MetNet, that implements the proposed methodology. MetNet makes it possible to automatically reconstruct the metabolic network of two organisms selected in KEGG and to compare their two networks both quantitatively and visually. We validate our methodology by presenting some experiments performed with MetNet.Metabolic pathway comparison and interaction between different species can detect important information for drug engineering and medical science. In the literature, proposals for reconstructing and comparing metabolic networks present two main problems: network reconstruction requires usually human intervention to integrate information from different sources and, in metabolic comparison, the size of the networks leads to a challenging computational problem. We propose to automatically reconstruct a metabolic network on the basis of KEGG database information. Our proposal relies on a two-level representation of the huge metabolic network: the first level is graph-based and depicts pathways as nodes and relations between pathways as edges; the second level represents each metabolic pathway in terms of its reactions content. The two-level representation complies with the KEGG database, which decomposes the metabolism of all the different organisms into “reference” pathways in a standardised way. On the basis of this two-level representation, we introduce some similarity measures for both levels. They allow for both a local comparison, pathway by pathway, and a global comparison of the entire metabolism. We developed a tool,  Metabolism is characterised by metabolic functions determining the structure and properties of cells in any organism. These functions interact with one another creating a complex network structure. While metabolism has traditionally been divided into metabolic pathways, subsystems of metabolism dealing with specific functions, it has become increasingly clear that metabolism operates as a highly integrated network , 2. The Various approaches to metabolic network reconstruction, analysis and comparison can be found in literature, see –17 for sAutoKEGGRec is presented. This tool automatically reconstructs the metabolic network of a single organism or a list of organisms from the KEGG database, retrieving all their reactions and corresponding linked genes. The reactions and compounds metabolic network is then created. Another resource for the reconstruction of metabolic reactions from newly annotated genomes is the KBase Predictive Biology platform . Note that the pathway similarity index Psim considers all pathways independently of their reaction content, while the weighted similarity index PsimW considers only reaction pathways, therefore ignoring pathways dealing with physical transformations.To compare the complete metabolism of G =  and G′ =  be the graphs representing the metabolic networks of two organisms O and O′, respectively, and let Pi be a reference pathway corresponding to a node in G or G′ (or in both). The Pistructure similarity index, SimSi, is defined as follows , SimSi is set to Case 4. if both G and G′ have the node corresponding to Pi and in both graphs the node is connected, Ei, G and G′, respectively.O and O′ at the structural level, we consider the arithmetic mean of the structure similarities of all pathways. Thus, the structure similarity index is defined by:n is the total number of pathways present in O or in O′, that is |V ∪ V′|.To compare the overall metabolism of combined similarity index is:n is the number of KEGG pathways in the two organisms, |V ∪ V′|. By definition, the index assumes values in .In our two-level representation, a global index is defined for comparing metabolic networks of two organisms, that is, taking into account their similarity at both levels. The O and in O′, the combined similarity index will stress their similarity/difference since it is set to 1/0. Note moreover that both SimSi and SimPi have values in , hence the combined similarity index will amplify the differences between metabolisms.Note that, when pathways based on physical mechanisms are present in the compared organisms MetNet, a tool that implements the proposed methodology and allows us to validate our approach. First we illustrate the tool’s functionalities and then we report three experiments performed with MetNet. The first experiment compares the metabolism of two selected organisms with the aim of highlighting their relationships. Two further experiments compare groups of organisms, with the aim of exploring whether the groups suggested by our similarity indexes agree with those established by well known evolutionary relationships.In this section we present MetNet is a Java tool that applies our reconstruction and comparison methods, the main steps of which are depicted in MetNet allows the user to select two organisms, to automatically retrieve their metabolic pathway data from KEGG using the public KEGG APIs, to reconstruct the metabolic network in our two-level representation, and to perform both a quantitative and a visual pairwise comparison of such networks. Being a Java application, MetNet is portable: it can run in any environment in which the Java Runtime Environment (JRE) is installed. Graph visualization is performed using the GraphStream library  stores the number of common compounds between pathways Pi and Pj. Such information is clearly symmetric, hence m = m. Entries of the main diagonal are instead used to store summary information about pathways: m = −1 indicates that pathway Pi is not present in the metabolism of the organism; m = 0 indicates that Pi is present in the metabolism but isolated wrt. the other pathways; m = k > 0 means that Pi has k connections with other pathways in total.The structural representation of the metabolic network of an organism is implemented by a squared adjacency matrix By taking into account all the KEGG reference pathways considered in the comparison, we standardise the representation as matrices of metabolic networks and greatly simplify their comparison, since each position in the different matrices corresponds to the same pathway.MetNet and illustrated later in this section: the pairwise comparison of the organisms Acyrthosiphon pisum (KEGG code api) and Buchnera aphidicola 5A (KEGG code bap).We present here the tool functionalities with the help of some images related to the first experiment performed with When starting the application, the initial view allows the user to download the latest list of KEGG organisms: if the download is requested, the local list is substituted with the new one. The user is then driven to the organisms selection window, see MetNet uses multithreading where possible: the workload is divided into different tasks that can be executed in parallel to increase performance. The computation requires an execution time that depends on the complexity of the networks. The average execution time is around one minute on a MacBook PRO with 16GB central memory.The comparison window allows the user to select the comparison method for the functional level (i.e. set or multi-set of reactions) and to start the comparison. The result of the comparison is shown in i, their names, Pi, the similarity values computed for the local structure SimSi and the pathway similarity values SimPi. Below the table, the global similarity indices, Psim and PsimW for the functional level, SimS for the structural level and the combined similarity index CSim, are shown as well. All the local and global similarity results are also automatically saved as an Excel file stored in the MetNet’s main folder.Note in MetNet also offers the possibility of visualising the metabolic network topology of the two organisms separately through the corresponding buttons on the main window. api and bap metabolic networks, respectively. We recall that nodes in the graphs are the pathways and edges are the connections between pathways due to shared compounds. Each node shows its corresponding KEGG pathway id as a short label, but also its name and degree appears as a tooltip when the user hovers over the node with the cursor. The two graphs can also be compared visually through the topology comparison button that displays the shared and unshared parts of the network with different colors allowing also to distinguish the organism to which the unshared parts belong, as reported in MetNet. When more than two organisms are involved in the comparison, we use MetNet as a command line tool and create a specific shell to include all the pairwise comparisons. Once all comparisons are completed, we make use of the excel files produced by MetNet to build the similarity matrices of all the obtained global indexes. Such matrices can be used to perform cluster analysis and check whether the organisms under exam are grouped according to well-known evolutionary relationships.We now proceed by showing some experiments performed with Acyrthosiphon pisum or pea aphid (KEGG code api), and Buchnera aphidicola 5A (KEGG code bap), a proteobacteria that is the primary endosymbiont of A. pisum [MetNet.In this experiment we perform the pairwise comparison of an insect, A. pisum . We are The results of their comparison is shown in Buchnera is a symbionelle of the insect A. pisum  [To understand this result, we must focus on the biology of these two organisms. They live together in symbiosis, the bacteria veloped) . This tyveloped) , 40. Theapi and bap networks topologies in api is an insect with a genome of 464 Mb that has a complex metabolic network [bap is a bacteria with a reduced genome of 640 Kb [The visual inspection of the  network , while bf 640 Kb  and has Glyoxylate and dicarboxylate metabolism pathway, and Methane metabolism of bap and api. Both of these pathways were found to be very important pathways for the production of amino acids inside the bacteriocytes of api [MetNet is able to clearly highlight this complementation in an easy and visual way.Moreover, s of api . As the s of api  concludes of api , 45. Notsce, zro, tpf, cal) and four Molds  that are listed in For this experiment we selected eight organisms among Fungi, four Yeasts  in the matrix shows the similarity value between the i-th and j-th organisms. The colors range from yellow to blue: yellow corresponds to organisms with the highest degree of similarity while blue corresponds to organisms with the lower degree of similarity. Clearly the main diagonal always shows the yellow color, being the result of the comparison of an organism with itself.Each depicted similarity matrix has rows and columns labeled according to the organisms ids 0−65 of Structure similarity index  and is graph-based; the functional level represents chemical reactions in the corresponding pathway and it is set-based. We introduce similarity indexes to quantify the pairwise comparison of the metabolism of two different organisms. Some indexes measure the local similarity between pathways, at the structural and functional levels separately. Others measure the global similarity between metabolisms, again at the two levels separately. A further index combines together the global structure and pathways similarities into a comprehensive similarity result.MetNet implements our proposal. It automatically reconstructs the metabolic network of an organism in KEGG and compares the metabolism between a pair of user selected organisms following the two-level methodology. It provides the quantitative results of the various similarity indexes and offers the possibility to visually explore and compare the metabolic networks.MetNet can be used both as an interactive application and as a command line tool. Moreover, thanks to its strong modular structure, the tool can be easily extended with new comparison methods both at a network and at a pathway level.The Java tool MetNet in order to validate the proposed methodology. The results we report and discuss are encouraging: the two-level methodology shows to be interesting and effective for metabolism comparisons. Moreover, the visualisation of the metabolic network offered by MetNet turns out to be a valuable feature to explore and compare the metabolisms of the organisms under examination. The symbiont-host experiment shows that MetNet is a very useful tool to discover and explore metabolic complementations of symbiotic partners in a very small time frame with an intuitive platform, which can then lead to proving relevant metabolic events experimentally with important a priori supportive information and specific targets. Also, MetNet has proven to be of use when considering the comparison of a set of very similar organisms, like the Yeasts and Molds experiment, as well as in the large scale comparison of all Mammals, being able in both cases to correctly classify them.Some experiments have been performed with MetNet has to be significantly extended to allow the input of genomic data and implement the reconstruction of the corresponding metabolic network.As far as future work is concerned, we would like to apply our two-level comparison to metabolic networks reconstructed from experimental genomic data, possibly belonging to more than one organism. This requires the development of an approach to metabolic network reconstruction compliant with the modular organization of the KEGG pathways. Moreover, the tool S1 TableThe table reports all mammals considered for the third experiment, identified by a numerical id.(PDF)Click here for additional data file.


