Abstract: Lung cancer is the most common cause of cancer death with high morbidity and mortality, which non-small-cell lung cancer (NSCLC) accounting for the majority. Traditional Chinese Medicine (TCM) is effective in the treatment of complex diseases, especially cancer. However, TCM is still in the conceptual stage. The interaction between different components remains unknown due to its multicomponent and multitarget characteristics. In this study, compound Liuju formula was taken as an example to isolate compounds with synergistic biological activity through systems pharmacology strategy. Through pharmacokinetic evaluation, 37 potentially active compounds were screened out. Meanwhile, 116 targets of these compounds were obtained by combing with the target prediction model. Through network analysis, we found that multicomponent drugs can present a synergistic effect through regulating inflammatory signaling pathway, invasion pathway, proliferation, and apoptosis pathway. Finally, it was confirmed that the bioactive compounds of compound Liuju formula have not only a killing effect on NSCLC tumor cells but also a synergistic effect on inhibiting the secretion of correlative inflammatory mediators, including TNF-alpha and IL-1beta. The systems pharmacology method was applied in this study, which provides a new direction for analyzing the mechanism of TCM.

Abstract: A key aim of post-genomic biomedical research is to systematically understand and model complex biomolecular activities based on a systematic perspective. Biomolecular interactions are widespread and interrelated, multiple biomolecules coordinate to sustain life activities, any disturbance of these complex connections can lead to abnormal of life activities or complex diseases. However, many existing researches usually only focus on individual intermolecular interactions. In this work, we revealed, constructed, and analyzed a large-scale molecular association network of multiple biomolecules in human by integrating associations among lncRNAs, miRNAs, proteins, drugs, and diseases, in which various associations are interconnected and any type of associations can be predicted. We propose Molecular Association Network (MAN)-High-Order Proximity preserved Embedding (HOPE), a novel network representation learning based method to fully exploit latent feature of biomolecules to accurately predict associations between molecules. More specifically, network representation learning algorithm HOPE was applied to learn behavior feature of nodes in the association network. Attribute features of nodes were also adopted. Then, a machine learning model CatBoost was trained to predict potential association between any nodes. The performance of our method was evaluated under five-fold cross validation. A case study to predict miRNA-disease associations was also conducted to verify the prediction capability. MAN-HOPE achieves high accuracy of 93.3% and area under the receiver operating characteristic curve of 0.9793. The experimental results demonstrate the novelty of our systematic understanding of the intermolecular associations, and enable systematic exploration of the landscape of molecular interactions that shape specialized cellular functions.

Abstract: Background: The healing process of the surgical wound of anal fistulotomy is much slower because of the presence of stool within the wound. Cuyuxunxi (CYXX) prescription is a Chinese herbal fumigant that is being used to wash surgical wound after anal fistulotomy. This study aimed at investigating the molecular mechanism of CYXX prescription using a network pharmacology-based strategy. Materials and Methods: The active compounds in each herbal medicine were retrieved from the traditional Chinese medicine systems pharmacology (TCMSP) database and in Traditional Chinese Medicine Integrated Database (TCMID) analysis platform based on the criteria of oral bioavailability >/=40% and drug-likeness >/=0.2. The disease-related target genes were extracted from the Comparative Toxicogenomics Database. Protein-protein interaction network was built for the overlapped genes as well as functional enrichment analysis. Finally, an ingredient-target genes-pathway network was built by integrating all information. Results: A total of 375 chemical ingredients of the 5 main herbal medicines in CYXX prescription were retrieved from TCMSP database and TCMID. Among the 375 chemical ingredients, 59 were active compounds. Besides, 325 target genes for 16 active compounds in 3 herbal medicines were obtained. Functional enrichment analysis revealed that these overlapped genes were significantly related with immune response, biosynthesis of antibiotics, and complement and coagulation cascades. A comprehensive network which contains 133 nodes (8 disease nodes, 3 drug nodes, 8 ingredients, 103 target gene nodes, 7 GO nodes, and 4 pathway nodes) was built. Conclusion: The network built in this study might aid in understanding the action mechanism of CYXX prescription at molecular level to pathway level.

Abstract: Pioglitazone, a type of insulin sensitizer, serves as an effective anti-hyperglycemic drug. The mechanism of action of pioglitazone is through the activation of the peroxisome proliferator-activated receptor (PPAR), which results in enhanced insulin sensitivity of peripheral tissues and the liver, causing a reduction in the production and output of liver sugar. It has been reported that pioglitazone increases the risk of bladder cancer, but the underlying mechanisms have remained elusive. It was hypothesized that modulation of pioglitazone activity may be predicted by systematically analyzing data published on drugs. This hypothesis was tested by querying the Drug-Target Interactome (DTome), a web-based tool that provides open-source data from three databases (DrugBank, PharmGSK and Protein Interaction Network analysis). A total of 4 direct target proteins (DTPs) and further DTP-associated genes were identified for pioglitazone. Subsequently, an enrichment analysis was performed for all DTP-associated genes using Cytoscape software. A total of 12 Kyoto Encyclopedia of Genes and Genomes pathways were identified, including the 'PPAR signaling pathway' as well as 'pathways in cancer' as relevant pathways. Functional network analysis was able to identify direct and indirect target genes of pioglitazone, resulting in a list of possible biological functions based on published databases. Furthermore, Kaplan-Meier analysis indicated that pioglitazone may affect the survival rate of patients with bladder cancer through genetic alterations (missense mutation, truncating mutation, amplification, deep deletion and fusion) of target genes. Therefore, it should be used with caution.

