Exploring the antiviral inhibitory activity of Niloticin against the NS2B/NS3 protease of Dengue virus (DENV2).

Dengue virus (DENV2) is the cause of dengue disease and a worldwide health problem. DENV2 replicates in the host cell using polyproteins such as NS3 protease in conjugation with NS2B cofactor, making NS3 protease a promising antiviral drug-target. This study investigated the efficacy of 'Niloticin' against NS2B/NS3-protease. In silico and in vitro analyses were performed which included interaction of niloticin with NS2B/NS3-protease, protein stability and flexibility, mutation effect, betweenness centrality of residues and analysis of cytotoxicity, protein expression and WNV NS3-protease activity. Similar like acyclovir, niloticin forms strong H-bonds and hydrophobic interactions with residues LEU149, ASN152, LYS74, GLY148 and ALA164. The stability of the niloticin-NS2B/NS3-protease complex was found to be stable compared to the apo NS2B/NS3-protease in structural deviation, PCA, compactness and FEL analysis. The IC50 value of niloticin was 0.14 µM in BHK cells based on in vitro cytotoxicity analysis and showed significant activity at 2.5 µM in a concentration-dependent manner. Western blotting and qRT-PCR analyses showed that niloticin reduced DENV2 protein transcription in a dose-dependent manner. Besides, niloticin confirmed the inhibition of NS3-protease by the SensoLyte 440 WNV protease detection kit. These promising results suggest that niloticin could be an effective antiviral drug against DENV2 and other flaviviruses.

Advances in functional lipid nanoparticles: from drug delivery platforms to clinical applications.

Since Doxil's first clinical approval in 1995, lipid nanoparticles have garnered great interest and shown exceptional therapeutic efficacy. It is clear from the licensure of two RNA treatments and the mRNA-COVID-19 vaccination that lipid nanoparticles have immense potential for delivering nucleic acids. The review begins with a list of lipid nanoparticle types, such as liposomes and solid lipid nanoparticles. Then it moves on to the earliest lipid nanoparticle forms, outlining how lipid is used in a variety of industries and how it is used as a versatile nanocarrier platform. Lipid nanoparticles must then be functionally modified. Various approaches have been proposed for the synthesis of lipid nanoparticles, such as High-Pressure Homogenization (HPH), microemulsion methods, solvent-based emulsification techniques, solvent injection, phase reversal, and membrane contractors. High-pressure homogenization is the most commonly used method. All of the methods listed above follow four basic steps, as depicted in the flowchart below. Out of these four steps, the process of dispersing lipids in an aqueous medium to produce liposomes is the most unpredictable step. A short outline of the characterization of lipid nanoparticles follows discussions of applications for the trapping and transporting of various small molecules. It highlights the use of rapamycin-coated lipid nanoparticles in glioblastoma and how lipid nanoparticles function as a conjugator in the delivery of anticancer-targeting nucleic acids. High biocompatibility, ease of production, scalability, non-toxicity, and tailored distribution are just a meager of the enticing allowances of using lipid nanoparticles as drug delivery vehicles. Due to the present constraints in drug delivery, more research is required to utterly realize the potential of lipid nanoparticles for possible clinical and therapeutic purposes.

Systematic review and meta-analysis on the efficacy and safety of Injectable Lentinan combined with chemotherapy in the treatment of gastric cancer.

BACKGROUND: This study employed a meta-analysis to evaluate the efficacy and safety of adjunctive treatment with injectable Lentinan (LNT) in combination with chemotherapy for gastric cancer (GC). METHODS: Computer-based searches of 6 databases were performed to identify randomized controlled trials (RCTs) relevant to the treatment of GC with LNT through mid-March 2023. Two independent researchers performed risk of bias assessment and trial sequential analysis(TSA), extracted the data and used Revman 5.3 software for data analysis. The certainty of evidence was graded based on the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach. RESULTS: A total of 31 RCTs with 2729 patients were included in the analysis. The results revealed that adjunctive therapy with LNT was associated with improved treatment efficacy (RR = 1.48, 95%CI: 1.36 ∼ 1.61, p < 0.00001), improvement in clusters of differentiation (CD3+, CD4+, and CD4+/CD8+), natural killer (NK) cells, and quality of life assessment (RR = 1.32, 95%CI: 1.20 ∼ 1.45, p < 0.00001) compared to using chemotherapy alone. In addition, there was a reduction in CD8+ levels, incidence of white blood cell decline, gastrointestinal reactions, and platelet decline. TSA results indicated that there was sufficient evidence to draw firm conclusions about these outcomes, and the GRADE scores showed 'high' or 'moderate' quality of evidence for these outcomes. CONCLUSION: The efficacy of treatment of GC with LNT in combination with chemotherapy was found to be better than chemotherapy alone. And no serious adverse effects were observed. However, further RCTs are needed to further validate the results of this study.

