Exploring pharmacological active ingredients of traditional Chinese medicine by pharmacotranscriptomic map in ITCM

Elucidation of the mechanism of the Yinhua Miyanling Tablet against urinary tract infection based on a combined strategy of network pharmacology, multi-omics and molecular biology

ETHNOPHARMACOLOGICAL RELEVANCE

Yinhua Miyanling Tablet (YMT), a traditional Chinese medicine consisting of 10 herbs, has been widely used clinically to treat urinary tract infections (UTIs), however, its therapeutic mechanism is not fully understood.

AIM OF THE STUDY

To investigate the mechanism of YMT in treating UTIs through network pharmacology, multi-omics and experimental validation.

MATERIALS AND METHODS

Clinically, blood and urine samples from YMT-treated UTI patients were collected for transcriptomic and metabolomic analyses. Computationally, compounds that are related to YMT were obtained from the databases, relevant targets were identified, and UTI-related targets were analyzed to determine the core signaling pathways. Subsequently, an integrated approach combining multi-omics and network pharmacology assisted in identifying the key pathways underlying therapeutic effects of YMT on UTI. Finally, a mouse model of UTI was established using uropathogenic Escherichia coli (UPEC), and the therapeutic mechanism of YMT on UTI was validated by ELISA, qRT-PCR and Western blotting.

RESULTS

After taking YMT, patients showed reduced levels of urinary bacteria, white blood cells, and serum inflammatory factors (CRP, IL-6 and TNF-α). Multi-omics analysis combined with network pharmacology demonstrated that YMT significantly inhibited the TLR/MAPK/NFκB signaling pathway. In vivo experiments confirmed that YMT attenuated UPEC-induced pathological changes in bladder structural, reduced the expression of bladder proteins (TLR4, MyD88, p-p38 MAPK and p-p65 NFκB), increased protein expression of IκB-α, and attenuated the release of inflammatory factors (TNF-α, IL-6 and IL-1β) in mice.

CONCLUSION

YMT is effective in treating UTI by down-regulating the TLR4/p38MAPK/p65NFκB pathway, thereby providing a scientific basis for its clinical application.

The latest research progress: Active components of Traditional Chinese medicine as promising candidates for ovarian cancer therapy

ETHNOPHARMACOLOGICAL RELEVANCE

Ovarian cancer ranks the first in the mortality of gynecological tumors. Because there are no obvious symptoms in the early stage of ovarian cancer, most patients are in the advanced stage of the disease at the time of diagnosis. The incidence of ovarian cancer is increasing year by year, and the incidence of ovarian cancer has a trend of younger age. In recent years. Traditional Chinese medicine (TCM) has a significant impact on improving the quality of life of cancer patients, reducing drug toxicity, preventing metastasis and recurrence, enhancing the efficacy of radiotherapy and chemotherapy, and prolonging survival time, so patients have benefited a lot.

AIM OF THE STUDY

This review summarizes the mechanisms and molecular pathways through which active ingredients of TCM act in ovarian cancer. It explores the advantages of TCM in treating ovarian cancer. This review provides theoretical support for the use of TCM in the treatment of ovarian cancer, offering new perspectives for its clinical prevention and treatment.

MATERIALS AND METHODS

This review conducted a literature search on PubMed, Web of Science, Wanfang Database, and China National Knowledge Infrastructure (CNKI) for relevant studies on TCM active ingredients in preventing ovarian cancer. The search terms included "ovarian cancer" combined with "Chinese herbal medicine," "Herbal medicine," "Traditional Chinese medicine," and "Active ingredients of Chinese medicine". Based on existing experimental and clinical research, the paper systematically summarized and analyzed the mechanisms of TCM in treating ovarian cancer.

RESULTS

Active ingredients of TCM inhibit the occurrence and development of ovarian cancer through inducing tumor cell apoptosis, inhibiting tumor cell proliferation, suppressing tumor cell migration and invasion, inducing tumor cell autophagy, promoting epithelial-mesenchymal transition, and enhancing the efficacy of radiotherapy and chemotherapy drugs. Chinese medicine provides a comprehensive treatment option for ovarian cancer patients, synergizing with radiotherapy and chemotherapy drugs to enhance treatment effectiveness and introduce new hope and possibilities in clinical therapy.

CONCLUSIONS

Active ingredients of TCM can inhibit the occurrence and development of ovarian cancer, but further clinical research is needed to support their application.

TCM-ADIP: A Multidimensional Database Linking Traditional Chinese Medicine to Functional Brain Zones of Alzheimer's Disease

Alzheimer's disease (AD) is a complex neurodegenerative disorder, with existing therapeutic drugs typically targeting specific disease stages. Traditional Chinese medicine (TCM), known for its multi-target and multi-mechanism therapeutic approach, has demonstrated efficacy in treating various stages of AD. In the present work, through a systematic review of classical Chinese medical texts, the formulae for preventing and treating AD were identified. Meanwhile, the active ingredients within these formulae were extracted and cataloged. A comprehensive bioinformatics analysis of omics data was performed to identify differentially expressed genes across different functional brain zones in AD patients at various stages. Finally, by integrating the multidimensional data, we proposed the first database, TCM-ADIP, dedicated to TCM based AD prevention and treatment, which is freely available at https://cbcb.cdutcm.edu.cn/TCM-ADIP/. TCM-ADIP not only supports interactive searching of multidimensional data, but also provides tools for gene localization and functional enrichment analysis of formulae, herbs, and ingredients for AD intervention in specific brain zones. TCM-ADIP fills a crucial gap in existing databases, offering a comprehensive resource for TCM in the treatment of AD.

