In 80 EOA-patients, we determined the EOAD+-prevalence in association with MRI-abnormalities. Subsequently, we explored underlying biological pathways by genetic network and functional enrichment analysis. We checked pathway-outcomes in specific EOAD+-genotypes by comparing results with non-specifically (in-silico-determined) shared genes in up-to-date EOA, AOA and dystonia gene panels (that could concurrently cause ataxia and dystonia). In the majority (65%) of EOA-patients, mild EOAD+-features concurred with extra-cerebellar MRI abnormalities ). Genetic network and functional enrichment analysis in EOAD+-genotypes indicated an association with organelle- and cellular-component organization . In non-specifically, in-silico-determined shared EOA, AOA and dystonia genes, pathways were enriched for Krebs-cycle and fatty acid/lipid-metabolic processes. In frequently occurring EOAD+-phenotypes, clinical, anatomical and biological pathway analyses reveal shared pathophysiology between ataxia and dystonia, associated with cellular energy metabolism and network signal transduction. Insight in the underlying pathophysiology of heterogeneous EOAD+-phenotype-genotype relationships supports the rationale for testing with complete, up-to-date movement disorder gene lists, instead of single EOA gene-panels.In degenerative adult onset ataxia (AOA), dystonic comorbidity is attributed to one disease continuum. However, in early adult onset ataxia (EOA), the prevalence and pathogenesis of dystonic comorbidity (EOAD The diagnosis “early onset ataxia” refers to a group of rare, genetically inheritable diseases with an estimated prevalence of 14.6 per 100,000 individuals, initiated before the 25th year of life. These “ataxic syndromes” involve a heterogeneous group of underlying disorders that may phenotypically involve: (a) pure ataxic features; (b) predominant ataxic features in combination with other comorbid movement disorder features; (c) mild ataxic features in combination with other primary movement disorder features; (d) hardly discernible, disputable or even absent ataxic features, but with an underlying diagnosis that is phenotypically described as ataxic in the Online Mendelian Inheritance in Man (OMIM) database [+) concerns a relatively frequently observed clinical phenotype in adulthood-onset ataxias [+) may vary between 0% up and 53% [+, we now aimed to explore the prevalence and underlying pathogenesis in paediatric and young adult patients with EOA, with the underlying hypothesis that EOAD+ could be associated with abnormal regulation of developmental genes and aberrations of neurotransmitter pathways as well. In mixed dystonic and ataxic EOAD+-phenotypes, we anticipated that pathogenetic insight would contribute to an insightful diagnostic approach.Previous studies in patients with AOA have shown that the presence of ataxia with comorbid dystonia . We hypothesized that EOAD+-phenotypes could be associated with: (1) extra-cerebellar neuro-degenerative alterations determinable by MRI; (2) identifiable shared genetic/molecular pathways determinable by gene co-expression networks in specific EOAD+ genotypes; and (3) non-specifically (in-silico) determinable genetic/molecular pathways in shared genes between AOA, EOA and dystonia gene panels, that may induce ataxia and dystonia in a concurrent way. We hypothesized that if comorbid dystonia could be explained by neurodegenerative processes, we would expect an association between the prevalence of comorbid dystonia and disease duration and/or age of the patient, both in our cohort, as well as in literature. This could also implicate a higher prevalence of comorbid dystonia in adult patients with AOA than in young patients with EOA. When the comorbid occurrence of dystonia in EOA would rather be attributable to shared molecular pathways and pathogenetic mechanisms, one would expect potentially corresponding results between two different “genetic-network-analyses” groups: (1) in EOAD+ genotypes, with specifically identified dystonic comorbidity; and (2) in non-specifically (in-silico-determined) shared genes in up-to-date with EOA, AOA and dystonia gene panels, that could theoretically cause ataxia and dystonia in a concurrent way [In the present EOA study, we therefore aimed to elucidate the underlying key biological pathways of dystonic comorbidity  the association between prevalent EOAD+ comorbidity and disease duration and/or age of the patient, and (3) the association between EOAD+ comorbidity and patterns of extra-cerebellar MRI abnormalities.In perspective of the above, we conducted this study in two parts: Part I: in a cohort of 80 EOA-patients, we investigated: (1) the prevalence of EOAD+ genotypes; (2) the shared underlying pathways by determining co-expression networks in (in-silico-determined) shared genes between AOA, EOA and dystonia gene lists (panels); and (3) comparative outcomes between specifically identified EOAD+ genotypes (from our database) and non-specifically (in-silico determined) shared genes in up-to-date EOA, AOA and dystonia gene lists (panels).Part II: By genetic network and functional enrichment analysis, we investigated: (1) the shared underlying pathways by determining co-expression networks in the identified EOAD+.To the best of our knowledge, this is the first study providing a comprehensive approach to explore the prevalence and pathogenesis of EOADThe study was carried out following the rules of the Declaration of Helsinki of 1975 (revised in 2013), in accordance with the research and integrity codes of the University Medical Center Groningen (UMCG). The Medical Ethical Committee of UMCG had approved the study . According to Dutch medical ethical law, both parents and children older than 12 years provided informed consent whereas children younger than 12 years of age provided informed assent for phenotypic assessment.http://omim.org/). In accordance with international criteria for EOA databases, we included patients with congenital, developmental, metabolic, degenerative, and/or unknown causes of ataxia starting before the 25th year of life [We included the video-recordings from a cohort of 80 EOA-patients that had visited the paediatric neurology outpatient clinic at UMCG over the last 10 years. Included patients fulfilled the criteria for “EOA”, implicating: symptomatic initiation of ataxia before the 25th year of life or an underlying genetic diagnosis associated with a primary ataxic phenotype, as indicated by the OMIM database , 24 December 2016. WorldWide Web:  of life . Patient+ study group when both assessors had indicated that comorbid dystonia was present. Patients were assigned to the EOAD− control group, when both assessors had indicated that comorbid dystonia was absent. In the remaining patients , both assessors explained their phenotypic choice in a separate after-session by play-back at the indicated time frames from the “print screens”.In accordance with previously described methodology , we incl− (control) group with additional ataxia genes that were reported without comorbid dystonia, in literature (PubMed and OMIM). For genes included in the EOAD+ study and EOAD− control group, see To allow subsequent statistical comparison on a sufficient number of genes in the study- and control-group, we had to supplement the EOAD+ study-group and EOAD− control group, respectively). In both groups, we subsequently associated the underlying genotypes with the corresponding brain abnormalities reported in literature (PubMed and OMIM databases). We characterized cerebral MRI abnormalities in EOAD+ and EOAD− groups for: (1) the neuro-anatomical location and (2) the nature of cerebral abnormalities.We subdivided the local cohort of 80 EOA patients into phenotypes with and without comorbid dystonia  to generate gene networks using the gene set enrichment feature. The pathway enrichment prediction of the clusters in the disease-specific networks was also performed by GeneNetwork and only the top significant gene ontology (GO) biological pathways were considered. In order to obtain sufficient genes for statistical analysis of the EAOD− control group, we added ataxia genes that were not reported with comorbid dystonia in literature .In the EOADn = 152 genes), AOA (n = 80 genes) and dystonia (n = 100 genes); see https://toppgene.cchmc.org). The GO biological pathways were considered significant up to p-values 0.005 . In accordance with previously published methods [www.genenetwork.nl) a co-expression tool by integrating 31,499 public RNA-seq samples [p-values of 5 × 10−5.In order to compare our specific pathway and network results (obtained ad IIa), with the non-specific outcomes derived from (in-silico-determined) shared genes between complete, up-to-date clinically applied gene panels (that could concurrently induce ataxia and dystonia), we compiled the most recent disease associated gene lists , including EOA  (in silico determined) non-specifically shared pathways, derived from up-to-date EOA, and AOA, dystonia gene lists panels, that could concurrently induce ataxia and dystonia. For this purpose, we used the EOA, and AOA, dystonia gene lists that are included in the gene panels at the University Medical Center Groningen.Finally, we compared the underlying shared pathways between: (1) specific EOADk < 0); slight (k 0–0.20); fair (k 0.21–0.40); moderate (k 0.41–0.60); substantial (k 0.61–0.80) and almost perfect (k 0.81–1.00) [The reliability of the agreement between the observers, was indicated by Cohen’s kappa. Results were interpreted in accordance with Landis and Koch as: poor (81–1.00) . We detep = 0.001)). In 84% (67/80) EOA patients, the underlying association with the disease symptom ataxia was confirmed by genetic, metabolic and/or radiologic findings. In 78/80 (98%) of the recorded EOA-patients, either one of the two observers had recognized the presence of the symptom ataxia. In 76/80 (95%) of the recorded EOA-patients, both observers had recognized the presence of the symptom ataxia. The two patients in whom none of the observers had recognized ataxia, were diagnosed with an ATP1A3 and TUBB2A mutation, respectively. Both patients had been described with ataxic features in the records of the outpatient clinic, but these features could apparently not be identified during the off-line video-assessment of the specific SARA video-recording. For rough scoring data and specific gene