ErbB signaling pathway, are likely caused by early events in clonal evolution followed by expansion of minor clonal populations in metastasis, a finding that may have translational implications for early detection or prevention of metastasisMetastasis is the primary mechanism by which cancer results in mortality and there are currently no reliable treatment options once it occurs, making the metastatic process a critical target for new diagnostics and therapeutics. Treating metastasis before it appears is challenging, however, in part because metastases may be quite distinct genomically from the primary tumors from which they presumably emerged. Phylogenetic studies of cancer development have suggested that changes in tumor genomics over stages of progression often result from shifts in the abundance of clonal cellular populations, as late stages of progression may derive from or select for clonal populations rare in the primary tumor. The present study develops computational methods to infer clonal heterogeneity and dynamics across progression stages via deconvolution and clonal phylogeny reconstruction of pathway-level expression signatures in order to reconstruct how these processes might influence average changes in genomic signatures over progression. We show, via application to a study of gene expression in a collection of matched breast primary tumor and metastatic samples, that the method can infer coarse-grained substructure and stromal infiltration across the metastatic transition. The results suggest that genomic changes observed in metastasis, such as gain of the  Metastatic disease is the primary mechanism by which cancer results in patient mortality  occur in around 10–30% of metastatic breast cancers cases  , where fold enrichment is defined as the EASE score of the current module to the geometric mean of EASE scores in all modules i,j, was calculated by taking the sum of expressions of all the genes in the i-th module. Then BM was rescaled row-wise by taking the z-scores across samples to compensate for the effect of variable module sizes.Functionally similar genes are usually affected by a common set of somatic alterations , Breast cancer (hsa05224), and Glioma (hsa05214). An additional cancer pathway RET pathway was added, since it was found to be recurrently gained in the prior research  with constraints on the pathway-level expression signatures to deconvolve the communities/populations from primary and metastatic tumor samples , Step 2 We define a cell community to be a set of clones/clonal subpopulations and other cell types that propagate as a group during the evolution of a tumor. A community may be just a single subpopulation/clone, but is a more general concept in the sense that it usually involves multiple related clones and their associated stroma. For example, a set of immunogenic clones and the immune cells infiltrating them might collectively form a community that has a collective expression signature mixing signatures of the clones and associated immune cells, even if the individual cell types are not distinguishable from bulk expression data alone. While much work in this space has classically aimed to separate individual clones, or perhaps individual cell types more broadly defined, we note that deconvolution may be unable in principle to resolve distinct cell types if they are always co-located in similar proportions. It is particularly true when data is sparse and cell types are fit only approximately, as in the present work, that a model with large complexity to deconvolve the fine-grained populations is prone to overfit. The community concept is intended in part to better describe the results we expect to achieve from the kind of data examined here and in part because identifying these communities is itself of interest in understanding how tumor cells coevolve with their stroma during progression and metastasis. Single-cell methods may provide an alternative, but are not amenable to preserved samples, such as are needed when retrospectively studying primary tumors and metastases that may have been biopsied years apart.B ∈ ℝm×n, the deconvolution problem is to find a component matrix With a matrix of bulk pathway values X||Fr is the Frobenius norm. The column-wise normalization in Equation (3) aims for recovering the biologically meaningful cell communities. In addition, they are equivalent to applying ℓ1 regularizers and therefore enforce sparsity to the fraction matrix F (where ||matrix F .https://pytorch.org/)  is column-wise normalization, so that each column sums up to 1. The two operations of Equation (5) naturally rephrase and remove the two constraints in Equations (2) and (3), and meanwhile fit the framework of neural networks. An alternative to the absolute value operation |X| might be rectified linear unit ReLU(X) = max. However, this activation function is unstable and leads to inferior performance in our case, since Xlj will be fixed to zero once it becomes negative and will lose the chance to get updated in the following iterations. One may also want to replace the column-wise normalization cwn(X) with softmax operation softmax(X). However, the non-linearity introduced by softmax actually changes the original optimization problem (Equations 1–3) and the fitted F is therefore not sparse.where |1 = 0.9, β2 = 0.999 and learning rate of 1 × 10−5 . The training is run until convergence, which is defined as when the relative decrease of training loss is smaller than ϵ = 1 × 10−10 every 20,000 iterations. This implementation has two main advantages: First, the method can be easily adapted to a wide range of optimization scenarios with various constraints, when existing methods do not or are hard to apply. Second, the NND has the flexibility of allowing for cross-validation, which is important for us in choosing the number of components k and preventing overfitting.Based on the revised NND optimization problem (Equations 4 and 5), we built the neural network with the architecture shown in B and the fitted samples One might be suspicious whether the neural network fits precisely in practice, since it is based on a simple gradient descent optimization. To validate the fitting ability of NND, we plotted the PCA of original samples mples B^ . One cank = 5 that has the smallest test error  with the “masking” method to choose the optimal number of components/communities st error . In eachX ⊙ Y is the Hadamard (element-wise) product. At the time of evaluation, given optimized Mtrain and 5% of positions of Mtest were 1s. Note that the actual number of cell populations is probably considerably larger than 5, and therefore each one of the five communities may contain multiple cell populations. Furthermore, it is likely that with sufficient numbers and precision of measurements, these communities could be more finely resolved into their constituent cell types. However k = 5 represents the largest hypothesis space of NND model that can be applied to the current dataset without severe overfitting.where We built “phylogenies” of cell subcommunities and estimated the pathway representation of unobserved (Steiner) nodes  algorithm  or extant node (v ≥ k − 1). Denote the set of weights duv is the output of NJ. For each dimension i of the pathway vectors, we consider them independently and separately, so that each dimension of the Steiner nodes can be solved in the same way. Now let us consider the i-th dimension (and omit the subscript i for brevity) of extant nodes k = 5. The inference of the i-th element in the pathway vector of the Steiner nodes can be formulated as minimizing the following elastic potential energy Denote the phylogeny of cell subcommunities as which can be rephrased as a quadratic programming problem and solved easily, as we show below.Theorem 1. Equation (7) can be further rephrased as a quadratic programming problem:whereis a function that takes as input edge weightsand outputs a matrixP ∈ ℝk−2) × (k−2), v ≤ k − 2) can be written as:wherev ≥ k − 1) inside the second summation can be rephrased as:Each term (wherex. Therefore the optimization in Equation (7) can be calculated and written as below:and emark 1R. x⋆ of the Equation (7), or the solution to the quadratic programming problem Equation (8) can be solved by setting the gradient to beThe optimal 0:Therefore,emark 2R. Based on the proof, we can derive how to calculate the matrixand vectorInitialize the matrix and vector with zeros:For each edgewuv, there are two possibilities of nodesuandv: First, if both of them are Steiner nodes , we updatePand keepqthe same:Second, if u is Steiner node and v is an extant node , we update bothPandq:We apply the same procedure to all dimension of pathwaysi = 1, 2, ..., mto get the full pathway values for each Steiner node.Before we applied our deconvolution algorithm NND to the breast cancer brain metastatic samples, we first validated our algorithm on a semi-simulated dataset where the ground truth expressions and fractions of each cell clone in the mixture samples are known.in silicoThe semi-simulated dataset is based on the real data of pure clones from the GSE11103 dataset  is a uniform distribution in the interval . The semi-simulated bulk expression matrix B was then generated from C, F, with a log-normal noise:where s controls the noise level, which we set to 0, 0.4, 0.9, and 1.3 for test; σ is the standard deviation of log2-transformed original GSE11103 data.where B, we applied NND and other two algorithms to infer the estimated C, we used L1 loss :For s were added to test the robustness of models and the performance of different models under different conditions. We repeated all the experiments for 10 times to get the boxplot.Different levels of noise C in the network . We verified that the gene module/pathway representation is effective in the sense that it captures distinguishing features of primary/metastatic sites and individual samples well and is able to identify recurrently gained or lost pathways.zMSD: The labels of all nodes were shuffled and the MSD is recalculated for 1,000 times to get the mean μMSD and standard deviation σMSD, which were used to get the z-score of the current assignment zMSD = (MSD−μMSD)/σMSD. (3) rMSD: The ratio of MSD of edges that connect same label nodes and MSD of edges that connect distinct label nodes. (4) zrMSD: as with MSD, a z-score of rMSD was calculated by shuffling labels for 1,000 times. Intuitively, the smaller values the MSD, zMSD, rMSD, and zrMSD are, the better is the feature representation at grouping same label samples together. The shortest paths and distances between all pairs of nodes were calculated using the Floyd-Warshall algorithm . The deconvolution enables us to produce at least a coarse-grained landscape of major cell communities C and their distributions in primary and metastatic tumors F. The number of components (k = 5) was chosen through 20-fold cross-validation . Otherwise, the component is classified as a neutral community. Three components  are classified as metastatic communities; one (C3|P) as primary; and one (C5) as neutral  are greatly enriched relative to those of the other four communities. We might interpret this community as reflecting at least in part stromal contamination from neural cells specific to the metastatic site. C4|M also contributes most to the gains of ErbB in brain samples. The metastatic subcommunity C1|M is probably most closely related to the loss of immune response in metastatic samples as it has the lowest pathway values of Notch, JAK-STAT, and Cytokine-cytokine receptor interaction. This component might thus in part reflect the effect of relatively greater immune infiltration in the primary vs. the metastatic site. C1|M also has the lowest pathway values of Apoptosis, Wnt, and Hedgehog. The metastatic community C2|M is most responsible for the loss of PI3K-Akt and TGF-beta pathways. We also note that although RET does not show up in the list of C1|M and C4|M but not in the metastatic community C2|M.We are especially interested in the pathway part  samples , compareWe built phylogenies of cell communities and calculated the pathway representations of their Steiner nodes (section 2.5). The phylogenies' topologies provide