Pathway Database Research Articles

BIOM-62. CIRCULATING TUMOR CELLS IN MEDULLOBLASTOMA: A NOVEL PERIPHERAL BIOMARKER FOR CNS DISEASE

Abstract BACKGROUND Medulloblastoma is an embryonal tumor of the cerebellum and the most common pediatric malignancy of the central nervous system (CNS). Diagnosis, staging, and disease monitoring require invasive neurosurgical biopsy or MRI. No blood or cerebrospinal fluid (CSF) biomarkers exist. We demonstrate the presence of circulating tumor cell clusters (CTCCs) in patients with medulloblastoma and use a transcriptomic approach to describe their gene expression. Design/Method: We enrolled 34 total patients in a longitudinal study: 24 with medulloblastoma and 10 without malignancy. CTCCs are captured from unprocessed whole blood using a physics-based label-free approach to CTCC isolation. RNA sequencing was performed on 21 samples at time of diagnostic resection from 6 patients with medulloblastoma. Sequencing reads were assessed for quality control, trimmed, and aligned to the human genome. Differential gene expression (DGE) was performed against whole blood from healthy controls. Reactome pathway database of curated, peer-reviewed gene sets are used for gene set enrichment analysis (GSEA). RESULTS The presence of peripherally CTCC patients with medulloblastoma is not previously known and was detected in all patients with medulloblastoma whereas none were observed from patients with non-malignant hematological conditions. RNA-sequencing demonstrate separation on principal component analysis and gene-expression heatmap against whole blood healthy control. DGE shows upregulation of genes indicating stem-like state. GSEA show enrichment of medulloblastoma molecular pathways and gene sets defining integrin, extracellular matrix remodeling, and cell adhesion pathways. Transient elevation of peripheral CTCC followed by return to baseline levels was associated with durable disease response to therapy, whereas persistence or elevation of CTCC in CSF correlated with disease recurrence. CONCLUSION The universal and specific finding of peripherally captured CTCC arising from parent tumor in the CNS within our patient cohort suggests a utility as a tumor biomarker. Future sequencing is planned comparing CTCC transcriptome against parent tumor.

SYNERGISTIC POTENTIAL OF NIGELLA SATIVA L. AND TRIGONELLA FOENUM-GRAECUM: INTEGRATED NETWORK PHARMACOLOGY FOR DIABETIC WOUND HEALING

Objective: Diabetes Mellitus (DM) is a metabolic disorder marked by elevated blood glucose levels, and one of the issues linked to DM involves the development of Diabetic Wounds (DW). DW is susceptible to infection and develops into chronic wounds if not treated properly. This study aimed to investigate the network pharmacology of N. sativa L. and T. foenum-graecum, emphasizing on their potential as DW treatment candidates. Methods: Various databases were used in this study, including PubChem, Dr. Duke's phytochemistry and Ethnobotany, and KNApSAcK Family. Swiss Target Prediction and Way2Drug PASS Online were utilized for biological activity and protein target prediction. The DW pathway's protein-protein interactions were examined with the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Gene Cards, and STRING databases. STRING was used to predict the metabolite's action. The relationship between metabolites and target proteins was predicted using STITCH, and Cytoscape was used to visualize the network. Result: The results showed that ten active ingredients (five active ingredients in N. sativa L. and five active ingredients in T. foenoem-graecum) contributed to DW healing by affecting Tumor Necrosis Factor (TNF), Interleukin-1beta (IL1B), JUN, Caspase 3 (CASP3), Interleukin-6 (IL-6), Alpha Kinase Threonine-1 (AKT1), Vascular Endothelial Growth Factor-A (VEGFA), and Mitogen-Activated Protein Kinase 3 (MAPK3) genes. Furthermore, the ten active ingredients correlated with twenty-eight intracellular proteins, resulting in a mechanism involving eight DW signalling pathways. Conclusion: Based on network pharmacology analysis, we determine that N. sativa L. and T. foenoem-graecum combination can potentially treat DW.

