Multiscale Modeling of Inflammation-Induced Tumorigenesis Reveals Competing Oncogenic and Oncoprotective Roles for Inflammation

Harnessing network pharmacology in drug discovery: an integrated approach

Traditional drug discovery approach is based on one drug-one target, that is associated with very lengthy timelines, high costs and very low success rates. Network pharmacology (NP) is a novel method of drug designing, that is based on a multiple-target approach. NP integrates systems such as biology, pharmacology and computational techniques to address the limitations of traditional methods of drug discovery. With help of mapping biological networks, it provides deep insights into biological molecules' interactions and enhances our understanding to the mechanism of drugs, polypharmacology and disease etiology. This review explores the theoretical framework of network pharmacology, discussing the principles and methodologies that enable the construction of drug-target and disease-gene networks. It highlights how data mining, bioinformatics tools and computational models are utilised to predict drug behaviour, repurpose existing drugs and identify novel therapeutic targets. Applications of network pharmacology in the treatment of complex diseases-such as cancer, neurodegenerative disorders, cardiovascular diseases and infectious diseases-are extensively covered, demonstrating its potential to identify multi-target drugs for multifaceted disease mechanisms. Despite the promising results, NP faces challenges due to incomplete and quality of biological data, computational complexities and biological system redundancy. It also faces regulatory challenges in drug approval, demanding revision in regulatory guidelines towards multi-target therapies. Advancements in AI and machine learning, dynamic network modelling and global collaboration can further enhance the efficacy of network pharmacology. This integrative approach has the potential to revolutionise drug discovery, offering new solutions for personalised medicine, drug repurposing and tackling the complexities of modern diseases.

Organochlorine pesticides: occurrence, spatial distribution of residues, toxicity, and toxic mechanisms

Organochlorine pesticides (OCPs) are a class of synthetic, broad-spectrum insecticides that have been widely used for plant pest control over the last century. OCPs are persistent organic pollutants (POPs) with mutagenic, teratogenic, and carcinogenic properties. Although most OCPs are banned to use now, they are ubiquitous in the environment and food, and identified in the serum and urine of humans. Exposure to OCPs could affect the human nervous system, auditory system, and endocrine system, leading to neurodegenerative diseases, hearing loss, cancer, and other diseases. Further, the toxic mechanisms of OCPs are explored from oxidative stress, DNA damage, and inflammatory response. Overall, this review offers a comprehensive insight into the occurrence, spatial distribution of residues, toxicity, and toxic mechanisms of OCPs.

Exploring the mechanism of action of Lobetyolin in the treatment of allergic rhinitis based on network pharmacology and molecular docking

Lobetyolin (LBT) is an important active ingredient in the traditional medicinal plant Codonopsis pilosula (Franch.) Nannf. However, the pharmacological targets and mechanisms of action of LBT against allergic rhinitis (AR) are not known. The aim of this study was to evaluate the possible functional role and potential mechanism of LBT as an anti-AR treatment through a combination of network pharmacology and molecular docking. The disease database and target screening database were used to find potential targets for screening LBT for the treatment of AR. Further network visualization analysis, gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed for potential targets. Finally, we performed some molecular docking with LBT and core targets to verify their relevant effects. The results revealed that a total of 64 target genes were obtained for LBT for AR. The top 10 targets with the highest enrichment scores were tumor necrosis factor, epidermal growth factor receptor, matrix metalloproteinase 9, recombinant toll-like receptor 4, erb-b2 receptor tyrosine kinase 2, JUN protooncogene, C-X-C motif chemokine receptor 4, HSP90AA1, kinase insert domain receptor, and matrix metalloproteinase 2. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that multiple signaling pathways are involved in LBT for AR. Molecular docking results showed that LBT binds strongly to the target proteins matrix metalloproteinase 2, matrix metalloproteinase 9, tumor necrosis factor, JUN protooncogene, and epidermal growth factor receptor through intermolecular forces. This study reveals for the first time the pharmacological targets and related pathways of LBT for the treatment of AR, indicating that LBT can intervene in the intrinsic molecular mechanism of AR through multiple targets and pathways.

Integrating multiscale mathematical modeling and multidimensional data reveals the effects of epigenetic instability on acquired drug resistance in cancer

Biological and dynamic mechanisms by which Drug-tolerant persister (DTP) cells contribute to the development of acquired drug resistance have not been fully elucidated. Here, by integrating multidimensional data from drug-treated PC9 cells, we developed a novel multiscale mathematical model from an evolutionary perspective that encompasses epigenetic and cellular population dynamics. By coupling stochastic simulation with quantitative analysis, we identified epigenetic instability as the most prominent kinetic feature related to the emergence of DTP cell subpopulations and the effectiveness of intermittent treatment. Moreover, we revealed the optimal schedule for intermittent treatment, including the optimal area for therapeutic time and drug holidays. By leveraging single-cell RNA-seq data characterizing the drug tolerance of lung cancer, we validated the predictions made by our model and further revealed previously unrecognized biological features of DTP cells, such as cell autophagy and migration, as well as new biomarker genes of therapeutic tolerance. Our work not only provides a paradigm for the integration of multiscale mathematical models with newly emerging genomics data but also improves our understanding of the crucial roles of DTP cells and offers guidance for developing new intermittent treatment strategies against acquired drug resistance in cancer.

General expert consensus on the application of network pharmacology in the research and development of new traditional Chinese medicine drugs

The research and development of new traditional Chinese medicine (TCM) drugs have progressively established a novel system founded on the integration of TCM theory, human experience, and clinical trials (termed the "Three Combinations"). However, considering TCM's distinctive features of "syndrome differentiation and treatment" and "multicomponent formulations and complex mechanisms", current TCM drug development faces challenges such as insufficient understanding of the material basis and the overall mechanism of action and an incomplete evidence chain system. Moreover, significant obstacles persist in gathering human experience data, evaluating clinical efficacy, and controlling the quality of active ingredients, which impede the innovation process in TCM drug development. Network pharmacology, centered on the "network targets" theory, transcends the limitations of the conventional "single target" reductionist research model. It emphasizes the comprehensive effects of disease or syndrome biological networks as targets to elucidate the overall regulatory mechanism of TCM prescriptions. This approach aligns with the holistic perspective of TCM, offering a novel method consistent with TCM's holistic view for investigating the complex mechanisms of TCM and developing new TCM drugs. It is internationally recognized as a "next-generation drug research model". To advance the research of new tools, methods, and standards for TCM evaluation and to overcome fundamental, critical, and cutting-edge technical challenges in TCM regulation, this consensus aims to explore the characteristics, progress, challenges, applicable pathways, and specific applications of network pharmacology as a new theory, method, and tool in TCM drug development. The goal is to enhance the quality of TCM drug research and development and accelerate the efficiency of developing new TCM products.

Network Pharmacology Study on the Underlying Mechanism of Danggui-Kushen Herb Pair in Adjuvant Chemotherapeutics against Breast Cancer

BACKGROUND

The Danggui-Kushen herb pair (DKHP) is a classic prescription that has long been used in combination with chemotherapeutic drugs to improve the immune status of patients with breast cancer (BC), however, the active components and the underlying pharmacological mechanisms remain unclear. Therefore, this study aimed to elucidate the possible mechanism of action of DKHP against BC-based comprehensive strategy combining network pharmacology, molecular docking, and cellular experiments.

METHODS

The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform was used to obtain the relevant compounds in DKHP. Genecards and the National Center for Biotechnology Information databases were used to predict BC targets. Then, drug-compound- target, and protein-protein interaction networks were constructed to forecast the promising protein targets of DKHP and identify the primary interactions that occur between the protein targets and compounds. Finally, the predicted candidate targets were validated using docking techniques and in vitro experiments.

RESULTS

A total of 30 potential active compounds and 173 intersecting pharmacological targets were identified in DKHP. Gene Ontology enrichment analysis revealed that the inflammatory response, positive regulation of protein phosphorylation, and cellular response to lipopolysaccharide were closely related to DKHP treatment in BC. Kyoto Encyclopedia of Genes and Genomes pathway analysis suggested that the PI3K/AKT pathway may be crucial for DKHP intervention in BC. Therefore, key targets could be AKT1, TP53, VEGR, CASP3, TNF, and IL6. Molecular docking analysis suggested that hyperforin, kushenin, and kushenol T had good binding ability to Akt, p53, and Caspase 3. The in vitro experiment showed that the DKHP extract promoted the apoptosis of MCF-7 cells via the PI3K/Akt signaling pathway. These results corresponded to the predictions produced using the network pharmacology approach.

CONCLUSION

Hyperforin, kushenin, kushenol T, and other active compounds in DKHP can regulate multiple signaling pathways and targets, such as AKT1, TP53, and CASP3, thereby playing preventive and therapeutic roles in BC.

