Yiqi Tuomin Decoction (YTD), which originated from the theory of lung deficiency and cold in Chinese medicine, is a common Chinese herbal formula used against allergic rhinitis (AR). In our otolaryngology department, this prescription has been used to treat so many AR patients with lung-deficiency-related colds for nearly 30 years. However, the mechanism of its ingredient–target is still unclear. Based on our early experiments and clinical case studies, in this paper, we explore the mechanism of YTD systematically against AR using bioinformatic methods of network pharmacology and molecular docking.The Traditional Chinese Medicine Systems Pharmacology (TCMSP) database was used to screen the active ingredients and targets of YTD. The AR-related targets were retrieved from OMIM, GeneCards, TTD, DisGeNET, DrugBank databases, and PharmGKB. The Venn database was used to screen the potential core targets. After that, the STRING database was used to construct the protein–protein interaction (PPI) of the core targets and then visualize it by Cytoscape. The Gene Ontology (GO)-enriched processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the core targets were analyzed by the KOBAS-I database and Sangerbox. Molecular docking was used to assess interactions between potential targets and active ingredients.A total of 169 active ingredients and 238 targets of YTD were predicted. YTD shared 115 common targets with AR from the Venn database. The GO-enriched processes and KEGG pathways indicate that genes involved in inflammation and oxidative stress, accompanying the MAPK signaling pathway, Th17 cell differentiation, IL-17 signaling pathway, and Th1 and Th2 cell differentiation, may play a mediated effect in YTD. The docking results showed good binding ability between the active ingredients and the selected targets.Our study systematically indicated the underlying mechanism of YTD against AR from the perspective of bioinformatics. By studying the active ingredients of YTD, we obtained molecular mechanisms and established a reliable method and molecular theoretical basis for the sensible development of Chinese medicine in the treatment of AR. HighlightsYiqi Tuomin Decoction (YTD) by network pharmacology and molecular docking.Investigating the anti-allergic rhinitis (AR) pharmacological mechanism of Key anti-AR targets include JUN, STAT3, and IL6.Wogonin, 7-methoxy-2-methyl isoflavone, formononetin, and beta-sitosterol might be the core bioactive ingredients from YTD that can treat AR.1. As early as 2001, the World Health Organization (WHO) released a report on AR and its impact on asthma2. The epidemiological studies suggested that 20–30% of adults and ~40% of children were affected3. In industrialized areas, allergic sensitization and airway responsiveness to allergens are initially presented due to the increasing combustion of fossil fuels4. The symptoms of AR may be aggravated due to subsequent airway responsiveness to environmental allergens5. AR is a chronic inflammatory disease of the airways that is caused by an allergic response mediated by immunoglobulin E and Th2 lymphocytes in the nasal mucosa. AR itself is not a serious disease, but it is a substantial socioeconomic burden. The induced symptoms, such as headache, nasal congestion, sneezing, reduced operating efficiency, and frequent complications with allergic asthma, can worsen the illness year by year. This can eventually lead to perennial asthma, emphysema, and cor pulmonale6, which can have a significant negative impact on patients. In the process of treating AR, allergen immunotherapy (AIT) is an acknowledged and irreplaceable clinical choice for the treatment of AR8. However, currently, the authorized biomarkers of AIT that predicted success have not been presented. Some patients with AR still experience exacerbations associated with allergen exposure despite being under drug treatment. Therefore, the easier the new drugs and treatments available, the better for AR patients.Allergic rhinitis (AR) is a common condition10, TCM is known for possessing ‘multi-ingredient, multi-target and multi-pathway’ characteristics. TCM has a long history in treating AR. AR is classified as ‘Bi Qiu’ in TCM11, which first appeared in Huangdi Neijing. TCM adopts several approaches in the treatment of AR, such as oral or external applications of Chinese medicine and acupuncture11. Yiqi Tuomin Decoction (YTD), which originated from the theory of lung deficiency and cold of Chinese medicine, comprises 12 Chinese herbal ingredients and treats different patients with different ingredient ratios. Clinical cases have shown that YTD has therapeutic effects on AR by improving the deficiency of Lung Qi, consolidating the defensive Qi, reducing airway inflammation, and regulating mucus secretion. However, the underlying pharmacological mechanism is not fully clear.Traditional Chinese medicine (TCM) has existed in China for thousands of years as a traditional medicine. Different from orthodox medicine, aiming to examine the pathogenic mechanisms12 and is an emerging avenue for drug research and development for TCM herbs or herbal formulas13. Therefore, firstly, we screened the active ingredients and targets of YTD. Next, we identified the common targets of AR from databases. Finally, we understood the underlying mechanism and the key shared targets. Secondly, we constructed the core PPI network, categorized the PPI network to investigate the key pharmacological mechanisms of YTD against AR through cluster analysis, and investigated the signaling pathways of biological function by performing the enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Finally, the molecular docking method was taken to verify the interaction between the screened bioactive ingredients and the key common targets.Network pharmacology belongs to the category of systems biology and multidirectional pharmacologyIn our study, we provided information on predicting the bioactive ingredients, common targets, and signaling pathways of YTD against AR using network pharmacology and molecular docking methods. Figure https://tcmsp-e.com/tcmsp.php)14 were used to screen the compounds and the corresponding targets of Hedysarum Multijugum Maxim (HMM), Saposhnikoviae Radix (SR), Paeoniae Radix Alba (PRA), Radix Bupleuri (RB), Cinnamomi Ramulus (CR), Xanthii Fructus (XF), Magnoliae Flos (MaF), A. Dahurica (Fisch.) Benth. Et Hook (ADBEH), Angelicae Sinensis Radix (ASR), Mume Fructus (MF), and Licorice (Li) of YTD. In the TCMSP database, we could capture information related to herbal ingredients and drug-target by entering Chinese herbs15. Oral bioavailability (OB)16 and Drug-likeness (DL)17 were used as a method of evaluation to screen active molecules of Chinese herbs which were or not suitable for future research. Filter indicators of OB ≥30% and DL ≥0.18 were set to retrieve pharmacokinetic data from the TCMSP database. After that, the main ingredients and target proteins of YTD were obtained. Subsequently, The Uniprot (http://www.uniprot.org/) database was used to unite the genes of proteins, in which the reviewed and human ones were picked to filter by. Ultimately, the active ingredients of YTD and its target proteins were identified.Traditional Chinese Medicine Systems Pharmacology (TCMSP) databases (https://www.genecards.org/), the OMIM database (http://www.omim.org/), TTD (http://db.idrblab.net/ttd/), DisGeNET (http://www.disgenet.org/) database, PharmGKB (https://www.pharmgkb.org/) database and DrugBank database (https://www. drugbank.ca/) to collect disease-related targets. By combining and deleting duplicates, the screened targets were the objective targets of AR for the next research.The keyword ‘allergic rhinitis’, was entered in the GeneCards (18. The Venn database (http://bioinformatics.psb.ugent.be/webtools/Venn/) could be used to obtain the overlapped targets of active ingredients and diseases. The common targets of YTD and AR could be potential targets for YTD against AR. Then, we imported the related targets of AR into the STRING (http://stringdb.org/) database to construct the PPI network with the Multiple Proteins tool, in which we limited the Homo sapiens organism with the required score of highest confidence (0.900)19. Subsequently, the core targets network was visualized by using Cytoscape 3.9.0. Topographical values for the three parameters, which referred to betweenness centrality (BC), closeness centrality (CC), and degree centrality (DC), calculated by Cytoscape indicated the significance of relevant nodes in the network20. And then, the network parameters were systematically analyzed21. The corresponding target nodes with higher topographical values would be selected in the PPI network.Proteins often act in conjunction with other molecules22 for visualization. The network of ingredient–target represented the relationship between them.Active ingredients and matched targets of YTD were imported into Cytoscape 3.9.0http://kobas.cbi.pku.edu.cn/)23 was performed to analyze the enrichment of GO24. The enrichment of the KEGG pathway was analyzed by Sangerbox25.In the above respective databases, the target organism was set as H. sapiens, with false discovery rate (FDR) <0.05 and P<0.05 judged as cutoff values.The KOBAS-I database 27. The structures of picked active ingredients were downloaded from TCMSP. The procedure of hydrogenating, dehydrating, constructing the docking grid box, spilling the ligands from the receptors, and molecular docking and free binding energies calculation of putative targets and active ingredients were determined using AutoDockTools-1.5.7. The PyMOL-2.2.0 and Discovery Studio Visualizer-v4.5.0 were used for the visual analysis of the interaction among the active ingredients and predicted targets.Molecular docking is a theoretical simulation method that is used to investigate the interaction between receptors and ligands and to predict their binding modes and affinityhttp://links.lww.com/MS9/A135. As shown in Table 28. Therefore, we identified effective targets for bioactive ingredients through a ligand-based prediction strategy and constructed a compound–target network from a holistic view using Cytoscape, as shown in Figure http://links.lww.com/MS9/A136). Based on the topological analysis, we determined the degree value of the node to be the primary screening criterion. In the network, the average degree value of bioactive ingredients was 14.86. With the degree value setting as ≥29.72 (twice the median degree), 12 candidate ingredients were identified, which showed that the demonstrated synergistic played a role in different herbs and ingredients. Meaningfully, we discovered that MOL000098 (quercetin), MOL000422 (kaempferol), MOL000173 (wogonin), MOL000378 (7-O-methylisomucronulatol), MOL003896 (7-methoxy-2-methyl isoflavone), MOL000392 (formononetin), MOL004328 (naringenin), MOL000354 (isorhamnetin), MOL000358 (beta-sitosterol), and MOL000497  interacted, respectively, with 144, 58, 43, 41, 39, 35, 35, 32, 31, and 31 targets, which became the key active ingredients in YTD. Polygenic predisposition is one of the major characteristics of AR. Studies related to the interaction between genes and the environment are helpful in revealing the pathogenesis of AR. AR-related targets were searched from GeneCards, TTD, DisGeNet, OMIM, PharmGKB, and Drugbank databases, resulting in a total of 2136 targets. As shown in Figure In our study, there were 12 herbs in YTD. Based on the TCMSP database, a total of 169 active ingredients of YTD were identified after removing duplicates. These were listed in Supplementary 1, Supplemental Digital Content 1, To further investigate the core pharmacological underlying mechanisms of YTD against