SLC12A3: A Novel Prognostic Biomarker of Clear Cell Renal Cell Carcinoma.

Objective: Clear renal cell carcinoma (ccRCC) is a common and deadly urinary system tumor. The TNM system determines treatment and prognosis based on cancer advancement. While nephron-sparing surgery is an option for localized ccRCC, advanced cases are challenging, and molecular-targeted therapy is crucial. Methods: Here, we implemented microarray datasets to identify a total of 119 differentially expressed genes (DEGs) and ten hub genes by a protein-protein interaction network (PPI) and performed module analysis through STRING and Cytoscape. Results: Data from this analysis shed light on a positive correlation between SLC12A3 (solute carrier family 12 member 3) and tumor-correlated cells. SLC12A3 can predict prognosis and immune infiltration levels in KIRC patients. Conclusion: Our findings demonstrated that SLC12A3 expression accounts for favorable prognosis and increased immune infiltration of various cell types. This could lead to potential therapeutic aims and biomarkers for KIRC (kidney renal clear cell carcinoma).

Transcriptomic analysis of the HPG axis-related tissues reveals potential candidate genes and regulatory pathways associated with testicular size in Hu sheep.

Testicular size is an excellent proxy for selecting high-fertility rams. The hypothalamus-pituitary-gonadal (HPG) axis plays an important role in regulating reproductive capacity in vertebrates, while key genes and regulatory pathways within the HPG axis associated with testicular size remain largely unknown in sheep. This study comprehensively compared the transcriptomic profiles in the hypothalamus, pituitary and testis of rams after sexual maturity between the large-testis group (LTG, testicular weight = 454.29 ± 54.24 g) and the small-testis group (STG, testicular weight = 77.29 ± 10.76 g). In total, 914, 795 and 10518 differentially expressed genes (DEGs) were identified in the hypothalamus, pituitary and testis between LTG and STG, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses showed that these DEGs were mainly involved in the biological processes of reproduction, biological regulation, and development process. Notably, the neuroactive ligand-receptor interaction and cAMP signaling pathways, commonly enriched by the DEGs in the hypothalamus and pituitary between two groups, were considered as two key signal pathways regulating testicular development through the HPGs axis. Weighted gene co-expression network analysis (WGCNA) identified two modules that were significantly associated with testicular size, and 97 key genes were selected with high module membership (MM) and gene significance (GS) in these two modules. Finally, a protein-protein interaction (PPI) network was constructed, and ten genes with the highest degree were represented as hub genes, including FOS, NPY, SST, F2, AGT, NTS, OXT, EDN1, VIP and TAC1. Taken together, these results provide new insights into the molecular mechanism underlying the HPG axis regulating testicular size of Hu sheep.

Analysis of Enrichment Pathway, Hub Gene, and Protein-Protein Interaction Network in Rheumatoid Arthritis and Construction of Molecular Subtypes in Peripheral Blood.

Objective: To analyze the enrichment pathway, hub gene, and Protein-protein interaction (PPI) network of rheumatoid arthritis (RA) and construct peripheral blood subtypes based on integrated bioinformatics analysis. Methods: Suitable datasets were screened from the GEO database based on titles and abstracts, batch positive analysis was performed using R language, and KEGG enrichment analysis and GO enrichment analysis were performed. After screening the differential genes, the PPI network was constructed, and the hubba plug-in of Cytoscape software was used to obtain the top 10 hub genes(key regulatory genes). hub genes were used as the typing condition to identify the molecular subtypes of synovial tissue and peripheral blood of arthritis. Results: GSE12021 and GSE93272 have been chosen for analysis. GSE12021 presents the transcriptome analysis of human joint synovial tissue, comprising 12 samples from patients with rheumatoid arthritis and 9 samples from normal healthy individuals. On the other hand, GSE93272 includes human peripheral blood samples, comprising 232 samples from patients with rheumatoid arthritis and 43 samples from normal healthy individuals. The main results of GSE12021 KEGG enrichment were Parathyroid hormone synthesis, Relaxin signaling pathway, TNF signaling pathway, Rheumatoid arthritis, T cell receptor signaling pathway, Th1 and Th2 cell differentiation, Th17 cell differentiation, Toll-like receptor signaling pathway and so on. The main results of GSE12021 GO enrichment were regulation of feeding behavior, regulation of neuron death, positive regulation of cell-cell adhesion, and positive regulation of leukocyte activation. The top 10 hub genes were CD8A, JUN, CTLA4, CD19, LCK, FOS, CCL5, IL7R, CCR7 and CD247. Synovial tissue and peripheral blood subtypes of rheumatoid arthritis showed that the two classification methods maintained consistency. Conclusion: Identifying the Hub gene in peripheral blood helps screen molecular subtypes of rheumatoid arthritis.