Tongue cancer is one of the most common types of cancer, but its molecular etiology and pathogenesis remain unclear. The aim of the present study was to elucidate the pathogenesis of tongue cancer and investigate novel potential diagnostic and therapeutic targets. Four matched pairs of tongue cancer and paracancerous tissues were collected for RNA sequencing (RNA-Seq), and the differentially expressed genes were analyzed. The RNA-Seq data of tongue cancer tissues were further analyzed using bioinformatics and reverse transcription-quantitative PCR analysis. The sequenced reads were quantified and qualified in accordance with the analysis demands. The transcriptomes of the tongue cancer tissues and paired paracancerous tissues were analyzed, and 1,700 upregulated and 2,249 downregulated genes were identified. Gene Ontology analysis uncovered a significant enrichment in the terms associated with extracellular matrix (ECM) organization, cell adhesion and collagen catabolic processes. Kyoto Encyclopedia of Genes and Genomes analysis demonstrated that these differentially expressed genes were mainly enriched in the focal adhesion pathway, ECM-receptor interaction pathway, phosphoinositide 3-kinase (PI3K)-Akt pathway, and cell adhesion molecules. Comprehensive analyses of the gene tree and pathway network revealed that the majority of cell cycle genes were upregulated, while the majority of the genes associated with intracellular response, cell adhesion and cell differentiation were downregulated. The ECM-receptor interaction, focal adhesion kinase (FAK) and PI3K-Akt pathways were closely associated with one another and held key positions in differential signaling pathways. The ECM-receptor, FAK and PI3K-Akt signaling pathways were found to synergistically promote tongue cancer occurrence and progression, and may serve as potential diagnostic and therapeutic targets for this type of cancer. Oral cancer is a prevalent malignant tumor. Owing to the frequent mechanical stimulation, the incidence of tongue squamous cell carcinoma ranks first among oral cancer cases, and its incidence continues to increase ,2. The iBioinformatics analyses of gene expression profiles have been extensively performed in recent years. As an assemblage of RNAs, the transcriptome is mainly transcribed from specific tissues or cells at a certain phase or functional state. Analyzing transcriptome data enables assessing overall gene function and structure, thereby elucidating the potential molecular mechanisms underlying pathological conditions, and has previously been applied extensively in cancer research . Non-codin vivo experiments or single-gene studies are currently preferred. To the best of our knowledge, this is the first study to date to investigate differentially expressed mRNAs and ncRNAs in tongue cancer and paracancerous tissues via transcriptome analysis, in the hope that the findings may help identify potential targets for the diagnosis and clinical treatment of early-stage tongue cancer.Transcriptome analyses have been well documented in cancer research; however, few transcriptome analyses have been reported for tongue cancer, and traditional The present study was approved by the Medical Ethics Committee of Nantong Municipal Tumor Hospital . Tongue cancer tissues and matched paracancerous tissues (located ~3 cm from the cancerous tissues) were surgically extracted from 4 patients with tongue cancer . The RNA concentration and purity were determined using an ultraviolet spectrophotometer . The cDNA library was constructed and sequenced as previously described . In briecDNA fragments were subjected to PCR amplification, and the complementary cDNA library was constructed. ncRNAs and mRNAs were sequenced using the high-throughput and high-sensitivity HiSeq 2500 sequencing platform . Sequenced data were analyzed and processed to dynamically remove the sequence fragments at the 3′-end and low-quality fragments using Trim Galore −0.6.5 software (Babraham Institute). Finally, Fast-QC version 0.11.8 (Babraham Institute) was used to evaluate the quality of the preprocessed data.RNA-Seq data were compared with the reference database  using Hisat2 software version 2.1.0 (Johns Hopkins University). Data were mapped with a 56-kb index by genetic fate mapping. Reads were quickly and accurately located on the genome to obtain the genomic structure of the sequencing data.2 FC >1 or <-1 and a false discovery rate ≤0.05 were selected.Gene expression was quantified via reads per kilobase per million mapped reads (RPKM), which was calculated using Htseq software version 0.9.0  as follows: RPKM = total exon reads/[mapped reads × exon length (kb)]. Fold changes (FCs) of the differentially expressed genes in the tongue cancer and paracancerous tissues were calculated. Genes with logGO analysis revealed significant enrichment of the terms associated with biological processes, molecular functions and cellular components. GO terms were assigned based on differentially expressed mRNAs and host genes with significantly different circRNAs. The P-value of each GO term was calculated using Fisher's exact test, and P<0.05 was considered to indicate statistically significant differences.Pathway annotation of differentially expressed genes was performed using the KEGG database. The P-value of each pathway involved was analyzed using Fisher's exact test based on hypergeometric distribution. P<0.05 was considered to indicate statistically significant differences.Tongue cancer and paracancerous tissues from each patient were considered as a group of samples to verify the changes of cancer-related genes by RT-qPCR. The total sample size was 20, and basic patient information is provided in According to the results of gene expression and signaling pathway analysis, 10 genes that were significantly different and were associated with cancer were selected for RT-qPCR. Their primers are listed in The signaling pathway networks were depicted using Cytoscape 3.4.0 software (Institute for Systems Biology). Each pathway network was depicted based on the pathway terms, and those with P<0.05 were analyzed by KEGG.2FC to explain the upregulation of gene expression in cancer tissues.The data are presented as mean ± standard error of the mean. RNA-sequencing data were obtained from 4 independent experiments, while RT-qPCR data were obtained from 20. These data were mainly compared by logRepresentative pathological images are shown in Transcriptome sequencing and data filtering were performed on 4 matched pairs of tongue cancer and paracancerous tissues. After removing adaptor sequences, the acquired data were compared with the reference genome. Large data sequences and high unique mapping rates of the samples indicated that the quantity and quality of the sequenced data were in accordance with the requirements. The raw data have been uploaded to the NCBI online database, and the accession number is GSE143950.Principal component analysis  revealed2FC=11.35), KRTAP13-2 (log2FC=−10.52), KRT36 (log2FC=−10.07) and S100A7A (log2FC=10.00). The heatmap revealed that the trend in gene expression change was consistent in all 4 patients, and the differences between the cancerous and paracancerous tissues were statistically significant.Gene expressions were quantified via RPKM. Expression abundances were calculated and depicted as volcano plots. In total, 24,582 genes were examined, of which 1,700 were upregulated and 2,249 were downregulated . The topThe genes that were differentially expressed in tongue cancer and paracancerous tissues were analyzed. GO analysis uncovered significantly enriched terms associated with three items: i) Biological processes: Extracellular matrix (ECM) organization, cell adhesion, collagen catabolic processes, ECM disassembly and the integrin-mediated signaling pathway; ii) molecular functions: Calcium ion binding, integrin binding, growth factor activity and ECM structural constituents; and iii) cellular components: Proteinaceous ECM, extracellular region and ECM. The most significantly enriched terms were analyzed as the ECM organization and cell adhesion  and 5.Of the pathways identified by KEGG pathway annotation and analysis, 61 were significantly downregulated and 43 were upregulated . The mosThe differences of fold changes were compared between RNA-seq and RT-qPCR. The results are shown as Potential interactions among the differentially expressed pathways were depicted in the pathway network, including mitogen-activated protein kinase signaling, PI3K-Akt signaling, cancer, calcium signaling and focal adhesion pathways .Tongue cancer is a prevalent malignant disease that is difficult to diagnose in its early stages and is characterized by high rates of metastasis and postoperative recurrence . Hence, GO analysis uncovered significantly enriched terms associated with ECM organization (upregulated), cell adhesion (downregulated) and collagen catabolic processes (upregulated). KEGG analysis demonstrated that these differentially expressed genes were mainly enriched in the focal adhesion pathway (upregulated), ECM-receptor interaction pathway (upregulated), PI3K-Akt pathway (upregulated) and CAMs (downregulated). RT-qPCR was used to verify the sequencing results.The occurrence, progression and metastasis of tongue cancer are closely linked to the ECM . ProlifeThe mammalian ECM consists of ~300 proteins . The ECM1 to the S phase  usually occurs during tumor progression and embryonic development, leading to transformation of cells from an epithelial phenotype with strong adhesions to a mesenchymal phenotype with invasive ability and increased motility . EMT caugression .−/− mice, the rate of papilloma formation decreased by 50%; once benign tumors had formed, loss of FAK inhibited malignant progression  are the main mediators of the connection between cells and the ECM. FAs induce enhanced tumor cell motility and EMT through integrin-based signaling transition and mechanical structural support. Upregulation of genes in the FA pathway is closely associated with tongue cancer progression. During malignant progression from atypical dysplasia of the oral mucosal epithelium into oral squamous cell carcinoma, focal adhesion kinase (FAK) is gradually upregulated. Thus, FAK may be used as a diagnostic marker for precancerous oral epithelial lesions . In FAK−gression . Blockingression . FAK is gression . A relevgression . Therefogression ,47. The Collectively, the activity of the FAK, ECM-receptor, PI3K-Akt and CAM pathways varies greatly during tongue cancer occurrence and progression. The activated PI3K-Akt pathway stimulates cell proliferation, alters the cell cycle, inhibits apoptosis and induces angiogenesis. Phosphorylated glycogen synthases further downregulate E-cadherin and, thus, block the CAM pathway. Impaired CAMs result in compositional changes in the signaling molecules in the ECM, which, in turn, cause alterations in the relevant pathways. In addition, a unique microenvironment affected by component and rigidity alterations of the ECM further affect tumor cell metastasis. In summary, the progression, invasion and metastasis of malignant tumors is a dynamic process. The interaction between the PI3K-Akt pathway and CAMs was shown to trigger changes in the ECM, thus further aggravating an unfavorable microenvironment in tongue cancer.In the present study, potential molecular mechanisms and tumor-related pathways during tongue cancer progression were analyzed through RNA-Seq. However, certain limitations should be addressed. First, the small sample size may have led to individual bias. Since transcriptome sequencing is a sensitive detection method, our sequencing data only provided referential information. Second, differentially expressed pathways were abundant, and some were significant during tongue cancer progression. The critical pathways and underlying mechanisms of tongue cancer require further investigation.In conclusion, transcriptomes from four tongue cancer tissues and four paired paracancerous tissues were analyzed, and 1,700 upregulated and 2,249 downregulated genes were identified. These differentially expressed genes were mainly enriched in the FA, ECM-receptor interaction, PI3K-Akt and CAM pathways. These pathways synergistically promoted tongue cancer occurrence and progression, and may be potential biological markers and therapeutic targets for early-stage tongue cancer. However, due to the small sample size and sensitivity of RNA-Seq, further molecular biology research is required to elucidate the roles of differentially expressed pathways in tongue cancer progression, in order to provide a wider theoretical and experimental basis for the clinical diagnosis, treatment and prognosis of patients with tongue cancer.


