Protein-protein Interaction Network Construction Research Articles

Comprehensive analysis ofmRNAs in the cerebral cortex inAPP/PS1 double-transgenic mice withAlzheimer's disease based onhigh-throughput sequencing ofN4-acetylcytidine.

N4-acetylcytidine (ac4C), a significant modified nucleoside, participates in the development of many diseases. Messenger RNAs (mRNAs) contain most of the information of the genome and are the molecules that transmit information from genes to proteins. Alzheimer's disease (AD) is a progressive neurodegenerative disease in which fibrillar amyloid plaques are present. However, it remains unknown how mRNA ac4C modification affects the development of AD. In the current study, ac4C-modified mRNAs were comprehensively analyzed in AD mice by ac4C-RIP-seq and RNA-seq. Next, a protein-protein interaction (PPI) network was constructed to examine the relationships between the genes with differential ac4C modification levels and their RNA expression levels. The differentially expressed genes (DEGs) acquired above were subjected to Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to further analyze the molecular mechanisms in AD. In total, 3312 significant ac4C peaks were found on 2512 mRNAs, 1241 of which were hyperacetylated and 1271 of which were hypoacetylated. In addition, 956 mRNAs with differential expression were found, including 520 upregulated mRNAs and 436 downregulated mRNAs. Overall, 134 mRNAs with simultaneous changes at the ac4C levels as well as RNA expression levels were identified via joint analysis. Then, through PPI network construction and functional enrichment analysis, 37 key mRNAs were screened, which were predominantly enriched in GABAergic synapses and the PI3K/AKT signaling pathway. The significant difference in the abundance of mRNA ac4C modification indicates that this modification is associated with AD progression, which may provide insight for more investigations of the potential mechanisms.

Bioinformatics-based analysis of core genes and pathway enrichment in early diabetic nephropathy.

Diabetic nephropathy (DN) is the main cause of end-stage renal disease (ESRD). Bioinformatics technology was adopted to screen and analyze the core genes of early DN to explore its pathogenesis. GSE30528, GSE96804, and GSE30122 chip data were obtained from Gene Expression Omnibus (GEO) database to screen DN and healthy controls for differentially expressed genes. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) for functional annotation and signaling pathway enrichment; String and Cytoscape were used for protein-protein interaction (PPI) network construction and core gene screening, NCBI-Genes to search the expression profile of core genes. 17 common genes were screened, with 6 genes up-regulated and 11 genes down-regulated. The major functional annotations of differential genes were the generation of precursor metabolites and energy, Immune response, and Phosphorylation. They were concentrated on Focal adhesion, PI3K/Akt signaling pathway, and Human papillomavirus infection pathway. The expressions of VEGFA and THBS1 were down-regulated, while the expressions of ITGB1, MMP7, and SOX9 were up-regulated. The core genes VEGFA and THBS1 were highly expressed in Thyroid and Appendix, but lowly expressed in Testis. MMP7 was highly expressed in the Gall bladder and low in the Adrenal. SOX9 was highly expressed in Thyroid and lowly expressed in the bone marrow. ITGB1 was highly expressed in Fat and low in Pancreas. Bioinformatics technology can mine chip data and present new targets for diagnosing and treating DN, but further verification is needed.

CD3D and CD247 are the molecular targets of septic shock.