Uncovering the role of traditional Chinese medicine in immune-metabolic balance of gastritis from the perspective of Cold and Hot: Jin Hong Tablets as a case study

Chronic gastritis (CG) is a common inflammatory disease of chronic inflammatory lesion of gastric mucosa and in the diagnosis of gastritis in traditional Chinese medicine (TCM), CG can be classified into Cold ZHENG (syndrome in TCM) and Hot ZHENG. However, the molecular features of Cold/Hot ZHENG in CG and the mechanism of Cold/Hot herbs in formulae for CG remained unclear. In this study, we collected a transcriptomics data including 35 patients of Cold/Hot ZHENG CG and 3 scRNA-seq CG samples. And 25 formulae for CG and 89 herbs recorded in these formulae were also collected. We conduct a comprehensive analysis based on the combination of transcriptomics datasets and machine learning algorithms, to discover biomarkers for Cold/Hot ZHENG CG. Then the target profiles of the collected formulae and Cold/Hot herbs were predicted to uncover the features and biomarkers of them against Cold/Hot ZHENG CG. These biomarkers suggest that Hot ZHENG CG might be characterized by over-inflammation and exuberant metabolism, and Cold ZHENG CG showed a trend of suppression in immune regulation and energy metabolism. Biomarkers and specific pathways of Hot herbs tend to regulate immune responses and energy metabolism, while those of Cold herbs are more likely to participate in anti-inflammatory effects. Finally, the findings were verified based on public transcriptomics datasets, as well as transcriptomics and ELISA detection, taking Jin Hong tablets as a case study. Biomarkers like leptin and IL-6 together with proportions of immune cells showed significant changes after the intervention. These findings might reflect the mechanism and build a bridge between macro and micro views of Cold/Hot ZHENG as well as Cold/Hot herbs.

Interactions between fibroblasts and monocyte-derived cells in chronic lung injuries induced by real-ambient particulate matter exposure

Long-term exposure to fine particulate matter (PM2.5) can lead to chronic lung injury, including inflammation, idiopathic pulmonary fibrosis, and cancer. Mesenchymal cells, such as fibroblasts, myeloid-derived suppressor cells (MDSCs), and interstitial macrophages (IMs), contribute to immune regulation in lung, yet their diversity and functions upon long-term exposure to particulate matter (PM) remain inadequately characterized. In this study, we conducted a 16-week real-ambient PM exposure experiment on C57BL/6 J male mice in Shijiazhuang, China. We used single-cell RNA sequencing to analyze the cellular and molecular changes in lung tissues. Notably, we revealed a significant increase in specific fibroblast (ATX+, Col5a1+Meg3+, universal fibroblasts) and monocyte-derived cell subpopulations (monocytic-MDSCs (M-MDSCs), Lyve1loMHC-Ⅱhi IMs, Lyve1hiMHC-Ⅱlo IMs) that exhibited pro-inflammatory and pro-fibrotic functions. These cell subpopulations engaged in immunosuppressive signaling pathways and interactions with various cytokines, shaping a pulmonary microenvironment similar to those associated with cancer-associated fibroblasts (CAFs) and tumor-associated macrophages (TAMs). This altered immune environment may promote the development of pulmonary fibrosis caused by PM exposure, underscoring the intricate roles of mesenchymal cells in chronic lung injury and highlighting the cancer-causing potential of PM2.5 exposure.

A modelling framework for cancer ecology and evolution

Cancer incidence increases rapidly with age, typically as a polynomial. The somatic mutation theory explains this increase through the waiting time for enough mutations to build up to generate cells with the full set of traits needed to grow without control. However, lines of evidence ranging from tumour reversion and dormancy to the prevalence of presumed cancer mutations in non-cancerous tissues argue that this is not the whole story, and that cancer is also an ecological process, and that mutations only lead to cancer when the systems of control within and across cells have broken down. Aging thus has two effects: the build-up of mutations and the breakdown of control. This paper presents a mathematical modelling framework to unify these theories with novel approaches to model the mutation and diversification of cell lineages and of the breakdown of the layers of control both within and between cells. These models correctly predict the polynomial increase of cancer with age, show how germline defects in control accelerate cancer initiation, and compute how the positive feedback between cell replication, ecology and layers of control leads to a doubly exponential growth of cell populations.

Biomarker Profile of Colorectal Cancer: Current Findings and Future Perspective

OBJECTIVE

Breakthroughs in omics technology have led to a deeper understanding of the fundamental molecular changes that play a critical role in the development and progression of cancer. This review delves into the hidden molecular drivers of colorectal cancer (CRC), offering potential for clinical translation through novel biomarkers and personalized therapies.

METHODS

We summarizes recent studies utilizing various omics approaches, including genomics, transcriptomics, proteomics, epigenomics, metabolomics and data integration with computational algorithms, to investigate CRC.

RESULTS

Integrating multi-omics data in colorectal cancer research unlocks hidden biological insights, revealing new pathways and mechanisms. This powerful approach not only identifies potential biomarkers for personalized prognosis, diagnosis, and treatment, but also predicts patient response to specific therapies, while computational tools illuminate the landscape by deciphering complex datasets.

CONCLUSIONS

Future research should prioritize validating promising biomarkers and seamlessly translating them into clinical practice, ultimately propelling personalized CRC management to new heights.

Causal relationship between anti-inflammatory drugs and cancer: a pan-cancer study with Mendelian randomization

Background

Numerous epidemiological studies have elucidated the intricate connection between inflammation and cancer, highlighting how sustained inflammatory responses can fuel carcinogenesis by fostering proliferation, angiogenesis, and metastasis, while dampening immune responses and sensitivity to chemotherapy. Previous clinical investigations have underscored the potential of anti-inflammatory medications in either preventing or mitigating tumor formation. Here, the causal relationship between anti-inflammatory drugs and cancer was further explored through Mendelian randomization studies.

Methods

Employing Mendelian randomization, we scrutinized the causal links between three anti-inflammatory drugs-NSAIDs, Aspirin, and Anilide-and 37 types of cancer. We primarily utilized inverse variance weighting (IVW) as the primary analytical approach to delineate the causal association between these drugs and cancer types. Concurrently, sensitivity analyses were conducted to ascertain the absence of horizontal pleiotropy and heterogeneity.

Results

Our investigation revealed a discernible causal relationship between certain anti-inflammatory drugs and a subset of cancers, albeit without a pervasive impact across all cancer types. Specifically, NSAIDs exhibited a risk-reducing effect on non-small cell lung cancer (OR: 0.76, 95% CI: 0.59-0.97, p-value: 0.03) and gastric cancer (OR: 0.57, 95% CI: 0.34-0.98, p-value: 0.04). Conversely, aspirin was associated with an increased risk of oral malignant tumors (OR: 2.18, 95% CI: 1.13-4.21, p-value: 0.02). Notably, no statistically significant findings were observed for anilide drugs (p < 0.05).

Conclusion

We identified several cancers with potential causal links to NSAIDs, including non-small cell lung cancer and gastric cancer. Despite our extensive analysis, we did not identify a substantial causal relationship between the use of anti-inflammatory drugs and the development of various cancers.

Multiorgan locked-state model of chronic diseases and systems pharmacology opportunities

With increasing human life expectancy, the global medical burden of chronic diseases is growing. Hence, chronic diseases are a pressing health concern and will continue to be in decades to come. Chronic diseases often involve multiple malfunctioning organs in the body. An imminent question is how interorgan crosstalk contributes to the etiology of chronic diseases. We conceived the locked-state model (LoSM), which illustrates how interorgan communication can give rise to body-wide memory-like properties that 'lock' healthy or pathological conditions. Next, we propose cutting-edge systems biology and artificial intelligence strategies to decipher chronic multiorgan locked states. Finally, we discuss the clinical implications of the LoSM and assess the power of systems-based therapies to dismantle pathological multiorgan locked states while improving treatments for chronic diseases.

Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine

Network pharmacology (NP) provides a new methodological perspective for understanding traditional medicine from a holistic perspective, giving rise to frontiers such as traditional Chinese medicine network pharmacology (TCM-NP). With the development of artificial intelligence (AI) technology, it is key for NP to develop network-based AI methods to reveal the treatment mechanism of complex diseases from massive omics data. In this review, focusing on the TCM-NP, we summarize involved AI methods into three categories: network relationship mining, network target positioning and network target navigating, and present the typical application of TCM-NP in uncovering biological basis and clinical value of Cold/Hot syndromes. Collectively, our review provides researchers with an innovative overview of the methodological progress of NP and its application in TCM from the AI perspective.

Mechanisms of Regulation of the Expression of miRNAs and lncRNAs by Metformin in Ovarian Cancer

Ovarian cancer (OC) is one of the most lethal gynecological malignancies. The use of biological compounds such as non-coding RNAs (ncRNAs) is being considered as a therapeutic option to improve or complement current treatments since the deregulation of ncRNAs has been implicated in the pathogenesis and progression of OC. Old drugs with antitumoral properties have also been studied in the context of cancer, although their antitumor mechanisms are not fully clear. For instance, the antidiabetic drug metformin has shown pleiotropic effects in several in vitro models of cancer, including OC. Interestingly, metformin has been reported to regulate ncRNAs, which could explain its diverse effects on tumor cells. In this review, we discuss the mechanism of epigenetic regulation described for metformin, with a focus on the evidence of metformin-dependent microRNA (miRNAs) and long non-coding RNA (lncRNAs) regulation in OC.