mutations, see For the underlying diagnosis, age of onset, disease duration of the included patients, see TTPA, ATP1A3 and TUBB2A gene mutations), both observers had indicated that dystonia was severely present and that dystonia was presented as the main phenotype. In two of these patients (ATP1A3 and TUBB2A gene mutations), ataxia had not been identified. In the other 49/52 (94%) of EOA-patients with comorbid dystonia, both observers had indicated that dystonia was (mostly mildly) present and that dystonia concerned the secondary phenotype. Either presence, or absence of comorbid dystonia was not significantly associated with EOA disease duration and/or age of the patient , see In 52/80 (65%) of the EOA-patients, comorbid presence of dystonia was indicated by both observers, characterized by “comorbid dystonia”. In 11/80 (14%) of the EOA-patients, the symptom dystonia was assessed by one observer and in 17/80 (21%) dystonia was assessed as absent by both observers. In 3/52 (6%) of the EOA-patients with comorbid dystonia , congenital malformations of the fossa posterior  and unknown causes , see n = 1; 6%), and unknown causes , see + and EOAD− phenotypes); see EOAD+ phenotypes were associated with genetic mutations (p < 0.001). The kappa value was interpreted as sufficient to good in accordance with Landis and Koch [+ and the EOAD− groups.The reliability of the agreement between the observers, was indicated by Cohen’s kappa of 0.668 . For the included EOAD+ and EOAD− groups and associated MRI abnormalities, see + and EOAD− phenotypes, revealed a significant association between EOAD+ phenotypes and abnormalities at the pons and/or basal ganglia and/or thalamus (p = 0.001), see + versus EOAD− groups, did not reveal statistical differences, see In the investigated cohort, there were 25 genotypes in association with EOAD+ gene group, pathway analysis revealed the strongest enrichment for GO biological processes involved in organelle organization (p = 8.853 × 10−17), and additionally in cellular component organization or biogenesis (p = 2.315 × 10−12), chromosome organization (p = 7.158 × 10−8) and cytoskeleton organization (p = 3.441 × 10−7). These are cellular processes resulting in the assembly, (re-) arrangement or disassembly of organelles, cellular components, chromosomes and cytoskeleton in a cell. For pathway and network analysis in EOAD+ and EOAD− groups in association with allocated MRI damage, see In the EOADATP1A3  [POLG , NPC1 , TUBB4A (DYT4), MTTP , SPG7 –(spastic paraplegia 7) and SLC2A1 (GLUT1 deficiency syndrome). Two of these genes, NPC1 and MTTP, are associated with plasma lipoprotein particle organization and cholesterol homeostasis.In EAO, AOA and dystonia gene lists , we idend FIPWE) , POLG (mn = 5) unique biological pathways with EOA but not with AOA.We identified 90 significant GO biological pathways in EOA, 39 in AOA and 132 in dystonia genes –S10. Of n = 1210), compared to EOA-dystonia (n = 1004) and AOA-dystonia (n = 500). The 472 shared genes between the three networks were enriched for GO pathways (top 10 ToppGene) involved in carboxylic acid—and organic acid metabolic process, fatty acid—and lipid metabolic process, and organic—and carboxylic acid catabolic process . In the majority of EOA-patients, we observed only mildly dystonic features. The prevalence of dystonic comorbidity (65%) was apparently higher than previously reported prevalence in AOA-patients  [+-phenotypes revealed a strong association with MRI abnormalities at the basal ganglia and/or thalamus and/or pons . As such pathways are not implicated in EOA alone, these findings are in line with previous studies demonstrating a pathophysiologic role for cytoskeletal reorganization in the underlying biology of dystonia [+) and non-specifically (in silico determined) shared pathways and networks analyses implicate an underlying role for cellular energy production and network signal transduction in the pathogenesis of EOA with comorbid dystonia. This may have implications for genetic testing. Instead of testing with a single EOA gene panel, one may consider using Whole Exome Sequencing (WES), a complete movement disorder panel and copy number variation analysis, whereas diagnostics by Whole Genome Sequencing (WGS) may have a wider application, in the future. Previous studies in neuro-degenerative AOA disorders, have implicated that the comorbid presence of dystonia should be regarded as an expression of the same disease continuum [+) in the majority (65%) of patients. This EOAD+ subgroup revealed a large heterogeneity in genotype-phenotype relationships, reflected by: (1) identical genetic mutations that were associated with EOAD+ and also with EOAD− phenotypes (in different patients), (2) absence of EOAD+ features in genotypes that have been identified with comorbid dystonia in literature and (3) presence of EOAD+ features in EOA genotypes that have not been reported with comorbid dystonic features, before. As expected, we observed that MRI abnormalities of the basal ganglia and/or pons and/or thalamus were associated with the EOAD+ phenotype. In addition to the well-known association between abnormalities at the basal ganglia and thalamus with dystonia, the pedunculo-pontine tegmental nucleus (PPTg) at the pons has been shown to connect between the basal ganglia and cerebellar nuclei and thalamus [To the best of our knowledge, this is the first study targeting at the underlying biological pathways in patients with EOA with comorbid dystonia . Thedystonia . Compariontinuum ,22,23. Continuum ,25 and continuum , eye bliontinuum  and saccontinuum . Howeverthalamus  implicatthalamus ,20.+; 65%) with previously reported “adult disease onset” AOA  [+ and young age and/or shorter disease duration. Furthermore, the majority of patients were older than 10 years of age. After this age, physiologic dystonic-like features have mostly disappeared [Comparing the dystonic prevalence in “early disease onset” EOA EOAD+; 6% with prenotype) ,31,32,33enotype) ,35. Howeappeared ,35 and, + could both involve: (1) shared pathways inducing a specific EOAD+ phenotype, and/or (2) non-specifically shared pathways by genes that may be concurrently expressed in EOA, AOA and dystonia, inducing comorbid features. Investigating shared pathways in the specific EOAD+ group, revealed an association with organelle- and cellular- organization. Until now, these pathways have not been described in EOAD+ before. Mitochondria are important organelles generating most of the cellular energy by the TCA (Krebs cycle). Pathways of cellular organizations are involved in the axonal cytoskeleton providing the basis for axonal transport and network signaling. This may imply that novel EOAD+-phenotype related gene mutations could be found in association with these molecular pathways.In the present study, we hypothesized that the underlying genetic mechanisms for EOADBy investigating the non-specifically shared genes and pathways between EOA and AOA genes, we identified quite similar pathway enrichment for the EAO and AOA gene list, that was different from dystonia. The top biological pathways observed for EAO and AOA were involved in locomotion and neurogenesis. These biological pathways have also been implicated in the pathogenesis of ataxia syndromes ,37. The + group, reveals a similar role for organelle and cellular organization in dystonic comorbidity. Although not identical, both specifically and non-specifically shared pathways may thus implicate an association with hampered cellular energy production and network signal transduction.The enrichment for lipid and fatty acid homeostasis in the shared genes B of the + genes are not associated with dystonia in literature, and vice versa. However, considering the fact that (1) EOA is a rare disorder, (2) we were able to include a considerable cohort of 80 EOA patients, (3) the identified EOAD+ phenotypes were linked with extra-cerebellar MRI alterations at the basal-ganglia-ponto-thalamic network, and (4) pathway- and network-analyses in both specific EOAD+ phenotypes and in silico determined shared genes reflected similarly underlying biological processes, we would suggest that the present results can be interpreted as indicative. Hopefully, future collaboration with European and even world-wide based ataxia databases will elucidate this.We are aware of some weaknesses to this study. In the first place, the presently studied EOA gene panel cannot be considered complete, since new genes are being, and will be added in the future. Furthermore, by using an EOA database from a single center, we cannot exclude local influences on the outcome data. For instance, we noticed that some of the EOAD+) and non-specific (in silico determined) pathway- and network-analyses implicated associated biological pathways involved in organelle and cellular organization, respectively in TCA cycle processes and lipid and fatty-acid homeostasis. Both outcomes suggest that hampered energy production and network signal transduction may play an underlying role in the pathophysiology of ataxia with comorbid dystonia.In summary, in a local cohort of 80 EOA-patients, we observed dystonic comorbidity in the majority of patients. Exploration of the underlying clinical, anatomical and biological pathways revealed shared pathophysiology, despite genotype-phenotype heterogeneity. Both patient specific  the heterogeneity of genotype-phenotype relationships, (3) the shared anatomical pathways and (4) the shared underlying biological pathways that contribute to the same disease continuum, it might be a rationalistic approach to test EOA patients with a complete, up to date movement disorder panel (including EOA and dystonia gene lists), instead of with a single EOA gene panel. In the future, we aim to investigate the pathogenesis of other mixed EOA phenotypes by determining shared pathways between EOA and other comorbid movement disorders, as well.Altogether, in perspective of: (1) the high prevalence of EOADComorbid dystonia is prevalent in the majority of EOA patients. The underlying biological pathways can be linked with energy depletion and hampered signal transduction involving the cortical-basal-ganglia-pontine-cerebellar network. Hopefully, future insight in the underlying processes causing the heterogeneous, mixed EOA phenotypes may contribute to the yield of diagnostic testing and innovative therapeutic strategies.


