Dataset Information

Modular Characteristics and Mechanism of Action of Herbs for Endometriosis Treatment in Chinese Medicine: A Data Mining and Network Pharmacology-Based Identification.

ABSTRACT: Endometriosis is a common benign disease in women of reproductive age. It has been defined as a disorder characterized by inflammation, compromised immunity, hormone dependence, and neuroangiogenesis. Unfortunately, the mechanisms of endometriosis have not yet been fully elucidated, and available treatment methods are currently limited. The discovery of new therapeutic drugs and improvements in existing treatment schemes remain the focus of research initiatives. Chinese medicine can improve the symptoms associated with endometriosis. Many Chinese herbal medicines could exert antiendometriosis effects via comprehensive interactions with multiple targets. However, these interactions have not been defined. This study used association rule mining and systems pharmacology to discover a method by which potential antiendometriosis herbs can be investigated. We analyzed various combinations and mechanisms of action of medicinal herbs to establish molecular networks showing interactions with multiple targets. The results showed that endometriosis treatment in Chinese medicine is mainly based on methods of supplementation with blood-activating herbs and strengthening qi. Furthermore, we used network pharmacology to analyze the main herbs that facilitate the decoding of multiscale mechanisms of the herbal compounds. We found that Chinese medicine could affect the development of endometriosis by regulating inflammation, immunity, angiogenesis, and other clusters of processes identified by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The antiendometriosis effect of Chinese medicine occurs mainly through nervous system-associated pathways, such as the serotonergic synapse, the neurotrophin signaling pathway, and dopaminergic synapse, among others, to reduce pain. Chinese medicine could also regulate VEGF signaling, toll-like reporter signaling, NF-?B signaling, MAPK signaling, PI3K-Akt signaling, and the HIF-1 signaling pathway, among others. Synergies often exist in herb pairs and herbal prescriptions. In conclusion, we identified some important targets, target pairs, and regulatory networks, using bioinformatics and data mining. The combination of data mining and network pharmacology may offer an efficient method for drug discovery and development from herbal medicines.

SUBMITTER: Zheng W

PROVIDER: S-EPMC7069061 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Modular Characteristics and Mechanism of Action of Herbs for Endometriosis Treatment in Chinese Medicine: A Data Mining and Network Pharmacology-Based Identification.

Zheng Weilin W Wu Jiayi J Gu Jiangyong J Weng Heng H Wang Jie J Wang Tao T Liang Xuefang X Cao Lixing L

Frontiers in pharmacology 20200306

Endometriosis is a common benign disease in women of reproductive age. It has been defined as a disorder characterized by inflammation, compromised immunity, hormone dependence, and neuroangiogenesis. Unfortunately, the mechanisms of endometriosis have not yet been fully elucidated, and available treatment methods are currently limited. The discovery of new therapeutic drugs and improvements in existing treatment schemes remain the focus of research initiatives. Chinese medicine can improve the ...[more]

PMID: 32210799

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Project description:BackgroundTraditional Chinese medicine (TCM) is increasingly extensively being applied as a complementary and alternative therapy for gastric cancer (GC); however, there is a lack of large-scale evidence-based deep learning for the guidance of its clinical prescription.MethodsThe combinational search terms of "Gastric cancer and/or gastric malignancy" and "Traditional Chinese Medicine" were used to retrieve clinical study-based herbal prescriptions from public database over the past 3 decades [1990-2020]. Association rules mining (ARM) was used to analyze the prescription patterns of the herbs extracted from the eligible studies. Deep machine learning and computational prediction were conducted to explore candidate prescriptions with general applicability for GC. The action mechanism of the preferred prescription was investigated through network pharmacology, and further validated via in vivo and in vitro experiments.ResultsA total of 194 clinical study-based herbal prescriptions with good efficacy for GC were collected. TCM with focus on invigorating the Spleen and tonifying the vital-Qi is a promising adjuvant therapy for GC. The preferred prescription is composed of Atractylodis Macrocephalae Rhizoma, Astragali Radix, Pinelliae Rhizoma, Citri Reticulatae Pericarpium, Herba Hedyotidis Diffusae, Crataegi Fructus, and so on. We screened 74 bioactive compounds and 2,128 predictive targets of the preferred prescription from public databases. Eventually, 135 GC-related genes were identified as the targets of the preferred prescription. The compound-target network revealed that the crucial substances in the preferred prescription are quercetin, kaempferol, baicalein, and nobiletin. Experimentally, the preferred prescription was validated to modulate GC cell survival and inhibit tumor progression mainly via the hTERT/MDM2-p53 signaling pathway in vivo and in vitro.ConclusionsTCM aimed at invigorating the Spleen and tonifying the vital-Qi is a promising adjuvant therapy for GC, which offers a guidance for worldwide use of TCM in the treatment of GC.