Abstract: Ellagic acid (EA) is a polyphenol found in several fruits and plants. EA has been reported to exert antitumor activity in many types of cancers. However, the effect and potential molecular mechanism of EA in endometrial cancer are still unclear. Therefore, the aim of this study was to explore the underlying antitumor function and targets by which EA inhibits endometrial cancer. By using multiplatform bioinformatics analysis tools, including DrugBank, STRING, WebGestalt and cBioPortal, the core targets of EA were identified as PIK3CA and PIK3R1. In addition, through transwell assays, EA was strongly found to inhibit cell invasion and migration. Based on CCK8 assays and flow cytometry, EA exhibited a suppressive effect on endometrial cancer cell proliferation by causing cell cycle arrest and inducing apoptosis. The results of real-time PCR confirmed that the expression of PIK3CA and PIK3R was decreased by EA. Furthermore, western blotting analysis demonstrated that EA inhibited PI3K phosphorylation, downregulating the expression of MMP9. In vivo, EA suppressed lung metastasis in BALB/c nude mice based on the SUVmax value determined from PET scans and HE staining. According to all these data, it comprehensively demonstrated the inhibitory effect of EA on endometrial cancer through bioinformatics analysis and experimental verification. Our findings suggest that EA may potentially be beneficial for treating endometrial cancer.

Abstract: A body of research demonstrates examples of in vitro and in vivo synergy between natural products and anti-neoplastic drugs for some cancers. However, the underlying biological mechanisms are still elusive. To better understand biological entities targeted by natural products and therefore provide rational evidence for future novel combination therapies for cancer treatment, we assess the targetable space of natural products using public domain compound-target information. When considering pathways from the Reactome database targeted by natural products, we found an increase in coverage of 61% (725 pathways), relative to pathways covered by FDA approved cancer drugs collected in the Cancer Targetome, a resource for evidence-based drug-target interactions. Not only is the coverage of pathways targeted by compounds increased when we include natural products, but coverage of targets within those pathways is also increased. Furthermore, we examined the distribution of cancer driver genes across pathways to assess relevance of natural products to critical cancer therapeutic space. We found 24 pathways enriched for cancer drivers that had no available cancer drug interactions at a potentially clinically relevant binding affinity threshold of < 100nM that had at least one natural product interaction at that same binding threshold. Assessment of network context highlighted the fact that natural products show target family groupings both distinct from and in common with cancer drugs, strengthening the complementary potential for natural products in the cancer therapeutic space. In conclusion, our study provides a foundation for developing novel cancer treatment with the combination of drugs and natural products.

Abstract: In spite of the increasing availability of genomic and transcriptomic data, there is still a gap between the detection of perturbations in gene expression and the understanding of their contribution to the molecular mechanisms that ultimately account for the phenotype studied. Alterations in the metabolism are behind the initiation and progression of many diseases, including cancer. The wealth of available knowledge on metabolic processes can therefore be used to derive mechanistic models that link gene expression perturbations to changes in metabolic activity that provide relevant clues on molecular mechanisms of disease and drug modes of action (MoA). In particular, pathway modules, which recapitulate the main aspects of metabolism, are especially suitable for this type of modeling. We present Metabolizer, a web-based application that offers an intuitive, easy-to-use interactive interface to analyze differences in pathway metabolic module activities that can also be used for class prediction and in silico prediction of knock-out (KO) effects. Moreover, Metabolizer can automatically predict the optimal KO intervention for restoring a diseased phenotype. We provide different types of validations of some of the predictions made by Metabolizer. Metabolizer is a web tool that allows understanding molecular mechanisms of disease or the MoA of drugs within the context of the metabolism by using gene expression measurements. In addition, this tool automatically suggests potential therapeutic targets for individualized therapeutic interventions.