Integrated network pharmacology analysis and in vitro validation revealed the underlying mechanism of Xiyanping injection in treating coronavirus disease 2019.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has spread rapidly worldwide, leading to a pandemic. In China, Xiyanping injection (XYP) has been recommended as a drug for COVID-19 treatment in the Guideline on Diagnosis and Treatment of COVID-19 by the National Health Commission of the People Republic of China and National Administration of Traditional Chinese Medicine (Trial eighth Edition). However, the relevant mechanisms at the molecular-level need to be further elucidated. METHODS: In this study, XYP related active ingredients, potential targets and COVID-19 related genes were searched in public databases. Protein-protein interaction network and module analyzes were used to screen for key targets. gene ontology and Kyoto encyclopedia of genes and genomes were performed to investigate the potentially relevant signaling pathways. Molecular docking was performed using Autodock Tools and Vina. For the validation of potential mechanism, PolyI:C was used to induce human lung epithelial cells for an inflammation model. Subsequently, CCK-8 assays, enzyme-linked immunosorbent assay, reverse transcription quantitative polymerase chain reaction and western blot were employed to determine the effect of XYP on the expression of key genes. RESULTS: Seven effective active ingredients in XYP were searched for 123 targets in the relevant databases. Furthermore, 6446 COVID-19 disease targets were identified. Sodium 9-dehydro-17-hydro-andrographolide-19-yl sulfate was identified as the vital active compounds, and IL-6, TNF, IL-1ß, CXCL8, STAT3, MAPK1, MAPK14, and MAPK8 were considered as the key targets. In addition, molecular docking revealed that the active compound and the targets showed good binding affinities. The enrichment analysis predicted that the XYP could regulate the IL-17, Toll-like receptor, PI3K-Akt and JAK-STAT signaling pathways. Consistently, further in vitro experiments demonstrated that XYP could slow down the cytokine storm in the lung tissue of COVID-19 patients by down-regulating IL-6, TNF-α, IL-1ß, CXCL8, and p-STAT3. CONCLUSION: Through effective network pharmacology analysis and molecular docking, this study suggests that XYP contains many effective compounds that may target COVID-19 related signaling pathways. Moreover, the in vitro experiment confirmed that XYP could inhibit the cytokine storm by regulating genes or proteins related to immune and inflammatory responses.

Identification of an immune-related 6-lncRNA panel with a good performance for prognostic prediction in hepatocellular carcinoma by integrated bioinformatics analysis.

Hepatocellular carcinoma (HCC) is one of the most malignant tumors with a poor prognosis. The long non-coding RNA (lncRNA) has been found to have great potential as a prognostic biomarker or therapeutic target for cancer patients. However, the prognostic value and tumor immune infiltration of lncRNAs in HCC has yet to be fully elucidated. To identify prognostic biomarkers of lncRNA in HCC by integrated bioinformatics analysis and explore their functions and relationship with tumor immune infiltration. The prognostic risk assessment model for HCC was constructed by comprehensively using univariate/multivariate Cox regression analysis, Kaplan-Meier survival analysis, and the least absolute shrinkage and selection operator regression analysis. Subsequently, the accuracy, independence, and sensitivity of our model were evaluated, and a nomogram for individual prediction in the clinic was constructed. Tumor immune microenvironment (TIME), immune checkpoints, and human leukocyte antigen alleles were compared in high- and low-risk patients. Finally, the functions of our lncRNA signature were examined using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and gene set enrichment analysis. A 6-lncRNA panel of HCC consisting of RHPN1-AS1, LINC01224, CTD-2510F5.4, RP1-228H13.5, LINC01011, and RP11-324I22.4 was eventually identified, and show good performance in predicting the survivals of patients with HCC and distinguishing the immunomodulation of TIME of high- and low-risk patients. Functional analysis also suggested that this 6-lncRNA panel may play an essential role in promoting tumor progression and immune regulation of TIME. In this study, 6 potential lncRNAs were identified as the prognostic biomarkers in HCC, and the regulatory mechanisms involved in HCC were initially explored.