HERB 2.0: an updated database integrating clinical and experimental evidence for traditional Chinese medicine

Clinical trials and meta-analyses are considered high-level medical evidence with solid credibility. However, such clinical evidence for traditional Chinese medicine (TCM) is scattered, requiring a unified entrance to navigate all available evaluations on TCM therapies under modern standards. Besides, novel experimental evidence has continuously accumulated for TCM since the publication of HERB 1.0. Therefore, we updated the HERB database to integrate four types of evidence for TCM: (i) we curated 8558 clinical trials and 8032 meta-analyses information for TCM and extracted clear clinical conclusions for 1941 clinical trials and 593 meta-analyses with companion supporting papers. (ii) we updated experimental evidence for TCM, increased the number of high-throughput experiments to 2231, and curated references to 6 644. We newly added high-throughput experiments for 376 diseases and evaluated all pairwise similarities among TCM herbs/ingredients/formulae, modern drugs and diseases. (iii) we provide an automatic analyzing interface for users to upload their gene expression profiles and map them to our curated datasets. (iv) we built knowledge graph representations of HERB entities and relationships to retrieve TCM knowledge better. In summary, HERB 2.0 represents rich data type, content, utilization, and visualization improvements to support TCM research and guide modern drug discovery. It is accessible through http://herb.ac.cn/v2 or http://47.92.70.12.

Formononetin Exerts Neuroprotection in Parkinson's Disease via the Activation of the Nrf2 Signaling Pathway

Parkinson's disease (PD) is a prevalent neurodegenerative disease for which no effective treatment currently exists. In this study, we identified formononetin (FMN), a neuroprotective component found in herbal medicines such as Astragalus membranaceus and Glycyrrhiza uralensis, as a potential agent targeting multiple pathways involved in PD. To investigate the anti-PD effects of FMN, we employed Caenorhabditis elegans (C. elegans) PD models, specifically the transgenic strain NL5901 and the MPP(+)-induced strain BZ555, to investigate the effects of FMN on the key pathological features of PD, including dyskinesia, dopamine neuron damage, and reactive oxygen species (ROS) accumulation. The MPP(+)-induced SH-SY5Y cell PD model was utilized to evaluate the effects of FMN on cell viability, ROS accumulation, and mitochondrial dysfunction. The signaling pathway induced by FMN was analyzed using transcriptomic techniques and subsequently validated in vitro. Our results indicate that FMN significantly reduced ROS accumulation and improved both dopaminergic neuron vitality and dyskinesia in the C. elegans PD models. In the cell PD model, FMN significantly reduced ROS accumulation and enhanced mitochondrial membrane potential (MMP) and cell viability. A transcriptomic analysis suggested that the effects of FMN are associated with Nrf2 activation. Furthermore, ML385, a specific Nrf2 inhibitor, blocked the beneficial effects of FMN in vitro, indicating that FMN ameliorates dyskinesia and protects dopaminergic neurons through Nrf2 signaling pathway activation. In addition, the effects of FMN on ameliorating dyskinesia and protecting dopamine neurons were comparable to those of the Nrf2 agonist of sulforaphane (SFN) in vivo. The results of this study confirm that FMN exerts significant anti-PD effects primarily through the Nrf2 signaling pathway. These findings provide crucial insights for the development of anti-PD therapies.

Self-Medication with Herbal Medicine Among Young Adult Cancer Patients: A Prospective Monocentric Study

Objective: The aim of this study was to determine the incidence of the self-use of plants in cancer patients and its impact on overall survival (OS). Methods: This was a prospective study including young adult patients collected between January 15, 2018 and January 15, 2019 in the department of medical oncology at the Habib Bourguiba University Hospital. All patients were questioned about the concept of taking herbs. We compared OS among those who received plants versus the nonusers. Results: A total of 223 patients were included. The average age was 35 ± 4 years. Ninety-seven patients had taken plants. Sixty patients had consumed alenda (61.8%), and 36 patients had received graviola (37.1%). Ten patients have had diarrhea (10.3%), 10 cytolysis (10.3%), 11 cholestasis (11.3%), 15 thrombocytosis (15.4%), 17 leukocytosis (17.5%), and 13 anemia (13.4%). OS at 5 years was 67.1%. It was lower in patients consuming the plants (54% vs. 83%, p = 0.023). Conclusion: Factors associated with a decrease in the 5-year OS were metastatic stage and plant consumption.

TarKG: a comprehensive biomedical knowledge graph for target discovery

MOTIVATION

Target discovery is a crucial step in drug development, as it directly affects the success rate of clinical trials. Knowledge graphs (KGs) offer unique advantages in processing complex biological data and inferring new relationships. Existing biomedical KGs primarily focus on tasks such as drug repositioning and drug-target interactions, leaving a gap in the construction of KGs tailored for target discovery.