AR, a topological approach was employed to assess the core network.PPI network is recognized as a good way to investigate the effect of multiple proteins in gene-related diseases. Consequently, the PPI network of shared targets was constructed from the STRING database Fig. A. For beTo illuminate the biological functions of shared targets, we classified the central PPI network into two clusters using two Cytoscape plugins, Cytohubba and MCODE. We selected the top 10 genes ranked by MCC  using the Cytohubba plugin, and also screened the top 10 genes with interaction neighbors Fig. C. As shoGO enrichment analysis was performed on the 115 predicted targets to indicate the various mechanisms of YTD against AR, including biological processes (BP), molecular function (MF), and cellular ingredient (CC). The top 20 GO-enriched terms were determined , MOL000422 (kaempferol), MOL000173 (wogonin), MOL000378 (7-O-methylisomucronulatol), MOL003896 (7-methoxy-2-methyl isoflavone), MOL000392 (formononetin), MOL004328 (naringenin), MOL000354 (isorhamnetin), MOL000358 (beta-sitosterol), and MOL000497 . Then molecular docking was implemented gradually with JUN (PDB ID:1JNM), STAT3 (PDB ID:5AX3), and IL6 (PDB ID:7NXZ), which were the top three target proteins. The binding energy of ingredients and core proteins is shown in Figure Figure Figure Figure Figure Figure The stability of conformation between receptor and ligand is proportional to the binding energy and the affinity. As Figure The above results indicate that, among the active ingredients, the proton and oxygen of the hydroxyl groups, as well as the ketone oxygen, tended to form hydrogen bonds with the active site residues of proteins.We found in our study that beta-sitosterol revealed better binding activity with JUN, 7-methoxy-2-methyl isoflavone revealed better binding activity with STAT3, and wogonin, 7-methoxy-2-methyl isoflavone, and formononetin revealed better binding activity with IL6. Therefore, we identified these herbal active ingredients as drug-candidate molecules. Molecular docking technology provided a strategy for evaluating the binding mode between the herbal ingredients and related targets. However, the results indicate that reliable experiments still need to be performed to validate the herbal active ingredients.29. It is characterized by symptoms of sneezing, itching, rhinorrhea, nasal congestion, and nasal hypersensitivity30. It is driven by type 2 cells of mucosal inflammation and caused by IgE-mediated reactions31. The complexity of the pathogenesis of AR and the multiple pathways involved provide many targets for drug treatment. However, to date, no single drug has been reported to be totally effective. For patients, AR is not only a physical trauma but also an economic burden. It disturbs sleep quality, reduces working passion, weakens cognitive functions, and affects the quality of life and irritability.AR is recognized as an abnormal inflammation of the membrane lining the nose15. In terms of pathophysiology, we understood the pathogenesis of AR and provided an approach to treat AR by combining pharmacology.TCM herbal formulas can help improve the treatment of chronic diseases through the ingredient–target-effect mechanism and the concept of syndrome differentiation and treatment. YTD is a clinical treatment method for allergic reactions and inflammation induced by hypersensitivity. However, the specific pathophysiological and pharmacologic mechanisms of YTD against AR have not yet been fully understood. In our study, to prove the underlying mechanisms of the curative effects of YTD against AR, we focused on two aspects: pharmacology and pathophysiology. In terms of pharmacology, we screened the active ingredients and identified core targets that could play crucial regulatory roles in treatment and then identified key enriched terms and biological pathways32, combines traditional pharmacology with bioinformatics, cheminformatics, and network biology. The network pharmacology method analyzes complex network relationships concerning the multi-ingredient, multi-target, and multi-channel aspects, similar to the theory of TCM based on syndrome differentiation33.A network pharmacology approach, which is related to the analysis of network models and systems biologyThe purpose of the study was to explore the underlying mechanism of YTD against AR using the methods of network pharmacology and molecular docking. From the TCMSP database, 169 chemical ingredients were screened. Among these chemical ingredients, quercetin, kaempferol, and wogonin were found to act on more targets compared to other active ingredients in the network.We collected AR-related targets from OMIM, GeneCards, Drugbank, PharmGKB, TTD, and DisGeNet databases. From the above databases, YTD and AR have been found to have about 115 common targets. These targets were considered potential targets of YTD against AR. To predict the core targets of YTD against AR, a PPI network was constructed to express protein–protein interactions. The results indicated that JUN, STAT3, IL6, AKT1, IL1B, RELA, HSP90AA1, TP53, VEGFA, EGFR, and CXCL8, particularly JUN, STAT3, and IL6, may be the core targets.34.GO and KEGG pathway enrichment analyses were conducted to predict the mechanism of YTD against AR. GO terms analysis results showed that the target genes were chiefly enriched in biological functions such as response to hormone (steroid hormone), inflammatory response, blood circulation, oxygen levels, nutrient levels, G protein-coupled amine receptor activity, nitric-oxide synthase regulator activity, transcription factor binding, and Hsp90 protein binding. KEGG pathway analysis results showed that YTD involved multiple signaling pathways in treating AR, with the key signaling pathways referring to the MAPK signaling pathway, IL-17 signaling pathway, Th17 cell differentiation, and Th1 and Th2 cell differentiation. The MAPK signaling pathway is one of the primary regulators of inflammatory response through transcriptional regulation of cytokines production. IL-17A plays a key role in the immune expression of IL-17, and sometimes Th17 cells could induce the production of IL-17. STAT3 (activator of transcription 3) and IL6 activate the IL-17-induced gene expression in the analysis of signal transduction35. The inflammatory pathway involved in AR is an IgE-mediated inflammatory response36. Interactions between allergens and nasal mucosa result in allergen-specific IgE binding to eosinophils, basophils, and mast cells. Meanwhile, the IgE-mediated mast cell response enhances the recruitment of T helper cells (Th1 and Th2), eosinophils, and basophils37. Th1 and Th2 cells could affect the production of IgE. Th1 cells secrete IFN‑γ and IL‑12, which inhibit the production of IgE39. Th2 cells secrete IL-13, IL‑4, and IL‑5, which provoke IgE production and induce an IgG isotype switch to IgG140. Hsieh et al.41 indicated that Th2 cells played a modulated role in regulating downstream cell differentiation and proliferation, switching to secreting IgE, IgG1, and eosinophils. Normally, to maintain a normal immune status, Th1 and Th2 cells were relatively balanced in number.Some studies indicate that T helper (Th) cells play a vital role in the pathological process of AR42, and decreasing IgE antibody releasing by B cells43. Kaempferol is a flavonoid that has shown effective inhibitory factors on inflammation in the airways. It can inhibit the production and release of inflammatory cytokines and chemokines, as well as the stimulation of mast cells44. Lee et al.45 investigated the effects of kaempferol and quercetin on TNF-α and IL4 levels and found that they decreased. However, in sensitized human mast cells, kaempferol showed a better suppressing rate than quercetin. Wogonin is a type of polyphenol commonly found in plants that is used in the treatment of allergies and inflammation due to its toxin-removing and thermal-cleansing properties46. It plays an inhibitory effect on IgE production due to its antioxidative effect. The anti-inflammatory action of wogonin played a crucial role in anti-edema activity47. 7-O-methylisomucronulatol, 7-methoxy-2-methyl isoflavone, isorhamnetin, and formononetin are commonly found in the Fabaceae family48. They reduced the production of inflammatory cytokines by inhibiting the MAPK and NF-κB signaling pathways49. Naringenin, a flavanone flavonoid, has been reported to ameliorate inflammatory response by suppressing MAPK activation50. From molecular docking results, beta-sitosterol showed better binding activity with JUN, 7-methoxy-2-methyl isoflavone showed better binding activity with STAT3, and wogonin, 7-methoxy-2-methyl isoflavone, and formononetin showed better binding activity with IL6. This indicates that they may be potential pharmaceutical active ingredients of YTD against AR.The enriched results of GO and KEGG pathways indicate that YTD could play an anti-allergic effect by inhibiting inflammatory and allergy-related pathways. The results of network pharmacology show that the top 10 ingredients, which are MOL000098 (quercetin), MOL000422 (kaempferol), MOL000173 (wogonin), MOL000378 (7-O-methylisomucronulatol), MOL003896 (7-methoxy-2-methyl isoflavone), MOL000392 (formononetin), MOL004328 (naringenin), MOL000354 (isorhamnetin), MOL000358 (beta-sitosterol), and MOL000497  may be the core bioactive chemical ingredients in our studies. Quercetin which is a naturally occurring polyphenol flavonoid, is known for restraining histamine production, being anti-allergic and anti-inflammatory, regulating the Th1/Th2 stabilityBased on network pharmacology and the theory of syndrome differentiation and treatment, our study illustrated the features of YTD that are responsible for drug synergy and predicted the possible pharmacological mechanisms of YTD against AR.In our study, we implemented the methods of network pharmacology and molecular docking technology to conduct a preliminary exploration of the active ingredients and potential targets of YTD against AR. Our results showed wogonin, 7-methoxy-2-methyl isoflavone, formononetin, and beta-sitosterol might be the core bioactive ingredients from YTD in the treatment of AR. Additionally, the targets JUN, STAT3, and IL6 might be potential therapeutic target proteins of YTD against AR. The results also suggested that the mechanism of YTD against AR may involve the bioactive ingredients acting on JUN, STATS, IL6, and other targets, while regulating the MAPK signaling pathway, IL-17 signaling pathway, Th17 cell differentiation, and Th1 and Th2 cell differentiation, thus exerting immunomodulatory and anti-inflammatory effects.Our results showed that it not only provided new insights for a more comprehensive investigation of the chemical substance basis and pharmacology of YTD but also demonstrated a feasible method for potential drug discovery in herbal medicine.None declared.None declared.This study was financially supported by the Key Project of the Jiangsu Provincial Health Commission (ZD2021016) and the Construction program of the Jiangsu Provincial Administration of Traditional Chinese Medicine .W.Z.: conceptualization, methodology, formal analysis, data curation, and writing – original draft; Q.Z.: data curation and review and editing; X.C.: funding acquisition and review and editing; J.Z.: validation; J.S. and L.C.: funding acquisition and project administration.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.Name of the registry: not applicable.Unique identifying number or registration ID: not applicable.Hyperlink to your specific registration (must be publicly accessible and will be checked): not applicable.Weixin Zhang.Not commissioned, externally peer-reviewed.