Identification and Experimental Verification of PDK4 as a Potential Biomarker for Diagnosis and Treatment in Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a chronic autoimmune disorder marked by sustained joint inflammation, with an etiology that remains elusive. Achieving an early and precise diagnosis poses significant challenges. This study aims to elucidate the molecular pathways involved in RA pathogenesis by screening genes associated with its occurrence, analyzing the related molecular activities, and ultimately developing more effective molecular-level treatments for RA. Microarray expression profiling datasets GSE1919, GSE10500, GSE15573, GSE77298, GSE206848, and GSE236924 were sourced from the Gene Expression Omnibus (GEO) database. Samples were divided into experimental (RA) and control (normal) groups. Differentially expressed genes (DEGs) were identified using R software packages such as limma, glmnet, e1071 as well as randomForest. Cross-validation of DEGs was conducted using lasso regression and the random forest (RF) algorithm in R software to pinpoint intersecting genes that met the criteria. Among these, one gene was selected as the target for correlation analysis to identify DEGs related to the target gene. Enrichment analysis utilized the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databases and Gene Ontology (GO) data. Gene Set Enrichment Analysis (GSEA) was performed to compare the expression levels of the target gene (PDK4) across various biological pathways and functions in groups with high and low expression. The relationship between target gene expression levels and cellular immune function was assessed using the immune function score technique. The discrepancy in immune cell distribution between the control and experimental groups, as well as their correlation with target gene expression levels, was elucidated using CIBERSORT. The relationships between mRNA, lncRNA, and miRNA were depicted in the ceRNA regulation network. The expression levels of the target gene were validated using Western blot and qRT-PCR. In this study, six intersecting genes meeting the criteria were identified through cross-validation, and PDK4 was chosen as the target gene for further investigation. Functional analysis using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) revealed that PDK4-associated DEGs are primarily enriched in the PPAR signaling pathway, thereby regulating synovial cell proliferation and migration, ultimately influencing the onset and progression of rheumatoid arthritis (RA). Immune infiltration analysis suggested that eosinophil quantity may influence the progression of RA. Experimental results from PCR and Western blot confirmed the downregulation of PDK4 in the RA group. The significant downregulation of PDK4 expression in patients diagnosed with rheumatoid arthritis (RA) was confirmed. PDK4 may function as a novel regulatory factor in the onset and progression of RA, with potential applications as a diagnostic biomarker for the condition.

Silver nanoparticles regulate antibiotic resistance genes by shifting bacterial community and generating anti-silver genes in estuarine biofilms

Biofilms are thought to be sinks for antibiotic resistance genes (ARGs) and nanoparticles (NPs), however, studies on the interactions between NPs and ARGs in biofilms are limited. This study focused on the occurrence and regulatory mechanisms of ARGs during the formation of biofilms with continuous treatment of zero-valent silver nanoparticles (Ag0-NPs) and Ag ions at an environmental concentration of 10 µg/L in the Yangtze Estuary. The biofilms could enrich large amounts of Ag, with the highest concentration of 97.60 mg/kg and 111.08 mg/kg in the Ag0-NPs and Ag ions group at 28 days. Compared to the blank at 28 days, the abundance of ARGs was reduced 2.2 times in the Ag0-NPs group, whereas it increased 1.3 times in the Ag ion group. Ag0-NPs and Ag ions induced the production of silver resistance genes (SRGs) or selected bacteria with SRGs in biofilms. Based on machine learning, the bacterial community, SRGs, and Ag concentration were the top three dominant regulators of ARGs, with 27.74 %, 25.57 %, and 17.93 % contributions, respectively. Structural equation modeling revealed that Ag could indirectly regulate ARGs by regulating the bacterial community in the Ag0-NPs group. Metagenomic sequencing further showed that most of the decreased ARGs were hosted by Betaproteobacteria in the Ag0-NPs groups. According to the KEGG pathway database, the possible molecular mechanism of Ag0-NPs/Ag ions regulating ARGs may be through the two-component system (arlS/silS-arlR) and beta-lactam resistance system (mexI-mexV-oprM/oprZ/smeF). Overall, this study provides new insights into the effects of Ag0-NPs at environmental concentrations on the ecological environment, especially regarding the mechanism of regulating ARGs in estuarine biofilms.