Septic shock is a serious systemic disease with circulatory failure and abnormal cell metabolism caused by sepsis. However, the relationship between CD3D and CD247 and septic shock remains unclear. The septic shock datasets GSE33118 and GSE142255 profiles were generated from the gene expression omnibus databases GPl570, GPl17586. Differentially expressed genes (DEGs) were screened and weighted gene co-expression network analysis was performed. The construction and analysis of protein-protein interaction (PPI) network, functional enrichment analysis, gene set enrichment analysis (GSEA) were performed. Gene expression heat map was drawn. Immune infiltration analysis was performed. Comparative toxicogenomics database (CTD) analysis were performed to find the disease most related to the core gene. Targets can was used to screen miRNAs regulating the hub DEGs. 467 DEGs were identified. According to the gene ontology analysis, they were mainly enriched in the regulation of immune response, cell activation, signaling receptor activity, enzyme binding. Kyoto encyclopedia of genes and genomes analysis showed that they were mainly enriched in the TCR signaling pathway, Fc epsilon RI signaling pathway. GSEA showed that the DEGs were mainly enriched in immune response regulation, cell activation, TCR signaling pathway, Fc epsilon RI signaling pathway. Positive regulation of Fc receptor signaling pathway, PID IL12 2 pathway, immune response was observed in go enrichment items in the enrichment items of metascape. PPI networks got 5 core genes. Gene expression heat map showed that 5 core genes (CD247, Lck, cd3e, cd3d, ITK) were lowly expressed in the sepsis shock samples and highly expressed in the normal samples. CTD analysis showed that 5 core genes (CD247, Lck, cd3e, cd3d, ITK) were found to be associated with hemorrhage and necrosis. Low expression of cd3d, CD247 was observed in septic shock, and the lower the level of cd3d, CD247, the worse the prognosis.

Systematical analysis of underlying markers associated with Marfan syndrome via integrated bioinformatics and machine learning strategies

Marfan syndrome (MFS) is a hereditary disease with high mortality. This study aimed to explore peripheral blood potential markers and underlying mechanisms in MFS via a series bioinformatics and machine learning analysis. First, we downloaded two MFS datasets from the GEO database. A total of 215 differentially expressed genes (DEGs) and 78 differentially expressed miRNAs (DEMs) were identified via “Limma” package. 60 DEGs, mainly enriched in abnormal transportation of structure and energy substances, were selected after protein-protein interaction (PPI) network construction, of which 20 were chosen for machine learning after three algorithms (betweenness, closeness, and degree) filtration using Cytoscape. Four overlapping DEGs (ACTN1, CFTR, GCKR, LAMA3) were finally selected as the candidate markers based on three machine-learning approaches (Lasso, random forest, and support vector machine-recursive feature elimination). Furthermore, we collected peripheral blood from MFS patients and healthy control to validate the findings and the results showed that compared with the control, the expression of the four DEGs was all statistically different in MFS patients validated by qRT-PCR. Besides, the area under the receiver operating characteristics curve was greater than 0.8 for each DEG. Single-sample gene-set enrichment analysis showed that the four DEGs were strongly associated with inflammation and myogenesis pathway. Finally, we constructed the mRNA-miRNA network based on the intersection of DEMs and predicted miRNAs targeting DEGs. In conclusion, our study partially provided four potential markers for MFS pathogenesis. Communicated by Ramaswamy H. Sarma

Identification of Key lncRNAs, circRNAs, and mRNAs in Osteoarthritis via Bioinformatics Analysis.

Osteoarthritis (OA) is a common degenerative joint disorder that adversely affects the quality of life of patients. Identification of novel diagnostic biomarkers is pivotal for the early detection and prevention of OA. Dataset GSE185059 was selected from Gene Expression Omnibus database to obtain differentially expressed lncRNAs (DE-lncRNAs), mRNAs (DE-mRNAs), and circRNAs (DE-circRNAs) between OA and normal samples. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses as well as protein-protein interaction (PPI) network construction of DE-mRNAs were conducted. Hub genes were identified from PPI networks and validated by RT-qPCR. starBase database was utilized for predicting miRNAs binding with hub genes, selected DE-lncRNAs and DE-circRNAs, respectively. The competing endogenous RNA (ceRNA) networks were constructed. A total of 818 DE-mRNAs, 191 DE-lncRNAs, and 2053 DE-circRNAs were identified. The DE-mRNAs were significantly enriched in several inflammation-related GO terms and KEGG pathways such as positive regulation of cell-cell adhesion, TNF-alpha signaling pathway and NF-kappa B signaling pathway. Thirteen hub genes were identified, which were CFTR, GART, SMAD2, NCK1, TJP1, UBE2D1, EFTUD2, PRKACB, IL10, SNRPG, CHD4, RPS24, and SRSF6. OA-related DE-lncRNA/circRNA-miRNA-hub gene networks were constructed. We identified 13 hub genes and constructed the ceRNA networks related to OA, providing a theoretical basis for further research.

