The method mimics a biologist's traditional approach of explaining changes in data using prior knowledge but does this at the scale of hundreds of thousands of reactions. This is a specific example of how to automate scientific reasoning processes and illustrates the power of mapping from experimental data to prior knowledge via logic programming. The identified mechanisms can explain how experimental and physiological perturbations, propagating in a network of reactions, affect cellular responses and their phenotypic consequences. Causal pathway analysis is a powerful and flexible discovery tool for a wide range of cellular profiling data types and biological questions. The automated causation inference tool, as well as the source code, are freely available at   •CausalPath builds mechanistic models from proteomic profiles•It integrates biological pathway models with molecular measurements•It supports logical reasoning with post-translational modifications•A web server, free software, and a source code are available Molecular profiling of biological organisms provides us with a great amount of information on cellular differences, but converting it to mechanistic insights is still a very challenging task. A prominent approach is to integrate new measurements with the mechanistic knowledge described in the scientific literature and build a model that is supported by both. Although this can be done in many ways, an adept approach will use the literature knowledge in detail and follow high standards of logical reasoning while integrating the known and the new. This article describes an approach that utilizes the details in human biological pathways to identify pairs of changes with a likely cause-effect relation within. The approach automatically converts comparative proteomic and other molecular profiles into hypotheses of differentially active mechanistic relations that explain how the profiles came to be. CausalPath integrates detailed biological pathways with proteomic and other molecular profiles to generate mechanistic models explaining how the observed changes are related. It is applicable to a wide range of contexts and a variety of experiment types. The method can be accessed at causalpath.org. The newer, data-driven inference approach leverages the recent developments in proteomics and other molecular technologies to directly infer graphical models, ab initio, from high-throughput measurements of controlled perturbations and natural variation.3Central to a cell's decision-making processes is a vast network of biochemical reactions. A comprehensive, predictive model of cell biological mechanisms would revolutionize our scientific understanding and have tremendous clinical utility. Modeling efforts can be categorized roughly into two branches. The more established approach is to compile extensive, interconnected pathway models through the curation of reactions based on carefully designed low-throughput controlled experiments. This classic approach led to the first large-scale metabolic maps and later was extended to signaling and transcriptional processes. Today this knowledge is represented in hundreds of pathway and interaction databases  detection of causal priors from pathway databases, performed once and reused in multiple analyses, and (2) matching causal priors with supporting correlated changes in the analyzed data, performed for every analysis. We define a “causal prior” as a set of prior knowledge that as a group suggests a possible causal link between two measurable molecular features.Existing kinase-substrate databases and transcription factor-target databases are valuable sources for causal priors, but they capture only a small part of the known biology; hence, they are limited for comprehensive causal reasoning. There are other databases that take a more detailed modeling approach for biochemical processes, such as Reactome. The Pathway Commons database provides integration of such detailed models collected from publicly available resources in the format of the BioPAX modeling language. Such models include details like post-translational modifications, molecular complexes, abstractions such as homologies, involvement of small molecules in signaling, and so on. Detailed process models provide a great opportunity to identify causal relations between the molecular measurements, but they require sophisticated algorithms to reason over them.To detect the causal prior relations, i.e., structures that imply causal relationships between proteins in the Pathway Commons database, we used the BioPAX-pattern softwareThe imbalance in the number of relations reflects the representation bias of these relationship types in the scientific literature. We assessed the overlap of these prior relations with the “canonical pathways” gene sets in MSigDB to understand its coverage. This collection has 2,815 gene sets curated from the databases BioCarta, KEGG, NCI-PID, Reactome, and WikiPathways. The genes in our prior relations have a nonzero overlap with 99% of these gene sets. If we redefine “overlap” focusing on relations instead of genes, and require that both the source and the target gene of a relation be in a gene set to count as overlap, then our prior relations have nonzero overlap with 68% of the canonical pathway gene sets.change here can be detected in two different forms based on the experimental setting: it can be up/downregulation for individual features in a “test versus control” comparison setting, or it can be positive/negative correlations applying to pairs of features in an uncontrolled study, as is common in cancer biology. We call an analysis in the former setting “comparison-based” and the latter “correlation-based.” As an example of comparison-based generation of a causal conjecture, consider the following chain of items from a study that detects a set of proteomic changes after stimulation by EGF:We define a “causal conjecture” as a pairing of a causal prior with supporting measurements in the molecular dataset that together declare that “one molecular change is the cause of another molecular change.” The Items 1 and 4 are direct observations from proteomic profiles and they are observed within the EGF stimulation context, and items 2 and 3 are the knowledge fragments that constitute the causal prior, as reported in publications from other experiments and subsequently curated into pathway databases. The causal conjecture here is that the increase in phospho-GAB1 (GAB1-pY406) after EGF stimulation causes an increase in its activity of helping phosphorylation of MAPK3 and hence an increase in the level of MAPK3-Y204. This is a well-defined, mechanistic, and falsifiable conjecture that is easily testable by perturbations  To demonstrate the method on a simple test case, we reanalyzed proteomic profiles from an EGF stimulation experiment. (2) We measured the precision of CausalPath results, where we analyze ligand stimulation of four different breast cancer cell lines and test the predictions with protein inhibitors. (3) Using a proteomic experiment that measures the effects of 31 different drugs on PC3 cell lines, we measured CausalPath's ability to relate observed changes to altered drug targets.We provide analyses for the robustness and reproducibility of CausalPath results, as well as a survey of other methods related to pathway analysis for proteomic datasets in Data S1.We reanalyzed a recent cell-line EGF stimulation phosphoproteomic datasetInterestingly, an explanation for MAPK1/3 phosphorylation is missing in this result. It is known that EGF signaling can activate MAPK1/3 through several steps and multiple paths, but none was captured. In its most strict configuration, CausalPath forces phosphorylation sites in the literature to exactly match the detected sites in the phosphoproteomic data. When we slightly relax this constraint by allowing two amino acids difference in site locations, we detect that SHC1 and GAB1 phosphorylations can causally link EGF stimulation to MAPK3 phosphorylation . Site loEach frame of the animated GIF corresponds to a time point in the experiment, which is compared with the initial control time point.We used a recently published RPPA experiment to estimate the precision of CausalPath results. This experiment stimulates four different breast cancer cell lines with seven different ligands, and also treats each cell line/ligand combination using five targeted drugs.unchanged, then the precision estimate drops to 0.56. In reality, the insignificant results are a mix of changed and unchanged, because we chose a threshold to have a reasonably low false positive rate at the expense of a possible high false negative rate. To estimate the false negative rate, we assume that the noise is symmetrically distributed around 0 and assume all three cases on the negative side are noise, predicting three additional unchanged cases on the positive side. This brings the estimate of changed-in-expected-direction cases to 26, suggesting a more realistic precision estimate of 0.81. This level of precision is very reasonable for most studies to justify following up with experimental verification.We first ignored the drug inhibition samples, and used the ligand stimulation experiments to predict their associated causal relations using CausalPath. Then, we identified the relations in the result networks whose source protein is targeted by one of the inhibitor drugs in the study. For these relations, the inhibition experiments provide validation of their inference. When the drug targeting the upstream protein is applied, if the CausalPath result relation is valid, we expect to see a reverse change downstream of the drug targets A. We ideTo evaluate CausalPath on a series of perturbations systematically, we analyzed the mass spectrometry data from a recent set of drug perturbation experiments on PC3 prostate cancer cell lines where a total of 3,979 phosphopeptides were measured.Four hundred eighty-nine high-grade serous ovarian cancer (HGSOC) samples were previously profiled by the TCGA project.Using CausalPath on this dataset, we generated explanations for the observed correlations in the measured peptide levels, using phosphorylation and expression regulation pathway relations. The first case explains phosphopeptide changes through phospho relations, and the second case explains total protein changes through expression regulation relations. In both cases, the upstream “cause” in the explanations is either a total protein or a phosphoprotein change. The resulting phosphorylation network contains 139 relations and the expression network contains 243 relations when we use a 0.1 FDR threshold for correlations. Interestingly, while the size of the phosphorylation network is significantly large , we do not observe this for the expression network (p = 0.6283). The most notable parts of the phosphorylation network include CDK1 and CDK2 downstream, MAPK1 and MAPK3 downstream, and several immune-related proteins such as SRC family kinases, PRKCD, and PRKCQ A.Figure Potential reasons for the radically different significance values for expression-regulation relations compared with phosphorylations include lower quality of expression regulation priors, higher number of confounding factors, and the relatively weak correlation between total protein measurements and their corresponding RNA expression. To investigate this, we modified CausalPath to use TCGA RNA-sequencing (RNA-seq) data instead of proteomic data for the target genes of expression regulation controls. We obtained 192 expression regulations that explain RNA measurements of 140 genes with proteomic changes of 92 transcription factors or their modulators. The size of the resulting network became significant after this change (p < 0.0001), confirming that the proteomic change is not a very good proxy for RNA expression (and vice versa). In addition, the downstream changes in four transcription factors  are significantly large (0.1 FDR), suggesting that these factors are significant sources of variance in ovarian cancer.The correlation-based causal network provides hypotheses for the signaling network parts that are differentially active across samples, but it does not indicate which parts are activated together or whether they align with previously defined molecular subtypes. The original TCGA study on HGSOC samples identifies four molecular subtypes based on RNA expression, termed as immunoreactive, differentiated, proliferative, and mesenchymal.Another CPTAC project produced proteomic and phosphoproteomic profiles for 105 of the original TCGA breast cancer samples with mass spectrometry,Similar to the ovarian cancer results, we detected that the breast cancer phosphorylation network is significant in size (p < 0.0001), while the expression network is not (p = 0.5521), suggesting that the known phosphorylation relations have a much higher impact on the proteomic correlations than known expressional relations. When we use TCGA RNA-seq data instead of proteomic data for the targets of expression regulation, we detect 248 relations that explain RNA changes in 155 target genes by proteomic changes of 120 transcription factors or their modulators. With RNA-seq data, the size of the network is highly significant (p < 0.0001), with 3 transcription factors  having correlated targets enriched in the results B.Next, we compared the PAM50 expression subtypes of breast cancer to see if we could get causal explanations of their proteomic differences. We were again challenged by decreased sample sizes and missing values, but we detected that luminal A and luminal B subtypes have significant differences from the basal-like subtype. This time, CausalPath results were not significant in terms of the overall network size (p = 0.2218); nevertheless, they indicate that ESR1 is significantly more active in luminal breast cancers, suggested by both its protein levels and the changes in its downstream C. TranscThere are 32 TCGA studies that provide proteomic and phosphoproteomic measurements of tumor biopsies from various types of cancer patients. Those studies provide RPPA profiles of a total of 7,694 patients using 259 antibodies. The low number of protein measurements in the datasets prevents a comprehensive pathway analysis, but the antibodies are selected for the proteins’ relevancy to cancer in general, and