RESULTS

We established a comprehensive biomedical KG focusing on target discovery, termed TarKG, by integrating seven existing biomedical KGs, nine public databases, and traditional Chinese medicine knowledge databases. TarKG consists of 1 143 313 entities and 32 806 467 relations across 15 entity categories and 171 relation types, all centered around 3 core entity types: Disease, Gene, and Compound. TarKG provides specialized knowledges for the core entities including chemical structures, protein sequences, or text descriptions. By using different KG embedding algorithms, we assessed the knowledge completion capabilities of TarKG, particularly for disease-target link prediction. In case studies, we further examined TarKG's ability to predict potential protein targets for Alzheimer's disease (AD) and to identify diseases potentially associated with the metallo-deubiquitinase CSN5, using literature analysis for validation. Furthermore, we provided a user-friendly web server (https://tarkg.ddtmlab.org) that enables users to perform knowledge retrieval and relation inference using TarKG.

AVAILABILITY AND IMPLEMENTATION

TarKG is accessible at https://tarkg.ddtmlab.org.

AI empowering traditional Chinese medicine?

For centuries, Traditional Chinese Medicine (TCM) has been a prominent treatment method in China, incorporating acupuncture, herbal remedies, massage, and dietary therapy to promote holistic health and healing. TCM has played a major role in drug discovery, with over 60% of small-molecule drugs approved by the FDA from 1981 to 2019 being derived from natural products. However, TCM modernization faces challenges such as data standardization and the complexity of TCM formulations. The establishment of comprehensive TCM databases has significantly improved the efficiency and accuracy of TCM research, enabling easier access to information on TCM ingredients and encouraging interdisciplinary collaborations. These databases have revolutionized TCM research, facilitating advancements in TCM modernization and patient care. In addition, advancements in AI algorithms and database data quality have accelerated progress in AI for TCM. The application of AI in TCM encompasses a wide range of areas, including herbal screening and new drug discovery, diagnostic and treatment principles, pharmacological mechanisms, network pharmacology, and the incorporation of innovative AI technologies. AI also has the potential to enable personalized medicine by identifying patterns and correlations in patient data, leading to more accurate diagnoses and tailored treatments. The potential benefits of AI for TCM are vast and diverse, promising continued progress and innovation in the field.

Signature Search Polestar: a comprehensive drug repurposing method evaluation assistant for customized oncogenic signature

SUMMARY

The burgeoning high-throughput technologies have led to a significant surge in the scale of pharmacotranscriptomic datasets, especially for oncology. Signature search methods (SSMs), utilizing oncogenic signatures formed by differentially expressed genes through sequencing, have been instrumental in anti-cancer drug screening and identifying mechanisms of action without relying on prior knowledge. However, various studies have found that different SSMs exhibit varying performance across pharmacotranscriptomic datasets. In addition, the size of the oncogenic signature can also significantly impact the result of drug repurposing. Therefore, finding the optimal SSMs and customized oncogenic signature for a specific disease remains a challenge. To address this, we introduce Signature Search Polestar (SSP), a webserver integrating the largest pharmacotranscriptomic datasets of anti-cancer drugs from LINCS L1000 with five state-of-the-art SSMs (XSum, CMap, GSEA, ZhangScore, XCos). SSP provides three main modules: Benchmark, Robustness, and Application. Benchmark uses two indices, Area Under the Curve and Enrichment Score, based on drug annotations to evaluate SSMs at different oncogenic signature sizes. Robustness, applicable when drug annotations are insufficient, uses a performance score based on drug self-retrieval for evaluation. Application provides three screening strategies, single method, SS_all, and SS_cross, allowing users to freely utilize optimal SSMs with tailored oncogenic signature for drug repurposing.

AVAILABILITY AND IMPLEMENTATION

SSP is free at https://web.biotcm.net/SSP/. The current version of SSP is archived in https://doi.org/10.6084/m9.figshare.26524741.v1, allowing users to directly use or customize their own SSP webserver.

Unveiling disulfidptosis-related biomarkers and predicting drugs in Alzheimer's disease

Alzheimer's disease is the predominant form of dementia, and disulfidptosis is the latest reported mode of cell death that impacts various disease processes. This study used bioinformatics to analyze genes associated with disulfidptosis in Alzheimer's disease comprehensively. Based on the public datasets, the differentially expressed genes associated with disulfidptosis were identified, and immune cell infiltration was investigated through correlation analysis. Subsequently, hub genes were determined by a randomforest model. A prediction model was constructed using logistic regression. In addition, the drug-target affinity was predicted by a graph neural network model, and the results were validated by molecular docking. Five hub genes (PPEF1, NEUROD6, VIP, NUPR1, and GEM) were identified. The gene set showed significant enrichment for AD-related pathways. The logistic regression model demonstrated an AUC of 0.952, with AUC values of 0.916 and 0.864 in validated datasets. The immune infiltration analysis revealed significant heterogeneity between the Alzheimer's disease and control groups. High-affinity drugs for hub genes were identified. Through our study, a disease prediction model was constructed using potential biomarkers, and drugs targeting the genes were predicted. These results contribute to further understanding of the molecular mechanisms underlying Alzheimer's disease.

Network medicine analysis for dissecting the therapeutic mechanism of consensus TCM formulae in treating hepatocellular carcinoma with different TCM syndromes

Introduction

Hepatocellular carcinoma (HCC) is a major cause of cancer-related mortality worldwide. Traditional Chinese Medicine (TCM) is widely utilized as an adjunct therapy, improving patient survival and quality of life. TCM categorizes HCC into five distinct syndromes, each treated with specific herbal formulae. However, the molecular mechanisms underlying these treatments remain unclear.