Immune perturbation network identifies an EMT subtype with chromosomal instability and tumor immune-desert microenvironment

Most gastric cancer (GC) subtypes are identified through transcriptional profiling overlooking dynamic changes and interactions in gene expression. Based on the background network of global immune genes, we constructed sample-specific edge-perturbation matrices and identified four molecular network subtypes of GC (MNG). MNG-1 displayed the best prognosis and vigorous cell cycle activity. MNG-2 was enriched by immune-hot phenotype with the potential for immunotherapy response. MNG-3 and MNG-4 were identified with epithelial-mesenchymal transition (EMT) peculiarity and worse prognosis, termed EMT subtypes. MNG-3 was characterized by low mutational burden and stromal cells and considered a replica of previous subtypes associated with poor prognosis. Notably, MNG-4 was considered a previously undefined subtype with a dismal prognosis, characterized by chromosomal instability and immune-desert microenvironment. This subtype tended to metastasize and was resistant to respond to immunotherapy. Pharmacogenomics analysis showed three therapeutic agents (NVP-BEZ235, LY2606368, and rutin) were potential interventions for MNG-4.

Quantitative Analysis on Molecular Characteristics Evolution of Gastric Cancer Progression and Prognosis

The dynamic changes of key biological characteristics from gastric low-grade intraepithelial neoplasia (LGIN) to high-grade intraepithelial neoplasia (HGIN) to early gastric cancer (EGC) are still unclear, which greatly affect the accurate diagnosis and treatment of EGC and prognosis evaluation of gastric cancer (GC). In this study, bioinformatics methods/tools are applied to quantitatively analyze molecular characteristics evolution of GC progression, and a prognosis model is constructed. This study finds that some dysregulated differentially expressed mRNAs (DEmRNAs) in the LGIN stage may continue to promote the occurrence and development of EGC. Among the LGIN, HGIN, and EGC stages, there are differences and relevance in the transcription expression patterns of DEmRNAs, and the activation related to immune cells is very different. The biological functions continuously changed during the progression from LGIN to HGIN to EGC. The COX model constructed based on the three EGC-related DEmRNAs has GC prognostic risk prediction ability. The evolution of biological characteristics during the development of EGC mined by the authors provides new insight into understanding the molecular mechanism of EGC occurrence and development. The three-gene prognostic risk model provides a new method for assisting GC clinical treatment decisions.

Single-cell transcriptomic analysis reveals crucial oncogenic signatures and its associative cell types involved in gastric cancer

The intricate association of oncogenic markers negatively impacts accurate gastric cancer diagnosis and leads to the proliferation of mortality rate. Molecular heterogeneity is inevitable in determining gastric cancer's progression state with multiple cell types involved. Identification of pathogenic gene signatures is imperative to understand the disease's etiology. This study demonstrates a systematic approach to identifying oncogenic gastric cancer genes linked with different cell types. The raw counts of adjacent normal and gastric cancer samples are subjected to a quality control step. The dimensionality reduction and multidimensional clustering are performed using Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) techniques. The adjacent normal and gastric cancer sample cell clusters are annotated with the Human Primary Cell Atlas database using the "SingleR." Cellular state transition between the distinct groups is characterized using trajectory analysis. The ligand-receptor interaction between Vascular Endothelial Growth Factor (VEGF) and cell clusters unveils crucial molecular pathways in gastric cancer progression. Chondrocytes, Smooth muscle cells, and fibroblast cell clusters contain genes contributing to poor survival rates based on hazard ratio during survival analysis. The GC-related oncogenic signatures are isolated by comparing the gene set with the DisGeNET database. Twelve gastric cancer biomarkers (SPARC, KLF5, HLA-DRB1, IGFBP3, TIMP3, LGALS1, IGFBP6, COL18A1, F3, COL4A1, PDGFRB, COL5A2) are linked with gastric cancer and further validated through gene set enrichment analysis. Drug-gene interaction found PDGFRB, interacting with various anti-cancer drugs, as a potential inhibitor for gastric cancer. Further investigations on these molecular signatures will assist the development of precision therapeutics, promising longevity among gastric cancer patients.

Antioxidant, Anti-Inflammatory and Antiproliferative Effects of Osmanthus fragrans (Thunb.) Lour. Flower Extracts

Osmanthus fragrans (Thunb.) Lour. flowers (OF-F) have been traditionally consumed as a functional food and utilized as folk medicine. This study evaluated the antioxidant, anti-inflammatory and cytotoxic effects of OF-F extracts on prostate cancer cells (DU-145) and determined possible protein-ligand interactions of its compounds in silico. The crude OF-F extracts-water (W) and ethanol (E) were tested for phytochemical screening, antioxidant, anti-inflammatory, and anti-cancer. Network and molecular docking analyses of chemical markers were executed to establish their application for anticancer drug development. OF-F-E possessed higher total polyphenols (233.360 ± 3.613 g/kg) and tannin (93.350 ± 1.003 g/kg) contents than OF-F-W. In addition, OF-F-E extract demonstrated effective DPPH scavenging activity (IC50 = 0.173 ± 0.004 kg/L) and contained a high FRAP value (830.620 ± 6.843 g Trolox/kg). In cell culture experiments, OF-F-E significantly reduced NO levels and inhibited cell proliferation of RAW-264.7 and DU-145 cell lines, respectively. Network analysis revealed O. fragrans (Thunb.) Lour. metabolites could affect thirteen molecular functions and thirteen biological processes in four cellular components. These metabolites inhibited key proteins of DU-145 prostate cancer using molecular docking with rutin owning the highest binding affinity with PIKR31 and AR. Hence, this study offered a new rationale for O. fragrans (Thunb.) Lour. metabolites as a medicinal herb for anticancer drug development.

Gastric precancerous lesions:occurrence, development factors, and treatment

Patients with gastric precancerous lesions (GPL) have a higher risk of gastric cancer (GC). However, the transformation of GPL into GC is an ongoing process that takes several years. At present, several factors including H.Pylori (Hp), flora imbalance, inflammatory factors, genetic variations, Claudin-4, gastric stem cells, solute carrier family member 26 (SLC26A9), bile reflux, exosomes, and miR-30a plays a considerable role in the transformation of GPL into GC. Moreover, timely intervention in the event of GPL can reduce the risk of GC. In clinical practice, GPL is mainly treated with endoscopy, acid suppression therapy, Hp eradication, a cyclooxygenase-2 inhibitor, aspirin, and diet. Currently, the use of traditional Chinese medicine (TCM) or combination with western medication to remove Hp and the use of TCM to treat GPL are common in Asia, particularly China, and have also demonstrated excellent clinical efficacy. This review thoroughly discussed the combining of TCM and Western therapy for the treatment of precancerous lesions as conditions allow. Consequently, this review also focuses on the causes of the development and progression of GPL, as well as its current treatment. This may help us understand GPL and related treatment.

Network Pharmacology and Molecular Docking Analysis Exploring the Mechanism of Tripterygium wilfordii in the Treatment of Oral Lichen Planus

Background: Oral lichen planus (OLP) is an infrequent autoimmune disease of the oral mucosa, which affects up to 2% of the world population. An investigation of Tripterygium wilfordii's mechanism of action for treating OLP was conducted, and a theoretical basis was provided for improving current treatment regimens. Materials and Methods: We used a network pharmacological approach to gain insight into the molecular mechanism of Tripterygium wilfordii in the treatment of OLP. Then, potential protein targets between Tripterygium wilfordii and OLP were analyzed through a drug-target network. This was followed by KEGG enrichment analysis and Gene Ontology (GO) classification. Finally, for molecular docking, AutoDock Vina was used. Results: A protein-protein interaction (PPI) network was constructed by analyzing the common targets of a total of 51 wilfordii-OLP interactions from different databases. The GO and KEGG enrichment analyses showed that the treatment of OLP with Tripterygium wilfordii mainly involves lipopolysaccharide response, bacterial molecular response, positive regulation of cytokine production, and leukocyte proliferation, and the signaling pathways mainly include the AGE-RAGE, NF-κB, Toll-like receptor, IL-17, HIF-1, and TNF signaling pathways. The molecular docking results showed that β-sitosterol, kaempferol, hederagenin, and triptolide have a higher affinity for AKT1, TNF, CASP3, and PTGS2, respectively. Based on the CytoNCA analysis of common targets, 19 key targets, including AKT1, TNF, VEGFA, STAT3, CXCL8, PTGS2, TP53, and CASP3, and their connections were identified. Conclusions: Preliminarily, this study reveals that Tripterygium wilfordii interferes with OLP by interacting with multiple targets through multiple accesses, as validated by molecular docking.

Uncovering the mechanisms of Yi Qi Tong Qiao Pill in the treatment of allergic rhinitis based on Network target analysis

OBJECTIVE

The purpose of this study is to reveal the mechanism of action of Yi Qi Tong Qiao Pill (YQTQP) in the treatment of allergic rhinitis (AR), as well as establish a paradigm for the researches on traditional Chinese medicine (TCM) from systematic perspective.

METHODS

Based on the data collected from TCM-related and disease-related databases, target profiles of compounds in YQTQP were calculated through network-based algorithms and holistic targets of TQTQP was constructed. Network target analysis was performed to explore the potential mechanisms of YQTQP in the treatment of AR and the mechanisms were classified into different modules according to their biological functions. Besides, animal and clinical experiments were conducted to validate our findings inferred from Network target analysis.