they are typically well studied with many established relations between them. We sought to determine which of these relations most frequently have evidence in the form of correlation across cancer types. We generated a correlation-based causal network for each cancer type using a strict FDR threshold of 0.001, then we ranked these relations according to how many cancer datasets they can explain . We foun27priors, which can inform the hybrid pathway inference methods that benefit from prior data.CausalPath is a novel pathway extraction method to aid researchers in understanding experimental observations using known mechanisms with a focus on post-translational modifications. Experimental data reveal protein features that change in coordination, and CausalPath automates the search for causal explanations in the literature. Loosely speaking, context-specific correlations are derived from the data and causality is derived from the literature. Compared with the methods that infer causality from data through mathematical modeling (pathway inference), this method has a much wider application area. Pathway inference methods have a potential to offer more complete results, but they require numerous perturbations and/or time points in the experiments, whereas the pathway extraction strategy is applicable to any simple comparison, or a set of profiles from a cohort with some variance to explain. This is extremely important, since even with the large CPTAC datasets, we are still data limited, especially when we try to understand subtype-specific mechanisms. We believe that we will see parallel progress in both strategies as the data and knowledge increase respectively with a potential convergence in the future. In addition, CausalPath is a great resource for high-confidence Even though pathway extraction cannot hypothesize the existence of new relations that were never seen before in any context, its results cannot be dismissed as not novel. Existing relations in pathway databases are collected from diverse contexts, cell types, disease models, etc. For a new context of focus, it is very challenging to identify which of the previously described relations are applicable. Causality-focused pathway extraction approaches provide a means of transferring knowledge between contexts. CausalPath does this by detecting variation patterns of proteomic abundances and detecting their consistency with prior knowledge. A limitation of this approach is its dependency on observable variance; therefore, it cannot identify a signaling relation that does not significantly vary across the compared samples.The added value that our method brings to the field of pathway extraction is three-fold: (1) interpretation of complex mechanistic pathway models, (2) site-specific evaluation of phosphoproteomic measurements, and (3) a logical test for causality between measurements. When all these are combined, pathway extraction becomes a useful tool for “mechanistic model building.” We expect future research will take these ideas further, potentially addressing these two challenges: (1) instead of a binary evaluation of causality (between two proteins), n-ary systems can be developed, and (2) instead of a binary classification of protein modifications as activating and inhibiting, a site can be more accurately mapped to a distinct subset of activities of the protein. These challenges can be tackled gradually as we have more detailed and more complete models of cellular processes in pathway databases.Our results show that evidence of known phospho regulations is more consistently observed in the proteomic data compared with the known expression regulations. In the ovarian and breast cancer datasets, the sizes of the resulting phosphorylation networks are significantly higher compared with background, while expression networks remain similar. In the recurrence study with TCGA RPPA datasets, 34% of the resulting phosphorylation controls recur in at least 15 cancer types . This raOne major limiting factor in this analysis is a large number of protein phosphorylation sites whose functions are not known; hence, their downstream cannot be included in the causality network. We are actively working to mine these data from the literature using natural language processing tools.Rarely, in the causality analysis results, we encounter relations that are erroneous. These are generally results of manual curation issues. In these cases, we report them to the source databases, and we remove these erroneous pathway interactions from our network so that future analyses are not affected. We encourage researchers to report such errors to source databases , if they come across any, to improve the accuracy of our collective knowledge of biochemical pathways. We are actively working on a collaborative data-explaining platform that will further streamline the curation and error reporting steps.causalpath.org. Users can either use the website to execute the analysis or run the analysis locally using CausalPath's open-source Java code. The result networks can be visualized using the viewer embedded in the CausalPath website or by loading to the pathway visualization tools ChiBE,CausalPath can be applied to the results of any proteomic and phosphoproteomic experiments to identify differential signaling that is supported by literature knowledge. To use CausalPath, the measurement values need to be comparable  and need to be associated with related gene symbols, and phosphopeptide measurements need to specify the phosphorylation sites with respect to their canonical UniProt sequence, in a special format that is described at the website, ozgun.babur@umb.edu).Further information and requests for digital resources should be directed to and will be fulfilled by the lead contact, Özgün Babur (This study did not generate new unique reagents.http://causalpath.org. Users can upload the proteomic data in a tab-delimited format, along with the analysis parameters, such as how to detect a change in the values. Options include averaging a group of values, getting difference/fold-change of two groups of columns, comparing two groups with a t test, or using correlations in a single group. The results are visualized as an interactive network using Cytoscape.js,https://github.com/PathwayAndDataAnalysis/causalpath. This repository additionally includes examples running CausalPath from R and Python.,causalpath.org. All network figures in this article were generated with ChiBE.CausalPath is freely available at https://www.synapse.org/#!Synapse:syn17014378. An alternative URL for this archive is https://doi.org/10.5281/zenodo.4477801.The results presented in this article can be reproduced using the datasets, parameters, and software in the supplementary archive at


Latent tuberculosis infection (LTBI) poses a major roadblock in the global effort to eradicate tuberculosis (TB). A deep understanding of the host responses involved in establishment and maintenance of TB latency is required to propel the development of sensitive methods to detect and treat LTBI. Given that LTBI individuals are typically asymptomatic, it is challenging to differentiate latently infected from uninfected individuals. A major contributor to this problem is that no clear pattern of host response is linked with LTBI, as molecular correlates of latent infection have been hard to identify. In this study, we have analyzed the global perturbations in host response in LTBI individuals as compared to uninfected individuals and particularly the heterogeneity in such response, across LTBI cohorts. For this, we constructed individualized genome-wide host response networks informed by blood transcriptomes for 136 LTBI cases and have used a sensitive network mining algorithm to identify top-ranked host response subnetworks in each case. Our analysis indicates that despite the high heterogeneity in the gene expression profiles among LTBI samples, clear patterns of perturbation are found in the immune response pathways, leading to grouping LTBI samples into 4 different immune-subtypes. Our results suggest that different subnetworks of molecular perturbations are associated with latent tuberculosis. Mycobacterium tuberculosis (Mtb) is one of the most successful pathogens known to humans. Despite the availability of an array of anti-mycobacterial drugs, tuberculosis (TB) is still the leading cause of death among infectious agents. Although it is the active form of TB that causes morbidity, contagiousness and mortality, a majority of Mtb infected individuals remain latently infected , the most sensitive technique currently available, lacks the specificity to differentiate between active and latent TB as well as new and treated infections . Moreovediseases –9. Thesediseases –12 as wediseases , 13–17. diseases . Recentldiseases . Howeverdiseases .In this work, we seek to obtain an unbiased and personalized view of the key immune responses that are either activated or repressed in response to a latent TB infection as compared to uninfected individuals. Towards this, we built sample-specific network models of the immune processes by using a computational pipeline previously developed in the laboratory that integrates transcriptomes of LTBI individuals with a human protein-protein interaction network to make precision networks for each subject. Whole blood transcriptomes for LTBI subjects were publicly available generated in multiple different studies. Our network that was recently reconstructed in the laboratory , is knowWe searched the Gene expression repository GEO  for micrWe obtained raw data for each dataset from the GEO. The datasets were pre-processed and normalized separately since the platform chemistry was different for each dataset. EdgeR and Limma package in Bioconductor in the R statistical environment was used for all gene expression analysis –28. OverIn case of the datasets with only LTBI samples but not uninfected cases, the gene expression profiles were computed in comparison to the uninfected samples from other datasets. The tool ComBat  was usedWe used a comprehensive in-house human protein-protein interaction network (hPPiN) from Sambarey et al., 2017 in this study . In summTo mine the weighted LTBI networks, we use our previously developed computational method , 21, 35,Pathway enrichment analysis of the precision networks, LTBINetP, was carried out with Enrichr . EnrichmSince the pathway description in the databases includes all the genes involved in the pathways and many pathways share multiple signal mediators, there is a significant overlap of genes between different pathways. Therefore, a pathway can be statistically over-represented if some of the signal mediators are captured in the gene list, but not the initiating genes of the pathway, i.e. the ligands or receptors. In order to avoid the selection of such pathways from further analysis, another curation step was applied to the enrichment analysis to select only those pathways for which the ligand or receptor genes (as per the source databases) were present in the LTBINet analyzed.Each of the target pathways was given a binary score of 1 or 0 based on the over- representation of the pathway associated genes  in the top perturbed network of individual samples. The precise pattern of pathway perturbation was used for clustering the samples. Unsupervised clustering of LTBI samples was performed with the pathway perturbation scores using the ‘ConsensusClusterPlus’ package in R .To obtain a systems perspective of the host immune responses associated with LTBI, we first analyzed 3 publicly available transcriptome datasets, GSE19439, GSE19444 and GSE28623, that contained information on samples from LTBI, uninfected healthy controls (HC) as well as active TB cases and subsequently validated our findings in 2 other independent LTBI transcriptome datasets (GSE107993 and GSE107994). The 3 datasets together contained samples from 63 LTBI  and 55 healthy individuals. We found very few differentially expressed genes (DEGs) between LTBI and HC, with a fold change ≥ ± 1.5, FDR adjusted p value ≤0.05   and represented each sample as a binary barcode of perturbations of IPLTB Figure 4We performed a 2 fold validation of the clustering patterns. In the first step, we analyzed the 2 RNAseq based transcriptome datasets that contained samples from both LTBI and uninfected cases. There were a total of 73 LTBI samples in GSE107993 and GSE107994 to which a similar network analysis pipeline followed by an enrichment analysis was applied. For each sample, a binary score was computed for each of the 10 immune pathways. Each of the 73 samples thus scored were added to the binary score table of the 63 previous samples and consensus clustering was performed. 63 of the 73 LTBI samples clustered with one of the four major clusters  showing the patterns to be significant for LTBI condition  between nodes ‘i1’ and ‘i2’ is calculated based on the node weights as,Mtb, Mycobacterium tuberculosis; PDGFR, Platelet derived growth factor receptor; TB, Tuberculosis; TGFβ, Transforming growth factor beta; TLR, Toll like receptor; TNFα, Tumor necrosis factor alphaEGFR, Epidermal growth factor receptor; FGFR, Fibroblast growth factor receptor; IFNγ, Interferon gamma; IL, Interleukin; HC, Healthy (Uninfected) control; LTBI, Latent tuberculosis infection; https://www.ncbi.nlm.nih.gov/geo/ under the IDs GSE19439, GSE19444, GSE28623, GSE107993, GSE107994, GSE84076,GSE40553, GSE79362, GSE37250, and GSE101705. Codes used for analysis in this work are available at https://github.com/chandralab-iisc/LTB_immune_Subtype.Publicly available datasets were analyzed in this study. This data can be found here: NC conceptualized, designed, and supervised the study. UB performed data curation, methodology, analysis, and interpretation. PB performed data curation and methodology. AS provided supervision and critical insights. UB wrote the first draft of manuscript. All authors revised and approved the submitted manuscript.The authors thank the Department of Biotechnology, Government of India for providing the funding for this study in the form of a DBT-IISc partnership grant.NC is a co-founder of the companies qBiome Research Pvt Ltd and HealthSeq Precision Medicine Pvt Ltd. They had no role in this manuscript.The remaining 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.