Methods

We employed a network medicine approach to explore the therapeutic mechanisms of TCM in HCC. By constructing a protein-protein interaction (PPI) network, we integrated genes associated with TCM syndromes and their corresponding herbal formulae. This allowed for a quantitative analysis of the topological and functional relationships between TCM syndromes, HCC, and the specific formulae used for treatment.

Results

Our findings revealed that genes related to the five TCM syndromes were closely associated with HCC-related genes within the PPI network. The gene sets corresponding to the five TCM formulae exhibited significant proximity to HCC and its related syndromes, suggesting the efficacy of TCM syndrome differentiation and treatment. Additionally, through a random walk algorithm applied to a heterogeneous network, we prioritized active herbal ingredients, with results confirmed by literature.

Discussion

The identification of these key compounds underscores the potential of network medicine to unravel the complex pharmacological actions of TCM. This study provides a molecular basis for TCM's therapeutic strategies in HCC and highlights specific herbal ingredients as potential leads for drug development and precision medicine.

Overview and limitations of database in global traditional medicines: A narrative review

The study of traditional medicine has garnered significant interest, resulting in various research areas including chemical composition analysis, pharmacological research, clinical application, and quality control. The abundance of available data has made databases increasingly essential for researchers to manage the vast amount of information and explore new drugs. In this article we provide a comprehensive overview and summary of 182 databases that are relevant to traditional medicine research, including 73 databases for chemical component analysis, 70 for pharmacology research, and 39 for clinical application and quality control from published literature (2000-2023). The review categorizes the databases by functionality, offering detailed information on websites and capacities to facilitate easier access. Moreover, this article outlines the primary function of each database, supplemented by case studies to aid in database selection. A practical test was conducted on 68 frequently used databases using keywords and functionalities, resulting in the identification of highlighted databases. This review serves as a reference for traditional medicine researchers to choose appropriate databases and also provides insights and considerations for the function and content design of future databases.

Computational identification of mitochondrial dysfunction biomarkers in severe SARS-CoV-2 infection: Facilitating therapeutic applications of phytomedicine

BACKGROUND

Currently, SARS-CoV-2 has not disappeared and continues to prevail worldwide, with the ongoing risk of mutations and the potential for severe COVID-19. The impairment of monocyte mitochondrial function caused by SARS-CoV-2, leading to a metabolic and immune dysregulation, is a crucial factor in the development of severe COVID-19.

PURPOSE

Discover effective phytomedicines based on mitochondrial-related biomarkers in severe SARS-CoV-2 infection.

METHODS

Firstly, differential gene analysis and gene set enrichment analysis (GSEA) were conducted on monocytes datasets to identify genes and pathways distinguishing severe patients from uninfected individuals. Then, GO and KEGG enrichment analysis on the differentially expressed genes (DEGs) obtained. Take the DEGs and intersect them with the MitoCarta 3.0 gene set to obtain the differentially expressed mitochondrial-related genes (DE-MRGs). Subsequently, machine learning algorithms were employed to screen potential mitochondrial dysfunction biomarkers for severe COVID-19 based on score values. ROC curves were then plotted to assess the distinguish capability of the biomarkers, followed by validation using two additional independent datasets. Next, the effects of the identified biomarkers on metabolic pathways and immune cells were explored through Gene Set Variation Analysis (GSVA) and CIBERSORT. Finally, potential nature products for severe COVID-19 were screened from the expression profile dataset based on dysregulated mitochondrial-related genes, followed by in vitro experimental validation.

RESULTS

There are 1812 DEGs and 17 dysregulated mitochondrial processes between severe COVID-19 patients and uninfected individuals. A total of 77 DE-MRGs were identified, and the potential biomarkers were identified as RECQL4, PYCR1, PIF1, POLQ, and GLDC. In both the training and validation sets, the area under the ROC curve (AUC) for these five biomarkers was greater than 0.9. And they did not show significant changes in mild to moderate patients (p > 0.05), indicating their ability to effectively distinguish severe COVID-19. These biomarkers exhibit a highly significant correlation with the dysregulated metabolic processes (p < 0.05) and immune cell imbalance (p < 0.05) in severe patients, as demonstrated by GSVA and CIBERSORT algorithms. Curcumin has the highest score in the predictive model based on transcriptomic data from 496 natural compounds (p = 0.02; ES = 0.90). Pre-treatment with curcumin for 8 h has been shown to alleviate mitochondrial membrane potential damage caused by the SARS-CoV-2 S1 protein (p < 0.05) and reduce elevated levels of reactive oxygen species (ROS) (p < 0.01).

CONCLUSION

The results of this study indicate a significant correlation between severe SARS-CoV-2 infection and mitochondrial dysfunction. The proposed mitochondrial dysfunction biomarkers identified in this study are associated with the disease progression, metabolic and immune changes in severe SARS-CoV-2 infected patients. Curcumin has a potential role in preventing severe COVID-19 by protecting mitochondrial function. Our findings provide new strategies for predicting the prognosis and enabling early intervention in SARS-CoV-2 infection.