Transparent sparse graph pathway network for analyzing the internal relationship of lung cancer.

While it is important to find the key biomarkers and improve the accuracy of disease models, it is equally important to understand their interaction relationships. In this study, a transparent sparse graph pathway network (TSGPN) is proposed based on the structure of graph neural networks. This network simulates the action of genes in vivo, adds to prior knowledge, and improves the model's accuracy. First, the graph connection was constructed according to protein-protein interaction networks and competing endogenous RNA (ceRNA) networks, from which some noise or unimportant connections were spontaneously removed based on the graph attention mechanism and hard concrete estimation. This realized the reconstruction of the ceRNA network representing the influence of other genes in the disease on mRNA. Next, the gene-based interpretation was transformed into a pathway-based interpretation based on the pathway database, and the hidden layer was added to realize the high-dimensional analysis of the pathway. Finally, the experimental results showed that the proposed TSGPN method is superior to other comparison methods in F1 score and AUC, and more importantly, it can effectively display the role of genes. Through data analysis applied to lung cancer prognosis, ten pathways related to LUSC prognosis were found, as well as the key biomarkers closely related to these pathways, such as HOXA10, hsa-mir-182, and LINC02544. The relationship between them was also reconstructed to better explain the internal mechanism of the disease.

Evaluating the Bias in Hospital Data: Automatic Preprocessing of Patient Pathways Algorithm Development and Validation Study.

The optimization of patient care pathways is crucial for hospital managers in the context of a scarcity of medical resources. Assuming unlimited capacities, the pathway of a patient would only be governed by pure medical logic to meet at best the patient's needs. However, logistical limitations (eg, resources such as inpatient beds) are often associated with delayed treatments and may ultimately affect patient pathways. This is especially true for unscheduled patients-when a patient in the emergency department needs to be admitted to another medical unit without disturbing the flow of planned hospitalizations. In this study, we proposed a new framework to automatically detect activities in patient pathways that may be unrelated to patients' needs but rather induced by logistical limitations. The scientific contribution lies in a method that transforms a database of historical pathways with bias into 2 databases: a labeled pathway database where each activity is labeled as relevant (related to a patient's needs) or irrelevant (induced by logistical limitations) and a corrected pathway database where each activity corresponds to the activity that would occur assuming unlimited resources. The labeling algorithm was assessed through medical expertise. In total, 2 case studies quantified the impact of our method of preprocessing health care data using process mining and discrete event simulation. Focusing on unscheduled patient pathways, we collected data covering 12 months of activity at the Groupe Hospitalier Bretagne Sud in France. Our algorithm had 87% accuracy and demonstrated its usefulness for preprocessing traces and obtaining a clean database. The 2 case studies showed the importance of our preprocessing step before any analysis. The process graphs of the processed data had, on average, 40% (SD 10%) fewer variants than the raw data. The simulation revealed that 30% of the medical units had >1 bed difference in capacity between the processed and raw data. Patient pathway data reflect the actual activity of hospitals that is governed by medical requirements and logistical limitations. Before using these data, these limitations should be identified and corrected. We anticipate that our approach can be generalized to obtain unbiased analyses of patient pathways for other hospitals.

Visual analysis of multi-omics data.

We present a tool for multi-omics data analysis that enables simultaneous visualization of up to four types of omics data on organism-scale metabolic network diagrams. The tool's interactive web-based metabolic charts depict the metabolic reactions, pathways, and metabolites of a single organism as described in a metabolic pathway database for that organism; the charts are constructed using automated graphical layout algorithms. The multi-omics visualization facility paints each individual omics dataset onto a different "visual channel" of the metabolic-network diagram. For example, a transcriptomics dataset might be displayed by coloring the reaction arrows within the metabolic chart, while a companion proteomics dataset is displayed as reaction arrow thicknesses, and a complementary metabolomics dataset is displayed as metabolite node colors. Once the network diagrams are painted with omics data, semantic zooming provides more details within the diagram as the user zooms in. Datasets containing multiple time points can be displayed in an animated fashion. The tool will also graph data values for individual reactions or metabolites designated by the user. The user can interactively adjust the mapping from data value ranges to the displayed colors and thicknesses to provide more informative diagrams.