Huang-Lian-Jie-Du decoction (HLJDD) has been used to treat pneumonia for thousands of years in China. However, our understanding of its mechanisms on treating pneumonia is still unclear. In the present work, network pharmacology was used to analyze the potential active ingredients and molecular mechanisms of HLJDD on treating pneumonia. A total of 102 active ingredients were identified from HLJDD, among which 54 were hit by the 69 targets associated with pneumonia. By performing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, we obtained the main pathways associated with pneumonia and those associated with the mechanism of HLJDD in the treatment of pneumonia. By constructing the protein–protein interaction network of common targets, 10 hub genes were identified, which were mainly involved in the tumor necrosis factor (TNF) signaling pathway, interleukin 17 (IL-17) signaling pathway, and nucleotide-binding oligomerization domain (NOD)-like receptor signaling pathway. Moreover, the results of molecular docking showed that the active ingredients of HLJDD had a good affinity with the hub genes. The final results indicate that HLJDD has a greater effect on bacterial pneumonia than on viral pneumonia. The therapeutic effect is mainly achieved by regulating the host immune inflammatory response and oxidative stress reaction, antibacterial microorganisms, alleviating the clinical symptoms of pneumonia, repairing damaged cells, and inhibiting cell migration. Pneumonia refers to infectious inflammation of the alveoli, distal airways, and lung interstitium, which can be caused by bacteria, viruses, and other pathogens. Respiratory infections, reported by the Global Burden of Disease (GBD), are the second leading cause of death , and theAs a common respiratory infection, pneumonia has an excessively high morbidity and mortality rate. It has been a huge burden to the medical system and seriously threatened the quality of human life . At presHuang-Lian-Jie-Du decoction (HLJDD), derived from the “Elbow Reserve Emergency Prescription,” is composed of Huanglian (HL), Huangbo (HB), Huangqin (HQ), and Zhizi (ZZ). As a classic Chinese medicine formula for clearing away heat and detoxification, HLJDD is widely utilized in the treatment of inflammation-related diseases , such asIn order to decode the mechanism of HLJDD in the treatment of pneumonia, we explored the targets and pathways of HLJDD in treating pneumonia through network pharmacology. The flowchart of our analysis is shown in 1, updated on May 31, 2014) with the criteria OB ≥ 30% and DL ≥ 0.18  and drug likeness (DL) are the main variables that affect drug absorption across the gastrointestinal mucosa. Therefore, by using “Huanglian,” “Huangbo,” “Huangqin,” and “Zhizi” as keywords, we screened the active ingredients of HLJDD in the Traditional Chinese Medicine Systems Pharmacology   networks . The MCO15 (version 3.6.1)  . The Cytn 3.6.1) . The netn 3.6.1) .p≤0.05 (Gene Ontology (GO) annotation is used to define and describe the functions of gene products from three aspects, namely, biological processes (BPs), cell components (CCs), and molecular functions (MFs). The Kyoto Encyclopedia of Genes and Genomes (KEGG) is a database that integrates genome, chemistry, and system function information. In order to obtain more accurate GO and KEGG functional enrichment information, the clusterProfiler software package of the R platform was used to perform GO and KEGG functional enrichment analysis, and the screening criterion was adjusted p≤0.05 .16, and the structures of the active ingredients were obtained from the TCMSP database. Before molecular docking, the AutoDock 4.2.6 software was used to remove ligands, structure water molecules, and add polar hydrogen atoms and charges to the protein crystal structures  with the largest degree values, which play important roles in the genesis and progression of pneumonia. The MCODE plug-in in Cytoscape was then used to cluster the PPI network of pneumonia targets. Accordingly, 16 clusters were obtained (p ≤ 0.05). The top 10 significant terms in BPs, MFs, and CCs and the top 20 significant KEGG pathways are shown in Protein–protein interaction network analysis on the pneumonia targets was performed by using the String database online service platform, and the results are shown in obtained . Accordiobtained  and perfGO enrichment analysis showed that the biological processes of pneumonia are mainly related to chemokines and cell migration, such as response to chemokine, cellular response to chemokine, leukocyte migration, and neutrophil migration. The molecular functions of pneumonia would be related to cytokine activity, cytokine receptor binding, and chemokine receptor binding. The cellular components associated with pneumonia could be activated in lamellar body, external side of the plasma membrane, and rough endoplasmic reticulum . The KEGp ≤ 0.05). The top 10 significant terms in BPs, MFs, and CCs and the 20 significant KEGG pathways are shown in In order to explore the mechanism of HLJDD in the treatment of pneumonia, we used the ClusterProfiler software package of the R platform to perform KEGG and GO functional enrichment analysis on the 69 common targets. Accordingly, we obtained 1,730 BPs, 110 MFs, 53 CCs, and 88 KEGGs . In terms of immune response, the categories of KEGG pathways were IL-17 signaling pathway (hsa04657) and nucleotide-binding oligomerization domain (NOD)-like receptor signaling pathway (hsa04621). In terms of inflammatory response, the category of BPs was regulation of inflammatory response (GO:0050727), and the category of KEGG pathways was TNF signaling pathway (hsa04668). In terms of signal transduction, the categories of KEGG pathways were MAPK signaling pathway (hsa04010) and NF-kappa B signaling pathway (hsa04064).The critical molecular functions were mainly related to receptor ligand activity (GO:0048018), cytokine activity (GO:0005125), and cytokine receptor binding (GO:0005126). The critical cellular components were membrane raft (GO:0045121) and membrane microdomain (GO:0098857). The critical BPs and KEGG pathways were mainly enriched in the categories of bacterial stimulation, immune response, signal transduction, and inflammatory response. In terms of bacterial stimulation, the categories of BPs were response to lipopolysaccharide (GO:0032496), response to molecule of bacterial origin (GO:0002237), cellular response to lipopolysaccharide (GO:0071222), cellular response to molecule of bacterial origin (GO:0071219), and cellular response to biotic stimulus (GO:0071216). The categories of KEGG pathways were Chagas disease (hsa05142) and Based on the active ingredients related to the hub genes, the disease targets–active ingredients–herbs network was constructed . This neThe 10 hub targets and six pivotal active ingredients were used as receptors and ligands, respectively. The docking results are listed in As indicated in Pneumonia was caused either by bacteria or viruses, called bacterial pneumonia and viral pneumonia, respectively. As the eighth leading cause of death in the world, pneumonia still has a high mortality . As a trIL1B, IL6, CCL2, MMP9, PTGS2, TNF, CRP, EGFR, TLR4, and NOS3 were found to be the hub genes of HLJDD in treating pneumonia. These genes are mainly related to host immunity, oxidative stress, bacteria, and microorganisms and are covered by the TNF signaling pathway , IL-17 signaling pathway , and Chagas disease pathway . We noticed that only four of the hub genes were overlapped with the 10 important genes of pneumonia. This might explain why HLJDD is only effective for special kinds of pneumonia rather than for all kinds of pneumonia.By constructing the PPI network for the common targets of HLJDD and pneumonia, According to the analysis of the hub genes, pivotal active ingredients, and main significant KEGG pathways, the potential mechanisms of HLJDD in the treatment of pneumonia might be attributed to the following aspects.IL1B and CCL2 were the most upregulated genes in COPD small airway epithelial cells and IL1B is an airway inflammatory molecule in COPD  can inhibit the production of pro-inflammatory factors IL6 and IL1B, thereby reducing the inflammatory response and oxidative stress caused by cerebral ischemia–reperfusion (CI/R) and increasing the levels of anti-inflammatory factors and superoxide dismutase . QuercetTrypanosoma cruzi is an intracellular protozoan parasite that causes Chagas disease. It can also invade lung cells and affect the host’s innate immunity and interference T lymphocytes. The TLR4–TRAF6–AP1–TNFα pathway in Chagas disease and Yersinia infection can indirectly inhibit the growth of parasites and inhibit the response of pro-inflammatory cytokines by regulating TNFα, IL6, and IL1B. Beta-sitosterol (MOL000358) and sitosterol (MOL000359) have antimicrobial and immunomodulatory effects. Its derivative 2-naphthoyl Sit ester can inhibit the overexpression of TLR4 induced by LPS, thereby inhibiting inflammation and regulating antioxidants , block pulmonary blood vessels, activate platelet-activating factor receptors, change the permeability of endothelial cells, and mediate the activity of Src family kinases, thereby making the lungs susceptible to parasitic infections and forming parasitic pneumonia . IL1B isoxidants .Matrix metalloproteinase-9 plays an important role in the local proteolysis of the extracellular matrix and leukocyte migration . When epIL1B, IL6, CCL2, MMP9, PTGS2, TNF, CRP, EGFR, TLR4, and NOS3. The significant biological processes, molecular functions, cellular components, and KEGG pathways were obtained by performing GO and KEGG pathway analysis. The pivotal active ingredients, hub genes, and enrichment analysis results indicate that HLJDD may have a greater effect on bacterial pneumonia than on viral pneumonia, which can explain why HLJDD does not appear frequently in the process of using Chinese medicine to prevent and treat COVID-19 (We obtained the pivotal active ingredients of HLJDD in treating pneumonia, namely, quercetin, rutaecarpine, sitosterol, beta-sitosterol, crocetin, and stigmasterol. By constructing a compound–target–disease network, 10 hub genes were screened out, i.e., COVID-19 . The theThe original contributions presented in the study are included in the article/WC and XL conceived and designed the experiments. XL and QT performed the experiments. XL, HT, and WC wrote the manuscript. All authors read and approved the final 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.