Potential molecular targets and drugs for basement membranes-related intervertebral disk degeneration through bioinformatics analysis and molecular docking.

BACKGROUND: Through bioinformatics analysis to identify the hub genes of Intervertebral disc degeneration (IVDD) associated with basement membranes (BMs) and find out the potential molecular targets and drugs for BMs-related annulus fibrosus (AF) degeneration based on bioinformatic analysis and molecular approach. METHODS: Intervertebral disc degeneration (IVDD) related targets were obtained from GeneCards, DisGenet and OMIM databases. BMs related genes were obtained from Basement membraneBASE database. The intersection targets were identified and subjected to protein-to-protein interaction (PPI) construction via STRING. Hub genes were identified and conducted Gene ontology (GO) and pathway enrichment analysis through MCODE and Clue GO in Cytospace respectively. DSigDB database was retrieved to predict therapeutic drugs and molecular docking was performed through PyMOL, AutoDock 1.5.6 to verify the binding energy between the drug and the different expressed hub genes. Finally, GSE70362 from GEO database was obtained to verify the different expression and correlation of each hub gene for AF degeneration. RESULTS: We identified 41 intersection genes between 3 disease targets databases and Basement membraneBASE database. PPI network revealed 25 hub genes and they were mainly enriched in GO terms relating to glycosaminoglycan catabolic process, the TGF-ß signaling pathway. 4 core targets were found to be significant via comparison of microarray samples and they showed strong correlation. The molecular docking results showed that the core targets have strong binding energy with predicting drugs including chitosamine and retinoic acid. CONCLUSIONS: In this study, we identified hub genes, pathways, potential targets, and drugs for treatment in BMs-related AF degeneration and IVDD.

Dissecting the Imbalance of Synovial Macrophages in Rheumatoid Arthritis.

Objective: This study sought to identify candidate genes of rheumatoid arthritis (RA) synovial macrophages using bioinformatics and to explore their pathways in the pathogenesis of RA. Methods: The microarray datasets GSE10500 and GSE97779 were obtained from the Gene Express Omnibus and analyzed with synovial macrophages of 14 RA patients and 8 healthy donors. The researchers used R software to identify differentially expressed genes and determine functional enrichment pathways. A protein-protein interaction network was then constructed using STRING and Cytoscape. Gene expression was validated with the GSE71370 dataset and RT-qPCR analysis. Results: 102 DEGs were identified in RA synovial macrophages relative to normal samples. Of these, 72 were upregulated; 30 were downregulated. GO and KEGG pathway analyses suggested that DEGs mainly regulated the immune response and signaling pathways associated with inflammatory activation, apoptosis, and cancer. The top five hub genes and top 1 gene module from the PPI network of DEGs were VEGFA, MMP9, FN1, IGF1, CXCL9, ISG20, RSAD2, IFI27, GBP2, and GBP1. The GSE71370 dataset and RT-qPCR analysis showed that CXCL9 and GBP1 were significantly upregulated (P ≤ .05). Conclusions: CXCL9 and GBP1 may contribute to RA pathogenesis and serve as potential biomarkers and therapeutic targets for RA.

Brief literature review and comprehensive bioinformatics analytics unravel the potential mechanism of curcumin in the treatment of periodontitis.