Silencing LCN2 enhances RSL3-induced ferroptosis in T cell acute lymphoblastic leukemia

BackgroundT-cell acute lymphoblastic leukemia (T-ALL) is a life-threatening malignancy and therapeutic toxicity remains a huge challenge for survival rates. A novel iron-dependent form of cell death, ferroptosis, shows potentials in cancer therapy. This study aimed to identify ferroptosis-associated hub genes within a proteinprotein interaction (PPI) network. MethodsWe screened differential expressed genes (DEGs) in GSE46170 dataset and obtained ferroptosis-related genes from FerrDb database. Through overlapping between DEGs and ferroptosis-related genes, ferroptosis-associated DEGs were identified for further PPI network construction. Molecular complex detection (MCODE) algorithm in Cytoscape was employed to determine tightly connected protein clusters. Chord diagram of Gene Ontology (GO) was generated to reveal the potential biological process of hub genes. Through transfection with siRNA of lipocalin 2 (LCN2) into TALL cells, the regulatory role of LCN2 in ferroptosis was investigated. ResultsVenn diagram identified a total of 37 ferroptosis-associated DEGs between GSE46170 and ferroptosis-associated genes, which were mainly enriched in ferroptosis and necroptosis. Based on PPI network analysis, 5 hub genes (LCN2, LTF, HP, SLC40A1 and TFRC) were found. These hub genes were involved in iron ion transport and could distinguish T-ALL from normal individuals. Further experimental studies demonstrated that LCN2 was highly expressed in T-ALL, while silencing LCN2 promoted RSL3-induced ferroptotic cell death in T-ALL cells. ConclusionThis study identified novel ferroptosis-associated hub genes, which shed new insights into the underlying mechanism of ferroptosis in T-ALL and also provide promising therapeutic targets for T-ALL.

Bioinformatics Analysis of Programmed Cell Death in Spinal Cord Injury

Programmed cell death (PCD) in the development of spinal cord injury (SCI) is complicated, including apoptosis, necroptosis, pyroptosis, ferroptosis, cuproptosis, and autophagy. It is necessary to make clear the expression levels of PCD and potential molecular targets after SCI for formulating relevant treatment strategies. We downloaded the rats' SCI expression matrix GSE45006, and the ssGSEA method was used to analyze the PCD after SCI. Then the related differentially expressed genes (DEGs) were identified, and the gene ontology (GO) and pathway analysis, protein-protein interaction (PPI) network construction, and HUB genes were identified. Finally, the correlation between HUB genes and PCD was analyzed. Apoptosis, necroptosis, pyroptosis, ferroptosis, and autophagy increased significantly in acute SCI, and then decreased gradually in the subacute and chronic stages; cuproptosis in acute SCI decreased significantly, and then gradually increased. In addition, we also screened 116 DEGs during the development of SCI. GO and pathway analysis showed that DEGs was related to mitosis and cell cycle. The identified hub genes are closely related to cell apoptosis, necroptosis, pyroptosis, ferroptosis after injury, and autophagy. PCD occurs differently in different stages after SCI. To inhibit apoptosis, necroptosis, pyroptosis, and ferroptosis after injury and induce autophagy may be the therapeutic strategy. In addition, intervention therapy based on related HUB genes may be the therapeutic target of SCI.

Phosphoproteomic and proteomic profiling in post-infarction chronic heart failure.

Background: Post-infarction chronic heart failure is the most common type of heart failure. Patients with chronic heart failure show elevated morbidity and mortality with limited evidence-based therapies. Phosphoproteomic and proteomic analysis can provide insights regarding molecular mechanisms underlying post-infarction chronic heart failure and explore new therapeutic approaches. Methods and results: Global quantitative phosphoproteomic and proteomic analysis of left ventricular tissues from post-infarction chronic heart failure rats were performed. A total of 33 differentially expressed phosphorylated proteins (DPPs) and 129 differentially expressed proteins were identified. Bioinformatic analysis indicated that DPPs were enriched mostly in nucleocytoplasmic transport and mRNA surveillance pathway. Bclaf1 Ser658 was identified after construction of Protein-Protein Interaction Network and intersection with Thanatos Apoptosis Database. Predicted Upstream Kinases of DPPs based on kinase-substrate enrichment analysis (KSEA) app showed 13 kinases enhanced in heart failure. Proteomic analysis showed marked changes in protein expression related to cardiac contractility and metabolism. Conclusion: The present study marked phosphoproteomics and proteomics changes in post-infarction chronic heart failure. Bclaf1 Ser658 might play a critical role in apoptosis in heart failure. PRKAA1, PRKACA, and PAK1 might serve as potential therapeutic targets for post-infarction chronic heart failure.

