To explore the active components and mechanism of Jiaotai Pill (JTP) in the treatment of primary insomnia (PI) based on gene expression omnibus.The main active components of Jiaotai Pills were obtained by TCMSP and literature mining, and the targets of the active components of Jiaotai Pills were predicted. The targets were verified and standardized by Uniprot database. PI-related targets were obtained from GeneCards, OMIM, DrugBank, PharmGKB, and TTD databases. Obtaining an intersection action target point of the Jiaotai pill and the PI by using a Venny diagram; Gene chip data (GSE208668) was downloaded from gene expression omnibus database, and then gene probe enrichment analysis (GSEA) was used to screen the differentially expressed genes between PI patients and normal controls, and molecular docking was used to virtually verify the screened differentially expressed genes with potential active compounds.21 active components and 263 potential targets of Jiaotai Pill were screened by database analysis and literature mining, 112 of which were intersected with PI. Molecular docking results showed that quercetin, EGCG, kaempferol, R-kanatin, stigmasterol, berberine and other core active components had good docking activity with related differential genes.Jiaotai Pill can regulate the release of inflammatory factors through multiple active ingredients, multiple disease targets, multiple biological pathways and multiple pathways to achieve the purpose of treating PI, which provides a theoretical basis for the clinical treatment of PI and broadens the clinical use of Jiaotai Pill. In a nationwide health plan survey of more than 10,000 members in the United States, about 30 to 40% of adults experienced insomnia symptoms, of which the prevalence of short-term insomnia was about 9.5%, and one-fifth of patients with short-term insomnia could turn into chronic insomnia. At the same time, the Canadian Association used the same method to study and found that the incidence of insomnia is increasing year by year. According to the analysis of national health survey data, the number of adults with insomnia symptoms increased from 13.4% in 2002 to 23.8% in 2015. At present, an epidemiological survey on PI in China shows that about 10% to 15% of the natural population suffer from PI. At the same time, insomnia is associated with a variety of diseases, including cardiovascular and cerebrovascular diseases, digestive diseases, cancer, hypertension and diabetes. Li L et al. Conducted a study on the relationship between insomnia and mental illness, in which about 40% of insomnia patients had mental illness, insomnia patients had a high risk of depression and anxiety disorders in a year, and insomnia increased the risk of suicide in depression patients.,6 At present, the incidence of PI has increased significantly with the COVID-19 pandemic, while the risk of anxiety, depression, and suicide has increased significantly. For the treatment of insomnia, Western medicine recommended sedative hypnotics as the main treatment, the clinical effect is obvious, but there are dependence, addiction, liver, and kidney damage and other adverse reactions. Therefore, there are more and more traditional Chinese medicine treatments for PI. Traditional Chinese medicine treatment has exact effect, less clinical adverse reactions, no addiction, and dependence. At the same time, in the long-term medical practice, doctors of past Dynasties have provided rich clinical experience for traditional Chinese medicine treatment of PI.Primary insomnia (PI) is defined as the most common sleep problem in today’s society, which excludes insomnia caused by other diseases, and is a subjective experience that patients are not satisfied with the time and quality of daily sleep, and at the same time, it has an impact on daytime work and social interaction.,10 There are many reports on the mechanism of Jiaotai Pill in the treatment of PI, and there is no clear clinical trial to verify it.The prescription and name of Jiaotai Pill are clearly recorded in Wang Shixiong, a famous doctor in the Qing Dynasty, in the chapter of “Brief Prescriptions of Four Departments, Anshen.” “Jiaotai Pill” is used to treat insomnia due to disharmony between the heart and the kidney, which is the main prescription for the treatment of insomnia due to disharmony between the heart and the kidney, and is modified on the basis of it in clinical treatment. So far, the study of Jiaotai Pill in the treatment of PI is mainly focused on experimental animals or clinical pharmacological observation. Molecular docking technology can obtain the affinity of small molecules and receptor proteins by computer simulation, and further realize the screening and design of drugs, which provides a reference for the clinical application of Jiaotai Pill and the subsequent research of related traditional Chinese medicine.Network pharmacology is an emerging discipline integrating systems biology, medicinal chemistry, pharmacology, and other disciplines, which can reflect the complex interaction network between “drug-gene-target-disease,” and is widely used in the interpretation of drug pairs and prescription compatibility rules.Coptis chinensis and Cinnamomum cassia in Jiaotai pills were searched by TCMSP (https://old.tcmsp-e.com/tcmsp.php) database and NCBI (https://www.ncbi.nlm.nih.gov/) database, and the related literatures were also searched. The main active ingredient selected from all active ingredients according to relevant biological function criteria.The related active components of https://pubchem.ncbi.nlm.nih.gov/) platform were used to confirm the structure of the active ingredients retrieved, and they were saved as mol2 format files.Oral bioavailability (OB) and drug likeness (DL) are one of the main parameters of drug metabolism. Substances with OB ≥30% and DL index ≥0.18 are highly druggable (33). Therefore, in the TCMSP database, the chemical components with the screening conditions of OB = 30% and DL = 0.18 are selected as the candidate active components to obtain the active components of Jiaotai pills. At the same time, TCMSP and PubChem (https://www.uniprot.org/) was selected. The database selection was limited to “Reviewed (Swiss-Prot),” and the species was “Human,” and the target was queried to obtain the gene target of the active ingredient of Jiaotai Pill. The PI disease target is screened through 5 databases including Gene cards, OMIM, DrugBank, PharmGKB, and TTD, wherein the higher the relevance score value in the Gene cards database is, the closer the relationship between the target and the disease is, The target of relevance score ≥5 was selected as the target of PI disease (36), and the reported genes related to PI were searched by inputting the keywords “Primary insomnia” or “PI,” and the duplicate and false positive genes were screened out and verified by literature search. O as to obtain a potential sedative and hypnotic mechanism target of the Jiaotai pill. The online software Venny 2. 1 mapping platform was used to draw the Venny map of PI potential target screening and Jiaotai pill active ingredient-PI potential target respectively.The gene target information of the screened Jiaotai Pill core protein targets was mined, and the UniProt KB search function in the UniProt database (33). Homo sapiens was defined as the species by using the Multiple proteins tool, and the protein interaction was obtained. In the process of obtaining, the minimum interaction threshold of protein target was set as “highest confidence” (>0.9), and the free protein was hidden and saved in TSV format. The node1 and node2 information in the file were imported into Cytoscape software to draw the protein interaction network, and the network was topologically analyzed, and the core target data was visualized to construct the PPI network diagram.The target of Jiaotai Pill was introduced into the database of functional protein association networkshttps://david.ncifcrf.gov/) was used for GO analysis and Kyoto Encyclopedia of Genes and Genomes  enrichment analysis of the core targets. Three modules, biological process (BP), cell component (CC), and molecular function (MF), were selected for GO enrichment analysis, and KEGG was used for pathway analysis. O as to obtain a pathway which is related to the pathogenic mechanism of the PI and enriched by most of key genes, meanwhile, the statistical significance threshold is set to be P < .05, the data result are subjected to visualization processing through a microbioinformation platform (http://www.bioinformatics.com.cn/), and a core target and pathway diagram is drawn, to analyze the approach of Jiaotai Pill in the treatment of PI.The DAVID database (http://www. ncbi.nlm. nih. gov/geo) of the NCBI) searches for “Primary insomnia” samples. Inclusion criteria: samples should include PI samples and normal samples. The sample was of human tissue origin (apiens), and the chip data type was gene expression profile.The National Center for Biotechnology Information, The gene expression omnibus (GEO) database . The 3D structure of the core active compound of Jiaotai Pill was downloaded from Pubchem database (https://pubchem.ncbi.nlm.nih.gov/), and AutoDockTools 1.5.7 software was used to remove water molecules and ligands from the above file, and at the same time, hydrogenation and electron addition were carried out. The molecular docking of the core active ingredients with the potential core targets is carried out in turn.The core active components screened from the network of “active components-T2DM targets” of Jiaotai Pill were selected for molecular docking with the differentially expressed intersection targets. The 3-dimensional structures of the potential targets and the screened targets to be verified are searched and obtained through the RCSB PDB database ” and “Human” were defined, and 263 gene targets were obtained. PI potential targets were predicted in GeneCards, OMIM, DrugBank, PharmGKB, TTD, and other databases, and 873 potential targets were obtained by sorting and removing duplicates. At the same time, the potential targets of screened PI and the active ingredient targets of Jiaotai Pill were mapped with the potential targets of PI by Venny 2.1 online drawing tool, and Venn Figure The 21 active ingredients and 112 predicted targets of Jiaotai Pill were imported into Cytoscape _ v3.9.1 software to construct a “drug-active ingredient-PI-target” interaction network diagram . The top 20 key targets ranked by MCC value were related to 247 BPs. Among them, RNA polymerase II promoter is closely related to the intuitive regulation of transcription, the positive regulation of gene expression, the positive regulation of DNA template transcription and the response to inflammation. It is closely related to 17 cellular locations (CC), including nucleus, nucleoplasm, chromatin, extracellular region, and transcription factor complex. It is related to 45 MF, among which the same protein binding, transcription factor binding, and enzyme binding are closely related. The GO functional enrichment analysis showed that Jiaotai Pill may play a role in the treatment of PI by affecting the 3 processes of BP, CC, and MF  is the most abundant alkaloid, followed by R-kanatin, palmatine, coptisine and epiberberine, and jatrorrhizine Studies have shown that TNF, a mediator of TNF signaling pathway, has a role in sleep regulation and brain development, while Toll-like receptor signaling pathway is closely related to PI. Animal experiments have shown that Toll-like receptors activate their downstream pathways and signal to activate the nuclear factor [NF]-κB signaling pathway in sleep deprivation models, and that TNF-α/NF-κB signaling pathway activation is enhanced in sleep deprived rats, both of which are responsible for neuroinflammation and are involved in the pathogenesis of sleep. At the same time, the inhibition of IL-17/TNF-α/Toll-like receptor signaling pathway can also inhibit the production of inflammatory factors, suggesting that the inhibition of IL-17/TNF-α/Toll-like receptor signaling pathway can stabilize the sleep-wake mechanism and reduce the release of inflammatory factors. Inhibiting the hyperactivation of hypothalamus-pituitary-adrenal axis may be the mechanism of Jiaotai Pill in the treatment of PI.The results of this study show that Jiaotai Pill may treat PI through 112 potential targets such as MAPK3, RELA, MAPK1, IL-6, and HIF1A. PPI network analysis showed that the top 20 targets with MCC value in the PPI network formed by 112 potential targets were considered as the core targets of Jiaotai Pill in the treatment of PI. The GO function enrichment analysis of the screened core targets showed that they were involved in multiple BPs, cell composition, and MFs, such as the intuitive regulation of RNA polymerase II promoter on transcription, the positive regulation of gene expression, the positive regulation of DNA template transcription, inflammatory response, nucleus, chromatin, extracellular region, same protein binding, enzyme binding, and so on. At the same time, the KEGG pathway enrichment analysis of core targets was carried out, and the potential core targets were enriched in 139 pathways, and the most enriched targets were IL-17 signaling pathway. IL-17 signaling pathway produces IL-17, a highly versatile proinflammatory cytokine that is critical for host defense, the pathogenesis of inflammatory diseases, and the progression of cancer. Enhanced activation of the IL-17 signaling pathway after sleep deprivation promotes the production of proinflammatory cytokines, leading to a series of inflammatory responses in the body.