OBJECTIVE: Periodontitis is a chronic oral disease prevalent worldwide, and natural products are recommended as adjunctive therapy due to their minor side effects. Curcumin, a widely used ancient compound, has been reported to possess therapeutic effects in periodontitis. However, the exact mechanism underlying its activity remains unclear. In this context, the present study aimed to conduct computational simulations to uncover the potential mechanism of action of Curcumin in the treatment of periodontitis. MATERIALS AND METHODS: Single-cell analysis was conducted using a dataset (i.e., GSE164241) curated from the Gene Expression Omnibus (GEO) database through an R package "Seurat package." Bulk RNA sequencing data were curated from GSE10334 and GSE16134 and processed by R package "Limma." Then, the marker genes in the single-cell transcriptome and differentially expressed genes (DEGs) in the bulk transcriptome were integrated. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were also carried out to reveal their functionalities. Key targets were mined from their protein-protein interaction (PPI) network topologically. Afterward, molecular docking was performed. The top-ranked pose was subjected to molecular dynamics simulations to investigate the stability of the docking result. RESULTS: FOS, CXCL1, CXCL8, and IL1B, were filtered after a series of selected processes. The results of molecular modeling suggested that except for IL1B, the Vena Scores of the rest exceeded -5 kcal/mol. Furthermore, the molecular dynamic simulation indicated that the binding of the CXCL8-Curcumin complex was stable over the entire 100 ns simulation. CONCLUSION: The present study unlocked the binding modes of CXCL1, FOS, and CXCL8 with the Curcumin molecule, which were relatively stable, especially for CXCL8, hindering its promising potential to serve as the critical targets of Curcumin in periodontitis treatment.

Identification of prognostic hub genes and therapeutic targets for selenium deficiency in chicks model through transcriptome profiling.

Selenium deficiency is a prevalent micronutrient deficiency that poses a major health concern worldwide. This study aimed to shed light on the molecular mechanisms underlying selenium deficiency using a chick model. Chickens were divided into control and selenium deficient groups. Plasma samples were collected to measure selenium concentration and transcriptome analyse were performed on oviduct samples. The results showed that selenium deficiency led to a significant reduction in plasma selenium levels and altered the expression of 10,266 differentially expressed genes (DEGs). These DEGs primarily regulated signal transduction and cell motility. The molecular function includes GTPase regulatory activity, and KEGG pathway analysis showed that they were mainly involved in the signal transduction. By using Cytoscape and CancerGeneNet tool, we identified 8 modules and 10 hub genes (FRK, JUN, PTPRC, ACTA2, MST1R, SDC4, SDC1, CXCL12, MX1 and EZR) associated with receptor tyrosine kinase pathway, Wnt and mTOR signaling pathways that may be closely related to cancer. These hub genes could be served as precise diagnostic and prognostic candidate biomarkers of selenium deficiency and potential targets for treatment strategies in both animals and humans. This study sheds light on the molecular basis of selenium deficiency and its potential impact on public health.

Unraveling the Mechanism of Zhibaidihuang Decoction against IgA Nephropathy Using Network Pharmacology and Molecular Docking Analyses.

Zhibaidihuang Decoction (ZBDHD) is a traditional Chinese medicine with immense potential to treat IgA nephropathy. However, its core ingredients and representative mechanism remain unclear. In this study, we uncovered the key component and underlying mechanisms of ZBDHD for IgA nephropathy by applying network pharmacology and molecular docking approaches. This was done by first identifying the active ingredients and, subsequently, their corresponding gene targets in ZBDHD with the help of the Traditional Chinese Medicine Systems Pharmacology and analysis platform (TCMSP) database, thereby constructing the drug-compound-target network. The IgA nephropathy-associated genes were then identified using GeneCards, Drugbank, and OMIM databases. The overlapped targets were later obtained to establish Protein-Protein Interaction (PPI) networks, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Finally, we performed molecular docking among active compounds and hub genes, and thereby verified the key compound of ZBDHD. The drug-compound-gene network consisted of 289 nodes and 1,113 edges. The top four active ingredients were beta-sitosterol, kaempferol, quercetin and stigmasterol. The top five hub genes in the PPI network were AKT1, ILB1, IL-6, TNF, and TP53. Molecular docking results could demonstrate that there was high affinity among active compounds and the core targets, while quercetin may possibly be the key compound of ZBDHD. We first identified the positive compound and the candidate molecular mechanisms of ZBDHD in an IgA nephropathy treatment and discovered that quercetin might be the core compound of ZBDHD in the treatment of IgA nephropathy.