Atac2, Rrm1, Rrm2, and Gla. The relevant signaling pathways are the Ras signaling pathway, HIF-1 signaling pathway, and the p53 signaling pathway. In conclusion, compared with SASCs, our multi-omics analysis identified that EASCs with higher Acat2 expression may be more correlated to fat metabolism and insulin resistance, while PASCs with abnormal expression of Rrm1/2 and Gla may be more correlated with some malignant tumors and cardiac-cerebral vascular disease.This study aimed at exploring the gene expression and metabolites among multisite adipose-derived mesenchymal stem cells (ASCs) and investigate the metabolic pathway using a multi-omics analysis. Subcutaneous adipose-derived mesenchymal stem cells (SASCs), perirenal adipose-derived mesenchymal stem cells (PASCs), and epididymal adipose-derived mesenchymal stem cells (EASCs) were isolated from Sprague Dawley rats. RNA and metabolites were extracted and sequenced using transcriptomics and metabolomics analyses, respectively. There were 720 differentially expressed genes (DEGs) in EASCs and 688 DEGs in PASCs compared with SASCs; there were 166 unique DEGs in EASCs, 134 unique DEGs in PASCs, and 554 common DEGs between EASCs and PASCs. Furthermore, there were 226 differential metabolites in EASCs, 255 differential metabolites in PASCs, 83 unique differential metabolites in EASCs, 112 unique differential metabolites in PASCs, and 143 common differential metabolites between EASCs and PASCs. The transcriptomics and metabolomics analyses identified four hub genes, one in EASCs and three in PASCs. There are functional differences among multisite ASCs that may be related to the hub genes  Obesity, which poses a grave threat to humankind, has become one of the most important chronic diseases. By 2016, more than 1.9 billion adults over 18 years of age were overweight worldwide, of whom more than 650 million were obese . ObesityAs early as 1956, Adipose tissue is mainly composed of adipocytes (90%), nonadipocytes , connective tissue matrix, blood vessels, and nervous tissues . GiroussP-value (FDR) <0.05, 497 downregulated, and 223 upregulated), and 688 DEGs between PASCs and SASCs  were found by RNA-Seq. Using Metacore system, the top 10 different enrichments in pathway maps, GO processes, and process networks of DEGs in EASCs and PASCs are shown in To explore the functional differences of SASCs, PASCs, and EASCs, 720 differentially expressed genes (DEGs) between EASCs and SASCs  process enrichment analyses show the common DEGs are most relevant to actin cytoskeleton organization, the PASCs are strongly associated with supramolecular fiber organization, and the EASCs are most enriched in the mitotic cell cycle. In addition, actin filaments in common DEGs, integrin-mediated cell-matrix adhesion in PASCs, and mitosis in EASCs were seen in process networks .In particular, the top scored network analyses of common upregulated DEGs were found to be involved in positive regulation of blood pressure by epinephrine-norepinephrine (16.0%), regulation of systemic arterial blood pressure by norepinephrine-epinephrine (16.0%), positive regulation of heart rate by epinephrine-norepinephrine (14.0%), adenylate cyclase-activating adrenergic receptor signaling pathway (16.0%), and adrenergic receptor signaling pathway (16.0%) . The univia Jak-Stat about pathway maps, cellular response to organic substance about GO processes, and amyloid proteins about process networks , mitochondrial translational elongation (12.0%), mitochondrial translational termination (12.0%), translation (20.0%), and gene expression (42.0%) . The uniTo reduce systematic and technical bias, metabolomics data were normalized and the metabolic data were normally distributed . PrincipTwo hundred and fifty-five differential metabolites in PASCs were found compared with SASCs . MetaboAnalysis 3.0 was used to analyze the metabolic pathways of significantly different metabolites. The results showed that the differential metabolites were primarily involved in seven metabolic pathways . SimilarTo further explore the differences of differential metabolites of ASCs among the three adipose deposits, we analyzed the differential metabolites of SASCs vs. EASCs and SASCs vs. PASCs and found that compared with SASCs, there were 143 common differential metabolites between EASCs and PASCs  , 83 uniqP-value <0.05 (acetyl coenzyme A acetyltransferase 2 (Acat2) gene  gene   and 143 Adipose tissue, which is divided into BAT and WAT, including SAT and VAT, is the main metabolic store in the human body. Compared with VAT, the enlargement of the SAT can be beneficial to patients by improving insulin sensitivity and reducing the risk of type II diabetes . ConversAccording to previous studies, VAT secretes higher levels of proinflammatory factors and lower levels of anti-inflammatory factors, decomposes FFA, and increases plasma more easily than SAT. As reported, high concentrations of FFA can inhibit insulin signaling pathways in skeletal muscle and liver, induce insulin resistance, and eventually increase the risk of diabetes and cardiovascular diseases. In addition, blood pressure can be elevated by activation of the renin-angiotensin-aldosterone system, the sympathetic nervous system, and mechanisms related to insulin resistance . TherefoIn this study, SASCs, EASCs, and PASCs were extracted from three different adipose deposits in rats, and transcriptomics and metabolomics analyses were used to reveal that there were 720 DEGs in EASCs and 688 DEGs in PASCs compared with SASCs. In addition, there were 166 unique DEGs in EASCs , 134 uniCollectively, we found that immune response and cytoskeleton remodeling in pathway maps, response to organic substance and response to organic cyclic compound in GO processed, and cytoskeleton and cell adhesion in process networks are common in EASCs and PASCs. Interestingly, cytoskeleton remodeling was the most enrichment of upregulated DEGs both in EASCs and PASCs, while immune response was the most enrichment of downregulated DEGs both in EASCs and PASCs. Also, KEGG analysis  showed AUnder the conditions of obesity, excessive FFAs will lead to glucose and lipid metabolism disorders, which eventually weaken the PI3K-AKT signaling pathway, thereby inducing insulin resistance , which wPhagosomes, which are individually involved in EASCs, are associated with inflammation by producing proinflammatory cytokines, such as IL-1β, IL-6, TNF-α, and IL-12 ; PI-3 kiThe unique signaling pathways of PASCs were the HIF-1 signaling pathway, p53 signaling pathway, cell cycle, and apoptosis. Previous studies indicated that the HIF-1 signaling pathway is associated with multiple tumor progressions , and dysAcat2 was found to be associated with nine metabolites in 10 metabolic pathways. Acat2 is a cellular enzyme that converts cholesterol and fatty acids into cholesterol esters and is mainly expressed in the liver and intestine  reported that obese and overweight patients had an increased risk of colon cancer, postmenopausal breast cancer, endometrial cancer, renal cancer, and esophageal cancer  were purchased from Vital River Biological Co., . Then the rats were raised under SPF conditions according to the breeding regulations and adapted to the environment for 3 days. All experiments were conducted with the approval of the Animal Experimental Ethics Committee. All procedures were approved  by the Experimental Animal Care and Use Committee of Nanjing Medical University and conducted in accordance with the Guide for the Care and Use of Laboratory Animals .°C incubator for 2 h. The upper-medium was discarded, high glucose medium containing 10% FBS was added, and SASCs, PASCs, and EASCs were obtained. After culturing at 37°C in an incubator for 3 days, RNA and metabolites were extracted from the third passage (P3) cells and sequenced by transcriptomics and metabolomics, respectively , centrifuged at 1,000 rpm for 5 min, and the supernatant was discarded. DMEM/F12 medium containing 10% fetal bovine serum (FBS) was added to the sample. Then the suspension was evenly spread in culture dishes and cultured into a 37ectively .°C for subsequent transcriptome sequencing . In order to ensure the accuracy of sequencing data, the RNA samples were detected by agarose gel electrophoresis, Nanodrop One/Onec, Qubit 2.0, and Agilent 2100 to estimate the effectiveness, concentration, purity, and integrity of RNA samples, and the cDNA library could be constructed only after the detection results meet the requirements. The HiSeq 3000 System (Illumina®) was used for sequencing that generated 150 bp paired-end reads. Before data analysis, the adaptor was removed by cutadapt software (version 3.1), and the low-quality sequence was removed by FastX-Toolkit software (version 0.0.13). The alignment was performed with TopHat softwar (version 2.1.1) , and the reference genome was rn6 (comparison efficiency >80%). Based on Bowtie v2, transcriptome sequencing reads were compared with the reference genome and splicing junction between exons were identified. Fragments per kilobase of transcript per million reads mapped (FPKM) was used as an index to measure gene expression. The differential expression analysis was performed by DEGseq, which was based on binomial distribution and combined with Fisher’s exact test and likelihood ratio test, and the genes with fold change >2 and FDR <0.05 were identified as DEGs. KOBAS v2.0 R language package was used for functional enrichment analysis. The upregulated or downregulated DEGs were subjected to network analyses1 as previously described  and precipitated by isopropanol and absolute ethanol. Finally, RNA was precipitated with DEPC water, and then stored at −80escribed .°C for liquid chromatography-mass spectrometry (LC-MS/MS).P3 SASCs, PASCs, and EASCs were cultured in 10 cm diameter cell culture dishes with six replicates in each group. When the cells grew to 90% their original size, the metabolites were extracted as follows: the cells were washed three times with PBS, then 0.5 ml of 80% (HPLC grade) methanol was added. After repeated blowing and washing of the residual cells, as much of the cell mixture was collected as possible, centrifuged at room temperature at high speed, and the supernatant was dried with nitrogen and stored at −802O/0.1% formic acid and mobile phase B consisted of acetonitrile with 0.1% formic acid which was followed by a program: 1% B at 0–1 min, 15% B at 3 min, 70% B at 5 min, 85% at 9 min, 100% at 10–12 min, and subsequently return to the initial conditions with 2 min for equilibration. Furthermore, the MS program was conducted on a 6545 Quadrupole-Time of Flight system  as follows: both positive and negative ion modes with drying gas 300°C flow 6 L/min, sheath gas 340°C flow 11 L/min, nebulizer gas 35 psig, capillary voltage 4,000 V, and fragmental voltage 135 V. The data collection  was acquired by both centroid and profile stored in autoMSMS scan mode with reference masses at m/z 112.05087 and 922.009798 were set as online accurate mass calibration.Before metabolomic sequencing, the sample was reconstituted with 10 μl of double-distilled water (HPLC grade). Then metabolites were separated by reverse-phase LC  using a reversed-phase XBridge C18 column  and 2 μl of each sample was injected on the C18 column for analysis with a 0.4-ml/min flow rate. A quality control sample was run first followed by 10 test samples. Mobile phase A consisted of H2 for peak discrimination, filtering, and alignment. A table of the intensities of all the peaks was created with the retention time and the mass to ratio for each ion after being aligned by time domain, automatic integration, and extraction of the peak intensities. Then, metabolites were qualitatively analyzed with MS/MS spectra by retrieving and matching the data from Metlin, Massbank, and Human Metabolome Database (HMDB).MassHunter Workstation software  was used to export mzdata format from the acquired MS data (.d). Data pretreatment procedures were performed by using XCMS Online3. Briefly, missing values were replaced by 1/5 of min positive values of their corresponding variables. Sample normalization by sum and data scaling by auto scaling were applied (mean centered and divided by the standard deviation of each variation)  cutoff <0.05). The different metabolites were subjected to enrichment analysis and pathway analysis in MetaboAnalyst 3.0 by KEGG.The MS peak list was subjected to MetaboAnalyst 3.0 for statistical analysis. PCA and pathway analysis were performed with the web-based software MetaboAnalyst 3.0riation) . Fold chP-value were used to assess the relationship between metabolites with genes which are shown by Heatmap and association analysis network diagram.Top 100 DEGs and differential metabolites (top 50 upregulated and top 50 downregulated) were selected for further correlation analysis by calculating Pearson’s correlation coefficients on the relative gene expression and metabolite content data between different VASCs with SASCs . The cor4 to get the common pathway. KGML file, a subset in the KEGG database, contains the relationships of the graph objects and information about lineal homologous genes in the KEGG genes database. The differences in genes and metabolites in our study were subjected to the database, and the matched network relationships were set as a basic network. Metabolome and transcriptome relationships were visualized and interpreted using Cytoscape (version 3.4.0), and hub genes were calculated by CytoHubba (a Cytoscape plug-in for identifying hub nodes) through MCC (the top 10 nodes ranked by MCC) and degree (degree >6) algorithm.The DEGs and differential metabolites were subjected to KEGG MapperIn conclusion, similar to SAT, PAT, and EAT, functional differences were found among SASCs, PASCs, and EASCs in our study. The possible metabolic pathways of these functional differences were found using both transcriptomic and metabolomic analyses, and our data provide a theoretical basis for future studies.https://www.ebi.ac.uk/metabolights/, MTBLS2100 and Gene Expression Omnibus, GSE161312.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: The animal study was reviewed and approved by All procedures were approved  by the Experimental Animal Care and Use Committee of Nanjing Medical University and conducted in accordance with the Guide for the Care and Use of Laboratory Animals .XK and WS designed the work and prepared the manuscript. CY, JZ, and TW performed the experiments, conducted the data analysis, and wrote the manuscript. KZ, XW, and JS analyzed and interpreted the data. All authors discussed the results and read and approved the final version of the manuscript for publication.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.