Identification of Mitophagy-Related Genes in Sepsis

Background: Numerous studies have shown that mitochondrial damage induces inflammation and activates inflammatory cells, leading to sepsis, while sepsis, a systemic inflammatory response syndrome, also exacerbates mitochondrial damage and hyperactivation. Mitochondrial autophagy eliminates aged, abnormal or damaged mitochondria to reduce intracellular mitochondrial stress and the release of mitochondria-associated molecules, thereby reducing the inflammatory response and cellular damage caused by sepsis. In addition, mitochondrial autophagy may also influence the onset and progression of sepsis, but the exact mechanisms are unclear. Methods: In this study, we mined the available publicly available microarray data in the GEO database (Home - GEO - NCBI (nih.gov)) with the aim of identifying key genes associated with mitochondrial autophagy in sepsis. Results: We identified four mitophagy-related genes in sepsis, TOMM20, TOMM22, TOMM40, and MFN1. Conclusion: This study provides preliminary evidence for the treatment of sepsis and may provide a solid foundation for subsequent biological studies.

Metabolomic profiling analysis reveals the benefits of ginseng berry intake on mitochondrial function and glucose metabolism in the liver of obese mice.

Ginseng berry (GB) has previously been demonstrated to improve systemic insulin resistance and regulate hepatic glucose metabolism and steatosis in mice with diet-induced obesity (DIO). In this study, the role of GB in metabolism was assessed using metabolomics analysis on the total liver metabolites of DIO mice. Metabolomic profiling was performed using capillary electrophoresis time-of-flight mass spectrometry (CE-TOF/MS) of liver tissue from mice on a 12-wk normal chow diet (NC), high-fat diet (HFD), and HFD supplemented with 0.1% GB (HFD + GB). The detected metabolites, its pathways, and functions were analyzed through partial least square discriminant analysis (PLS-DA), the small molecular pathway database (SMPDB), and MetaboAnalyst 5.0. The liver metabolite profiles of NC, HFD, and GB-fed mice (HFD + GB) were highly compartmentalized. Metabolites involved in major liver functions, such as mitochondrial function, gluconeogenesis/glycolysis, fatty acid metabolism, and primary bile acid biosynthesis, showed differences after GB intake. The metabolites that showed significant correlations with fasting blood glucose (FBG), insulin, and homeostatic model assessment for insulin resistance (HOMA-IR) were highly associated with mitochondrial membrane function, energy homeostasis, and glucose metabolism. Ginseng berry intake increased the levels of metabolites involved in mitochondrial membrane function, decreased the levels of metabolites related to glucose metabolism, and was highly correlated with metabolic phenotypes. This study demonstrated that long-term intake of GB changed the metabolite of hepatosteatotic livers in DIO mice, normalizing global liver metabolites involved in mitochondrial function and glucose metabolism and indicating the potential mechanism of GB in ameliorating hyperglycemia in DIO mice.

In silico Identification and Characterization of Novel Drug Targets in Treponema denticola (strain ATCC 35405 / DSM 14222 / CIP 103919 / JCM 8153 / KCTC 15104): A Subtractive Genomics Approach