RESULTS

Network target analysis showed that YQTQP targeted 12 main pathways or biological processes related to AR, represented by those related to IL-4, IFN-γ, TNF-α and IL-13. These results could be classified into 3 biological modules, including regulation of immune and inflammation, epithelial barrier disorder and cell adhesion. Finally, a series of experiments composed of animal and clinical experiments, proved our findings and confirmed that YQTQP could improve related symptoms of AR, like permeability of nasal mucosa epithelium.

CONCLUSION

A combination of Network target analysis and the experimental validation indicated that YQTQP was effective in the treatment of AR and might provide a new insight on revealing the mechanism of TCM against diseases. Trial registration Name of the registry: Chinese Clinical Trial Registry: Trial registration number: ChiCTR-TRC-13,003,137: Date of registration: Registered 29 March 2013 - Retrospectively registered: URL of trial registry record: https://www.chictr.org.cn/showproj.html?proj=6422 .

Anti-inflammatory strategies for photothermal therapy of cancer

High temperature generated by photothermal therapy (PTT) can trigger an inflammatory response at the tumor site, which not only limits the efficacy of PTT but also increases the risk of tumor metastasis and recurrence. In light of the current limitations posed by inflammation in PTT, several studies have revealed that inhibiting PTT-induced inflammation can significantly improve the efficacy of cancer treatment. In this review, we summarize the research progress made in combining anti-inflammatory strategies to enhance the effectiveness of PTT. The goal is to offer valuable insights for developing better-designed photothermal agents in clinical cancer therapy.

Integrated network pharmacology and DSS-induced colitis model to determine the anti-colitis effect of Rheum palmatum L. and Coptis chinensis Franch in granules

ETHNOPHARMACOLOGICAL RELEVANCE

Rheum palmatum L. (RP) and Coptis chinensis Franch. (CC), frequently used as herbal pair (HP) in clinical practicing of traditional Chinese medicine, exerted predominate efficacies in colitis treatment. However, the mechanism of their synergism lacks scientific explanation.

AIM OF THE STUDY

By integrating network pharmacology and DSS-induced colitis model, the anti-colitis effects and synergistic molecular mechanisms of RP-CC combination was determined.

MATERIALS AND METHODS

In vivo study, mice were divided into control, model, RP, CC and RP-CC (low, middle, high) groups, 2.5% DSS was administrated to induce colitis for consecutive 7 days, subsequently, the therapeutic effects were evaluated from body weight changes, disease activity index (DAI), and pathological conditions. After determining the shared and exclusive targets of RP and CC, respectively by network pharmacology, CETSA, WB, and qPCR were utilized to verify the action modes of RP and CC on specific targets.

RESULTS

Compared to RP or CC used alone, RP-CC combination can significantly protect colon tissues from inflammatory damage in a dose-dependent manner via remarkably alleviating DAI and colon shortening. Network pharmacological analysis suggested that AKT1 would be the core target for RP-CC synergism since these two herbs could simultaneously but non-competitively bind to AKT1 at different sits. Furthermore, RP and CC could also influencing HIF and MAPK pathways, respectively, these additional actions attribute to more optimizing effectiveness towards colitis.

CONCLUSION

In contrast to the mild therapeutic effects of RP or CC individually, RP-CC herb pair could exert strong and synergistic effects in treatment of colitis via non-competitive binding to AKT1 simultaneously, as well as exclusively influencing MAPK and HIF pathways. Our study not only provides the evidence for understanding the combined effect of RP and CC, but also brings up a new strategy and suggestive thoughts for the rationality of HP-based TCM formula.

The mechanism of triptolide in the treatment of connective tissue disease-related interstitial lung disease based on network pharmacology and molecular docking

BACKGROUND

Interstitial lung disease (ILD) is associated with substantial morbidity and mortality, which is one of the key systematic manifestations of connective tissue disease (CTD). Tripterygium wilfordii, known as Leigongteng in Chinese, has been applied to treat connective tissue disease-related interstitial lung disease (CTD-ILD) for many years. Triptolide is a key effective component from Tripterygium wilfordii. But the molecular mechanism of Triptolide for treating CTD-ILD is not yet clear.

METHODS

Gaining insight into the molecular mechanism of Triptolide intervention CTD-ILD, we used the method of network pharmacology. And then we conducted drug-target networks to analyse the potential protein targets between Triptolide and CTD-ILD. Finally, AutoDock Vina was selected for molecular docking.

RESULTS

By analysing the interaction genes between Triptolide and CTD-ILD, 242 genes were obtained. The top 10 targets of the highest enrichment scores were STAT3, AKT1, MAPK1, IL6, TP53, MAPK3, RELA, TNF, JUN, JAK2. GO and KEGG enrichment analysis exhibited that multiple signalling pathways were involved. PI3K-Akt, multiple virus infections, cancer signalling, chemokine, and apoptosis signalling pathway are the main pathways for Triptolide intervention CTD-ILD. And it is related to various biological processes such as inflammation, infection, cell apoptosis, and cancer. Molecular docking shows Triptolide can bind with its target protein in a good bond by intermolecular force.

CONCLUSIONS

This study preliminarily reveals the internal molecular mechanism of Triptolide interfere with CTD-ILD through multiple targets, multiple access, validated through molecular docking.KEY MESSAGESTriptolide intervention CTD-ILD, which are related to various biological processes such as inflammation, infection, cell apoptosis, and cancer.PI3K-Akt, multiple virus infections, and apoptosis signalling pathway are the main pathways for Triptolide intervention CTD-ILD.Triptolide can bind with related target protein in a good bond by Intermolecular force, exhibiting a good docking activity.

Capric Acid Behaves Agonistic Effect on Calcitriol to Control Inflammatory Mediators in Colon Cancer Cells

Inflammation prompts cancer development and promotes all stages of tumorigenesis. Calcitriol is a nutraceutical essential regulator for host health benefits. However, the influence of calcitriol on inflammatory mediators involved in cancer cells is not clear. This study aimed to assess the sensitivity of calcitriol alone and combined with capric acid, and identify the possible influence of calcitriol on inflammatory mediators. The colorectal cancer cell line (HCT116) was induced by LPS/TNF-α and the inflammation and metastatic mediators (IL-1β, IL-6, IL-17) were quantified in calcitriol and capric acid supplemented colon cancer cells. The mRNA and protein expression of MMP-2, NF-κB and COX-2 were quantified. The significant reduction in MMP-2 expression was confirmed at combination treatment by zymogram analysis. Our findings demonstrated the anti-inflammatory and anti-metastatic potentials of capric acid and calcitriol in individual exposure in a combination of human colon cancer cell lines (HCT116). These abilities may be due to the inhibition of COX-2 mediators and NF-κB transcription factor and reciprocally regulated MMP-2 and MMP-9 signaling pathways. These findings elucidate the activation of COX-2 and NF-κB via disruption of the cellular outer matrix could be considered a novel molecular target suitable for colorectal cancer therapy. This study confirmed that capric acid activates calcitriol sensitization in colon cancer cells and could be used as a successful supplement for intestinal diseases and colon aberrations.

Gene Differential Expression and Interaction Networks Illustrate the Biomarkers and Molecular Biological Mechanisms of Unsaponifiable Matter in Kanglaite Injection for Pancreatic Ductal Adenocarcinoma

Background

Kanglaite injection (KLTi) has shown good clinical efficacy in the treatment of pancreatic ductal adenocarcinoma (PDAC). While previous studies have demonstrated the antitumor effects of the oil compounds in KLTi, it is unclear whether the unsaponifiable matter (USM) also has antitumor effects. This study used network pharmacology, molecular docking, and database verification methods to investigate the molecular biological mechanisms of USM.

Methods

Compounds of USM were obtained from GC-MS, and targets from DrugBank. Next, the GEO database was searched for differentially expressed genes in cancerous tissues and healthy tissues of PDAC to identify targets. Subsequently, the protein-protein interaction of USM and PDAC targets was constructed by BisoGenet to extract candidate genes. The candidate genes were enriched using GO and KEGG by Metascape, and the gene-pathway network was constructed to screen the key genes. Molecular docking and molecular dynamic simulations of core compound targets were finally performed and to explore the diagnostic, survival, and prognosis value of targets.

Results

A total of 10 active compounds and 36 drug targets were screened for USM, 919 genes associated with PDAC, and 139 USM candidate genes against PDAC were excavated. The enrichment predicted USM by acting on RELA, NFKB1, IKBKG, JUN, MAPK1, TP53, and AKT1. Molecular docking and dynamic simulations confirmed the screened core targets had good affinity and stability with the corresponding compounds. In diagnostic ROC validation, the above targets have certain accuracy for diagnosing PDAC, and the combined diagnosis is more advantageous. As the most diagnostic value of RELA, it is equally significant in predicting disease-specific survival and progression-free interval.

Conclusions

USM in KLTi plays an anti-PDAC role by intervening in the cell cycle, inducing apoptosis, and downregulating the NF-κB, MAPK, and PI3K-Akt pathways. It might participate in the pancreatic cancer pathway, and core target groups have diagnostic, survival, and prognosis value biomarker significance.