Corydalis decumbens (Thunb.) Pers. in NSCLC based on network pharmacology and bioinformatics. The overlapping genes between Corydalis decumbens (Thunb.) Pers. and NSCLCs were screened using Venn analysis. Cytoscape 3.7.1 software was used to analyze the overlapping target protein-protein interaction (PPI) network. Gene ontology and pathway enrichment analysis using the Kyoto Encyclopedia of Genes and Genomics database were performed to exploring biological functions of the overlapping genes. The gene expression profiling interactive analysis dataset was used to analyze the correlation between hub gene expression and disease. This study revealed 38 nodes with 191 edges, which may be therapeutic targets for NSCLC. PPI network analysis showed that the most likely association was between the genes AR and NCOA2, NCOA2, and RXRA and ESR1 and NCOA2. These overlapping genes were mainly enriched in the estrogen signaling pathway, calcium signaling pathway, cholinergic synapse, and PI3K-Akt signaling pathway. ESR2 mRNA levels were signiﬁcantly downregulated in patients with lung adenocarcinoma (LUAD) getting worse, and KDR levels were lower in lung squamous cell carcinoma (LUSC) than those in normal tissue. PTGS2 expression was correlated with the median survival time of LUAD, and ESR1 expression was correlated with the median survival time of LUSC. The application of network pharmacology revealed the potential mechanism underlying the effects of Corydalis decumbens (Thunb.) Pers. in NSCLC treatment and provided a theoretical basis for further in-depth research in this field.Non-small cell lung cancer (NSCLC) is one of the most malignant tumors worldwide. The main treatment for NSCLC is based on Western medicine; however, the overall effect is unsatisfactory. This study aimed to investigate the potential therapeutic targets and pharmacological mechanisms of action of the traditional Chinese medicine  Lung cancer is one of the most malignant tumors with high morbidity and mortality rates worldwide. It is divided into non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) based on its different morphological characteristics. NSCLC accounts for 80% of all lung cancer cases and mainly includes lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), and large cell lung cancer . Recent Corydalis decumbens (Thunb.) Pers. is botanical medicine that is rich in alkaloids and has mainly focused on treating cardiovascular and cerebrovascular diseases, arthritis, and myopia [Corydalis decumbens (Thunb.) Pers. play role in treating NSCLC? Which ingredients of Corydalis decumbens (Thunb.) Pers. have an anti-NSCLC effect? What is the molecular mechanism underlying the effect of Corydalis decumbens (Thunb.) Pers. in NSCLC treatment?Traditional Chinese medicine (TCM) has a history of more than 2000 years in China and mainly includes botanical, animal, and mineral medicines. It has been widely used to prevent and treat diseases, particularly in Asia , 7. Studd myopia –13; howeCorydalis decumbens (Thunb.) Pers. using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform . The Chinese pinyin name “xiatianwu” was entered in the “herb name” column. The filter conditions were oral bioavailability (OB) ≥ 30% and drug similarity (DL) ≥ 0.18.We searched the active ingredients of https://www.symmap.org/), and the Encyclopedia of Traditional Chinese Medicine . The reviewed human database from UniPort (http://www.uniport.org/) was used to translate target names to gene names. Cytoscape 3.7.1 software was used to construct the drug ingredients target network.The corresponding targets of the active ingredients were obtained from TCMSP, SymMap (https://www.Genecards.org) to identify the symbols of genes reported in NSCLC using keyword “NSCLC.” UniProt online databases were used to translate symbol names to gene names. The overlapping genes of Corydalis decumbens (Thunb.) Pers. and NSCLC were identified by drawing a Venn diagram. The overlapping genes were considered as potential Corydalis decumbens (Thunb.) Pers. genes playing a role in treating NSCLC. Cytoscape 3.7.1 software was used to construct the ingredients-potential target network.We used online tools such as GeneCards (http://www.string-db.org/) and Cytoscape 3.7.1 software were used to elucidate the molecular mechanism underlying the anti-NSCLC effect of Corydalis decumbens (Thunb.) Pers. According to the STRING database with the species limited to “Homo sapiens” and a confidence score >0.4, the interaction proteins were screened. The PPI network was constructed using Cytoscape 3.7.1 for further visualization and analysis .The STRING database (https://david.ncifcrf.gov) was used for functional enrichment analysis of potential genes. “Homo sapiens” was selected as the species and “OFFICIAL GENE SYMBOL” was used for identification. Gene ontology (GO) functions and the Kyoto Encyclopedia of Genes and Genomics (KEGG) pathway enrichment analysis in the DAVID online tool were performed for exploring the biological function of the genes used in the treatment of NSCLC; P < 0.05 was considered as the cutoff criterion. In particular, GO functions include biological process (BP), molecular function (MF), and cellular component (CC). P < 0.01 was used as the cutoff criterion.The Database for Annotation, Visualization and Integrated Discovery  was used to analyze the expression of hub genes, such as the differential expression analysis between NSCLC tissues and normal tissues, differential expression analysis according to pathological stages of cancer, and the expression analysis associated with overall survival (OS) of the NSCLC patients (P < 0.05). “MCC” was used as the calculate mode, and the “display the shortest path” as the displayed options, LUAD and LUSC, were selected as the cancer type dataset. Log2 FC cutoff = 2, q value cutoff = 0.05, and the other options were set as the default values.The top 10 hub genes in the PPI network were verified using the Cytoscape 3.7.1 plugin cytoHubba. The gene expression profiling interactive analysis dataset  Pers. were screened  Pers. participated in the regulation of NSCLC progression  Pers. contained 38 nodes and 191 edges  Pers. against NSCLC, the interacting proteins were screened using the STRING online tool. We found 31 nodes (PRSS1 did not interact with any of the genes) with 90 PPI relationships in the network  Pers. and NSCLC-associated targets. The most likely association was between AR and NCOA2 (score = 0.999). The remaining interactions were as follows: NCOA2 (interacts with) RXRA, ESR1 (interacts with) NCOA2, CALM1 (interacts with) NOS2, CALM1 (interacts with) NOS3, ESR1 (interacts with) NOS3.Based on the potential pharmacodynamics of  network . The PPI network . The res network  and 40 e network . We founCorydalis decumbens (Thunb.) Pers. were used for further GO function and pathway enrichment analyses using the DAVID online tool. The results showed that the genes could be assigned to different GO terms, which mainly involved biological processes (BP) (90 items), cell components (CC) (27 items), and molecular functions (MF) (41 items). The cutoff for statistical significance was set P < 0.01, and the top 20 items with significant enrichment in biological processes, cell components, and molecular functions were screened, as shown in Corydalis decumbens (Thunb.) Pers. were significantly enriched in “adenylate cyclase-inhibiting G-protein coupled acetylcholine receptor signaling pathway” and “phospholipase C-activating G-protein coupled acetylcholine receptor signaling pathway.” For cell components, “axon terminus” and “asymmetric synapse” were mainly enriched. In addition, “RNA polymerase II transcription factor activity,” ligand-activated sequence-specific DNA binding,” “steroid hormone receptor activity”, and “G-protein coupled acetylcholine receptor activity” belonged to molecular functions. Additionally, the KEGG pathway enrichment analysis (P < 0.01) showed that these overlapping genes were mainly enriched in pathways such as the estrogen signaling pathway, calcium signaling pathway, cholinergic synapse, cAMP signaling pathway, PI3K-Akt signaling pathway, and VEGF signaling pathway  Pers. for treating NSCLC were selected based on the Cytoscape 3.7.1 plugin cytoHubba  Pers. included CALM1, AR, ESR1, ESR2, FOS, GJA1, KDR, NOS3, OPRM1, and PRKACA. Using the GEPIA dataset, we compared the mRNA expression of hub genes among NSCLC (LUAD and LUSC) at different stages. The results showed that not all of the ESR2 mRNA expression levels were the same in different stage LUAD of patients (P < 0.05)  . Additiol tissue . We furtof PTGS2 . The gro of ESR1 .Corydalis decumbens (Thunb.) Pers. against NSCLC based on network pharmacology analysis and bioinformatics. Network pharmacology is an interdisciplinary subject that combines systems biology and pharmacology using high-throughput sequencing, genomics, and other technologies to analyze and explore the multichannel regulation of signaling pathways [Corydalis decumbens (Thunb.) Pers. based on network pharmacology analysis. The results showed that the main ingredients of Corydalis decumbens (Thunb.) Pers. were PDSP1_000624, palmatine, fumarine, isocorypalmine, bicuculline, and C09367. Studies have shown that alkaloids exhibit good antitumor activity. Bicuculline can effectively inhibit the migration and invasion of human hepatocellular carcinoma MHCC97-H cells which is related to the downregulation of VEGF, MMP-2, and MMP-9 gene expression and inhibition of JAK2/STAT3 signaling pathway activation [Lung cancer is one of the most malignant cancers worldwide, and the effect of clinical treatment for this disease is not satisfactory ; therefopathways . In thistivation . Fumarintivation , and thetivation .2+ is an important small-molecule signal that regulates cell function. The high expression of the Ca2+ pathway transcription factor NFATc2 is considered to be a new regulator for lung cancer to initiate cell phenotype, and high NFATc2 expression predicted poor tumor differentiation, adverse recurrence-free, and cancer-specific overall survivals in human lung cancers [2+ concentration in the cytoplasm of cancer cells can also induce apoptosis [Babita et al. [Corydalis decumbens (Thunb.) Pers. mainly act on the key factors in the above pathways to effectively treat NSCLC.A total of 32 overlapping genes were identified between the drug targets and NSCLC molecular targets. GO and KEGG analyses showed that they were mainly assigned to different GO terms and pathways. For example, transcription factor binding, protein binding, drug binding, estrogen signaling pathway, and calcium signaling pathway promote the occurrence and metastasis of NSCLC. Shi et al.  found th cancers . The incpoptosis . Increaspoptosis . Babita a et al.  indicatea et al. . Hormonea et al. . The PI3a et al. , 26. TheCorydalis decumbens (Thunb.) Pers.: CALM1, AR, ESR1, ESR2, FOS, GJA1, KDR, NOS3, OPRM1, and PRKACA. The PPI network showed that the most likely association was between AR and NCOA2; NCOA2 and RXRA, ESR1 and NCOA2, FOS and ESR2, ESR2 and NCOA2, and FOS and ESR2. In tumor models, the adrenaline signaling pathway plays a regulatory role in tumor growth and development. Moreover, the activation of β-adrenergic receptors promotes tumor progression and development of resistance to treatment [AR [Chen et al. [Furthermore, we analyzed the top 10 potential therapeutic targets of reatment , 28. Fosreatment ; moreovement [AR . Studiesment [AR –33. VEGFment [AR . Chen etn et al.  showed tCorydalis decumbens (Thunb.) Pers. and preliminarily verified the effects of Corydalis decumbens (Thunb.) Pers. in multicomponent, multitarget, and multichannel treatment of lung cancer. The application of network pharmacology revealed the potential mechanism underlying the treatment of NSCLC by Corydalis decumbens (Thunb.) Pers. and provides a theoretical basis for further in-depth research. However, this study did not consider the influence of drug dosage, drug ingredients, and drug delivery methods on the treatment results. Therefore, further experimental study is required to verify the results of this study.This study is used the network pharmacology analysis to understand the mechanism of action of