Identification of hub genes and candidate herbal treatment in obesity through integrated bioinformatic analysis and reverse network pharmacology.

Obesity is a global epidemic elevating the risk of various metabolic disorders. As there is a lack of effective drugs to treat obesity, we combined bioinformatics and reverse network pharmacology in this study to identify effective herbs to treat obesity. We identified 1011 differentially expressed genes (DEGs) of adipose tissue after weight loss by analyzing five expression profiles (GSE103766, GSE35411, GSE112307, GSE43471, and GSE35710) from the Gene Expression Omnibus (GEO) database. We identified 27 hub genes from the protein-protein interaction (PPI) network by performing MCODE using the Search Tool for the Retrieval of Interacting Genes (STRING) database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed that these hub genes have roles in the extracellular matrix-receptor interaction, cholesterol metabolism, PI3K-Akt signaling pathway, etc. Ten herbs (Aloe, Portulacae Herba, Mori Follum, Silybum Marianum, Phyllanthi Fructus, Pollen Typhae, Ginkgo Semen, Leonuri Herba, Eriobotryae Folium, and Litseae Fructus) targeting the nine hub genes (COL1A1, MMP2, MMP9, SPP1, DNMT3B, MMP7, CETP, COL1A2, and MUC1) using six ingredients were identified as the key herbs. Quercetin and (-)-epigallocatechin-3-gallate were determined to be the key ingredients. Lastly, Ingredients-Targets, Herbs-Ingredients-Targets, and Herbs-Taste-Meridian Tropism networks were constructed using Cytoscape to elucidate this complex relationship. This study could help identify promising therapeutic targets and drugs to treat obesity.

Network Pharmacology and Molecular Docking Validation to Reveal the Pharmacological Mechanisms of Kangai Injection against Colorectal Cancer.

Background: Kangai injection is a traditional Chinese medicine (TCM) mixed by extracts from astragalus, ginseng, and kurorinone with modern technology. It is a commonly used antitumor injection in China, but the mechanism of Kangai injection in the treatment of colorectal cancer (CRC) is still unclear. The purpose of this study is to explore the mechanism of Kangai injection against CRC using network pharmacology and molecular docking technology. Methods: Targets of Kangai injection in CRC were predicted by SwissTargetPrediction and DisGeNET databases. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed by using the DAVID database. A component-disease-target gene-pathway network was constructed by Cytoscape 3.8.0 software. Results: 114 overlapping targets of Kangai injection and CRC were used to construct a PPI network, and the top 10 hub targets of Kangai injection were rated from high to low as TP53, VEGFA, EGFR, TNF, ESR1, STAT3, HSP90AA1, HDAC1, AR, and MMP9. The ingredient-target-disease interactive network was constructed, which included 22 compounds and 114 overlapping targets with 161 nodes and 707 edges. Entries of enrichment analysis were obtained based on P value (<0.05), which included 19 of GO-MF, 217 of GO-BP, 8 of GO-CC, and 13 KEGG. Molecular docking analysis showed that Kangai injection strongly interacted with top 10 hub target proteins. Conclusion: Network pharmacology intuitively showed the multicomponent, multiple targets, and multiple pathways of Kangai injection in the treatment of CRC. The molecular docking experiment verified that compounds of Kangai injection had good binding ability with top 10 hub target proteins as well.

Mechanism of saikogenin G against major depressive disorder determined by network pharmacology.