“Drug-active ingredient-PI-target” network results showed that 21 effective active ingredients were screened in Jiaotai Pill, which mainly used quercetin, EGCG, kaempferol, R-kanatin, stigmasterol, berberine, and other core effective active ingredients to combine with 20 potential core targets to treat PI. The potential core targets screened in the PPI network are intersected with the targets obtained in the GEO database, and are subjected to molecular docking verification with the core active component rows, and the result shows that all active components can be spontaneously combined with the intersected targets, The binding energies of quercetin, R-kanatin, kaempferol with HIF1A, EGCG with HSP90AA1, stigmasterol with IL-10, and berberine with HSP90AA1 and CREB1 were low, and there were multiple hydrogen bonds.To sum up, Jiaotai Pill may act on 20 core targets such as IL-6, HIF1A and IL1B through 21 effective active ingredients such as quercetin, EGCG, and kaempferol in Coptis chinensis and Cinnamomum cassia, and treat PI by affecting a variety of BPs, cell composition, and MFs in conjunction with corresponding signaling pathways. Its specific mechanism of action is to combine active ingredients such as quercetin, EGCG, kaempferol, R-kanatin, stigmasterol, and berberine with core targets such as MAPK3, IL-6, HIF1A, and TNF, inhibit IL-17/TNF-α/Toll-like receptor signaling pathway and enhance the body’s sedation threshold. Inhibit the activation of the hypothalamic-pituitary-adrenal axis. In this study, network pharmacology was used as a research method to systematically analyze and summarize the pharmacological effects of the active ingredients of Jiaotai Pills on regulating sleep-wake mechanism through their corresponding target proteins, and the GEO database was used to screen the differentially expressed genes between PI patients and ordinary people, and to intersect with the drug target genes. The rationality of this study was verified by molecular docking technology. This study is of great value to the clinical application of Jiaotai Pill.Conceptualization: Limin Pan.Data curation: Limin Pan, Yaolei Wang, Ruiqian Guan, Qingchun Shi.Formal analysis: Limin Pan.Funding acquisition: Limin Pan, Yaolei Wang, Qingchun Shi.Investigation: Limin Pan, Qingchun Shi.Methodology: Limin Pan.Project administration: Limin Pan.Resources: Limin Pan, Qingchun Shi.Software: Limin Pan, Qingchun Shi.Supervision: Limin Pan, Qingchun Shi.Validation: Limin Pan, Qingchun Shi.Visualization: Limin Pan.Writing—original draft: Limin Pan, Yaolei Wang, Ruiqian Guan, Qingchun Shi.Writing—review and editing: Limin Pan, Qingchun Shi.


Moderate to severe fatigue occurs in up to 94% of patients with cancer. Recent evidence suggests that morning and evening fatigue are distinct dimensions of physical fatigue. The purposes of this study were to evaluate the transcriptome for common and distinct perturbed inflammatory pathways in patients receiving chemotherapy who reported low versus high levels of morning or low versus high levels of evening cancer‐related fatigue.n = 357) and microarray (n = 360). Patterns of interactions between and among these perturbed pathways were evaluated using a knowledge network (KN).Patients completed questionnaires during the week prior to their chemotherapy treatment. Severity of morning and evening fatigue was evaluated using the Lee Fatigue Scale. Gene expression and pathway impact analyses (PIA) were performed in two independent samples using RNA‐sequencing  were common to both morning and evening fatigue, six were distinct for morning fatigue, and four were distinct for evening fatigue. KN  identified the phosphatidylinositol 3‐kinase (PI3K)‐Akt pathway node (perturbed in evening fatigue) with the highest betweenness (0.255) and closeness (0.255) centrality indices. The next highest betweenness centrality indices were seen in pathways perturbed in evening fatigue .This study describes perturbations in common and distinct inflammatory pathways associated with morning and/or evening fatigue. PI3K‐Akt was identified as a bottleneck pathway. The analysis identified potential targets for therapeutic interventions for this common and devastating clinical problem. This study describes perturbations in common and distinct inflammatory pathways associated with morning and/or evening fatigue associated with clinically meaningful levels of morning and evening fatigue severity in oncology patients receiving chemotherapy. A knowledge network represents the pathway‐to‐pathway interconnections of common and distinct perturbed inflammatory pathways. The distinct pathways for morning (blue nodes) and evening (red nodes) fatigue group together within the knowledge network. The remaining interconnections between the two symptoms are common perturbed pathways (green nodes). PI3K‐Akt was identified as a bottleneck pathway. The analysis identified potential targets for therapeutic interventions for this common and devastating clinical problem. Of the 2234 patients approached, 1343 consented to participate. A major reason for refusal was being overwhelmed with their cancer treatment. Eligible patients were approached in the infusion unit during their first or second cycle of chemotherapy by a member of the research team to discuss participation and obtain written informed consent. Blood was collected at the enrollment assessment.2.32.3.1Patients completed a demographic questionnaire, Karnofsky Performance Status (KPS) scale,2.3.2The 18‐item LFS was used to assess physical fatigue and energy.2.42.4.1Of the 717 patients who provided a blood sample, 357 were processed using RNA‐sequencing  and 360 were processed using microarrays .2.4.2k‐nearest‐neighbors method, with k = 9. For continuous variables, the Euclidean distance was used to find the nearest neighbors. The imputed value was the weighted average of the nearest neighbors, with each weight originally exp, after which the weights were scaled to one. For categorical variables, the distance was 0 if the predictor and the neighbor had the same value and 1 if they did not and the imputed value was the mode of the nearest neighbors.Missing data for demographic and clinical characteristics were imputed by the 2.4.3https://www.R‐project.org/). Patients were classified into low (<3.2) and high (≥3.2) morning fatigue and low (<5.6) and high (≥5.6) evening fatigue groups using clinically meaningful cutoff scores.p‐value of <0.05) were entered into a logistic regression analysis to adjust for covariates in the gene expression analyses. For the final model, variables were selected using a backward stepwise logistic regression approach based on the likelihood ratio test. The area under the curve of the receiver operating characteristic curve was used to evaluate the overall adequacy of the regression models.Data from the two samples were analyzed separately using R  formation. This pathway is involved in the creation and release of extracellular lattices of decondensed chromatin and granule proteins that contain circulating cell‐free DNA from neutrophils .4.2Of the six distinct pathways for morning fatigue, neither C‐type lectin receptor signaling nor necroptosis was identified in previous gene expression studies of CRF. Interestingly, both pathways are implicated in the activation of the NF‐κb signaling pathway and associated inflammatory responses.The necroptosis pathway models a programmed form of cell death induced by the binding of a specific set of death receptors .The four remaining pathways that are distinct for morning fatigue . The betweenness centrality index identifies “bottleneck” nodes that form bridges so that two communities  can communicate with each other and can play a key role in the modularization of a network.Given that the PI3K/AKT pathway plays a significant role in oncogenesis,Platelet activation is involved in hemostasis, thrombosis, and intercellular communication that mediate inflammatory and immunomodulatory mechanisms.4.4While this study had a relatively large sample size, had well‐phenotyped patients, included rigorous quality controls, set strict criteria for differential expression and pathway perturbation selection, and provided results from independent tests across two samples, some limitations warrant consideration. First, given that this evaluation is the first to report on associations between morning fatigue and gene expression changes, these findings warrant confirmation. Second, because morning and evening fatigue were assessed during chemotherapy, future studies need to examine associations between CRF from other types of cancers and cancer treatments and various pathway perturbations. Longitudinal studies are needed that assess for associations between changes in CRF and changes in gene expression and pathway perturbations. Given the heterogeneity of CRF instruments,5This study is the first to describe perturbations in common and distinct inflammatory pathways associated with morning and/or evening fatigue and to use a knowledge network approach to identify pathway‐to‐pathway interactions. Our findings suggest the PI3K‐Akt signaling pathway is a “bottleneck” pathway. Given that some of the signaling pathways are cell‐type specific, future research should evaluate within and across specific immune cells for differences in expression and epigenetic regulatory processes. Finally, given the strengths of our findings and the evidence that suggests that synergistic interactions occur between inflammatory and neuroendocrine processes,Kord M. Kober: Conceptualization (lead); data curation ; formal analysis ; funding acquisition (lead); investigation (lead); methodology (lead); project administration (lead); resources ; software ; visualization ; writing – original draft (lead); writing – review and editing (lead). Carolyn Harris: Conceptualization (supporting); formal analysis (supporting); methodology (supporting); writing – review and editing . Yvette P Conley: Conceptualization (supporting); methodology (supporting); writing – review and editing . Anand Dhruva: Methodology (supporting); writing – review and editing . Vasuda Dokiparthi: Formal analysis ; writing – review and editing . Marilyn J Hammer: Methodology (supporting); writing – review and editing . JD Levine: Conceptualization (supporting); methodology (supporting); writing – review and editing . Kate Oppegaard: Writing – review and editing . Steven M Paul: Formal analysis (supporting); writing – review and editing . Joosun Shin: Writing – review and editing . Anatol Sucher: Investigation (supporting); methodology (supporting); writing – review and editing . Fay Wright: Conceptualization (supporting); writing – review and editing . Brian Yuen: Investigation (supporting); writing – review and editing . Adam Olshen: Conceptualization (supporting); formal analysis (supporting); methodology ; writing – review and editing . Christine Miaskowski: Conceptualization ; funding acquisition ; methodology ; resources ; writing – original draft ; writing – review and editing .This study was funded by grants from the American Cancer Society and the National Institutes of Health/National Cancer Institute . Dr. Miaskowski is an American Cancer Society Clinical Research Professor.The authors declare that they have no conflict of interest.Figure S1.Click here for additional data file.Figure S2.Click here for additional data file.Table S1.Click here for additional data file.Table S2.Click here for additional data file.Table S3.Click here for additional data file.Table S4.Click here for additional data file.