Network pharmacology is in line with the holistic characteristics of TCM and can be used to elucidate the complex network of interactions between disease-specific genes and compounds in TCM herbal medicines. Here, we investigate the pharmacological mechanism of Xiaokui Jiedu decoction (XJD) for the treatment of ulcerative colitis (UC).  The Computational Systems Biology Laboratory Platform (TCMSP) database was searched and screened for the active ingredients of all drugs in XJD. The Uniport database was used to retrieve possible gene targets for the therapeutic effects of XJD. GeneCards, PharmGKB, TTD, and OMIM databases were used to retrieve XJD-related gene targets. A herb-compound-protein network and a protein-protein interaction (PPI) network were constructed, and hub genes were screened for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Finally, molecular docking was performed to validate the interrelationship between disease target proteins and active drug components. RB1, MAPK1, TP53, JUN, NR3C1, MAPK3, and ESR1. GO enrichment analysis showed 741 biofunctional enrichments, and KEGG enrichment analysis showed 124 related pathway enrichments. Molecular docking showed that the active components of XJD  showed good binding activities to five of the six hub gene targets. Discussion. The active ingredients of XJD  may regulate the inflammatory and oxidative stress-related pathways of colon cells during the course of UC by binding to the hub gene targets. This may be a potential mechanism of XJD in the treatment of UC. A total of 135 XJD potential action targets, 5097 UC-related gene targets, and 103 XJD-UC intersection gene targets were screened. The hub gene targets of XJD that exert therapeutic effects on UC are  Ulcerative colitis (UC) is a chronic nonspecific inflammatory disease of the rectum and colon of unknown etiology , 2. The In recent years, the incidence of ulcerative colitis (UC) has increased significantly, with 505 and 214 cases per 100,000 people in Europe and the United States, respectively, and the annual costs for UC-related screening and treatment amount to 12.5–29.1 billion euros and 8.1–14.9 billion dollars . AminosaUlcerative colitis belongs to the category of “diarrhea” and “dysentery” in TCM and is mainly caused by damp-heat and a diet that damages the spleen and stomach. Network pharmacology is in line with the holistic characteristics of TCM and can elucidate the complex network of interactions between disease-specific genes and compounds in TCM herbal medicines –10.In summary, our study combines network pharmacology, molecular docking, and cellular experimental analysis to investigate the pharmacological mechanism of XJD for UC treatment.Pulsatilliae radix), huanglian (Coptidis rhizoma), huangbai (Phellodendri chinensis cortex), qinpi (Fraxini cortex), kusheng (Sophorae flavescentis radix), baiji (Bletilla striata), gegen (Radix puerariae), huaihua (Sophora japonica L.), xianhecao (Agrimonia eupatoria), and gancao (licorice)) [In the Computational Systems Biology Laboratory Platform (TCMSP), herbs are searched separately (baitouweng (corice)) . The scrhttps://www.uniprot.org/), the Uniport KB function was used to search for human gene abbreviations related to the targets of the active ingredients of XJD and to obtain possible gene targets for the therapeutic effects of XJD.In the Uniport database  [The R software package was used to plot the Venn diagram to obtain the intersection of drug and disease gene targets to achieve the likely therapeutic targets. These were then entered into the string system, and a protein-protein interaction (PPI) network was constructed with a confidence interval of ±0.950 . Cytoscae <0.05) , 15.Molecular docking refers to the “docking theory” that relies on receptor-ligand interactions to predict the potential binding mode of a compound to a protein –18. Hub Pulsatilliae radix), huanglian (Coptidis rhizoma), huangbai (Phellodendri chinensis cortex), qinpi (Fraxini cortex), kusheng (Sophorae flavescentis radix), baiji (Bletilla striata), gegen (Radix puerariae), huaihua (Sophora japonica L.), xianhecao (Agrimonia eupatoria), and gancao (licorice). Animal welfare was carried out in strict accordance with the internationally accepted principles for laboratory animals . Sprague Dawley (SD) rats with a weight range of 270 ± 10 g were randomly divided into three groups according to body mass: blank group, XJD treatment group, and model group. Dosages administered were calculated with reference to the previous literature [μm microporous membrane, and stored at −20°C for quantitative real-time PCR (qPCR). Followed by cDNA synthesis using the Prime-Script RT Master Mix (TaKaRa), total mRNA was isolated from the cell cultures using the Mini-BEST Universal RNA extraction kit . And, qPCR assays were detected with PCR LightCycler480.XJD is a combination of baitouweng , six active ingredients of huaihua (Sophora japonica L.), four active ingredients of gegen (Radix puerariae), nine active ingredients of baiji (Bletilla striata), 45 active ingredients of kusheng (Sophorae flavescentis radix), three active ingredients of qinpi (Fraxini cortex), 27 active ingredients of huangbai (Phellodendri chinensis cortex), 11 active ingredients of huanglian (Coptidis rhizoma), and 12 active ingredients of baitouweng (Pulsatilliae radix). A total of 168 human gene targets corresponding to the active ingredients were searched through the Uniport database.The study flow chart is described in Using the keyword “ulcerative colitis,” the GeneCards database retrieved 5,079 disease-related gene targets, the OMIM database retrieved 19 disease-related gene targets, the DrugBank database retrieved 13 disease-related gene targets, and the PharmGKB database retrieved 54 disease-related gene targets. A total of 5,097 UC-related gene targets were obtained by combining all disease-related gene targets .β-sitosterol, kaempferol, formononetin, quercetin, and luteolin  were enriched in the endocrine resistance pathway , CCs , and MFs   were found to exhibit the same effects as ESR1, JUN, NR3C1, MAPK1, MAPK3, RB1, and TP53  had a molecular docking binding free energy less than −7.0 kcal/mol and 94% had less than −5 kcal/mol, indicating that the targets had good binding power with the components. Therefore, we suggest that XJD binds to these genes  very well and this may be the potential mechanism of XJD in the treatment of UC.Molecular docking binding free energy less than −5.0 kcal/mol indicates strong binding activity, and that less than −7.0 kcal/mol indicates very strong binding activity. In the present study, 47% of targets  . Therefore, the expression of key mRNAs  in UC was significantly downregulated after XJD administration. Our study reveals, to some extent, the pharmacological mechanism of XJD in the treatment of UC.FOS, TNF- < 0.01) . The expHu et al. recently found that XJD was effective in treating UC, with its ability to effectively regulate neuroendocrine factors, improve the intestinal immune response, and reduce patient symptoms . NetworkRB1, MAPK1, TP53, JUN, NR3C1, MAPK3, and ESR1. GO enrichment analysis showed 741 biofunctional enrichments, and KEGG enrichment analysis showed 124 related pathway enrichments. Molecular docking showed that the active components of XJD  showed good binding activity to six of the seven hub gene targets . β-Sitosterol, kaempferol, formononetin, quercetin, and luteolin have shown potential functions in the treatment of UC in many previous studies [ESR1 were associated with the development of Crohn's disease in patients [JUN and MAPK1 [NR3C1 [α, and IL-1) in UC was also significantly downregulated after XJD administration. Binding to these six genes, XJD therefore likely regulates the inflammatory response and pathways associated with the oxidative stress response in colon cells during the course of UC.A total of 135 potential action targets of XJD, 5,097 UC-related gene targets, and 103 XJD-UC intersection gene targets were screened, and 103 potential protein targets were found to be possible UC-related genes. The hub gene targets of XJD that exert therapeutic effects on UC are  studies –25. Varipatients . The potnd MAPK1 , 28 and 1 [NR3C1  on UC isα, IL-1, and FOS) expression, which promotes apoptosis of intestinal epithelial cells and increases permeability, further destroying the intestinal barrier and further aggravating local intestinal mucosal damage [α, and IL-1) was downregulated after treatment of UC model mice with XJD. While our findings are quite valuable for UC treatment options, further experiments are still necessary to determine the specific mechanism of action of each active ingredient, and clinical studies need to be further refined.Many factors contribute to the development of UC, including genetic factors, environmental factors, bacterial infections, and hormonal drugs , 31. Sevl damage –33. We fIn summary, XJD may have important therapeutic implications in treatment of UC lesions. It may be possible to correct colonic mucosal barrier dysregulation by regulating the expression of the core genes in UC.