Compound Kushen Injection inhibits epithelial-mesenchymal transition of gastric carcinoma by regulating VCAM1 induced by the TNF signaling pathway.

BACKGROUND: Gastric carcinoma (GC) treatment needs to be developed rapidly. Compound Kushen Injection (CKI), a formula from traditional Chinese medicine, has been used clinically in combination with chemotherapy to treat GC with satisfactory results. However, the molecular mechanism by which CKI acts to cure GC is still unclear. METHODS: In the present study, in vivo and in vitro experiments were used to assess the efficacy of CKI. Using ceRNA microarray and TMT technologies, the molecular mechanism of CKI was further investigated at the transcriptional and protein levels, and a bioinformatics approach was employed to investigate and functionally validate key CKI targets in GC. RESULTS: When combined with cisplatin (DDP), CKI significantly increased its efficacy in preventing the proliferation and metastasis of GC cells and malignant-looking tumors in mice. High-throughput sequencing data and bioinformatics analysis showed that CKI regulated the TNF signaling pathway, epithelial-mesenchymal transition (EMT), with VCAM1 as a key target. The transcription factors CEBPB, JUN, RELA, NFKB1, the EMT mesenchymal-like cell markers N-cadherin and vimentin, as well as the expression of VCAM1 and its upstream signaling driver TNF, were all downregulated by CKI. In contrast, the expression of the EMT epithelial-like cell marker E-cadherin was upregulated. CONCLUSION: CKI can effectively inhibit GC growth and metastasis, improve body's immunity, and protect normal tissues from damage. The molecular mechanism by which CKI inhibits metastasis of GC is by regulating VCAM1 induced by the TNF signaling pathway to inhibit EMT of GC. Our results provide an important clue to clarify precisely the multi-scale molecular mechanism of CKI in the treatment of GC.

The Strength of hERG Inhibition by Erythromycin at Different Temperatures Might Be Due to Its Interacting Features with the Channels.

Erythromycin is one of the few compounds that remarkably increase ether-a-go-go-related gene (hERG) inhibition from room temperature (RT) to physiological temperature (PT). Understanding how erythromycin inhibits the hERG could help us to decide which compounds are needed for further studies. The whole-cell patch clamp technique was used to investigate the effects of erythromycin on hERG channels at different temperatures. While erythromycin caused a concentration-dependent inhibition of cardiac hERG channels, it also shifted the steady-state activation and steady-state inactivation of the channel to the left and significantly accelerated the onset of inactivation at both temperatures, although temperature itself caused a profound change in the dynamics of hERG channels. Our data also suggest that the binding pattern to S6 of the channels changes at PT. In contrast, cisapride, a well-known hERG blocker whose inhibition is not affected by temperature, does not change its critical binding sites after the temperature is raised to PT. Our data suggest that erythromycin is unique and that the shift in hERG inhibition may not apply to other compounds.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq unravels T cell-related prognostic risk model and tumor immune microenvironment modulation in triple-negative breast cancer.

BACKGROUND: Triple negative breast cancer (TNBC) is an aggressive and fatal malignancy. The current success of tumor immunotherapy has focused attention on intermediate T-cell subsets and the tumor microenvironment, which are essential for activation of the anti-tumor response. Therefore, both areas require further research to accelerate progress in developing tailored immunotherapeutic approaches for patients with TNBC. METHODS: We obtained scRNA-seq data of TNBC from the GEO database. A multiplex strategy was used to analyze and identify the T-cell heterogeneity of TNBC. By combining the METABRIC and GEO databases, a prognostic risk model for T-cell marker genes was constructed and validated. In addition, the immune-infiltrating cells of TNBC was analyzed using CIBERSORT, and the association between the risk model and response to immunotherapy was investigated. RESULTS: Based on scRNA-seq data, 25,932 cells were identified for multiple analyzes. T cells were studied with a focus on 2 subtypes, including CD8+ and CD4+. There were also communication relationships between T cells and multiple cell types. The results of the enrichment analysis showed that the T-cell marker genes were focused in pathways related to the immune system. In addition, OPTN, TMEM176A, PKM and HES1 deserve attention as prognostic markers in TNBC. The immune infiltration results showed that the high-risk group had significant immune cell infiltration and immunosuppression status. CONCLUSION: This study provides a resource for understanding T-cell heterogeneity and the associated prognostic risk model for TNBC. The results show that the model helps predict prognosis and response to treatment in breast cancer.