To explore the main components and unravel the potential mechanism of simiao pill (SM) on rheumatoid arthritis (RA) based on network pharmacological analysis and molecular docking. R packages. Molecular docking analysis was constructed by the Molecular Operating Environment (MOE).  Related compounds were obtained from TCMSP and BATMAN-TCM database. Oral bioavailability and drug-likeness were then screened by using absorption, distribution, metabolism, and excretion (ADME) criteria. Additionally, target genes related to RA were acquired from GeneCards and OMIM database. Correlations about SM-RA, compounds-targets, and pathways-targets-compounds were visualized through Cytoscape 3.7.1. The protein-protein interaction (PPI) network was constructed by STRING. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed via  A total of 72 potential compounds and 77 associated targets of SM were identified. The compounds-targets network analysis indicated that the 6 compounds, including quercetin, kaempferol, baicalein, wogonin, beta-sitosterol, and eugenol, were linked to ≥10 target genes, and the 10 target genes  were core target genes in the network. Enrichment analysis indicated that PI3K-Akt, TNF, and IL-17 signaling pathway may be a critical signaling pathway in the network pharmacology. Molecular docking showed that quercetin, kaempferol, baicalein, and wogonin have good binding activity with IL6, VEGFA, EGFR, and NFKBIA targets.  The integrative investigation based on bioinformatics/network topology strategy may elaborate on the multicomponent synergy mechanisms of SM against RA and provide the way out to develop new combination medicines for RA. Rheumatoid arthritis (RA) is a chronic polyarticular symmetric disease. It is characterized by chronic inflammation of the synovial membrane, which can destroy articular cartilage and juxta-articular bone . RA affeTraditional Chinese medicine (TCM) has been extensively applied for the treatment of RA for centuries in Asia and has been gradually accepted for worldwide clinical applications , 7. NumeAtractylodes lancea (Thunb.) Dc. (Cang Zhu), achyranthis bidentatae radix (Niu Xi), and Coicis Semen (Yi Yi Ren). Previous studies have indicated the anti-inflammation pharmacological effect of SM [κB)/pyrin domain containing 3 (NLRP3) inflammasome activation [Simiao pill (SM), a traditional TCM formula, comprises four herbs, including Phellodendri Chinensis Cortex (Huang Bo), ct of SM  and thattivation . Recentltivation . In additivation . Howevertivation , the theNowadays, network pharmacology integrates network biology and polypharmacology based on existing databases, providing a novel approach for exploring the mechanisms and synergistic effect of TCM formulas as disease treatments –16. CombTherefore, in this study, a network pharmacology-based study was conducted to predict bioactive compounds and elucidate the comprehensive pharmacological mechanisms about the antirheumatic effect of SM. In addition, molecular docking analysis was performed to validate in silico to predict molecular interactions between compounds and targets.Network pharmacology-based prediction of SM treating RA was constructed by the following : (1) dathttp://lsp.nwu.edu.cn/tcmsp.php) and a Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine .The related composite compounds of SM were obtained from the Traditional Chinese Medicine Systems Pharmacology Database  model administrated by TCMSP is employed for pharmaceutical research. As an oral drug, two related-ADME models, oral bioavailability (OB), and drug-likeness (DL) are applied to identify the potential bioactive compounds in this study. Only the compounds with OB ≥ 30 and DL ≥ 0.18 that satisfied the criteria suggested by the TCMSP database (removed the duplicated) are retained as the candidate compounds for further study . In addihttp://www.uniprot.org/) to search for target gene names selected by human species.All the potential compounds were input into TCMSP to capture the relationships between drugs and targets. Since the obtained targets include various biological species, all target names were also put into UniProt databases  and Online Mendelian Inheritance in Man , and only “Homo sapiens” target genes linked to RA are selected.Information of known RA-related therapeutic target genes was collected by keywords “rheumatoid arthritis” as queries from The Human Gene Databases , respectively, to produce a Venn diagram, which indicates the intersection of identified targets of drug and disease.https://cytoscape.org/, ver. 3.7.1) to systemically explore the molecular mechanisms of SM treating RA.SM-RA mechanism network, compounds-targets network, and pathways-targets-compounds network were visualized through Cytoscape  to construct a PPI network for understanding protein interaction systematically. The PPI network is constructed by setting the organism as “human sapiens”, setting the minimum required interaction score to “medium confidence (0.40)”, and excluding the disconnected protein nodes. In addition, statistics of protein interactions are figured out according to the PPI network, and a related bar plot diagram is constructed with R 3.6.0 subsequently.The above 77 target genes acquired from the Venn diagram intersection were imported into STRING  are applied to carry out Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of intersection target genes of SM-RA. P values ＜0.05 and q values ＜0.05 are considered statistically significant based on Fisher's test.R 3.6.0 and related http://www.rcsb.org/) in PDB format by setting the organism to “Homo sapiens only”. The 3D conformers of candidate compounds are acquired from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) with SDF format. Subsequently, they were imported to the Molecular Operating Environment (MOE) to get the docking score. The greater the absolute value of the docking score, the better.The 3D structures of candidate targets were obtained from the PDB database (Atractylodes lancea (Thunb.) Dc. (Cang Zhu), 176 in achyranthis bidentatae radix (Niu Xi), and 38 in Coicis Semen (Yi Yi Ren), and in TCMSP, also including 37 in Phellodendri Chinensis Cortex (Huang Bo), 26 in Atractylodes lancea (Thunb.) Dc. (Cang Zhu), 10 in achyranthis bidentatae radix (Niu Xi), and 3 in Coicis Semen (Yi Yi Ren) in BATMAN-TCM.A total of 479 compounds were identified in SM, including 140 in Phellodendri Chinensis Cortex (Huang Bo), 49 in All the identified compounds were selected through ADME screening, with 90 of 479 compounds satisfying the suggested criteria OB ≥ 30 and DL ≥ 0.18 . Of the The target genes related to RA were searched in GeneCards and OMIM databases, which include 3768 genes in GeneCards and 1 gene in OMIM, with no overlapping target gene.In the Venn diagram intersection of identified targets about identified compounds and of RA , a totalE, rutin, and kaempferol) that connected to more than four genes, 55 target genes are associated with quercetin, 22 target genes are associated with kaempferol, 15 target genes are associated with baicalein, 14 target genes are associated with wogonin, and 10 target genes are associated with beta-sitosterol and eugenol, respectively , cellular component (CC), and molecular function (MF). As showed in P < 0.05 and q < 0.05) shown by a bar plot diagram  in the SM-RA network. These 4 target genes and 4 compounds are imported into MOE for molecular docking verification. The docking scores are shown in In the present network pharmacological analysis, a total of 479 compounds were identified in the four herbal medicines of SM, and 72 compounds were yielded by ADME criteria screening. A total of 386 targets related to potential compounds and 3769 targets associated with RA were identified, and 77 target genes were obtained from the interaction of targets about SM identified compounds and RA. SM-RA network analysis visualized the interaction of multicomponents and multitargets about SM on RA. The compounds-targets network analysis indicated that the 6 compounds, including quercetin, kaempferol, baicalein, wogonin, beta-sitosterol, and eugenol, were linked to ≥10 target genes, and the 10 target genes  were core target genes in the network. GO enrichment analysis indicated that numerous targets are involved in response to a steroid hormone, oxidative stress, and regulation of the apoptotic signaling pathway in BP, are localized to the cellular membrane and nuclear chromatin part in CC, and are associated with nuclear receptor activity and transcription factor activity in MF. KEGG pathways analysis results indicated that numerous targets are associated with certain virus infections and cancer. Molecular docking showed that quercetin, kaempferol, baicalein, and wogonin have good binding activity with IL6, VEGFA, EGFR, and NFKBIA targets.α (TNF-α), interleukin-1β (IL-1β), interleukin-17 (IL-17), and monocyte chemotactic protein-1 (MCP-1) [κB) transcriptional activity and recombinant macrophage migration inhibitory factor- (MIF-) mediated signaling [β-Sitosterol could modulate the functions of macrophages and attenuates rheumatoid inflammation in CIA mice [κB signaling pathway [About 72 identified compounds, particularly the 6 compounds, including quercetin, kaempferol, baicalein, wogonin, beta-sitosterol, and eugenol, were linked to more than 10 targets, indicating that these compounds might play a vital role in the process of RA treatment. Furthermore, certain compounds have exhibited the potential antirheumatic therapeutic activities except for wogonin . For ins (MCP-1)  and sign (MCP-1) . Besides (MCP-1) . In addiignaling . What's CIA mice . For eugCIA mice , 26. Mor pathway , and ber pathway . CollectAmong the main target genes (top 30) in the PPI network is INS, ranking first with the highest connection, which may affect the local inflammatory process of joint in RA , though KEGG pathway enrichment analysis indicated that certain types of virus infection and cancer might also be crucial in the network. The evidence that viral infection contributes to RA, such as Epstein-Barr virus infection , is stroOf the leading 30 target genes with a higher connection in the PPI network, IL6, VEGFA, EGFR, and NFKBIA play a critical role in the development of RA, which has been aforementioned. Besides, in the visualized pathways-targets network, IL6, VEGFA, EGFR, and NFKBIA are involved in numerous pathways, indicating that SM may exert anti-RA effects through multipathways and multitargets combined interaction. Furthermore, the molecular docking analysis was constructed to investigate the interaction of some candidate compounds and targets. For example, the absolute value of docking scores about NFKBIA and quercetin, kaempferol, baicalein, and wogonin is the highest in each group, indicating that NFKBIA has a higher binding affinity than other target genes. For wogonin, although there have been no relevant studies about the effect in RA, the docking results indicated that wogonin performed good binding activity with IL6, VEGFA, EGFR, and NFKBIA. In brief, the high binding affinities of these active components indicated that the therapeutic effects of SM treating RA were probably through the modulation of several related targets.As shown, the anti-RA effect of identified compounds  is partially associated with the potential target genes, including NFKBIA, IL6, and MAPK, and potential signals, including PI3K-Akt, TNF, and IL-17, indicating the interaction between multicomponents, multitargets, and multisignaling of SM treating RA.This study has some limitations. It provides only a predictive overview of the pharmacological mechanisms of SM against RA based on the existing database, and further experiment verification in vivo and in vitro is necessary to ensure the reliability and reasonability of predicted results. First, posttranscriptional processing, translation regulation, and posttranslational processing and regulation play a critical role in gene expression regulation, and most of the research on mechanisms about SM treating RA is gene level in this study; therefore, an in-depth study needs to explore the related mechanism. Second, the key proteins and KEGG pathways need to be verified. Third, the anti-RA effect needs to be further verified in the animal model.Besides, for clinical applying of SM treating RA, owing to the ethnic, genetic, and possible etiological differences, potential mechanisms about the related therapeutic module coinciding with clinical applications are worthy of further experimental investigation. In addition, importantly, dosage exploration, oral bioavailability, water-solubility, pharmacokinetics, and potential side effects of SM will also need a thorough exploration.In summary, a bioinformatics/topology-based strategy, including ADEM screening, bioinformatics, network topology, enrichment analysis, and molecular analysis, was applied for identification of the molecular mechanisms of SM against RA. The integrated strategy might make the decipherment of biological mechanisms more accurate and efficient. The SM-RA network, compounds-targets network, and pathways-targets network analysis visualized the interaction of multicomponents and multitargets about SM treating RA. In particular, quercetin, kaempferol, baicalein, wogonin, beta-sitosterol, and eugenol might be the candidate therapeutic agents, and PTGS1, ESR1, AR, PGR, CHRM3, PPARG, CHRM2, BCL2, CASP3, and RELA were identified as potential drug targets. The enrichment and PPI analysis revealed the biological functions of the grouping networks related to the pathogenesis of RA. The multicomponent cosynergism of the herbal combinations about SM was elaborated. The study also revealed the multifunctional synergetic mechanisms of SM, including certain virus infection and cancer, and PI3K-Akt, TNF, and IL-17 signaling pathway.