a way to infer a likely evolutionary history of cancer cell communities and thus their constitutive cell types. At the same time, the perturbed pathways along their edges suggest the order of genomic alterations or changes in community composition.C1|M missing . Case 3: only C2|M missing . Case 4: only C5 missing . Although not all communities exist in Cases 2–4, the topologies are similar to that of Case 1 and can be seen as special cases of Case 1, representing some inferred common mechanisms of progression across all the BrM patients.All five cell components do not appear in each BrM patient. We analyze the distribution of communities in each patient based on whether the community is inferred to be present in the patient . There aErbB, PI3K-Akt, and RET  from unrelated infiltrating cell types . The methods are likely finding only a small fraction of the true clonal heterogeneity of the tumors and stroma, and might benefit from algorithms capable of better resolution or from integration of multi-omics data  that might have complementary value in finer discrimination of cell types. The present methods are also using only a limited form of temporal constraint in considering a two-stage progression process and without use of quantitative time measurements. Models might be extended in future work to consider true time-series data, such as is becoming available through “liquid biopsy” technologies. In addition, we know of no data with known ground truth that models the kind of progression process studied here nor of other tools designed for modeling similar progression processes from expression data, leaving us reliant on validating based on consistency with prior research on brain metastasis (Brastianos et al., https://github.com/lizhu06/TILsComparison_PBTvsMET. The simulated dataset generated, and data for analysis for this study can be found on Github: https://github.com/CMUSchwartzLab/NND.The breast cancer brain metastases dataset analyzed for this study can be found on Github: RS, JM, and AL contributed to the conceptualization. RS, JM, AL, YT, and HL contributed to the methodology. YT contributed to the software. RS, JM, AL, and YT contributed to the formal analysis, writing-review and editing, and funding acquisition. AL contributed to the resources. YT and HL contributed to the writing of the original draft. RS supervision. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Poria cocos (Fuling), a natural plant, has recently emerged as a promising strategy for cancer treatment. However, the molecular mechanisms of Poria cocos action in breast cancer remain poorly understood.  TCMSP database was used to screen the potential active ingredients in Poria cocos. GEO database was used to identify differentially expressed genes. Network pharmacology was used to identify the specific pathways and key target proteins related to breast cancer. Finally, molecular docking was used to validate the results.  In our study, 237 targets were predicted for 15 potential active ingredients found in Poria cocos. An interaction network of predicted targets and genes differentially regulated in breast cancers was constructed. Based on the constructed network and further analysis including network topology, KEGG, survival analysis, and gene set enrichment analysis, 3 primary nodes were identified as key potential targets that were significantly enriched in the PPAR signaling pathway.  The results showed that potential active ingredients of Poria cocos might interfere with breast cancer through synergistic regulation of PTGS2, ESR1, and FOS. As one of the most common cancers in women, although screening and early detection significantly aid prevention and treatment, almost 1 in 4 newly diagnosed cancer cases in women are breast cancers . Over thArtemisia annua, is a Chinese herbal medicine used to treat malaria. Furthermore, single-target interventions may prove ineffective for complex systemic diseases like cancer with powerful biological networks [Traditional Chinese medicine (TCM) from natural compounds has been developed since several years. TCM is not only used clinically in China and other Asian countries but also becoming increasingly popular in many developed countries like Australia and the United States . The devnetworks . Coincidnetworks . Poria cnetworks . Recent networks . In addinetworks . TherefoNetwork pharmacology is a new drug development method based on multipharmacology and network biology, which provides a valuable tool for extracting new targets for rational drug discovery . NetworkIn order to elucidate the comprehensive mechanism of action of Poria cocos intervention in breast cancer, we adopted the methods based on network pharmacology and bioinformatics, to screen for key network targets and pathway information. The results obtained were further validated by molecular docking. Our study hopes to provide insight into the Poria cocos mechanism of action and its use as a potential therapy for breast cancer.http://lsp.nwu.edu.cn/), which is a unique systems pharmacology platform of Chinese herbal medicines and composed of a large number of entries with their absorption, distribution, metabolism, and excretion (ADME) properties. PubMed database was used for literature mining to prevent omitting of relevant compounds. We chose components that met the requirements of OB ≥ 30% and DL ≥ 0.18 as potential active ingredients of Poria cocos. The FDA defines OB  as “the rate and extent to which the active ingredient or active moiety is absorbed from a drug product and becomes available at the site of action” [We collected data regarding the chemical compounds present in Poria cocos from the Traditional Chinese Medicine Systems Pharmacology Database  to obtain possible targets of potential active components in Poria cocos. All the potential active components were searched with PubChem (http://pubchem.ncbi.nlm.nih.gov/) to obtain their canonical SMILES strings. The canonical SMILES were then imported into SwissTargetPrediction to obtain targets of each potential active compound. We used UniProt KB database (http://www.uniprot.org/) to search for gene names of each protein target of active components that were obtained from TCMSP. Finally, we deleted the redundant targets of the same components obtained from the two databases to finally obtain 237 targets for the potential active ingredients of Poria cocos.We used the TCMSP database and SwissTargetPrediction (http://www.ncbi.nlm.nih.gov/geo/) for the keywords “breast cancer miRNA” and used the following criteria to filter the datasets: (a) the study used human samples; (b) data were from breast cancer tissues and nonbreast cancer tissues; and (c) sample size in each dataset was n > 20. Finally, GSE42128 dataset based on GPL15018 and GSE38867 dataset based on GPL15019 were selected, which included 76 tissue samples . Furthermore, we used the GEO2R tool (http://www.ncbi.nlm.nih.gov/geo/geo2r/) to identify miRNAs differentially expressed in normal versus breast cancer tissues. Differentially expressed miRNAs were defined as having a relative expression change of |Log2FC| > 2 and p < 0.05. TargetScan (http://www.targetscan.org/vert_72/), miRWalk , and miRDB (http://www.mirdb.org/miRDB/) were used to confirm the potential targets of miRNAs. High confidence targets that appeared at least twice among the 3 prediction software programs were analyzed further.We manually queried the GEO database , with http://string-db.org/) was used to produce PPI predictions of all genes that were targets of Poria cocos and the shortlisted DEGs in breast cancer, which were visualized using the network visualization software Cytoscape . The topology scores of the nodes, including closeness centrality, betweenness centrality, and degree were measured using CentiScaPe plugin (version 2.2) and the top 5 genes were identified as hub genes. The Kaplan–Meier plotter , an online tool, enlists the effect of 54,675 genes on the survival of 10,461 cancer samples. We used the Kaplan–Meier plotter breast cancer mRNA database to assess the prognostic values of hub genes in breast cancer.Analyzing protein-protein interaction (PPI) networks provides a useful tool to evaluate the functions of proteins and interactions of genes. By setting the minimum interaction score to 0.4, the web-based STRING database (version 10.5) (https://david.ncifcrf.gov/). GO terms and enrichment pathways that had p < 0.05 were considered significant.We performed Gene Ontology (GO) enrichment and pathway enrichment analyses using the online Database of Annotation, Visualization, and Integrated Discovery (DAVID) (http://www.broad.mit.edu/gsea/) using the gene sets from the Molecular Signatures Database (MSigDB) as a reference. Enrichment results with nominal p < 0.05 and FDR value < 0.25 were considered statistically significant.GSEA is a computational method that is based on gene sets and establishes a database of molecular characteristics based on the known information about gene characteristics, location, and biological functions. The correlation between the key potential genes and biological mechanisms was analyzed with GSEA v3.0  and potential active components (n = 15) were used for docking. The docking scores of 15 pairs of potential active component-key potential gene interactions were greater than 6.0, indicating that they had strong binding free energy. In addition, our results also showed that the docking points of each potential target with at least one potential active ingredient were greater than 6.0. We further optimized the docking of specific protein-ligand interactions of 3 gene-compound pairs, with the highest docking scores using PyMoL  and reduces pain related to inflammation . PachymiNetwork pharmacology is a multidisciplinary, highly informative field of research, that seeks to identify the key targets in disease-related networks through data mining, statistical methods, modeling, graph theory, and information visualization . At the Based on the constructed PPI network, we carried out pathway enrichment analysis and found that target genes were significantly enriched in PPAR signaling pathway. Interestingly, each key candidate target was characterized based on GSEA, and it was found that differentially expressed PTGS2, ESR1, and FOS were also significantly enriched in this pathway. One of the mechanisms of drug resistance in cancer cells involves the release of exosomes mainly through the transfer of drug transporter proteins and nucleic acids or accumulation of anticancer drugs . CeramidPoria cocos may play a therapeutic role in breast cancer by binding and regulating specific protein targets. We speculate that the top 5 nodes identified by topology analysis of the PPI network could be the hub targets for breast cancer treatment by Poria cocos. Further screening of the targets showed three targets of PTGS2, ESR1, and FOS as the key potential genes. PTGS2 was targeted by 2 potential active compounds from Poria cocos–pachymic acid and hederagenin. PTGS2, a polymorphic gene, encodes cyclooxygenase-2 (COX-2) which is overexpressed in breast cancer and is related to invasive parameters such as tumor size, angiogenesis, and low overall survival , 37. In In addition, our results identified five miRNAs including miR-490-5p, miR-3200-5p, miR-3666, miR-338-5p, and miR-3925-5p associated with key potential genes. miR-490-5p is downregulated in bladder cancers, renal cell carcinoma, and liver cancers . OverexpThe current research combines network pharmacology and bioinformatics methods to clarify the molecular and pharmacological mechanism of Poria cocos action against breast cancer, from a systematic perspective. Although further experiments are needed to demonstrate the accurate action  of the potential compounds on key potential genes, it provides a significant basis for further optimizations of experimental design for mechanistic studies.In summary, these biological studies further confirmed the reliability of our research model in exploring the mechanism of Poria cocos in the treatment of breast cancer. Potential active ingredients of Poria cocos may interfere with breast cancer through synergistic regulation of key genes  that participate in the PPAR signaling pathway.