Treponema denticola is a gram-negative, highly drug resistant bacterium found in primary dentition infections and around teeth. It causes inflammation and tissue homeostasis, linked to periodontal diseases like earlyonset periodontitis, necrotizing ulcerative gingivitis, and acute pericoronitis. Research is needed to develop powerful, cost-effective, secure, and environmentally friendly antibiotics and techniques to control and manage infections caused by this bacterium. This study exploits the sophisticated in silico subtractive genomics approach to investigate potential therapeutic targets that are exclusive to the pathogen T. denticola. The full sequencing data of the Treponema denticola (strain ATCC 35405 / DSM 14222 / CIP 103919 / JCM 8153 / KCTC 15104) proteome has facilitated the computational analysis of its genome. Our analysis found 126 proteins of the pathogen that had no resemblance to the human genome. The Database of Essential Genes (DEG) identified 12 bacterial proteins which were indispensable to the pathogen, while the KEGG Automated Annotation Server (KAAS) found in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways database analysis of the non-homologous proteins revealed 11 T. denticola enzymes that can be targeted for drug development. Moreover, based on sub-cellular localization prediction, all selected proteins were cytoplasmic proteins and the no outer membrane proteins were present among the non-homologous proteins, and virulent protein predictions revealed no virulent proteins among the selected proteins. Therefore, eleven proteins were selected based on their involvement in unique metabolic pathways inside the pathogen that were not present in the host. The study further revealed the protein-protein interactions of these eleven essential proteins, and successfully anticipated, assessed, and verified the three-dimensional structures of these proteins. Undertaking a screening process to detect functional inhibitors for these newly revealed targets may lead to the discovery of novel therapeutic drugs that can effectively combat Treponema denticola. CBMJ 2024 July: vol. 13 no. 02 P: 251-264

Kidney whole-transcriptome profiling in primary antiphospholipid syndrome reveals complement, interferons and NETs-related gene expression.

Pathogenesis of antiphospholipid syndrome (APS) remains poorly elucidated. We aimed to evaluate for the first time, kidney transcriptome profiles in primary APS vs systemic lupus erythematosus (SLE) and control subjects. We performed RNA-sequencing on archival formalin-fixed paraffin-embedded kidney biopsies from APS (n = 4) SLE (n = 5), and control (n = 3) individuals, differential gene expression analysis (DGEA), and enrichment analysis using gene ontology (GO), and CORUM, KEGG and Reactome pathway databases. Two-dimensional projection showed a distinct gene profile in primary APS vs control kidneys samples, but similar to SLE. DGEA in APS vs controls returned 276 upregulated and 217 downregulated genes, while the comparison between APS and SLE identified 75 upregulated and 111 downregulated genes. In 276 upregulated genes, enriched GO terms were (innate) immune response, inflammatory response, leucocyte and lymphocyte activation, cytokine production and T cell activation. CORUM and KEGG revealed complement-related genes (C3, C4A, C4B). Expression levels showed logFC values of 2.25 (p= 1.58e-05) for C3, 2.17 (p= 2.69e-06) for C4A, and 2.135 (p= 3.7e-06) for C4B in APS vs controls, without differences between APS and SLE. Interferon (IFN) alpha/beta signalling was revealed by Reactome. Expression levels of nine IFN-regulated genes found upregulated in APS vs control kidneys (p-values ≤ 0.001 for all). Examining neutrophil-extracellular traps (NETs)-related gene expression, 13 of 15 upregulated NETs-related genes exhibited higher expression in APS vs controls but not vs SLE. Complement, interferon and NETs-related genes are highly expressed in APS kidney tissues, similarly to SLE, pointing out the role of innate immunity in APS nephropathy pathogenesis and potential treatment targets.

FatPlants: a comprehensive information system for lipid-related genes and metabolic pathways in plants.

FatPlants, an open-access, web-based database, consolidates data, annotations, analysis results, and visualizations of lipid-related genes, proteins, and metabolic pathways in plants. Serving as a minable resource, FatPlants offers a user-friendly interface for facilitating studies into the regulation of plant lipid metabolism and supporting breeding efforts aimed at increasing crop oil content. This web resource, developed using data derived from our own research, curated from public resources, and gleaned from academic literature, comprises information on known fatty-acid-related proteins, genes, and pathways in multiple plants, with an emphasis on Glycine max, Arabidopsis thaliana, and Camelina sativa. Furthermore, the platform includes machine-learning based methods and navigation tools designed to aid in characterizing metabolic pathways and protein interactions. Comprehensive gene and protein information cards, a Basic Local Alignment Search Tool search function, similar structure search capacities from AphaFold, and ChatGPT-based query for protein information are additional features. Database URL: https://www.fatplants.net/.