Exploring the pathogenesis of colorectal carcinoma complicated with hepatocellular carcinoma via microarray data analysis.

Background: Despite the increasing number of research endeavors dedicated to investigating the relationship between colorectal carcinoma (CRC) and hepatocellular carcinoma (HCC), the underlying pathogenic mechanism remains largely elusive. The aim of this study is to shed light on the molecular mechanism involved in the development of this comorbidity. Methods: The gene expression profiles of CRC (GSE90627) and HCC (GSE45267) were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the common differentially expressed genes (DEGs) of psoriasis and atherosclerosis, three kinds of analyses were performed, namely, functional annotation, protein-protein interaction (PPI) network and module construction, and hub gene identification, survival analysis and co-expression analysis. Results: A total of 150 common downregulated differentially expressed genes and 148 upregulated differentially expressed genes were selected for subsequent analyses. The significance of chemokines and cytokines in the pathogenesis of these two ailments is underscored by functional analysis. Seven gene modules that were closely connected were identified. Moreover, the lipopolysaccharide-mediated signaling pathway is intricately linked to the development of both diseases. Finally, 10 important hub genes were identified using cytoHubba, including CDK1, KIF11, CDC20, CCNA2, TOP2A, CCNB1, NUSAP1, BUB1B, ASPM, and MAD2L1. Conclusion: Our study reveals the common pathogenesis of colorectal carcinoma and hepatocellular carcinoma. These common pathways and hub genes may provide new ideas for further mechanism research.

Identification of MiR-223 Associated with Diagnosis in Ectopic Pregnancy.

In this study, we conducted an integrated study of the diagnostic value of MiR-223 in ectopic pregnancy (EP). We used GSE44731 downloaded from GEO and GEO2R to identify differentially expressed miRNA. The hub genes corresponding to the differential miRNA were then identified by using the Xiantao academic tool, GO (Gene Ontology), and KEGG (Kyoto Encyclopedia of Genes and Genomes). Afterward, we used the miEAA database to perform gene set enrichment analysis (GSEA) of differential miRNA, and used Xiantao academic tools again to conduct the ceRNA network based on the target genes. Protein-protein interaction (PPI) network construction and lncRNA of hub miRNA target genes were then predicted by the starbase database. For validation, the villus tissue from intrauterine pregnancy and tubal pregnancy was collected and assayed by qPCR. In total 19 differentially expressed miRNAs were screened out, of which MiR-223 had a relatively clear diagnostic significance. Hub genes were enriched and analyzed by GO, KEGG, and GSEA, and the results showed that regulation of NF-κB and other signaling pathways are primarily enriched in ectopic pregnancy. We also obtained 215 key genes from PPI analysis. Our ceRNA analysis indicated that LRRC75A-AS1 and PITPNA-AS1 were associated with MiR-223, and the expression of MiR-223 in qPCR was significantly high in tubal pregnancy group. We found that MiR-223 can be used in the diagnosis of EP. Our findings provide valuable information and direction for future research into novel targets for EP diagnosis.

Machine learning-based solution reveals cuproptosis features in inflammatory bowel disease.