Abstract: Background: Kang-bai-ling (KBL), a Chinese patent medicine, has been demonstrated as an effective therapy for vitiligo in China. However, the pharmacological mechanisms of KBL have not been completely elucidated. Methods: In this study, the potential multicomponent, multitarget, and multipathway mechanism of KBL against vitiligo was clarified by using network pharmacology-based strategy. In brief, potential targets of KBL were collected based on TCMSP databases, followed by network establishment concerning the interactions of potential targets of KBL with well-known therapeutic targets of vitiligo by using protein-protein interaction (PPI) data. As a result, key nodes with higher level of seven topological parameters, including "degree centrality (DC)," "betweenness centrality (BC)," "closeness centrality (CC)," "eigenvector centrality (EC)," "network centrality (NC)," and "local average connectivity (LAC)" were identified as the main targets in the network, followed by subsequent incorporation into the ClueGO for GO and KEGG signaling pathway enrichment analysis. Results: In accordance with the topological importance, a total of 23 potential targets of KBL on vitiligo were identified as main hubs. Additionally, enrichment analysis suggested that targets of KBL on vitiligo were mainly clustered into multiple biological processes (associated with DNA translation, lymphocyte differentiation and activation, steroid biosynthesis, autoimmune and systemic inflammatory reaction, neuron apoptosis, and vitamin deficiency) and related pathways (TNF, JAK-STAT, ILs, TLRs, prolactin, and NF-kappaB), indicating the underlying mechanisms of KBL on vitiligo. Conclusion: In this work, we successfully illuminated the "multicompounds, multitargets" therapeutic action of KBL on vitiligo by using network pharmacology. Moreover, our present outcomes might shed light on the further clinical application of KBL on vitiligo treatment.

Abstract: Diffuse gliomas with K27M histone mutations (H3K27M glioma) are generally characterized by a fatal prognosis, particularly affecting the pediatric population. Based on the molecular heterogeneity observed in this tumor type, personalized treatment is considered to substantially improve therapeutic options. Therefore, clinical evidence for therapy, guided by comprehensive molecular profiling, is urgently required. In this study, we analyzed feasibility and clinical outcomes in a cohort of 12 H3K27M glioma cases treated at two centers. Patients were subjected to personalized treatment either at primary diagnosis or disease progression and received backbone therapy including focal irradiation. Molecular analyses included whole-exome sequencing of tumor and germline DNA, RNA-sequencing, and transcriptomic profiling. Patients were monitored with regular clinical as well as radiological follow-up. In one case, liquid biopsy of cerebrospinal fluid (CSF) was used. Analyses could be completed in 83% (10/12) and subsequent personalized treatment for one or more additional pharmacological therapies could be recommended in 90% (9/10). Personalized treatment included inhibition of the PI3K/AKT/mTOR pathway (3/9), MAPK signaling (2/9), immunotherapy (2/9), receptor tyrosine kinase inhibition (2/9), and retinoic receptor agonist (1/9). The overall response rate within the cohort was 78% (7/9) including one complete remission, three partial responses, and three stable diseases. Sustained responses lasting for 28 to 150 weeks were observed for cases with PIK3CA mutations treated with either miltefosine or everolimus and additional treatment with trametinib/dabrafenib in a case with BRAFV600E mutation. Immune checkpoint inhibitor treatment of a case with increased tumor mutational burden (TMB) resulted in complete remission lasting 40 weeks. Median time to progression was 29 weeks. Median overall survival (OS) in the personalized treatment cohort was 16.5 months. Last, we compared OS to a control cohort (n = 9) showing a median OS of 17.5 months. No significant difference between the cohorts could be detected, but long-term survivors (>2 years) were only present in the personalized treatment cohort. Taken together, we present the first evidence of clinical efficacy and an improved patient outcome through a personalized approach at least in selected cases of H3K27M glioma.

Abstract: Aim: Achyranthes bidentata is one of the most commonly used Chinese herbal medicines (CHM) that is currently considered for the treatment of osteoarthritis. The purpose of this study was to reveal the mechanism of Achyranthes bidentata in osteoarthritis treatment based on the network pharmacology. Methods: The effective components of Achyranthes bidentata were firstly screened out from the TCMSP database with ADME property parameters. Then, osteoarthritis-related proteins targeted by the effective components were predicted based on the DrugBank and CTD databases. Subsequently, enrichment analysis and interaction network between targets of effective components and pathways were also studied. In addition, the differentially expressed genes (DEGs) of GSE55457 were used for validation of the osteoarthritis-related target proteins. Finally, the effective components-target molecular docking models were predicted. Results: A total of 10 effective components were identified, of which kaempferol and quercetin had 1 and 29 targets, respectively. There were 26 target proteins of quercetin related to the osteoarthritis. These targets were mainly enriched in mitochondrial ATP synthesis coupled proton transport, cellular response to estradiol stimulus, and nitric oxide biosynthetic process. In addition, there were three common proteins, PIM1, CYP1B1, and HSPA2 based on the DEGs of GSE55457, which were considered as the key targeted proteins of the quercetin. Conclusion: The docking of PIM1-quercetin, CYP1B1-quercetin, and HSPA2-quercetin may play important roles during the treatment of osteoarthritis by Achyranthes bidentata.