Identification of AKIRIN2 as a potential biomarker and correlation with immunotherapy in gastric adenocarcinoma by integrated bioinformatics analysis

Gastric adenocarcinoma is major type of gastric cancer that endangers human health. AKIRIN2 has been shown to be associated with cholangiocarcinoma promoting invasion and angiogenesis. In this study, AKIRIN2 is highly expressed in Gastric adenocarcinoma through bioinformatics analysis based on Stomach adenocarcinoma samples data from The Cancer Genome Atlas. Correlation analysis showed that the high-expression of AKIRIN2 was associated with poor survival rate compared to the low-expression group. Univariate and multivariate Cox regression analyses determined the correlation between clinical characteristics and overall survival. Next, the correlation between AKIRIN2 and immune infiltration was evaluated. The distribution of 24 immune cells and their correlation with the expression of AKIRIN2 were explored using the immune cell database. In addition, three Immune cell methods were used to verify the positive correlation between immune cells and AKIRIN2. Also, Genomics of Drug Sensitivity in Cancer database was utilized to verify the correlation between AKIRIN2 expression level and the efficacy of chemotherapy and immunotherapy. The results showed that AKIRIN2 is an effective biomarker of Gastric adenocarcinoma prognosis, which can guide chemotherapy and immunotherapy and clarify the progress of Gastric adenocarcinoma promoted by immune microenvironment.

Interrogating Patterns of Cancer Disparities by Expanding the Social Determinants of Health Framework to Include Biological Pathways of Social Experiences

The objective of this article is to call for integrating biological pathways of social experiences in the concept model of cancer disparities and social determinants of health (SDH) fields. Black, Indigenous, and People of Color (BIPOC) populations experience more negative outcomes across the cancer continuum. Social conditions are instrumental in better understanding the contemporary and historical constructs that create these patterns of disparities. There is an equally important body of evidence that points to the ways that social conditions shape biological pathways. To date, these areas of research are, for the most part, separate. This paper calls for a bridging of these two areas of research to create new directions for the field of cancer disparities. We discuss inflammation, epigenetic changes, co-morbidities, and early onset as examples of the biological consequences of social conditions that BIPOC populations experience throughout their lifespan that may contribute to disproportionate tumorigenesis and tumor progression.

Network Pharmacology- and Molecular Docking-Based Identification of Potential Phytocompounds from Argyreia capitiformis in the Treatment of Inflammation

The methanolic extract of Argyreia capitiformis stem was examined for anti-inflammatory activities following network pharmacology analysis and molecular docking study. Based on gas chromatography-mass spectrometry (GC-MS) analysis, 49 compounds were identified from the methanolic extract of A. capitiformis stem. A network pharmacology analysis was conducted against the identified compounds, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Ontology analysis of biological processes and molecular functions were performed. Six proteins (IL1R1, IRAK4, MYD88, TIRAP, TLR4, and TRAF6) were identified from the KEGG pathway analysis and subjected to molecular docking study. Additionally, six best ligand efficiency compounds and positive control (aspirin) from each protein were evaluated for their stability using the molecular dynamics simulation study. Our study suggested that IL1R1, IRAK4, MYD88, TIRAP, TLR4, and TRAF6 proteins may be targeted by compounds in the methanolic extract of A. capitiformis stem to provide anti-inflammatory effects.

Network pharmacology to unveil the mechanism of Moluodan in the treatment of chronic atrophic gastritis

BACKGROUND

Moluodan (MLD) is a traditional Chinese patent medicine for the treatment of chronic atrophic gastritis (CAG). However, the mechanism of action (MoA) of MLD for treating CAG still remain unclear.

PURPOSE

Elucidate the MoA of MLD for treating CAG based on network pharmacology.

STUDY DESIGN

Integrate computational prediction and experimental validation based on network pharmacology.

METHODS

Computationally, compounds of MLD were scanned by LC-MS/MS and the target profiles of compounds were identified based on network-based target prediction method. Compounds in MLD were compared with western drugs used for gastritis by hierarchical clustering of target profile. Key biological functional modules of MLD were analyzed, and herb-biological functional module network was constructed to elucidate combinatorial rules of MLD herbs for CAG. Experimentally, MLD's effect on different biological functional modules were validated from both phenotypic level and molecular level in 1- Methyl-3-nitro-1-nitrosoguanidine (MNNG)-induced GES-1 cells.

RESULTS

Computational results show that the target profiles of compounds in MLD can cover most of the biomolecules reported in literature. The MoA of MLD can cover most types of MoA of western drugs for CAG. The treatment of CAG by MLD involved the regulation of various biological functional modules, e.g., inflammation/immune, cell proliferation, cell apoptosis, cell differentiation, digestion and metabolism. Experimental results show that MLD can inhibit cell proliferation, promote cell apoptosis and differentiation, reduce the inflammation level and promote lipid droplet accumulation in MNNG-induced GES-1 cells.

CONCLUSION

The network pharmacology framework integrating computational prediction and experimental validation provides a novel way for exploring the MoA of MLD.

Ferroptosis Patterns Correlate with Immune Microenvironment Characterization in Gastric Cancer

Objective

We aimed to build a ferroptosis-based classifier to characterize the molecular features of gastric cancers (GC) and investigate the relationship between different ferroptosis patterns and GC tumor microenvironment (TME).

Methods

Based on the genomic and clinical information from TCGA portal and GEO database, non-negative matrix factorization (NMF) was used to identify ferroptosis subtypes in GC patients. In order to estimate the ferroptosis levels, we established ferroptosis subtype score (FSS) to quantify ferroptosis patterns and ferroptosis potential index (FPI) by principal component analysis (PCA). The correlations of different ferroptosis patterns with TME cell-infiltrating characteristics (including immune cell infiltration, immune checkpoints expression levels, tumor mutational burden (TMB) and immunotherapy response) were systematically analyzed.

Results

Two ferroptosis subtypes, C1 (with lower FSS) and C2 (with higher FSS), were determined. C2 displayed a significantly lower FPI than C1. Besides, C2 was associated with diffuse subtype while C1 with intestinal subtype. As for TME characteristics, C2 was in accordance with the immune-excluded phenotype as it showed more active immune and stromal activities but lower TMB, less probability of immunotherapy response and poorer prognosis. C1 was linked to immune-inflamed phenotype as it had lower stromal activities but increased neoantigen load, enhanced response to immunotherapy and relatively better prognosis.

Conclusion

The systematic assessment of ferroptosis patterns and ferroptosis levels presented in our study implied that ferroptosis serves as an important factor in the formation of TME, which may expand the understanding of TME and provide a novel perspective for the development of targeted immunotherapeutic strategies for GC patients.

The network pharmacology integrated with pharmacokinetics to clarify the pharmacological mechanism of absorbed components from Viticis fructus extract

ETHNOPHARMACOLOGICAL RELEVANCE

Viticis fructus (VF) has been widely used in alleviating the swelling and pain, owning to its pharmacologically active components including agnuside, 10-O-vanilloylaucubin, luteolin and casticin.

AIM OF THE STUDY

The pharmacokinetic profiles of the absorbed components from aqueous and ethanolic extracts of VF in rat plasma were performed, and explored the molecular mechanisms of absorbed components via network pharmacology.

MATERIALS AND METHODS

Ultra-performance liquid chromatography-tandem mass spectroscopy (UHPLC-MS/MS) was employed to identify the absorbed components from rat plasma. Liquid-liquid extraction with ethyl acetate was used to purify the plasma samples. Plasma pharmacokinetics parameters of the components absorbed were analyzed after oral administration of both extracts. Network pharmacology was used to predict the biological functions and potential signaling pathways of VF. The anti-cancer effects of VF extract and absorbed components have been confirmed by in vitro experiments.

RESULTS

The method was very sensitive with lower limit of quantification (LLOQ) of 1.0, 2.5, 0.2 and 0.5 ng/mL for agnuside, 10-O-vanilloylaucubin, luteolin and casticin, respectively. With the exception of 10-O-vanilloylaucubin which was not detected in the ethanolic extract of VF, all other components were detected in both extracts in plasma. The pharmacokinetic parameters of the four components from rat plasma were significantly different between the two extracts. According to the results of network pharmacology, the absorption components of VF are enriched in 32 key pathways, and 15 pathways are related to cancer. Ultimately, the anti-cancer effects, as well as the signaling pathways of VF ethanolic extract and absorbed components were verified by in vitro experiments.

CONCLUSION

The optimized, sensitive and validated UHPLC-MS/MS method was successfully applied for the plasma pharmacokinetics comparison analysis of the two VF extracts. The combination of network pharmacology and pharmacokinetics provides a useful method to elucidate the biological effects and molecular mechanism of the absorbed components of VF.