The investigation of sets of genes using biological pathways is a common task for researchers and is supported by a wide variety of software tools. This type of analysis generates hypotheses about the biological processes that are active or modulated in a specific experimental context.The Network Data Exchange Integrated Query (NDEx IQuery) is a new tool for network and pathway-based gene set interpretation that complements or extends existing resources. It combines novel sources of pathways, integration with Cytoscape, and the ability to store and share analysis results. The NDEx IQuery web application performs multiple gene set analyses based on diverse pathways and networks stored in NDEx. These include curated pathways from WikiPathways and SIGNOR, published pathway figures from the last 27 years, machine-assembled networks using the INDRA system, and the new NCI-PID v2.0, an updated version of the popular NCI Pathway Interaction Database. NDEx IQuery’s integration with MSigDB and cBioPortal now provides pathway analysis in the context of these two resources.https://www.ndexbio.org/iquery and is implemented in Javascript and Java.NDEx IQuery is available at  Investigating sets of proteins and genes of interest using biological pathways and networks is a common task for researchers. Networks describe relationships between genes, proteins, small molecules, and other entities and express the mechanisms associated with a biological process or the function of a cellular component. Pathway resources such as KEGG , ReactomThis rich landscape of tools serves the biomedical research community well, but unmet needs remain. First, many sources of molecular interaction data are not aggregated and cannot be queried or have limited query interfaces. Second, where network content is aggregated, it is integrated into a single database with a common schema. While this benefits consistency, it nevertheless loses the data's original structure and can obscure qualitative differences between sources. This normalization process also increases the effort to add and update content, slowing the pace at which databases can grow and making community contributions more complex. Third, the diversity of sources poses challenges for the reuse of query results in subsequent analyses, with each source exporting in a different format or accessed via a different API. Last but not least, sharing and publishing the results of network analysis in a computable and editable format is difficult, and most networks are published and presented only as static images in figures. Here, we present Network Data Exchange Integrated Query (NDEx IQuery), a new tool for network and pathway-based gene set interpretation. NDEx IQuery addresses the unmet needs described above, providing functionality that complements or extends existing resources. It combines novel sources of pathways/networks, and its integration with NDEx  provideshttps://www.ndexbio.org/iquery accessible through any major web browser. The NDEx IQuery service is implemented in Java; source code and documentation are available on GitHub at https://github.com/ndexbio/iquery. To report a bug, please use the contact form at https://home.ndexbio.org/report-a-bug/.The NDEx IQuery web user interface is implemented in Javascript by using the React framework. The NDEx IQuery web application is available at https://home.ndexbio.org/data-model.Cytoscape Exchange (CX) is a JSON-based network exchange format, a flexible structure for the transmission of networks. It is designed for flexibility, modularity, and extensibility and as a message payload in common REpresentational State Transfer (REST) protocols. It is not intended as an in-memory data model for use in applications. The flexibility of CX enables straightforward strategies for lossless encoding of potentially any network. At the most basic level, this means that CX imposes very few restrictions: networks can be cyclic or acyclic, and edges are implicitly directed, but networks can use custom annotation schemes to override this. CX does not make any commitment to a single ‘correct’ model of biology or graphic markup scheme. CX is designed to facilitate streaming, potentially reducing the memory footprint burden on applications processing large CX networks. The CX format documentation is available at P-value, and overlap.NDEx IQuery calculates measures of alignment of a query gene set against a large collection of networks and then returns ranked lists of results. To create this ranking, NDEx IQuery implements three different metrics, each highlighting a different aspect of alignment: similarity, This score characterizes the similarity between the query set and the genes in the network while considering that some genes are much more universal than others and will appear in many more networks. The cosine similarity calculation uses values derived from each gene's term frequency-inverse document frequency (TF-IDF) in the query set and the network. Rare genes that are shared between the query set and the network will contribute more to the similarity score than common genes, resulting in a higher similarity score. When sorting by similarity, networks with high similarity are at the top of the list, and networks with low similarity are at the bottom of the list.This score is the probability that the query set and the network overlap, calculated using the complementary cumulative distribution function of a hypergeometric distribution, where:N) is equal to the number of genes in the database ;The population size (K) is equal to the number of genes in the network;The number of success states in the population (n) is equal to the size of the network; andThe sample size (k) is equal to the number of genes that are in both the query set and the network.The number of observed successes . When sorting by P-value, networks with a low P-value are at the top of the list, and networks with a high P-value are at the bottom of the list.The P-value and then by overlap will result in a list where networks are sorted by overlap, and networks that are tied for overlap are sorted by P-value.When sorting by overlap, networks with a high number of overlapping genes are at the top of the list, and networks with a low number of overlapping genes are at the bottom of the list. Sorting is stable, so sorting by https://www.ndexbio.org/). It draws on curated pathway content from WikiPathways (https://www.wikipathways.org/) and the SIGNOR database (https://signor.uniroma2.it/), as well as machine-assembled networks from the INDRA system (https://www.indra.bio/) and published pathway diagrams from the Pathway Figures OCR (https://pfocr.wikipathways.org/) open science project. NDEx IQuery is also integrated with the popular Molecular Signatures Database (MSigDB) (https://gsea-msigdb.org) and cBioPortal for Cancer Genomics .NDEx IQuery is part of the Network Data Exchange (NDEx) infrastructure (https://ndexbio.org/iquery) may become very slow, although we are working to improve response time. Queries made from other websites (e.g. www.cytoscape.org) via an HTTP GET request are limited to roughly 100 gene symbols because larger queries would exceed the maximum size of a URL.NDEx IQuery v1.4 supports only human gene queries. While we recommend using HGNC-approved gene symbols, synonyms, and aliases are also acceptable and will be normalized to approved symbols. Queries with over 400 gene symbols made on the NDEx IQuery website  provides a search box to input query genes, augmented with buttons to clear the query, view it in full, or select one of the available example gene sets. On the right-hand side, the black ‘question mark’ icon lets users access a short tutorial on the interface and its modules.Analysis selection element. The analysis tabsbelow the query input element (green outline) select between the four currently supported analyses described in the analysis section below.Results element. The results panel on the bottom left (blue outline) displays the query results of each analysis along with details about the matched query genes.At the top of the panel, the ‘Sort by’ radio buttons let users choose among three different ordering methods:Similarity: the cosine similarity of the query genes and the network genes.P-value: the hypergeometric test adjusted for false discovery.Overlap: the number of genes that are in both the query set and the network.Additional details about the three methods are available by clicking the blue info icon next to the ‘Sort by’ controls. Each query result displays the number of matched genes and the number of genes in the network. When selected, the result expands to show all the query genes with matched genes emphasized in red. If the original gene set includes invalid or unofficial gene names, NDEx IQuery will normalize those names and provide details about the normalization performed.Toolbar element. The toolbar above the displayed network shows the name of the selected query result and provides common controls to perform several actions. The network’s name can be clicked in the toolbar to display a window showing its description, reference, and other attributes. This network information window also includes tabs to browse the network’s nodes and edges in tabular form. The same tabs are also displayed when individual nodes and edges are selected in the graphical rendering to explore their annotations. Annotations are specific to the various types of networks and can include information such as publications supporting an interaction. Other toolbar controls let users change the network's layout, toggle the highlighting of query genes, and display the network’s legend. Finally, two additional dedicated toolbar buttons integrate NDEx IQuery with Cytoscape and NDEx.Integration with Cytoscape. NDEx IQuery is tightly integrated with Cytoscape .Pathways  is an opSIGNOR is a repository of manually annotated experimental evidence about causal interactions between proteins and other entities of biological relevance. SIGNOR includes signaling, metabolic, disease, and cancer pathways as well as COVID-19 network hallmarks, providing a total of 90 pathway models.NCI-PID v2.0 is an updated version of the Pathway Interaction Database (PID) . The PIDAll protein node names are normalized to official gene symbols, but each pathway preserves the semantics and curation approaches intended by the authors. For example, the WikiPathways models retain their diagram format and heterogeneous visual styling features as expected from a community-curated resource, while the SIGNOR pathways keep their causal evidence edge annotations.Pathway figures. Drawing on the Pathway Figure OCR open science project  lists of networks as query results and (ii) networks in CX format . The integration of NDEx IQuery into a web application is simple. It can be embedded in the application via the ‘IFRAME’ HTML tag and queried via an HTTP “GET’ transaction . Alterna cBioPortal provides a web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The cBioPortal query interface, combined with customized data storage, enables researchers to interactively explore genetic alterations across samples, genes, and pathways. A customized, cancer-focused version of IQuery was embedded in the cBioPortal interface, enhanced to enable users to view their genes of interest annotated with genetic alteration frequency data from selected datasets . The cusP-value score if it contains more overlapping genes or if the proteins are clustered in a small subnetwork.Given the diversity of the pathway networks in NDEx IQuery, the values of the scores for a given pathway or its rank in the results should be seen as a general advice, a means of directing the user's attention to potentially relevant mechanisms. Pathways relevant to a given mechanism from different sources reflect alternative choices of genes and may significantly differ in size. This can provide complementary information about the relationships of the genes and proteins within the pathways but can also affect the ranking of those pathways. For example, a gene set related to Apoptosis finds pathways of various sizes from SIGNOR, NCI-PID 2.0, and WikiPathways. A larger pathway may be more informative than a smaller pathway with a better With NCI-PID v2.0, we took the first step toward a sophisticated, machine-driven approach to refresh an outdated pathway resource, augmenting the original PID pathways with the addition of up-to-date relationships. However, the proteins, chemicals, and complexes selected for inclusion in each pathway must also be updated to reflect the new research findings generated in the last 10 years. In the next step, we will generate a new pipeline that combines both the INDRA and Pathway Figure OCR technologies to expand the pathways with additional entities relevant to the mechanisms and current members. In this way, the NCI-PID will continue to grow and be maintained as a unique, dynamic reference pathway resource.The next steps with NDEx IQuery will focus on (i) expansion via community-sourced pathway and interaction data, (ii) new collaborations in which NDEx IQuery is embedded in applications, (iii) enabling organizations to operate customized IQuery servers focused on their own data, and (iv) enabling users to tailor their IQuery experience.NDEx IQuery provides collaborators with a simple way to distribute their experimentally derived content and expose it to the community for immediate validation and reuse. Projects that output large datasets, such as BioPlex, HIPPIE , or ProtWe will build on the methods developed in our collaboration with cBioPortal to streamline the creation of customized NDEx IQuery instances with alternative sources of pathway data. For example, the cBioPortal uses a selected set of pathways sourced from NCI-PID, WikiPathways, SIGNOR, and NetPath but does not provide access to PFOCR, INDRA GO, and interactome networks.We will foster new collaborations to enable other projects to integrate NDEx IQuery into their web applications. The methods developed in these collaborations will also enable organizations to deploy private instances of NDEx IQuery and NDEx behind firewalls for use with protected or proprietary pathway data.Finally, planned extensions of NDEx IQuery will enable users to (i) select among the available public pathway sources to be used for their queries and (ii) include additional sets of pathways/networks stored in their NDEx accounts.To conclude, NDEx IQuery is a flexible framework for pathway and interaction network-based analysis of gene sets; it encourages collaborations within the research community while providing a channel for researchers to make their network models immediately available and usable by other scientists.