Pathway enrichment analysis provides a knowledge-driven approach to interpret differentially expressed genes associated with disease status. Many tools have been developed to analyze a single study. However, when multiple studies of different conditions are jointly analyzed, novel integrative tools are needed. In addition, pathway redundancy introduced by combining multiple public pathway databases hinders interpretation and knowledge discovery. We present a meta-analytic integration tool, Comparative Pathway Integrator (CPI), to address these issues using adaptively weighted Fisher’s method to discover consensual and differential enrichment patterns, a tight clustering algorithm to reduce pathway redundancy, and a text mining algorithm to assist interpretation of the pathway clusters. We applied CPI to jointly analyze six psychiatric disorder transcriptomic studies to demonstrate its effectiveness, and found functions confirmed by previous biological studies as well as novel enrichment patterns. CPI’s R package is accessible online on Github metaOmics/MetaPath. In a typical transcriptomic study, a set of candidate genes associated with diseases or other outcomes are first identified through differential expression analysis. Then, to gain more insight into the underlying biological mechanism, pathway analysis  is usually applied to pursue functional annotation of the candidate biomarker list. The goal behind pathway analysis is to determine whether the detected biomarkers are enriched in pre-defined biological functional domains. These functional domains might come from one of the publicly available databases such as GO , ReactomMany transcriptomic datasets have been generated with the rapid advances of high-throughput experimental technologies in the past decade. Meta-analysis, a set of statistical methods for combining multiple studies of a related hypothesis, has thus become popular . Some meA second issue emerges with pathway enrichment analysis is the pathway redundancy across pathway databases. Researchers often have difficult time to infer and interpret the underlying biological mechanism without presumed bias due to the large number of pathways identified. This kind of redundancy frequently occurs in a regular pathway enrichment analysis since different pathway databases contain similar annotated pathways with highly overlapped genes. The DAVID Bioinformatics Resources  partiallp-values from multiple omics studies for detecting homogeneous and heterogeneous differentially expressed genes. Next, cluster analysis based on pathway similarity (defined by gene overlap) is applied to remove the level of pathway redundancy. But unlike DAVID, we adopt a tight clustering algorithm similar to [In light of the aforementioned drawbacks in existing tools, we propose a meta-analytic integrative framework to combine multiple transcriptomic studies for identifying consensual and differential pathway enrichment, wrapped in a tool named Comparative Pathway Integrator (CPI). CPI incorporates 25 pathway databases, including GO , Reactommilar to  to allowmilar to .p-value matrices and pathway clustering details including gene composition and functional keywords.CPI is a comprehensive tool incorporating several widely accepted mature methods as well as multiple novel algorithms/approaches. It is mainly composed of three steps . The firp-values as alternative input , many genes are shared among different pathways. Similar pathways can also repeat in different pathway databases with slightly different gene composition, annotation or description. Such redundancies often stumble interpretation of pathway analysis results. In CPI, we perform pathway clustering to reduce the redundancy among detected pathways. The similarity between different pathways is calculated based on kappa statistics , which dWe next assign detected pathways into clusters. For most clustering algorithms including the aforementioned consensus clustering, all pathways are forced into clusters although it is well-known that leaving scattered subjects out of clusters often generate tighter clusters and improve the clustering performance in such high-dimensional data ,22,23. IAlthough the pathway cluster analysis in spacyr [spaCy package [spaCy is an industrial strength text-mining package employing a large library database as well as some machine learning algorithms to detect information from texts. The stop words in English, such as “the”, “a”, “that”, which are common and carry no important information, are removed from those noun phrases by using the English stop words database from R package tm [textstem [For each pathway description, we firstly extracted unique noun phrases from it. This step was done using the spacy_extract _nounphrases function from R package spacyr  which is package . spaCy ickage tm . After rtextstem . The toptextstem  were thej contains the noun phrase i and 0 otherwise.In total, we provide 25 pathway databases  with 26,801 pathways in CPI for users to select in the analysis. The above preprocessing and filtering steps were repeated for each pathway to generate standard noun phrases for all pathways. Based on the results, we constructed a binary matrix where each row being a noun phrase and each column being a pathway with element wordnet [Once the matrix was constructed, R package wordnet  was usedj contains the noun phrase i and 0 otherwise. For a given pathway , the pathway name (“NADH dehydrogenase complex”) is usually more concise while the pathway description  gives a more detailed illustration of the pathway.For later penalized permutation test, the above text mining matrix construction procedure was also applied to pathway names of 26,801 pathways, producing a similar matrix of 36,037 rows and 26,801 columns with j to indicate whether it contains phrase i: j and i in a pathway cluster C, we define the test statistics:A simple strategy to test for the significance of a phrase frequently appearing in a cluster is by simple counting and conducting Fisher exact test. Yet we found this method to be less powerful and less biologically justifiable from real data analysis, because the phrases in the term name or a shorter description of a pathway are deemed to be more representative than those in a full or longer description. In other words, phrases appearing in a pathway with shorter description should statistically contribute more weights than in a pathway with lengthy description because the latter is more likely to happen by chance. Therefore, we down-weighted the phrase count with long description by assigning a score between 0 and 1 to each pathway i in the b-th permutation, pathways are randomly sampled to form subset C. Test statistics p-value could be calculated by i is seen in cluster C. Multiple comparison is then corrected by Benjamini–Hochberg procedure [To test for the null hypothesis that a phrase is not enriched in a certain cluster, we adopt a permutation analysis. For each phrase rocedure  to contrp-value matrix (pathways sorted by clusters on the rows and studies on the columns)  of studies in each cluster  heatmap of kappa statistics matrix for pair-wise pathways see  (2) heatmns) see  (3) multmns) see  and (4) ster see . CPI alsWe provide 22 Homo sapiens pathway databases in CPI, including 14 pathway databases from MsigDB , 2 databases from Connectivity Map, transcription factor target database JASPAR, Protein-Protein interaction database and 3 microRNA target databases as options for enrichment analysis. In addition, GO and KEGG for Mus musculus and Saccharomyces cerevisiae, and JASPAR database for Mus musculus are also provided see . Users mTo demonstrate its utility, we applied CPI to an integrative analysis of a postmortem microarray dataset from three psychiatric disorders . Brieflyp-value matrix provided by users. Pathway enrichment analysis was performed in each study using the top 400 DE genes and the results were meta analyzed by adaptively weighted Fisher’s method in CPI to obtain the final integrative p-values and q-values in each pathway. We filtered out pathways containing less than 15 genes or more than 500 genes in the pathway databases. Of the 1901 pathways analyzed, 96 pathways had meta-analyzed q-values smaller than 0.0005 and were entered for pathway cluster analysis. The number of pathway clusters were selected to be 8 which was justified by the elbow plot and consensus CDF plot  analysis was conducted by limma embedded in the MAPE2.0 function from our package which allows both raw transcriptomic data input as well as DE plot see  and 18 pp-values with pathways on the rows and six studies on the columns. Dendrograms of hierachical clustering of studies in each cluster are shown in p-values. But in general, we do observe clear pattern in almost all 8 clusters. For example, cluster VI, VII and VIII contain highly enriched pathways in SCZ-L3 and SCZ-L5, marginal enrichment in BP-L3 and BP-L5 but almost no significance in MDD-L3 and MDD-L5. Based on text mining results, clusters VI and VIII contain pathways related to mitochondrion, ATP synthesis, NAD, etc. Our results also suggest these alternations across DLPFC layer 3 and layer 5 are mainly related to ATP production rather than other aspects of mitochondrial function. Cluster VII with keywords degradation, multiubiquitination, ubiquitin 26s proteasome system, etc. is significantly altered in schizophrenia DLPFC layers and to a lesser extent in bipolar disorder. Similar results have been reported in a blood-based microarray investigation of both schizophrenia and bipolar disorder [disorder .The result of cluster VI, VII and VIII is consistent to several biological findings in the literature. Firstly, our results are highly consistent with the original publication in . Secondlmetabolism, mRNA, ribosome, viral protein, NUMB, etc. is significantly altered only in schizophrenia DLPFC layers, which indicates protein synthesis dysfunction. However, similar altered expression of gene sets related to protein synthesis has been found in postmortem hippocampus and orbitofrontal cortex of patients with major depression, bipolar disorder, and schizophrenia consensually [insulin, NGF, BDNF, neurotrophins, Trk tyrosine kinase receptor, etc. shows