured single-cell and bulk transcriptomic data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), implementing quality assurance, dimensional reduction, clustering, and annotation on the single-cell sequencing data. To examine cellular interactions, CellChat was utilized, while single-cell regulatory network inference and clustering (SCENIC) was applied to deduce transcription factors (TFs) and their associated targets. Through gene enrichment, survival, and immune infiltration correlation analyses, we sought to pinpoint and validate influential genes. A TAM model under HCC conditions was then established to confirm the expression levels of these key genes. Results: Our analysis encompassed 74,742 cells and 23,110 genes. Through postdimensional reduction and clustering, we identified seven distinct cell types and nine TAM subtypes. Analysis via CellChat highlighted a predominance of M2-phenotype-inclined TAM subsets within the tumor's core. SCENIC pinpointed the transcription factor PRDM1 and its target genes as pivotal in this region. Further analysis indicated these genes' involvement in macrophage polarization. Employing trajectory analysis, survival analysis, and immune infiltration correlation, we scrutinized and validated genes likely directing M2 polarization. Experimental validation confirmed PRDM1's heightened expression in TAMs conditioned by HCC. Conclusions: Our findings suggest the PRDM1 gene is a key regulator of M2 macrophage polarization, contributing to the immunosuppressive TME in HCC.

Prolonged elevated heart rate and 90-Day mortality in acute pancreatitis.

Prolonged elevated heart rate (peHR) is recognized as a risk factor for poor prognosis among critically ill patients. However, there is currently a lack of studies investigating the association between peHR and patients with acute pancreatitis. Multiparameter Intelligent Monitoring in Intensive Care IV (MIMIC-IV) database was used to identify patients with acute pancreatitis. PeHR was defined as a heart rate exceeding 100 beats per minute for at least 11 out of 12 consecutive hours. Cox regression analysis was used to assess the association between peHR and the 90-Day mortality. A total of 364 patients (48.9%) experienced a peHR episode. The 90-day mortality was 25%. PeHR is an independent risk factor for 90-day mortality (HR, 1.98; 95% CI 1.53-2.56; P < 0.001). KM survival curves exhibited a significant decrease in the survival rate at 90 days among patients who experienced a peHR episode (P < 0.001, 84.5% vs. 65.1%). We revealed a significant association of peHR with decreased survival in a large cohort of ICU patients with acute pancreatitis.

Identification of m6A-associated autophagy genes in non-alcoholic fatty liver.

Background: Studies had shown that autophagy was closely related to nonalcoholic fat liver disease (NAFLD), while N6-methyladenosine (m6A) was involved in the regulation of autophagy. However, the mechanism of m6A related autophagy in NAFLD was unclear. Methods: The NAFLD related datasets were gained via the Gene Expression Omnibus (GEO) database, and we also extracted 232 autophagy-related genes (ARGs) and 37 m6A. First, differentially expressed ARGs (DE-ARGs) and differentially expressed m6A (DE-m6A) were screened out by differential expression analysis. DE-ARGs associated with m6A were sifted out by Pearson correlation analysis, and the m6A-ARGs relationship pairs were acquired. Then, autophagic genes in m6A-ARGs pairs were analyzed for machine learning algorithms to obtain feature genes. Further, we validated the relationship between feature genes and NAFLD through quantitative real-time polymerase chain reaction (qRT-PCR), Western blot (WB). Finally, the immuno-infiltration analysis was implement, and we also constructed the TF-mRNA and drug-gene networks. Results: There were 19 DE-ARGs and four DE-m6A between NAFLD and normal samples. The three m6A genes and five AGRs formed the m6A-ARGs relationship pairs. Afterwards, genes obtained from machine learning algorithms were intersected to yield three feature genes (TBK1, RAB1A, and GOPC), which showed significant positive correlation with astrocytes, macrophages, smooth muscle, and showed significant negative correlation with epithelial cells, and endothelial cells. Besides, qRT-PCR and WB indicate that TBK1, RAB1A and GOPC significantly upregulated in NAFLD. Ultimately, we found that the TF-mRNA network included FOXP1-GOPC, ATF1-RAB1A and other relationship pairs, and eight therapeutic agents such as R-406 and adavosertib were predicted based on the TBK1. Conclusion: The study investigated the potential molecular mechanisms of m6A related autophagy feature genes (TBK1, RAB1A, and GOPC) in NAFLD through bioinformatic analyses and animal model validation. However, it is critical to note that these findings, although consequential, demonstrate correlations rather than cause-and-effect relationships. As such, more research is required to fully elucidate the underlying mechanisms and validate the clinical relevance of these feature genes.

Identification roles of NFE2L3 in digestive system cancers.

BACKGROUND: Morbidity and mortality rates of Digestive System Cancers (DSC) continue to pose human lives and health. Nuclear factor erythroid 2-like protein 3 (NFE2L3) is aberrantly expressed in DSC. This study aimed to explore the clinical value and underlying mechanisms of NFE2L3 as a novel biomarker in DSC. METHODS: We utilized data from databases and clinical gastric cancer specimens to validate the aberrant expression level of NFE2L3 and further assessed the clinical value of NFE2L3. To investigate the potential molecular mechanism of NFE2L3, we analyzed the correlation of NFE2L3 with immune molecular mechanisms, constructed PPI network, performed GO analysis and KEGG analysis, and finally explored the biological function of NFE2L3 in gastric cancer cells. RESULTS: NFE2L3 expression is up-regulated in DSC and has both prognostic and diagnostic value. NFE2L3 correlates with various immune mechanisms, PPI network suggests proteins interacting with NFE2L3, GSEA analysis suggests potential molecular mechanisms for NFE2L3 to play a role in cancer promotion, and in vitro cellular experiments also confirmed the effect of NFE2L3 on the biological function of gastric cancer cells. CONCLUSION: Our study confirms the aberrant expression and molecular mechanisms of NFE2L3 in DSC, indicating that NFE2L3 could serve as a novel biomarker for diagnosis and prognosis of DSC.