Many classic decoctions of Chinese medicine including Radix Bupleuri are used to treat major depressive disorder (MDD). Saikosaponin D is a representative bioactive ingredient discovered in Radix Bupleuri. The mechanism of saikogenin G (SGG) as a metabolite in MDD remains unclear to date. This study aims to elucidate the mechanism of SGG in treating MDD with network pharmacology. We evaluated the drug likeness of SGG with SwissADME web tool and predicted its targets using the SwissTargetPrediction and PharmMapper. MDD-related targets were identified from the following databases: DisGeNET, DrugBank, Online Mendelian Inheritance in Man, and GeneCards. The common targets of SGG and MDD were imported to the STRING11.0 database, and then a protein-protein interaction network was constructed. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment were analyzed with DAVID 6.8 database. The molecular weight of SGG was 472.7 g/mol, the topological polar surface area was 69.92 A2 <140 A2, the octanol/water partition coefficient (Consensus LogP0/W) was 4.80, the rotatable bond was 1, the hydrogen bond donors was 3, and the hydrogen bond acceptors was 4. A total of 322 targets of SGG were obtained and there were 1724 MDD-related targets. A total of 78 overlapping genes were selected as targets of MDD treatment including albumin, insulin-like growth factor I, mitogen-activated protein kinase 1, proto-oncogene tyrosine-protein kinase Src, and epidermal growth factor receptor. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis suggested that proteoglycans in cancer, pathways in cancer, prostate cancer, hypoxia-inducible factor-1, central carbon metabolism in cancer, estrogen, PI3K-Akt, ErbB, Rap1, and prolactin signaling pathways played an important role(P < .0001). This study showed that SGG exhibits good drug-like properties and elucidated the potential mechanisms of SGG in treating MDD with regulating inflammation, energy metabolism, monoamine neurotransmitters, neuroplasticity, phosphocreatine-creatine kinase circuits, and so on.

Network Pharmacology Prediction: The Possible Mechanisms of Cinobufotalin against Osteosarcoma.

OBJECTIVE: To explore the active compounds and targets of cinobufotalin (huachansu) compared with the osteosarcoma genes to obtain the potential therapeutic targets and pharmacological mechanisms of action of cinobufotalin on osteosarcoma through network pharmacology. METHODS: The composition of cinobufotalin was searched by literature retrieval, and the target was selected from the CTD and TCMSP databases. The osteosarcoma genes, found from the GeneCards, OMIM, and other databases, were compared with the cinobufotalin targets to obtain potential therapeutic targets. The protein-protein interaction (PPI) network of potential therapeutic targets, constructed through the STRING database, was inputted into Cytoscape software to calculate the hub genes, using the NetworkAnalyzer. The hub genes were inputted into the Kaplan-Meier Plotter online database for exploring the survival curve. Functional enrichment analysis was identified using the DAVID database. RESULTS: 28 main active compounds of cinobufotalin were explored, including bufalin, adenosine, oleic acid, and cinobufagin. 128 potential therapeutic targets on osteosarcoma are confirmed among 184 therapeutic targets form cinobufotalin. The hub genes included TP53, ACTB, AKT1, MYC, CASP3, JUN, TNF, VEGFA, HSP90AA1, and STAT3. Among the hub genes, TP53, ACTB, MYC, TNF, VEGFA, and STAT3 affect the patient survival prognosis of sarcoma. Through function enrichment analysis, it is found that the main mechanisms of cinobufotalin on osteosarcoma include promoting sarcoma apoptosis, regulating the cell cycle, and inhibiting proliferation and differentiation. CONCLUSION: The possible mechanisms of cinobufotalin against osteosarcoma are preliminarily predicted through network pharmacology, and further experiments are needed to prove these predictions.

Reveals of quercetin's therapeutic effects on oral lichen planus based on network pharmacology approach and experimental validation.