Data mining combines bioinformatics discover immunoinfiltration-related gene SERPINE1 as a biomarker for diagnosis and prognosis of stomach adenocarcinoma.

Stomach adenocarcinoma (STAD) is a type of cancer which often at itsadvanced stage apon diagnosis and mortality in clinical practice. Several factors influencethe prognosis of STAD, including the expression and regulation of immune cells in the tumor microenvironment. We here investigated the biomarkers related to the diagnosis and prognosis of gastric cancer, hoping to provide insights for the diagnosis and treatment of gastric cancer in the future. STAD and normal patient RNA sequencing data sets were accessed from the cancer genome atlas (TCGA database). Differential genes were determined and obtained by using the R package DESeq2. The stromal, immune, and ESTIMATE scores are calculated by the ESTIMATE algorithm, followed by the modular genes screening using the R package WGCNA. Subsequently, the intersection between the modular gene and the differential gene was taken and the STRING database was used for PPI network module analysis. The R packages clusterProfiler, enrichplot, and ggplot2 were used for GO and KEGG enrichment analysis. Cox regression analysis was used to screen survival-related genes, and finally, the R package Venn Diagram was used to take the intersection and obtain 7 hub genes. The time-dependent ROC curve and Kaplan-Meier survival curve were used to find the SERPINE1 gene, which plays a critical role in prognosis. Finally, the expression pattern, clinical characteristics, and regulatory mechanism of SERPINE1 were analyzed in STAD. We revealed that the expression of SERPINE1 was significantly increased in the samples from STAD compared with normal samples. Cox regression, time-dependent ROC, and Kaplan-Meier survival analyses demonstrated that SERPINE1 was significantly related to the adverse prognosis of STAD patients. The expression of SERPINE1 increased with the progression of T, N, and M classification of the tumor. In addition, the results of immune infiltration analysis indicated that the immune cells' expression were higher in high SERPINE1 expression group than that in low SERPINE1 expression group, including CD4+ T cells, B cells, CD8+ T cells, macrophages, neutrophils and other immune cells. SERPINE1 was closely related to immune cells in the STAD immune microenvironment and had a synergistic effect with the immune checkpoints PD1 and PD-L1. In conclusion, we proved that SERPINE1 is a promising prognostic and diagnostic biomarker for STAD and a potential target for immunotherapy.

Identification of key pharmacological components and targets for Aidi injection in the treatment of pancreatic cancer by UPLC-MS, network pharmacology, and in vivo experiments.

BACKGROUND: Pancreatic cancer is one of the most lethal cancers worldwide. Aidi injection (ADI) is a representative antitumor medication based on Chinese herbal injection, but its antitumor mechanisms are still poorly understood. MATERIALS AND METHODS: In this work, the subcutaneous xenograft model of human pancreatic cancer cell line Panc-1 was established in nude mice to investigate the anticancer effect of ADI in vivo. We then determined the components of ADI using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS) and explored the possible molecular mechanisms against pancreatic cancer using network pharmacology. RESULTS: In vivo experiments, the volume, weight, and degree of histological abnormalities of implanted tumors were significantly lower in the medium and high concentration ADI injection groups than in the control group. Network pharmacology analysis identified four active components of ADI and seven key targets, TNF, VEGFA, HSP90AA1, MAPK14, CASP3, P53 and JUN. Molecular docking also revealed high affinity between the active components and the target proteins, including Astragaloside IV to P53 and VEGFA, Ginsenoside Rb1 to CASP3 and Formononetin to JUN. CONCLUSION: ADI could reduce the growth rate of tumor tissue and alleviate the structural abnormalities in tumor tissue. ADI is predicted to act on VEGFA, P53, CASP3, and JUN in ADI-mediated treatment of pancreatic cancer.

T cell-related prognostic risk model and tumor immune environment modulation in lung adenocarcinoma based on single-cell and bulk RNA sequencing.