Pulmonary fibrosis (PF) is a chronic and potentially fatal lung disease and disorder. Although the active ingredients of ginseng honeysuckle superfine powdered tea (GHSPT) have been proven to have anti-inflammatory and antioxidant effects, the mechanism of GHSPT on PF remains unclear. The present study was to explore the underlying mechanism of GHSPT in treating PF based on proteomics and network pharmacology analysis and to confirm it in vivo.We used intratracheal instillation of bleomycin to induce the PF mouse model and GHSPT (640 mg/kg) intragastrically administrated to PF mice for 21 days. The lung tissues were harvested for TMT-based proteomics. The UPLC-Q-Exactive MS/MS analyze the serum migrant compounds of GHSPT in the PF mice. Moreover, components of GHSPT were harvested from the pharmacology database of the TCMSP system. PF-related targets were retrieved using NCBI and GeneCards databases.Our results showed that GHSPT significantly alleviated PF mice. Proteomics analysis showed that 525 proteins had significantly changed in the lung of untreated PF mice. Among them, 19 differential proteins were back-regulated to normal levels after GHSPT therapy. Moreover, 25 compounds originating from GHSPT were identified in the serum sample. Network analysis showed 159 active ingredients and 92 drug targets against PF. The signaling pathways include apoptosis, ferroptosis, cytokine-cytokine receptor, P53, and PI3K-Akt signaling pathway.The evidence suggests that GHSPT might play an effective role in the treatment of PF by multi-target interventions against multiple signaling pathways.The online version contains supplementary material available at 10.1186/s13020-023-00769-x. Pulmonary fibrosis (PF) is a diffuse inflammatory disease that is the inevitable progression of many lung diseases at the end stage, resulting in respiratory failure and death with high morbidity and mortality . PF is cIn order to develop the cure for PF, scientists from various countries have carried out a lot of exploration. Until now, there are very few drugs that can delay the onset of pulmonary fibrosis internationally, with modest benefits and considerable side effects. There is still a lack of effective treatment for PF. Therefore, the development of new drugs for the treatment of pulmonary fibrosis has important practical significance to improve the quality of life and prolong the survival time of patients.Panax ginseng C.A. Mey, Lonicera japonica Thunb, Wurfbainia villosa var. villosa, Citrus × aurantium f. deliciosa, Poria cocos, Glycyrrhiza uralensis Fisch. ex DC, Gardenia jasminoides J. Ellis, and Camellia sinensis L. Kuntze. Many researchers have reported that ginseng, honeysuckle, and licorice all play an important role against PF -88027-20220607, which is derived from the network address The six serum samples of in each group (GH and PF) were selected to extract metabolites. 100 µL serum sample was thoroughly mixed with 400 µL of cold methanol acetonitrile  via vortexing. The samples were obtained by ultrasonic 1 h in the ice bath and placed at −20 ℃ for 1 h and centrifuged at 16,000 g and 4 °C for 20 min. The supernatants were then harvested and dried under vacuum UPLC-MS analysis.® 5600 + MS system which was equipped with a Waters UPLC BEH Amide column ( 1.7 μm 2.1 × 100 mm). The injection volume is 5ul, the column temperature is 40 ℃, the flow rate is 300ul/min, the chromatographic mobile phase A: water + 20mM ammonium acetate, B: acetonitrile, the chromatographic gradient elution procedure is as follows: 0–0.1 min, 2% A; 0.1–12 min, A changes linearly from 2 to 25%; 12–13.5 min, A changes linearly from 25 to 60%; 13.5–15 min, A holds at 60%; 15–15.1 min, A changes linearly from 60 to 2%; 15.1–18 min, A holds at 2%. The experiments of ESI-MS were conducted in positive and negative ion modes, respectively. Mass spectrometry parameters were set as follows: spray voltage: 3.8 kv (positive) and 3.2 kv (negative). Capillary temperature 320 ℃; sheath gas (nitrogen) flow: 30 arbitrary units; Aux Gas flow: 5 arb; Probe Heater Temp: 350 ℃; S-Lens RF Level 50. Full mass scan (m/z 60–1200) and MS/MS spectra were recorded at a resolution of 70,000 at m/z 200, and 17,500 at m/z 200.Chromatographic separation was completed on the Water UPLC I-class-TripleTOFhttp://tcmspw.com/tcmsp.php). The potential active ingredients in GHPST were screened according to the pharmacokinetic parameters of oral bioavailability (OB) ≥ 30% and drug likeness (DL) ≥ 0.18 screening conditions.GHSPT has a total of 8 botanical drug components, and the potential active ingredients and serum migrant compounds were searched in the TCMSP  and NCBI (http://www.ncbi.nlm.nih.gov). All collected targets were mixed together, and repeated targets were removed to find the unique project to establish a PF-related target set. With R software (version 4.0.3) and Venn Diagram package, we mapped the potential gene targets (PF), and related targets , and found the potential targets of GHPST in treating PF.The targets related to the PF pathogenesis were acquired by retrieving GeneCards . Among them, “node” represents active ingredients and related targets, and “edge” represents the relationship between them. Network topology analysis was carried out for the obtained action network, and the degree value of the node was used to reflect the importance of the “node”. The degree value was positively correlated with the importance of “node”.The global protein changes data in the lung were analyzed with the Perseus software, Microsoft Excel, and R statistical computing software. Proteins whose abundance was down-regulated more than 0.83-fold, and up-regulated less than 1.20-fold were ignored to increase statistical sensitivity. The potential targets of GHSPT and the differently expressed proteins of lung tissues were analyzed. To annotate the sequences, information was extracted from the Swiss-Prot, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). GO and KEGG enrichment analyses were carried out with Fisher’s exact test, and FDR correction for multiple testing was also performed. GO terms were grouped into three categories: cell component (CC), biological process (BP), and molecular function (MF). Construction of protein-protein interaction (PPI) networks was also conducted by using the STRING database with the cytoscape software.The lung samples were homogenized in ice-cold lysis buffer containing PMSF . An equal amount of protein was separated by SDS-PAGE (10%) and transferred onto polyvinylidene difluoride membranes. The membrane was sealed with PBST-5% skimmed milk or PBST-5% BSA, and then incubated overnight with the antibody at 4 °C as follows: E-cadherin (1:5000), α-SMA (1:1000), PIKfyve (1:500), AFF4 (1:2000), Amotl2 (1:1000), and GLP1R (1:1000), HSP90AA1 (1:2000), JUN (1:2000), MMP1 (1:1000), TGF-β1 (1:1000), P53 (1:1000), Akt (1:1000), pho-Akt , STAT3 (1:2000), pho-STAT3 . Secondary antibody  was applied for 1 h at room temperature. The intensity of immunoblot bands was normalized to that of β-actin (1:2000). Densitometry was obtained for quantification of each identified protein band and analyzed with Image J densitometry software.P-value < 0.05 was considered statistically significant. All analyses were performed with SPSS for Windows software version 22.0 .Data were expressed as mean ± standard deviation. Statistical analysis between groups was made using one-way analysis of variance (ANOVA) and Student’s t-test for comparisons. P < 0.01). GHSPT treatment group significantly increased body weight and survival rate at 21 days . GHSPT treatment group significantly decreased the ashcroft score and mass intensity  Kuntze  Kuntze in GHSPT. The GHSPT-component- serum migrant compounds network was developed , heat shock protein 90 alpha family class A member 1 (HSP90AA1), peroxisome proliferator-activated receptor gamma, proto-oncogene serine/threonine-protein kinase Pim-1, trypsin-1, mitogen-activated protein kinase 14, prothrombin, acetylcholinesterase, phosphatidylinositol-4, 5-bisphosphate 3-kinase catalytic subunit gamma isoform. PTGS2 and HSP90AA1 can interact with up to 113 and 80 related active ingredients  between CC group and PF group, majority of obtained proteins was involved in cellular process, metabolic process, and related to intracellular anatomical structure, cytoplasm, and included protein binding, catalytic activity Fig. A. For thGO enrichment analyzed 92 drug targets of potential active ingredients and 33 drug targets of serum migrant compounds GHSPT in treating PF in BP, CC and MF Fig. E and F. KEGG signaling pathways analyzed 525 DEPs in PF groups, and 19 proteins treated with GHSPT  and plays a key role in pulmonary fibrosis . InflammThe enriched signaling pathways mainly include PI3K-Akt, P53, Wnt, NOD-like receptor, and VEGF signaling pathways in the proteomics and network pharmacology. Moreover, Akt, JUN, P53 and STAT3 are molecular hubs connecting many signaling pathways in cells including inflammatory response and biosynthesis of cytokines. Inhibition of PI3K-Akt, JUN, P53 and STAT3 can suppress inflammatory protein production and pulmonary fibrosis –37. ThisThe treatment of pulmonary fibrosis by GHPST may be achieved by inhibiting inflammatory response, enhancing antioxidant effects, and decreasing the degree of EMT by multi-targets intervention and multiple signaling pathways. This study provides important molecular mechanisms and theoretical support of GHSPT in the treatment of pulmonary fibrosis. Further studies are needed to investigate the detailed molecular mechanisms at the cellular level. Therefore, these components of GHSPT can be used as dietary supplements or nutraceuticals in the treatment of fibrosis-related diseases including pulmonary fibrosis.Additional file 1. Protein identification table.Additional file 2. Peptide identification table.Additional file 3. Pos-neg table.Additional file 4: Table S1. Identified significant differenced expression proteinsof the lung in the pulmonary fibrosis mice. TableS2. The potential activecomponents and ADME parameters of Ginseng honeysucklesuperfinepowdered tea. TableS3. The potential activeingredients and related targets of Ginseng honeysuckle superfinepowderedtea against pulmonary fibrosis. TableS4. The possible targets of Ginseng honeysuckle superfine powdered tea against pulmonaryfibrosis. TableS5. Serum migrant compounds and related targets of Ginseng honeysuckle superfinepowdered tea against pulmonaryfibrosis. Table S6. Pathway enrichment analysis of differential expressionproteins of the lungin the pulmonary fibrosis mice. Table S7. Pathway enrichment analysis of differential expressionproteins of the lung in the pulmonary fibrosis mice as reversed by Ginsenghoneysuckle superfine powdered tea. Table S8. Pathway enrichment analysis ofGinseng honeysuckle superfine powdered tea and pulmonaryfibrosis co-targeted genes. Figure S1. PCA plots for the proteomics of mice lung tissues. Figure S2. Metabolomic investigation of mice blood samples. .