Oral lichen planus (OLP) is a localized autoimmune disease of the oral mucosa, with an incidence of up to 2%. Although corticosteroids are the first-line treatment, they cause several adverse effects. Quercetin, a naturally occurring compound, has fewer side-effects and provides long-term benefits. Besides, it has powerful anti­inflammatory activities. Here, we combined network pharmacology with experimental verification to predict and verify the key targets of quercetin against OLP. First, 66 quercetin-OLP common targets were analyzed from various databases. The protein-protein interaction (PPI) network was constructed. Topology analysis and MCODE cluster analysis of common targets were conducted to identify 12 key targets including TP53, IL-6 and IFN-γ and their connections. Gene functions and key signaling pathways, including reactive oxygen species metabolism, IL-17 pathway and AGE-RAGE pathway, were enriched by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Then, in vitro experiments showed that quercetin interfered with Th1/Th2 balance by acting on IL-6 and IFN-γ to modulate the immune system in treating OLP. Quercetin considerably affected the apoptosis and migration of T lymphocytes in OLP patients. Our study reveals the potential therapeutic targets and signaling pathways of quercetin associated with OLP, and establishes the groundwork for future clinical applications.

Analysis and Identification Genetic Effect of SARS-CoV-2 Infections to Alzheimer's Disease Patients by Integrated Bioinformatics.

BACKGROUND: COVID-19 pandemic is a global crisis which results in millions of deaths and causes long-term neurological sequelae, such as Alzheimer's disease (AD). OBJECTIVE: We aimed to explore the interaction between COVID-19 and AD by integrating bioinformatics to find the biomarkers which lead to AD occurrence and development with COVID-19 and provide early intervention. METHODS: The differential expressed genes (DEGs) were found by GSE147507 and GSE132903, respectively. The common genes between COVID-19 and AD were identified. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interactions (PPI) network analysis were carried out. Hub genes were found by cytoscape. A multivariate logistic regression model was constructed. NetworkAnalyst was used for the analysis of TF-gene interactions, TF-miRNA coregulatory network, and Protein-chemical Interactions. RESULTS: Forty common DEGs for AD and COVID-19 were found. GO and KEGG analysis indicated that the DEGs were enriched in the calcium signal pathway and other pathways. A PPI network was constructed, and 5 hub genes were identified (ITPR1, ITPR3, ITPKB, RAPGEF3, MFGE8). Four hub genes (ITPR1, ITPR3, ITPKB, RAPGEF3) which were considered as important factors in the development of AD that were affected by COVID-19 were shown by nomogram. Utilizing NetworkAnalyst, the interaction network of 4 hub genes and TF, miRNA, common AD risk genes, and known compounds is displayed, respectively. CONCLUSION: COVID-19 patients are at high risk of developing AD. Vaccination is required. Four hub genes can be considered as biomarkers for prediction and treatment of AD development caused by COVID-19. Compounds with neuroprotective effects can be used as adjuvant therapy for COVID-19 patients.

Genome-wide promoter analysis, homology modeling and protein interaction network of Dehydration Responsive Element Binding (DREB) gene family in Solanum tuberosum.

Dehydration Responsive Element Binding (DREB) regulates the expression of numerous stress-responsive genes, and hence plays a pivotal role in abiotic stress responses and tolerance in plants. The study aimed to develop a complete overview of the cis-acting regulatory elements (CAREs) present in S. tuberosum DREB gene promoters. A total of one hundred and four (104) cis-regulatory elements (CREs) were identified from 2.5kbp upstream of the start codon (ATG). The in-silico promoter analysis revealed variable sets of cis-elements and functional diversity with the predominance of light-responsive (30%), development-related (20%), abiotic stress-responsive (14%), and hormone-responsive (12%) elements in StDREBs. Among them, two light-responsive elements (Box-4 and G-box) were predicted in 64 and 61 StDREB genes, respectively. Two development-related motifs (AAGAA-motif and as-1) were abundant in StDREB gene promoters. Most of the DREB genes contained one or more Myeloblastosis (MYB) and Myelocytometosis (MYC) elements associated with abiotic stress responses. Hormone-responsive element i.e. ABRE was found in 59 out of 66 StDREB genes, which implied their role in dehydration and salinity stress. Moreover, six proteins were chosen corresponding to A1-A6 StDREB subgroups for secondary structure analysis and three-dimensional protein modeling followed by model validation through PROCHECK server by Ramachandran Plot. The predicted models demonstrated >90% of the residues in the favorable region, which further ensured their reliability. The present study also anticipated pocket binding sites and disordered regions (DRs) to gain insights into the structural flexibility and functional annotation of StDREB proteins. The protein association network determined the interaction of six selected StDREB proteins with potato proteins encoded by other gene families such as MYB and NAC, suggesting their similar functional roles in biological and molecular pathways. Overall, our results provide fundamental information for future functional analysis to understand the precise molecular mechanisms of the DREB gene family in S. tuberosum.