BACKGROUND: T cells are present in all stages of tumor formation and play an important role in the tumor microenvironment. We aimed to explore the expression profile of T cell marker genes, constructed a prognostic risk model based on these genes in Lung adenocarcinoma (LUAD), and investigated the link between this risk model and the immunotherapy response. METHODS: We obtained the single-cell sequencing data of LUAD from the literature, and screened out 6 tissue biopsy samples, including 32,108 cells from patients with non-small cell lung cancer, to identify T cell marker genes in LUAD. Combined with TCGA database, a prognostic risk model based on T-cell marker gene was constructed, and the data from GEO database was used for verification. We also investigated the association between this risk model and immunotherapy response. RESULTS: Based on scRNA-seq data 1839 T-cell marker genes were identified, after which a risk model consisting of 9 gene signatures for prognosis was constructed in combination with the TCGA dataset. This risk model divided patients into high-risk and low-risk groups based on overall survival. The multivariate analysis demonstrated that the risk model was an independent prognostic factor. Analysis of immune profiles showed that high-risk groups presented discriminative immune-cell infiltrations and immune-suppressive states. Risk scores of the model were closely correlated with Linoleic acid metabolism, intestinal immune network for IgA production and drug metabolism cytochrome P450. CONCLUSION: Our study proposed a novel prognostic risk model based on T cell marker genes for LUAD patients. The survival of LUAD patients as well as treatment outcomes may be accurately predicted by the prognostic risk model, and make the high-risk population present different immune cell infiltration and immunosuppression state.

Investigation on the efficiency of Brucea javanica oil emulsion injection with chemotherapy for treating malignant pleural effusion: A meta-analysis of randomized controlled trials.

Introduction: Systematic evaluation of the clinical efficacy and safety of Brucea javanica oil emulsion injection (BJOEI) in combination with chemotherapy in the treatment of malignant pleural effusion (MPE). Methods: The study searched CNKI, Wanfang database, VIP database, SinoMed, PubMed, Embase, the Cochrane Library, and the Web of Science database and retrieved randomized controlled trials (RCTs) on the treatment of MPE with BJOEI in combination with chemotherapy from seven electronic databases from inception to 31 March 2022. Meta-analysis and sensitivity analysis were performed using Revman 5.4 and Stata 13.0 software. Results: Ultimately, 30 RCTs with 2035 patients were included, including 1002 cases in the control group and 1033 cases in the treatment group. The results of the meta-analysis showed that the overall efficacy rate of BJOEI combined with chemotherapy was higher in the treatment of MPE compared with chemotherapy alone (RR = 1.45, 95%CI: 1.36-1.54, p < 0.00001). And it could improve the Karnofsky (KPS) score (RR = 1.54, 95%CI: 1.41-1.68, p < 0.00001), reduce adverse reactions such as fever (RR = 0.82, 95%CI:0.60-1.12), chest pain (RR = 0.90, 95%CI: 0.67-1.21), gastrointestinal reactions (RR = 0.70, 95%CI: 0.57-0.87, p < 0.005), and leukopenia (RR = 0.51, 95%CI: 0.43-0.61, p < 0.00001). Conclusion: BJOEI combined with chemotherapy has better clinical efficacy than chemotherapy alone in the treatment of MPE. It can further improve KPS score, improve patients' quality of life, and reduce the occurrence of adverse reactions. However, the conclusions of this study need to be confirmed by further randomized, double-blind, controlled trials with large sample size, reasonable design, and strict implementation.

Single-Cell and Bulk RNA Sequencing Reveal Malignant Epithelial Cell Heterogeneity and Prognosis Signatures in Gastric Carcinoma.

Gastric carcinoma (GC) heterogeneity represents a major barrier to accurate diagnosis and treatment. Here, we established a comprehensive single-cell transcriptional atlas to identify the cellular heterogeneity in malignant epithelial cells of GC using single-cell RNA sequencing (scRNA-seq). A total of 49,994 cells from nine patients with paired primary tumor and normal tissues were analyzed by multiple strategies. This study focused on the malignant epithelial cells, which were divided into three subtypes, including pit mucous cells, chief cells, and gastric and intestinal cells. The trajectory analysis results suggest that the differentiation of the three subtypes could be from the pit mucous cells to the chief cells and then to the gastric and intestinal cells. Lauren's histopathology of GC might originate from various subtypes of malignant epithelial cells. The functional enrichment analysis results show that the three subtypes focused on different biological processes (BP) and pathways related to tumor development. In addition, we generated and validated the prognostic signatures for predicting the OS in GC patients by combining the scRNA-seq and bulk RNA sequencing (bulk RNA-seq) datasets. Overall, our study provides a resource for understanding the heterogeneity of GC that will contribute to accurate diagnosis and prognosis.