Cuproptosis, a new cell death mode, is majorly modulated by mitochondrial metabolism and protein lipoylation. Nonetheless, cuproptosis-related genes (CRGs) have not yet been thoroughly studied for their clinical significance and relationship with the immune microenvironment in inflammatory bowel disease (IBD). We screened CRGs that had a significant correlation with immune status, which was determined utilizing single-sample GSEA (ssGSEA) and Gene Expression Omnibus datasets (GSE75214). Furthermore, utilizing the R package "CensusClusterPlus", these CRGs' expression was used to obtain different patient clusters. Subsequently, gene-set enrichment analysis (GSEA), gene set variation analysis (GSVA), and CIBERSORT assessed the variations in the enrichment of gene function and the abundance of immune cell infiltration and immune functions across these clusters. Additionally, weighted gene co-expression network analysis (WGCNA) and analysis of differentially expressed genes (DEGs) were executed, and for the purpose of identifying hub genes between these clusters, the construction of protein-protein interaction (PPI) network was done. Lastly, we used the GSE36807 and GSE10616 datasets as external validation cohorts to validate the immune profiles linked to the expression of CRG. ScRNA-seq profiling was then carried out using the publicly available dataset to examine the CRGs expression in various cell clusters and under various conditions. Three CRGs, PDHA1, DLD, and FDX1, had a significant association with different immune profiles in IBD. Patients were subsequently classified into two clusters: low expression levels of DLD and PDHA1, and high expression levels of FDX1 were observed in Cluster 1 compared to Cluster 2. According to GSEA, Cluster 2 had a close association with the RNA processes and protein synthesis whereas Cluster 1 was substantially linked to environmental stress response and metabolism regulations. Furthermore, Cluster 2 had more immune cell types, which were characterized by abundant memory B cells, CD4+ T memory activated cells, and follicular helper T cells, and higher levels of immune-related molecules (CD44, CD276,CTLA4 and ICOS) than Cluster 1. During the analysis, the PPI network was divided into three significant MCODEs using the Molecular Complex Detection (MCODE) algorithm. The three MCODEs containing four genes respectively were linked to mitochondrial metabolism, cell development, ion and amino acid transport. Finally, external validation cohorts validated these findings, and scRNA-seq profiling demonstrated diverse intestinal cellular compositions with a wide variation in CRGs expression in the gut of IBD patients. Cuproptosis has been implicated in IBD, with PDHA1, DLD, and FDX1 having the potential as immune biomarkers and therapeutic targets. These results offer a better understanding of the development of precise, dependable, and cutting-edge diagnosis and treatment of IBD.

Identification of immune-related genes in acute myocardial infarction based on integrated bioinformatical methods and experimental verification.

Acute myocardial infarction (AMI) is one of the leading causes of death worldwide. The etiology of AMI is complex and has not been fully defined. In recent years, the role of immune response in the development, progression and prognosis of AMI has received increasing attention. The aim of this study was to identify key genes associated with the immune response in AMI and to analyze their immune infiltration. The study included a total of two GEO databases, containing 83 patients with AMI and 54 healthy individuals. We used the linear model of microarray data (limma) package to find the differentially expressed genes associated with AMI, performing weighted gene co-expression analysis (WGCNA) to further identify the genes associated with inflammatory response to AMI. We found the final hub genes through the protein-protein interaction (PPI) network and least absolute shrinkage and selection operator (LASSO) regression model. To verify the above conclusions, we constructed mice AMI model, extracting myocardial tissue to perform qRT-PCR. Furthermore, the CIBERSORT tool for immune cells infiltration analysis was also carried out. A total of 5,425 significant up-regulated and 2,126 down-regulated genes were found in GSE66360 and GSE24519. A total of 116 immune-related genes in close association with AMI were screened by WGCNA analysis. These genes were mostly clustered in the immune response on the basis of GO and KEGG enrichment. With construction of PPI network and LASSO regression analysis, this research found three hub genes (SOCS2, FFAR2, MYO10) among these differentially expressed genes. The immune cell infiltration results revealed that significant differences could be found on T cells CD4 memory activated, Tregs (regulatory T cells), macrophages M2, neutrophils, T cells CD8, T cells CD4 naive, eosinophils between controls and AMI patients.