Artemisiae scopariae Herba (ASH) is used to treat various liver diseases. The purpose of this study was to explore the mechanisms of ASH for treating chronic hepatitis B (CHB) using a network pharmacological method.  As a traditional Chinese medicine,  Bioactive ingredients and related targets of ASH were obtained from Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. Gene names of targets were extracted from UniProt database. Differentially expressed genes (DEGs) of CHB were obtained from microarray dataset GSE83148. The intersect genes between DEGs and target genes were annotated using clusterProfiler package. The STRING database was used to obtain a network of protein-protein interactions. Cytoscape 3.7.2 was used to construct the “ingredient-gene-pathway” (IGP) network. Molecular docking studies were performed using Autodock vina.  A total of 13 active components were extracted from TCMSP database. Fifteen intersect genes were obtained between 183 target genes and 403 DEGs of GSE83148. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis results showed that ASH against CHB mainly involved in toll-like receptor signaling pathway, cellular senescence, hepatitis B, and chemokine signaling pathway. We screened one hub compound, five core targets, and four key pathways from constructed networks. The docking results indicated the strong binding activity between quercetin and AKT1.  This study provides potential molecular mechanisms of ASH against CHB based on exploration of network pharmacology. Chronic hepatitis B (CHB) is a chronic liver inflammatory disease caused by persistent infection of hepatitis B virus (HBV). Despite the availability of effective vaccines, HBV still affects more than 250 million people worldwide . The majArtemisiae scopariae Herba, ASH), the main constituents of which include coumarins, flavonoids, organic acids, and volatile oils, is a traditional Chinese medicine (TCM). ASH has multiple biological activities, such as liver protection, antivirus, antitumor, neuroprotection, prevention of Alzheimer's disease, and metabolic regulation [Yin-Chen  [http://www.uniprot.org/). The structures of effective compounds were obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov/).The bioactive compounds of ASH were obtained from the TCMSP database (ion 2.3) . The effion 2.3) , 15. Tarp value <0.05 and |log2FoldChange| > 1. The intersect genes were identified between target genes and DEGs using Venny online tool (http://bioinformatics.psb.ugent.be/webtools/Venn/).Microarray datasets GSE83148 were downloaded from Gene Expression Omnibus (GPL570 Affymetrix Human Genome U133 Plus 2.0 Array). These datasets consisted of 122 HBV infected liver tissues and 6 healthy controls. Differentially expressed genes (DEGs) were obtained using Limma package with adjusted http://string-db.org/) for the protein interaction analysis (the combined score >0.4) [http://www.cytoscape.org) was performed to construct the PPI network. Five effective ingredients, fifteen intersect genes, and the top 10 KEGG pathways were also introduced into Cytoscape to establish a visualized ingredient-gene-pathway (IGP) network. The tool of network analyzer was used to calculate topological parameters. Three topological parameters including degree centrality (DC), betweenness centrality (BC), and closeness centrality (CC) were used to estimate the central properties of the nodes in PPI and IGP network [Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were implemented using the “clusterProfiler” package. The intersect genes were imported into STRING database (re >0.4) , and the network . DC ≥ me network . The corhttp://www.pdb.org/) and PubChem, respectively. Docking studies were performed between core targets and quercetin or entecavir using Autodock vina  [The structures of core targets, quercetin, and positive control drug (entecavir) were obtained from PDB  . The doc, 1.1.2) , 20.A total of 13 bioactive compounds of ASH were obtained from the TCMSP database, including 4′-methylcapillarisin, areapillin, beta-sitosterol, isorhamnetin, skrofulein, isoarcapillin, eupalitin, eupatolitin, capillarisin, demethoxycapillarisin, quercetin, artepillin A, and genkwanin. 2D structures of these compounds were extracted from PubChem Database see . We got A total of 403 DEGs were screened from GSE83148 and visualized by a volcano plot see , 383 of GO analysis showed that 15 intersect genes mainly participated in response to lipopolysaccharide, cellular response to lipopolysaccharide, cellular response to biotic stimulus, chemokine-mediated signaling pathway, response to chemokine, cellular response to chemokine, neutrophil, chemotaxis, and lymphocyte migration. KEGG analysis showed that intersect genes involved in toll-like receptor (TLR) signaling, cellular senescence, hepatitis B, chemokine signaling pathway, IL−17 signaling pathway, viral protein interaction with cytokine and cytokine receptor, and TNF signaling pathway see . These rThe IGP network comprised 30 nodes  and 63 edges see . In thisWe did not adopt the SPP1 in a molecular docking study because 3D structure of SPP1 did not have a human source in the PDB database. We docked quercetin and entecavir with four of above core targets and found quercetin had certain binding activity with CCL2, FOS, and CXCL8  and strong binding activity with AKT1  see . InteresIn the IGP network, we identified five active compounds, including quercetin, eupatolitin, isorhamnetin, areapillin, and Isoarcapillin. Among them, quercetin had the largest node value, indicating that quercetin played a pivotal role in the network. Quercetin is classified as flavonol, which possesses various biological and pharmacological activities including antioxidative, antiviral, anti-inflammatory, and antifibrotic effects , 22. Par+Ly-6Chi monocytes to develop into Ly-6C+ macrophages in the damaged liver; then, these Ly-6C+ macrophages provoked hepatic stellate cells (HSCs) and promoted hepatic fibrosis in mice [+ macrophages infiltration in mice [In PPI network, we ascertained 5 core targets: AKT1, FOS, CCL2, SPP1, and CXCL8. Then, we used quercetin as a ligand to dock with the core protein. The results of molecular docking showed that AKT1, CXCL8, CCL2, and FOS had binding activity to quercetin, which suggested that the above proteins may be the key target proteins for the pharmacological action of ASH. Prior study has reported CXCL8 and its receptor CXCR1 are upregulated intrahepatically in chronic liver diseases . In addi in mice , 29. Fur in mice . Some re in mice , 32. Ano in mice . Sun et  in mice . c-Fos w in mice , 36. Kal in mice . These fKEGG pathway analysis showed that the intersect genes were enriched in the top 10 pathways as follows: TLR signaling pathway, cellular senescence, hepatitis B, chemokine signaling pathway, IL-17 signaling pathway, viral protein interaction with cytokine and cytokine receptor, and TNF signaling pathway. Then, we identified four key signaling pathways in the IGP network: TLR signaling pathway, cellular senescence, hepatitis B, and chemokine signaling pathway. Some studies have shown that these key pathways are involved in the pathogenesis of CHB. Isogawa et al. reported that TLR signaling inhibited HBV replication by trigging the production of cytokines  in mice liver . PreviouThere are some defects in present work. This study lacks experimental verification, which will be further validated in future study. The composition of ASH is complex and some components or related prediction targets may be missed due to incomplete database information and slow update.This study reflects the characteristics of “multicomponent, multitarget, and multipathway” of ASH in the treatment of CHB. We identified quercetin acted on the targets  through key signal pathways  to exert treatment effects on CHB by use of a network pharmacological method. This study provides clues for understanding the molecular mechanisms of potential targets of ASH in the treatment of CHB and provides a reference for the further experimental study.