Bergenia stracheyi, Bergenia ciliata, Bergenia pacumbis, and Bergenia purpurascens, which belong to the family Saxifragaceae, to treat HCC using an integrated network pharmacology and molecular docking approach. Four active phytochemicals were selected based on oral bioavailability (OB) and drug likeness (DL) parameters. The criteria of phytochemical selection were set to OB > 30% and DL > 0.18. Similarly, the gene targets related to Bergenia spp. and the genes related to HCC were retrieved from different databases. The integration of these genes revealed 98 most common overlapping genes, which were mainly interrelated with HCC pathogenesis. Ultimately, the 98 Bergenia-HCC associated genes were used for protein–protein interaction (PPI), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and Gene Ontology (GO) enrichment analyses. Finally, the topological analysis revealed the top ten hub genes with maximum degree rank. From the top ten genes, STAT3, MAPK3, and SRC were selected due to their involvement in GO annotation and KEGG pathway. To confirm the network pharmacology results, molecular docking analysis was performed to target STAT3, MAPK3, and SRC receptor proteins. The phytochemical (+)-catechin 3-gallate exhibited a maximum binding score and strong residue interactions with the active amino acids of MAPK3-binding pockets , SRC , and STAT3  as receptor proteins. (+)-Catechin 3-gallate and β-sitosterol induced a significant reduction in cell viability in HepG2 after 24 h of treatment in a dose-dependent manner. The results of this study explore the potential of (+)-catechin 3-gallate and β-sitosterol, which can be used in the future as potential drug candidates to suppress HCC.Hepatocellular carcinoma (HCC) is the fifth most common and fatal cancer reported, representing 72.5% of malignancies around the world. The majority of HCC incidents have been associated with infections caused by hepatitis B and C viruses. Many first- and second-line conventional drugs, e.g., sorafenib, cabozantinib, or ramucirumab, have been used for the management of HCC. Despite different combinational therapies, there are still no defined biomarkers for an early stage diagnosis of HCC. The current study evaluated the potential of  Hepatocellular carcinoma (HCC) or liver cancer is the second death-related cancer worldwide with late-stage diagnosis due to its severely fatal tumor . ChronicDifferent pathways including Wnt/ß-catenin pathway, phosphatidylinositol-3-kinase, MET receptor tyrosine kinase pathway, and Hedgehog (Hh) signaling pathway play a leading role in the proliferation of the tumor, which ultimately leads to HCC. Any mutational changes in the genetic makeup and signaling of the metabolic cascade mainly cause the onset of carcinogenesis . RecentlBergenia is a genus of flowering plants and belongs to the Saxifragaceae family with about 35 known species. Different Ayurvedic formulations have used Bergenia species over the centuries to treat piles, kidney stones, bladder, and pulmonary infections [Plant-based herbal remedies have gained much interest in recent years for the treatment of various cancers. Plants are a natural source of biologically active compounds, which play a leading role as potential drug candidates for the treatment of multiple disorders and infections . Bergenifections . Almost fections .Bergenia species was accomplished via network targeting of multidrug or multi-targeting approach. Using multi-therapeutics with the networking of targeted genes, biologically active phytochemicals have been used as leading drug candidates against key targeted genes with their role in different signaling mechanisms involved in the pathogenesis of HCC.Network pharmacology is an elaborative domain that predicts the drug mechanism to treat different diseases, which is similar to Chinese medicine’s multitargeting functions. Network pharmacology is a multidimensional field in pharmacology, interpreting the network targeting and interrelation of plant phytochemicals to target enormous types of disorders . In thisB. ciliate, 6 from B. pacumbis, 64 from B. purpurascens, and 61 from B. stracheyi. These phytochemicals were collected from different plant parts  reported in the literature and IMMPAT database. For network pharmacology analysis, only four compounds -catechin gallate, and leucocianidol were selected based on their pharmacokinetic criteria of drug likeness (DL > 0.18) and oral bioavailability (OB > 30%). In A library was constructed which contained 16 phytochemicals from Bergenia spp.-related gene targets were predicted from the SwissTargetPrediction and STITCH database. In total, 313 targets were predicted for the four phytochemicals of Bergenia spp. Then, 228 unique drug-related gene targets were predicted after removal of duplicates by aligning the UniprotKB protein IDs.The Bergenia spp.-related targets and HCC-related predicted targets was performed for mapping of common overlapping gene targets. Out of these, a total of 98 targets were selected after interaction of drug-related gene targets and HCC-related gene targets -catechin gallate, β-sitosterol, and leucocianidol. Therefore, the phytochemical cianidanol was eliminated from further analysis. In addition, the network density of 0.094, network heterogeneity of 0.907, and network centralization of 0.329 were also calculated by the Network Analyzer tool of the software Cytoscape. The plug-in cytoHubba was also employed to examine the hub genes. The network degree is the number of interactions a node  makes with other nodes within a biological network. A node with a higher network degree is considered as a hub node, as it shows significant control or influence on the functioning of the network. The degrees of the top targeted genes are represented in The 98 potential targets were imported into STRING 11.5 for protein–protein interactions with a high confidence score of 0.700 and by selecting organism . In the The GO and KEGG enrichment analyses were performed through the DAVID database to predict the functional annotation and pathway enrichment associated with active plant constituents for the treatment of hepatocellular carcinoma. The GO annotation revealed 373 biological processes (BPs), 108 molecular functioning (MF), 40 cellular components (CCs), and 135 KEGG pathways. The top 20 terms in GO and KEGG analyses were identified as mainly involved in cancer pathways . The BPsThe ShinyGO tool was used for interpretation and visualization of the top 20 selected pathways in the bar plot. The target–pathway network was constructed with the cystoscope software to fully understand the interrelation of targets with the associated signaling pathways .The Cytoscape software was employed for the integration of the compound–target network and the target–pathway network for the construction of the compound–target–pathway network. The Network Analyzer represents 166 edges and 33 nodes in three active phytochemicals, 10 hub genes, and 20 signaling pathways .The molecular docking approach was used to explore potential drug candidates to target screened genes from a network pharmacology study for the precise targeting of HCC genes for the treatment of liver cancer. The top three genes  were selected based on their topological analysis, GO, and KEGG enrichment analyses results. These three genes were at the top of the hub genes, sub-gene clustering, and common in the top 20 GO terms and KEGG pathways. The 3D structures of these target proteins STAT3 (with PDB ID 6TLC), MAPK3 (with PDB ID 6GES), and SRC (with PDB ID 2H8H) were retrieved from the Protein Data Bank in PDB format. The removal of solvent molecules and already bound ligand, energy minimization, and 3D protonation of these proteins were performed using the MOE tool. The 3D structures of the selected phytochemicals were retrieved from the PubChem database in .sdf format.The PyRx software was employed for ligand-based molecular docking of the selected compounds with three target proteins. Among the three phytochemicals, (+)-catechin 3-gallate showed the maximum binding score and the strongest interactions with the active residues of all target proteins. The (+)-catechin 3-gallate, with a docking score of −10.2 kcal/mol, interacted with LysA:71, AspA:128, CysA:183, and AspA:184 residues of the binding pocket of MAPK3 via hydrogen bonding . SimilarThe ADMET analysis classifies the drug into five parameters . These attributes of a potential drug classify it from the chemical perspective . All selp ˂ 0.001) increased the cytotoxicity of HepG2 cells -catechin 3-gallate, β-sitosterol, and leucocianidol were estimated to be approximately 5.258, 1.784, and 49.94 μg/mL, respectively, compared to cisplatin  -catechin 3-gallate, β-sitosterol, and leucocianidol in HepG2 cells, the MTT assay was used. Initially, different concentrations of these compounds, including a standard drug cisplatin  were applied to HepG2 cells for 24 h and their cytotoxicity potentials were evaluated through an MTT test. The cytotoxicity potential and percent cell viability of control, (+)-catechin 3-gallate, β-sitosterol, and leucocianidol-treated cells are shown in G2 cells . After t5 μg/mL) .The pathogenesis of HCC is complex due to sneaking symptoms which make HCC difficult to diagnose. Hepatocarcinogenesis involves the complex cellular dysfunctioning that predominantly transforms into primary liver carcinoma. The lack of predictable biomarkers and continuous resistance make it limited for treatment and sequential therapies. Conventional anticancer therapies have been associated with severe side effects due to lack of diagnosis and selectivity . The vasB. ciliate, B. pacumbis, B. purpurascens, and B. stracheyi) from the Saxifragaceae family have been used due to their anti-inflammatory history as anticancer therapy. The current study is based on previously used Bergenia spp. in different cancer therapies. B. ciliate, B. pacumbis, B. purpurascens, and B. stracheyi have been reported with great potential as natural sources of antioxidant, anti-inflammatory, and anticancer compounds. Studies have reported the anticancer effects of Bergenia spp. on the inhibition of protein kinase and the induction of apoptosis [Bergenia ligulata silver nanoparticles for arresting p53-mediated mitochondrial apoptosis in breast cancer. The results of their study reveal the potential of BgAgNps as anticancer agents via the cleavage of caspase-3 and downstream targeting of p53 like Bax. In another study, Dulta et al. [Bergenia ciliate. The prepared ZnONPs exhibited antibacterial and antioxidant activity against Gram-negative bacterial strains. The ZnONPs with an absorbance band of 340 nm displayed maximum cytotoxic potential in human cervical cancer and human colon cancer cell lines. This indicated the green synthesis of natural NPs to combat with different cancers.In the current study, four plants  in each top signaling pathway for the onset of primary liver carcinoma were selected for precise targeting for the selective treatment of HCC. The ten hub genes with their highest degree ranks in topological analysis and PPIs were further selected for pathway and enrichment analysis. The genes STAT3, MAPK3, SRC, EP300, VEGFA, PIK3CA, TNF, PTPN11, ESR1, and HIF1A were on the top of