Herb-CMap: a multimodal fusion framework for deciphering the mechanisms of action in traditional Chinese medicine using Suhuang antitussive capsule as a case study

Herbal medicines, particularly traditional Chinese medicines (TCMs), are a rich source of natural products with significant therapeutic potential. However, understanding their mechanisms of action is challenging due to the complexity of their multi-ingredient compositions. We introduced Herb-CMap, a multimodal fusion framework leveraging protein-protein interactions and herb-perturbed gene expression signatures. Utilizing a network-based heat diffusion algorithm, Herb-CMap creates a connectivity map linking herb perturbations to their therapeutic targets, thereby facilitating the prioritization of active ingredients. As a case study, we applied Herb-CMap to Suhuang antitussive capsule (Suhuang), a TCM formula used for treating cough variant asthma (CVA). Using in vivo rat models, our analysis established the transcriptomic signatures of Suhuang and identified its key compounds, such as quercetin and luteolin, and their target genes, including IL17A, PIK3CB, PIK3CD, AKT1, and TNF. These drug-target interactions inhibit the IL-17 signaling pathway and deactivate PI3K, AKT, and NF-κB, effectively reducing lung inflammation and alleviating CVA. The study demonstrates the efficacy of Herb-CMap in elucidating the molecular mechanisms of herbal medicines, offering valuable insights for advancing drug discovery in TCM.

Integrated network pharmacology and bioinformatics to identify therapeutic targets and molecular mechanisms of Huangkui Lianchang Decoction for ulcerative colitis treatment

BACKGROUND

Huangkui Lianchang Decoction (HLD) is a traditional Chinese herbal formula for treating ulcerative colitis (UC). However, its mechanism of action remains poorly understood. The Study aims to validate the therapeutic effect of HLD on UC and its mechanism by integrating network pharmacology, bioinformatics, and experimental validation.

METHODS

UC targets were collected by databases and GSE19101. The active ingredients in HLD were detected by ultra-performance liquid chromatography-tandem mass spectrometry. PubChem collected targets of active ingredients. Protein-protein interaction (PPI) networks were established with UC-related targets. Gene Ontology and Kyoto Encyclopedia (KEGG) of Genes and Genomes enrichment were analyzed for the mechanism of HLD treatment of UC and validated by the signaling pathways of HLD. Effects of HLD on UC were verified using dextran sulfate sodium (DDS)-induced UC mice experiments.

RESULTS

A total of 1883 UC-related targets were obtained from the GSE10191 dataset, 1589 from the database, and 1313 matching HLD-related targets, for a total of 94 key targets. Combined with PPI, GO, and KEGG network analyses, the signaling pathways were enriched to obtain IL-17, Toll-like receptor, NF-κB, and tumor necrosis factor signaling pathways. In animal experiments, HLD improved the inflammatory response of UC and reduced UC-induced pro-inflammatory factors such as Tumor Necrosis Factor Alpha (TNF-α), interleukin 1β (IL-1β), and interleukin 6 (IL-6). HLD suppressed proteins TLR4, MyD88, and NF-κB expression.

CONCLUSIONS

This study systematically dissected the molecular mechanism of HLD for the treatment of UC using a network pharmacology approach. Further animal verification experiments revealed that HLD inhibited inflammatory responses and improved intestinal barrier function through the TLR4/MyD88/NF-κB pathway.

Network Medicine: A Potential Approach for Virtual Drug Screening

Traditional drug screening methods typically focus on a single protein target and exhibit limited efficiency due to the multifactorial nature of most diseases, which result from disturbances within complex networks of protein-protein interactions rather than single gene abnormalities. Addressing this limitation requires a comprehensive drug screening strategy. Network medicine is rooted in systems biology and provides a comprehensive framework for understanding disease mechanisms, prevention, and therapeutic innovations. This approach not only explores the associations between various diseases but also quantifies the relationships between disease genes and drug targets within interactome networks, thus facilitating the prediction of drug-disease relationships and enabling the screening of therapeutic drugs for specific complex diseases. An increasing body of research supports the efficiency and utility of network-based strategies in drug screening. This review highlights the transformative potential of network medicine in virtual therapeutic screening for complex diseases, offering novel insights and a robust foundation for future drug discovery endeavors.

Computer-aided drug discovery strategies for novel therapeutics for prostate cancer leveraging next-generating sequencing data

INTRODUCTION

Prostate cancer (PC) is the most common malignancy and accounts for a significant proportion of cancer deaths among men. Although initial therapy success can often be observed in patients diagnosed with localized PC, many patients eventually develop disease recurrence and metastasis. Without effective treatments, patients with aggressive PC display very poor survival. To curb the current high mortality rate, many investigations have been carried out to identify efficacious therapeutics. Compared to de novo drug designs, computational methods have been widely employed to offer actionable drug predictions in a fast and cost-efficient way. Particularly, powered by an increasing availability of next-generation sequencing molecular profiles from PC patients, computer-aided approaches can be tailored to screen for candidate drugs.

AREAS COVERED

Herein, the authors review the recent advances in computational methods for drug discovery utilizing molecular profiles from PC patients. Given the uniqueness in PC therapeutic needs, they discuss in detail the drug discovery goals of these studies, highlighting their translational values for clinically impactful drug nomination.

EXPERT OPINION

Evolving molecular profiling techniques may enable new perspectives for computer-aided approaches to offer drug candidates for different tumor microenvironments. With ongoing efforts to incorporate new compounds into large-scale high-throughput screens, the authors envision continued expansion of drug candidate pools.