Ellagic acid (EA) is a natural polyphenolic compound. Recent studies have shown that EA has potential anticancer properties against gastric cancer (GC). This study aims to reveal the potential targets and mechanisms of EA against GC. This study adopted methods of bioinformatics analysis and network pharmacology, including the weighted gene co‐expression network analysis (WGCNA), construction of protein–protein interaction (PPI) network, receiver operating characteristic (ROC) and Kaplan–Meier (KM) survival curve analysis, Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, molecular docking and molecular dynamics simulations (MDS). A total of 540 EA targets were obtained. Through WGCNA, we obtained a total of 2914 GC clinical module genes, combined with the disease database for screening, a total of 606 GC‐related targets and 79 intersection targets of EA and GC were obtained by constructing Venn diagram. PPI network was constructed to identify 14 core candidate targets; TP53, JUN, CASP3, HSP90AA1, VEGFA, HRAS, CDH1, MAPK3, CDKN1A, SRC, CYCS, BCL2L1 and CDK4 were identified as the key targets of EA regulation of GC by ROC and KM curve analysis. The enrichment analysis of GO and KEGG pathways of key targets was performed, and they were mainly enriched in p53 signalling pathway, PI3K‐Akt signalling pathway. The results of molecular docking and MDS showed that EA could effectively bind to 13 key targets to form stable protein–ligand complexes. This study revealed the key targets and molecular mechanisms of EA against GC and provided a theoretical basis for further study of the pharmacological mechanism of EA against GC. The WGCNA analysis process of this step was performed in R 4.1.2 software (https://www.r‐project.org/); the goodSamplesGenes function was used to remove outliers and screen out the genes with the top 10% variance as input genes for analysis; clinical features (control samples and tumour samples) were imported; sample clustering was performed; pickSoftThreshold function was used to calculate soft threshold power; then, the adjacency was converted into a topological overlap matrix; hierarchical clustering and dynamic shearing was used; the minimum number of genes in the module was set to 30; and similar modules were merged; the gene modules related to clinical features of GC were constructed, and GC clinical module genes were identified.We obtained RNA‐Seq data and clinical data of TCGA‐STAD patients from the TCGA database  to construct a protein–protein interaction (PPI) network and selected the creature as ‘Homo sapiens’; the confidence parameter was set as ‘medium confidence > 0.4’, exported the PPI network, usedWe imported the intersection targets of EA/GC into the STRING 11.5 database2.4http://kmplot.com) to analyse the prognostic value of the core targets. The patients were divided into high expression group and low expression group according to the median expression level of mRNA, and the two groups were compared, and the hazard ratio with 95% confidence intervals and the log rank p value were calculated.We extracted the expression data of core candidate targets in the TCGA‐STAD dataset and used the bioinformatics online platform to generate the receiver operating characteristic (ROC) curve to verify the core candidate targets. The overall survival (OS) analysis of the core targets was performed using GC data from the Kaplan–Meier (KM) plotter (2.5https://metascape.org/gp/index.html) for Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for key targets; the enrichment conditions were p < 0.01 and the minimum number of enriched genes ≥3, used KEGG Mapper to draw the KEGG map of the key signalling pathways that were significantly enriched, used bioinformatics online platform to visualize the top 15 biological processes (BP), cellular components (CC), molecular functions (MF) and the top 20 KEGG pathway enrichment results, used Cytoscape 3.8.2 software to build interaction network of EA, the key Targets, and the top 15 GO functional items  and the top 20 KEGG pathways.We used the Metascape database2.6https://www.rcsb.org/),We obtained the SDF format file of the ligand EA from the Pubchem database, used the Chem3D module of the ChemOffice 2018 software to minimize the energy of the EA and saved it in mol2 format, obtained the receptor protein structures of key targets from the RCSB PDB database  and H bonds between proteins, between backbones and between small molecule ligands; combined with APBS software version 3.0.0, the binding free energy of the protein–ligand complexes was calculated using the molecular mechanics Poisson–Boltzmann surface area (MM‐PBSA) method33.1We obtained ADME parameters of EA from the TCMSP database, OB = 43.06% and DL = 0.43, where OB represents the speed and degree of drug absorption into human circulation, which is an important indicator for evaluating drug efficacy, and DL reflects the properties of drugs with specific functional groups or the same or similar physical properties,We collected candidate targets of EA from nine databases, namely TCMSP, BATMAN‐TCM, Binding DB, SwissTargetPrediction, STITCH, pharmmapper, CTD, GeneCards and SEA database, as shown in Figure 3.2β = 4 was set, and a scale‐free network was constructed with R2 ≈ 0.95, as shown in Figure We obtained data on TCGA‐STAD patients from the TCGA database, which included 32 control samples and 375 tumour samples. A total of 57,067 genes were obtained by removing outliers, and 5707 genes with the top 10% variance were screened for analysis. The WGCNA package was used to construct a weighted gene co‐expression network, selecting a soft threshold To make the obtained GC‐related genes more accurate, we obtain the intersection of the targets of GeneCards database and GC clinical module genes of WGCNA as GC‐related targets for subsequent analysis. We used ‘Gastric cancer’ as keyword to search the GeneCards database and obtained 5475 GC‐related targets. With the relevance score greater than or equal to the median 0.784, 2792 targets with higher correlation with the disease were selected for subsequent analysis.Tbtools software was used to construct the Venn diagram to obtain the intersection of EA and GC. The intersection Venn diagram contains three parts, which are GC clinical module genes, GC database related genes, and EA related candidate targets. By constructing the Venn diagram, 79 intersection targets of EA and GC were obtained, as shown in Figure 3.3Homo sapiens’ and set the confidence parameter as ‘medium confidence > 0.4’. A PPI network with 79 nodes and 678 edges was obtained, which was visualized with Cytoscape 3.8.2 software, as shown in Figure We imported the obtained 79 intersection targets of EA/GC into the STRING 11.5 database to construct a PPI network, selected the organism as ‘3.4We extracted the expression data of the core candidate targets in the TCGA‐STAD dataset and generated the ROC curve to verify the core candidate targets. When the area under the curve (AUC) was within the range of , it indicated that the gene had good predictive ability. According to the ROC curves of the core candidate targets, we generated, as shown in Figure p < 0.05). Based on the results of ROC curve and KM survival analysis, we predicted that 13 key targets TP53, JUN, CASP3, HSP90AA1, VEGFA, HRAS, CDH1, MAPK3, CDKN1A, SRC, CYCS, BCL2L1 and CDK4 may be the key targets of EA against GC.Then, in order to further reveal the prognostic value of 14 core targets, Kaplan–Meier plotter was used to analyse the survival of core targets, as shown in Figure 3.5p < 0.01 and the minimum number of enriched genes ≥3, a total of 283 GO entries and 99 KEGG pathways were obtained. The results with significant enrichment were visualized in order of p value, as shown in Figure We used the Metascape database to perform enrichment analysis of GO functions and KEGG pathways for 13 key targets; under the conditions of Among the top 20 KEGG pathways from KEGG pathway enrichment results, signalling pathways related to GC development included pathways in cancer, viral infection, lipid and atherosclerosis, endocrine resistance, apoptosis, platinum drug resistance, p53 signalling pathway, other cancer‐related pathways, etc. KEGG Mapper was used to draw KEGG maps of the distribution of key targets in the p53 signalling pathway and PI3K‐Akt signalling pathway, as shown in Figure Then, Cytoscape 3.8.2 software was used to construct the interaction network of EA related targets‐key targets‐top 15 GO entries ‐top 20 KEGG pathways, as shown in Figure 3.6We used Autodock Tools v.1.5.6 and Autodock Vina software to perform molecular docking of EA with key target receptor proteins to evaluate the interaction of EA with key targets and verify the accuracy of the above analysis. It is generally believed that the binding energy less than 0 kcal/mol indicates the existence of binding activity between protein ligands, and the binding energy less than −5.0 kcal/mol indicates the existence of good binding activity.3.7We selected the protein–ligand complexes of EA with HSP90AA1, EA with MAPK3 and EA with SRC as examples for molecular dynamics simulation to evaluate the binding stability and binding ability of proteins and ligands. The RMSD and H were calculated to evaluate the binding stability of protein–ligand complexes; as shown in Figure The hydrogen‐bonding interaction has an important contribution to the stability of protein–ligand complexes. We analysed the 3–10 ns MDS trajectories of the stabilized systems for hydrogen‐bonding interaction analysis and calculated the number of hydrogen bonds formed between proteins and ligands in the protein–ligand complexes of EA and HSP90AA1, EA and MAPK3, and EA and SRC, as shown in Figure We performed MM‐PBSA binding energy calculations on the 3–10 ns MDS trajectories after stabilization, providing a more accurate estimate of binding affinity based on the binding free energy. We calculated van der Waals energy, electrostatic energy, polar solvation energy, nonpolar solvation energy and binding energy, as shown in Table 4Currently, GC remains one of the most prevalent cancers worldwide, with high mortality rates, especially in developing countries. In recent years, more and more studies have shown that many natural components in plants can enhance sensitivity to chemotherapy and have synergistic effects with anti‐GC drugs,Currently, network pharmacology has become a powerful tool for predicting the molecular mechanisms of drug–disease interactions; however, limitations such as lack of clinical information may limit the value and application of network pharmacology.Through network pharmacology analysis, we identified 13 key targets, which may be the key targets of EA anti‐GC, namely TP53, JUN, CASP3, HSP90AA1, VEGFA, HRAS, CDH1, MAPK3, CDKN1A, SRC, CYCS, BCL2L1 and CDK4. Among them, tumour protein p53 (TP53), a well‐known tumour suppressor gene, is the most frequently mutated gene in GC, accounting for about 50%.We conducted molecular docking between EA and 13 key targets, and the results showed that the binding energy of EA to these key targets was all less than −5.0 kcal/mol, and EA could effectively bind to key targets. Then, we took the protein–ligand complexes of EA and HSP90AA1, EA and MAPK3, EA and SRC with the highest binding energy as examples to carry out 10 ns MDS. By calculating RMSD and the results showed that EA formed stable protein–ligand complexes with HSP90AA1, MAPK3 and SRC. MM‐PBSA was used