Single-cell Transcriptomes Reveal Characteristics of MicroRNA in Gene Expression Noise Reduction

Isogenic cells growing in identical environments show cell-to-cell variations because of the stochasticity in gene expression. High levels of variation or noise can disrupt robust gene expression and result in tremendous consequences for cell behaviors. In this work, we showed evidence from single-cell RNA sequencing data analysis that microRNAs (miRNAs) can reduce gene expression noise at the mRNA level in mouse cells. We identified that the miRNA expression level, number of targets, target pool abundance, and miRNA–target interaction strength are the key features contributing to noise repression. miRNAs tend to work together in cooperative subnetworks to repress target noise synergistically in a cell type-specific manner. By building a physical model of post-transcriptional regulation and observing in synthetic gene circuits, we demonstrated that accelerated degradation with elevated transcriptional activation of the miRNA target provides resistance to extrinsic fluctuations. Together, through the integrated analysis of single-cell RNA and miRNA expression profiles, we demonstrated that miRNAs are important post-transcriptional regulators for reducing gene expression noise and conferring robustness to biological processes.

Characterization of the immune cell infiltration landscape in bladder cancer to aid immunotherapy

The tumor microenvironment (TME) is composed mainly of tumor cells, tumorinfiltrating immune cells, and matrix components. Recent clinical studies have indicated that tumor immune cell infiltration (ICI) is related to the sensitivity of immunotherapy and the prognosis of patients with bladder cancer (BC). Nevertheless, up to now, the landscape of immune infiltration in BC has not been clearly defined. Here we present two algorithms to reveal the landscape of ICI in 277 cases of BC. Two kinds of ICI patterns were established, and ICI scores were based on the analysis of the main components. In sub-types with high ICI scores, we found highly expressed immunecheckpoint and activated transforming growth factor b and WNT signal pathways. These might be the cause of poor prognosis. A low ICI score indicated a better prognosis. Our study showed that ICI scores in immunotherapy could be a valid biomarker for the prognosis of patients and a predictive indicator. The evaluation of ICI patterns of a larger cohort of samples would expand our cognition of TME, and the present study might guide the strategies of immunotherapy for patients with BC.

Modeling the effects of EMT-immune dynamics on carcinoma disease progression

During progression from carcinoma in situ to an invasive tumor, the immune system is engaged in complex sets of interactions with various tumor cells. Tumor cell plasticity alters disease trajectories via epithelial-to-mesenchymal transition (EMT). Several of the same pathways that regulate EMT are involved in tumor-immune interactions, yet little is known about the mechanisms and consequences of crosstalk between these regulatory processes. Here we introduce a multiscale evolutionary model to describe tumor-immune-EMT interactions and their impact on epithelial cancer progression from in situ to invasive disease. Through simulation of patient cohorts in silico, the model predicts that a controllable region maximizes invasion-free survival. This controllable region depends on properties of the mesenchymal tumor cell phenotype: its growth rate and its immune-evasiveness. In light of the model predictions, we analyze EMT-inflammation-associated data from The Cancer Genome Atlas, and find that association with EMT worsens invasion-free survival probabilities. This result supports the predictions of the model, and leads to the identification of genes that influence outcomes in bladder and uterine cancer, including FGF pathway members. These results suggest new means to delay disease progression, and demonstrate the importance of studying cancer-immune interactions in light of EMT.

Targeting tumor-derived NLRP3 reduces melanoma progression by limiting MDSCs expansion

The nucleotide-binding domain, leucine-rich containing family, pyrin domain-containing-3 (NLRP3) inflammasome, an intracellular complex that regulates maturation and release of interleukin (IL)-1β, is active in biopsies of metastatic melanoma. Here, we demonstrate that NLRP3 activation in melanoma cells drives tumor progression in mice. Subsequent to NLRP3 activation in melanoma cells, IL-1β induces melanoma-associated inflammation, resulting in immunosuppression. Oral administration of a single NLRP3 inhibitor (OLT1177) reduces melanoma growth and melanoma-associated myeloid-derived suppressor cell expansion. Inhibition of the NLRP3 signaling in combination with anti–PD-1 revealed augmented efficacy compared to monotherapy. These data propose that NLRP3 is a therapeutic target for human melanoma.

Interleukin-1β (IL-1β)–mediated inflammation suppresses antitumor immunity, leading to the generation of a tumor-permissive environment, tumor growth, and progression. Here, we demonstrate that nucleotide-binding domain, leucine-rich containing family, pyrin domain-containing-3 (NLRP3) inflammasome activation in melanoma is linked to IL-1β production, inflammation, and immunosuppression. Analysis of cancer genome datasets (TCGA and GTEx) revealed greater NLRP3 and IL-1β expression in cutaneous melanoma samples (n = 469) compared to normal skin (n = 324), with a highly significant correlation between NLRP3 and IL-1β (P < 0.0001). We show the formation of the NLRP3 inflammasome in biopsies of metastatic melanoma using fluorescent resonance energy transfer analysis for NLRP3 and apoptosis-associated speck-like protein containing a CARD. In vivo, tumor-associated NLRP3/IL-1 signaling induced expansion of myeloid-derived suppressor cells (MDSCs), leading to reduced natural killer and CD8⁺ T cell activity concomitant with an increased presence of regulatory T (Treg) cells in the primary tumors. Either genetic or pharmacological inhibition of tumor-derived NLRP3 by dapansutrile (OLT1177) was sufficient to reduce MDSCs expansion and to enhance antitumor immunity, resulting in reduced tumor growth. Additionally, we observed that the combination of NLRP3 inhibition and anti–PD-1 treatment significantly increased the antitumor efficacy of the monotherapy by limiting MDSC-mediated T cell suppression and tumor progression. These data show that NLRP3 activation in melanoma cells is a protumor mechanism, which induces MDSCs expansion and immune evasion. We conclude that inhibition of NLRP3 can augment the efficacy of anti–PD-1 therapy.

Recent Advances in Systemic Treatments for HER-2 Positive Advanced Gastric Cancer

Gastric cancer (GC) is the fifth most common cancer worldwide. Despite recent improvements in treatment quality and options, advanced gastric cancer remains one of the hardest to cure cancers, with a median overall survival (OS) of 10–12 months and a 5-year OS of approximately 5–20%. There is an unmet need for further efforts to palliate disease-related symptoms, improve quality of life, increase tumor response rate, and prolong progression free and overall survival while balancing the toxicities of therapy. The most common type of GC is adenocarcinoma, which demonstrates morphological, biological, and clinical heterogeneity. A plethora of genomic alterations and the activation of numerous molecular pathways including human epidermal growth receptor 2 (HER2), epidermal growth factor receptor (EGFR), fibroblast growth factor receptor-2 (FGFR2), mesenchymal epidermal transforming factor receptor (MET), and the phosphatidylinositol 3-kinase (PI3K)/mammalian target of rapamycin (mTOR) are responsible for the complex heterogeneity of GC. Efforts to validate the therapeutic effects of inhibiting some of these aberrantly expressed pathways have failed to lead to a clinically meaningful outcome apart from the overexpression/amplification of the HER2 gene, inhibition of which has had a significant impact on clinical practice. The only available biomarkers to guide the effective treatment of patients with advanced GC are HER2 overexpression, MSI/PD-L1 status, and FGFR alterations. Various anti-HER2 agents have been evaluated after the success of the ToGA trial, but none led to a significant enough clinical improvement to be considered a viable alternative for HER2-targeted therapy in advanced GC until the global Keynote-811 trial, which added pembrolizumab to trastuzumab in combination with chemotherapy. This combination demonstrated a survival advantage for the first time in the 11 years since ToGA. Trastuzumab deruxtecan (T-DXd) was also found to be effective in patients who had already received >2 previous lines of treatment. Despite these promising avenues, the optimal management of HER-2 positive GC still requires further development.

FSTL3 is a Prognostic Biomarker in Gastric Cancer and is Correlated with M2 Macrophage Infiltration

Purpose

Follistatin-related gene 3 (FSTL3), an established oncogene, can modulate target gene expression via members of the transforming growth factor β (TGF-β) superfamily. The present study was conducted to evaluate the expression of FSTL3 in gastric cancer (GC) and to determine its prognostic significance. We also evaluated the possible mechanisms involved in the oncogenic role of FSTL3 in gastric carcinogenesis and development.

Methods

We obtained data from the Human Protein Atlas, MethSurv, cBioPortal, UALCAN, TIMER, GEPIA, STRING, GeneMANIA, ONCOMINE, and MEXPRESS databases and examined it using R software. RNAi was used to establish stable FSTL3-knockdown (shFSTL3) and overexpression (OE) cell strains. Western blot; enzyme-linked immunosorbent (ELISA); and immunohistochemical (ICH), immunofluorescence, and phalloidin staining were used for examining protein expression. Cell invasion and migration were determined using transwell and scratch-wound assays. After tumor-associated macrophage (TAM) generation, co-culturing of cancer cells with TAMs was performed to confirm the relationship between FSTL3 and TAMs.

Results

In GC patients, FSTL3 mRNA and protein levels were upregulated. FSTL3 expression was significantly linked to cancer stage as well as to pathological tumor grade in GC. Moreover, a high expression of FSTL3 was associated with a dismal survival duration in patients with GC. Furthermore, functional enrichment analysis demonstrated that FSTL3 overexpression could activate epithelial-mesenchymal transition (EMT) by promoting F-actin expression and BMP/SMAD signaling. Finally, immunofluorescence staining confirmed that the overexpression of FSTL3 promoted the proliferation of M2 TAMs.

Conclusion

Taken together, our findings suggest that FSTL3 may be involved in GC progression via the promotion of BMP/SMAD signaling-mediated EMT and M2 macrophage activation.