Project description:Colorectal cancer (CRC) is now the second most deadly cancer globally. Chinese herbal medicine (CHM) plays an indispensable role in CRC treatment in China. However, the core herbs (the CHs) in the treatment of CRC and their underlying therapeutic mechanisms remain unclear. This study aims to uncovering the CHs and their mechanisms of action of CRC treatment, applying data mining and network pharmacology approach. First, CHM prescriptions treating CRC were collected from clinical studies from the Chinese National Knowledge Infrastructure (CNKI) and MEDLINE databases, and the CHs were identified through data mining. Then, the bioactive compounds and the corresponding putative targets of the CHs were obtained from three traditional Chinese medicine (TCM) databases. CRC related targets were acquired from three disease databases; the overlapping targets between the CHs and CRC were identified as the therapeutic targets. Subsequently, functional enrichment analysis was performed to elucidate the mechanisms of the CHs on CRC. Moreover, networks were constructed to screen the major bioactive compounds and therapeutic targets. Finally, prognostic values of the major target genes were evaluated by survival analysis, and molecular docking simulation was performed to assess the binding affinity of key targets and major bioactive compounds. It came out that 10 the CHs from 113 prescriptions and 190 bioactive compounds with 118 therapeutic targets were identified. The therapeutic targets were mainly enriched in the biological progress of transcription, apoptosis, and response to cytokine. Various cancer-associated signaling pathways, including microRNAs, TNF, apoptosis, PI3K-Akt, and p53, were involved. Furthermore, 15 major bioactive compounds and five key target genes (VEGFA, CASP3, MYC, CYP1Y1, and NFKB1) with prognostic significance were identified. Additionally, most major bioactive compounds might bind firmly to the key target proteins. This study provided an overview of the anti-CRC mechanisms of the CHs, which might refer to the regulation of apoptosis, transcription, and inflammation.

Project description:Chronic heart failure (CHF) is a major public health problem in huge population worldwide. The detailed understanding of CHF mechanism is still limited. Zheng (syndrome) is the criterion of diagnosis and therapeutic in Traditional Chinese Medicine (TCM). Syndrome prediction may be a better approach for understanding of CHF mechanism basis and its treatment. The authors studied disturbed metabolic biomarkers to construct a predicting mode to assess the diagnostic value of different syndrome of CHF and explore the Chinese herbal medicine (CHM) efficacy on CHF patients. A cohort of 110 patients from 11 independent centers was studied and all patients were divided into 3 groups according to TCM syndrome differentiation: group of Qi deficiency syndrome, group of Qi deficiency and Blood stasis syndrome, and group of Qi deficiency and Blood stasis and Water retention syndrome. Plasma metabolomic profiles were determined by UPLC-TOF/MS and analyzed by multivariate statistics. About 6 representative metabolites were highly possible to be associated with CHF, 4, 7, and 5 metabolites with Qi deficiency syndrome, Qi deficiency and Blood stasis syndrome, and Qi deficiency and Blood stasis and Water retention syndrome (VIP > 1, p < 0.05). The diagnostic model was further constructed based on the metabolites to diagnose other CHF patients with satisfying sensitivity and specificity (sensitivity and specificity are 97.1 and 80.6% for CHF group vs. NH group; 97.1 and 80.0% for QD group vs. NH group; 97.1 and 79.5% for QB group vs. NH group; 97.1 and 88.9% for QBW group vs. NH group), validating the robustness of plasma metabolic profiling to diagnostic strategy. By comparison of the metabolic profiles, 9 biomarkers, 2-arachidonoylglycerophosphocholine, LysoPE 16:0, PS 21:0, LysoPE 20:4, LysoPE 18:0, linoleic acid, LysoPE 18:2, 4-hydroxybenzenesulfonic acid, and LysoPE 22:6, may be especially for the effect of CHM granules. A predicting model was attempted to construct and predict patient based on the related symptoms of CHF and the potential biomarkers regulated by CHM were explored. This trial was registered with NCT01939236 (https://clinicaltrials.gov/).