to calculate the binding free energy of the three protein–ligand complexes to provide more accurate affinity evaluation, the results showed that EA had good binding force with HSP90AA1, MAPK3, and SRC, and could form stable complexes. Above, through molecular docking and MDS, the accuracy of the predicted targets has been preliminarily verified, and EA may exert anticancer properties on GC by regulating these key targets.The results of the KEGG pathway enrichment analysis showed that 13 key target genes were mainly enriched in Apoptosis, p53 signalling pathway and PI3K‐Akt signalling pathway. Apoptosis is an evolutionarily conserved program of cell death, and the activation of apoptotic pathways and related molecular targets is an important anticancer strategyAlthough this study provides evidence for the potential effect of EA against GC, however, it should be mentioned that this study still has certain limitations. The relevant targets are obtained from the database, which may depend on the accuracy of the database. Further in vitro and in vivo experimental verification is required to better develop EA for clinical application.5In conclusion, this study adopted bioinformatics analysis and network pharmacology revealed that EA anti‐GC mainly regulated TP53, JUN, CASP3, HSP90AA1, VEGFA, HRAS, CDH1, MAPK3, CDKN1A, SRC, CYCS, BCL2L1 and CDK4 as well as p53 signalling pathway, PI3K‐Akt signalling pathway, involved in the inhibition of tumour angiogenesis, cell cycle, cell proliferation, invasion and migration and induction of apoptosis. It reveals that EA has great potential for the prevention and treatment of GC, and provides a theoretical basis for the further study of EA against GC.Zhiyao Liu: Conceptualization ; methodology ; writing – original draft . Hailiang Huang: Funding acquisition ; writing – review and editing . Ying Yu: Data curation ; software . Lingling Li: Data curation ; software . Xin Shi: Validation . Fangqi Wang: Validation .This study was funded by the High Level Key Disciplines of Traditional Chinese Medicine Basic Theory of Traditional Chinese Medicine, National Administration of Traditional Chinese. Medicine, Shandong University of Traditional Chinese Medicine, Jinan 250355, PR China and the Key Laboratory of Traditional Chinese Medicine Classical Theory, Ministry of Education, Shandong University of Traditional Chinese Medicine, Jinan 250355, PR China.The authors declare that there are no competing interests associated with the manuscript.


Background: Oral lichen planus (OLP) is an infrequent autoimmune disease of the oral mucosa, which affects up to 2% of the world population. An investigation of Tripterygium wilfordii’s mechanism of action for treating OLP was conducted, and a theoretical basis was provided for improving current treatment regimens. Materials and Methods: We used a network pharmacological approach to gain insight into the molecular mechanism of Tripterygium wilfordii in the treatment of OLP. Then, potential protein targets between Tripterygium wilfordii and OLP were analyzed through a drug–target network. This was followed by KEGG enrichment analysis and Gene Ontology (GO) classification. Finally, for molecular docking, AutoDock Vina was used. Results: A protein–protein interaction (PPI) network was constructed by analyzing the common targets of a total of 51 wilfordii–OLP interactions from different databases. The GO and KEGG enrichment analyses showed that the treatment of OLP with Tripterygium wilfordii mainly involves lipopolysaccharide response, bacterial molecular response, positive regulation of cytokine production, and leukocyte proliferation, and the signaling pathways mainly include the AGE-RAGE, NF-κB, Toll-like receptor, IL-17, HIF-1, and TNF signaling pathways. The molecular docking results showed that β-sitosterol, kaempferol, hederagenin, and triptolide have a higher affinity for AKT1, TNF, CASP3, and PTGS2, respectively. Based on the CytoNCA analysis of common targets, 19 key targets, including AKT1, TNF, VEGFA, STAT3, CXCL8, PTGS2, TP53, and CASP3, and their connections were identified. Conclusions: Preliminarily, this study reveals that Tripterygium wilfordii interferes with OLP by interacting with multiple targets through multiple accesses, as validated by molecular docking. Oral lichen planus (OLP), an inflammatory autoimmune disease of the oral mucosa, is characterized by chronic or recurrent disease. According to the WHO guidelines, OLP is designated as an oral pathology that may lead to malignancy, and one of its most dangerous complications is the development of oral squamous cells . CurrentAs far as OLP is concerned, immune dysregulation plays an important role, and CD8+ cytotoxic lymphocytes and CD4+ Th1-polarized T lymphocytes drive this process, which is triggered by antigens that do not originate from the body, thereby activating T cells that are directed toward oral keratinocytes and causing their death . In addiTripterygium wilfordii is a common herbal medicine used to suppress immune function, inhibit fibrosis, inhibit tumor growth, and reduce inflammation in the body [Tripterygium wilfordii, for instance, psoriasis, diabetic kidney, and rheumatoid arthritis, which benefit from its pharmacological effects on multiple targets and pathways [Tripterygium wilfordii, including liver and kidney toxicity, as well as reproductive toxicity [Tripterygium wilfordii, as shown in clinical trials [Tripterygium wilfordii’s action is still unclear. Network pharmacology could guide us in understanding the complex relationships between Chinese herbs and diseases. The network pharmacological approach proposed by Hopkins et al. focuses on the interactions among diseases, drugs, and genes at multiple levels [the body . Since ipathways ,8,9. Thetoxicity . OLP coul trials . Howevere levels . Molecule levels .Tripterygium wilfordii on OLP. The initial step was to screen Tripterygium wilfordii for its active chemical constituents and determine their target proteins. A signaling pathway analysis of gene targets was performed through GO and KEGG. Additionally, cross-validations were conducted on PPI and target proteins. Finally, Tripterygium wilfordii’s main target proteins and its active components’ mechanism of binding were determined with the utilization of molecular docking. The detailed procedures can be seen in Tripterygium wilfordii may act to treat OLP and provides a reference for future pharmacological laboratory investigations employing molecular docking and network pharmacology.In this study, a network pharmacological analysis was conducted to investigate the mechanism of action of http://tcmspw.com) (accessed on 11 April 2023), which provided us with valuable insights into this complex process. To ensure high-quality results, we employed an ADME  screening approach, which allowed us to impose stringent selection criteria of drug-likeness (DL) ≥ 0.18 and oral bioavailability (OB) ≥ 30% to efficiently filter out unsatisfactory active ingredients.We utilized the extensive and comprehensive resources of the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) (http://herb.ac.cn) (accessed on 13 April 2023) is a high-throughput experiment- and reference-guided database of traditional Chinese medicine, to which the identified active compounds were subjected to obtain their potential targets. In addition, DrugBank (https://www.drugbank.ca/) (accessed on 13 April 2023) was used to identify potential candidate targets. Finally, all target proteins identified during the screening process were annotated with gene names using the UniProt database (https://www.uniprot.org/) (accessed on 13 April 2023), excluding any nonhuman targets.HERB (https://www.genecards.org) (accessed on 14 April 2023), the Online Mendelian Inheritance in Man (OMIM) database (https://omim.org) (accessed on 14 April 2023), the DisGeNET database (https://www.disgenet.org) (accessed on 14 April 2023), and the CTD database (https://www.ctdbase.org) (accessed on 14 April 2023). We generated a comprehensive list of OLP genes from these four disease databases and removed any duplicative entries.In the pursuit of discovering potential therapeutic targets for “Oral lichen planus” (OLP), sophisticated retrieval systems were employed to scour the vast expanse of relevant data available. We utilized a multi-database approach and turned to the GeneCard database  (accessed on 16 April 2023), we aggregated the intersection targets and deployed them to construct a PPI network. We set the species type to “Homo sapiens”, ensured the minimum interaction threshold was set to the “highest confidence” (>0.9), and the remaining values were set as the default values. In the end, the results we obtained were imported into the Cytoscape (version 3.9.1) software, which enabled us to construct a PPI network with multiple components, thus providing novel insights into the intricate mechanisms underlying the detected Tripterygium wilfordii targets and related OLP targets.Utilizing the R package “VennDiagram”, we computed the intersection of the detected Tripterygium wilfordii. The nodes in the network represent the genes, activated components, or targets, and the lines indicate the interactions that exist between them. In addition, we picked the top four active components that correspond to the most important potential therapeutic targets of OLP for molecular docking.We harnessed Cytoscape (version 3.9.1) to generate a network to depict the complicated relationships between potential active components and the corresponding OLP targets of Tripterygium wilfordii and OLP nexus, the CytoNCA algorithm was employed in CytoScape [To extract the hub genes of the PPI network in connection with the ytoScape . This alTripterygium wilfordii on gene and gene functions in signaling pathways, we performed GO and KEGG enrichment analyses of potential targets of Tripterygium wilfordii intervention in OLP via the R software (version 4.1.2 for Windows). Three aspects were included in the GO enrichment analysis: biological process (BP), molecular function (MF), and cell component (CC). All the GO and KEGG enrichment analysis results were selected based on p-values of ≤0.05.To elucidate the effects of 51 target proteins interacting with the target genes of http://www.rcsb.org/) (accessed on 18 April 2023), while the drug compounds’ MOL2 structures were obtained from the TCMSP and PubChem (https://pubchem.ncbi.nlm.nih.gov/) (accessed on 18 April 2023) databases. Through the use of PyMOL, we removed water molecules and original ligands from the target proteins and drug compounds. We then utilized the AutoDock Tools (version 1.5.6) to generate charge calculation, non-polar hydrogen combination, and hydrogenation and stored these results in the PDBQT format. Finally, Autodock Vina (version 1.1.2) was used for molecular docking and calculation of minimal binding affinity. According to previous research studies and methodologies, the binding activity of a small-molecule drug to a protein is considered satisfactory when the binding energy is less than −4.25 kcal/mol. Moreover, the binding activity between two molecules is considered excellent when the binding energy is less than −5.0 kcal/mol [The core proteins with the greatest node degree values in the PPI network were docked to their active components. To acquire a complete understanding of the molecular interactions between the compounds and core proteins, we utilized molecular docking techniques. The