An advanced network pharmacology study to explore the novel molecular mechanism of Compound Kushen Injection for treating hepatocellular carcinoma by bioinformatics and experimental verification.

BACKGROUND: Compound Kushen Injection (CKI) is a Chinese patent drug that exerts curative effects in the clinical treatment of hepatocellular carcinoma (HCC). This study aimed to explore the targets and potential pharmacological mechanisms of CKI in the treatment of HCC. METHODS: In this study, network pharmacology was used in combination with molecular biology experiments to predict and verify the molecular mechanism of CKI in the treatment of HCC. The constituents of CKI were identified by UHPLC-MS/MS and literature search. The targets corresponding to these compounds and the targets related to HCC were collected based on public databases. To screen out the potential hub targets of CKI in the treatment of HCC, a compound-HCC target network was constructed. The underlying pharmacological mechanism was explored through the subsequent enrichment analysis. Interactive Gene Expression Profiling Analysis and Kaplan-Meier plotter were used to examine the expression and prognostic value of hub genes. Furthermore, the effects of CKI on HCC were verified through molecular docking simulations and cell experiments in vitro. RESULTS: Network analysis revealed that BCHE, SRD5A2, EPHX2, ADH1C, ADH1A and CDK1 were the key targets of CKI in the treatment of HCC. Among them, only CDK1 was highly expressed in HCC tissues, while the other 5 targets were lowly expressed. Furthermore, the six hub genes were all closely related to the prognosis of HCC patients in survival analysis. Molecular docking revealed that there was an efficient binding potential between the constituents of CKI and BCHE. Experiments in vitro proved that CKI inhibited the proliferation of HepG2 cells and up-regulated SRD5A2 and ADH1A, while down-regulated CDK1 and EPHX2. CONCLUSIONS: This study revealed and verified the targets of CKI on HCC based on network pharmacology and experiments and provided a scientific reference for further mechanism research.

Exploring the Multicomponent Synergy Mechanism of Yinzhihuang Granule in Inhibiting Inflammation-Cancer Transformation of Hepar Based on Integrated Bioinformatics and Network Pharmacology.

Background: The Chinese patent drug Yinzhihuang granule (YZHG) is used to treat hepatitis B. This research is aimed at exploring the multicomponent synergistic mechanism of YZHG in the treatment of inflammation-cancer transformation of hepar and at providing new evidence and insights for its clinical application. Methods: To retrieve the components and targets of Yinzhihuang granules. The differentially expressed genes (DEGs) of hepar inflammation-cancer transformation were obtained from TTD, PharmGKB, and GEO databases. Construct the compound-prediction target network and the key module network using Cytoscape 3.7.1. Results: The results show that hepatitis B and hepatitis C shared a common target, MMP2. CDK1 and TOP2A may play an important role in the treatment with YZHG in hepatitis B inflammatory cancer transformation. KEGG pathway enrichment showed that key genes of modules 1, 2, and 4 were mainly enriched in the progesterone-mediated oocyte maturation signaling pathway and oocyte meiosis signaling pathway. Conclusion: The multicomponent, multitarget, and multichannel pharmacological benefits of YZHG in the therapy of inflammation-cancer transition of hepar are directly demonstrated by network pharmacology, providing a scientific basis for its mechanism.

A novel strategy to reveal clinical advantages and molecular mechanism of aidi injection in the treatment of pancreatic cancer based on network meta-analysis and network pharmacology.