Project description:Chinese medicine is a complex system guided by traditional Chinese medicine (TCM) theories, which has proven to be especially effective in treating chronic and complex diseases. However, the underlying modes of action (MOA) are not always systematically investigated. Herein, a systematic study was designed to elucidate the multi-compound, multi-target and multi-pathway MOA of a Chinese medicine ,QSYQ, on myocardial infarction. Male Sprague Dawley rat model of myocardial infarction were administered QSYQ intragastrically for 7 days while the control group was not treated. The differentially expressed genes (DEGs) were identified from myocardial infarction rat model treated with QSYQ, followed by constructing a cardiovascular disease (CVD)-related multilevel compound-target-pathway network connecting main compounds to those DEGs supported by literature evidences and the pathways that are functionally enriched. Three conditions were compared with three replicates each: (1) sham, i.e. without left anterior descending coronary artery (LAD) ligation; (2) model, with LAD ligation; (3) QSYQ, with LAD ligation and treated with QSYQ intragastrically for 7 days, the dosage was 105.6 mg/kg once a day. Rats were sacrificed after 7 days of i.g. administration under 10% chloral hydrate anesthesia (300mg/kg). Three tissue samples on the border between infarct and non-infarct area were dissected from left ventricles of each group. The tissue samples were stored at -80M-bM-^DM-^C refrigerator. Total RNA was extracted using TRIZol Reagent (Invitrogen) and purified using RNeasy Mini kit (QIAGEN), following manufacturersM-bM-^@M-^Y protocols. RNA quality was evaluated using an Agilent 2100 Bioanalyzer and electrophoresis in 2% (w/v) agarose gels. Only RNA with RNA integrity numbers (RINs) greater than 7.0 and 28SrRNA/18S rRNA ratio greater than 0.7 was used for microarray analyses. Whole genome microarray analysis was performed using Affymetrix rat Genome 230 2.0 chips.

Project description:BackgroundAction ("gongxiao" in Chinese) of traditional Chinese medicine (TCM) is the high recapitulation for therapeutic and health-preserving effects under the guidance of TCM theory. TCM-defined herbal properties ("yaoxing" in Chinese) had been used in this research. TCM herbal property (TCM-HP) is the high generalization and summary for actions, both of which come from long-term effective clinical practice in two thousands of years in China. However, the specific relationship between TCM-HP and action of TCM is complex and unclear from a scientific perspective. The research about this is conducive to expound the connotation of TCM-HP theory and is of important significance for the development of the TCM-HP theory.MethodsOne hundred and thirty-three herbs including 88 heat-clearing herbs (HCHs) and 45 blood-activating stasis-resolving herbs (BAHRHs) were collected from reputable TCM literatures, and their corresponding TCM-HPs/actions information were collected from Chinese pharmacopoeia (2015 edition). The Kennard-Stone (K-S) algorithm was used to split 133 herbs into 100 calibration samples and 33 validation samples. Then, machine learning methods including supported vector machine (SVM), k-nearest neighbor (kNN) and deep learning methods including deep belief network (DBN), convolutional neutral network (CNN) were adopted to develop action classification models based on TCM-HP theory, respectively. In order to ensure robustness, these four classification methods were evaluated by using the method of tenfold cross validation and 20 external validation samples for prediction.ResultsAs results, 72.7-100% of 33 validation samples including 17 HCHs and 16 BASRHs were correctly predicted by these four types of methods. Both of the DBN and CNN methods gave out the best results and their sensitivity, specificity, precision, accuracy were all 100.00%. Especially, the predicted results of external validation set showed that the performance of deep learning methods (DBN, CNN) were better than traditional machine learning methods (kNN, SVM) in terms of their sensitivity, specificity, precision, accuracy. Moreover, the distribution patterns of TCM-HPs of HCHs and BASRHs were also analyzed to detect the featured TCM-HPs of these two types of herbs. The result showed that the featured TCM-HPs of HCHs were cold, bitter, liver and stomach meridians entered, while those of BASRHs were warm, bitter and pungent, liver meridian entered.ConclusionsThe performance on validation set and external validation set of deep learning methods (DBN, CNN) were better than machine learning models (kNN, SVM) in sensitivity, specificity, precision, accuracy when predicting the actions of heat-clearing and blood-activating stasis-resolving based on TCM-HP theory. The deep learning classification methods owned better generalization ability and accuracy when predicting the actions of heat-clearing and blood-activating stasis-resolving based on TCM-HP theory. Besides, the methods of deep learning would help us to improve our understanding about the relationship between herbal property and action, as well as to enrich and develop the theory of TCM-HP scientifically.

Dataset Information

Modular Characteristics and Mechanism of Action of Herbs for Endometriosis Treatment in Chinese Medicine: A Data Mining and Network Pharmacology-Based Identification.

Publications

Modular Characteristics and Mechanism of Action of Herbs for Endometriosis Treatment in Chinese Medicine: A Data Mining and Network Pharmacology-Based Identification.

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