structure of the target proteins was obtained from the PDB database  to perform GO and KEGG pathway analyses on the 51 common targets. The resulting visual data shown in p-value), with brighter shades indicating higher levels of enrichment and larger p-values being represented by more intense shades of red. The enriched BPs  were staggering and spanned the gamut from responses to lipopolysaccharide to positive regulation of cytokine production, and even leukocyte proliferation. The CCs (cellular components) were equally diverse, ranging from the external side of plasma membranes to membrane rafts, membrane microdomains, and organelle outer membranes. The MFs (molecular functions) were just as impressive, with close associations to DNA-binding transcription factor binding, cytokine receptor binding, and cytokine activity. And the KEGG analysis revealed the top 30 biological pathways, including those involved in lipid metabolism and atherosclerosis, Kaposi’s sarcoma-associated herpesvirus infection, AGE-RAGE signaling pathway , MOL000358 (β-sitosterol), MOL003187 (triptolide), and MOL000296 (hederagenin) have more targets, thus showing the higher rank these components have in pharmacological action.Through Cytoscape, we constructed a g figure  reveals We took our research to the next level by submitting the intersection target proteins to STRING version 11.0, which helped us construct a PPI network consisting of 51 nodes and an astonishing number of 523 edges A. By utiWe performed molecular docking between the top four compounds  and the top four proteins . The results of binding energy are shown in Tripterygium wilfordii may indeed be a highly promising and safe therapeutic intervention for individuals suffering from OLP [Despite decades of research, the pathophysiological underpinnings of OLP remain shrouded in mystery. Several studies have suggested that immunological and psychological variables may play a significant role in the development and progression of this complex disorder . Furtherfrom OLP . NeverthThe innovative methodology of network pharmacology is a revolutionary approach to comprehending the complexity of systems biology and network theory . NetworkTripterygium wilfordii’s efficacy in treating OLP. To bolster the reliability of our findings and conclusions regarding the identified therapeutic targets, we integrated molecular docking into our analytical approach. We found that the main active components of Tripterygium wilfordii  play vital roles in the treatment of OLP. Kaempferol, the main ingredient of Tripterygium wilfordii, has been suggested to protect the vascular endothelial function by reducing oxidative stress and inflammation [Tripterygium wilfordii and is widely employed in the treatment of a myriad of autoimmune and inflammatory diseases, such as rheumatoid arthritis, psoriasis, systemic lupus erythematosus, and nephritis [In this research, we harnessed the power of network pharmacology to unravel the interplay between the major constituents, pivotal pathways, and potential therapeutic targets that underlie ammation . Manifesammation . In a stammation . The comephritis . Researcephritis . Furtherephritis , therebyephritis .Tripterygium wilfordii in the treatment of OLP, according to the analyses using STRING and CytoNCA. AKT1 is a member of the AKT family and participates in the PI3K/AKT signaling pathway. By responding to extracellular signals, the PI3K/AKT pathway is a necessary regulator of critical cellular functions, such as cell proliferation, growth, angiogenesis, and metabolic processes [AKT1, TNF, VEGFA, STAT3, CXCL8, PTGS2, TP53, and CASP3 may be significant targets of rocesses . This parocesses . Additiorocesses ,34,35, urocesses , therebyrocesses . Moreoverocesses .Tripterygium wilfordii in the treatment of OLP may be related to BPs involved in leukocyte proliferation, positive regulation of the production of cytokines, response to bacterial-derived molecules, and response to lipopolysaccharide. These BP signaling pathways are related to immune response and inflammation. Our investigation suggests Tripterygium wilfordii’s capabilities in controlling key cellular processes, such as the regulation of cell growth, apoptosis, and immune response, all of which are of great significance in the etiology and pathogenesis of OLP. A series of intensive research studies have uncovered a myriad of connections between the pathophysiology of OLP and the intricate workings of aberrant T cell activation, the delicate balance of oral keratin-forming cell death, and subtle shifts in the body’s redox state [Tripterygium wilfordii on OLP are closely related to well-known OLP-associated pathways, such as the NF-κB signaling pathway, the AGE-RAGE signaling pathway, the IL-17 signaling pathway, the Toll-like receptor signaling pathway, the TNF signaling pathway, and the HIF−1 signaling pathway. It has been reported that the Toll-like receptor signaling pathway and the NF-κB signaling pathway may interact in the perpetuation of OLP [Tripterygium wilfordii could hinder OLP growth via various targets and signaling pathways. Furthermore, Tripterygium wilfordii’s probable methods of treating OLP include actions on several targets and interactions between the targets. Drugs with multi-target regulatory activity may be more appropriate for diseases with a complicated etiology such as OLP, opening up new avenues for treatment.Based on the GO analysis results, the potential mechanism of ox state ,39. The n of OLP . Toll-lin of OLP . Severaln of OLP . AccordiTripterygium wilfordii’s potential molecular mechanism for treating OLP, we screened four vital biologically active ingredients and four representational targets. The four active compounds, namely kaempferol, β-sitosterol, triptolide, and hederagenin, were molecularly docked with a series of vital proteins, including AKT1, TNF, CASP3, and PTGS2, respectively, to validate our network pharmacological predictions. Indeed, with the binding energies below the threshold of −5 kcal/mol, the affinity between these biological molecules is powerful.To investigate Tripterygium wilfordii in its treatment of OLP, we recognize that further experiments are required to verify these findings. Additionally, we recognize that current network information technology is by no means perfect, and the databases that we used, while comprehensive, are not without minor flaws, as they occasionally yield false positive results due to limited data availability.Despite the significant findings of our investigation, we must acknowledge that there are certain limitations to our study. While our use of molecular docking and network pharmacological methodologies allowed us to identify several key components and target proteins of Tripterygium wilfordii in the treatment of OLP. Our results reveal that kaempferol, β-sitosterol, triptolide, and hederagenin are the primary active compounds of Tripterygium wilfordii in the treatment of OLP. And AKT1, TNF, CASP3, and PTGS2 are the potential therapeutic targets of Tripterygium wilfordii in the treatment of OLP. The above compounds interfere with these targets through various signaling pathways, such as the AGE-RAGE, NF-κB, Toll-like receptor, IL-17, HIF-1, and TNF signaling pathways. Our research provides positive aspects and new horizons for future experimental validation of Tripterygium wilfordii in the treatment of OLP and development of Chinese patent medicine.In the present study, by means of network pharmacology and molecular docking, we conducted a preliminary investigation on the active compounds and mechanism of


Atopic dermatitis and abnormalities in tooth development  have been observed to co-occur in some patients. A common pathogenesis pathway that involves genes and protein interactions has been hypothesized. This review aims to first provide a description of the key gene mutations and signaling pathways associated with atopic dermatitis and tooth agenesis  and identify the possible association between the two diseases. Second, utilizing a list of genes most commonly associated with the two diseases, we conducted a protein–protein network interaction analysis using the STRING database and identified a novel association between the Wnt/β-catenin signaling pathway (major pathway responsible for TA) and desmosomal proteins (component of skin barrier that affect the pathogenesis of AD). Further investigation into the mechanisms that may drive their co-occurrence and underlie the development of the two diseases is warranted. Atopic dermatitis (AD), also known as eczema and atopic eczema, is the most common chronic inflammatory skin disease  and is eInterestingly, some studies have shown epidemiological commonalities between AD and dental caries, and dental structural abnormalities such as hypomineralization and hypodontia . For example, in the GUSTO birth cohort study in Singapore, children diagnosed with AD in the first 18 months of life had a 3-times higher risk of developing tooth decay by age 3, despite controlling for several potential confounders . AnotherDue to the shared ectodermal tissue origin of the teeth and skin, an “ectodermal subclinical development defect” has been suggested, whereby genetic mutations associated with AD share a common pathogenic pathway with abnormalities in tooth development and can cause structural defects in the tooth, such as hypo-mineralization of the enamel. This structural defect in turn increases the tooth’s susceptibility to dental caries and may have resulted in the AD-caries associations observed. A longitudinal cohort of 6-year-old twins, which demonstrated moderate to strong associations between hypo-mineralization of the second molars (HSPM) and infantile eczema [Tooth agenesis (TA) is an extreme case of abnormality in tooth development, where there is an absence of teeth due to developmental failure, and is one of the most prevalent dental and craniofacial malformations in humans . TA can Non-syndromic TA is TA that is not associated with any other systemic abnormalities or genetic syndromes. However, as the main cause of tooth agenesis is genetic, TA may involve other organs or tissues, as the involved networks of signaling molecules and transcription factors in TA and epithelial–mesenchymal interactions play essential and extensive roles during embryogenesis ,9. A few studies offer direct evidence for the association between AD and hypodontia. While only allergy  was significantly associated, atopy (which includes atopic dermatitis) and asthma were also among the top conditions experienced by patients with hypodontia . A 2017 While the published review articles have focused on either AD or TA ,15,16,17Therefore, the aim of this review is to provide a description of the key gene mutations and signaling pathways associated with atopic dermatitis and tooth agenesis, respectively, identify the possible associations between the two diseases, and to propose exploratory hypotheses and mechanisms to narrow down the possible shared pathogenic pathways for future research to interrogate.The two major pathophysiological pathways in AD are abnormalities of epidermal structure and function, and cutaneous inflammation due to inappropriate immune responses to antigens encountered in the skin . Both pastratum corneum (SC), which consists of corneocytes surrounded by intercellular lipid lamellae and attached by corneodesmosomes [tight junctions attached to lateral walls of keratinocytes in the upper stratum