Rheumatoid arthritis (RA) is a common chronic autoimmune disease characterized by inflammation of the synovial membrane. However, the etiology and underlying molecular events of RA are unclear. Here, we applied bioinformatics analysis to identify the key genes involved in RA.GSE77298 was downloaded from the Gene Expression Omnibus (GEO) database. We used the R software screen the differentially expressed genes (DEGs). Gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway were analyzed by using the DAVID online tool. The STRING database was used to analyze the interaction of differentially encoded proteins. PPI interaction network was divided into subnetworks using MCODE algorithm and was analyzed using Cytoscape. Gene set enrichment analysis (GSEA) was performed to identify relevant biological functions. qRT-PCR analysis was also performed to verify the expression of identified hub DEGs.A total of 4062 differentially expressed genes were selected, including 1847 upregulated genes and 2215 downregulated genes. In the biological process, DEGs were mainly concentrated in the fields of muscle filament sliding, muscle contraction, intracellular signal transduction, cardiac muscle contraction, signal transduction, and skeletal muscle tissue development. In the cellular components, DEGs were mainly concentrated in the parts of cytosol, Z disk, membrane, extracellular exosome, mitochondrion, and M band. In molecular functions, DEGs were mainly concentrated in protein binding, structural constituent of muscle, actin binding, and actin filament binding. KEGG pathway analysis shows that DEGs mainly focuses on pathways about lysosome, Wnt/β-catenin signaling pathway, and NF-κB signaling pathway. CXCR3, GNB4, and CXCL16 were identified as the core genes that involved in the progression of RA. By qRT-PCR analysis, we found that CXCR3, GNB4, and CXCL16 were significantly upregulated in RA tissue as compared to healthy controls.In conclusion, DEGs and hub genes identified in the present study help us understand the molecular mechanisms underlying the progression of RA, and provide candidate targets for diagnosis and treatment of RA. Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic inflammation, hyperproliferation of synovial tissue, and progressive destruction of multiple joints , 2. RA mChip technology has improved the ability to study disease pathogenesis and is an important technology for functional genomics research . In receWith the development of next-generation sequencing technologies and the improvement of biological database, using bioinformatics methods to explore the relevant mechanisms is significant.https://www.ncbi.nlm.nih.gov/geo/) using “rheumatoid arthritis” as the keyword. The microarray dataset GSE77298 from GPL570 platform contains 7 samples of RA  and 16 healthy controls . The expression micro-array datasets was Affymetrix Human Genome U133 Plus 2.0 Array.The microarray datasets including GSE77298 was downloaded from the GEO database  was used for log2 transformation of the data, and the two datasets were merged . “SVA” phttps://david.ncifcrf.gov/) to perform Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment [P < 0.05 and the gene counts > 3 were considered statistically significant.DEGs were inputted into David 6.8 online tools  database to analyze the PPI of proteins encoded by DEGs (medium confidence = 0.04) [Using the Search Tool for the Retrieval of Interacting Genes  . Cytosca = 0.04) .P value < 5% and a false discovery rate (FDR) < 25%.Further GSEA was carried out for all genes that were detected by use of GSEA software (version 4.0.0), providing us another option to screen out significant differential biological functions derived after bariatric surgery . The genRA was diagnosed based on the revised RA classification criteria by the American College of Rheumatology. For non-RA control, the synovial tissue samples were collected from 30 patients who underwent emergency trauma amputation. Synovial tissue was obtained and stored in liquid nitrogen and kept at −80 °C. Extraction of total RNA from tissues and cell lines was used Trizol reagent  , 22. Revp value of less than 0.05 was considered statistically significant.Each experiment was performed at least three times. All data were expressed as mean ± standard deviation (SD). Statistical analyses were determined by Student’s t test, and the differences between two groups or more than two groups were detected using ANOVA. A The total samples were analyzed by hierarchical clustering, no samples were with high heterogeneity and eliminated. Finally, 23 samples were included for analysis.The blue bar represents the data before normalization, and the red bar represents the normalized data. After normalization, Fig. A total of 4062 DEGs were screened out, including 1847 high expression genes and 2215 low expression genes. R was used to make results visualization and draw volcano map Fig. .Fig. 2VIn GO analysis, DEGs were divided into three categories: biological process, cellular component, and molecular function. In the biological process, DEGs were mainly concentrated in the fields of muscle filament sliding, muscle contraction, intracellular signal transduction, cardiac muscle contraction, signal transduction, skeletal muscle tissue development, sarcomere organization, antigen processing and presentation of peptide antigen via MHC class I, tricarboxylic acid cycle, and regulation of release of sequestered calcium ion into cytosol by sarcoplasmic reticulum Fig. .Fig. 4GIn the cellular components, DEGs were mainly concentrated in the parts of cytosol, Z disk, membrane, extracellular exosome, mitochondrion, M band, cytoplasm, T-tubule, myofibril, sarcomere and so on Fig. .In molecular functions, DEGs were mainly concentrated in protein binding, structural constituent of muscle, actin binding, actin filament binding, signal transducer activity, calmodulin binding, sodium channel regulator activity, SH3 domain binding, metal ion transmembrane transporter activity, and ATP binding Fig. .KEGG pathway analysis shows that DEGs mainly focuses on pathways about lysosome, Wnt/β-catenin signaling pathway, metabolic pathways, regulation of actin cytoskeleton, focal adhesion, chemokine signaling pathway, adrenergic signaling in cardiomyocytes, biosynthesis of antibiotics, NF-κB signaling pathway, and proteoglycans in cancer Fig. .Protein-protein interaction network with a total of 198 nodes and 356 relationship pairs was obtained, and genes in protein-protein interaction, such as RNF4, CDC20, UBE2D4, and UBE2Q2, were recognized as key nodes in protein-protein interaction  results indicated that CXCR3 expression was significantly upregulated in RA synovial tissue compared with healthy control Fig. .Fig. 8PMoreover, GNB4 was significantly upregulated in RA synovial tissue compared with healthy control Fig. .However, CXCL16 was significantly downregulated in RA synovial tissue compared with healthy control Fig. .In the present study, we analyzed GSE77298 microarray dataset to screen DEGs between end-stage RA synovial biopsies and 16 synovial biopsies from individuals without a joint disease.GO and KEGG enrichment analyses were performed to explore interactions among the DEGs. CXCR3 was identified as the core gene that involved into the progression of RA. Bakheet et al.  found thThe main innate immune-related signaling pathways include NF-κB signaling pathway and TRIM32 signaling pathway. Wang et al.  found thRheumatoid arthritis is an autoimmune nature joint disease with irreversible cartilage destruction and bone erosion. The DEGs were mainly enriched in muscle filament sliding, muscle contraction, intracellular signal transduction, and cardiac muscle contraction. KEGG pathway analysis shows that DEGs mainly focuses on pathways about lysosome, Wnt/β-catenin signaling pathway, metabolic pathways, regulation of actin cytoskeleton, focal adhesion, chemokine signaling pathway, adrenergic signaling in cardiomyocytes, biosynthesis of antibiotics, NF-κB signaling pathways, and proteoglycans in cancer.Studies have shown that the development of RA may depend on the common changes in the expression of specific key genes. Xiong et al.  revealedKEGG pathway analysis revealed that NF-KB signaling pathway involved into the progression of OA. Xing et al.  revealedLysosomes are membrane-bound organelles with roles in processes involved in degrading and recycling cellular waste. In KEGG pathway enrichment analysis, we found that DEGs also enriched into the lysosomes pathway. Lysosomes can be as a therapeutic target for RA .There were some limitations in our study. First, all patients had a pathological diagnosis of RA; however, correlation between DEGs and disease severity did not examine in depth. Second, though we examined the expression of the DEGs between RA and healthy controls, the potential pathway that involved into the RA was not examined. Future studies should be performed to identify the detailed pathway that participated into the progression of RA.In conclusion, DEGs and hub genes identified in the present study help us understand the molecular mechanisms underlying the progression of RA, and provide candidate targets for diagnosis and treatment of RA.