PPIs and mostly involved in the top twenty GO annotations and KEGG pathways. Among these, the genes STAT3, MAPK3, and SRC were evaluated for HCC treatment. Network pharmacology can be used to create a complex integrated network of compound–target–pathways to understand the complex relation of drugs with their respected targets and signaling mechanisms to manage those specific diseases. These networks are based on targets, biologically active compounds, and biological signaling pathways of the gene that addresses the potential of compounds to tackle the involvement of that specific gene to target that pathway. The selection and screening of targets from the pool of genes make it more reliable to point out the involved gene(s) in common cancer pathways .Signal transducer and activator of transcription 3 (STAT3) modulates chronic inflammation in tumor formulation and mediates interactions between tumor cells and stromal cells. STAT3 belongs to the STAT family with seven members which mediate the signal transduction from the plasma membrane toward the nucleus. The overexpression and ubiquitous activation of STAT3 has been associated with metastasis, immune suppression, and tumor progression in liver cancer. The participation of STAT3 in oncogenesis, angiogenesis, anti-apoptosis, and drug resistance has attracted the attention of researchers as a therapeutic target in liver cancer. Different clinical trials have favored STAT3 gene transcription targeting as effective for cancer treatment ,22.Mitogen-activated protein kinase 3 (MAPK3) is a member of the MAP kinase family and called extracellular signal-regulated kinases (ERKs). The overexpression or genetic mutation in the MAPK/ERK signaling cascade has been frequently reported in liver cancer. MAPK/ERK acts as a signaling cascade being involved in differentiation, proliferation, and cell cycle signaling in response to outer signals. Any mutation in this signaling cascade leads to tumor initiation, progression, and modulation of the primary liver tumor, thus leading to HCC .SRC kinase is predominantly involved in multiple cellular signaling pathways including mitochondrial oxidative phosphorylation (OXPHOS). Any abhorrent change in the signaling cascade due to SRC kinase mutation ultimately leads to cancer development and metastasis. The change in OXPHOS signaling and expression has been reported in liver cancer biopsies, which showed the involvement of SRC kinase in HCC . Differep < 0.01), compared to controls, fibroblasts, and hepatocytes in a dose-dependent manner.The results obtained from network pharmacology were further evaluated through experimental work and the cytotoxic effects of (+)-catechin 3-gallate, β-sitosterol, and leucocianidol were explored. The findings show that (+)-catechin 3-gallate and β-sitosterol have cytotoxic effects on HepG2 cell viability in a dose-dependent manner. In a study, Pal et al.  assessed50 values for (+)-catechin 3-gallate and β-sitosterol were found to be 5.258 and 1.784 µg/mL, respectively, in the current study. In a study, HepG2 cells were treated with EGCG and metformin and their cell viability exhibited a dose-dependent decrease. The IC50 values were determined for each treatment. At 24 h of exposure, the IC50 values for EGCG and metformin were calculated to be 31.4 μg/mL and 7.57 μg/mL, respectively [p < 0.001) induction of dose-dependent cytotoxicity in HepG2 cells following β-sitosterol treatment. The highest level of cytotoxicity was observed at a concentration of 1 mM/mL. At 24 h, the IC50 value of β-sitosterol in HepG2 cells was determined to be 0.6 mM/mL. Similarly, Raj [p < 0.001). The IC50 value of BSS-SNPs in HepG2 cells was determined to be 7 ng/mL.The ICectively . Similarectively  conducterly, Raj  also con50 values of 156 and 937 μg/mL, respectively, when tested on the control non-cancer cell line. The Caco-2 cell line demonstrated significant cytotoxic activity when exposed to both compounds, with IC50 values of 81 μg/mL for β-amyrin and 54 μg/mL for β-sitosterol-3-O-glucoside [In order to investigate the cytotoxic effects of β-amyrin and β-sitosterol-3-O-glucoside, the MTT cell viability assay was employed on two cancer cell lines (HepG2 and Caco-2) in addition to a non-cancer cell line (HEK293). The results reveal a dose-dependent cytotoxicity of the tested compounds on the cancer cell line. Notably, both β-amyrin and β-sitosterol-3-O-glucoside exhibited selective cytotoxicity toward cancer cells, as indicated by their higher IClucoside .Through network pharmacology, three main targets  were evaluated in this study as potential targets for (+)-catechin 3-gallate to treat HCC. These proteins are the main parts of different cancer-leading pathways, which make them perfect targets for the treatment of cancer. The molecular docking has confirmed the potential of (+)-catechin 3-gallate as the lead drug candidate for the treatment of HCC. The minimum energy score has displayed the greater binding affinity of (+)-catechin 3-gallate as a ligand for the selected receptor proteins. This study provides a systemic layout that illustrates the key targets and molecular mechanisms as suggestions or recommendations for a detail study of HCC treatment in the future.Bergenia spp.  were also retrieved from the literature and the publicly available database of Indian Medicinal Plants, Phytochemistry and Therapeutics (IMPPAT) [The biologically active compounds from four (IMPPAT) . All pre(IMPPAT) . Only th(IMPPAT) . Similar(IMPPAT) .Homo sapiens in species option. Protein IDs of each identified protein were aligned with UniProtKB to eliminate duplicates [The putative gene targets related to selected active phytochemicals were identified and collected by providing SMILES with SwissTargetPrediction tool  and STITplicates .Different keywords related to the research studies  were searched to retrieve HCC-related target genes from GeneCard  and DisGB. ciliate, B. pacumbis, B. purpurascens, and B. stracheyi with associated target genes. A plug-in “Network Analyzer” was used to access the topological features of the network.The software Cytoscape 3.9.1  was emplH. sapiens as target species to construct the PPI maps. The constructed network was then imported into Cytoscape 3.9.1 software to visualize the topological analysis using Network Analyzer tool. A plug-in “CytoHubba” was used to obtain the hub genes with the highest degree.Protein–protein interactions (PPIs) are mandatory to reveal the underlying mechanisms and co-expression of genes . The ovep < 0.05 was set to filter the top twenty enriched pathways for pathway–target network construction through Cytoscape 3.9.1 [The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) gene enrichment analysis of selected genes was performed via the DAVID database . DAVID ipe 3.9.1 . Bubble pe 3.9.1 .Cytoscape 3.9.1 was used to build the compound–target–pathway (C-T-P) network by integration of compound–target and target–pathway networks. The C-T-P network helps to understand the interrelations of each gene with relevant pathways, which are involved in certain biological and cellular signaling cascades. This networking was used to determine the HCC-associated primary key target associations with the active phytochemicals.https://www.rcsb.org/) in .pdb format [https://pubchem.ncbi.nlm.nih.gov/) in .sdf format [The molecular docking study identifies the ligand–target protein interactions to verify the potential of selected ligands as drug candidates . The resb format . Similarf format . The recThe PyRx software was used for ligand–protein docking to explore the binding patterns of ligands to the active site residues of selected receptor proteins . The docThe SwissADME  and pkCS2 incubator, Hep-G2 cells were maintained and allowed to grow at 37 °C with a 5% carbon dioxide source in a moist environment. For treatment purposes, the cells were seeded when sufficient confluence had been reached and 0.25% trypsin-EDTA was added to separate the cells [The DMEM (Dulbecco’s Modified Eagle Medium) with 10% fetal bovine serum (FBS) and 100 μL/mL each of streptomycin and penicillin was used to develop the hepatocellular carcinoma cell line Hep-G2. In a COhe cells .The MTT assay was used to determine the cytotoxicity-inducing ability of (+)-catechin 3-gallate, β-sitosterol, and leucocianidol. Cisplatin and DMSO were used as a standard drug and control, respectively. Using the MTT assay, the anticancer activities of the best selected phytochemicals were evaluated. For this work, Hep-G2 cells were seeded and planted on 96-well plates. After a 12 h incubation period, different concentrations  of the best selected phytochemicals and cisplatin were delivered to the cancer cells. Following that, cells were treated with 50 µL/mL of MTT solution for 4 h at 37 °C. Then, 0.1% DMSO was also added before aspirating the media. Finally, the ELIZA plate reader was used to note the absorbance at 540 nm [p-value > 0.05 were considered as statistically significant [The software Graph Pad Prism 8 was used for the statistical analysis and the significance of the inhibition data was measured using one-way ANOVA. The results with nificant .Bergenia spp. could be used as a natural drug source to treat HCC. Furthermore, clinical and in vivo studies would be required to validate the potential of (+)-catechin gallate, specifically, and β-sitosterol and leucocianidol, generally, as the leading drug candidates for the treatment of HCC in the future.Hepatocellular carcinoma (HCC) is the most lethal cancer type with malignant tumors mostly reported in Asia and Europe. Despite systemic therapies, there is a continuous increase in death cases due to the limited diagnosis at the advanced stages of cancer and lack of diagnosis biomarkers. Moreover, combinational therapies are associated with adverse side effects that increase the mortality rate with the median postdiagnostic survival of 6–12 after an advanced stage. The network pharmacology approach has pointed out the compound-related targets for the management of HCC. The PPI, GO, and KEGG analyses have explored the top genes involved in the pathogenesis of HCC. The viability on HepG2 cells confirmed the cytotoxic effects of (+)-catechin 3-gallate and β-sitosterol in a dose-dependent manner. In the light of the current results, it has been concluded that