ETHNOPHARMACOLOGICAL RELEVANCE: Pancreatic cancer is a common malignancy worldwide due to its poor prognosis and high mortality rate. It is clinically proven that the combination of chemotherapeutic drugs and Traditional Chinese Medicine injections (TCMIs) significantly improves the therapeutic effect. AIM OF THE STUDY: To evaluate the efficacy and clinical benefits of TCMIs in combination with chemotherapy in the treatment of pancreatic cancer and to explore the mechanism of clinical advantage of Aidi injection. METHODS: Randomized controlled trials (RCTs) were searched in databases by NMA before December 29, 2020. WinBUGS 1.4, Stata 14.0, and R 4.0.4 software were used for calculations. All results were expressed as odds ratios and 95% credible intervals. Through the network pharmacology method, the chemical components and their targets, as well as the disease targets were further analyzed. And then, biological experiments were integrated to verify the results of network pharmacology analysis. (PROSPERO ID: CRD42021283559). RESULTS: A total of 33 RCTs with 8 TCMIs and 2011 patients were included. The results of NMA showed that Aidi injection can significantly improve the clinical efficacy (OR = 0.34, 95%CI: 0.16-0.74), and the clinical advantage was that it can significantly alleviate the leukopenia and thrombocytopenia caused by chemotherapy (OR = 5.65, 95%CI: 1.18-28.13). A total of 23 chemical compounds and 280 potential targets for Aidi injection were obtained from the online databases. Among them, there were 22 compounds, 50 targets and 211 signaling pathways closely related to leukopenia. Five genes were predicted to be core targets of ADI in alleviating leukopenia, and 2 of them (TP53 and VEGFA) were confirmed by biological experiments as regulatory targets of ADI in the treatment of PC. CONCLUSIONS: In conclusion, TCMIs in combination with chemotherapy, can improve clinical efficacy and safety in the treatment of pancreatic cancer. However, the overall evidence base is low, and large samples with multi-center RCTs are still needed to support further research findings. Aidi injection can alleviate leukopenia mainly by intervening in oxidative stress, regulating cell proliferation and apoptosis, and regulating the inflammatory response. The combined application of NMA, network pharmacology, and biological experiments provides a reference for clinical evaluation and mechanism of action exploration of other drugs.

Computational analysis of single nucleotide polymorphisms (SNPs) in PPAR gamma associated with obesity, diabetes and cancer.

The single nucleotide polymorphisms (SNPs) are the common genetic variations in human genomes and act as markers for molecular susceptibility of complex traits and diseases in humans. Amino acid variations in the non-synonymous SNPs (nsSNPs) in coding and non-coding regions affect the function/structure of the proteins. The Peroxisome proliferator-activated receptor gamma (PPARγ or PPARG) is a nuclear receptor that plays a significant role in lipid metabolism and insulin production and is associated with diabetes, obesity, and cancer. In this study, the PPARG sequence was retrieved from the NCBI database (dbSNP: NP_619726.2), and an analysis was done to predict the damaged/harmful mutated amino acids. We identified five mutated variants (C162S, R166W, Q286P, or Q314P and P467L), which were mostly expressed in cancer tissues and associated with insulin resistance and partial lipodystrophy. The identified mutations were induced, and the analysis of molecular dynamics simulation was established to determine the dynamic stability/flexibility of PPARG. The dynamic trajectories were analyzed by RMSD, RMSF, and Radius of Gyration (Rg) analysis; a vast difference was noticed in each of the protein structure when compared with the PPARG wild-type, and the mutations in PPARG impaired its functions, leading to more significant problems in humans.Communicated by Ramaswamy H. Sarma.

Exploring potential mechanisms of Suhexiang Pill against COVID-19 based on network pharmacology and molecular docking.

BACKGROUND: The traditional Chinese medicine prescription Suhexiang Pill (SHXP), a classic prescription for the treatment of plague, has been recommended in the 2019 Guideline for coronavirus disease 2019 (COVID-19) diagnosis and treatment of a severe type of COVID-19. However, the bioactive compounds and underlying mechanisms of SHXP for COVID-19 prevention and treatment have not yet been elucidated. This study investigates the mechanisms of SHXP in the treatment of COVID-19 based on network pharmacology and molecular docking. METHODS: First, the bioactive ingredients and corresponding target genes of the SHXP were screened from the traditional Chinese medicine systems pharmacology database and analysis platform database. Then, we compiled COVID-19 disease targets from the GeneCards gene database and literature search. Subsequently, we constructed the core compound-target network, the protein-protein interaction network of the intersection of compound targets and disease targets, the drug-core compound-hub gene-pathway network, module analysis, and hub gene search by the Cytoscape software. The Metascape database and R language software were applied to analyze gene ontology biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Finally, AutoDock software was used for molecular docking of hub genes and core compounds. RESULTS: A total of 326 compounds, 2450 target genes of SHXP, and 251 genes related to COVID-19 were collected, among which there were 6 hub genes of SHXP associated with the treatment of COVID-19, namely interleukin 6, interleukin 10, vascular endothelial growth factor A, signal transducer and activator of transcription 3 (STAT3), tumor necrosis factor (TNF), and epidermal growth factor. Functional enrichment analysis suggested that the effect of SHXP against COVID-19 is mediated by synergistic regulation of several biological signaling pathways, including Janus kinase/ STAT3, phosphatidylinositol 3-kinase (PI3K)-protein kinase B (Akt), T cell receptor, TNF, Nuclear factor kappa-B, Toll-like receptor, interleukin 17, Chemokine, and hypoxia-inducible factor 1 signaling pathways. SHXP may play a vital role in the treatment of COVID-19 by suppressing the inflammatory storm, regulating immune function, and resisting viral invasion. Furthermore, the molecular docking results showed an excellent binding affinity between the core compounds and the hub genes. CONCLUSION: This study preliminarily predicted the potential therapeutic targets, signaling pathways, and molecular mechanisms of SHXP in the treatment of severe COVID-19, which include the moderate immune system, relieves the "cytokine storm," and anti-viral entry into cells.