Compound Xueshuantong capsule (CXC) and Hexuemingmu tablet (HXMMT) are two important Chinese patent medicines (CPMs) frequently used to treat proliferative diabetic retinopathy (PDR), especially when complicated with vitreous hemorrhage (VH). However, a network pharmacology approach to understand the therapeutic mechanisms of these two CPMs in PDR has not been applied. Materials and methods. The differentially expressed messenger RNAs (mRNAs) between normal retinal tissues in healthy individuals and active fibrovascular membranes in PDR patients were retrieved from the Gene Expression Omnibus database. The active ingredients of CXC and HXMMT and the targets of these ingredients were retrieved from the Traditional Chinese Medicine Systems Pharmacology database. The intersections of the CPM (CXC and HXMMT) targets and PDR targets were determined. Then, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed, and the ingredient-target networks, protein-protein interaction networks, and KEGG-target (KEGG-T) networks were constructed.  To identify differences in the active ingredients between CXC and HXMMT and to comparatively predict and further analyze the molecular targets shared by these CPMs and PDR. Radix salviae is the only herb common to both. CXC had 34 potential therapeutic targets in PDR, while HXMMT had these 34 and 10 additional targets. Both CPMs shared the following main processes: response to reactive oxygen species and oxidative stress, regulation of blood vessel diameter and size, vasoconstriction, smooth muscle contraction, hemostasis, and blood coagulation. The shared pathways included the AGE-RAGE signaling pathway in diabetic complications, TNF signaling pathway, relaxin signaling pathway, and IL-17 signaling pathway.  CXC contains 4 herbs, and HXMMT contains 19. Radix salviae, the only herb common to both CPMs, contains many useful active ingredients. The PDR-CXC and PDR-HXMMT networks shared 34 common genes , and the PDR-HXMMT network has 10 additional genes . The top 5 pathways with the highest gene ratio in both networks were the AGE-RAGE signaling pathway in diabetic complications, TNF signaling pathway, relaxin signaling pathway, IL-17 signaling pathway, and focal adhesion. Additional pathways such as neuroactive ligand-receptor interaction, chemokine signaling pathway, and AMPK signaling pathway were enriched with HXMMT targets. Thus, HXMMT has more therapeutic targets shared by different active ingredients and more abundant gene functions than CXC, which may be two major reasons why HXMMT is more strongly recommended than CXC as an auxiliary treatment for new-onset VH secondary to PDR. However, the underlying mechanisms still need to be further explored. Both CXC and HXMMT include components effective at treating PDR and affect the following main processes: response to reactive oxygen species and oxidative stress, regulation of blood vessels, and blood coagulation.  Diabetic retinopathy (DR), a serious complication of diabetes mellitus (DM) caused by microvascular ischemia and hypoxemia, affects approximately 35% of DM patients and an estimated > 90 million people worldwide , 2. The For thousands of years, traditional Chinese medicines (TCMs) have been used by Chinese people to treat DM and its complications . CompounNetwork pharmacology analysis is a convenient and systematic approach to identify core targets shared by drugs and diseases . It can The general procedure was as follows (described in detail in http://www.ncbi.nlm.nih.gov/gds/). The mRNA expression data were acquired from dataset GSE60436, which contains 3 samples from normal retinal tissues and 3 from active fibrovascular membranes in PDR patients. The raw expression data were first normalized, and analysis of DEmRNAs was then performed using the limma package based on the R language. The criteria for the selection of DEmRNAs were an adjusted P value of <0.05 and a |log2FC| value of >1, and the selected DEmRNAs were defined as PDR targets.The microarray data used in this study were retrieved from the Gene Expression Omnibus (GEO) database (https://tcmspw.com/index.php), a platform that provides pharmacokinetic characteristics and targets of each ingredient in these herbs. Oral bioavailability (OB) and drug-likeness (DL) are two parameters commonly used to screen active ingredients. The OB is the percentage of the orally administered dose of the unchanged drug that enters the systemic blood circulation, and it is an important pharmacokinetic indicator. DL is used to assess whether the ingredients function as known drugs.A total of 499 Chinese herbs and 12144 chemical ingredients from the Chinese pharmacopoeia (2010) were registered in the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database  was used to generate all visual network diagrams, including the I-T, PPI, and KEGG-T networks. The intersections of the formulas' targets (CXC and HXMMT targets) and the PDR targets were determined (CXC-PDR and HXMMT-PDR targets). Accordingly, two I-T networks were constructed. PPI networks were constructed using the Bisogenet 3.0.0 plugin in Cytoscape 3.6.1 based on the following databases: the Database of Interacting Proteins, the Biological General Repository for Interaction Datasets, the Human Protein Reference Database, the IntAct Molecular Interaction Database, the Molecular INTeraction Database and the Binding Database. The CytoNCA plugin was used to perform topological analyses. Degree centrality and betweenness centrality were the measures selected to represent the topological features of each node in the network. Degree centrality represents the number of edges linked by a node, while betweenness centrality represents the proximity of a node to other nodes. KEGG-T networks were constructed after enrichment analyses were performed.Cytoscape 3.6.1 . The criteria for the selection of KEGG pathways were a P value of <0.01 and a gene count of ≥3. Visualizations were performed using the ggplot2 R package.GO analyses were performed using the clusterProfiler package based on the R language. The criterion for the selection of GO processes was a Analysis of the microarray dataset GSE60436 showed that 1915 mRNAs (819 upregulated mRNAs and 1096 downregulated mRNAs) were differentially expressed in PDR patients compared with individuals without PDR .Radix salviae is the only herb common to both CPMs. Through screening the active ingredients, we found 34 potential therapeutic targets in PDR for CXC . In addition, RNF2, VNL, RPS27, COPS5, XPO1, PARP1, RACK1, YWHAB, and ITGA4 were included only in the PDR-HXMMT network. The topological screening processes of the PPI networks are shown in The top 20 or all GO processes  in the biological process (BP), cellular component (CC), and molecular function (MF) categories were identified . Both CXAfter KEGG pathway analyses were performed using KOBAS 3.0, possible PDR-related pathways were selected, and visualizations were generated using the ggplot2 R package. All genes and pathways enriched with CXC targets were also enriched with HXMMT targets, and HXMMT had 5 additional target genes: IGFBP3, CYCS, CD36, PTGER2, and COL1A2. The pathway that was the most significantly enriched and had the highest gene ratio in both analyses was the AGE-RAGE signaling pathway in diabetic complications. Other common pathways included the TNF signaling pathway, relaxin signaling pathway, IL-17 signaling pathway, and focal adhesion. Additional pathways, such as neuroactive ligand-receptor interaction, chemokine signaling pathway, and AMPK signaling pathway, were enriched with HXMMT targets. Then, the enriched pathways and their related target genes were used to construct KEGG-T networks for CXC and HXMMT. These results are shown in The GO terms enriched with CXC and HXMMT targets were similar and focused mainly on the response to reactive oxygen species and oxidative stress, regulation of blood vessels, and blood coagulation.Radix salviae is an important component that is closely related to the response to reactive oxygen species and oxidative stress. In an in vitro model of hypoxia and reoxygenation, Hu's [Radix salviae obviously alleviated cardiomyocyte apoptosis and protected mitochondrial function and cell membrane skeleton integrity in H9c2 cells. In addition, Zhang's experiment in rodents [ dripping pill inhibited apoptosis and exerted neuroprotective effects in the retinas of diabetic rats by increasing the expression of Bcl-2, Bcl-2-associated X, and caspase-3 in diabetic rats. Moreover, according to the I-T networks of CXC and HXMMT, Radix salviae contained a greater number of active ingredients related to potential therapeutic targets in PDR compared with the other herbal components, suggesting that Radix salviae may play a crucial role in PDR treatment. A randomized controlled trial (RCT) performed by Lian and colleagues [Radix salviae-containing Chinese herbal product was effective in treating DR and in delaying the progression from non-PDR to PDR by reducing the area of capillary nonperfusion and degree of vascular leakage. Our study identified tanshinone as one of the most important active ingredients of Radix salviae. According to previous studies, tanshinone exerts protective effects on retinal pigment epithelium and retinal endothelial cells [on, Hu's  study sh rodents  revealedlleagues  showed tal cells –17.α and improving microcirculation. Further studies should be performed to clarify the mechanism of HXMMT.Regarding circulatory-related effects, CXC is considered to be an effective complementary medicine to treat ischemic vascular diseases, such as cerebral infarction and cardiovascular diseases , 19. LyuRadix salviae) may be key therapeutic targets in DR. Consistent with Piao's result, we found that MMP9 was included in both the CXC and HXMMT I-T networks. Matrix metalloproteinases (MMPs) play an important role in the migration, differentiation, and proliferation of cells [Radix salviae. IGF is expressed in many tissues, including the retina, where it is found in cells such as retinal endothelial cells and retinal pigment epithelial cells. IGF is a crucial regulator of cell differentiation and is closely related to blood-retinal barrier breakdown and retinal neovascularization [Huangqi and Sanqi) and 9 herbal components of HXMMT , implying that different active ingredients may share common therapeutic targets. Combined and stronger therapeutic effects may be exerted on a therapeutic target shared by a greater number of active ingredients. HXMMT contains more components than CXC; therefore, HXMMT may have more therapeutic targets. In addition, many of the genes targeted only by HXMMT but not by CXC were related to circulation and blood coagulation. For instance, CYCS was shown to be involved in blood platelet formation and regulatory processes [Utilizing a network pharmacology approach, Piao et al.  found thof cells . Hyperglof cells . A previof cells  suggesteof cells . In addirization , 29. Howrocesses , 31. APOrocesses . PECAM1 rocesses . Similarrocesses . RACK1 mrocesses . PARP1, rocesses , 37. In Similar to Li et al.'s research , the AGEα in diabetic retina is likely to play a role in dysfunction and vulnerability related to DR progression via TNF-α.According to the pathway map, the AGE-RAGE signaling pathway in diabetic complications is also closely associated with the PI3K-Akt signaling pathway and VEGF. The PI3K-Akt signaling pathway is one of the most frequently studied pathways in DR –45. The The genes and pathways mentioned above and whether CXC/HXMMT regulates their functions are summarized in Radix salviae, the only herb common to both CPMs, contains many useful active ingredients. The PDR-CXC and PDR-HXMMT networks shared 34 common genes , and the PDR-HXMMT network has 10 additional genes . The top 5 pathways with the highest gene ratio in both networks were the AGE-RAGE signaling pathway in diabetic complications, TNF signaling pathway, relaxin signaling pathway, IL-17 signaling pathway, and focal adhesion. Additional pathways, such as neuroactive ligand-receptor interaction, chemokine signaling pathway, and AMPK signaling pathway, were enriched with HXMMT targets. Thus, HXMMT has more therapeutic targets shared by different active ingredients and more abundant gene functions than CXC, which may be two major reasons why HXMMT is more strongly recommended than CXC as an auxiliary treatment for new-onset VH secondary to PDR. However, the underlying mechanisms need to be further elucidated.Both CXC and HXMMT include components effective in treating PDR and affect the following main processes: response to reactive oxygen species and oxidative stress, regulation of blood vessels, and blood coagulation.