When analyzing patient-reported outcome (PRO) data, sources of differential item functioning (DIF) can be multiple and there may be more than one covariate of interest. Hence, it could be of great interest to disentangle their effects. Yet, in the literature on PRO measures, there are many studies where DIF detection is applied separately and independently for each covariate under examination. With such an approach, the covariates under investigation are not introduced together in the analysis, preventing from simultaneously studying their potential DIF effects on the questionnaire items. One issue, among others, is that it may lead to the detection of false-positive effects when covariates are correlated. To overcome this issue, we developed two new algorithms (namely ROSALI-DIF FORWARD and ROSALI-DIF BACKWARD). Our aim was to obtain an iterative item-by-item DIF detection method based on Rasch family models that enable to adjust group comparisons for DIF in presence of two binary covariates. Both algorithms were evaluated through a simulation study under various conditions aiming to be representative of health research contexts. The performance of the algorithms was assessed using: (i) the rates of false and correct detection of DIF, (ii) the DIF size and form recovery, and (iii) the bias in the latent variable level estimation. We compared the performance of the ROSALI-DIF algorithms to the one of another approach based on likelihood penalization. For both algorithms, the rate of false detection of DIF was close to 5%. The DIF size and form influenced the rates of correct detection of DIF. Rates of correct detection was higher with increasing DIF size. Besides, the algorithm fairly identified homogeneous differences in the item threshold parameters, but had more difficulties identifying non-homogeneous differences. Over all, the ROSALI-DIF algorithms performed better than the penalized likelihood approach. Integrating several covariates during the DIF detection process may allow a better assessment and understanding of DIF. This study provides valuable insights regarding the performance of different approaches that could be undertaken to fulfill this aim. Studies involving PRO measures often aim to compare patient levels on a latent variable by means of group comparisons and/or to study change in the latent variable. To make valid comparisons, one must ensure that individuals with different characteristics interpret the items in the same way and/or that their perception of the items remains the same over time . DIF occurs when patients do not interpret items in the same way according to their group membership and thus have differing item endorsement probabilities despite having the same latent variable level. In case of DIF, there is a violation of the assumption of between-group measurement invariance (response shift (Patient-reported outcome (PRO) measures have gained interest in health research to take into account patients’ perspectives on healthcare . PRO meaver time . Howevervariance . Ignorinvariance . Changesse shift .There is a wide range of DIF detection methods in the literature. Among them, we can mention the Mantel-Haenszel method , the loga priori) and (iii) the sequential-free baseline (uses a DIF-free item assumed to be invariant and designated based on the constrained baseline approach). In their simulations, two binary covariates and their interaction could induce DIF and were under investigation. While MIMIC methods  appeared to be efficient for detecting DIF items, the identification of the covariates inducing DIF seemed problematic (MIMIC-model methods for DIF detection are very popular in the literature for this purpose . This apblematic .Within the item response theory (IRT) or Rasch measurement theory (RMT) frameworks, statistical approaches have also been recently developed to consider several covariates simultaneously. On the one hand, we can mention iterative detection methods such as the IRT with covariates (IRT-C) procedure , 2016 anIn a broader issue of measurement invariance assessment, the ROSALI algorithm  has been-Identify items that function differently between the two groups defined by the covariate at the first measurement occasion (first part of ROSALI).-Determine whether the perception of some items changes between two time points and assess whether or not these changes over time are similar in both groups (second part of ROSALI).Of note, ROSALI ends by a final model allowing to adjust latent variable levels comparisons for the lack of invariance previously evidenced, if appropriate. Simulations showed that ROSALI does not erroneously infer DIF when DIF has not been simulated  and its The aim of this study is twofold:(1)To extend the first part of ROSALI (dedicated to the detection of DIF at one time point) to enable the simultaneous introduction of two binary covariates,(2)To compare by simulations the detection performance of this extension to the one obtained with the approach using likelihood penalization under various conditions, including moderate numbers of polytomous items , moderate sample sizes, potentially correlated covariates, and various forms of DIF.J polytomous items with Mj response categories for item j  is given by:Rasch measurement theory is a family of models derived from the Rasch model for dichotomous items . For poli answers x  to item j is a function of:The conditional probability that an individual -i: θiThe latent variable level of individual i is the realization of Θ, a random variable assumed normally distributed (with mean μ and standard deviation σ). This latent variable is assumed to represent the target construct .Where θ-item threshold parameters δjp associated with each response category p > 0 of item j (1 ≤ p ≤ Mj − 1). δjp represents the latent variable level at which the probabilities of answering category p or p-1 to item j are equal. When tracing the probability curves of each response category, item threshold parameters  correspond to the intersection between two adjacent category probability curves as pictured in The Rasch family models are often used to detect DIF in health research . Within (1)Have the same direction and the same magnitude, e.g., (2)Have the same direction but vary in magnitude, e.g., (3)Vary in direction and magnitude, e.g., non-homogeneous DIF  and (3) will be referred to as eous DIF . Of noteF .C on the latent variable level accounting for a potential DIF induced by C through the introduction of group effects on the latent variable level and on the item threshold parameters:The PCM can be used to assess the impact of a binary covariate In addition to the above-mentioned parameters, we have:Ci the realization of covariate C for individual i. Ci equals either 0 (reference group) or 1.- C on the latent variable level (sometimes referred to as the group effect). β equals the difference between μ1 and μ0, where μ1 designates the latent variable mean in the group of individuals with Ci = 1, and μ0 designates the latent variable mean in the group Ci = 0 (β = μ1−μ0).- β the effect of covariate jp the DIF parameters interfering with the item thresholds and modeling the DIF effects of covariate C. These DIF parameters operationalize the difference in item threshold parameters between the groups. Item threshold parameters in the reference group are δjp and item threshold parameters in the focal group are equal to δjp + γjp. If there is no DIF on item j, then γjp = 0.- γ1 and C2, without interaction:Additional binary covariates can be added in the same way. For instance, with two covariates CThe first part of the ROSALI algorithm with one binary covariate has been described elsewhere . We exteStep 1. Estimation of a fully non-invariant PCM where the two covariates are assumed to induce DIF on all items.Step 2. Estimation of a fully invariant PCM (no DIF is assumed).Step 3. Test of the global occurrence of DIF by comparing the two previous models using a likelihood-ratio test (LRT).Step 4. If the LRT is significant, screen all item-covariate pairs for DIF separately based on the fully non-invariant model. Otherwise, go to step 6.Step 5. Forward iterative selection of the significant DIF item-covariate pairs found in step 4 (starting from the fully invariant model) and assessment of the form of DIF involved. A Bonferroni correction is performed to account for multiple testing.Step 6. Estimation of a final model giving the covariates effect on the latent variable level adjusted for DIF (if appropriate).This extension of the first part of ROSALI will be referred to as ROSALI-DIF FORWARD. All steps are comprehensively described in A DIF detection method for polytomous items using likelihood penalization of a PCM or a generalized PCM (GPCM) has been comprehensively described by DIF parameters paths  are estimated across a wide range of tuning parameters . All estimates of the DIF parameters aths see . The optWe chose to evaluate this approach, named PCMLasso, enabling the detection of both forms of DIF using a PCM. Of note, the PCMLasso approach relies on a PCM where the item discrimination parameters are constrained to be equal over all items. In the ROSALI-DIF algorithms, the discrimination parameters equal 1 for all items. Besides, neither the ROSALI-DIF algorithms nor PCM-Lasso enable to consider that the DIF effect of one covariate may depend on the level of another covariate.n = 400 or 800 individuals to a unidimensional questionnaire composed of J = 4 or 7 polytomous items  with M = 4 response categories, numbered from 0 to M-1. Individual latent variable levels were drawn from a standard normal distribution and responses were generated by a PCM. Item threshold parameters δjp were chosen to cover all the latent variable continuum .We simulated the responses of The structure of the questionnaire (number of items and number of response categories) was chosen to be in accordance with the unidimensional subscales of PRO instruments commonly encountered in health research; e.g., SF-36 , QLQ-C30(i)Studies on psychometric properties of a PRO instrument. (ii)Analyses of data from observational studies including PRO measures. Based on our practical experience in France, cohorts generally contain about 300 to 500 patients . Analysa priori on which procedure could perform better. However, we expected that performance would increase with increasing sample size and DIF size. For the ROSALI-DIF algorithms, we also expected that we might lack power to identify non-constant shifts in item thresholds  as previous issues were found in a longitudinal framework  and PCMLasso were able to recover the DIF conditions that were simulated, that is: conclude that DIF is absent when DIF has not been simulated and identify the correct DIF item-covariate pairs when DIF has been simulated. Scenarios considered within the simulation study aimed to be representative health research contexts . We explored three broad settings. The first one (setting No. 1) aimed to evaluate the procedures’ performance in a simple situation where two uncorrelated covariates induce DIF on a different item. The second setting was considered to determine whether the procedures could disentangle DIF effects when both covariates induce DIF on the same item. Finally, with setting No. 3, we determined whether the procedures could correctly identify the DIF-inducing covariate when to correlated covariates are introduced in the analysis. As it was an exploratory simulation study, we had no ramework , 2022.n nor the number of items J seemed to impact these rates. However, rates of false detection of DIF were usually slightly lower when the two covariates C1 and C2 were correlated than when they weren’t. For each scenario, the rate of false detection of DIF was systematically lower than the rate of datasets with a significant LRT. It means that for some datasets (1–3%), overall occurrence of DIF was initially suspected following the LRT, but finally not retained at the end of the algorithm. Of note, the proportion of datasets with a significant likelihood-ratio test (LRT) was close to 5% (the significance level used for this test). Same results were obtained for ROSALI-DIF BACKWARD . Neither the sample size BACKWARD .DIF was wrongly detected on at least one item-covariate pair at the end of the PCMLasso in almost half of the datasets whatever the scenario. None of the simulation characteristics seemed to have an impact on these results. As the number of item-covariate pairs incorrectly flagged for DIF by PCMLasso among the “No DIF scenarios” remained small (one item-covariate pair on average among replications where DIF was wrongly inferred), we chose to continue the investigations in order to draw a comprehensive view of its performance .n equals 800 or when the DIF size is medium (with either n = 400 or 800).The performance of the three procedures varied depending on the values of the simulation characteristics. First, all procedures failed to detect DIF within scenarios with weak DIF and a sample size of 400: Most Flexible detection rates did not exceed 10% under these conditions. Therefore, results regarding these scenarios will not be further developed. The following paragraphs focus only on the results observed when the sample size n = 800 (from 16 to 34%). However, when DIF size was medium, Most Flexible detection rates were moderate when n = 400 (between 35 and 56%) and high when n = 800 (from 76 to 96%). As a reminder, these rates indicate to what extent the different procedures are able to detect DIF on at least the item-covariate pairs on which DIF was simulated. Best performance regarding Most Flexible detection rates was observed for ROSALI-DIF FORWARD  but ROSALI-DIF BACKWARD also showed quite similar performance (rates did not differ by more than 5%). The performances of PCMLasso were generally slightly lower . Of note, the three methods showed usually higher Most Flexible detection rates when DIF was non-homogeneous than when it was homogeneous, all other scenario characteristics being equal, with a maximal difference up to +17% (mean difference of +8%).Whatever the procedure, Most Flexible detection rates were low when DIF size was weak and n = 800 (from 13 to 32%), moderate Flexible detection rates when DIF size was medium and n = 400 (from 30 to 49%), and high Flexible detection rates when DIF size was medium and n = 800 . Regarding PCMLasso, we observed poor Flexible detection rates in all scenarios except those with a sample size of 800 and medium DIF size where rates ranged from 37 to 54%. Hence, based on the Flexible criteria, the best-performing methods are the ROSALI-DIF algorithms. Of note, ROSALI-DIF BACKWARD outperformed ROSALI-DIF FORWARD when DIF size was medium and n = 800 (Flexible detection rates of both methods differed from +5 to +16%) while their performance was similar under other scenarios. Finally, the three methods showed generally higher Flexible detection rates when DIF was non-homogeneous than when it was homogeneous , with a maximal difference up to +17% (mean difference of +7%).Both ROSALI-DIF algorithms showed poor Flexible detection rates when DIF size was weak and J. These gaps were always the smallest for ROSALI-DIF BACKWARD and the largest for PCMLasso.Flexible detection rates were lower than the Most Flexible detection rates whatever the procedure. It means that, in addition to the correct DIF item-covariate pairs, all procedures wrongly detected other pairs (on which DIF was not simulated). Gaps between the Most Flexible and Flexible detection rates usually increased with increasing Most Flexible detection rates for all procedures . For both ROSALI-DIF algorithms, gaps also increased with increasing number of items n = 800, low rates ranging from 12 to 26% otherwise). It indicates that once PCMLasso correctly identified the item-covariate pairs affected by DIF, it also correctly identified the DIF form. Therefore, when DIF was non-homogeneous, the PCMLasso approach showed larger Perfect detection rates than the ROSALI-DIF algorithms. Based on the Perfect criterion, PCMLasso outperformed ROSALI-DIF algorithms when the simulated DIF was non-homogeneous. Note that, as observed for Flexible detection rates, ROSALI-DIF BACKWARD showed higher Perfect detection rates than ROSALI-DIF FORWARD in scenarios with medium DIF and n = 800. Both algorithms showed similar performance otherwise.Among scenarios with homogeneous DIF, ROSALI-DIF algorithms both showed Perfect detection rates close to the Flexible detection rates . Thus, within datasets meeting the Flexible criteria, both algorithms correctly determined the form of DIF involved when the simulated DIF was homogeneous. As for PCMLasso, Perfect detection rates did not exceed 2% indicating that PCMLasso failed to identify the correct DIF form when the simulated DIF was homogeneous. Hence, based on the Perfect criterion, both ROSALI-DIF algorithms outperformed PCMLasso within scenarios with homogeneous DIF. However, when the simulated DIF was non-homogeneous, Perfect detection rates associated with ROSALI-DIF algorithms were substantially lower than Flexible detection rates . Hence, both algorithms struggled to identify the correct form of DIF  among scenarios meeting the Flexible criteria. On the contrary, PCMLasso showed Perfect detection rates very close to the Flexible detection rates . Performance was poorer for PCMLasso under the same conditions as Most Flexible and Flexible detection rates ranged from 19 to 78% and from 3 to 34%, respectively. Of note, we observed similar effects to those highlighted in settings Nos. 1 and 2 regarding these rates and the associated gaps.1 and β2 (the respective effect of the covariates C1 and C2 on the latent variable level) are given in 1  remained small when J = 7 but it increased when J = 4 for all procedures . Across all settings, the better the methods detected DIF, the lower the bias.Bias, empirical standard errors, and average model standard errors associated with the estimation of βOn the one hand, PCMLasso always underestimated the DIF parameters This study aimed to extend the first part of the ROSALI algorithm (dedicated to DIF detection at one time point using RMT) in order to consider two binary covariates instead of one. We proposed two extensions: ROSALI-DIF FORWARD and ROSALI-DIF BACKWARD. The novelty characterizing these extensions is the screening step that aims to identify the item-covariate pairs candidate for DIF detection and the item-covariate pairs that will be considered as anchors . This further step was inspired by the iterative Wald test procedure proposed by M and J. Moreover, ROSALI-DIF algorithms should generally not wrongly detect items-covariates pairs without DIF. However, one must be cautious regarding the form of DIF evidenced by these algorithms, as non-homogenous DIF is rarely identified as such. It means that the test performed during step 5 generally lacked power as it failed to reject the null hypothesis of homogeneous DIF when DIF was actually non-homogenous. Correct identification of the DIF form may require larger sample sizes.In light of the rates of false detection of DIF, both ROSALI-DIF algorithms satisfactorily prevent from inferring DIF when it has not been simulated. This good performance may be explained by: (i) combining the LRT performed at a 5% significance level with the screening and iterative steps, and (ii) the Bonferroni correction applied during the iterative step. In light of the Flexible and Most Flexible detection rates, both ROSALI-DIF algorithms can detect item-covariate pairs having medium DIF (as simulated in this manuscript) with good power for studies with two correlated or uncorrelated binary covariates, a sample size of 800, and a questionnaire similar to the ones simulated with regards to Regarding PCMLasso, rates of false detection of DIF among scenarios without DIF were high. Indeed, PCMLasso was prone to erroneously detect DIF in at least one item-covariate pair in almost half of the datasets, no matter the scenario. This drawback is also highlighted by the large gaps between the Flexible and Most flexible detection rates among scenarios where DIF was simulated. Indeed, PCMLasso was likely to wrongly flag other item-covariate pairs in addition to the ones truly affected by DIF. However, we noticed that the estimated size of the wrongly evidenced DIF effects remained small on average, which did not result in a meaningful measurement bias at the scale level . One must be cautious regarding the form of DIF evidenced by this approach. Indeed, it almost always suspected the occurrence of non-homogeneous DIF (whatever the form of DIF simulated). After further investigations, we also noticed that in some datasets, DIF parameters estimates were very close . However, the focus of these criteria is different from the criteria we used:-Regarding DIF effects erroneously identified:On the one hand, the rate of false DIF detection that we used in our simulation study quantifies the risk of drawing an erroneous conclusion when performing the procedure on a given dataset where no DIF was simulated . It does not quantify whether there are many item-covariate pairs flagged for DIF among the datasets in which DIF was wrongly inferred. On the other hand, the average FPR quantifies whether or not many item-covariate pairs are incorrectly flagged for DIF among the datasets without DIF. However, it does not inform about the proportion of datasets where the adequate conclusion was arrived at (that is: no DIF is present within the set of items considered).-Regarding DIF effects correctly identified:The rate of correct DIF detection  quantifies the probability of reaching the right conclusion when performing the procedure on a given dataset where DIF was simulated . As for the average TPR, it indicates whether the DIF item-covariate pairs are often rightly flagged for DIF, but it does not inform about the proportion of datasets where the adequate conclusion was arrived at (that is: DIF is present within the set of items considered and the correct item-covariate pairs are flagged).The estimation of the average FPR and TPR was not planned in the initial aims of our simulation study, but since they may interest readers, they are provided in The DIF parameters hlighted . Of note1 and β2 when DIF was weak (despite the procedures’ low performance) could mean that such a DIF condition does not trigger meaningful bias if DIF is not accounted for. Among scenarios with DIF simulated under setting No. 3, results were more mixed: bias related to the estimation of β1  remained small when J = 7 but it increased when J = 4. This latter condition corresponds to a test composed of four items and half of them affected by DIF induced by a single covariate. As demonstrated by Finally, all procedures provided a globally unbiased estimation of the effect of the covariates on the latent variable level adjusted for DIF. Besides, the better the methods detected DIF, the lower the bias. Among scenarios with DIF simulated under settings Nos. 1 and 2, bias remained small for all methods . This observation of globally unbiased estimations of βn = 400. Between the three methods, ROSALI-DIF algorithms (FORWARD and BACKWARD) seemed to be preferable if one aims to correctly identify the item-covariate pairs affected by DIF. A Stata module automating both algorithms is in preparation. Of note, all the methods evaluated within this manuscript were based on Rasch measurement