Application of network pharmacology and molecular docking to elucidate the potential mechanism of Astragalus-Scorpion against prostate cancer

The present study aimed to investigate the molecular mechanism of the Astragalus-Scorpion drug pair in the treatment of prostate cancer (PCa). We employed network pharmacology and molecular docking technology to retrieving the active ingredients and corresponding targets of Astragalus-Scorpion by using TCMSP, BATMAN-TCM, TCMID and Swiss Target Prediction Databases. The targets related to PCa were retrieved through GeneCards. Cytoscape software was used to construct the 'active ingredient-target disease' network, and GO and KEGG enrichment analyses were performed on the common targets. Autodock software was used for molecular docking verification. In total, 26 active ingredients, 340 potential targets related to active ingredients and 122 common targets were screened from Astragalus-Scorpion drug pair. The core targets of the protein-protein interaction (PPI) network were JUN, AKT1, IL6, MAPK1 and RELA, whereas the core active ingredients were quercetin, kaempferol, formononetin, 7-o-methylisomucronulatol and calycosin. Nearly 762 GO entries and 154 pathways were obtained by using the pathway enrichment analysis. Molecular docking results revealed that quercetin and kaempferol bind to AKT1 and formononetin binds to RELA, all of which were found to be stable bounds.

A metabolic data-driven systems pharmacology strategy for decoding and validating the mechanism of Compound Kushen Injection against HCC

ETHNOPHARMACOLOGICAL RELEVANCE

Compound Kushen Injection (CKI) is a widely used TCM formula for treatment of carcinomatous pain and tumors of digestive system including hepatocellular carcinoma (HCC). However, the potential mechanisms of CKI for treatment of HCC have not been systematically and deeply studied.

AIM OF STUDY

A metabolic data-driven systems pharmacology approach was utilized to investigate the potential mechanisms of CKI for treatment of HCC.

MATERIALS AND METHODS

Based on phenotypic data generated by metabolomics and genotypic data of drug targets, a propagation model based on Dijkstra program was proposed to decode the effective network of key genotype-phenotype of CKI in treating HCC. The pivotal pathway was predicted by target propagation mode of our proposed model, and was validated in SMMC-7721 cells and diethylnitrosamine-induced rats.

RESULTS

Metabolomics results indicated that 12 differential metabolites, and 5 metabolic pathways might be involved in the anti-HCC effect of CKI. A total of 86 metabolic related genes that affected by CKI were obtained. The results calculated by propagation model showed that 6475 shortest distance chains might be involved in the anti-HCC effect of CKI. According to the results of propagation mode, EGFR was identified as the core target of CKI for the anti-HCC effect. Finally, EGFR and its related pathway EGFR-STAT3 signaling pathway were validated in vivo and in vitro.

CONCLUSION

The proposed method provides a methodological reference for explaining the underlying mechanism of TCM in treating HCC.

Uncovering the Mechanism of Curcuma in the Treatment of Ulcerative Colitis Based on Network Pharmacology, Molecular Docking Technology, and Experiment Verification

Aim

The incidence of ulcerative colitis (UC) is increasing steadily in developed countries, it is plaguing nearly 1 million people in the United States and European countries, while developing countries have had a rapidly increased incidence over the past decades. Curcuma is widely used in treating malaria, UC, Crohn's disease, and colon cancer, which lead to diarrhea and bloody stool. However, the systemic mechanism of curcuma in treating UC is still unclear. Our work was supposed to expound how does curcuma alleviate UC in a comprehensive and systematic way by network pharmacology, molecular docking, and experiment verification.

Methods

Traditional Chinese Medicine System Pharmacology Database (TCMSP), Shanghai Chemistry & Chemical Industry Data Platform (SGST), and papers published in Chinese Network Knowledge Infrastructure (CNKI) and PubMed were used to collect the chemical constituents of curcuma based on ADME (absorption, distribution, metabolism, and excretion). And effective targets were predicted by Swiss Target Prediction to establish the curcuma-related database. The disease targets of UC were screened by GeneCards and DrugBank databases, and Wayne (Venn) analysis was carried out with curcuma targets to determine the intersection targets. AutoDock software and TCMNPAS system were used to dock the core chemical components of curcuma with key UC targets. Protein interaction (PPI) network was constructed based on the STRING database and Cytoscape software. Gene function GO analysis and KEGG pathway enrichment analysis were carried out by using Metascape database. Finally, HE staining was performed to identify the inflammatory infiltration and expression difference in TNF-α and STAT3 before and after the treatment of curcuma which was verified by immunoblotting.

Results

Twelve active components containing 148 target genes were selected from curcuma. Potential therapeutic targets of curcuma in the treatment of UC were acquired from 54 overlapped targets from UC and curcuma. Molecular docking was used to filter the exact 24 core proteins interacting with compounds whose docking energy is lower than -5.5 and stronger than that of 5-aminosalicylic acid (5-ASA). GO and KEGG analyses showed that these targets were highly correlated with EGFR tyrosine kinase inhibitor resistance, PI3K-Akt signaling pathway, JAK-STAT signaling pathway, MAPK signaling pathway, and inflammatory bowel disease (IBD). Experiments verified curcuma relieved pathological manifestation and decreased the expression of TNF-α and STAT3.

Conclusion

Curcuma relieved the colon inflammation of ulcerative colitis via inactivating TNF pathway, inflammatory bowel disease pathway, and epithelial cell signaling in Helicobacter pylori infection pathway, probably by binding to STAT3 and TNF-α.

Network pharmacology to explore the anti-inflammatory mechanism of Xuebijing in the treatment of sepsis

BACKGROUND

Xuebijing (XBJ) is a traditional Chinese patent medicine for sepsis. However, the mechanism of action (MoA) of XBJ on sepsis remain unclear.

PURPOSE

Elucidate the MoA of XBJ for treating sepsis based on network pharmacology.

STUDY DESIGN

Integrate computational prediction, experimental validation and literature reported clinical results analysis based on network pharmacology.

METHODS

Computationally, representative compounds of XBJ were characterized by LC-MS/MS and the target profiles of each compound were identified using network-based method. Compounds from XBJ were compared with FDA approved drugs or experimental agents for sepsis by hierarchical clustering of target profile. Key biological functional modules of XBJ for treating sepsis were identified by enrichment analysis. Differential expressed analysis for each biological functional module was conducted from sepsis related public omics datasets. Herb-biological functional module network was constructed to reveal part of the traditional combinatorial rules of herbs for modules. Experimentally, the action of XBJ compounds on genes in biological functional module was validated by detecting quantitative transcriptional profiling and sepsis animal model. Clinically, combined with the clinical results recorded in literature, computational and experimental results were used to interpret the anti-inflammatory effect of XBJ for treating sepsis.

RESULTS

The target profiles of compound cover most of the compound's related biomolecules reported in literature, which can characterize the comprehensive function of compound. XBJ has similar pharmacological effect as FDA approved drugs or experimental agents. Four key biological functional modules including inflammation, immune, cell apoptosis and coagulation of XBJ for treating sepsis were identified. Cell line experimental results show that part of ingredients in XBJ regulate the expression of genes in inflammation modules as predicted. Animal experiments show that compounds from XBJ could reduce the expression level of IL-1β. Combined with literature reported clinical results, XBJ was found to exert anti-inflammatory effect through regulating the NF-kappa B signaling pathway.

CONCLUSION

The network pharmacology framework integrating computational prediction, experimental validation and literature reported clinical results analysis provides a novel approach for analyzing MoA of XBJ for treating sepsis.

A Network Pharmacology Study Based on the Mechanism of Citri Reticulatae Pericarpium-Pinelliae Rhizoma in the Treatment of Gastric Cancer

Objective

To explore the mechanism of action of Citri Reticulatae Pericarpium-Pinelliae Rhizoma (CRP-PR) in treating gastric cancer (GC) by using pharmacology network.

Methods

Based on oral bioavailability and drug-likeness, the main active components of CRP-PR were screened using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). DisGeNET Database was used to establish target databases for GC. Cytoscape software was used to construct a visual interactive network diagram of “Active Component-Target” and screen out the key targets. The STRING database was used to construct a protein interaction network. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on the key targets. Additionally, TCGA and HPA databases were used for key target verification.

Results

Thirty-seven active components of CRP-PR were screened. The results of network analysis showed that the main components include 8-octadecenoic acid, stigmasterol, ferulic acid, and naringenin of the CRP-PR herb pair. The key targets of the PPI network mainly involved GAPDH, MAPK3, JUN, STAT3, GSK3B, SIRT1, ERBB2, and SMAD2. GO enrichment analysis involves 540 biological processes, 118 cellular components, and 171 molecular functions. CRP-PR components were predicted to exert their therapeutic effect on the tumor signaling pathway, PI3K-Akt signaling pathway, MAPK signaling pathway, and estrogen signaling pathway. The validation of the key genes in the TCGA and HPA database showed that most of the key target verification results were consistent with this article.