Machine learning survival prediction using tumor lipid metabolism genes for osteosarcoma

Osteosarcoma is a primary malignant tumor that commonly affects children and adolescents, with a poor prognosis. The existence of tumor heterogeneity leads to different molecular subtypes and survival outcomes. Recently, lipid metabolism has been identified as a critical characteristic of cancer. Therefore, our study aims to identify osteosarcoma's lipid metabolism molecular subtype and develop a signature for survival outcome prediction. Four multicenter cohorts-TARGET-OS, GSE21257, GSE39058, and GSE16091-were amalgamated into a unified Meta-Cohort. Through consensus clustering, novel molecular subtypes within Meta-Cohort patients were delineated. Subsequent feature selection processes, encompassing analyses of differentially expressed genes between subtypes, univariate Cox analysis, and StepAIC, were employed to pinpoint biomarkers related to lipid metabolism in TARGET-OS. We selected the most effective algorithm for constructing a Lipid Metabolism-Related Signature (LMRS) by utilizing four machine-learning algorithms reconfigured into ten unique combinations. This selection was based on achieving the highest concordance index (C-index) in the test cohort of GSE21257, GSE39058, and GSE16091. We identified two distinct lipid metabolism molecular subtypes in osteosarcoma patients, C1 and C2, with significantly different survival rates. C1 is characterized by increased cholesterol, fatty acid synthesis, and ketone metabolism. In contrast, C2 focuses on steroid hormone biosynthesis, arachidonic acid, and glycerolipid and linoleic acid metabolism. Feature selection in the TARGET-OS identified 12 lipid metabolism genes, leading to a model predicting osteosarcoma patient survival. The LMRS, based on the 12 identified genes, consistently accurately predicted prognosis across TARGET-OS, testing cohorts, and Meta-Cohort. Incorporating 12 published signatures, LMRS showed robust and significantly superior predictive capability. Our results offer a promising tool to enhance the clinical management of osteosarcoma, potentially leading to improved clinical outcomes.

Exosome-Mediated Communication in Thyroid Cancer: Implications for Prognosis and Therapeutic Targets

Thyroid cancer (THCA) is one of the most common malignancies of the endocrine system. Exosomes have significant value in performing molecular treatments, evaluating the diagnosis and determining tumor prognosis. Thus, the identification of exosome-related genes could be valuable for the diagnosis and potential treatment of THCA. In this study, we examined a set of exosome-related differentially expressed genes (DEGs) (BIRC5, POSTN, TGFBR1, DUSP1, BID, and FGFR2) by taking the intersection between the DEGs of the TCGA-THCA and GeneCards datasets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of the exosome-related DEGs indicated that these genes were involved in certain biological functions and pathways. Protein‒protein interaction (PPI), mRNA‒miRNA, and mRNA-TF interaction networks were constructed using the 6 exosome-related DEGs as hub genes. Furthermore, we analyzed the correlation between the 6 exosome-related DEGs and immune infiltration. The Genomics of Drug Sensitivity in Cancer (GDSC), the Cancer Cell Line Encyclopedia (CCLE), and the CellMiner database were used to elucidate the relationship between the exosome-related DEGs and drug sensitivity. In addition, we verified that both POSTN and BID were upregulated in papillary thyroid cancer (PTC) patients and that their expression was correlated with cancer progression. The POSTN and BID protein expression levels were further examined in THCA cell lines. These findings provide insights into exosome-related clinical trials and drug development.

Pan-cancer analysis of prognostic and immunological role of IL4I1 in human tumors: a bulk omics research and single cell sequencing validation

BACKGROUND

Interleukin-4 inducible gene 1 (IL4I1) regulates tumor progression in numerous tumor types. However, its correlation with immune infiltration and prognosis of patients in a pan-cancer setting remains unclear.

METHODS

Data from the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), UALCAN, Clinical Proteomic Tumor Analysis Consortium (CPTAC), Gene Expression Omnibus (GEO), cBioPortal, Cancer Single-cell State Atlas (CancerSEA), and Tumor IMmune Estimation Resource(TIMER) databases were used to evaluate IL4I1 expression, clinical features and prognostic effects, gene set enrichment, and correlation with immune cell infiltration, as well as the relationship between IL4I1 methylation and expression and survival prognosis. Correlations with 192 anticancer drugs were also analyzed.

RESULTS

IL4I1 was significantly overexpressed in the majority of tumors, and the imbalance of IL4I1 was significantly correlated with overall survival and pathological stage. Moreover, total IL4I1 protein was increased in cancer. Therefore, IL4I1 may be used as a prognostic biomarker or protective factor in numerous types of cancer. The methylation level of IL4I1 may also be used as a prognostic marker. The functional enrichment of IL4I1 was closely related to the immunomodulatory pathway. In addition, the level of tumor-associated macrophage infiltration was positively correlated with the expression of IL4I1 in pan-cancerous tissues. scRNA-seq analysis suggested that IL4I1 differ significantly among different cells in the tumor microenvironment and was most enriched in macrophages. Various immune checkpoint genes were positively correlated with IL4I1 expression in most tumors. In addition, patients with high IL4I1 expression may be resistant to BMS-754807 and docetaxel, but sensitive to temozolomide.

CONCLUSION

IL4I1 may play a role as promoter of cancer and prognostic indicator in patients. High expression of IL4I1 is associated with the state of tumor immunosuppression and may contribute to tumor-associated macrophage invasion. Therefore, IL4I1 may be a new therapeutic target for the treatment and prognosis of patients with cancer.