Scientific Reports 10.1038/s41598-020-60294-x, published online 25 February 2020Correction to: This Article contains typographical errors in the Results and Discussion section.Under subheading ‘Definition of the quality-significant events using X-ray radiography’,11. The former is responsible for maintaining the keyhole channel, while the latter for its collapse. It was shown that high keyhole depth leads to increased surface tension while the recoil pressure is reduced11,42.”“It is characterized by geometrical fluctuations of the keyhole channel. This phenomenon can be explained by the fact that a stable keyhole requires a balance between several factors, where the main ones are the surface tension and recoil pressureshould read:11. The former is responsible for maintaining the keyhole channel, while the latter for its collapse. It was shown that high keyhole depth leads to increased surface tension while the recoil pressure is reduced11,42.”“It is characterized by geometrical fluctuations of the keyhole channel. This phenomenon can be explained by the fact that a stable keyhole requires a balance between several factors, where the main ones are the recoil pressure and surface tensiont  =  10.5–12 ms. Interestingly, the pore merges with the keyhole channel formed by the second pulse and disappear, as shown in Fig. 3B, at t  =  3.3–6.9 ms.”“The formation of a pore at the end of the first laser pulse  can be seen at should read:t  =  10.5–12 ms. Interestingly, the pore merges with the keyhole channel formed by the second pulse and disappear, as shown in Fig. 4B, at t  =  3.3–6.9 ms.”“The formation of a pore at the end of the first laser pulse  can be seen at Additionally, under subheading ‘LBR and AE signatures for laser welding’,unstable keyhole was classified with an accuracy rate of 87% using temporal CNN (bold red). The errors are due to an overlap with the categories conduction welding, stable keyhole and keyhole explosion with error rates of 4% (bold blue), 5% (bold green) and 4% (bold black), respectively.”“For example, for the LBR ), the classification results for the category should read:unstable keyhole was classified with an accuracy rate of 87% using temporal CNN. The errors are due to an overlap with the categories conduction welding, stable keyhole and keyhole explosion with error rates of 4%, 5% and 4%, respectively.”“For example, for the LBR ), the classification results for the category


Acute-on-chronic liver failure (ACLF) is a clinical syndrome with acute jaundice and coagulation dysfunction caused by various inducements on the basis of chronic liver disease. Western medical treatment is limited. Previous studies have confirmed that Jieduan-Niwan Formula (JDNW Formula), an empirical prescription for the treatment of ACLF, can inhibit inflammation and resist hepatocyte apoptosis. However, potential targets and mechanisms still need to be explored.  In this study, network pharmacological analysis was performed to investigate the key components and potential mechanisms of JDNW Formula treating ACLF. Firstly, we predicted the potential active ingredients of JDNW Formula and the corresponding potential targets through TCMSP, BATMAN-TCM platform, and literature supplement. Then, the ACLF targets database was built using OMIM, DisGeNET, and GeneCard database. Based on the matching targets between JDNW Formula and ACLF, the PPI network was constructed for MCODE analysis and common targets were enriched by Metascape. Furthermore, the ACLF rat model was used to verify the potential mechanism of JDNW Formula in treating ACLF. α signaling pathway as a prediction.  132 potential bioactive components of JDNW Formula and 168 common targets were obtained in this study. The enrichment analysis shows that the AMPK signaling pathway was associated with the treating effects of JDNW Formula. Quercetin was hypothesized to be the key bioactive ingredient in JDNW Formula and has a good binding affinity to AMPK based on molecular docking verification. JDNW Formula and quercetin were verified to treat ACLF by regulating the AMPK/PGC-1 The study predicted potential mechanisms of JDNW Formula in the treatment of ACLF, involving downregulation of inflammatory factor expression, antioxidant stress, and inhibition of hepatocyte apoptosis. JDNW Formula may improve mitochondrial quality in ACLF via the AMPK signaling pathway, which serves as a guide for further study. Acute-on-chronic liver failure (ACLF) is a clinical syndrome characterized by short-term and high mortality in patients with chronic liver disease who suffer an acute injury within a short period, resulting in acute or subacute decompensation of liver function and intrahepatic and extrahepatic organ failure . The shoJieduan-Niwan Formula (JDNW Formula) is an empirical prescription developed by Professor Ying Qian, a nationally celebrated TCM expert, for the treatment of ACLF. This formula consists of ten Chinese herbs, including Salvia miltiorrhiza , Astragali Radix , Trichosanthes Trichosanthes , Phyllanthus amarus , Herba Lysimachiae , Viscum coloratum , Panax notoginseng , Zedoary Rhizome , Rehmanniae Radix , and Radix Aconiti Lateralis Preparata . Through the clinical observation of the efficacy of patients with ACLF, it is found that the comprehensive curative effect of JDNW Formula combined with the western medicine treatment group has an advantage compared with the western medicine treatment group [However, benefited by the characteristics of multicomponents, multitargets, and multipathways , Traditint group . Our prent group , 10. HowIn order to investigate the potential mechanism of JDNW Formula in the treatment of ACLF, the method of network pharmacology was adopted , 12. Thihttp://tcmspw.com/tcmsp.php) and a Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine [http://bionet.ncpsb.org/batman-tcm/) were searched to preliminarily collect the candidate ingredients of ten herbs in JDNW Formula. Then, two ADEM parameters, oral bioavailability (OB) and druglikeness (DL), were selected to screen the potential bioactive compounds in candidate ingredients. OB, a parameter of pharmacokinetics in drug screening, represents the ability of the active ingredient to be absorbed into the system circulation to exert a biological effect [Traditional Chinese Medicine System Pharmacology Database   was used to search the molecular chemical structure information of the default bioactive ingredients in TCMSP. The target was screened by submitting the molecular chemical structure of bioactive ingredients to Swiss Target Prediction (http://www.swisstargetprediction.ch/), a tool for target prediction based on 2-dimensional and 3-dimensional chemical similarity of ligands [Firstly, the target information of the bioactive ingredients of JDNW Formula was obtained on the TCMSP platform. Besides, the PubChem database  , DisGeNET [https://www.disgenet.org/), and GeneCard [https://www.genecards.org/). Disease targets were obtained by deleting duplicate target information. After all the targets were normalizing by gene symbols according to the Uniprot database (https://www.uniprot.org/), the targets of bioactive ingredients of JDNW Formula were matched with ACLF-related targets, and the target network was constructed. The overlapping targets were selected as the relevant target of the JDNW Formula for treating ACLF.The known targets related to ACLF were integrated by using the key work “Acute-on-Chronic Liver Failure” from Online Mendelian Inheritance in Man  , a platform with fast data updating, wide coverage, integration of multiple database resources, and rich annotation, to perform PPI network construction, Gene Ontology (GO), and KEGG pathway enrichment analysis. After constructing the PPI network, common targets were grouped into corresponding clusters according to the Molecular Complex Detection (MCODE) algorithm in Metascape. By setting min overlap = 3, min enrichment = 1.5, P ≤ 0.01, biological processes (BPs), molecular functions (MFs), cellular components (CCs), and metabolic pathways which the common genes involved were obtained.The common targets of JDNW Formula and ACLF were summited to Metascape  (http://http://cao.labshare.cn/cb-dock/) is a web server for blind docking for cavity detection based on the widely used docking tool AutoDock Vina. The benchmark tests show that CB-Dock is superior to other state-of-the-art blind docking tools in predicting binding sites and binding conformations [http://rcsb.org). The files of ligand and receptor were input to CB-Dock to elevate the binding activities. The binding models were visualized using CB-Dock and Ligplot (Version 2.2) [CB-Dock (rmations . Quercetrmations  (http://ion 2.2) .Quercetin , human serum albumin , D-galactosamine , and lipopolysaccharide  were purchased from SigmaAldrich .n = 6), ACLF model group , JDNW Formula treatment group , and quercetin treatment group . Rats were adaptively housed for 5 days before the experiment. As described in previous studies [μg/kg LPS intraperitoneal injection for the acute attack to construct the ACLF model. Rats in the NC group were given the same volume of normal saline instead. Before giving D-GalN/LPS, rats in the JDNWF group were given 21.7 g/kg JDNW Formula decoction and those in the Que group were given 100 mg/kg quercetin by intragastric administration for 7 days.30 male Wistar rats, weighing 180–200 g, were housed in an SPF environment with normal water and food. All procedures were approved by the Animal Experiments and Experimental Animal Welfare Committee of Capital Medical University. The rats were randomly divided into 4 groups: normal control group . The stained samples were scanned by Pannoramic SCAN (3D HISTECH) at 400×.