The objective of the current study is to accomplish a relative exploration of the biological roles of differentially dysregulated genes (DRGs) in type 2 diabetes mellitus (T2DM). The study aimed to determine the impact of these DRGs on the biological pathways and networks that are related to the associated disorders and complications in T2DM and to predict its role as prospective biomarkers.Datasets obtained from metabolomic and proteomic profiling were used for investigation of the differential expression of the genes. A subset of DRGs was integrated into IPA software to explore its biological pathways, related diseases, and their regulation in T2DM. Upon entry into the IPA, only 94 of the DRGs were recognizable, mapped, and matched within the database.The study identified networks that explore the dysregulation of several functions; cell components such as degranulation of cells; molecular transport process and metabolism of cellular proteins; and inflammatory responses. Top disorders associated with DRGs in T2DM are related to organ injuries such as renal damage, connective tissue disorders, and acute inflammatory disorders. Upstream regulator analysis predicted the role of several transcription factors of interest, such as STAT3 and HIF alpha, as well as many kinases such as JAK kinases, which affects the gene expression of the dataset in T2DM. Interleukin 6 (IL6) is the top regulator of the DRGs, followed by leptin (LEP). Monitoring the dysregulation of the coupled expression of the following biomarkers  highlights that they could be used as potential prognostic biomarkers.The integration of data obtained by advanced metabolomic and proteomic technologies has made it probable to advantage in understanding the role of these biomarkers in the identification of significant biological processes, pathways, and regulators that are associated with T2DM and its comorbidities. In summary, the incorporation of metabolomic and proteomic data through integrative pathway analysis using different tools would help in understanding the role of various biomarkers in the identification of the biological processes, pathways, upstream regulators, and pathophysiology that are associated with Type 2 Diabetes Mellitus (T2DM) and comorbidities. Therefore, such a study could help to recognize those patients at higher risk for a specific complication and its response to a particular class of anti-diabetic drugs. This study could help in personalized medicine for T2DM.Recent decades had indicated a remarkable upsurge in the prevalence of diabetes mellitus (DM) worldwide, particularly of type 2 diabetes.Identification of biomarkers for T2DM and its complications is a challenging issue because of the diverse nature of this disease. Different factors contribute to the heterogenicity of type DM such as the glycemic control, treatment response, duration of the diseases, age of onset and biochemical profile, body mass index, and variations in environmental exposures, which could affect the disease diversity.Metabolites symbolize intermediate and end products of metabolic pathways that reflect the physiology and dysfunctions of metabolic processes and disorders. Recent technology allows for the assessment of metabolites opening new opportunities to study changes in biochemical pathways for insight into the biological mechanisms of disorders such as T2DM and its comorbidities.The personalized medicine could help in the screening of subjects at risk of developing T2DM, as well as one or all of the complicating morbidities associated with microangiopathies, such as retinopathy, neuropathy, nephropathy, and macroangiopathy or large-vessel disease. They also have the potential to direct treatment planning, regarding personalized goal setting, choice of treatments, and treatment prioritization.5In this study, the aim is to accomplish a comparative and integrative investigation of metabolomic and proteomic datasets of gene expression to identify differentially regulated genes (DRGs) as potential predictive biomarkers associated with type 2 diabetes and its complications. The integration of the dataset of DRGs is used to reveal significant pathways and biological functions and diseases that are relevant to understand the pathogenesis of associated comorbidities of diabetes and its complications. The study pursued to recognize biological processes and metabolic pathways of DRGs, which are interrelated to T2DM that were differentially up- or downregulated in comparison to healthy controls. Therefore, the overall target of the current study is to recognize those patients at higher risk for a disorder or complication associated with T2DM through understanding the biological networks and pathways underlie these diseases that could respond better to management and drug treatment.Supplementary Table 1 was provided. The study was approved by the Institutional Review Board of the Hamad Medical Corporation, Qatar University, and Georgetown University . The study was performed according to the principles expressed in the Declaration of Helsinki. Written informed consent was obtained from each subject after a full explanation of the purpose, nature, and risk of all procedures used.Datasets from two previously published studies on T2DM by our group were selected for gene expression integration.After overnight fasting, venous blood was collected as previously described.5For the metabolomic and lipidomic profiling experiment levels of high-density lipoprotein cholesterol, total cholesterol, and triglycerides were assayed by automated clinical laboratory methods using a diagnostic analyzer. Low density lipoprotein cholesterol levels were estimated using the Friedewald formula: LDL-C = TC – HDL-C (TG/5).11For the protein expression profiling experiment, serum samples were delipidated according to the protocol described by Cham and Knowles in preparation for iTRAQ analysis.Centroided and integrated UPLC-TOFMS data were pre-processed using the XCMS software and normalized to the ion intensity of the respective internal standards for the metabolomic and lipidomic profiling experiment.Nano UPLC-MS/MS analysis was conducted by an electrospray quadrupole time of flight (ESI-QTOF) mass spectrometer coupled with a Nano-Acquity-UPLC system. Relative abundance quantitation, peptide, and protein identifications were performed using Protein Pilot software 3.0 (ABSCIEX). Data were analyzed with MMTS as a fixed modification of cysteine, and the database was searched with a 95% confidence interval rate for protein identifications. High confidence peptides of the target proteins exhibiting rich production spectrum were selected for multiple reaction monitoring (MRM) assays. MRM data were processed using TargetLynx 2.0, while Graph Pad Prism program v 5.0 was used for statistical analysis and to generate the receiver operating characteristics. The Wilcoxon test was used for the comparison of each peptide.The serum samples of the study subjects (Diabetics and controls) were used to evaluate the different panels of biomarkers, including the inflammatory, adipokines, oxidative stress, metabolic, CVD, and bone markers. The following kits were used to evaluate such different panels, including the following multiplex assays, HMHMAG-34, HCVD1-67AK, APOMAG-62K, HADK1MAG-61k-03, HBN1A‐51K, and HCVD2MAG-67K. All assays were performed in triplicates according to the manufacturer’s instructions from Millipore . The assays were performed using a Luminex200 . Of note, PCA analysis was performed for all dataset of the study to obtain DRGs as we previously published.6t-test or Mann–Whitney/Wilcoxon when appropriate. ANCOVA was used to perform analysis, including the age to compare the biochemical data of the study subjects and AUC (see Supplementary Table 1). Two-tailed p value is significant when p < 0.05.Clinical and biochemical data are expressed as mean ± SD. All statistical analyses were performed using the SPSS program for Windows . Differences between control and T2DM were performed using Student’s http://www.ingenuity.com/) was performed to identify canonical pathways, diseases and functions, and gene networks that are most significant to the dataset and to categorize differentially dysregulated genes in specific diseases and functions. We also used Pathway Studio 9 (Elsevier) for Integrated pathway mapping. For Network generation, a data set containing gene/protein/metabolite identifier and corresponding fold change and UniProt ID was uploaded into the application. Each protein/metabolite ID was mapped to its corresponding gene/protein/metabolite in the Ariadne ResNet Mammalian database. The metabolites were grouped based on their fold change and used to develop networks based on regulation and connectivity.Ingenuity Pathway Analysis , triglycerides, ALT, and CRP than control healthy subjects. Diabetic subjects have significantly lower values for C-peptide and insulin than healthy control subjects. Other variables are not significantly different between the two studied groups.The characteristics of the study population are described in Data represent the differentially expressed regulated proteins were further evaluated to obtain details of biological processes, cellular functions, networks, and signaling pathways related to comorbidities and diseases associated with T2DM. The dataset was integrated into different software such as IPA software, and Pathway Studio 9 software for the analysis of molecular pathways and networks.−2 of the present dataset to the numbers of genes of the IPA knowledge data of each pathway. Supplementary Table S2 for details). The top 5 enriched signaling pathways in rank were LXR/RXR Activation, FXR/RXR Activation, Acute Phase Response Signaling, Atherosclerosis Signaling, and Clathrin-mediated Endocytosis, as shown in The dataset was analyzed using IPA core analysis to achieve a fundamental profile of the molecular processes underlying T2DM and its complications. Related canonical pathways categorized the differentially expressed genes. Categorization was based on a multiple testing correction of p-value less than 10Supplementary Table S3). RXRs are nuclear receptors that exert the biological effects of retinoids by the participation of retinoic acid-mediated gene, which affects biological functions such as lipid metabolism, molecular transport, small molecule biochemistry. The second pathway is The Farnesoid X receptor (FXR), which includes 29 upregulated genes and 3 downregulated genes of the 137 genes of IPA base knowledge (Table S4). FXR is a member of the nuclear family of receptors and has a fundamental function in the regulation of numerous metabolic pathways, such as bile acid metabolism and its control (Table S5). This pathway is a cytokine signaling pathway where it is activated by tissue injury, trauma, surgery, cancer, immunologic disorders, and in response to microorganisms as a protective pathway. Following that, the next fourth pathway is the Atherosclerosis Signaling, whereas 20 DRGs of the data set were detected, of which two were downregulated, and 18 were upregulated of the 127 genes of IPA base knowledge (Table S6). This pathway is a specific form of a chronic inflammatory process that functions as a cell to cell signaling and interactions and cellular movement in the cardiovascular system. The next pathway is Clathrin-mediated Endocytosis Signaling, whereas 19 DRGs of the data set were detected, of which 2 genes were downregulated, and 17 were upregulated out of the 196 genes of IPA base knowledge shown in (Table S7). Clathrin-mediated Endocytosis Signaling is involved in endocytosis, which is the principal pathway for the movement of nutrients, hormones, and other signaling molecules from the extracellular into intracellular structures across the plasma membrane. Other canonical biological pathways were IL-12 Signaling and Production in Macrophages pathway, whereas 16 DEGs of the dataset were detected of which one gene was downregulated, and 15 were upregulated of the 136 genes of the IPA base knowledge (Table S2). IL-12 is produced primarily by dendritic cells, macrophages, and monocytes, and affecting Th1 immune response and Th17 cells activation. Following that, the Production of Nitric Oxide and Reactive Oxygen Species in Macrophages, whereas 16 DEGs of the data set were detected of which one gene is downregulated, and 