Elucidating the Mechanism of Action of Salvia miltiorrhiza for the Treatment of Acute Pancreatitis Based on Network Pharmacology and Molecular Docking Technology.

Using network pharmacology and molecular docking, this study investigated the molecular mechanisms by which the active components in Salvia miltiorrhiza can alleviate acute pancreatitis. Initially, the active components of Salvia miltiorrhiza and the targets collected from the GeneCards database were screened based on the platform of systematic pharmacology analysis of traditional Chinese medicine. Subsequently, the active components were intersected with the disease targets. Also, interactions among the targets were computed using the STRING database. Biological function and pathway enrichment were analyzed using the Cluster Profiler package in the R software. Protein-protein interaction and component target pathway network were constructed using the Cytoscape software. Ultimately, the key targets and their corresponding components in the network were verified using the AutoDock Vina software. The results showed Salvia miltiorrhiza had 111 targets for acute pancreatitis. The biological process (BP) analysis showed that the active components of Salvia miltiorrhiza induced a drug response, positive regulation of transcription by RNA polymerase II promoter, signal transduction, positive regulation of cell proliferation, and negative regulation of apoptosis. Furthermore, the KEGG enrichment analysis screened 118 (P < 0.05) signaling pathways, such as the pathways related to cancer, neuroactive ligand-receptor interaction, PI3K-Akt signaling pathway, and cAMP signaling pathway, to name a few. Finally, molecular docking showed that the active components of Salvia miltiorrhiza had a good binding affinity with their corresponding target proteins. Through network pharmacology, this study predicted the potential pharmacodynamic material basis and the mechanisms by which Salvia miltiorrhiza can treat acute pancreatitis. Moreover, this study provided a scientific basis for mining the pharmacodynamic components of Salvia miltiorrhiza and expanding the scope of its clinical use.

Regulation of cell signaling pathways by Schisandrin in different cancers: Opting for "Swiss Army Knife" instead of "Blunderbuss".

There has been an exponential growth in the field of molecular oncology and cutting-edge research has enabled us to develop a better understanding of therapeutically challenging nature of cancer. Based on the mechanistic insights garnered from decades of research, puzzling mysteries of multifaceted nature of cancer have been solved to a greater extent. Our rapidly evolving knowledge about deregulated oncogenic cell signaling pathways has allowed us to dissect different oncogenic transduction cascades which play critical role in cancer onset, progression and metastasis. Pharmacological targeting of deregulated pathways has attracted greater than ever attention in the recent years. Henceforth, discovery and identification of high-quality biologically active chemicals and products is gaining considerable momentum. There has been an explosion in the dimension of natural product research because of tremendous potential of chemopreventive and pharmaceutical significance of natural products. Schisandrin is mainly obtained from Schisandra chinensis. Schisandrin has been shown to be effective against different cancers because of its ability to inhibit/prevent cancer via modulation of different cell signaling pathways. Importantly, regulation of non-coding RNAs by schisandrin is an exciting area of research that still needs detailed and comprehensive research.   However, we still have unresolved questions about pharmacological properties of schisandrin mainly in context of its regulatory role in TGF/SMAD, SHH/GLI, NOTCH and Hippo pathways.

Combining Network Pharmacology with Molecular Docking for Mechanistic Research on Thyroid Dysfunction Caused by Polybrominated Diphenyl Ethers and Their Metabolites.