α , p-AMPK , and GAPDH . Then, the membranes were incubated with corresponding secondary antibodies, including Donkey Anti-Mouse IgG  and Goat Anti-Rabbit IgG  at room temperature. Immunoreactivity was detected by electrochemiluminescence (ECL) reagent . The levels of protein expression were normalized relative to GAPDH. Quantitative analysis was performed by ImageJ software.Liver tissues in each group were disrupted in lysis buffer and the supernatant was collected after centrifugation. The protein concentration of each supernatant was determined by BCA protein assay. Equal amounts of protein were separated by 10% sodium dodecyl sulfate-polyacrylamide gel (SDS-PAGE) electrophoresis and electrophoretically transferred to PVDF membranes . After blocking with 5% nonfat dry milk, bands were incubated overnight at 4°C with appropriate primary antibodies against PGC-1P values < 0.05 were considered to be statistically significant.The experiment data were analyzed using GraphPad software (Prism 9.00). All the results were presented as mean ± standard deviation (SD) and analyzed by one-way ANOVA. The least-significant difference (LSD) method was applied when the variance was equal; otherwise, Tamhane's T2 test was performed. Dan Shen, Huang Qi, Gua Lou, Ku Wei Ye Xia Zhu, Jin Qian Cao, Hu Ji Sheng, San Qi, E Zhu, Sheng Di Huang, and Pao Fu Zi. After the deletion of duplicate values, 132 ingredients of JDNW Formula were eventually collected as potential active compounds and shown in Supplementary Through TCMSP, BATMAN-TCM, and literature mining, the ingredients of the JDNW Formula were initially obtained. Further, screening by two ADEM parameters  and supplementing with active compounds with actual targets but in low OB or DL, 148 compounds were collected and were considered as potential active ingredients, including 59, 17, 10, 20, 10, 6, 7, 2, 10, and 7 from By combining known targets and chemical similarity predictions, a total of 336 corresponding potential targets were identified from 132 potential active ingredients of JDNW Formula , cellular components (CCs), molecular function (MF), and molecular pathway involved in the treatment of ACLF with JDNW Formula  was performed on CB-Dock using default parameters . Vina scIn the NC group, the hepatic cords in the liver were regularly arranged and structurally intact. The morphology of hepatocytes was normal, without inflammatory cell infiltration. In the ACLF group, hepatic lobular structures were destroyed, and the hepatic sinusoid also has obvious hyperemia. Hepatocytes are seen to be swollen and necrotic, with the loss of the nucleus. There is a large amount of inflammatory cell infiltration in the portal area and around the focal point of hepatocellular necrosis. However, lighter inflammatory cell infiltration can be seen in the JDNWF group and quercetin group, accompanied by hepatic fibrous tissue hyperplasia and regular hepatic cord structures .α were evaluated by Western Blot. Compared with the NC group, the level of p-AMPK and PGC-1α, the downstream signaling molecule, was inhibited in the ACLF group. However, JDNW Formula and quercetin treatment attenuated this decrease  and PGC1-decrease .ACLF is a clinical syndrome with acute jaundice and coagulation dysfunction caused by various inducements on the basis of chronic liver disease. It can be complicated with hepatic encephalopathy, ascites, electrolyte disturbance, infection, hepatorenal syndrome, and even extrahepatic multiple organ failure . ACLF isBy mining the database, 132 potential bioactive ingredients were identified in JDNW Formula, 1471 ACLF-related targets, and 168 common targets shared in JDNW Formula and ACLF. The node of quercetin, with the highest degree value in the compound-common targets network, was presumed to be the potential active ingredient that plays a key role in the mechanisms of the JDNW Formula. In fact, quercetin, with hepatoprotective effects, exhibits strong antioxidant activity and anti-inflammatory effects  and inhiAs the PPI network of common targets showed, JUN, TP53, EGFR, MAPK1, AKT1, RELA, GSK3B, MYC, PRKCA, and STAT3 were the top 10 targets ranking by degree, which indicated that JDNW Formula may play a potential role in improving ACLF by regulating these targets. What is more, targets that are highly correlated with each other were grouped into the same cluster with a corresponding description of GO-BP and KEGG pathway enrichment analysis to explore the biological mechanisms, suggesting that the therapeutic mechanism of JDNW Formula in ACLF is a combination of multiple targets. According to the network, targets of JDNW Formula for treating ACLF are involved in the responses to oxidative stress, responses to the pathogen-associated molecular pattern (PAMP), regulation of inflammatory responses, apoptotic pathways, and cell proliferation. In fact, our previous study has proved that JDNW Formula can alleviate ACLF by regulating JNK-induced mitochondrial apoptotic pathway and E2F1-mediated intrinsic apoptosis pathway in vivo , 10.α by inducing the secretion of IFN-1 and IFN-3 [κB to further transmit inflammatory signals [κB and MAPK signaling pathways. The level of TNF is related to the severity and prognosis of ACLF [α and IL-6 [From the results of the present study, based on the KEGG enrichment analysis, the potential pathways of JDNW Formula in the treatment of ACLF were closely related to pathways in cancer, hepatitis B, TNF signaling pathway, cAMP signaling pathway, AMPK signaling pathway, and calcium signaling pathway. HBV (enriched in cluster C) and HCV (enriched in cluster B) infection and reactivation are important causes of ACLF. HCV can be recognized by RIG-I-like receptors and Toll-like receptors. It can activate natural killer cells and promote the expression of TNF-nd IFN-3 . However signals , 40. TNF of ACLF . In our and IL-6 . By reguand IL-6 . As a keand IL-6 . The caland IL-6 .It is worth mentioning that the cancer pathway is also important in the treatment of ACLF. Because of the pathology of massive hepatocyte necrosis in ACLF, the treatment goal of ACLF patients is to protect hepatocytes and promote hepatocyte proliferation. Studies have shown that the transformation from fatty acid oxidation to glycolysis can produce effects similar to promoting the proliferation and apoptosis escape in tumor cells and improve the survival of hepatocytes under hypoxic and hyperammonemia conditions –47. The As discussed above, the enrichment pathway mainly involves the regulation of inflammation, oxidative stress, lipid metabolism, apoptosis, and cell proliferation. Mitochondrial dysfunction is closely related to inflammatory response, oxidative stress, and lipid metabolism , 49. Resα to promote mitochondrial biogenesis and ultimately maintain mitochondrial homeostasis [α was detected. In the present study, JDNW Formula and quercetin can attenuate the decrease of AMPK phosphorylation levels and the expression of PGC-1α in the ACLF group, suggesting that JDNW Formula and quercetin can modulate the AMPK/PGC-1α signaling pathway to treat ACLF.AMPK, a major regulator of mitochondria, can induce expression and acetylation of its downstream effector PGC-1eostasis , 50. TheIn this study, the means of network pharmacology was used to explore the effects of JDNW Formula in the treatment of ACLF at the molecular level and explained the possible mechanisms of its effects. Except for the anti-inflammatory effects and inhibition of apoptosis, which we have proved, our study predicted that protecting mitochondria may be a potential mechanism of JDNW Formula for ACLF. Quercetin and the AMPK signaling pathway could be the key to the mechanism. However, there were still some limitations. For example, the potential active ingredients are screened primarily by databases using ADME parameters. Although the components were supplemented by reviewing the literature, there is no denying that certain ingredients will be omitted. In addition, quercetin and other potentially active compounds should be verified in subsequent studies and metabolomic analysis, for example, to find biomarkers.In this study, the potential active compounds, genes, and the signaling pathway that may play a key role in the biological process were predicted, screened, and analyzed. Quercetin was presumed to be the key active compound of the JDNW Formula. It was elucidated that the possible mechanism of JDNW Formula treating ACLF is the synergistic effect of “multicomponents, multitargets, and multipathways,” including downregulation of inflammatory factor expression and antioxidant stress, inhibiting hepatocyte apoptosis, and improving mitochondrial quality. According to the enrichment analysis, JDNW Formula may improve mitochondrial quality in ACLF via the AMPK signaling pathway, which needs further study. More importantly, the results of this study provide a theoretical direction for the JDNW Formula in the treatment of ACLF. Further experimental validation is still necessary to identify the key active ingredients in the JDNW Formula and to investigate how this formula acts as a therapeutic agent for ACLF through relevant pathways.


We explored the mechanism of Shugan Jianpi Formula (SGJPF) and its effective components for the treatment of liver fibrosis (LF).  We collected the active ingredients in SGJPF through the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform and screened the effective components by absorption, distribution, metabolism, and excretion. Herb-associated target proteins were predicted and screened based on the Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine and Search Tool for Interactions of Chemicals databases. LF-associated target proteins were predicted and screened based on the Online Mendelian Inheritance in Man® Database and Comparative Toxicogenomics Database. Common genes with LF and herbs were selected, and Cytoscape 3.5.1 software was used to construct an herb pathway and component-LF common target network. The Search Tool for the Retrieval of Interacting Genes/Proteins was used to build a protein-protein interaction, and quantitative PCR was used to verify the related target genes. Finally, clusterProfiler was applied for the analysis of Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways. β, and the nucleotide-binding and oligomerization domain-like receptor and Janus kinase-signal transducer and activator of transcription signaling pathways were selected as the critical target gene and critical signal pathway, respectively.  The pharmacological network contained 252 active compounds , 84 common target genes, and 94 significant signaling pathways. Among them, interleukin 6 (IL-6), tumor protein 53 p53 (TP53), prostaglandin-endoperoxide synthase 2 (PTGS2), AKT1, IL-1 The mechanisms of SGJPF in protecting against LF include the regulation of multiple targets such as IL-6, TP53, PTGS2, and AKT1. These target proteins affect LF through various signal transduction pathways. Liver fibrosis (LF) is a pathological reaction of the liver to chronic liver injury . Liver iTraditional Chinese Medicine (TCM) has received an increasing amount of attention due to its notable effectiveness and low side effects. TCM also has special advantages in antiliver fibrosis as it is characterized by holistic concepts and TCM syndrome differentiation and treatment . Shugan With the development of system biology and bioinformatics, analysis based on network pharmacology provides new methods that reveal the complex mechanisms of TCM. The methodologies of network pharmacology highlight the paradigm shift from “one drug, one target” to “multicomponent therapeutics, biological network” . TCM hasIn this study, to reveal the mechanism of SGJPF in the treatment of LF, we collected information on targets of active ingredients in SGJPF and targets of LF from several databases and performed network construction and topological structural analysis. The results of this study reveal the underlying synergistic mechanisms of SGJPF in treating LF .All data on the chemical ingredients of 11 herbs  were derived from the TCM Systems Pharmacology Database and Analysis Platform (TCMSP). TCMSP provides users with a platform for the