Revealing the role of the gut microbiota in enhancing targeted therapy efficacy for lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death globally. Although the gut microbiota's role in the antitumor efficacy of many cancers has been revealed, its involvement in the response to gefitinib therapy for LUAD remains unclear. To fill this gap, we conducted a longitudinal study that profiled gut microbiota changes in PC-9 tumor-bearing mice under different treatments, including gefitinib monotherapy and combination therapies with probiotics, antibiotics, or Traditional Chinese Medicine (TCM). Our findings demonstrated that combining probiotics or TCM with gefitinib therapy outperformed gefitinib monotherapy, as evidenced by tumor volume, body weight, and tumor marker tests. By contrast, antibiotic intervention suppressed the antitumor efficacy of gefitinib. Notably, the temporal changes in gut microbiota were strongly correlated with the different treatments, prompting us to investigate whether there is a causal relationship between gut microbiota and the antitumor efficacy of gefitinib using Mediation Analysis (MA). Finally, our research revealed that thirteen mediators (Amplicon Sequence Variants, ASVs) regulate the antitumor effect of gefitinib, regardless of treatment. Our study provides robust evidence supporting the gut microbiota's significant and potentially causal role in mediating gefitinib treatment efficacy in mice. Our findings shed light on a novel strategy for antitumor drug development by targeting the gut microbiota.

Construction and validation of a colon cancer prognostic model based on tumor mutation burden-related genes

Currently, immunotherapy has entered the clinical diagnosis and treatment guidelines for colon cancer, but existing immunotherapy markers cannot predict the effectiveness of immunotherapy well. This study utilized the TCGA-COAD queue to perform differential gene analysis on high and low-mutation burden samples, and screen differentially expressed genes (DEGs). To explore new molecular markers or predictive models of immunotherapy by using DEGs for NMF classification and prognostic model construction. Through systematic bioinformatics analysis, the TCGA-COAD cohort was successfully divided into high mutation burden subtypes and low mutation burden subtypes by NMF typing using DEGs. The proportion of MSI-H between high mutation burden subtypes was significantly higher than that of low mutation burden subtypes, but there was no significant difference in immunotherapy efficacy between the two subtypes. Drug sensitivity analysis showed significant differences in drug sensitivity between the two subtypes. Subsequently, we constructed a prognostic model using DEGs, which can effectively predict patient survival and immunotherapy outcomes. The prognosis and immunotherapy outcomes of the low-risk group were significantly better than those of the high-risk group. The external dataset validation of the constructed prognostic model using the GSE39582 dataset from the GEO database yielded consistent results. At the same time, we also analyzed the TMB and MSI situation between the high and low-risk groups, and the results showed that there was no significant difference in TMB between the high and low-risk groups, but the proportion of MSI-H in the high-risk group was significantly higher than that in the low-risk group. Finally, we conclude that TMB is not a suitable molecular marker for predicting the efficacy of immunotherapy in colon cancer. The newly constructed prognostic model can effectively differentiate the prognosis of colon cancer patients and predict their immunotherapy efficacy.

CDS-DB, an omnibus for patient-derived gene expression signatures induced by cancer treatment

Patient-derived gene expression signatures induced by cancer treatment, obtained from paired pre- and post-treatment clinical transcriptomes, can help reveal drug mechanisms of action (MOAs) in cancer patients and understand the molecular response mechanism of tumor sensitivity or resistance. Their integration and reuse may bring new insights. Paired pre- and post-treatment clinical transcriptomic data are rapidly accumulating. However, a lack of systematic collection makes data access, integration, and reuse challenging. We therefore present the Cancer Drug-induced gene expression Signature DataBase (CDS-DB). CDS-DB has collected 78 patient-derived, paired pre- and post-treatment transcriptomic source datasets with uniformly reprocessed expression profiles and manually curated metadata such as drug administration dosage, sampling time and location, and intrinsic drug response status. From these source datasets, 2012 patient-level gene perturbation signatures were obtained, covering 85 therapeutic regimens, 39 cancer subtypes and 3628 patient samples. Besides data browsing, download and search, CDS-DB also supports single signature analysis (including differential gene expression, functional enrichment, tumor microenvironment and correlation analyses), signature comparative analysis and signature connectivity analysis. This provides insights into drug MOA and its heterogeneity in patients, drug resistance mechanisms, drug repositioning and drug (combination) discovery, etc. CDS-DB is available at http://cdsdb.ncpsb.org.cn/.

ccTCM: A quantitative component and compound platform for promoting the research of traditional Chinese medicine

Traditional Chinese medicine (TCM) databases play a vital role in bridging the gap between TCM and modern medicine, as well as in promoting the popularity of TCM. Elucidating the bioactive ingredients of Chinese medicinal materials is key to TCM modernization and new drug discovery. However, one drawback of current TCM databases is the lack of quantitative data on the constituents of Chinese medicinal materials. Herein, we present ccTCM, a web-based platform designed to provide a component and compound-content-based resource on TCM and analysis services for medical experts. In terms of design features, ccTCM combines resource distribution, similarity analysis, and molecular-mechanism analysis to accelerate the discovery of bioactive ingredients in TCM. ccTCM contains 273 Chinese medicinal materials commonly used in clinical settings, covering 29 functional classifications. By searching and comparing, we finally adopted 2043 studies, from which we collected the compounds contained in each TCM with content greater than 0.001 %, and a total of 1449 were extracted. Subsequently, we collected 40,767 compound-target pairs by integrating multiple databases. Taken together, ccTCM is a versatile platform that can be used by TCM scientists to perform scientific and clinical TCM studies based on quantified ingredients of Chinese medicinal materials. ccTCM is freely accessible at http://www.cctcm.org.cn.