Network pharmacology was used to illuminate the targets and pathways of polybrominated diphenyl ethers (PBDEs) causing thyroid dysfunction. A protein-protein interaction (PPI) network was constructed. Molecular docking was applied to analyze PBDEs and key targets according to the network pharmacology results. A total of 247 targets were found to be related to 16 PBDEs. Ten key targets with direct action were identified, including the top five PIK3R1, MAPK1, SRC, RXRA, and TP53. Gene Ontology (GO) functional enrichment analysis identified 75 biological items. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis identified 62 pathways mainly related to the regulation of the thyroid hormone signaling pathway, MAPK signaling pathway, PI3K-Akt signaling, pathways in cancer, proteoglycans in cancer, progesterone-mediated oocyte maturation, and others. The molecular docking results showed that BDE-99, BDE-153, 5-OH-BDE47, 5'-OH-BDE99, 5-BDE47 sulfate, and 5'-BDE99 sulfate have a good binding effect with the kernel targets. PBDEs could interfere with the thyroid hormone endocrine through multiple targets and biological pathways, and metabolites demonstrated stronger effects than the prototypes. This research provides a basis for further research on the toxicological effects and molecular mechanisms of PBDEs and their metabolites. Furthermore, the application of network pharmacology to the study of the toxicity mechanisms of environmental pollutants provides a new methodology for environmental toxicology.

Pharmacological Mechanism of Danggui-Sini Formula for Intervertebral Disc Degeneration: A Network Pharmacology Study.

BACKGROUND: Intervertebral disc degeneration (IVDD) is the most significant cause of low back pain, the sixth-largest disease burden globally, and the leading cause of disability. This study is aimed at investigating the molecular biological mechanism of Danggui-Sini formula (DSF) mediated IVDD treatment. METHODS: A potential gene set for DSF treatment of IVDD was identified through TCMSP, UniProt, and five disease gene databases. A protein interaction network of common targets between DSF and IVDD was established by using the STRING database. GO and KEGG enrichment analyses were performed using the R platform to discover the potential mechanism. Moreover, AutoDock Vina was used to verify molecular docking and calculate the binding energy. RESULTS: A total of 119 active ingredients and 136 common genes were identified, including 10 core genes (AKT1, IL6, ALB, TNF, VEGFA, TP53, MAPK3, CASP3, JUN, and EGF). Enrichment analysis results showed that the therapeutic targets of DSF for diseases mainly focused on the AGE-RAGE signaling pathway involved in diabetic complications, IL-17 signaling pathway, TNF signaling pathway, Toll-like receptor signaling pathway, apoptosis, cellular senescence, PI3K-Akt signaling pathway, and FoxO signaling pathway. These biological processes are induced mainly in response to oxidative stress and reactive oxygen species and the regulation of apoptotic signaling pathways. Molecular docking showed that there was a stable affinity between the core genes and the key components. CONCLUSIONS: The combination of network pharmacology and molecular docking provides a practical way to analyze the molecular biological mechanism of DSF-mediated IVDD treatment, which confirms the "multicomponent, multitarget and multipathway" characteristics of DSF and provides an essential theoretical basis for clinical practice.

Exploring anti-liver cancer targets and mechanisms of oxyresveratrol: in silico and verified findings.

The aim of current study was to exhume the potential targets and molecular mechanisms of oxyresveratrol, a structurally re-constructed resveratrol, for treating liver cancer through bioinformatics investigation and experimentative validation. To start with, the network pharmacology approach and molecular docking technology were used to uncover all candidate targets of oxyresveratrol to treat liver cancer, accompanied with identified anti-liver cancer targets including estrogen receptor 1 (ESR1), epidermal growth factor receptor (EGFR). In addition, more pharmacological mechanisms of oxyresveratrol against liver cancer were revealed in details. In experimental verification, the clinical samples of liver cancer showed elevated ESR1, EGFR mRNA expressions. The in-vitro data indicated that intracellular contents of ESR1, EGFR mRNAs in oxyresveratrol-treated liver cancer cells were reduced. Taken together, the bioinformatics and validated findings have highlighted detailed pharmacological targets and molecular mechanisms of oxyresveratrol for treating liver cancer. Following with experimental verification, the identified genes of ESR1, EGFR may function as potential screening anti-liver cancer markers.