analysis of TCM components, including the identification of active components .In modern drug discovery, early assessment of absorption, distribution, metabolism, and excretion (ADME) screening has become an essential process. The proper use of ADME results can give preference to those drug candidates that are more likely to have good pharmacokinetic properties and minimize potential drug-drug interactions. In the present work, two ADME-related models, oral bioavailability (OB) and drug-like (DL), were employed to screen the active components from SGJPF. DL helps to describe the pharmacokinetic and pharmaceutical properties of compounds, such as solubility and chemical stability . Usuallyhttp://www.omim.org/) with keywords of “liver fibrosis” or “hepatic fibrosis” and the Comparative Toxicogenomics Database (CTD) (https://www.ctdbase.org) [Different genes associated with LF were collected from Online Mendelian Inheritance in Man (OMIM) (ase.org)  with thehttp://bionet.ncpsb.org/batman-tcm) ranks potential drug-target interactions based on their similarity to known drug-target interactions [http://stitch.embl.de/) integrates many sources of experimental and manually curated evidence with text-mining information and interaction predictions [Two databases were combined to predict relevant targets of active ingredients in SGJPF comprehensively. The Bioinformatics Analysis Tool for Molecular mechANism of TCM (BATMAN-TCM)  pathway analysis was performed using clusterProfiler with each herb's targets. Of note, a pe 3.5.1 , an openIntegrated targets were matched with the LF-associated target, and the intersecting genes were preserved for further analysis. A component-target network was established to identify the key target. Then, Cytoscape 3.5.1 was used to visualize the network . Simultahttps://string-db.org/) is a database that provides comprehensive information about interactions between proteins, including prediction and experimental interaction data [The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING)  into the back skin of mice, twice a week for 12 weeks. From the day after injection with 20% CCl4, SGJPF (7.80 g/kg) was administered by gavage once a day for 12 weeks. Then, total RNA from the liver was extracted using the EZ-10 Total RNA Mini-Prep Kit  and first-strand cDNA was reverse-transcribed and synthesized using the ABscript II cDNA First-Strand Synthesis Kit . The reaction system was established by TB Green™ Premix Ex Taq™ II , and quantitative PCR (qPCR) was performed using the Bio-Rad iQ5 Real-Time PCR System . The primers are shown in P < 0.05.SPSS17.0 software was used for data analysis and processing, and the results are expressed as mean ± standard deviation (SD) In total, 11 types of herbs in the SGJPF contained 1048 compounds; detailed information about all compounds can be visualized in the Upset plot, which demonstrates the quantitative results of multiple interactive sets more effectively than the traditional Venn diagram . As showAccording to the screening threshold of OB > 30% and DL > 0.18, 252 active compounds from all compounds were selected as the active components of SGJPF including astragaloside A, saikosaponin, linoleic acid, and Poria acid A .To explore the biological functions of herb-target genes, the KEGG pathway was analyzed, and then the herb-pathway network was reconstructed using Cytoscape 3.5.1. As shown in TCM exerts its pharmacological action through the interaction of various components and targets. Therefore, in addition to the predictive component, studying the target is also necessary . HoweverTo reflect the intrinsic connection among compounds, diseases, and target genes, a network among compounds and common target genes was reconstructed and visualized. As shown in After removing isolated genes, the PPI network was delineated using the STRING database. As shown in P < 0.05, which were mainly related to the TLR and nucleotide-binding oligomerization domain (NOD)-like receptor signaling pathways. The top 30 pathways are shown in To explore the biological functions of core targets, Gene Ontology (GO) and KEGG pathways were analyzed. GO analysis revealed that there were 3626 GO terms. Among them, biological processes included positive regulation of type 2 immune response, chronic inflammatory response to antigenic stimulus, negative regulation of receptor biosynthetic process, and positive regulation of natural killer cell chemotaxis. The molecular functions mainly included the chemokine metabolic process, type II transforming growth factor beta receptor binding, interleukin-12 (IL-12) receptor binding, and toll-like receptor (TLR) binding. The top 30 significant GO terms are shown in β in the model group were significantly increased, while the expression levels of IL-6, TP53, AKT1, and IL-1β in the SCJPF group were significantly lower than those in the model group.We chose four target genes with higher degrees to verify the influence of SGJPF. As shown in LF is considered the common pathological process of all chronic liver diseases, with complex formation processes and mechanisms, many influencing factors, and great individual differences. Therefore, the clinical treatment of LF still faces great challenges . In viewIn light of the complexity of the active ingredients in SGJPF and the diversity of potential regulatory targets in the human body, we analyzed the main components of SGJPF by network pharmacology. The holistic and systematic nature of network pharmacology coincides with the holistic view of TCM and syndrome differentiation. It can use many existing databases and pathway and network analyses to explore the molecular mechanism of TCM for the treatment of diseases . Then, wAstragalus membranaceus [A is an active ingredient present in sputum and a well-known medicinal bacterium with various biological functions such as anti-inflammatory and antitumor effects. Studies have shown that Poria acid A has inhibitory effects on the formation of cell fibrosis [Astragaloside, the effective component of ranaceus , attenuaranaceus . Astragaranaceus . Saikosaranaceus , 25. Linranaceus . Poria afibrosis .T cell and macrophage, is an important central gene [β, a critical mediator of the inflammatory response, and can promote the proliferation and differentiation of HSCs in a dose-dependent manner, leading to the occurrence and development of LF [β in the pathogenesis of LF to play a role in the treatment of LF.According to the common target gene degree, this study predicted that SGJPF treats LF mainly through targeting IL-6, TP53, PTGS2, and AKT1, suggesting that SGJPF has multicomponents and multitargets characteristics for the treatment of LF . The abnral gene . Cytokinral gene . TP53 isral gene . AKT1 isral gene . By autoral gene , 34, regnt of LF , 36. ThrκB) [κB pathway [κB pathway promotes the release of proinflammatory cytokines, during the occurrence of liver inflammation. NLR Family Pyrin Domain Containing 3 (NLRP3) inflammatory bodies are activated, and the expression of NLRP3, apoptosis-associated speck-like protein containing a CARD, and caspase-1 in the liver increases, which induces pyroptosis, further expanding the inflammatory response and promoting the occurrence and development of LF [The results of KEGG metabolic pathway enrichment analysis showed that the TLR, NOD-like receptor, and JAK-STAT signaling pathways were mainly involved in the treatment of LF with SGJPF. The TLR signaling pathway is complex and has a wide range of effects. Its effect on LF is mainly through TLR4, TLR9, and its signal transduction pathway, especially the signal pathway of myeloid differentiation factor 88-nuclear factor kappa B (NF-κB) , 38. AftκB) . NOD-lik pathway . Activatnt of LF . The JAKnt of LF , 43. It nt of LF .There are still some limitations in using network pharmacology to predict the effective components and potential mechanism of SGJPF. First, when predicting the potential active components of SGJPF, OB and DL in TCM powder were used as screening indexes. However, compared with the data from other databases , the screening indicators are different. After consideration, we only retained the data from TCMSP, but did not rule out the existence of other active ingredients for the treatment of LF. Second, the data obtained in this study were based on previous experimental results and computer simulation predictions. Although we obtained some action targets and the pathways in which these targets are located to provide a direction for follow-up research, the more exact therapeutic mechanism of SGJPF on LF was not reflected in this study. Therefore, this still needs to be studied or verified in detail in follow-up animal and human experiments to further implement the development of new drugs and improve clinical medication thinking. Third, during the treatment of LF with SGJPF, the drug components actually absorbed into the patient's body after oral administration are not necessarily the same as the active components selected; thus, the effect of drugs on the target may also be different, and the effects of other factors may have led to deviations in the results. Therefore, to verify the theoretical prediction, further experiments on the effective components are needed.A, saikosaponin, Poria acid A, and other components may act on IL-6, TP53, PTGS2, AKT1, and other targets, affecting TLR, NLR, and JAK/STAT, among other signaling pathways and participating in the inflammatory response, HSC proliferation and differentiation, apoptosis, pyroptosis, and other processes. This appears to be the mechanism underlying the therapeutic effect of SGJPF on LF, and it broadens the train of thought for follow-up pharmacological research.In conclusion, the mechanism of SGJPF in the treatment of LF involves a variety of active components, targets, and pathways. In this study, 252 active components, 84 potential targets, and 94 related signaling pathways in SGJPF were predicted. Among them, astragaloside