Prognostic Values of Core Genes in Pilocytic Astrocytom

Pilocytic astrocytoma (PA) is the most common primary brain tumor in children and adolescents. Treatment strategy largely depends on its key genes and molecular mutations. This study aimed to identify potential biomarkers of PA closely related to its prognosis. The gene expression profiles (series numbers GSE50161, GSE66354, and GSE86574) of PA and normal brain tissues were downloaded from the Gene Expression Omnibus database. The Gene Expression Omnibus2R was used to identify differentially expressed genes. The overlapping differentially expressed genes were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database. A protein-protein interaction network was constructed using Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape. The Gene Expression Profiling Interactive Analysis 2 (GEPIA2) tool analyzed the impact of hub genes on PA prognosis based on the Kaplan-Meier curves. Compared with normal brain tissues (n= 36), a total of 37 upregulated and 144 downregulated genes were identified in PA (n= 40). In the protein-protein interaction network construction and GEPIA2 survival analysis, 2 of the top 10 hub genes were significantly associated with decreased overall survival of PA patients, namely Gamma-aminobutyric acid A receptor alpha 2 (hazard ratio = 2.8, P < 0.01) and regulating synaptic membrane exocytosis protein 1) (hazard ratio= 3.2, P < 0.01). This bioinformatics analysis reveals that low expression of Gamma-aminobutyric acid A receptor alpha 2 and regulating synaptic membrane exocytosis protein 1 is associated with a favorable prognosis for PA patients. These 2 hub genes could be novel biomarkers for prognosis assessment, furthermore a key element for treatment decisions in the future.

Molecular mechanism of ginsenoside Rg_1 against radiation enteritis: based on network pharmacology and in vitro experiment

Via network pharmacology, molecular docking, and cellular experiment, this study explored and validated the potential molecular mechanism of ginsenoside Rg_1(Rg_1) against radiation enteritis. Targets of Rg_1 and radiation enteritis were retrieved from BATMAN-TCM, SwissTargetPrediction, and GeneCards. Cytoscape 3.7.2 and STRING were employed for the construction of protein-protein interaction(PPI) network for the common targets, and screening of core targets. DAVID was used for Gene Ontology(GO) term and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment to predict the possible mechanism, followed by molecular docking of Rg_1 with core targets and cellular experiment. For the cellular experiment, ~(60)Co-γ irradiation was performed for mo-deling of IEC-6 cells, which were then treated with Rg_1, protein kinase B(AKT) inhibitor LY294002, and other drugs to verify the effect and mechanism of Rg_1. The results showed that 29 potential targets of Rg_1, 4 941 disease targets, and 25 common targets were screened out. According to the PPI network, the core targets were AKT1, vascular endothelial growth factor A(VEGFA), heat shock protein 90 alpha family class A member 1(HSP90AA1), Bcl-2-like protein 1(BCL2L1), estrogen receptor 1(ESR1), etc. The common targets were mainly involved in the GO terms such as positive regulation of RNA polymerase Ⅱ promoter transcription, signal transduction, positive regulation of cell proliferation, and other biological processes. The top 10 KEGG pathways included phosphoinositide 3-kinase(PI3K)/AKT pathway, RAS pathway, mitogen-activated protein kinase(MAPK) pathway, Ras-proximate-1(RAP1) pathway, and calcium pathway, etc. Molecular docking showed that Rg_1 had high binding affinity to AKT1, VEGFA, HSP90AA1, and other core targets. Cellular experiment indicated that Rg_1 can effectively improve cell viability and survival, decrease apoptosis after irradiation, promote the expression of AKT1 and B-cell lymphoma-extra large(BCL-XL), and inhibit the expression of the pro-apoptotic protein Bcl-2-associated X protein(BAX). In conclusion, through network pharmacology, molecular docking, and cellular experiment, this study verified the ability of Rg_1 to reduce radiation enteritis injury. The mechanism was that it regulated PI3K/AKT pathway, thereby suppressing apoptosis.

Transcriptome Analysis Identifies Biomarkers for the Diagnosis and Management of Psoriasis Complicated with Depression.