an enrichment pattern mainly in MDD and BP-L5 with little enrichment in SCZ of BP-L3. This suggests some similarity between the expression of these gene in layer 5 between BP and MDD subjects.Cluster I and cluster IV had a different pattern of pathway alterations. Cluster IV with keywords ensually . This imtranscription, nucleoplasm, chromosome, nuclear content, nucleolus, RNA, indicating alterations in general aspects of nuclear function. Finally, cluster II and V with moderate enrichment in all six studies contain general neural disease related pathways with keywords such as neuron, cell death and apoptotic process and likely represent processes involved in neuronal survival.Pathways in cluster III are enriched in layer 3 of all three diseases  but to a lesser extent in layer 5 . This cluster is annotated with keywords such as Interestingly hierarchical clustering of the six categories of samples (cells from two layers and three different diagnoses) for each of the eight identified pathway clusters shows a similar pattern. Specifically, each of the 8 pathway clusters are most tightly clustered by diagnosis across layers suggesting similar alterations in layers 3 and 5 within a disease. Furthermore, across diagnoses BP and SCZ cluster more strongly with one another than with MDD. This is consistent with transcriptome ,38,39 anapoptosis in cluster 7 was ranked low in the Fisher’s exact test  while prioritized in permutation analysis of the penalized test statistics . In our experience, p-value assessment while under a reasonable computing time. The entire permutation analysis for the example in In pathway databases, some pathways come with only pathway names  and some contains both pathway names and pathway descriptions (can be up to 1500 words). In Fisher’s exact test by simple count, occurrences of a noun phrase are treated the same when appearing in these two extreme cases. In this case, signals of important mechanism terms in pathway names can be masked due to its frequent occurrence in pathway descriptions, while some non-informative terms can be falsely detected from long pathway descriptions. The penalization by pathway description length in 0−4) see . Some meCPI has three advantages compared to existing methods. Firstly, CPI explores consensual and differential pathway enrichment pattern simultaneously when combining multiple related studies. To our knowledge, CPI is the first method for this purpose. Secondly, CPI clusters pathways by gene composition similarity  to reduce pathway redundancy. Finally, CPI uses a statistically evaluated text mining method to annotate mechanisms of each pathway cluster automatically without subjective human interpretation. In addition, the proposed penalized text mining algorithm by permutation test was shown to outperform conventional Fisher’s exact test in text mining. We applied the tool to six transcriptomic datasets spanning on three psychiatric disorders  and two layers in DLPFC (L3 and L5). The result identified multiple pathway clusters with enrichment patterns consistent with previous findings, such as mitochondrial ATP dysfunction in schizophrenia DLPFC layers, as well as other new findings.The current CPI package has several limitations. Firstly, for single study pathway analysis, our tool currently only provides Fisher’s exact test and Kolmogorov–Smirnov test. Users, however, can externally apply advanced methods such as GSEA  or otherp-value matrix or pathway-based enrichment p-value matrix for each study to start with. If p-values of genes are entered, pathway enrichment analysis is applied first within each study. Enriched pathways are passed down to meta-analysis where AW-Fisher is applied to discover consensually and differentially enriched pathways. In the second step, significant pathways from AW-Fisher meta-analysis are clustered using consensus clustering, with consensus CDF plot and elbow plot to assist users to choose the number of clusters [In this article, we developed an integrative framework for combining and comparing pathway analyses from multiple transcriptomic studies, namely Comparative Pathway Integrator (CPI). CPI performs meta-analytic pathway analysis, reduces pathway redundancy to condense knowledge discovered from the results and conducts text mining to provide statistically solid inference on interpreting results. CPI has three major steps. In the first step, users can input either gene-based differential expression clusters . SilhoueIn summary, CPI is a meta-analytic tool for integrating multiple related transcriptomic studies in pathway enrichment analysis. As more and more transcriptomic datasets accumulate in the public domain, the need of such integrative analysis will become more and more prevalent. CPI can fill the gap and provide biological insight in such comparative and integrative tasks. In addition to transcriptomic studies, the framework is readily extensible to integrate pathway analysis of multiple related proteomic studies or multiple metabolomics studies.


P value of Kaplan‐Meier curve < 0.001. GATA6 (TF) and COL3A1 (DEIG) were selected  as significant TF‐immune gene links. Type II IFN response (P < 0.001) was the significant immune pathway. Propanoate metabolism (P < 0.001) was the significant KEGG pathway. We proposed that COL3A1 was positively regulated by GATA6, and by effecting type II IFN response and propanoate metabolism, COL3A1 involved in LGG recurrence.Some studies suggested the prognosis value of immune gene in lower grade glioma (LGG). Recurrence in LGG is a tough clinical problem for many LGG patients. Therefore, prognosis biomarker is required. Multivariate prognosis Cox model was constructed and then calculated the risk score. And differential expressed transcription factors (TFs) and differential expressed immune genes (DEIGs) were co‐analysed. Besides, significant immune cells/pathways were identified by single sample gene set enrichment analysis (ssGSEA). Moreover, gene set variation analysis (GSVA) and univariate Cox regression were applied to filter prognostic signalling pathways. Additionally, significant DEIG and immune cells/pathways, and significant DEIG and pathways were co‐analysed. Further, differential enriched pathways were identified by GSEA. In sum, a scientific hypothesis for recurrence LGG including TF, immune gene and immune cell/pathway was established. In our study, a total of 536 primary LGG samples, 2,498 immune genes and 318 TFs were acquired. Based on edgeR method, 2,164 DEGs, 2,498 DEIGs and 31 differentials expressed TFs were identified. A total of 106 DEIGs were integrated into multivariate prognostic model. Additionally, the AUC of the ROC curve was 0.860, and  According to classification of WHO, glioma can be divided into Ⅰ, Ⅱ, Ⅲ and Ⅳ grade, and grade Ⅰ and Ⅱ are included in LGG. And estimated by the American Cancer Society, it will be 23,890 new cases and 18,020 death in America.In our study, differential expressed immune genes (DEIGs) were identified, and univariate Cox regression was utilized to access the prognostic value. Additionally, significant prognosis DEIGs were integrated into multivariate prognosis Cox model and then calculated the risk score. Univariate and multivariate Cox regression were applied to validate risk score as the independent prognosis factor. Similarly, differential expressed transcription factors (TFs) were identified. Then, co‐analysis for differential expressed TFs and DEIGs to identify the significant TF‐DEIG relationship was performed. Based on CIBERSORT, immune cells/pathways were identified, and significant immune cells/pathways were identified by ssGSEA. And significant DEIG and immune cells/pathways to identify the significant immune cell/pathway were co‐analysed. Moreover, GSVA was utilized to identify the expression level in clinical LGG sample, and univariate Cox regression evaluated the prognosis value of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Further, differential expressed KEGG pathways were identified by GSEA. Additionally, significant DEIG and KEGG pathways to identify the significant KEGG pathway were co‐analysed. In sum, a scientific hypothesis for LGG recurrence based on TF, immune gene, immune cell/pathway and KEGG pathway was established.22.1https://tcga‐data.nci.nih.gov), gene expression profiles and clinical information of 536 primary LGG samples were acquired. Data of 2498 immune genes were available from ImmPort (http://www.immport.org/) and MsigDB (http://software.broadinstitute.org/gsea/msigdb/) database. Besides, data of 318 TFs were downloaded from Cistrome database (http://cistrome.org).Based on TCGA database  and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were utilized to explore clinical significance of DEGs. Similarly, immune genes were performed by edgeR method, when log2 FC >1.0 or <−1.0, and FDR P value < 0.05, DEIGs were defined. In addition, to access the prognostic value, DEIGs were screened by univariate Cox regression. Also, TFs were screened by edgeR method, differential expressed TFs were identified as log2 >1.0 or <−1.0, and FDR P value < 0.05.Gene expression level of clinical samples in TCGA database was performed by edgeR method, differential expressed genes (DEGs) based on whether recurrence or not were identified by log2 FC (Fold charge) and false discovery rate (FDR) 2.3significance immune genes were integrated into multivariate model. Additionally, the accuracy of the model was tested by receiver operator characteristic (ROC) curve. And for each LGG sample, risk score was calculated by this following formula: To predict the prognosis of LGG, differential expressed prognostic For each single sample, ‘n’ represented the number of DEIG in the multivariate model, ‘β’ represented regression coefficient of each DEIG, and DEIGn represented the expression level of the Nth DEIG in the corresponding sample. Then, median of risk score was selected to distinguish the high and low‐risk score, and samples were divided into the high‐ and low‐risk group, respectively. Besides, Kaplan‐Meier curve was utilized to predict the prognosis value of the multivariate model based on risk score.Further, the value of independently prognostic for risk score was evaluated by univariate