Cancer has several phenotypic subtypes where the responsiveness towards drugs or capacity of migration or recurrence are different. The molecular networks are dynamically altered in various phenotypes of cancer. To reveal the network pathways in epithelial-mesenchymal transition (EMT), we have profiled gene expression in mesenchymal stem cells and diffuse-type gastric cancer (GC), as well as intestinal-type GC. Gene expression signatures revealed that the molecular pathway networks were altered in intestinal- and diffuse-type GC. The artificial intelligence (AI) recognized the differences in molecular network pictures of intestinal- and diffuse-type GC.male-specific lethal 3 homolog (Drosophila) pseudogene 1 (MSL3P1), CDC28 protein kinase regulatory subunit 1B (CKS1B), DEAD-box helicase 27 (DDX27), golgi to ER traffic protein 4 (GET4), chromosome segregation 1 like (CSE1L), translocase of outer mitochondrial membrane 34 (TOMM34), YTH N6-methyladenosine RNA binding protein 1 (YTHDF1), ribonucleic acid export 1 (RAE1), par-6 family cell polarity regulator beta (PARD6B), and MRG domain binding protein (MRGBP), which have differences in gene expression between intestinal- and diffuse-type GC. A total of 463 direct relationships with three molecules  were found in the biomarker-filtered network generated by network pathway analysis. The networks and features in intestinal- and diffuse-type GC have been investigated and profiled in bioinformatics. Our results revealed the signaling pathway networks in intestinal- and diffuse-type GC, bringing new light for the elucidation of drug resistance mechanisms in CSCs.Epithelial-mesenchymal transition (EMT) plays an important role in the acquisition of cancer stem cell (CSC) feature and drug resistance, which are the main hallmarks of cancer malignancy. Although previous findings have shown that several signaling pathways are activated in cancer progression, the precise mechanism of signaling pathways in EMT and CSCs are not fully understood. In this study, we focused on the intestinal and diffuse-type gastric cancer (GC) and analyzed the gene expression of public RNAseq data to understand the molecular pathway regulation in different subtypes of gastric cancer. Network pathway analysis was performed by Ingenuity Pathway Analysis (IPA). A total of 2815 probe set IDs were significantly different between intestinal- and diffuse-type GC data in cBioPortal Cancer Genomics. Our analysis uncovered 10 genes including  CDH1 and RHOA distinguished GC from colorectal and esophageal tumors, and these mutations were specific to diffuse-type GC, it is still challenging to discriminate the intestinal-type and diffuse-type GC in molecular gene expression networks [CDH2 to CDH1 distinguish the intestinal- and diffuse-type GC [Different cell types show a variety of molecular networks. Gastric cancer (GC) has several subtypes, which includes intestinal- and diffuse-type GC ,2. Intesnetworks . We haveEpithelial-mesenchymal transition (EMT) is associated with the malignancy of GC and diffuse-type GC . EMT is The importance and potential to use the molecular network profile to distinguish diffuse- and intestinal-type GC are increasing in the digital era to reveal the EMT mechanism . The premale-specific lethal 3 homolog (Drosophila) pseudogene 1 (MSL3P1), CDC28 protein kinase regulatory subunit 1B (CKS1B), DEAD-box helicase 27 (DDX27), golgi to ER traffic protein 4 (GET4), chromosome segregation 1 like (CSE1L), translocase of outer mitochondrial membrane 34 (TOMM34), YTH N6-methyladenosine RNA binding protein 1 (YTHDF1), ribonucleic acid export 1 (RAE1), par-6 family cell polarity regulator beta (PARD6B), and MRG domain binding protein (MRGBP). Gene expression profile of the top 10 genes in intestinal- and diffuse-type GC are shown in t-test, p < 0.00001)  type and genomically stable (GS) type samples in TCGA RNAseq data, respectively. 0.00001) .t-test, p < 0.00001). A total of 25 networks generated from genes that have significant difference between intestinal- and diffuse-type GC are shown in Networks of genes altered in intestinal- and diffuse-type GC were analyzed using Ingenuity Pathway Analysis (IPA). A total of 2815 IDs that had significant difference between intestinal- and diffuse-type GC were analyzed in IPA -related regulator effect networks were analyzed in intestinal- and diffuse-type GC . Table 5Upstream regulators of genes altered in intestinal- and diffuse-type GC were defined by IPA analysis. The top 25 upstream regulators of the altered genes in intestinal- and diffuse-type GC are shown in p value < 10−6, p < 0.005 in Bonferroni statistics). In the 2815 IDs, EMT-related genes, which have GO  term “epithelial to mesenchymal transition”, included BMP and activin membrane bound inhibitor (BAMBI), EPH receptor A3 (EPHA3), GLI pathogenesis related 2 (GLIPR2), MAD2 mitotic arrest deficient-like 2 (yeast) (MAD2L2), SMAD family member 4 (SMAD4), SRY-box 9 (SOX9), Wnt family member 11 (WNT11), adiponectin receptor 1 (ADIPOR1), bone morphogenetic protein 7 (BMP7), ephrin A1 (EFNA1), forkhead box A2 (FOXA2), hepatocyte growth factor (HGF), histone deacetylase 2 (HDAC2), low density lipoprotein receptor class A domain containing 4 (LDLRAD4), msh homeobox 2 (MSX2), ovo like zinc finger 2 (OVOL2), pleckstrin homology like domain family B member 1 (PHLDB1), transforming growth factor beta receptor 2 (TGFBR2), and tripartite motif containing 28 (TRIM28).Gene Ontology (GO) was analyzed in genes regulated in intestinal- and diffuse-type GC. A total of 2815 IDs were analyzed for enrichment analysis in the Database for Annotations, Visualization and Integrated Discovery (DAVID) database, which resulted in 2762 DAVID gene IDs analyzed in GO Biological Process. The top 21 GOs are shown in CCR4-NOT transcription complex subunit 3 (CNOT3), CD34 molecule (CD34), ETS variant 4 (ETV4), Fanconi anemia complementation group D2 (FANCD2), GATA binding protein 2 (GATA2), LDL receptor related protein 5 (LRP5), LIM domain binding 2 (LDB2), RNA polymerase II subunit C (POLR2C), RNA polymerase II subunit H (POLR2H), RNA polymerase II subunit J (POLR2J), SMAD family member 4 (SMAD4), SRY-box 2 (SOX2), SRY-box 9 (SOX9), Wnt family member 2B (WNT2B), abnormal spindle microtubule assembly (ASPM), alpha-2-macroglobulin (A2M), cullin 4A (CUL4A), cyclin dependent kinase inhibitor 2A (CDKN2A), endothelial PAS domain protein 1 (EPAS1), epithelial cell adhesion molecule (EPCAM), fibulin 1 (FBLN1), frizzled class receptor 1 (FZD1), growth arrest specific 6 (GAS6), hes family bHLH transcription factor 1 (HES1), insulin like growth factor 2 mRNA binding protein 1 (IGF2BP1), keratinocyte differentiation factor 1 (KDF1), lysine demethylase 1A (KDM1A), mediator complex subunit 10 (MED10), mediator complex subunit 24 (MED24), mediator complex subunit 30 (MED30), mesenchyme homeobox 1 (MEOX1), msh homeobox 2 (MSX2), notchless homolog 1 (NLE1), ovo like zinc finger 2 (OVOL2), paired related homeobox 1 (PRRX1), platelet activating factor acetylhydrolase 1b regulatory subunit 1 (PAFAH1B1), platelet derived growth factor receptor alpha (PDGFRA), programmed cell death 2 (PDCD2), proteasome 26S subunit, non-ATPase 11 (PSMD11), spalt like transcription factor 4 (SALL4), squamous cell carcinoma antigen recognized by T-cells 3 (SART3), ubiquitin associated protein 2 like (UBAP2L), and vacuolar protein sorting 72 homolog (VPS72).EMT may be related to the stemness of cancer. Genes related to stem cells have been investigated in the 2815 IDs, which have significant differences between intestinal- and diffuse-type GC. Genes which have the “stem cell” term in GO  included CTNNBL1, ZEB2, ERBB3, TGFBR2, SMAD4, CDH5, STAT5A, AKT3, STAT5A, AKT3, STAT5B, WNT11, STAT1, WNT2B, ERBB2, and TWIST2 [Metabolism is one of the possible EMT-modified processes. Genes which have “metabolism” term in KEGG PATHWAY in the analysis of DAVID of the 2815 IDs are listed in d TWIST2 .The results of upstream analysis of intestinal- and diffuse-type GC data were analyzed in DataRobot Automated Machine Learning version 6.0 for creating prediction models. The list of upstream regulators was up-loaded and linked with network picture data, followed by the target prediction setting as subtype differences in intestinal- and diffuse-type GC . Among vThe canonical pathways for Regulation of the EMT pathway include TGF-beta pathway, Wnt pathway, Notch pathway, and Receptor Tyrosine Kinase pathway . In eachCDH2 to CDH1 [CKS1B, CSE1L, DDX27, GET4, MRGBP, MSL3P1, PARD6B, RAE1, TOMM34, and YTHDF1. The network analysis of altered genes in intestinal- and diffuse-type GC generated networks related to cancer, gastrointestinal disease, organismal injury and abnormalities, amino acid metabolism, molecular transport, small molecule biochemistry, and so on. Several miRNAs including miR-205-5p, miR-21-5p, let-7a-5p, let-7, miR-24-3p, and miR-291a-3p were identified to regulate networks involved in intestinal- and diffuse-type GC. Since previous studies have revealed the involvement of miR-200s in promoting metastatic colonization by inhibiting EMT and promoting mesenchymal-epithelial transition (MET), it may be an intriguing approach to reveal miRNA networks in EMT [It is critical to distinguish the intestinal- and diffuse-type GC for effective therapeutic strategies, since the pathogenesis and prognosis are quite different in these subtypes. We previously revealed the gene signature of intestinal- and diffuse-type GC, which is indicated by the ratio of gene expression in  to CDH1 ,14,15,16s in EMT ,18. The s in EMT ,20,21. DCDH2 to CDH1, as previously shown [The intestinal- and diffuse-type GC can be distinguished with the mRNA ratios of The RefSeq data of intestinal- and diffuse-type GC are publicly available in The Cancer Genome Atlas (TCGA) of The cBioPortal for Cancer Genomics database ,14,15 inData of intestinal- and diffuse-type GC in TCGA cBioPortal Cancer Genomics were uploaded and analyzed through the use of Ingenuity Pathway Analysis (IPA)  .Gene Ontology (GO) was analyzed in the Database for Annotations, Visualization and Integrated Discovery (DAVID) Bioinformatics Resources 6.8 (Laboratory of Human Retrovirology and Immunoinformatics) ,25.To create a prediction model by using multi-modal data including images and text description of molecular networks, an enterprise AI platform  was used. For the modeling, the 116 molecular networks of IPA upstream analysis in intestinal- and diffuse-type GC were collected and input as image data in the DataRobot (58 images in each subtype), which automatically created and tuned prediction models using various machine learning algorithms  ,27. FinaThe results of gene expression data of RefSeq and network analysis were visualized by Tableau software.p value < 0.00001. For DAVID Gene Ontology (GO) enrichment analysis, data was analyzed in the default setting. GO enrichment was considered significant in modified Fischer Exact p value < 10−6. Bonferroni statistics showed p value < 0.005.The RefSeq data were analyzed by Student’s t-test. Z-score in intestinal- and diffuse-type GC samples were compared, and the difference was considered to be significant in The regulatory molecular networks are altered in intestinal- and diffuse-type GC. Networks generated from genes altered in intestinal- and diffuse-type GC included a network related to cancer, gastrointestinal disease, and organismal injury and abnormalities. We demonstrated that several miRNAs regulated the networks in intestinal- and diffuse-type GC. Machine learning of network image data created prediction models to distinguish the subtypes of the GC. The molecular mapping of intestinal- and diffuse-type GC may reveal the EMT mechanism. The miRNAs identified in the study may be regulated in EMT, which would be critical for progression and metastasis process. Our results support further identification of GC subtypes through visual changes in molecular networks.