granulosum (SG) have also been included in the basic skin barrier structure. Keratin filaments form macrofibrils by cross-linking with the cornified envelope (CE) of corneocytes. The SC lipid layer is covalently attached to the external surface of CE proteins, forming the cornified lipid envelope (CLE) [The epidermal barrier is the first line of defense between the host organism and the environment. As illustrated in smosomes . The tigpe (CLE) .Stratum corneum (SC): The most significant gene variants associated with AD are the loss-of-function mutations found in the filaggrin (FLG) gene. An estimated 27.5% of Caucasian Americans, 48% of Europeans, 31.4% of Chinese and 20% of Japanese populations with AD present mutations in the FLG gene . FilaggrFilaggrin-2 (FLG-2) is a filaggrin-like protein, and is part of the corneocyte envelope . The expOVOL1  is an upstream transcription factor that regulates FLG expression. FLG, OVOL1 and IL13 are reported to be the three genes most significantly associated with AD among the 31 susceptible gene loci reported in a meta-analysis of genome-wide association studies .Other gene mutations that result in epidermal barrier dysfunction include genes that encode SC proteases such as KLK7, and genes that encode protease inhibitors SPINK5 and LEKTI. Kallikreins are a family of 15 trypsin- or chymotrypsin-like secreted serine pro-teases (KLK1-KLK15). The expression of KLK5-8, KLK10, KLK13 and KLK14 is significantly increased in AD patients , and theThe multidomain serine protease inhibitor Kazal-type 5 (SPINK5), otherwise known as the lympho-epithelial Kazal-type-related inhibitor (LEKTI), plays a role in keratinocyte differentiation during skin and hair morphogenesis, and the protective barrier function of skin by inhibiting the activity of KLKs in the epidermis ,40. LossCornified envelope (CE): The corneocyte envelope (CE) serves as a scaffold for lipids to attach and provides a supportive force to the corneocytes. The envelope is formed from structural proteins, including involucrin, loricrin, and the small proline-rich (SPRR) proteins . An extrCorneodesmosomes and tight junctions: Mutations within genes that express corneodesmosomal proteins (desmogleins and desmocollins) and tight junction proteins (claudins and ocludins) also contribute to the progression of AD . DesmoglGenetic variants associated with these immunological events contribute to the aberrant inflammatory and immune response in AD. Mutations in pattern-recognition receptors (PRRs) have been observed to be related to AD; these include polymorphisms in toll-like receptors (TLRs)—TLR2, TLR4, TLR6, TLR9—and gene polymorphisms in nucleotide-binding oligomerization domain-like receptors (NLRs)—CARD4, CARD12, CARD15, NALP1, NALP12, and NOD1; several SNPs of the human β-defensin 1 (DEFB1) gene have also been found in AD patients . MutatioTooth development is a series of genetically regulated processes with successive and reciprocal interactions of the epithelium and mesenchyme . Four maPAX9 is the most prevalent gene for non-syndromic TA . It encoMSX1, a member of the homeobox genes, encodes for a protein that acts as a transcriptional repressor during embryogenesis and is critical for the development of teeth . The WntAXIN2 encodes an intracellular inhibitor of Wnt/β-catenin signaling and has been associated with lower incisor agenesis ,74. AXINEctodysplasin A, a protein of the tumor necrosis factor family, plays an important role during the development of ectodermal organs and teeth by activating the IKBKG-NF-κB signaling pathway. Mutations in EDA and EDA receptor genes have been reported to affect sporadic hypodontia in families . Most ofMost mutations in EDAR and EDARADD are associated with ectodermal dysplasia, with a few associated with non-syndromic TA. Several mutations of IKBKG will lead to incontinentia pigmenti and ectodermal dysplasia (HGMD); therefore, NF-κB activity is likely to be affected by these mutations in the aforementioned genes .In addition, single nucleotide polymorphisms in the EDAR gene have also been associated with other dental malformations. For example, the presence/absence of the V370A allele of the EDAR gene has been correlated with modern human shovel-shaped incisors . The 154Recently, the genetic link between the Wnt pathway and TA was highlighted once again through whole exome and Sanger sequencing, with the observation of many mutations in genes that encode for Wnt ligands and its receptors . The repWnt10B, a structurally related protein, is also expressed in the dental epithelium during the early bud and cap stages of tooth development. Similar to Wnt10A, genetic mutations in Wnt10B have been found in dental anomalies, such as TA and oligodontia ,87. ImpaLRP6 is important for cell differentiation and proliferation, as it encodes a protein of the Wnt-Fzd-LRP5-LRP6 complex, which triggers the Wnt/β-catenin signaling cascade. LRP6 mutants are unable to activate β-catenin, therefore preventing the activation of Wnt signaling . These LOther genes implicated in isolated TA or oligodontia accompanied with ectodermal dysplasia include mutations in DKK1 and associated KREMEN1; while mutations in ANTXR1 were also implicated with syndromic TA ,90,91. DThe implicated TGF-β/BMP-associated genes include GREM2—for mutations associated with TA, taurodontism, short tooth roots, and microdontia—and LTBP3—for mutations associated with inherited dental anomalies and isolated oligodontia ,93,94. GSecreted protein acidic and rich in cysteine (SPARC)-related modular calcium binding 2 (SMOC2) is an early dental developmental gene in human beings, supported by its high expression in areas such as the oral ectoderm and dental epithelium . This geFrom the above review of the gene mutations associated with epidermal barrier defects in AD and TA, no direct overlap of the genes involved was observed. Nevertheless, it is possible that there are shared biological pathways or processes, or indirect interactions through intermediary molecules between the two diseases. Recent research has shown that protein–protein interactions (PPI) are crucial for most biological activities, and examining the protein interaction networks can help to identify key proteins that may be involved in both diseases or that act as “hubs” linking multiple pathways. For example, PPIs between Parkinson’s disease and periodontitis have been found to indicate new candidate molecular mechanisms . Therefohttps://string-db.org/, accessed on 15 November 2022), together with association and analysis methods. The STRING database contains known and predicted protein–protein interactions, stemming from computational prediction, knowledge transfer between organisms, and interactions aggregated from other (primary) databases. In general, PPIs in STRING are derived from the following five main domains: Genomic Context Predictions, High-throughput Lab Experiments, (Conserved) Co-Expression, Automated Textmining and Previous Knowledge in Databases . Apart from in-house predictions and homology transfers, STRING also relies on many resources maintained elsewhere . The methodological details of the STRING database and network analysis have been reported in a recent paper [The protein–protein interaction networks for TA and AD were investigated using version 11.5 of the STRING  database  between the proteins known to be associated with TA and AD. Catenin beta-1 (CTNNB1) presents interactions with TA-associated genes, including AXIN2, WNT10A, WNT10B and LRP6, as a key downstream component of the canonical Wnt signaling pathway. Importantly, there is evidence that indicates functional links between CTNNB1 and desmosomal proteins (DSC1 and DSG3), which play an important role in the maintenance of skin barrier function, and thus affect the pathogenesis of AD . There aWhile the Wnt/b-catenin pathway to junctional/desmosomal proteins interaction may have been previously determined experimentally (as illustrated by the purple/pink connection line), this interaction has not been proposed to be involved in the mechanistic pathways through which AD and TA co-occur. The identification of the interaction of these proteins that are associated with the known gene mutations for these two diseases is the novel result of this database search.The β-catenin protein encoded by the CTNNB1 gene is part of a complex of proteins that constitute adherens junctions (AJs). AJs are necessary for the creation and maintenance of epithelial cell layers by regulating cell growth and adhesion between cells. Meanwhile, β-catenin is an integral part of the canonical Wnt signaling pathway.Tooth development is a dynamic process that goes through the bud, cap and bell stages, root development and tooth eruption . The WntResearch on the relationship between desmosomal components and β-catenin signaling has been carried out. It was shown that DSC3 regulated β-catenin in suprabasal keratinocytes by inducing β-catenin stabilization and transgene-mediated DSC3a and DSC3b expression in differentiating keratinocytes enhances β-catenin signaling . The abnThe canonical Wnt/β-catenin pathway is a fundamental mechanism that accounts for various biological activities, including cell proliferation, differentiation and development. Distinct from the non-canonical Wnt pathways that are independent of β-catenin, the canonical Wnt/β-catenin pathway has β-catenin as its typical characteristic . In the In summary, PAX9 and MSX1 are the most prevalent genes for non-syndromic TA. Mutated genes that encode the components in the canonical Wnt/β-catenin pathway and Wnt-associated genes account for the highest genetic risk for isolated TA compared to mutated genes involved in several other pathways. TGF-β/BMP and EDA/EDAR/NF-κB signaling pathways also contribute to TA. Skin epithelial function and immune responses are the two major biologic pathways responsible for AD and they interactionally affect each other. Genetic mutations that cause structural defects in the epidermal barrier include genes that encode epidermal barrier structural proteins, stratum corneum proteases and protease inhibitors.We identified protein interactions between desmosomal proteins and β-catenin. Desmosomes and tight junctions are essential components of the skin barrier, affecting the pathogenesis of AD. β-catenin is a key component of the Wnt/β-catenin signaling pathway, which is the major pathway responsible for TA. While experimental studies have shown that the DSG and DSC levels have a positive correlation with β-catenin expression and affect the Wnt signaling pathway, this protein interaction has not been previously proposed as one of the mechanistic pathways through which AD and TA co-occur. The specific mechanism by which these two diseases interact through this pathway remains unclear and more evidence is needed, for example, the GWAS risk of disease and EQTL analysis. Nevertheless, our findings help to narrow down the possible shared pathogenic pathways for future research to interrogate in this novel field, and help support the hypothesis that shared genetic mutations in the epidermal structure could increase the risk of tooth agenesis, thus linking structural defects in the skin barrier and tooth formation. Further studies may explore the mechanisms of gene variation in desmosomes and adherens in epithelial membranes, and their interactions with the Wnt signaling pathway linked to TA, in particular in the context of patients with TA and AD.