and multivariate Cox regression analysis. And other demographic information like age, gender and race was applied for correction.2.4P value < 0.001, the corresponding regulatory relationships based on TFs and DEIGs were considered to be statistically significant. Besides, the most significant regulatory relationship was selected, and the most significant TF and DEIG were selected, respectively.To validate the clinical relationship with recurrence/ new tumour event, non‐parametric test for DEIGs was utilized. The Pearson correlation analysis was utilized to co‐analyse differential TFs and DEIGs. When correlation coefficient more than 0.300 and 2.5P value < 0.05, the interplay was regarded as significant. And the most significant immune cell or pathway was decided by the largest correlation coefficient.To identify the characteristic of immune cells and pathways in the downstream of DEIGs in clinical samples, single sample gene set enrichment analysis (ssGSEA) was utilized to filter significant immune cells and pathways. Besides, the Pearson analysis was used to identify the correlation between the most significant DEIG and immune cells/pathways. When P value < 0.05 was defined as significant.Aiming to filter prognostic KEGG pathway, gene set variation analysis (GSVA) was applied to access the expression level of KEGG pathways, and all KEGG pathways were in the downstream of the most significant DEIG. In addition, univariate Cox regression was utilized to calculate the prognostic value of KEGG pathways. And survival‐related KEGG pathways were identified. What's more, to co‐analyse the most significant DEIG and survival‐related KEGG pathways, the Pearson analysis was utilized, and 2.6Gene set enrichment analysis (GSEA) was utilized to identify significant KEGG pathway between primary and recurrence LGG samples in clinical data. According to the result of GSVA and GSEA, the most significant survival‐related and recurrent‐related KEGG pathway was selected.The regulatory network was constructed based on the interaction of the most significant TF, DEIG, top 9 immune cells/pathways and top three KEGG pathways.2.7https://pathcards.genecards.org/) was applied. Gene Expression Profiling Interactive Analysis (GEPIA),To validate our scientific hypothesis and decrease inherent deficient in silico, multidimensional validation was applied based on the following online databases. Immune gene set was utilized to find related genes of corresponding immune cells/pathways.2.8P value < 0.05 was defined as statistically significant. And R version 3.6.1 was utilized for analysis in silico  .For all analysis process, tow‐sided 33.1P value < 0.05 was defined as expressed significantly and differentially. Heatmap plot   ,) Figure  and mult) Figure  Cox regr3.3P value < 0.05 was set. Heatmap . What's more, Beeswam plots for new tumour event (P = 0.006)   Figure  and recu) Figure  showed t3.4R = 0.300, P < 0.001), Treg  and CCR . The detail correlation of COL3A1 and type II IFN response (P < 0.001)    Figure , Treg (P) Figure  and CCR ) Figure  based onR = 0.21, P < 0.001), N‐glycan biosynthesis , small cell lung cancer  and glycosaminoglycan degradation .To find overall survival‐related KEGG pathways, 58 significant KEGG pathways were filtered by GSVA based on 184 KEGG pathways. And co‐analysed COL3A1 and 184 KEGG pathways, heatmap plot Figure  showed t3.5P < 0.001)   , systemic lupus erythematosus  and proteasome .A total of 184 KEGG pathways were screened by GSEA, and 12 KEGG pathways were significant differential expressed between primary and recurrent LGG samples. And Venn plot Figure  showed t) Figure , systemi) Figure  and prot) Figure  were all) Figure , systemi) Figure  and prot) Figure  based onTherefore, the regulatory network Figure  based onUltimately, the scientific hypothesis Figure : COL3A1 3.6To decrease the bias of different platform and validate our scientific hypothesis, multivariate validation was utilized. Based on immune gene set and Pathway Card, GPR146, SELP and AHR represented type II IFN response, SELP, AHR, BCKDHA, BCKDHB, DLD, SUCLG1 and DBT represented propanoate metabolism, respectively. GEPIA Figure , OncominGATA6 and SELP low‐expressed and AHR, BCKDHA, BCKDHB, DLD, SUCLG1 and DBT high‐expressed in LGG.P = 0.007, Linkedomics, Figure P < 0.001, GEPIA, Figure P = 0.007, PROGgeneV2, Figure P < 0.001, Linkedomics, Figure P = 0.014, Linkedomics, Figure P = 0.011, PROGgeneV2, Figure P = 0.041, Linkedomics, Figure P = 0.013, CGGA, Figure P < 0.001, GEPIA, Figure P < 0.001, Linkedomics, Figure P = 0.032, cBioportal, Figure P = 0.008, CGGA, Figure P = 0.021, cBioportal, Figure P < 0.001, cBioportal, Figure P = 0.021, CGGA, Figure P = 0.021, CGGA, Figure P = 0.012, CGGA, Figure GATA6 , P < 0.001) and multivariate , P < 0.001) Cox regression model showed risk score from multivariate prognostic model was an independent prognostic factor. Further, according to 14 clinical‐related DEIGs and significant TF‐immune gene links, GATA6 (TF) and COL3A1 (DEIG) were selected . Moreover, 4 immune cells and 5 immune pathways were selected by ssGSEA. And based on co‐analysed COL3A1 and 29 types of immune cells or pathways, type II IFN response  was significant. What’ s more, based on the comprehensive consideration of GSEA, GSVA and co‐analysis result of COL3A1 and 184 KEGG pathways, propanoate metabolism  was selected. Eventually, COL3A1 was positively regulated by GATA6, and type II IFN response and propanoate metabolism were the downstream of COL3A1 in recurrent LGG.In our study based on comprehensive bioinformation, a total of 536 LGG samples, 2,498 immune genes and 318 TFs were acquired. Based on edgeR method, 2,164 DEGs, 2,498 DEIGs and 31 differential expressed TFs were identified. A total of 106 DEIGs from univariate Cox regression were integrated into multivariate prognostic model. Additionally, the AUC of the ROC curve was 0.860, and Kaplan‐Meier curve GATA6 is a TF named GATA‐binding protein 6, which belongs to the GATA family, and it expressed in neuron, astrocyte, choroid plexus epithelial cells and endothelial cells.P < 0.001; normal vs higher grade glioma (HGG), P = 0.002; LGG vs HGG, P = 0.005).COL3A1 is an immune gene named collagen type III alpha 1 chain, and it codes the pro‐alpha 1 chains of type III collagen in the vascular system and other tissue.However, there was no study reported the direct interaction between GATA6 and COL3A1. Based on bioinformation analysis and other studies,Type II IFN response is a kind of defence mechanism, and the main molecule was IFN‐γ. However, IFN‐γ can positively and negatively regulate immune system; thus, it is an antitumour cytokine and also a promoter for tumour genesis.IFN‐γ can inhibit tumour angiogenesis to inhibit tumour genesis, and ICAM1 low‐expressed when knockout IFN‐γ.https://pathcards.genecards.org/ ), the main genes in propanoate metabolism are BCKDHA, BCKDHB, DLD, SUCLG1 and DBT. And the propanoate metabolism can be a carbon source for fatty acid biosynthesis and promote cell multiplication in pancreatic carcinoma.Propanoate metabolism is downstream of the lipid metabolism, and propanoate will convert into succinyl‐coenzyme A; then, the latter will enter the tricarboxylic acid cycle to supply energy or enhance lipogenesis. Based on Pathway Card (In tumour genesis, propanoate metabolism plays a role in lipogenesis, and by down‐regulating propanoate metabolism and lipogenesis, leading to inhibition of angiogenesis in vitro/in vivo.All in all, in our hypothesis, GATA6 was the most significant TF and COL3A1 was the most significant DEIG. And COL3A1 was positively regulated by GATA6, and have an influence on recurrence of LGG through two potential downstream pathways: type II IFN response and propanoate metabolism. However, the inherent vice of analysis in silico was bias base on different platform, and our scientific hypothesis only based on bioinformation instead of mechanism exploring.Therefore, multidimensional validation was applied to validate our hypothesis in different databases. Additionally, a serial experiment according to some studies to validate our hypothesis will be launched.Based on analysis in silico and multidimensional validation, we proposed that COL3A1 was positively regulated by GATA6, and by affecting type II IFN response and propanoate metabolism, COL3A1 involved in recurrence LGG. And the prognosis value of GATA6, COL3A1, type II IFN response and propanoate metabolism might play a role in recurrence mechanism.The authors declare that there is no potential conflict of interest and no commercial or financial relationships in this study.Conception/design: Runzhi Huang, Zhenyu Li, Xiaolong Zhu, Penghui Yan, Dianwen Song, Huabin Yin, Peng Hu, Ruoyi Lin, Shengyu Wu, Tong Meng, Jie Zhang, Zongqiang Huang. Collection and/or assembly of data: Runzhi Huang, Zhenyu Li, Xiaolong Zhu, Penghui Yan, Dianwen Song, Huabin Yin, Peng Hu, Ruoyi Lin, Shengyu Wu. Data analysis and interpretation: Runzhi Huang, Zhenyu Li, Xiaolong Zhu, Penghui Yan, Dianwen Song, Huabin Yin, Peng Hu, Ruoyi Lin, Shengyu Wu, Tong Meng, Jie Zhang, Zongqiang Huang. Manuscript writing: Runzhi Huang, Zhenyu Li, Xiaolong Zhu, Penghui YanTong Meng, Jie Zhang, Zongqiang Huang. Final approval of manuscript: Runzhi Huang, Zhenyu Li, Xiaolong Zhu, Penghui Yan, Dianwen Song, Huabin Yin, Peng Hu, Ruoyi Lin, Shengyu Wu, Tong Meng, Jie Zhang, Zongqiang Huang.The study was approved by the Ethics Committee of First Affiliated Hospital of Zhengzhou University. The authors declare that there is no conflict of interests.Fig S1Click here for additional data file.Fig S2Click here for additional data file.Fig S3Click here for additional data file.Fig S4Click here for additional data file.Fig S5Click here for additional data file.Fig S6Click here for additional data file.Fig S7Click here for additional data file.Fig S8Click here for additional data file.Fig S9Click here for additional data file.Fig S10Click here for additional data file.Fig S11Click here for additional data file.Fig S12Click here for additional data file.Fig S13Click here for additional data file.Fig S14Click here for additional data file.Fig S15Click here for additional data file.Table S1‐S3Click here for additional data file.