Psoriasis is a systemic inflammatory disease, and the mechanism that links psoriasis to depression is still elusive. Hence, this study aimed to elucidate the potential pathogenesis of psoriasis and depression comorbidity. The gene expression profiles of psoriasis (GSE34248, GSE78097 and GSE161683) and depression (GSE39653) were downloaded from the Gene Expression Omnibus (GEO) DataSets. Functional annotation, protein-protein interaction (PPI) network and module construction, and hub gene identification and co-expression analysis were performed, following identification of the common differentially expressed genes (DEGs) of psoriasis and depression. A total of 115 common DEGs (55 up-regulated and 60 down-regulated) were identified between psoriasis and depression. Functional analysis indicated that T cell activation and differentiation were predominantly implicated in the potential pathogenesis of these two diseases. In addition, Th17 cell differentiation and cytokines is closely related to both. Finally, 17 hub genes were screened, including CTLA4, LCK, ITK, IL7R, CD3D, SOCS1, IL4R, PRKCQ, SOCS3, IL23A, PDGFB, PAG1, TGFA, FGFR1, RELN, ITGB5 and TNXB, which re-emphasized the importance of the immune system in psoriasis and depression. Our study reveals the common pathogenesis of psoriasis and depression. These common pathways and hub genes may apply to a molecular screening tool for depression in psoriasis patients, which could help dermatologists optimize patient management in routine care.

DiDang decoction improves mitochondrial function and lipid metabolism via the HIF-1 signaling pathway to treat atherosclerosis and hyperlipidemia.

DiDang Decoction (DDD) is a traditional classical prescription that has been used to treat atherosclerosis (AS) and hyperlipidemia (HLP) in China. Nevertheless, the underlying mechanism of DDD remains unclear. To validate the mechanism of DDD in AS and HLP based on network pharmacology and in vitro experiments. The chemical components of DDD were obtained from the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP) database and literature mining, and the disease targets of AS and HLP were obtained from the Gencards, OMIM, and DisGeNET databases. The intersection genes were imported into the STRING database to construct protein-protein interaction (PPI) network, and the DAVID database was used for gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Combined with the results of KEGG pathway analysis, the HIF-1 signaling pathway was selected for further in vitro experiments. The results showed that network pharmacology predicted 112 targets related to DDD treatment of AS and HLP, and the top 10 related pathways are: Lipid and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, Chemical carcinogenesis - receptor activation, Pathways in cancer, Proteoglycans in cancer, Fluid shear stress and atherosclerosis, HIF-1 signaling pathway, Alcoholic liver disease, PPAR signaling pathway, and Coronavirus disease-COVID-19. In vitro experiments showed that DDD effectively reduced lipid accumulation in FFA-treated L02cells; DDD attenuated mitochondrial damage and reduced ROS content; DDD inhibited ferroptosis and apoptosis; DDD up-regulated the expression of HIF-1α, Glutathione Peroxidase 4(GPX4), and Bcl2 proteins, and down-regulated expression of Bax protein. DDD exerts therapeutic effects on AS and HLP through multiple targets and pathways, and improves mitochondrial function, reduces ROS content, inhibits ferroptosis and apoptosis by activating the HIF-1 signaling pathway, which provides reliable theoretical and experimental support for DDD treatment of AS and HLP.

Hub genes and pathways in gastric cancer: A comparison between studies that used normal tissues adjacent to the tumour and studies that used healthy tissues as calibrator.

Several bioinformatics studies have been performed on high-throughput expression data to determine the cellular pathways and hub genes affected by Gastric cancer (GC). However, these studies differ in using a healthy tissue or normal tissue adjacent to the tumour (NAT) as calibrator tissues. This study was designed to find how using healthy or NAT tissues as calibrator tissues could affect pathway enrichment data and hub genes in GC. Two gene expression datasets with NAT tissues (GSE79973 and GSE118916) and one dataset with healthy tissues (GSE54129) were downloaded and processed by the limma package to screen the differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analysis were performed by the Enrichr online tool. Protein-protein interaction network construction, module analysis, and hub genes selection were performed by Cytoscape software, Molecular Complex Detection plugin, and cytoHubba plugin, respectively. The gene expression profiling interactive analysis web server was used to analyse RNA sequencing expression data from The Cancer Genome Atlas Program. The Kaplan-Meier plotter was used to perform survival analysis. Our results showed that some KEGG and GO pathways were shared between studies with NAT and the study with healthy tissues. However, some terms, especially inflammation-related terms, were missed when NAT tissues were used as calibrator tissues. Also, only FN1 and COL1A1 are common hub genes between DEGs of the studies with NAT and healthy tissues. Since hub genes are usually extracted and suggested as candidate targets for GC diagnosis, prognosis, or treatment, selecting healthy or NAT tissues may affect the hub genes selection.