15 were upregulated of the 194 genes of IPA base knowledge as shown (Table S2). This pathway is central to the control of infection by microbes.The Retinoid X Receptors (RXRs) were on top of the signaling pathways, which includes 31 upregulated genes and 3 downregulated genes of the 128, as shown in  control . The thiTable S8 and S9). The top five affected diseases in rank based on higher p values for multiple testing for corrections were presented in Using IPA base knowledge, we detected several significant diseases associated with the present dataset of the current study . As presented in The damage of the genitourinary system is predicted to be increased with (z-score 2.203). As displayed in Figure S1)Degranulation of cells is predicted to be increased with active z-score 2.60. As displayed in Activation of leukocytes is predicted to be increased with active z-score 2.359. As displayed in Hypertriglyceridemia is on the top of the metabolic disorders and cardiovascular diseases (CVD) associated with T2DM, with Z-score of 2.035. Hypertriglyceridemia is predicted to increase with the following upregulated genes APOA2, APOB, APOC3, APOE, IL6, SERPINF1, TNF, and the downregulated gene INS as shown in Figures S1).Dyslipidemia is a metabolic disorder commonly associated with T2DM with a Z-score of 1.260. Dyslipidemia s predicted to increase with the following genes APOA2, APOB, APOC3, IL6, INS, and TNF, as shown in Furthermore, we analyzed the common functional disorders which are associated with DRGs of that set using IPA base knowledge. The top 5 functions affected are Cellular compromise, Protein synthesis, Molecular transport, Lipid metabolism, and Small molecule biochemistry. Figure S1), of which seven genes are PPBP, CCL2, F2, LEP, GCG, C4A/C4B, TNF increased the prediction of the degranulation. Fat Acid Metabolism under the lipid metabolism category with activation Z-score of 3.62, including 19 genes of the data set increased the prediction of FA metabolism , Protein synthesis under Protein Metabolism category with a P value of 7.74E-24, Z-score of 2.88, with 19 DRGs of the data set to increase the prediction of protein synthesis . The release of lipids (p=2.11E-12), Z-scores of 3.70 having 15 DRGs which increase the prediction of the release of lipids, and release of Ca+2, P=1.15E-11 and Z-scores of 2.75 having 11 DRGs of the data set which increased the prediction of the release of Ca+2, , and quantity of metal ions (p=1.61E-20), Z-scores of 3.16 having 10 DRGs which increase the prediction of the quantity of metal ions (Table S10) for details.Further, we explore the top five functions in each category associated with T2DM and identify the genes of the DRGs of the data set per each function, as shown in tal ions  under MoE-15  as shown in Supplementary figures (S3).Further, we used the IPA upstream regulator analysis that would explain the changes in gene expression as downstream targets. The aim is to understand the underline regulation of the expression changes seen in the dataset of T2DM. Transcription factors are proteins that control the rate of RNA transcription to regulate the gene expression (up and down) based on the cell state and organ activity to help in cell homeostasis. The five most significant upstream transcript regulators based on Figure S4.Further, we observed the top 5 activated kinases, which are ATM, JAK2, JAK1, MTOR, and AKT1, as upstream regulators that affect the expression of some genes of the data set as shown in Figure S5a), while among the upregulated proteins are TNF targets 32, IL6 targets 29 (Table S11), and Moreover, we look for the master regulator of proteins of the dataset, which regulate other proteins using IPA stream analysis. Also, this analysis identifies potential mechanisms linked with phenotype changes such as disease or functional disorders and explain the biological role of the upstream regulator, via its regulation on a gene or sets of genes. rgets 29 , LEP tarFigure S6). INS upregulates TNF gene expression directly or indirectly through intermediates such as D-glucose, SIRT1, EP 300, FOS, POMC, PI3 complex which in turn affects the transcription factors such as FOXO1, FOXO3, REL, STAT3, and via effects on ligand-dependent nuclear receptor such as PPARG, and NR3C1 and via the effect on lipid metabolism which acts directly or via c-JUN (Ap1) kinase . In similar basis, INS upregulates IL6 expression directly or indirectly , and IL6 targets 29 genes directly and 34 genes  and its uses as biomarkers in atherosclerosis and hypertension .We investigated the interactions among the DRGs, pathways, regulators, and other molecules in type 2 diabetics and identified 12 eligible networks associated with various biologic processes, functions, and diseases-as displayed in a table . IL6 interacting directly on PI3 (Peptidase Inhibitor 3) complex, P38MAPK , AKT (Serine/Threonine Kinase), and affects TNF gene expression and also through other regulatory molecules such as STAT3 , NFKBIB (NF-kappa-B inhibitor beta), and FOXO1 (forkhead box O1),  and 7B. 5. IL6 in18Furthermore, IL6 downregulates the expression of the insulin gene, which contributes to the pathogenesis of T2DM and its related comorbidities. IL6 is participating in numerous biological processes such as acute inflammatory response as one of the top canonical pathways associated with inflammatory response disease such as activation of leukocytes and degranulation of the cells, injury of different organs such as renal damage, connective tissue disorders such as rheumatoid arthritis. It also affects many molecular functions, such as fat and protein metabolism. The complex signal transduction mechanism of IL-6/STAT3 may explicate the widespread effects of the IL6 as a cytokine.Moreover, we detected several transcription factors (TF) as upstream regulators including Signal transducer and activator of transcription 3 (STAT3), Signal transducer and activator of transcription 1 (STAT1), Hypoxia-inducible factor 1-Alpha (HIF1A), CCAAT/enhancer-binding protein alpha (CEBPA), and CCAAT/enhancer-binding protein beta (CEBPB). Signal transducer and activator of transcription 3 is one of STAT family, known as acute-phase response factor, which regulates downstream target molecule such as; IL6, LEP, JAK2 (Janus Kinase 2), LIF (LIF Interleukin 6 Family Cytokine), IL10 (Interleukin 10), EGF , SRC , IL21 (Interleukin 21), SOCS3 , JAK (Janus Kinase), IL6ST , and binds with other TFs such as EGFR , STAT1, JAK2 (Janus Kinase2), FOS , PIAS3 (Protein Inhibitor Of Activated STAT 3), IL6ST, SRC, EP300 (E1A Binding Protein P300), JUN , RELA , JAK1, CDKN1A (Cyclin-Dependent Kinase Inhibitor 1A), HIF1A, STAT2. STAT3 is regulated by BCL2L1 , MYC , SOCS3, VEGFA , IL6, CCND1 (Cyclin D1), STAT3, VEGF , IL21, BIRC5 , and HIF1A, which indicates its pleiotropic cellular effects such as expression, proliferation, apoptosis, growth, differentiation, and migration. In the current study, STAT3 regulates the expression of AGT, AHSG (Alpha 2-HS Glycoprotein), APOA4 (Apolipoprotein A4), ATP, CCL2 (C-C Motif Chemokine Ligand 2), CFB (Complement Factor B), FN1 (Fibronectin 1), HP (Haptoglobin), ICAM1 (Intercellular Adhesion Molecule 1), and IL6 . The curFigure S4), which can target several genes of the data set. AKT  is one of 3 closely related serine/threonine-protein kinases , which regulates metabolism, proliferation, cell growth, and angiogenesis.Further, as upstream regulators that affect the gene expression of the dataset, we identified several kinases (supplementary Tables S2–S7). RXRs are nuclear receptors that affect biologic functions such as lipid metabolism, molecular transport, and small molecule biochemistry. Activation of RXR is involved in cholesterol efflux in macrophages through an effect by the following DRGs of apolipoproteins family; APOE, APOC1, APOC2, APOC4, while inhibiting APOA5. In hepatocytes, activation of APOA4 enhances cholesterol efflux. The apolipoprotein family is transporters that are involved in lipid metabolism, which altered in diabetes and predisposed to several disorders such as atherosclerosis and cardiovascular diseases.Figure S2), and are interconnected to hypertriglyceridemia as a top disease related to metabolic disorder and considered as the leading platform and risk factor for multiple disorders such as cardiovascular disorders, and progressive neurological disorders  as an upstream regulator, which is connected with 2761 nodes and could elucidate the plethoric effects of the acute phase response signaling pathway. One of these disorders associated with acute inflammatory response is the damage of the kidney.  and B diNumerous essential metals are essential for the biological functions of several enzymes, proteins, and transcriptional regulators. It is also vital in many biochemical reactions for cell functions in different tissues. For instance, Zn, Mg, and Mn are cofactors of several enzymes that participated in various biological pathways. Zn is implicated in the biosynthesis and secretion of insulin hormone from the beta-cells of the pancreas. Likewise, Cr augments the activity of insulin receptors on muscle cells, which increases the insulin-stimulated glucose uptake.33Figure S7b), shows the metabolite KNG1 (Kininogen 1) having a direct positive relationship of regulation for ROS generation,43Complications observed with T2DM include disorders affecting the heart, blood vessels, nerves, eyes, liver, and kidneys.Table S9), causing liver cirrhosis, nephrotic syndrome, and heart failure, respectively.Figure S7b), which eventually leads to the development of diabetic-related complications such as atherosclerosis, diabetic retinopathy, and nephropathy.Figure S1) from diabetic patients is impaired, which predispose to infections .48Amyloidosis is a group of diseases in which misfolded proteins that result in progressive organ damage are formed in different tissues . The cliFigure S7a) plays a critical role in the pathogenesis of cardiovascular disease in patients with neuropathy.Figures S7a showed many metabolites that affect the coagulation in some fashion. INS regulates vascularization; APOH, F2, and ICAM regulate platelet activation and adhesion; AGT regulates blood vessel contraction; TNF and CCL2 regulate blood chemokine in circulation; F9 regulates onset Hemophilia B, and PON1 inhibits onset cardiovascular disease. Directions of therapeutic goals should aim at monitoring and decreasing over-expression of the metabolites contributing to coagulation diseases and increasing the counterattack of PON1 (Paraoxonase 1) to prevent cardiovascular morbidity.51The coagulation pathway . Monitoring the coupled expression of dysregulated proteins such as IL6, INS, LEP, IAPP (Islet Amyloid Polypeptide), AGT, TNF, CCL2, GHRL, KNG1 (Kininogen 1), GCG, RETN, and C3 (Complement C3) could be used as a predictive biomarker of T2DM comorbidities. For instance, AGT (angiotensinogen) plays an essential role in counteracting both the cardiovascular and non-cardiovascular actions of AngII.55Insulin action pathway networks identified dysregulation of cellular processes such as glucose and lipid metabolism, which plays a vital role in the pathogenesis of T2DM . For example, ANG could be used to monitor the efficacy of Aliskiren and Irbesartan in the treatment of T2DM associated with renal disorders.61Using the analysis of current data integration, we were able to answer some questions of interest. For example, TNF as one of the upstream regulators that are involved in rheumatoid arthritis, and the clinical utility of Adalimumab as antibody and binder to TNF target, could be used for the treatment of active rheumatoid arthritis, which is predicted to inhibit TNF as a target.In summary, the incorporation of metabolomics and proteomics data through integrative pathway analysis using different tools would help in understanding the role of various biomarkers in the identification of the biological processes, pathways, and pathophysiology that are associated with the comorbidities of T2DM. We identified the most commonly expressed genes of the study are the following TNF, IL6, LEP, AGT, APOE, F2, SPP1, RETN, and INS that could be used as potential prognostic biomarkers. The data recognized that IL6 is the top regulator of the DRGs, followed by LEP. LXR/RXR and acute phase response signalling pathways are dominant pathways involved in renal damage, insulin resistance, dyslipidemia, and cardiovascular disorders. We identified the role of upstream regulators such as STAT3 as a transcription factor that is involved in connective tissue disorders and atherosclerosis as it targets many proteins involved in such disorders. ANG could be used as an efficacy biomarker of renal disorders. TNF is involved in the regulation of metals such as Zinc, which affects B-cell functions and insulin secretion. Therefore, such information could help to recognize those patients at higher risk for a specific complication and its response to a particular class of anti-diabetic drugs. Prospective studies should be performed to validate the results obtained from the current work for the utility of the biomarkers using clinical studies.supplementary Table 1, and previously published.A limitation of this study was a difference in the mean age of the control and diabetic: the age of the patents is older than the control, which could affect results. Therefore, we did an AUC analysis of some target biomarkers, as shown in