Nanoplastics exposure simplifies the network structure of sea cucumber (Apostichopus japonicus) gut microbiota and improves cluster randomness

Nanoplastics (NPs) are abundant in ocean environments, leading to environmental pollution and notable disruptions to the physiological functions of marine animals. To investigate the toxic effects of NPs on echinoderms, specifically sea cucumbers (Apostichopus japonicus), they were exposed to varying concentrations of NPs (0, 102, 104 particles/L) for 14 d. Subsequently, the 102 particles/L exposure group was purified for 35 d to elucidate the impact of both NPs exposure and purification on the intestinal bacteria structure and function. The results showed that the richness and variety of intestinal bacteria in sea cucumbers significantly reduced under NPs exposure, and then they could be restored to the pre-exposure treatment state after 35 d of purification. With the increase of NPs exposure concentration in the environment, the intestinal core bacteria gradually changed from Firmicutes and Proteobacteria to Pseudoalteromonas and Vibrio. The KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway database annotated that the gut microbiota of sea cucumbers was significantly downregulated in the glycosylation, carbohydratic and amino acid metabolic pathways (P < 0. 05), exogenous substance biodegradation and metabolism, DNA replication and repair pathways were significantly up-regulated (P < 0.05) under the exposure of NPs. In addition, nanoplastics exposure simplified the symbiotic network relationships of the gut bacteria, reduced the selective effect of host on the intestinal bacteria, and increased stochasticity. In conclusion, waterborne NPs can adversely affect the structure and function of sea cucumber intestinal bacteria, with these effects persisting for a duration. However, as the purification time lengthens, these adverse effects gradually diminish. This study aims to provide some theoretical basis for the biotoxic effects of NPs.

Biomedical literature mining: graph kernel-based learning for gene–gene interaction extraction

The supervised machine learning method is often used for biomedical relationship extraction. The disadvantage is that it requires much time and money to manually establish an annotated dataset. Based on distant supervision, the knowledge base is combined with the corpus, thus, the training corpus can be automatically annotated. As many biomedical databases provide knowledge bases for study with a limited number of annotated corpora, this method is practical in biomedicine. The clinical significance of each patient’s genetic makeup can be understood based on the healthcare provider’s genetic database. Unfortunately, the lack of previous biomedical relationship extraction studies focuses on gene–gene interaction. The main purpose of this study is to develop extraction methods for gene–gene interactions that can help explain the heritability of human complex diseases. This study referred to the information on gene–gene interactions in the KEGG PATHWAY database, the abstracts in PubMed were adopted to generate the training sample set, and the graph kernel method was adopted to extract gene–gene interactions. The best assessment result was an F1-score of 0.79. Our developed distant supervision method automatically finds sentences through the corpus without manual labeling for extracting gene–gene interactions, which can effectively reduce the time cost for manual annotation data; moreover, the relationship extraction method based on a graph kernel can be successfully applied to extract gene–gene interactions. In this way, the results of this study are expected to help achieve precision medicine.

Somatic Mutation Profile as a Predictor of Treatment Response and Survival in Unresectable Pancreatic Ductal Adenocarcinoma Treated with FOLFIRINOX and Gemcitabine Nab-Paclitaxel.