Bioinformatics analysis combined with molecular dynamics simulation validation to elucidate the potential molecular mechanisms of Jianshen Decoction for treatment of osteoporotic fracture.

Osteoporotic fracture (OPF) is a prevalent skeletal disease in the middle-aged and elderly. In clinical practice, Jianshen Decoction (JSD) has been used to treat OPFs. However, the specific effective components and mechanisms of JSD on OPF have not been explored. Therefore, this study used bioinformatics analysis combined with molecular dynamics simulation validation to explore the molecular mechanism of JSD treatment of OPF. Public databases (TCMSP, Batman TCM) were used to find the effective active components and corresponding target proteins of JSD (screening conditions: OB ≥ 30%, drug-likeness ≥ 0.18, half-life ≥ 4). Differentially expressed genes (DEGs) related to OPF lesions were obtained based on the gene expression omnibus database (screening conditions: adjust P value < .01, | log2 FC | ≥ 1.0). The BisoGenet plug-in and the CytoNCA plug-in of Cytoscape were used to derive the potential core target proteins of JSD in the treatment of OPF. The JSD active ingredient target interaction network and the JSD-OPF target protein core network were constructed using the Cytoscape software. In addition, the R language Bioconductor package and clusterProfiler package were used to perform gene ontology (GO)/Kyoto Encylopedia Of Genes And Genome (KEGG) enrichment analysis on core genes to explain the biological functions and signal pathways of core proteins. Finally, molecular docking and molecular dynamics simulations were carried out through PyMOL, AutoDockTools 1.5.6, Vina, LeDock, Discovery Studio (DS) 2019, and other software to verify the binding ability of drug active ingredients and core target proteins. A total of 245 targets and 70 active components were identified. Through protein-protein interaction (PPI) network construction, 39 core targets were selected for further research. GO/KEGG enrichment analysis showed that the DNA-binding transcription factor binding, RNA polymerase II-specific DNA-binding transcription factor binding, MAPK signaling pathway, and ErbB signaling pathway were mainly involved. The results of molecular docking and molecular dynamics simulations supported the good interaction between MYC protein and Quercetin/Stigmasterol. In this study, bioinformatics, molecular docking, and molecular dynamics simulations were used for the first time to clarify the active components, molecular targets, and key biological pathways of JSD in the treatment of OPF, providing a theoretical basis for further research.

Screening and identification of the core immune-related genes and immune cell infiltration in severe burns and sepsis.

Severe burns often have a high mortality rate due to sepsis, but the genetic and immune crosstalk between them remains unclear. In the present study, the GSE77791 and GSE95233 datasets were analysed to identify immune-related differentially expressed genes (DEGs) involved in disease progression in both burns and sepsis. Subsequently, weighted gene coexpression network analysis (WGCNA), gene enrichment analysis, protein-protein interaction (PPI) network construction, immune cell infiltration analysis, core gene identification, coexpression network analysis and clinical correlation analysis were performed. A total of 282 common DEGs associated with burns and sepsis were identified. Kyoto Encyclopedia of Genes and Genomes pathway analysis identified the following enriched pathways in burns and sepsis: metabolic pathways; complement and coagulation cascades; legionellosis; starch and sucrose metabolism; and ferroptosis. Finally, six core DEGs were identified, namely, IL10, RETN, THBS1, FGF13, LCN2 and MMP9. Correlation analysis showed that some core DEGs were significantly associated with simultaneous dysregulation of immune cells. Of these, RETN upregulation was associated with a worse prognosis. The immune-related genes and dysregulated immune cells in severe burns and sepsis provide potential research directions for diagnosis and treatment.

Integrative analyses of biomarkers and pathways for diabetic nephropathy.

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 (a total of 120 DEGs). 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 (TYROBP, ITGB2, CD53, IL10RA, LAPTM5, CD48, C1QA, and IRF8) 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.