Activities of the 5 pathways were calculated based on corresponding pathway signatures and associations between pathways and clinical outcomes were examined by survival analysis. Among the five PAM50-based subtypes, BLBC had the highest IFNα, IFNγ, TNFα pathway activities, and the lowest TGFβ activity. The IFNα, IFNγ, TNFα pathway activities were negatively correlated with BLBC recurrence. In contrast, positive association and no association with BLBC recurrence were observed for TGFβ and STAT3 pathways, respectively. TNFα/TGFβ pathway combination improved the prediction of recurrence and chemotherapy response of BLBCs. Immune cell subset analysis in BLBC showed that M0, M1 and M2 macrophage levels were associated with either TNFα or TGFβ pathways, whereas the level of activated memory CD4 T cells were associated with both pathways. Moreover, this T cell subset was most abundant in BLBCs with low TGFβ and high TNFα pathway activities. These results suggested that cooperation of TNFα and TGFβ signaling may be involved in the regulation of memory T cells and anti-cancer immunity in BLBCs. Our data also demonstrate that TNFα/TGFβ pathway combination may represent a better biomarker for BLBC prognosis and clinical management.Basal-like breast cancer (BLBC) is a type of high-grade invasive breast cancer with high risk of recurrence, metastases, and poor survival. Immune activation in BLBC is a key factor that influences both cancer progression and therapeutic response, although its molecular mechanisms are not well clarified. In this study, we examined five cancer immunity-related pathways  in four large independent breast cancer cohorts ( Breast cancer is the most common malignant tumor in women and a heterogeneous disease that has been demonstrated to be divided into five major biologically distinct intrinsic subtypes based on gene expression profiling, i.e. luminal A, luminal B, human epidermal growth factor receptor-2 (HER2) overexpressing, basal-like, and normal-like Recently, whole genome analysis has been supplemented to the equipment library of experimental techniques, which provides a new molecular classification for breast cancer and promotes the development of many prognostic multigene analyses Although there are a variety of multi-gene signatures that can predict the prognosis of patients with breast cancer, their prognostic value seems to come mainly from their ability to measure the expression of proliferation-related genes Although genetic/epigenetic alterations are key pathogenic events, the immunological signaling plays an important role in promoting progression and metastasis of BLBCs. Previously, we examined multiple cancer-related pathway signatures in colon and breast cancers In our previous study We previously merged the 42 microarray datasets into 4 cohorts Using the BinReg approach to generate pathway signatures and predict pathway activities of individual samples has been described in detail before The signatures for 11 pathways analyzed in this study, except for BRAF/MEK and AR pathways, were previously reported by Gatza et al BRAF mutation is rare in breast cancer, here we generated a BRAF/MEK signature based on the cells treated with mock or MEK-specific inhibitor . The conditions for the BRAF/MEK pathway signatures are listed in Supplementary Table S1.IFNα, IFNγ, TGFβ and TNFα pathway signatures were generated and validated using gene expression data from cells treated with or without IFNα, IFNγ, TGFβ and TNFα, respectively The pathway signature scores were first Z-sore transformed. The scores for combination of TGFβ with IFNα, IFNγ, and TNFα were calculated by subtracting the TGFβ signature score from IFNα, IFNγ, and TNFα signature scores respectively .The intrinsic subtypes luminal A (LumA), luminal B (LumB), HER2-enriched, normal-like and basal-like were predicted by using the PAM50 centroids in the Genefu R package https://cibersort.stanford.edu/) was used to calculate immune cell subtype fractions based on the gene expression profile of each breast cancer sample CIBERSORT  for the associations of neoadjuvant response with risk scores were calculated using logistic regression. The association between disease-free survival and risk scores was analyzed using Kaplan-Meier survival curves with log-rank test and cox proportional hazards regression. Disease free survival (DFS) was defined as the time from surgery to the first confirmed relapse or metastasis. In this study distant metastasis-free survival (DMFS) was preferred to be used in survival analysis, while when the data is not available, relapse-free survival (RFS) data was used. The overall hazard ratio (HR) of a variable of interest was calculated using a random-effects model. The significance of the overall effects across multiple datasets was estimated by Z test.p < 0.05.All statistical analyses were two-sided and considered significant when 1). The IFNα and IFNγ pathways showed higher levels in HER2-enriched BCs and BLBCs but lower levels in luminal A and B BCs. The TNFα pathway activity increased gradually from luminal A, luminal B, Normal-like, HER2-enriched and basal-like subtypes. Conversely, the TGFβ pathway exhibited an opposite activity pattern with TNFα pathway. Overall, the pathway activities of IFNα, IFNγ, TNFα were the highest, while that of TGFβ was the lowest in BLBC. No significant difference of the STAT3 pathway activity was observed among the five PAM50 subtypes .Activities of the IFNα, IFNγ, TNFα TGFβ and STAT3 pathways, which were predicted with corresponding signatures, were compared among five PAM50-based BC subtypes. The first four pathways showed different expression pattern among different BC subtypes . When all BC samples were tested or just luminal A, luminal B, Normal-like BC subtypes were tested, none of the five pathways were prognostic for BC recurrence. By contrast, when basal-like subtype was tested in Cox regression, the IFNγ  and TNFα  pathways were significant favorable factors while the TGFβ pathway  was significant unfavorable factors for disease-free survival. The IFNα pathway was also a prognostic factor  but had weaker effect than the IFNγ and TNFα pathways. Notably, higher IFNγ and TNFα pathway activity were also associated with lower recurrence risk in HER2-enriched BC. No association between the STAT3 pathway and BC recurrence was observed in any of BC subtypes .Associations of the five immunity-related pathways with BC recurrence risk were first examined using univariate Cox regression .Multivariate regression analysis revealed that the association of IFNα, IFNγ, TNFα and TGFβ pathways with recurrence risk remain significant in BLBC after adjusting for different clinical variables including age, tumor grade and size, and status of lymph node, estrogen receptor (ER), progesterone receptor (PR) and HER2 in the three cohorts . Similar to the results in Cox regression analysis, subgroups of BLBC with higher IFNγ  and TNFα  activity had significantly lower recurrence risk in all 3 BC cohorts . Recurrence risk of BLBC was also low in subgroups with high IFNα  or low TGFβ  pathway activities, which, however, was not as significant as that for IFNγ and TNFα pathways .The impact of the IFNα, IFNγ, TNFα and TGFβ pathways on BLBC recurrence was further supported by Kaplan-Meier analysis. As shown in Fig. 4A). In BLBC, the combination of TGFβ pathway with either IFNα, IFNγ or TNFα pathways archived more significant overall HR  with respect to the association with cancer recurrence than the 4 individual pathways  . Moreover, the synergistic effects of these 3 combinations in recurrence prediction for BLBC were observed in all the 3 individual cohorts . By contrast, although combination of TGFβ pathway with IFNα or IFNγ also archived more significant overall HR than individual pathways in HER2-enriched BC , we found the combination effects were only present in cohort 2 but not in the other 2 cohorts .It is well-known that IFNα, IFNγ and TNFα signaling mediate antitumor immunity whereas TGFβ signaling induces tumor immunosuppression S5A-C) was more significant than those stratified based on only TGFβ or TNFα pathway activity alone  in all the 3 cohorts. When BLBCs were stratified into 2 subgroups, the recurrence risks were much more different among subgroups stratified based on TGFβ & TNFα combination score than those based on TGFβ or TNFα pathway alone  in the 3 cohorts . By contrast, the synergistic effect of TGFβ pathway combined with IFNα or IFNγ pathway was only observed in BLBCs from cohorts 3 and 4, regardless of whether the samples were stratified into three subgroups . Cox regression analysis on BLBC in this cohort showed that none of the 4 pathways  were associated with neoadjuvant response . Combined application of TNFα and TGFβ signatures moderately enhanced the correlation with neoadjuvant therapy responses but did not reach statistical significance (p=0.11) .Chemotherapy is the main method for treating BLBC. We asked whether these phenomena observed above are the effects of these immune-related pathway signaling on response of BC cells to chemotherapy. Analysis of BCs in cohort 1 revealed that basal-like and HER2-enriched BCs had best response to neoadjuvant chemotherapy among the 5 subtypes . Combination of TGFβ with IFNγ or TNFα strongly enhanced this association in cohort 2 . In Kaplan-Meier analysis, however, TGFβ and TNFα pathway combination exhibited synergistic effects on enhancing the association in both cohorts  . In contrast, in Kaplan-Meier analysis, such synergistic effects were observed only in cohort 2 for TGFβ-IFNγ pathway combination .We also examined the impact of the above 4 pathways on the recurrence risk of BLBC treated with adjuvant chemotherapy. Only Cohorts 2 and 4 were used in this analysis because only 21 basal-like samples in Cohort 3 had chemotherapy information annotated. Cox regression analysis revealed that only TGFβ pathway was significantly associated with recurrence of BLBC after chemotherapy treatment . However, none of the 6 pathways showed specific enrichment or suppression in TNFα+TGFβ- or TNFα-TGFβ+ subgroups of BLBC .Besides affecting anti-cancer immunity, TNFα and TGFβ signaling also have direct tumor-suppressing or oncogenic effects on tumor cells. To uncover the potential molecular mechanisms that could explain the synergistic effects of TNFα/TGFβ pathway combination in prognostic prediction of BLBC, we first examined the associations of TNFα and TGFβ signaling with the alterations of six cancer-related pathways, including p53, BRAF, EGFR, androgen receptor (AR), beta-catenin (BCAT) and PI3K pathways that were reported to play important roles in the molecular pathogenesis of BLBC 6). However, only activated Tm cells in these 4 cell subsets showed specific enrichment in BLBCs with TNFα+TGFβ- or TNFα-TGFβ+ . Activated Tm cell infiltration was mainly observed in TNFα+ particularly TNFα+TGFβ- BLBC , and this trend was more clearly presented when using a scatter plot to visualize the corresponding data shown in the heatmap . TNFα-TGFβ- BLBC had higher level of activated Tm subset than TNFα-TGFβ+ BLBC , while TNFα+TGFβ+ BLBC had higher level of activated Tm cells than TNFα-TGFβ- BLBC (p < 1E-04) and TNFα+TGFβ- BLBC had higher level than TNFα+TGFβ+ BLBC (p = 1E-04), suggesting that both TNFα and TGFβ signaling affect the infiltration of activated Tm cells in BLBC .Considering that the TNFα and TGFβ signaling strength in cancer cells reflects the TNFα and TGFβ levels in intercellular matrix, we ask whether intracellular TNFα and TGFβ signaling is associated with infiltration of any specific type of immune cells in BLBC. CIBERSORT At present, few clinical variables show predictive ability for prognosis of BLBC. Hence, we tried to identify genomic predictors of prognosis in patients with BLBCs. In the present study, we use four large independent breast cancer cohorts to examine the association of five immunity-related pathways with breast cancer prognosis. Analysis of these cohorts showed that among the five PAM50 subtypes of BC, BLBC and HER2-enriched BC were most sensitive to neoadjuvant chemotherapy and have worst prognosis. These results are well consistent with previous reports 6). Although the association of IFNγ signaling as a single variable with cancer recurrence was more significant than that of TNFα signaling , the combined effects of TNFα/TGFβ signaling were more significant than combination of IFNγ/TGFβ signaling in BLBC . Interestingly, we observed that TNFα signaling strength increased gradually from luminal A, luminal B, Normal-like, HER2-enriched and basal-like subtypes, whereas the TGFβ signaling exhibited an exact opposite pattern . Probably, TNFα and TGFβ signaling are more complementary than IFNγ and TGFβ signaling in immune regulation, which may provide a potential explanation for better synergistic effects of TNFα/TGFβ combination in prediction of BLBC prognosis. Besides potential application in clinical outcome prediction, these findings may also help to understand the molecular pathogenesis of BLBCs.Here, we demonstrate that the combination of TNFα/TGFβ or IFNγ/TGFβ pathways can predict BLBC prognosis and activated CD4+ T cell levels. More importantly, TNFα/TGFβ combination has better synergistic effects in prognosis prediction than IFNγ/TGFβ combination. In BC, particularly in the aggressive triple-negative/basal-like subgroup, patient outcome is closely associated with the immune infiltration of tumors. While infiltration of cytotoxic T cells and assisting T-helper 1 cells are associated with good prognosis, infiltration of tumor-associated macrophages (TAMs) is linked to poor prognosis Memory T cells are largely resting cells that become active after antigen stimulation. Analysis of T cell subpopulations in circulation, lymph nodes, and tumor sites in tumor-bearing mice revealed severe loss of T memory cells during tumor progression, indicating a continuous competition between cancer and T memory cells There are currently no rigorously validated methods to guide the prognosis of BLBC patients. In fact, the data we provide here show that it is possible to develop such a test. Future studies will focus on extending these findings in more retrospective cohort of BLBC patients, and finally in a prospective-based clinical trial aimed at protecting low-risk BLBC patients from harmful and redundant adjuvant chemotherapy.Supplementary figures and tables.Click here for additional data file.