(1) Background: Pancreatic ductal adenocarcinoma (PDAC) has low survival rates despite treatment advancements. Aim: This study aims to show how molecular profiling could possibly guide personalized treatment strategies, which may help improve survival outcomes in patients with PDAC. (2) Materials and Methods: A retrospective analysis of 142 PDAC patients from a single academic center was conducted. Patients underwent chemotherapy and next-generation sequencing for molecular profiling. Key oncogenic pathways were identified using the Reactome pathway database. Survival analysis was performed using Kaplan-Meier curves and Cox Proportional Hazards Regression. (3) Results: Patients mainly received FOLFIRINOX (n = 62) or gemcitabine nab-paclitaxel (n = 62) as initial chemotherapy. The median OS was 13.6 months. Longer median OS was noted in patients with NOTCH (15 vs. 12.3 months, p = 0.007) and KIT pathway mutations (21.3 vs. 12.12 months, p = 0.04). Combinatorial pathway analysis indicated potential synergistic effects on survival. In the PFS, PI3K pathway (6.6 vs. 5.7 months, p = 0.03) and KIT pathway (10.3 vs. 6.2 months, p = 0.03) mutations correlated with improved PFS within the gemcitabine nab-paclitaxel subgroup. (4) Conclusions: Molecular profiling could play a role in PDAC for predicting outcomes and responses to therapies like FOLFIRINOX and gemcitabine nab-paclitaxel. Integrating genomic data into clinical decision-making can benefit PDAC treatment, though further validation is needed to fully utilize precision oncology in PDAC management.

High-throughput sequencing analysis of differential microRNA expression in the process of blocking the progression of chronic atrophic gastritis to gastric cancer by Xianglian Huazhuo formula.

To explore the mechanism of Xianglian Huazhuo formula (, XLHZ) blocking the development of chronic atrophic gastritis (CAG) to gastric cancer (GC) through bioinformatics analysis and in vitro. Pathological morphology of gastric mucosa of rats were observed. High-throughput sequencing was used to analyze the miRNA expression profile of gastric mucosa. The miRanda, miRDB and miRWalk databases were used to predict the differential target genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed for differential target genes. Real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to verify the differentially expressed miRNAs and target genes. Western blot, EdU, wound healing and flow cytometry were used to observe the effect of XLHZ on epithelial-mesenchymal transition (EMT) markers, proliferation, migration, apoptosis and cell cycle of CAG cells in vitro. A total of five differentially expressed miRNAs and four differential target genes were screened in this study. GO analysis showed that the target genes were enriched in regulation of neuron development, regulation of transcription factor activity and regulation of RNA polymerase. KEGG pathways database differences in gene enrichment of target genes in the Wnt signaling pathway, Phospholipase D signaling pathway and mitogen-activated protein kinase signaling pathway. qRT-PCR confirmed that miRNAs and its target genes were consistent with the screening results. In vitro, our study revealed that XLHZ could increase the expression of E-cadherin, decrease the expression of transforming growth factor β1, vimentin and β-catenin, inhibite the proliferation and migration of CAG cells, cause cell cycle arrest at G0/G1 and G2/M phase, induce the apoptosis of CAG cells, and prevent the progression of CAG to GC. This study provided a new idea for the mechanism of blocking the progression of CAG to GC by XLHZ, which may be related to the expression of miR-20a-3p, miR-320-3p, miR-34b-5p, miR-483-3p and miR-883-3p and their target genes transferrin receptor, nuclear receptor subfamily 4 member 2, delta like canonical Notch ligand 1 and a kinase anchor protein 12 in CAG. In the future, we will continue to investigate the linkage between the active ingredients of XLHZ and the relevant miRNAs and their target genes, so as to provide more sufficient experimental basis for clinically effective prevention of CAG to GC.

Interpreting and visualizing pathway analyses using embedding representations with PAVER.

Omics studies use large-scale high-throughput data to explain changes underlying different traits or conditions. However, omics analysis often results in long lists of pathways that are difficult to interpret. Therefore, it is of interest to describe a tool named PAVER (Pathway Analysis Visualization with Embedding Representations) for large scale genomic analysis. PAVER curates similar pathways into groups, identifies the pathway most representative of each group, and provides publication-ready intuitive visualizations. PAVER clusters pathways defined by their vector embedding representations and then identifies the term most cosine similar to its respective cluster's average embedding. PAVER can integrate multiple pathway analyses, highlight relevant biological insights, and work with any pathway database.

BPP: a platform for automatic biochemical pathway prediction.