In particular, AS-tDR-008924, AS-tDR-011690, tDR-003634, AS-tDR-013354, tDR-011031, AS-tDR-001008, and AS-tDR-007319 were predicted to be involved in podocyte injury by targeting the Gpr, Wnt, Rac1, and other genes. Furthermore, gene ontology analysis showed that these differential tRFs were strongly associated with podocyte injury processes such as protein binding, cell adhesion, synapses, the actin cytoskeleton, and insulin-activate receptor activity. KEGG pathway analysis predicted that they participated in the PI3K-Akt signaling pathway, Wnt signaling pathway, and Ras signaling pathway. It was reported that these pathways contribute to podocyte injury. In conclusion, our study revealed that changes in the expression levels of tRFs might be involved in INS. Seven of the differentially expressed tRFs might play important roles in the process of podocyte injury and are worthy of further study.Idiopathic nephrotic syndrome (INS) is a disease involving injury to podocytes in the glomerular filtration barrier, and its specific causes have not been elucidated. Transfer RNA-derived fragments (tRFs), products of precise tRNA cleavage, have been indicated to play critical roles in various diseases. Currently, there is no relevant research on the role of tRFs in INS. This study intends to explore the changes in and importance of tRFs during podocyte injury in vitro and to further analyze the potential mechanism of INS. Differentially expressed tRFs in the adriamycin-treated group were identified by high-throughput sequencing and further verified by quantitative RT-PCR. In total, 203 tRFs with significant differential expression were identified, namely, 102 upregulated tRFs and 101 downregulated tRFs ( Idiopathic nephrotic syndrome (INS) is a glomerular disease that predominantly occurs in children and is characterized by massive proteinuria, hypoalbuminemia, hyperlipidemia, and edema , 2. PodoSmall noncoding RNAs (sncRNAs) are members of the noncoding RNA family and have been found to play crucial roles under many pathological conditions . RecentlTo explore the potential function of tRFs in podocyte injury, we used adriamycin to establish a model of experimental nephrotic syndrome in vitro . The difγ. Cells were incubated at 33°C under 5% CO2 [γ (nonpermissive conditions), and differentiation was confirmed by assessment of podocyte differentiation markers. Then, differentiated cells were cultured in serum-free RPMI-1640 medium (GIBCO BRL) for 24 hours to synchronize all cells into a quiescent state. Then, the Adr group was treated with adriamycin (1 μg/ml) for 24 hours. Each treatment was repeated three times.The immortalized mouse podocyte cell line was a gift from Dr. Mundel  . Cells wr 5% CO2 , 15. To R or Python for calculations and graphical analysis of the differentially expressed tRFs. The differentially expressed tRFs are listed in Total RNA samples were pretreated to remove some RNA modifications that interfere with the construction of the RNA-seq library. Then, the 3′ and 5′ miRNA adapters were successively ligated to each sample. cDNA was then synthesized and amplified using Illumina's proprietary RT primers and amplification primers. Subsequently, a PCR-amplified fragment of approximately 135-160 bp was excised from the PAGE gel and purified. Finally, a quantitative analysis of completed libraries was performed using an Agilent 2100 Bioanalyzer. For sequencing using the Illumina NextSeq 500 system, the library samples were processed, denatured, and diluted to a loading volume of 1.3 ml and a loading concentration of 1.8 pM. A NextSeq 500/550 V2 kit  was used to load the diluted libraries and advance them for sequencing. The libraries were denatured and diluted to a loading volume of 1.3 ml and a loading concentration of 1.8 pM. The diluted libraries were loaded in a NextSeq 500/550 V2 Kit  and sent to the Illumina NextSeq 500 system for sequencing according to the manufacturer's instructions. The original step in Illumina NextSeq 500 sequencing involves reading the data through the Illumina filter for subsequent analysis. The pruned reading segments (5′ and 3′ adapter bases removed) were compared with mature and pre-tRNA reference sequences. After statistical analysis, valid sequences were retained for subsequent tRF expression profiling and differential expression analysis. The remaining reads were aligned with transcriptome data including miRNA biotypes. Hierarchical clustering was performed and volcano plots were generated in https://imagej.nih.gov/ij/).Total protein was extracted with RIPA lysis buffer (Sigma), and protein concentrations were determined by a BCA assay. Proteins were separated on a 10% SDS-polyacrylamide gel and transferred to a nitrocellulose membrane. The membrane was blocked with a solution of 5% powdered nonfat milk for 2 hours at room temperature. After blocking, the membrane was placed in a solution containing the primary antibody and incubated overnight at 4°C. Then, the membrane was washed and incubated with a 1 : 5000 dilution of the secondary antibody (Sigma) for 2 hours. After washing the membrane again, the enhanced chemiluminescence reagents were used to react with the horseradish peroxidase-conjugated secondary antibody to detect antibody binding, and band densities were quantified using ImageJ  according to the manufacturer's protocol. To improve the efficiency of qRT-PCR, rtStar™ tRF and tiRNA pretreatment kits  were used to remove the terminal and internal methyl groups from total RNA isolated from podocytes of the NC and Adr groups. The RNA concentration and purity were quantified for each sample by a NanoDrop ND-1000 instrument. Then, following the manufacturer's instructions, a Bulge-Loop™ miRNA qRT-PCR Starter Kit  was used to reverse transcribe RNA to cDNA and carry out PCR amplification. The primer sequences targeting tRFs are listed in Supplementary Table (available http://gtrnadb.ucsc.edu/). Target genes of tRFs were predicted by the miRanda algorithm and TargetScan miRNA prediction database (http://mirdb.org/). The target genes of the top 20 tRFs with the highest fold changes are listed in GtRNAdb-based readings were generated using the tRNA database Genomic tRNA Database  were selected and entered into the DAVID (https://david.ncifcrf.gov/tools.jsp) website for GO and KEGG pathway analysis.To validate the potential functions of differentially expressed tRFs, target genes of differentially expressed tRFs . The pie charts generated from the results display 551 tRFs, including 102 upregulated tRFs and 101 downregulated tRFs  with abundant expression were randomly selected for qRT-PCR validation . GO analysis results consist of biological processes, cellular components, and molecular functions. Enriched categories within these parent categories were obtained. The most highly enriched biological processes were protein binding, DNA template transcription, protein phosphorylation, transport, regulation of translation, cell adhesion, angiogenesis, apoptotic process, protein homooligomerization, and regulation of actin cytoskeleton organization (β-catenin binding, and protein complex binding were the most highly enriched molecular functions (GO and KEGG pathway analyses were performed to explore the potential functions and mechanisms of these dysregulated tRFs (nization . The mosnization . Sequencunctions . In addiunctions .Idiopathic nephrotic syndrome with proteinuria is predominantly related to podocyte disease. Podocytes serve as components of the glomerular filtration barrier and play an integral role in preventing urinary protein loss . While tCommonly, as adaptor molecules, tRNAs function primarily to carry nucleotides for the translation of mRNA and synthetic polypeptide chains . Recent Glu-derived tRF3E is decreased in the blood of epidermal growth factor receptor-positive patients. Through competitive interaction with nucleolin, tRF3E leads to the release of p53 mRNA, thereby exerting tumor-suppressive effects [Along with the advancement of genome sequencing technology, tRFs have been proven to participate in the occurrence of various diseases. For instance, tRF-1001 from the Ser-TGA tRNA precursor transcript is highly expressed in a wide range of cancer cell lines. The knockdown of tRF-1001 can impair cell proliferation and suppress DNA synthesis . In addi effects . Howeverβ-catenin signaling activation has been confirmed in adriamycin-induced mouse podocytes and may indicate a novel therapeutic strategy for proteinuric kidney diseases [Analysis of tRF target genes revealed that multiple tRFs are closely related to podocyte injury. According to these results, AS-tDR-008924 and AS-tDR-011690 can function by targeting G protein-coupled receptors. Several studies support the idea that podocyte damage may accumulate through persistent G protein-coupled receptor activation . In addidiseases , 26. Furdiseases . These sα3β1, α2β1 and αvβ3, α-dystroglycan, syndecan-4, and type XVII collagen are cell-matrix adhesion receptors expressed by podocytes and can tightly connect podocytes to GBM [GO analysis was used to investigate the potential functions of tRFs. In the biological process category, protein binding and cell adhesion, which are important for podocyte function, had high enrichment scores. As a key element of the GFB, podocytes are permanently exposed to transcapillary filtration pressure. Based on this physiological structure, podocytes must be adhered tightly to the underlying GBM. Integrin s to GBM , 29. Thes to GBM , 31. In s to GBM , 33. Theβ-catenin. While Wnt signaling appears to be silenced in the adult kidney, reactivation of Wnt/β-catenin signaling occurs in certain types of kidney diseases, including those with dysfunctional podocyte homeostasis [β1 and NF-κB signaling to promote renal inflammation and fibrosis. These results reveal that differentially expressed tRFs might play a key role in podocyte injury through various signaling pathways [KEGG pathway analysis revealed that the PI3K-Akt signaling pathway, Wnt signaling pathway, and Ras signaling pathway had high enrichment scores. A growing number of studies have revealed that the PI3K-Akt signaling pathway is one of the crucial antiapoptotic pathways in podocytes. Dai et al. established an Adr-induced podocyte injury model and found that blocking Akt activation promotes caspase-3 activation in podocytes, indicating that the protective effect against podocyte injury is largely mediated by the PI3K-Akt pathway . Wnts beeostasis , 35. In eostasis . As the pathways , 38 .In conclusion, a total of 203 differentially expressed tRFs were identified. Prediction of target genes for these tRFs revealed that seven tRFs, namely, AS-tDR-008924, AS-tDR-011690, tDR-003634, AS-tDR-013354, tDR-011031, AS-tDR-001008, and AS-tDR-007319, play a regulatory role by targeting the Gpr, Wnt, Rac1, and other genes. In addition, the bioinformatic analysis revealed that tRFs could be involved in podocyte injury through the PI3K-Akt signaling pathway, Wnt signaling pathway, and Ras signaling pathway. Our study revealed that changes in the expression levels of tRFs might be involved in INS. Seven of the differentially expressed tRFs might play important roles in the process of podocyte injury and are thus worthy of further study.