Background: Diabetic nephropathy (DN) is a widespread diabetic complication and a major cause of terminal kidney disease. There is no doubt that DN is a chronic disease that imposes substantial health and economic burdens on the world’s populations. By now, several important and exciting advances have been made in research on etiopathogenesis. Therefore, the genetic mechanisms underlying these effects remain unknown.Methods: The GSE30122, GSE30528, and GSE30529 microarray datasets were downloaded from the Gene Expression Omnibus database (GEO). Analyses of differentially expressed genes (DEGs), enrichment of gene ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed. Protein-protein interaction (PPI) network construction was completed by the STRING database. Hub genes were identified by Cytoscape software, and common hub genes were identified by taking intersection sets. The diagnostic value of common hub genes was then predicted in the GSE30529 and GSE30528 datasets. Further analysis was carried out on the modules to identify transcription factors and miRNA networks. As well, a comparative toxicogenomics database was used to assess interactions between potential key genes and diseases associated upstream of DN.Results: Samples from 19 DNs and 50 normal controls were identified in the GSE30122 dataset. 86 upregulated genes and 34 downregulated genes . GO analysis showed significant enrichment in humoral immune response, protein activation cascade, complement activation, extracellular matrix, glycosaminoglycan binding, and antigen binding. KEGG analysis showed significant enrichment in complement and coagulation cascades, phagosomes, the Rap1 signaling pathway, the PI3K-Akt signaling pathway, and infection. GSEA was mainly enriched in the TYROBP causal network, the inflammatory response pathway, chemokine receptor binding, the interferon signaling pathway, ECM receptor interaction, and the integrin 1 pathway. Meanwhile, mRNA-miRNA and mRNA-TF networks were constructed for common hub genes. Nine pivotal genes were identified by taking the intersection. After validating the expression differences and diagnostic values of the GSE30528 and GSE30529 datasets, eight pivotal genes  were finally identified as having diagnostic values.Conclusion: Pathway enrichment analysis scores provide insight into the genetic phenotype and may propose molecular mechanisms of DN. The target genes TYROBP, ITGB2, CD53, IL10RA, LAPTM5, CD48, C1QA, and IRF8 are promising new targets for DN. SPI1, HIF1A, STAT1, KLF5, RUNX1, MBD1, SP1, and WT1 may be involved in the regulatory mechanisms of DN development. Our study may provide a potential biomarker or therapeutic locus for the study of DN. Identify the potential DEGs that participate in the initiation and development of DN and analyze their expression, function, and interaction in order to serve as a guide for researching potential biomarkers or therapeutic targets for DN.Screen potential diabetic nephropathy-related genes using GEO datasets and text mining. The transcriptome expression profile datasets GSE30122, GSE30529, and GSE30528  were obtThe details of the three datasets are shown in These datasets were downloaded from the GEO database, and only the probes with the highest signal values for the same molecule were retained. The Limma software package was again used to normalize the data and analyze the variance between the two groups.p values adjusted < 0.05 and |log2FoldChange| > 1. The “Complex Heatmap” (version 2.2.0) and “ggplot2” packages (version 3.3.3) of R software (version 3.6.3) were used to create volcano maps, PCA maps, and heat maps. , cellular component (CC), and molecular function (MF) (www.frontiersin.org).To conduct GO and KEGG pathway analysis, the ClusterProfiler package (version 3.14.3) of R software was used . The orgGSEA was performed in order to explore biological signaling pathways. When the FDR <0.25 and the p. adjust value <0.05, it is thought to be a significant enrichment and is used as a screening index. Mainly, the clusterProfiler package (version 3.14.3) was used for GSEA analysis . Statisthttps://string-db.org/) was used to construct the PPI network to reveal general organizational principles of cellular function and predict protein-protein interactions . Then, the mRNA-miRNA regulatory networks and the mRNA-TF regulatory networks were constructed to profile the interactions with mRNAs and miRNAs/TF as target potential for DN renal cells. The regulatory network was visualized using Cytoscape software.Prediction of interactions with the miRNet database 2.0 between differentially expressed miRNAs and mRNAs  was used for receiver operating characteristic (ROC) curve analysis and computation of ROC curves and ROC AUC values. Visualization of charts is implemented with the ggplot2 package. Multi-gene ROC analysis is a predictor of probability based on the contribution of multiple genes to the outcome. A ROC analysis was performed on the results of binary logistic regression calculations for each sample. Regression was performed with the SPSS 22.0 version. Outcomes were quantified as the area under the ROC curve (AUC) of the results, and the genes with AUC > 0.7 were deemed diagnostic.http://ctdbase.org/, accessed December 10, 2022) is an integrated database that integrates information related to chemical gene-protein interactions, chemical disease, and genetic disease relationships and proposes postulates associated with disease mechanisms . GO analysis of the enrichment showed that the differentially expressed genes were mainly functional in the following 15 ways (www.spandidos-publications.com) GO: 0006956, “complement activation.” GO:0002455-humoral immune response mediated by circulating immunoglobulin (draco.cyverse.org); GO:0062023-collagen-containing extracellular matrix; GO: 0072562 (blood microparticle); GO: 005581 (collagen trimer); GO: 0034774 (secretory granule lumen); GO: 009897 ; GO: 0005539—Glycosaminoglycan binding; GO: 0008201—Heparin binding; GO: 1901681—Sulfur compound binding; GO: 0003823—Antigen binding; GO: 0005201—Extracellular matrix structural constituent (www.ncbi.nlm.nih.go). The results are presented in To further investigate the biofunction of the 120 DEGs obtained in GSE 30122, GO functional enrichment analysis was performed. There were 304 items for BP, 57 items for CC, and 52 items for MF based on the adjusted filtering criteria  indicate that 21 KEGG pathways were enriched in GSE 30122. Complement and coagulation cascades; The phagosome; protein digestion and absorption; the PI3K-Akt signaling pathway; Primary immunodeficiency; Focal adhesion; The NF-kappa B signaling pathway; The Rap1 signaling pathway; and other pathways were enriched . The adjusted filtering criteria  differed between DN and normal control patients . We creaDiabetic nephropathy (DN) is the major cause of CKD and ESRD , and it There is tremendous heterogeneity in DN susceptibility, and genetic regulation may be an essential factor contributing to this heterogeneity. The mainly damaged cells in DN are podocytes, and the severity of damage is highly related to disease progression. Podocyte apoptosis is the leading cause of podocyte reduction in DN . ROS-medvia the cell adhesion molecule (CAM) pathway. (rgd.mcw.edu), and the positive regulation of the MAPK cascade (TYROBP is located on chromosome 19 and can act as an adaptor molecule for TREM (trigger receptor expressed on myeloid cells) and induce cytokine production in macrophages , and is pathway. . Ligand pathway. . CD53 ispathway. , cell supathway. . IL10RA,pathway. . Lysosompathway. . LAPTM5 pathway. , the pos cascade . Studies cascade , inducin cascade . C1QA an cascade . IRF8, w cascade . High gl cascade . The ideThe mechanism of DN is very sophisticated, and present treatments can only slow down its development but cannot effectively prevent and cure it. The pathophysiology of DN is often believed to include problems in hemodynamics, metabolic function, and hormone production. Advanced glycosylation end products (AGE), renin-angiotensin-aldosterone system (RAAS), transforming growth factor-1 (TGF-1) expression, activation of protein kinase C (PKC), mitogen-activated protein kinase (MAPK), and reactive oxygen species (ROS) are all considered to be significant pathways in the initial stages and progression of diabetic nephropathy. However, various pathway factors regulate each other and overlap . FunctioIn our study, Transcription factors HIF1A, KLF5, RUNX1, SP1, SPI1, STAT1, MBD1 and WT1 may be related to diabetic nephropathy. The results were similar to previous studies . ChronicThe current study discusses eight potentially key genes in the development of diabetic nephropathy as potential mechanisms involved in diabetic nephropathy. The genes might be prospective biomarkers and treatment goals for diabetic nephropathy. Also, there are some limitations in this paper: the dataset samples included  may affect the stability of the results. The analysis of key potential molecules gained from this study needs to be further validated in the clinical trial.Our study explored the same platform GEO dataset for diabetic nephropathy bioinformatics analysis and identified eight potential key genes , screened eight transcription factors, and identified 93 miRNA nodes. It provides some contribution to identifying new biomarkers of diabetic nephropathy susceptibility and useful potential targets for therapy.