A biochemical pathway consists of a series of interconnected biochemical reactions to accomplish specific life activities. The participating reactants and resultant products of a pathway, including gene fragments, proteins, and small molecules, coalesce to form a complex reaction network. Biochemical pathways play a critical role in the biochemical domain as they can reveal the flow of biochemical reactions in living organisms, making them essential for understanding life processes. Existing studies of biochemical pathway networks are mainly based on experimentation and pathway database analysis methods, which are plagued by substantial cost constraints. Inspired by the success of representation learning approaches in biomedicine, we develop the biochemical pathway prediction (BPP) platform, which is an automatic BPP platform to identify potential links or attributes within biochemical pathway networks. Our BPP platform incorporates a variety of representation learning models, including the latest hypergraph neural networks technology to model biochemical reactions in pathways. In particular, BPP contains the latest biochemical pathway-based datasets and enables the prediction of potential participants or products of biochemical reactions in biochemical pathways. Additionally, BPP is equipped with an SHAP explainer to explain the predicted results and to calculate the contributions of each participating element. We conduct extensive experiments on our collected biochemical pathway dataset to benchmark the effectiveness of all models available on BPP. Furthermore, our detailed case studies based on the chronological pattern of our dataset demonstrate the effectiveness of our platform. Our BPP web portal, source code and datasets are freely accessible at https://github.com/Glasgow-AI4BioMed/BPP.

Enteropathway: the metabolic pathway database for the human gut microbiota.

The human gut microbiota produces diverse, extensive metabolites that have the potential to affect host physiology. Despite significant efforts to identify metabolic pathways for producing these microbial metabolites, a comprehensive metabolic pathway database for the human gut microbiota is still lacking. Here, we present Enteropathway, a metabolic pathway database that integrates 3269 compounds, 3677 reactions, and 876 modules that were obtained from 1012 manually curated scientific literature. Notably, 698 modules of these modules are new entries and cannot be found in any other databases. The database is accessible from a web application (https://enteropathway.org) that offers a metabolic diagram for graphical visualization of metabolic pathways, a customization interface, and an enrichment analysis feature for highlighting enriched modules on the metabolic diagram. Overall, Enteropathway is a comprehensive reference database that can complement widely used databases, and a tool for visual and statistical analysis in human gut microbiota studies and was designed to help researchers pinpoint new insights into the complex interplay between microbiota and host metabolism.

CCPA: cloud-based, self-learning modules for consensus pathway analysis using GO, KEGG and Reactome.

This manuscript describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' (https://github.com/NIGMS/NIGMS-Sandbox). The module delivers learning materials on Cloud-based Consensus Pathway Analysis in an interactive format that uses appropriate cloud resources for data access and analyses. Pathway analysis is important because it allows us to gain insights into biological mechanisms underlying conditions. But the availability of many pathway analysis methods, the requirement of coding skills, and the focus of current tools on only a few species all make it very difficult for biomedical researchers to self-learn and perform pathway analysis efficiently. Furthermore, there is a lack of tools that allow researchers to compare analysis results obtained from different experiments and different analysis methods to find consensus results. To address these challenges, we have designed a cloud-based, self-learning module that provides consensus results among established, state-of-the-art pathway analysis techniques to provide students and researchers with necessary training and example materials. The training module consists of five Jupyter Notebooks that provide complete tutorials for the following tasks: (i) process expression data, (ii) perform differential analysis, visualize and compare the results obtained from four differential analysis methods (limma, t-test, edgeR, DESeq2), (iii) process three pathway databases (GO, KEGG and Reactome), (iv) perform pathway analysis using eight methods (ORA, CAMERA, KS test, Wilcoxon test, FGSEA, GSA, SAFE and PADOG) and (v) combine results of multiple analyses. We also provide examples, source code, explanations and instructional videos for trainees to complete each Jupyter Notebook. The module supports the analysis for many model (e.g. human, mouse, fruit fly, zebra fish) and non-model species. The module is publicly available at https://github.com/NIGMS/Consensus-Pathway-Analysis-in-the-Cloud. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.