Ulcerative colitis (UC) is a complicated disease caused by the interaction between genetic and environmental factors that affect mucosal homeostasis and triggers inappropriate immune response. The purpose of the study was to identify significant biomarkers with potential therapeutic targets and the underlying mechanisms.  The gene expression profiles of GSE48958, GSE73661, and GSE59071 are from the GEO database. Differentially expressed genes (DEGs) were screened by the GEO2R tool. Next, the Database for Annotation, Visualization and Integrated Discovery (DAVID) was applied to analyze gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Then, protein-protein interaction (PPI) was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING).  There were a total of 128 common DEGs genes, including 86 upregulated genes enriched in extracellular space, regulation of inflammatory response, chemokine-mediated signaling pathway, response to lipopolysaccharide, and cell proliferation, while 42 downregulated genes enriched in the integral component of the membrane, the integral component of the plasma membrane, apical plasma membrane, symporter activity, and chloride channel activity. The KEGG pathway analysis results demonstrated that DEGs were particularly enriched in cytokine-cytokine receptor interaction, TNF signaling pathway, chemokine signaling pathway, pertussis, and rheumatoid arthritis. 18 central modules of the PPI networks were selected with Cytotype MCODE. Furthermore, 18 genes were found to significantly enrich in the extracellular space, inflammatory response, chemokine-mediated signaling pathway, TNF signaling pathway, regulation of cell proliferation, and immune response via reanalysis of DAVID.  The study identified DEGs, key target genes, functional pathways, and pathway analysis of UC, which may provide potential molecular targets and diagnostic biomarkers for UC. Ulcerative colitis (UC) is one of the major clinical phenotypes of inflammatory bowel disease (IBD), a complicated disease caused by the interaction between genetic and environmental factors that affect mucosal homeostasis and trigger inappropriate immunity response . The incAlthough great progress has been made in understanding the pathophysiology of UC, early diagnosis, therapeutic intervention, and the potential pathogenesis remain to be fully elucidated. At present, UC is mainly diagnosed through colonoscopy and biopsy, which brings great pain to UC patients. Furthermore, the diagnosis may be delayed for several years, and it is difficult to make even for trained physicians . TherefoTherefore, early and accurate diagnosis of biomarkers could help clinicians to improve the treatment of individual patients. Furthermore, the biomarkers can contribute to predict the disease courses and thus identify patients who require intensive treatment. Patients with a low risk of morbidity may avoid the medication usage accompanied by the risk of adverse events. Moreover, disease recognition and specific biomarkers could be applied to identify the biological pathways involved in disease development and treatment. Deepening the overall understanding of the disease mechanism can promote the development of prevention and treatment strategies in the future. Therefore, the clinical application of a set of biomarkers represents a potentially valuable tool for diagnosis and prognosis.Currently, there are no effective biomarkers or commercial tests for early diagnosis of UC in clinical practice. The bioinformatics analysis has been widely used for exploring the molecular mechanisms of various diseases –7, whichα), Intercellular adhesion molecule 1 (ICAM1), and Ring finger protein 186 (RNF186) have been identified by bioinformatics analyses  Affymetrix Human Gene 1.0 ST Array). The present study focused on colonic mucosal biopsies, which included 7 colonic tissues and 8 normal controls, 23 colonic biopsies and 12 normal tissues, and 97 UC patients and 11 controls, respectively.NCBI-GEO . And the KEGG pathway analysis was downloaded from the KEGG database (https://www.kegg.jp/). P < 0.05 was set as the cutoff criterion.In order to analyze the screened DEGs at the functional level, GO function enrichment was performed using the DAVID online tool (https://string-db.org/cgi/input.pl) in Cytoscape was applied to examine the potential correlation between these DEGs with the node association confidence score > 0.4.To further visualize and evaluate interactions among DEGs, the STRING online database (P < 0.05 was considered a significant difference.The plug-in Molecular Complex Detection (MCODE) was used to identify the hub gene in functional networks in Cytoscape. The Biological Networks Gene Ontology tool (BINGO) plug-in in Cytoscape was conducted to the GO network of hub genes from the PPI network. P < 0.05 and ∣logFC | >2. Furthermore, the selected common DEGs are shown in First, we analyzed all the genes from GSE59071, GSE48958, and GSE73661 in P < 0.05).GO analysis includes molecular function, biological processes, and cell composition. The GO enrichment analysis of upregulated DEGs is presented in The STRING database was applied to investigate the PPI networks of these DEGs. The result is shown in 18 central node modules of the PPI networks were selected with Cytotype MCODE . And theThe etiology of UC has involved a complex interaction between environmental factors, infectious agents, and genetic susceptibility, which results in the impairment of mucosal immune response and barrier function against the intestinal microbiota . For exaIn the present study, 128 DEGs were screened in the UC samples, including 86 upregulated and 42 downregulated genes. The results of the GO analysis indicated that DEGs were particularly enriched in extracellular space, regulation of inflammatory response, chemokine-mediated signaling pathway, response to lipopolysaccharide, cell proliferation, immune response, and integral component of membrane. Previous studies have indicated that the abovementioned GO terms are potentially significant events in the pathogenesis of UC. For example, the regulation of inflammatory response, chemokine-mediated signaling pathway, response to lipopolysaccharide, cell proliferation, and immune response have roles in the pathogenesis of UC , 19. Furα agents [Furthermore, the KEGG pathway analysis revealed that the common DEGs were particularly enriched in cytokine-cytokine receptor interaction, TNF signaling pathway, chemokine signaling pathway, pertussis, and rheumatoid arthritis. Multiple immune and inflammatory signaling pathways, including the cytokine-cytokine receptor interaction, TNF signaling pathway, and chemokine signaling pathway, are activated and involved in the process of intestinal inflammation , 21. Preα agents , 28.α), interleukin-1 beta (IL-1β), interleukin IL-1 receptor antagonist (IL1RN), and prostaglandin-endoperoxide synthase 2 (PTGS2), which were mainly associated with the inflammatory response.Subsequently, UC with highly relevant pathway and enriched genes were selected to construct the PPI network; then, the potential key genes were identified. The 18 central hub genes included nitric oxide synthase 2 (NOS2), Matrix metalloproteinase 1 (MMP1), Matrix metalloproteinase 3 (MMP3), Matrix metalloproteinase 9 (MMP9), Tissue inhibitors of matrix metalloproteinase 1(TIMP1), chemokine (C-C motif) ligand 20 (CCL20), intercellular cell adhesion molecule-1(ICAM1), motif chemokine receptor 1 (CXCR1), Chemokine (C-X-C motif) ligand 1 (CXCL1), Chemokine (C-X-C motif) ligand 2 (CXCL2), Chemokine (C-X-C motif) ligand 8 (CXCL8), Chemokine (C-X-C motif) ligand 10 (CXCL10), Chemokine (C-X-C motif) ligand 11 (CXCL11), E-selectin (SELE), Interleukin-1 alpha (IL-1NOS2 induction accounts for large nitric oxide amounts that promote oxidative stress. The LPS-induced inflammatory responses are often accompanied by a high level of nitric oxide (NO) through a macrophage expression of the inducible form of NOS2 in microglial cells , 30.CXCLs, a class of small cytokines or signal proteins, play important roles in inducing directed chemotaxis of nearby reactive cells. Some chemokines are considered to be proinflammatory cytokines, which can induce cells of the immune system to enter the infection site during the immune response . With reCXCR1/2 belongs to the G-protein coupled receptor family, which is expressed on monocytes, neutrophil, and other leukocytes. Some scholars pointed out that CXCR1 was involved in the pathogenesis of IBD . CXCL8 eα) and endotoxin, it can be expressed on many cells. The adhesion molecules ICAM-1, associated with macrophage infiltration, are directly related to cell migration in inflamed colonic tissue [ICAM-1 is a member of the immunoglobulin superfamily of adhesion molecules. When stimulated by inflammatory cytokines  antagonists  and anti-a4b7 antibody (vedolizumab), has been approved in the management of UC patients . HoweverThe purpose of the research on genes and biomarkers related to UC has been the possibility to predict the treatment response and ultimately minimize and prevent possible adverse reactions. On the other hand, it is committed to promoting the development of new therapeutic drugs. A recent meta-analysis had reported a weak association of TLR2, TLR4, TLR9, TNFRSF1A, IFNG, IL6, and IL1B with the treatment response to infliximab .The development of new molecules targeting simultaneously multiple cytokines has been proven to be effective in UC. The discovery of the Janus kinase (JAKs) family of tyrosine kinases elucidated their role in cytokine signaling pathways, which have been identified as potential therapeutic targets of UC . TofacitIt was worth noting that this study has limitations. In this study, the results of microarray expression profiling were analyzed using bioinformatics methods and were not verified by reverse transcription-quantitative (RT-q) PCR. Therefore, a large number of clinical samples and future studies for experimental verification are required.In summary, the study used a comprehensive analysis method to identify DEGs, as well as unique biological functions and pathways of UC, thereby enhancing the current understanding of the pathogenesis of UC. Moreover, these results may provide potential biomarkers for the early and accurate diagnosis of UC, as well as potential therapeutic targets for the development of novel UC treatments.