Based on the perturbed toxicity pathways during the process of Cd-induced nephrotoxicity, we established the MOA of Cd-induced renal tubular dysfunction and assessed its confidence with the tailored Bradford Hill criteria. Bioinformatics analysis showed that oxidative stress, DNA damage, cell cycle arrest, and cell death were the probable key events (KEs). Assessment of the overall MOA of Cd-induced renal tubular dysfunction indicated a moderate confidence, and there are still some evidence gaps to be filled by rational experimental designs.Although it is recognized that cadmium (Cd) causes renal tubular dysfunction, the mechanism of Cd-induced nephrotoxicity is not yet fully understood. Mode of action (MOA) is a developing tool for chemical risk assessment. To establish the mechanistic MOA of Cd-induced renal tubular dysfunction, the Comparative Toxicogenomics Database (CTD) was used to obtain genomics data of Cd-induced nephrotoxicity, and Ingenuity Epidemiological studies have demonstrated the association of Cd exposure and the increased risk of renal tubular impairment in both occupationally and environmentally exposed populations (Cadmium (Cd) is one of the most toxic metals and naturally exists in the environment. However, anthropogenic activities, including agricultural and industrial activities, contribute greatly to the pollution by Cd. The exposure sources in the general population include food, drinking water, smoking, and air. Dietary exposure is the main source in the general population, whereas occupational exposure in individuals working in alloy production, battery production, pigment production and use, plastics production, and smelting and refining mostly involves the respiratory tract . Cd can ulations . In blooulations , 2019. Culations . Althougulations . Cd expoulations . Therefoulations .in vitro studies in human cells for characterizing early toxicity pathway perturbation with the employment of computational biology techniques can be applied for chemical risk assessment with less use of animals and reduced uncertainty in species, thereby conforming to the vision of “Toxicity Testing in the 21st Century” , which are essential for the occurrence of an effect [usually an adverse outcome (AO)]. These KEs can be empirically observed steps or their markers. They are essential but not necessarily sufficient in their own right for the occurrence of AO, and can occur on different biological levels, such as molecular level, cellular level, tissue/organ level, and individual level . With thCentury” . To dateCentury” , providiTherefore, to provide a theoretical basis for Cd risk assessment, we constructed a putative MOA for Cd-induced renal tubular dysfunction based on bioinformatics analysis of genomics data from the Comparative Toxicogenomics Database (CTD). Weight of evidence analysis of the MOA revealed the potential data gaps in the present evidence, and further studies are needed to fill them.1 is a public database containing the association information between chemical exposure, gene products, and diseases. It is manually organized by professional biocurators to promote the understanding of the effects of environmental chemicals on human health . The remaining references were then categorized according to Cd-affected organs/tissues. In addition, due to the lack of kidney specificity, we excluded epidemiological studies where human blood or urine samples had been used. The final set included 107 references (see Comparative Toxicogenomics Database (CTD)n health . The datn health . Therefonces see  related ® Pathway Analysis (IPA) software (QIAGEN Inc.)2 was used to analyze the biological effects during Cd-induced nephrotoxicity by canonical pathway analysis and biofunction and disease analysis. Genes identified to be differentially expressed from CTD were input into IPA. Since CTD does not provide the detailed expression values or fold change values of the corresponding genes, we manually assigned the fold change value for those genes for the analysis in IPA. For transforming the qualitative expression alteration into a quantitative fold change value, we manually assigned the expression level of genes to 2, −2, or 0 based on the overall change in all of the included references. Genes with consistent change on either expression or protein activity (upregulated/downregulated/affected) in all the concerning references were assigned 2, −2, or 0, while the assignment of genes with contradictory expression change depended on the majority of cases. For example, SQSTM1 was involved in five references; in three of them, SQSTM1 was upregulated, while in two references, it was downregulated. Therefore, the expression level of SQSTM1 was assigned to 2. The 869 genes relevant to Cd-induced nephrotoxicity from 107 references in CTD were summarized in p < 0.05 as the threshold for significant perturbed pathways and excluded those that lacked z-score used to predict whether the pathway was activated or inhibited and the corresponding degree. The top 10 enriched pathways were selected to recognize the main effects relevant to the perturbed pathways.IngenuityMode of action (MOA) of a specific chemical consists of a series of events that are crucial to the occurrence of the AO; these KEs are measurable and sequential in chronological order . We recoTo initially confirm the reliability of the postulated MOA, the evolved Bradford Hill criteria were used to assess the evidence of the MOA, especially the causal relationship between the upstream KEs and downstream KEs. Five main aspects were considered in the evolved Bradford Hill criteria: biological plausibility, essentiality, empirical observation, consistency, and analogy. In the evaluation of essentiality, the focus was on the KEs, whereas in the evaluation of biological plausibility, empirical support, consistency, and analogy, the emphasis was on key event relationships (KERs). Biological plausibility required that the mechanistic relationships between KEs were consistent with the established biological knowledge. Essentiality was defined when the downstream KE would be prevented if the upstream KE was blocked. Empirical support included considerations about evidence for both dose–response and temporal concordance of KERs. Consistency referred to the consistency of empirical support for KERs across different species/strains/organs/test systems. Analogy support required that dependent alteration of KEs was observed after the exposure to the structurally related chemicals. Here, we put stress on the evaluation of KEs’ essentiality and empirical support for KERs because they have been weighted up to 80% in the assessment of MOA . The detp-value were as follows: nuclear factor erythroid 2-related factor 2 (Nrf2)-mediated oxidative stress response, aryl hydrocarbon receptor signaling, endoplasmic reticulum (ER) stress pathway, hypoxia signaling in the cardiovascular system, ferroptosis signaling pathway, unfolded protein response, mitotic roles of polo-like kinase, hypoxia-inducible factor-1-alpha (HIF1α) signaling, apoptosis signaling, and cyclins and cell cycle regulation  and REACTOME databases; thus, we also analyzed pathway enrichment by CTD with the support of KEGG. The results  were simIn addition to the bioinformatics analysis, we also retrieved review articles to understand the established cytotoxicity mechanisms of Cd. It has been shown that Cd could cause oxidative stress not by directly increasing free radicals due to its non-redox nature but by indirectly generating reactive oxygen species (ROS) by interaction with Fenton metal, such as Fe and affecting the antioxidative defense system . Cd is nKidneys are the recognized target organs of chronic Cd exposure, especially the renal proximal tubules. Renal tubular dysfunction is an early clinical feature of renal damage caused by Cd, during which proteinuria , glycosuria, amino aciduria, and polyuria have been observed in both humans and laboratory animals . FurtherIn the results of canonical pathway analysis, in addition to the NRF2 pathway, which was 1 of the top 10 significant pathways, there are other pathways associated with oxidative stress, such as xenobiotic metabolism signaling pathway and mitochondrial dysfunction pathway , which min vivo and in vitro studies have provided plenty of evidence of oxidative stress playing an important role in Cd-induced nephrotoxicity, especially in tubular damage. Cd has been shown to enhance oxidative stress by triggering ROS production and inhibiting the antioxidant system in renal tubular epithelial cells; such oxidative damage and nephrotoxicity could be alleviated by various substances with antioxidant activity   . After cductase) . HoweverAs shown in the result of canonical pathway , ATM sig2+ alone did not lead to DNA damage in plasmid DNA; however, it was able to cause DNA strand breaks in the presence of hydrogen peroxide in a dose-dependent manner due to the production of hydroxyl radicals   . Renal po lumen) . Therefoulations .According to the guidance document released by the Organization for Economic Co-operation and Development , assessmTo explore the confidence of supporting data for the essentiality of KEs in the MOA, we assembled evidence and summarized it in Since MOA consists of a series of sequential, measurable KEs essential to AO, in addition to the assessment of essentiality of each KE, evidence concordance of all KERs is also required. Thus, we assessed the evidence concordance including the dose–response and the temporal relationship between KEs.2+ could attack DNA and induce DNA damage by breaking DNA strands. DNA damage accumulation challenges DNA repair and subsequently blocks cell cycle progression and initiates cell death to avoid wrong replication entering into divided cells  and inductively coupled plasma mass spectrometry (ICP-MS) have widely been used in the determination of Cd content in biological samples, but they failed to differentiate the intracellular ionic Cd . Furthermore, not all KERs were well-supported by empirical evidence; indeed, in most cases, several KEs were observed simultaneously, especially the adjacent KEs. Thus, to increase the confidence of each KE, in vivo and in vitro studies involved in Cd-induced renal tubular dysfunction should set lower Cd concentrations and smaller time intervals to identify the dose–response and temporal sequence between each KE. Benchmark dose (BMD) analysis could help to verify the dose–response or temporal sequences of KEs.We used the Hill Bradford criteria to assess the weight of evidence of the postulated MOA. The biological plausibility supporting the MOA was strong; however, for the essentiality and empirical support, the confidence was weaker. The essentiality of KE1 was well-supported by evidence considering that blockages of this event successfully reversed the downstream KEs. However, the essentiality of later KEs was insufficient, as there were data gaps for direct evidence (e.g., inhibitors of DNA damage/promoters for DNA repair block cell death induced by Cd in kidney tissue or In addition, in view of the weaker confidence for KE3 and KE4, we considered some alternative pathways in the MOA of Cd-induced kidney damage. ER stress, a probable mechanism of Cd toxicity, was also shown to be a potential KE based on the toxicity pathway analysis by IPA. In the top 10 perturbed pathways, ER stress pathway and unfolded protein response pathway were significantly enriched and were predicted to be activated. Furthermore, previous studies have reported the potential role of ER stress in Cd-induced nephrotoxicity. It has been reported that ER stress can regulate the apoptosis induced by Cd exposure . HoweverMOA was recognized as a useful tool for chemical risk assessment. The use of quantitative transcriptomic data to determine BMDs based on a chemical’s MOA was demonstrated to be comparable to those derived from adverse apical effects . The KEsIn summary, based on the pathway-oriented approach, here we constructed the MOA framework for Cd-induced renal tubular dysfunction. Oxidative stress response was the KE1, followed by DNA damage, cell cycle arrest, and cell death as the later KEs, and eventually renal tubular dysfunction was the AO. Weight of evidence analysis for our MOA suggested that data gaps still exist, especially in terms of MIE. The MOA has to be verified in further studies before it can be used for chemical risk assessment.The original contributions presented in the study are included in the article/YZ wrote the main manuscript text, contributed to data collection and bioinformatics analysis, and prepared the figures. QW proposed and organized the study, contributed to data interpretation, and revised the manuscript. YJ, DY, YX, and WC contributed to the study design. ZL, QH, and FW contributed to the data analysis. All authors reviewed the manuscript.