TCMBank: bridges between the largest herbal medicines, chemical ingredients, target proteins, and associated diseases with intelligence text mining

Traditional Chinese Medicine (TCM) has long been viewed as a precious source of modern drug discovery. AI-assisted drug discovery (AIDD) has been investigated extensively. However, there are still two challenges in applying AIDD to guide TCM drug discovery: the lack of a large amount of standardized TCM-related information and AIDD is prone to pathological failures in out-of-domain data. We have released TCM Database@Taiwan in 2011, and it has been widely disseminated and used. Now, we developed TCMBank, the largest systematic free TCM database, which is an extension of TCM Database@Taiwan. TCMBank contains 9192 herbs, 61 966 ingredients (unduplicated), 15 179 targets, 32 529 diseases, and their pairwise relationships. By integrating multiple data sources, TCMBank provides 3D structure information of ingredients and provides a standard list and detailed information on herbs, ingredients, targets and diseases. TCMBank has an intelligent document identification module that continuously adds TCM-related information retrieved from the literature in PubChem. In addition, driven by TCMBank big data, we developed an ensemble learning-based drug discovery protocol for identifying potential leads and drug repurposing. We take colorectal cancer and Alzheimer's disease as examples to demonstrate how to accelerate drug discovery by artificial intelligence. Using TCMBank, researchers can view literature-driven relationship mapping between herbs/ingredients and genes/diseases, allowing the understanding of molecular action mechanisms for ingredients and identification of new potentially effective treatments. TCMBank is available at https://TCMBank.CN/.

COIMMR: a computational framework to reveal the contribution of herbal ingredients against human cancer via immune microenvironment and metabolic reprogramming

Immune evasion and metabolism reprogramming have been regarded as two vital hallmarks of the mechanism of carcinogenesis. Thus, targeting the immune microenvironment and the reprogrammed metabolic processes will aid in developing novel anti-cancer drugs. In recent decades, herbal medicine has been widely utilized to treat cancer through the modulation of the immune microenvironment and reprogrammed metabolic processes. However, labor-based herbal ingredient screening is time consuming, laborious and costly. Luckily, some computational approaches have been proposed to screen candidates for drug discovery rapidly. Yet, it has been challenging to develop methods to screen drug candidates exclusively targeting specific pathways, especially for herbal ingredients which exert anti-cancer effects by multiple targets, multiple pathways and synergistic ways. Meanwhile, currently employed approaches cannot quantify the contribution of the specific pathway to the overall curative effect of herbal ingredients. Hence, to address this problem, this study proposes a new computational framework to infer the contribution of the immune microenvironment and metabolic reprogramming (COIMMR) in herbal ingredients against human cancer and specifically screen herbal ingredients targeting the immune microenvironment and metabolic reprogramming. Finally, COIMMR was applied to identify isoliquiritigenin that specifically regulates the T cells in stomach adenocarcinoma and cephaelin hydrochloride that specifically targets metabolic reprogramming in low-grade glioma. The in silico results were further verified using in vitro experiments. Taken together, our approach opens new possibilities for repositioning drugs targeting immune and metabolic dysfunction in human cancer and provides new insights for drug development in other diseases. COIMMR is available at https://github.com/LYN2323/COIMMR.

Integrating animal experiments, mass spectrometry and network-based approach to reveal the sleep-improving effects of Ziziphi Spinosae Semen and γ-aminobutyric acid mixture

BACKGROUND

Ziziphi Spinosae Semen (ZSS) is a plant widely used as medicine and food in Asian countries due to its numerous health benefits. γ-aminobutyric acid (GABA), a non-proteinaceous amino acid, is one of the major inhibitory neurotransmitters with a relaxant function. In this study, a system pharmacology approach was employed to assess the effects of a mixture composed of ZSS and GABA (ZSSG) on sleep improvement.

METHODS

Mice were divided into five groups (n = 10) and received either no treatment, sodium pentobarbital, or sodium barbital with diazepam or ZSSG. The effects of ZSSG on sleep quality were evaluated in mice, and differential metabolites associated with sleep were identified among the control, ZSS, GABA, and ZSSG groups. Additionally, network-based ingredient-insomnia proximity analysis was applied to explore the major ingredients.

RESULTS

ZSSG significantly improved sleep quality by decreasing sleep latency and prolonging sleep duration in sodium pentobarbital-induced sleeping mouse model (P < 0.05). ZSSG significantly enhanced the brain content of GABA in mice. Furthermore, ZSSG also significantly decreased sleep latency-induced by sodium barbital in mice (P < 0.05). Metabolic analysis revealed significant differences in 10 metabolites between ZSSG group and the groups administering ZSS or GABA. Lastly, using the network-based ingredient screening model, we discovered potential four active ingredients and three pairwise ingredient combinations with synergistic effect on insomnia from ZSSG among 85 ingredients identified by UPLC-Q/TOF-MS. Also, we have constructed an online computation platform.

CONCLUSION

Our data demonstrated that ZSSG improved the sleeping quality of mice and helped to balance metabolic disorders-associated with sleep disorders. Moreover, based on the network-based prediction method, the four potential active ingredients in ZSSG could serve as quality markers-associated with insomnia. The network-based framework may open up a new avenue for the discovery of active ingredients of herbal medicine for treating complex chronic diseases or symptoms, such as insomnia.