Conclusion

CRP-PR can treat GC by mediating PI3K-Akt signal pathway, MAPK signal pathway, and other biological processes such as tumor cell proliferation, apoptosis, and vascular regeneration, which embodies the synergistic effect of multi-components, multi-targets, and multi-channels, and provides the theoretical basis and research ideas for further study of CRP-PR in treating GC. 8-octadecenoic acid, stigmasterol, ferulic acid, and naringenin may be the material basis for the treatment of GC.

Exploration of the potential mechanism of the Tao Hong Si Wu Decoction for the treatment of postpartum blood stasis based on network pharmacology and in vivo experimental verification

ETHNOPHARMACOLOGICAL RELEVANCE

Tao Hong Si Wu Decoction (THSWD) is a traditional prescription for blood management in traditional Chinese medicine, THSWD consists of Paeoniae Radix Alba (Paeonia lactiflora Pall.), Rehmanniae Radix Praeparata (Rehmannia glutinosa (Gaertn.) DC.), Angelicae Sinensis Radix (Angelica sinensis (Oliv.) Diels), Chuanxiong Rhizoma (Conioselinum anthriscoides 'Chuanxiong'), Persicae Seman (Prunus persica (L.) Batsch) and Carthami Flos (Carthamus tinctorius L.) at a weight ratio of 3: 4: 3: 2: 3: 2. THSWD is a commonly used prescription in the treatment of postpartum blood stasis disease.

AIM OF THE STUDY

To explore the potential mechanism of THSWD for the treatment of postpartum blood stasis using network pharmacology and experimental research.

MATERIALS AND METHODS

We extracted the active ingredients and targets in THSWD from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), and constructed a herbs-ingredients-targets-disease-network, devised a protein-protein interaction (PPI) network, performed GO enrichment analysis, and performed Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to discover potential treatment mechanisms. A postpartum blood stasis model was established in rats, and the results of network pharmacology were verified by in vivo experiments.

RESULTS

The results showed that 69 potential active ingredients and 207 THSWD target genes for the treatment of postpartum blood stasis disease were obtained after ADME filtering analysis. The targets were enriched in multiple gene functions and different signaling pathways. By exploring various different signaling pathways, it was found that mitochondrial regulation of oxidative stress plays a potentially important role in the treatment of postpartum blood stasis with THSWD. Compared to model group, THSWD alleviated mitochondrial damage, decreased levels of oxidative stress in the rat model of postpartum blood stasis and reduced apoptosis in uterine cells.

CONCLUSION

The therapeutic effect of THSWD on postpartum blood stasis is likely related to mitochondrial regulation of oxidative stress, which paves the way for further research investigating its mechanisms.

NOGEA: A Network-oriented Gene Entropy Approach for Dissecting Disease Comorbidity and Drug Repositioning

Rapid development of high-throughput technologies has permitted the identification of an increasing number of disease-associated genes (DAGs), which are important for understanding disease initiation and developing precision therapeutics. However, DAGs often contain large amounts of redundant or false positive information, leading to difficulties in quantifying and prioritizing potential relationships between these DAGs and human diseases. In this study, a network-oriented gene entropy approach (NOGEA) is proposed for accurately inferring master genes that contribute to specific diseases by quantitatively calculating their perturbation abilities on directed disease-specific gene networks. In addition, we confirmed that the master genes identified by NOGEA have a high reliability for predicting disease-specific initiation events and progression risk. Master genes may also be used to extract the underlying information of different diseases, thus revealing mechanisms of disease comorbidity. More importantly, approved therapeutic targets are topologically localized in a small neighborhood of master genes on the interactome network, which provides a new way for predicting drug-disease associations. Through this method, 11 old drugs were newly identified and predicted to be effective for treating pancreatic cancer and then validated by in vitro experiments. Collectively, the NOGEA was useful for identifying master genes that control disease initiation and co-occurrence, thus providing a valuable strategy for drug efficacy screening and repositioning. NOGEA codes are publicly available at https://github.com/guozihuaa/NOGEA.

Effects of maoto (ma-huang-tang) on host lipid mediator and transcriptome signature in influenza virus infection

Maoto, a traditional kampo medicine, has been clinically prescribed for influenza infection and is reported to relieve symptoms and tissue damage. In this study, we evaluated the effects of maoto as an herbal multi-compound medicine on host responses in a mouse model of influenza infection. On the fifth day of oral administration to mice intranasally infected with influenza virus [A/PR/8/34 (H1N1)], maoto significantly improved survival rate, decreased viral titer, and ameliorated the infection-induced phenotype as compared with control mice. Analysis of the lung and plasma transcriptome and lipid mediator metabolite profile showed that maoto altered the profile of lipid mediators derived from ω-6 and ω-3 fatty acids to restore a normal state, and significantly up-regulated the expression of macrophage- and T-cell-related genes. Collectively, these results suggest that maoto regulates the host’s inflammatory response by altering the lipid mediator profile and thereby ameliorating the symptoms of influenza.

Study of the active ingredients and mechanism of Sparganii rhizoma in gastric cancer based on HPLC-Q-TOF-MS/MS and network pharmacology

Sparganii rhizoma (SL) has potential therapeutic effects on gastric cancer (GC), but its main active ingredients and possible anticancer mechanism are still unclear. In this study, we used HPLC-Q-TOF–MS/MS to comprehensively analyse the chemical components of the aqueous extract of SL. On this basis, a network pharmacology method incorporating target prediction, gene function annotation, and molecular docking was performed to analyse the identified compounds, thereby determining the main active ingredients and hub genes of SL in the treatment of GC. Finally, the mRNA and protein expression levels of the hub genes of GC patients were further analysed by the Oncomine, GEPIA, and HPA databases. A total of 41 compounds were identified from the aqueous extract of SL. Through network analysis, we identified seven main active ingredients and ten hub genes: acacetin, sanleng acid, ferulic acid, methyl 3,6-dihydroxy-2-[(2-hydroxyphenyl) ethynyl]benzoate, caffeic acid, adenine nucleoside, azelaic acid and PIK3R1, PIK3CA, SRC, MAPK1, AKT1, HSP90AA1, HRAS, STAT3, FYN, and RHOA. The results indicated that SL might play a role in GC treatment by controlling the PI3K-Akt and other signalling pathways to regulate biological processes such as proliferation, apoptosis, migration, and angiogenesis in tumour cells. In conclusion, this study used HPLC-Q-TOF–MS/MS combined with a network pharmacology approach to provide an essential reference for identifying the chemical components of SL and its mechanism of action in the treatment of GC.

Effects of combined treatment with Indomethacin and Juglone on AOM/DSS induced colon carcinogenesis in Balb/c mice: Roles of inflammation and apoptosis

AIM

Indomethacin [IND] is reported to treat colon cancer. However, continuous exposure to IND causes gastric ulceration, an adverse side effect in humans. This study implies the therapeutic effect of IND and juglone [JUG] against colon carcinogenesis, without gastric ulceration - an adverse side effect of IND.

MATERIALS AND METHODS

Adult male Balb/C mice were divided into six groups randomly: control, AOM/DSS-induced, IND-treated, JUG-treated, IND + JUG-treated and drug-control. Levels of serum markers, haematoxylin & eosin staining to observe tissue architecture, toluidine blue staining to detect mast cells expression, Masson's trichrome and sirius-red staining were used to detect the collagen deposition. RT-PCR and western blot analysis were carried out to detect inflammation and apoptosis.

KEY FINDINGS

IND + JUG effectively decreased the levels of serum markers: CEA, AFP, LDH, AST and ALT. Although, IND restored colonic architecture by regulating the accumulation of mast cell and collagen content, it causes gastric ulceration. To address this adverse effect of IND, JUG was given along with IND and was shown to alleviate IND-induced gastric ulceration. AOM/DSS induced animals showed increased expression of inflammatory molecules - TNFα, NFκB and Cox-2, apoptosis regulator - Bcl-2 and decreased expression of pro-apoptotic molecules - Bad, Bax and caspase3; whereas, IND and JUG treated groups showed decreased inflammatory expression with increased expression of pro-apoptotic molecules.

SIGNIFICANCE

IND and JUG reduce the inflammatory activity and induce apoptotic cell death, while JUG effectively prevents IND induced gastric ulceration. These findings establish that a combination of IND + JUG may serve as a promising treatment regimen for colon cancer.

TCM network pharmacology: A new trend towards combining computational, experimental and clinical approaches

Traditional Chinese medicine (TCM) is a precious treasure of the Chinese nation and has unique advantages in the prevention and treatment of diseases. The holistic view of TCM coincides with the new generation of medical research paradigm characterized by network and system. TCM gave birth to a new method featuring holistic and systematic "network target", a core theory and method of network pharmacology. TCM is also an important research object of network pharmacology. TCM network pharmacology, which aims to understand the network-based biological basis of complex diseases, TCM syndromes and herb treatments, plays a critical role in the origin and development process of network pharmacology. This review introduces new progresses of TCM network pharmacology in recent years, including predicting herb targets, understanding biological foundation of diseases and syndromes, network regulation mechanisms of herbal formulae, and identifying disease and syndrome biomarkers based on biological network. These studies show a trend of combining computational, experimental and clinical approaches, which is a promising direction of TCM network pharmacology research in the future. Considering that TCM network pharmacology is still a young research field, it is necessary to further standardize the research process and evaluation indicators to promote its healthy development.