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Depression is a common mental disease that lacks effective therapeutic drugs with good curative effects and few adverse reactions. Traditional Chinese medicine (TCM) has the advantages of multiple components, multiple channels, and fewer adverse reactions in the treatment of depression. Although Xingpi Jieyu Decoction (XPJYD) demonstrates a good therapeutic effect on depression, the pharmacological mechanism underlying its antidepressant effect is still unclear.  We used a network pharmacology strategy, including the construction and analysis of a complex drug-disease network, to explore the complex mechanism of XPJYD treatment of depression. In addition, molecular docking technology was used to preliminarily study the binding ability of the potential active components and core therapeutic targets of XPJYD. −1, indicating a good docking effect. The GO enrichment analysis results showed that the XPJYD antidepression mechanism mainly involves the following biological processes such as apoptotic signaling pathway, cellular response to lipid, inflammatory response, and others. The KEGG analysis results indicated that XPJYD may regulate 13 pathways such as PI3K-Akt signaling pathway and estrogen signaling pathway in the treatment of depression.  The network pharmacology results showed 42 targets of XPJYD that are involved in depression. PPI network analysis demonstrated that the top 10 core targets were AKT1, VEGFA, MAPK8, FOS, ESR1, NR3C1, IL6, HIF1A, NOS3, and AR. The molecular docking results showed that the binding energies of beta sitosterol with AR, FOS, AKT1, VEGFA, NR3C1, and NOS3 were less than −7.0 kcal·mol This study reflects the characteristics of the mechanism of action by which XPJYD treats depression, which includes multiple components, multiple targets, and multiple pathways, and provides a biological basis for further verification and a novel perspective for drug discovery in depression. Depression F32.900) is a kind of mental disease with obvious and lasting symptoms, such as low mood, decreased interest, and impaired cognitive function, which can affect a person's thoughts, behavior, feelings, and sense of well-being . It has .900 is aβ-asarum, the main component of Acoritataninowii Rhizoma, had a protective effect on hippocampal neurons, increased the phosphorylation levels of ERK1/2, and CREB in the hippocampus, and improved the expression of brain-derived neurotrophic factor BDNF in depressed rats [Traditional Chinese herbs such as Herbahypericiperforati, Acoritataninowii Rhizoma, Turmeric, and Panax quinquefolium have better antidepressant effects. Compared with selective 5-HT reuptake inhibitor (SSRI) antidepressants, Herba Hyperici perforati showed better efficacy and higher safety in the treatment of mild to moderate depression . Some stsed rats , 17.Xingpi Jieyu Decoction (XPJYD) is composed of Panacis Quinquefolii Radix, Herbahypericiperforati, Acoritataninowii Rhizoma, and Curcumae Radix. Previous studies have shown that XPJYD significantly decreased the preference for sugar water and the open-field test movement distance of depressed rats with CUMS and improved depressed behavior in depressed rats. Moreover, it increased the serum corticosterone concentration and reversed the serum 5-HT content in rats with depression, which is similar to the effect of sertraline, an SSRI antidepressant . XPJYD iClinical evidence shows that, compared with traditional Chinese medicine placebo, XPJYD can reduce the scores of HAMD-24, SDS, and PHQ-15, improve the depressive mood and somatic symptoms of patients with depression, and increase the level of ATP in peripheral blood . IntestiThe research of network pharmacology on disease embodies the concept of systematicness, wholeness, and network, which is similar to the concept of wholeness in traditional Chinese medicine , 28. NetHypericum perforatum, Acorus gramineus, and Curcuma aromatica) were obtained by searching the Traditional Chinese Medicine Systems Pharmacology Database [Homo sapiens selected as the default species to predict the targets of potential drug molecules [All the components of XPJYD  Database  (TCMID).Database , 33. We olecules .Potential targets associated with depression were collected from the GeneCard , DisGeNEThe shared targets between the drug targets and the disease targets were imported into the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) . PPI nethttps://david.ncifcrf.gov/) to identify biological processes. The KOBAS database (http://kobas.cbi.pku.edu.cn/) [Gene Ontology (GO) enrichment analysis for overlapping genes was performed using the MetScape database (edu.cn/)  was usedhttp://www.rcsb.org/). We used PyMol 2.4 software to remove water molecules and separate the original ligand from the core target protein. We imported the processed protein targets into AutoDock Tools 1.5.6 software [The PDB format of the core target protein was downloaded from the RCSB PDB database  and tail suspension test (TST) in mice and improve the depression-like behavior induced by lipopolysaccharide (LPS), showing the highest antidepressant effect at 30 mg/kg . KaempfeThe corresponding target proteins of the active components were obtained by using TCMSP and SwissTargetPrediction, and the SwissTargetPrediction results were obtained according to a probability value above 0.5. Finally, 213 target proteins corresponding to 18 potentially active ingredients were acquired. The specific information of OB and DL values of 18 components is shown in After screening with relevance score greater than 0.2 in GeneCard and score greater than 0.2 in DisGeNET, a total of 942 depression-related targets were retrieved from the GeneCard, OMIM, and DisGeNET databases. After the intersection of drug targets and disease targets, 42 drug-disease intersection targets were obtained, namely, the targets of XPJYD involved in the treatment of depression. Cytoscape3.7.1 software was used to construct the drug-component-target-disease network, and the results are shown in α (ERα) and estrogen receptor β (ERβ), while the main membrane receptor of estrogen is the G-protein-coupled estrogen receptor (GPER) [β agonists were also found to reverse depression-like behavior in ovariectomized rats [The overlapping target genes were imported into the STRING database with medium confidence set to 0.4 to obtain a PPI network for the targets. The PPI network results were saved in TSV text format and imported into Cytoscape3.7.1 software for network topology analysis to obtain the top 10 hub genes. The results of the network topology analysis are shown in r (GPER) . It was r (GPER) . ERβ agozed rats . The GPEzed rats . Androgezed rats . The stuzed rats . The meczed rats , thereby−1 indicates good binding activity. Lower binding energy indicates a better docking efficiency. The molecular docking results showed that the binding energies of beta sitosterol with AR, FOS, AKT1, VEGFA, NR3C1, and NOS3 were less than −7.0 kcal·mol−1, indicating a good docking effect. Kaempferol, luteolin, and quercetin have a good docking effect with AR genes.Quercetin, kaempferol, luteolin, and beta sitosterol were used for molecular docking with the top 10 hub genes. The molecular docking results are presented in the form of a heat map . A bindiDiagrams of the interaction structures of the interaction between the active ingredient and the hub genes with good binding energy were drawn. The diagrams are shown in GO enrichment analysis mainly involves three aspects: cell composition, molecular function, and biological process. As shown in We used the KOBAS database to analyze the KEGG pathways of the overlapping targets. A total of 198 pathways were enriched, of which the 13 pathways associated with depression are shown in β-catenin cascade in the prefrontal cortex [β and TNFα in the prefrontal cortex of depressed mice, especially at a dose of 20 mg/kg [The network pharmacology results showed that, with conditions of OB ≥30% and DL ≥0.18, 14 main active components of XPJYD were identified, and these components were predicted to target 45 potential depression-related proteins. Four important active components of XPJYF were identified from the drug-component-target-disease network, namely, quercetin, luteolin, kaempferol, and beta sitosterol. Quercetin, which can improve the level of monoamine transmitters in the brain and regulate the activity of the HPA axis, plays an important role in the treatment of depression . Luteolil cortex . In addi20 mg/kg . Beta-si20 mg/kg . Relevanα, and other inflammatory factors in depression model rats and improved their learning and memory behaviors [PPI network analysis showed that VEGFA, EGFR, CASP3, IL6, ESR1, and other targets have important implications in the treatment of depression with XPJYD. Studies have shown that AKT1, VEGFA, ESR1, IL6, and AR play an important role in the treatment of depression , 59, 60.ehaviors . The molGO function analysis shows that XPJYD is involved in biological processes that can achieve antidepressant effects, such as apoptotic signaling pathway, cellular response to lipid, inflammatory response, neuron death, response to reactive oxygen species, and others. KEGG pathway analysis showed that XPJYD mainly regulates 13 pathways such as PI3K-Akt signaling pathway and estrogen signaling pathway to treat depression. The PI3k-Akt pathway, a classic antiapoptotic and prosurvival pathway, is widely found in various cells and has biological functions that include regulating cell growth, proliferation, differentiation, migration, and autophagy . In nervThe estrogen signaling pathway plays an important role in the treatment of depression. Some reports suggest that estrogen replacement therapy has antidepressant effects in both clinical and animal studies , 73. StuPreliminary experimental results showed that XPJYD may increase the content of 5-HT and DA in the brain, improve cerebral blood flow, and inhibit the inflammatory response and HPA axis hyperactivity. The network pharmacological results showed that XPJYD may regulate 13 pathways such as the PI3K-Akt signaling pathway and estrogen signaling pathway to treat depression. Most network pharmacology studies are based on virtual screening of databases, while TCM compounds can only show a therapeutic role on the basis of in vivo processes such as oral absorption, distribution, metabolism, and excretion. Due to the limitations of network pharmacology, this study needs further experimental verification.In summary, network pharmacological analysis revealed characteristics of the mechanism of action by which XPJYD treats depression, which involves multiple components, multiple targets, and multiple pathways. XPJYD is an important modulator of AKT1, VEGFA, ESR1, IL6, and AR through active ingredients such as beta sitosterol. XPJYD achieves antidepressant effects by regulating 13 pathways, including the PI3K-Akt signaling pathway and estrogen signaling pathway. Although these 13 pathways have been shown to be strongly associated with depression, some pathways have not yet been shown to be regulated by XPJYD. Therefore, these pathways provide experimental research directions for the further study of XPJYD in the treatment of depression.