Paeoniae Radix Alba, the root of the plant Paeonia lactiflora Pall, is a common blood-enriching drug in traditional Chinese medicine. Its effectiveness in the clinical treatment of anaemia is remarkable, but its potential pharmacologic mechanism has not been clarified.Paeoniae Radix Alba in the treatment of iron-deficiency anaemia was preliminarily elucidated through systematic and comprehensive network pharmacology.In this study, the potential pharmacologic mechanism of Paeoniae Radix Alba. The ingredients were predicted to target 77 genes associated with iron-deficiency anaemia. In-depth analyses of these targets revealed that they were mostly associated with energy metabolism, cell proliferation, and stress responses, suggesting that Paeoniae Radix Alba helps alleviate iron-deficiency anaemia by affecting these processes. In addition, we conducted a core target analysis and a cluster analysis of protein-protein interaction (PPI) networks. The results showed that four pathways, the p53 signalling pathway, the IL-17 signalling pathway, the TNF signalling pathway and the AGE-RAGE signalling pathway in diabetic complications, may be major pathways associated with the ameliorative effects of Paeoniae Radix Alba on iron-deficiency anaemia. Moreover, molecular docking verified the credibility of the network for molecular target prediction.Specifically, we obtained 15 candidate active ingredients from among 146 chemical components in Paeoniae Radix Alba and their targets and uncovered the mechanism of action of this drug, providing new insights for advanced research on Paeoniae Radix Alba and other traditional Chinese medicines.Overall, this study predicted the functional ingredients in  Worldwide, 46% of children aged 5 to 14 years and 48% of pregnant women suffer from iron-deficiency anaemia (IDA) . IDA is Paeoniae Radix Alba (PRA), the root of the plant Paeonia lactiflora Pall (family Ranunculaceae), is a TCM with the functions of nourishing blood, astringing Yin, preventing perspiration, regulating menstruation, extinguishing liver wind and relieving pain [In China, traditional Chinese medicine (TCM) is not simply a cultural practice, it also has a history of thousands of years of use for the treatment of various diseases. Under the guidance of the overall concepts and principles of syndrome differentiation and treatment, TCM has achieved satisfactory clinical results for anaemia treatment. ing pain , 12.In recent years, an unconventional novel analytical technique called network pharmacology has been widely used in TCM research –16. CombIn this study, network pharmacology was utilized to analyse the active ingredients, drug targets and key pathways of PRA in the treatment of IDA, as shown in Fig. Information on the chemical composition of PRA was gathered from three databases: the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform , the Encyclopedia of Traditional Chinese Medicine , and the Bioinformatics Analysis Tool for Molecular mechANisms of TCM  –23. In tHomo sapiens” to identify IDA-related genes.Targets of IDA were identified with four databases: GeneCards , DrugBank , DisGeNET (a platform to explore the relationships between genes and diseases), and the NCBI database  –26. In tP value of − 2 ~ 5, and a rotatable hydrogen bond number (RBN) ≤ 10. If a compound does not violate two or more of the above principles, it can be considered that the compound has soothing properties [Screening of dynamic components can be conducted on the basis of the five rules of Lipinski: a molecular weight (MW) < 500, a hydrogen acceptor number ≤ 10, a hydrogen donor (HDon) number ≤ 5, a log operties . Functiooperties . The OB operties ; a higheoperties , 31. In At present, the methods and techniques of drug target prediction can be divided into four parts according to their principles: (1) ligand prediction based on chemical structure similarity and pharmacophore models; (2) machine intelligence learning and prediction, for which standardized names and clear molecular target correspondence are required; (3) molecular docking, in which receptors are used to make predictions; and (4) combined prediction . BuildinHomo sapiens as the species. To ensure the accuracy of the results, we used P > 0.5 as the constraint condition for the predicted targets, yielding moderate credibility. After that, we obtained the drug active ingredient targets.First, the TCMSP, BATMAN-TCM, and ETCM were chosen as databases for the chemical components of PRA. The active components were screened with the criteria of an OB > 30% and a DL > 0.18, and the component targets were then rigorously predicted by SwissTargetPrediction  and Stitch  . In addiSecond, to consolidate and standardize the data, we identified the gene names of the predicted target proteins with Universal Protein (UniProt), a comprehensive resource of protein sequences and annotation data. UniProt is a compilation of the UniProt Knowledgebase, the UniProt Reference Cluster, and the UniProt Archive . We restFinally, to identify the universal targets between IDA and PRA, we uploaded the two target networks to the Venny 2.1 online server and obtained 77 common targets , and the “merge” tool was utilized to merge the network. The D-I-T-P-D network was obtained.The LibDock module of Discovery Studio 2016 was used to verify the molecular docking based on the functional components of PRA, and a heat map was constructed from the component-core target docking scores.We collected data on the chemical components of PRA from three databases, namely, the TCMSP, ETCM, and BATMAN-TCM. The numbers of chemical components derived from these three databases were 85, 59, and 35, respectively. After removing duplicates, we obtained 146 chemical components. Through screening of OB values and DL values, 13 qualified chemical components were obtained   that conThe development of a disease is usually associated with multiple genes or proteins, as is the case for IDA. In this study, we identified 1923, 60, 25 and 29 IDA-related genes from the GeneCards, DisGeNET, NCBI and DrugBank databases, respectively. Duplicates were removed, and 1939 related genes were obtained. A total of 77 genes were shared between PRA targets and IDA-related genes  > 2× the average degree for the core targets yielded 12 strategic targets. The results of the GO function and KEGG pathway enrichment analyses were very similar to the enrichment results for the 77 targets Fig. . The topP-values, including pertussis and the TNF signalling pathway -adjusted way Fig. . The detThe PPI network comparison of IDA and PRA revealed 75 core targets. To elucidate the biological functions of these targets, we divided the 75 targets into four clusters and subjected them to GO and KEGG pathway analyses . As illustrated in Fig. .1. As ilThe docking score diagram is given in Fig. IDA is a condition that occurs among children in both developing and developed countries, leading to impaired development, activity intolerance, behavioural changes, irritability, and reduced learning ability; women and elderly individuals are also affected , 46. TheIn this study, we identified 77 common targets between PRA and IDA. These targets were mainly enriched for energy metabolism-, cell proliferation-, and apoptosis-related terms. The main properties of these targets were associated with nucleic acid binding, receptors, and transcription factors (9/77); hydrolases and proteases (8/77); and oxidoreductases 13/77). Many other targets had unknown or other attributes. We reviewed several literature sources and found that most of the components downregulate TNF, IL6, PTGS2, CYP3A4, and CASP3 and upregulate PPARG and CAT, further indicating the reliability of the target prediction, as detailed in Table /77. ManyIn addition, we compared the KEGG and GO results for the 12 core targets, the 75 targets revealed in the cluster analyses, and the common targets, and the results showed an IDA correlation. We believe that the processes of nitric oxide biosynthesis, arachidonic acid metabolism, and Th17 cell differentiation play important roles in IDA –97. EffeSubsequently, we searched for the top four KEGG pathways and identified the p53 signalling pathway, the IL-17 signalling pathway, the TNF signalling pathway, and the AGE-RAGE signalling pathway in diabetic complications. We concluded that these pathways are involved in the treatment of anaemia –101. IntIn general, we elucidated the multi-component, multi-target, and multi-pathways characteristics of PRA in the context of IDA treatment through network pharmacology. Four pathways, the p53 signalling pathway, the IL-17 signalling pathway, the TNF signalling pathway, and the AGE-RAGE signalling pathway in diabetic complications, were identified, providing new directions for the development of drugs for IDA.Additional file 1: Table 1. Candidate active ingredients of PRA; Table 2. Common targets between PRA and IDA; Table 3. Correlations between components and targets.Additional file 2: File 2a: Table 1 Known therapeutic targets for IDA; File 2b: Table 2 Targets for PRA.Additional file 3. File for all pictures.Additional file 4. File for partial targets with components docking.