A network pharmacology approach to reveal the pharmacological targets and biological mechanism of compound kushen injection for treating pancreatic cancer based on WGCNA and in vitro experiment validation.

BACKGROUND: Compound kushen injection (CKI), a Chinese patent drug, is widely used in the treatment of various cancers, especially neoplasms of the digestive system. However, the underlying mechanism of CKI in pancreatic cancer (PC) treatment has not been totally elucidated. METHODS: Here, to overcome the limitation of conventional network pharmacology methods with a weak combination with clinical information, this study proposes a network pharmacology approach of integrated bioinformatics that applies a weighted gene co-expression network analysis (WGCNA) to conventional network pharmacology, and then integrates molecular docking technology and biological experiments to verify the results of this network pharmacology analysis. RESULTS: The WGCNA analysis revealed 2 gene modules closely associated with classification, staging and survival status of PC. Further CytoHubba analysis revealed 10 hub genes (NCAPG, BUB1, CDK1, TPX2, DLGAP5, INAVA, MST1R, TMPRSS4, TMEM92 and SFN) associated with the development of PC, and survival analysis found 5 genes (TSPOAP1, ADGRG6, GPR87, FAM111B and MMP28) associated with the prognosis and survival of PC. By integrating these results into the conventional network pharmacology study of CKI treating PC, we found that the mechanism of CKI for PC treatment was related to cell cycle, JAK-STAT, ErbB, PI3K-Akt and mTOR signalling pathways. Finally, we found that CDK1, JAK1, EGFR, MAPK1 and MAPK3 served as core genes regulated by CKI in PC treatment, and were further verified by molecular docking, cell proliferation assay, RT-qPCR and western blot analysis. CONCLUSIONS: Overall, this study suggests that the optimized network pharmacology approach is suitable to explore the molecular mechanism of CKI in the treatment of PC, which provides a reference for further investigating biomarkers for diagnosis and prognosis of PC and even the clinical rational application of CKI.

An Advanced Systems Pharmacology Strategy Reveals AKR1B1, MMP2, PTGER3 as Key Genes in the Competing Endogenous RNA Network of Compound Kushen Injection Treating Gastric Carcinoma by Integrated Bioinformatics and Experimental Verification.

Gastric carcinoma (GC) is a severe tumor of the digestive tract with high morbidity and mortality and poor prognosis, for which novel treatment options are urgently needed. Compound Kushen injection (CKI), a classical injection of Chinese medicine, has been widely used to treat various tumors in clinical practice for decades. In recent years, a growing number of studies have confirmed that CKI has a beneficial therapeutic effect on GC, However, there are few reports on the potential molecular mechanism of action. Here, using systems pharmacology combined with proteomics analysis as a core concept, we identified the ceRNA network, key targets and signaling pathways regulated by CKI in the treatment of GC. To further explore the role of these key targets in the development of GC, we performed a meta-analysis to compare the expression differences between GC and normal gastric mucosa tissues. Functional enrichment analysis was further used to understand the biological pathways significantly regulated by the key genes. In addition, we determined the significance of the key genes in the prognosis of GC by survival analysis and immune infiltration analysis. Finally, molecular docking simulation was performed to verify the combination of CKI components and key targets. The anti-gastric cancer effect of CKI and its key targets was verified by in vivo and in vitro experiments. The analysis of ceRNA network of CKI on GC revealed that the potential molecular mechanism of CKI can regulate PI3K/AKT and Toll-like receptor signaling pathways by interfering with hub genes such as AKR1B1, MMP2 and PTGERR3. In conclusion, this study not only partially highlighted the molecular mechanism of CKI in GC therapy but also provided a novel and advanced systems pharmacology strategy to explore the mechanisms of traditional Chinese medicine formulations.