Dengue virus and Streptococcus pneumoniae are the critical and common sources of infectious diseases. So, it is critical to understand the gene expression profiling and their inferred functions in comparison to the normal and virus infected conditions. Here, we have analyzed the gene expression profiling for dengue hemorrhagic fever, dengue fever, and normal human dataset. Similar to it, streptococcus pneumoniae infectious data were analyzed and both the outcomes were compared. Our study leads to the conclusion that the dengue hemorrhagic fever arises in result to potential change in the gene expression pattern, and the inferred functions obviously belong to the immune system, but also there are some additional potential pathways which are critical signaling pathways. In the case of pneumoniae infection, 19 pathways were enriched, almost all these pathways are associated with the immune system and 17 of the enriched pathways were common with dengue infection except platelet activation and antigen processing and presentation. In terms of the comparative study between dengue virus and Streptococcus pneumoniae infection, we conclude that cell adhesion molecules (CAMs), MAPK signaling pathway, natural killer cell mediated cytotoxicity, regulation of actin cytoskeleton, and cytokine-cytokine receptor interaction are commonly enriched in all the three cases of dengue infection and Streptococcus pneumoniae infection, focal adhesion was enriched between classical dengue fever — dengue hemorrhagic fever, dengue hemorrhagic fever—normal samples, and SP, and antigen processing and presentation and Leukocyte transendothelial migration were enriched in classical dengue fever —normal samples, dengue hemorrhagic fever—normal samples, and Streptococcus pneumoniae infection.Infectious diseases are the disorders caused by organisms such as bacteria, viruses, fungi, or parasites. Although many of them are permentantly hazardous, a number of them live in and on our bodies and they are normally harmless or even helpful. Under certain circumstances, some organisms may cause diseases and these infectious diseases may be passed directly from person to person or  Germs or microbes are found everywhere in the air, soil, and water as well as on the bodies of different host species. Many of them are usually harmless and a few of them could be even helpful to minimize the risk of acquiring sickness. However, Germs or microbes can be pathogenic and may lead to devistating outcomes. Indeed, the infectious diseases are among the leading cause of death and are responsible for approximately 14.9 million deaths globally . InfectiA. aegypti and A. albopictus female mosquitoes that feed both indoors and outdoors during the daytime (from dawn to dusk). The clinical manifestations of dengue range from acute self-limiting febrile illness to life-threatening dengue hemorrhagic fever and the dengue shock syndrome{Ubol:2008eq, Warsi:vd, Halstead:1988iy}. More than 2 billion people in tropical and subtropical regions are at risk of dengue virus infection, leading to 50 to100 million human infections and 24,000 deaths annually. Currently, there are a few antiviral therapies for dengue but they are not highly effective {Guzman:2016js, Monath:1994cq, Tripathi:2013wu, Pierson:2020ds, Firth:2013bb, Holmes:2003dx, Clarke:2002bp, Bardina:2017dd}. These viruses are transmitted through the bite of infected ffective . It has ffective . Moleculffective  have shoffective . It has ffective . There affective .Continuing with ongoing issues, we have mainly presented the comparative study of DENV and SP gene expression profiling and its potential impact on the signaling pathways in a simplified way. For this purpose we have not only analyzed the DEGs  and inferred pathways but also analyzed the networks of DEGs. Furthermore, distinct gene expression patterns were found in patients with various etiologies of respiratory infections. As a result, microarray studies of patient peripheral blood leukocytes may help with infectious disease differential diagnosis.In this study, we have used the publicly available array expression profiling dataset from GEO (Gene Expression Omnibus) which contains dengue hemorrhagic fever [DHF (9 patients)], dengue fever [DF (9 patients)], and control  sample [ND (8 uninfected with dengue)]. Here, our main goal is to study the role of NS5 in the immune system and associated pathways of dengue virus-infected humans. Furthermore, for pneumoniae, we have used the GSE6269 dataset and performed the expression profiling followed by the pathway enrichment and finally compared DENV and SP at both the expression and functional levels.For the selected datasets, the DEGs  and their inferred pathways have been identified in different combinations . We have applied a simplified approach for the entire analysis  and down  regulated) and analyzed the inferred pathways which leads to the critical pathways such as T-cell activation, Angiogenesis, EGFR signaling, G-protein signaling, & Wnt signaling (in DHF versus ND), Ubiquitin proteasome signaling, nicotine degradation, Wnt signaling, circadian clock, cell cycle, & p53 pathway (in DF versus ND), and Apoptosis, TLR, IGF, p53, blood coagulation, & metabolic pathways Table 1.We see a variety of altered pathways based on the inferred pathways that are either part of the immune system or theoretically control/contribute to normal immune system function. As a consequence of dengue infection, their pathway constituents (genes) are predominately present among the DEGs Table 1.An infectious disease occurs when the immune system of the host is weakened or when the infectious agent overwhelms the immune system and thus the major focus of the study was to deeply unravel the commonly linked immunological pathways and the components. Finally, we have investigated the linkage of dengue infection with pneumonia for which we have compared the DEGs of dengue infected patients with the pneumonia patients DEGs and similarly the compared the enriched pathways. Here, we observe that there are only 16 DEGs  which are common in two of the cases (DHF—ND and SP), PDLIM7 DEG appears common between DF–DHF, DHF–ND, and SP, and GPM6B is commonly differentially expressed between DF–ND, DHF–ND, and SP while there are comparatively higher numbers of commonly enriched pathways. Cell adhesion molecules (CAMs), MAPK signaling pathway, natural killer cell mediated cytotoxicity, regulation of actin cytoskeleton, and cytokine-cytokine receptor interaction are commonly enriched in all the three cases of DENV and SP, focal adhesion was enriched between DF—DHF, DHF—ND, and SP, and antigen processing and presentation and Leukocyte transendothelial migration were enriched in DF—ND, DHF—ND, and SP Figure 4via the HLA-DRB1, HLA-DRB 3, HLA-DRB4, and HLA-DQB1 DEGs while CXCL2 and CXCL8 control cytokine-cytokine interaction, TNF signaling, and NF-kB signaling pathways, and PKBP5 controls estrogen signaling.So, we could say that in terms of gene expression there is less commonality while in terms of the functions they are more common which also means that the commonly enriched pathways also have different sources of functional alteration than the dengue infection or vice-versa. Moreover, we have also explored the the commonly enriched pathways and the shared DEGs by representing the network of pathways and genes by using KEGG pathway database where the phagosome, hematopoetic cell lineage, and cell adhesion molecules are interconnected Dengue virus is among the leading infectious diseases which are a potential risk among the developing countries  and due As we have mentioned the top 30 genes (up and down regulated) and analyzed the inferred pathways which lead to the critical pathways such as T-cell activation, Angiogenesis, EGFR signaling, G-protein signaling, & Wnt signaling (in DHF versus ND), Ubiquitin proteasome signaling, nicotine degradation, Wnt signaling, circadian clock, cell cycle, & p53 pathway (in DF versus ND), and Apoptosis, TLR, IGF, p53, blood coagulation, and metabolic pathways. Based on this, we conclude that there are some pathways which are exclusively affected for DHF and DF.In the past few decades a large amount of research on dengue has resulted in a host of literature, which strongly suggests that the pathogenesis of DHF and DSS (dengue shock syndrome) involves viral virulence factors and detrimental host responses . Most imIn the previous work, we have exclusively explored , but morDengue fever is a life-threatening illness that affects approximately 2.5 billion people in dengue-endemic areas. Dengue viruses have a single-stranded positive RNA genome. By analysing gene expression results, we hoped to learn more about the pathways caused by dengue infection. Our findings suggest that in the case of dengue infection, possible dengue virus targets are associated with the immune system or mechanisms associated with it, as well as their components. These results have mechanistic implications for how the innate immune system modulates dengue virus transmission and pathogenesis. We also found that DENV and SP infection have the potential to affect immune signalling pathways through their components, and that there are many essential pathways that are shared between the two cases.In this study, we used GEO to obtain gene expression datasets  GSE1809  and eight as classical dengue fever (DF), as well as eight control samples (ND) from febrile patients who were confirmed to be dengue-free. At the time the samples used in the functional genomic characterization were obtained, none of the DHF patients had any signs or symptoms of vasculopathy. These patients had been sick for around 5 days at the time of collection, and the absence of fever was confirmed two to three days later. To avoid major variations in patient age, gender, dengue infection history, and days of symptoms, the samples from the DF, DHF, and ND patients were matched .Each infectious agent is made up of a different set of pathogen-associated molecular patterns that interact with immune cells’ pattern-recognition receptors. As a result, they have hypothesized that the blood immune cells of people with various infections may have distinct transcriptional signatures. For the peripheral blood samples from pediatric patients with acute infections, gene expression profiles were collected .In short the basic steps involved for the entire study are raw file processing, intensity calculation and normalization. For normalization , GCRMA , RMA, anFor differential gene expression prediction and statistical analysis, MATLAB functions  were used. Mattest performed a two-sample t-test to evaluate differential expression of genes from two experimental conditions or phenotypes and it processes a matrix of gene expression values where each row corresponds to a gene and each column corresponds to a replicate. Further, we proceeded to find p-value corrections and FDR . The basis of DEGs selection were fold changes and the p-values and the limit for fold changes were +/- 2.0 (upregulated and downregulated genes) and the p-values were less than 0.05. For pathway analysis, we used panther database  and haveFor generating the DEGs network, FunCoup2.0  was usedThe original contributions presented in the study are included in the article/Conceptualization, LB, SS, TA, MK, SP, and EA. methodology, LB, SS, MM, MK, and EA; software, EA; validation, LB, TA, and EA; formal analysis, LB, SS, MM, MK, EA; investigation, LB, SS, MK, and EA; resources, SS, MK, and EA; data curation, SS, MR, AF, MM, and EA; writing—original draft preparation, LB, SS, MM, MK, and EA; writing—review and editing, LB, SS, TA, MM, MK, SP, MR, AF, and EA; visualization, LB, SS, MR, AF, and EA; supervision, MK, and EA; project administration, EA; funding acquisition, MK and EA. All authors have read and agreed to the published version of the submitted version.This research was funded by Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia, grant number FP-5-42 and The APC was funded by FP-5-42.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.