theory. Hence, they assume that the assumptions of RMT modeling are met, that is: unidimensionality, local independence, monotonicity, and items all equally indicative of the latent variable. In practice, the adequacy of Rasch modeling should be investigated prior to conducting DIF detection.To summarize, the DIF detection methods presented in the manuscript allow to simultaneously model the DIF effects from different covariates. They are useful when one wants to grasp the potential multiple sources of DIF, especially when these sources are correlated. Indeed, in this latter case, such DIF detection methods should be preferred to the strategy that consists in performing the DIF detection independently for each covariable . However, we can see that the three methods considered require a large enough sample size, as their DIF detection performance was low to moderate when DIF size was medium and J = 4 or 7 items could either be due to: (i) an increase in the number of items, (ii) a decrease in the proportion of true DIF items, or (iii) a decrease in the proportion of item-covariate pairs on which DIF was simulated. Further investigations are hence needed to disentangle the effects of these different simulation parameters.Several limitations can be addressed. First, within our simulation study, the differences observed between DIF scenarios with In addition, neither ROSALI extensions nor the PCMLasso approach allows to consider that the DIF effect of one covariate may depend on the level of another covariate. To allow for such a phenomenon, it would be necessary to introduce an interaction term between the covariates. Yet, more developments are needed to know how to deal with such an interaction. Of note, the PCM-IFT approach systematically models such an interaction because of its philosophy of recursive partitioning. More broadly, it would be worth comparing our results with the performance of the PCM-IFT approach under the same conditions when it will be able to account for the effect of a covariate on the latent variable.Besides, we have considered simple situations with two well-balanced covariates that did not impact the latent variable level. Extending the simulation framework with: (i) less balanced covariates, (ii) covariates with an impact on the latent variable level, and (iii) more than two covariates, would be interesting to have more insight into the performance of the evaluated procedures. In addition, it would be worth evaluating these procedures under a wider DIF setting . Finally, all datasets were complete: there were no missing data with regards to item responses nor covariate. A simulation study with incomplete datasets could be of value to evaluate the performance of the DIF detection methods, in a situation more representative of real data. Of note, as parameters are estimated using marginal maximum likelihood, estimations should be asymptotically unbiased in case of incomplete data missing (completely) at random. Nonetheless, due to a loss of precision of the estimations, a loss in the performance can be expected.a priori clinical knowledge on the items on which DIF may be likely (depending on the patients’ population) in order to not only rely on statistical criteria.The methods evaluated in this manuscript are based on statistical results. Hence, it is critical to ensure that the evidenced DIF effects are relevant and meaningful. Regarding the ROSALI-DIF algorithms, they are more specifically based on statistical testing. One drawback of such an approach is that they are likely to detect minor DIF effects as soon as the sample is large enough. An alternative could be to use DIF effect size to evaluate whether a given difference in item parameters is substantial or negligible . BesidesAll these concerns about: (i) choosing which covariates to investigate for DIF, (ii) adding prior knowledge to the analysis, (iii) using univariate pre-selection  before multivariable modeling, and (iv) using forward and backward strategies are broader concerns that do not pertain to DIF detection; they are encountered in all regression modeling strategies. Of note, forward and backward strategies are traditional and straightforward way to filter out predictors of a given outcome from a pool of candidates containing both true predictors and noise variables. Although widespread, these approaches show disadvantages that have been extensively addressed in the regression modeling literature . Among oFurther developments are needed to better grasp the sources and determinants of lack of measurement invariance in health research. To that end, DIF detection methods allowing considering several covariates simultaneously (with two categories or more) could be of great practical interest.https://osf.io/hkp38/?view_only=81c3c3c42e194760a7accbd7cc9b431f.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s), simulated datasets, modules, and scripts related to this simulation study are openly available at: YD simulated and analyzed the data and drafted the manuscript. MB, VS, and J-BH revised substantially the manuscript. All authors participated in the development of this study, contributed to the manuscript revision, read, and approved the submitted version.


Sarcomas are a diverse group of malignant neoplasms of mesenchymal origin. They develop rarely, but due to poor prognosis, they are a challenging and significant clinical problem. Currently, available therapeutic options have very limited activity. A better understating of sarcomas’ pathogenesis may help develop more effective therapies in the future. The Sonic hedgehog (Shh) signaling pathway is involved in both embryonic development and mature tissue repair and carcinogenesis. Shh pathway inhibitors are presently used in the treatment of basal cell carcinoma. Its increased activity has been demonstrated in many sarcomas, including osteosarcoma, Ewing sarcoma, chondrosarcoma, rhabdomyosarcoma, leiomyosarcoma, and malignant rhabdoid tumor. In vitro studies have demonstrated the effectiveness of inhibitors of the Hedgehog pathway in inhibiting proliferation in those sarcomas in which the components of the pathway are overexpressed. These results were confirmed by in vivo studies, which additionally proved the influence of Shh pathway inhibitors on limiting the metastatic potential of sarcoma cells. However, until now, the efficacy of sarcomas treatment with Shh pathway inhibitors has not been established in clinical trials. The reason for that may be the non-canonical activation of the pathway or interactions with other signaling pathways, such as Wnt or Notch. In this review, we present the Shh signaling pathway's role in the pathogenesis of sarcomas, including both canonical and non-canonical signaling. We also propose how this knowledge could be potentially translated into clinics. Signaling pathways, crucial in the physiological functions of cells and tissues, may, through dysregulation and ensuing dysfunctions, be significant factors in the process of tumorigenesis  pathway is a ligand-dependent signaling pathway. In vertebrates, three Hh ligands have been described: Desert hedgehog (Dhh), Indian hedgehog (Ihh), and Sonic hedgehog (Shh), the last one being the primary ligand in humans  and long non-coding RNA (lncRNA)  have been shown to promote osteosarcoma cell growth by activating the Hh pathway. Hsp70 and CD63 expression has been detected in these exosomes  or vismodegib (Smo inhibitor) decreased osteosarcoma cell migration. Moreover, inhibition of the osteosarcoma metastasis to the lung was observed during this combination treatment  and, as a result, induces chromatin condensation and transcriptional repression  is the second most frequent bone sarcoma affecting children and adolescents  in the treatment of chondrosarcoma in primary xenografts was also evaluated. The observed effects included decreased volume and cellularity of the tumor, tumoral calcification, and decreased chondrocyte proliferation. The high efficacy of the treatment was likely due to the ligand-dependent nature of the Hh pathway present in chondrosarcoma  via mTOR/S6K1 signaling pathway  treatment in the embryonal subtype of rhabdomyosarcoma (ERMS) with a mutation in Leiomyosarcoma (LMS) is one of the most common soft-tissue sarcoma in adults, representing 10–20% of newly diagnosed cases. Uterine LMS is, in turn, the most common type of uterine sarcoma  are a group of mainly soft-tissue cancers which most commonly develop in the kidney or the brain but can be found in any body part. Those located in the brain are referred to as atypical teratoid/rhabdoid tumors (ATRT). MRT mainly affect infants and is believed to develop during embryogenesis. They remain one of the most lethal pediatric cancers and have a particularly bad prognosis in case of metastases , and sonidegib . While cyclopamine is chemically unstable . There is an ongoing discussion about their potential therapeutic value, particularly in the case of the non-canonical activation of the Shh pathway, where Smo inhibitor therapies have no effect  is a natural anthraquinone derivative which has been reported to have various desired pharmacological effects, such as an anti-neoplastic, antioxidant, anti-inflammatory, and anti-apoptotic potential , a substance extracted from the plant Dysregulation of the Hh pathway, leading to overexpression of its components, has been observed in some sarcomas, including osteosarcoma, Ewing-sarcoma, chondrosarcoma, rhabdomyosarcoma, leiomyosarcoma, and malignant rhabdoid tumor. This points to the critical role played by the Hh pathway in the tumorigenesis of sarcomas; however, Hh components do not seem to be the core mechanism in the pathogenesis of these tumors. Subsequently, the efficacy of sarcomas treatment with Hh inhibitors was assessed. Both in vitro and in vivo studies showed promising results, but they have not yet been confirmed in clinical trials. We believe that the observed lack of effect may result from two factors: the non-canonical pathway activation and interactions with other signaling pathways.Due to its mechanism, the non-canonical pathway activation is less sensitive to Smo inhibition, making Gli inhibitors the only potentially effective Hh-targeted treatment in this group of sarcomas. Understanding which mechanism of Hh pathway activation is present in each sarcoma subtype Table  is cruciAnother aspect of the Shh pathway’s role in the treatment of sarcomas is its possible influence on susceptibility to immunotherapy. Reduced expression of the Hedgehog signaling pathway and the presence of CD8 + T cells have been shown to correlate with the best clinical outcome in patients with sarcoma undergoing immunotherapy (D’Angelo et al. A better understanding of the role played by the Shh pathway in the pathogenesis of sarcomas is necessary. We propose that further research should primarily focus on the role of the non-canonical pathway and the development of its inhibitors as well as potential combination therapies, which would simultaneously target not only the Shh pathway but also other signaling pathways. Such studies could be used to develop an effective therapy for sarcomas connected with Shh pathway dysregulations and thus help solve one of oncology’s most prominent problems (see Table