Pathway Enrichment Analysis Of Differentially Expressed Genes Research Articles

Two hub genes of bipolar disorder, a bioinformatics study based on the GEO database

Bipolar disorder is a mood illness that affects many people. It has a high recurrence frequency and will cause significant damage to the patient's social function. At present, the pathogenesis of BD is not clear. The National Center for Biotechnology Information (NCBI) established and maintained the Gene Expression Omnibus (GEO) database, a gene expression database. For bioinformatics analysis, researchers can obtain expression data from the internet. At present, the samples of the dataset used in the research of BD are mostly from brain tissue, and the data containing blood samples are rarely used. GEO databases (GSE46416, GSE5388, and GSE5389) were used to retrieve public data, and utilizing the online tool GEO2R, differentially expressed genes (DEGs) were retrieved. The common DEGs between the samples of patients with BD and the samples of the normal population were screened by Venn diagrams. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to perform functional annotation and pathway enrichment analysis of DEGs. A protein-protein interaction network (PPI) was built to investigate hub genes on this basis. There were 117 up-regulated DEGs and 38 down-regulated DEGs discovered, with two hub genes [SRC, CDKN1A] among the up-regulated DEGs. These two hub genes were also highly enriched in the oxytocin signaling pathway, proteoglycans in cancer and bladder cancer, according to KEGG analysis. The results of the receiver operating characteristic curve (ROC) of SRC and CDKN1A in the three datasets strongly suggested that SRC and CDKN1A were potential diagnostic markers of BD. The results strongly suggest that SRC and CDKN1A are related to the pathogenesis of BD.

The role of differentially expressed genes and immune cell infiltration in the progression of nonalcoholic steatohepatitis (NASH) to hepatocellular carcinoma (HCC): a new exploration based on bioinformatics analysis

Nonalcoholic fatty liver disease (NAFLD) is a spectrum of chronic liver disease characterized. The condition ranges from isolated excessive hepatocyte triglyceride accumulation and steatosis (nonalcoholic fatty liver (NAFL), to hepatic triglyceride accumulation plus inflammation and hepatocyte injury (nonalcoholic steatohepatitis (NASH)) and finally to hepatic fibrosis and cirrhosis and/or hepatocellular carcinoma (HCC). However, the mechanism driving this process is not yet clear. Obtain sample microarray from the GEO database. Extract 6 healthy liver samples, 74 nonalcoholic hepatitis samples, 8 liver cirrhosis samples, and 53 liver cancer samples from the GSE164760 dataset. We used the GEO2R tool for differentially expressed genes (DEGs) analysis of disease progression (nonalcoholic hepatitis healthy group, cirrhosis nonalcoholic hepatitis group, and liver cancer cirrhosis group) and necroptosis gene set. Gene set variation analysis (GSVA) is used to evaluate the association between biological pathways and gene features. The STRING database and Cytoscape software were used to establish and visualize protein-protein interaction (PPI) networks and identify the key functional modules of DEGs, drawn factor-target genes regulatory network. Gene Ontology (GO) and KEGG pathway enrichment analyses of DEGs were also performed. Additionally, immune infiltration patterns were analyzed using the cibersort, and the correlation between immune cell-type abundance and DEGs expression was investigated. We further screened and obtained a total of 152 intersecting DEGs from three groups. 23 key genes were obtained through the MCODE plugin. Transcription factors regulating common differentially expressed genes were obtained in the hTFtarget database, and a TF target network diagram was drawn. There are 118 nodes, 251 edges, and 4 clusters in the PPI network. The key genes of the four modules include METAP2, RPL14, SERBP1, EEF2; HR4A1; CANX; ARID1A, UBE2K. METAP2, RPL14, SERBP1 and EEF2 was identified as the key hub genes. CREB1 was identified as the hub TF interacting with those gens by taking the intersection of potential TFs. The types of key gene changes were genetic mutations. It can be seen that the incidence of key gene mutations is 1.7% in EEF2, 0.8% in METAP2, and 0.3% in RPL14, respectively. Finally, We found that the most significant expression differences of the immune infiltrating cells among the three groups, were Tregs and M2, M0 type macrophages. We identified four hub genes METAP2, RPL14, SERBP1 and EEF2 being the most closely with the process from NASH to cirrhosis to HCC. It is beneficial to examine and understand the interaction between hub DEGs and potential regulatory molecules in the process. This knowledge may provide a novel theoretical foundation for the development of diagnostic biomarkers and gene-related therapy targets in the process.

Identification of potential diagnostic biomarkers for tenosynovial giant cell tumour by integrating microarray and single-cell RNA sequencing data

PurposeTenosynovial giant cell tumour (TGCT) is a benign hyperplastic and inflammatory disease of the joint synovium or tendon sheaths, which may be misdiagnosed due to its atypical symptoms and imaging features. We aimed to identify biomarkers with high sensitivity and specificity to aid in diagnosing TGCT.MethodsTwo scRNA-seq datasets (GSE210750 and GSE152805) and two microarray datasets (GSE3698 and GSE175626) were downloaded from the Gene Expression Omnibus (GEO) database. By integrating the scRNA-seq datasets, we discovered that the osteoclasts are abundant in TGCT in contrast to the control. The single-sample gene set enrichment analysis (ssGSEA) further validated this discovery. Differentially expressed genes (DEGs) of the GSE3698 dataset were screened and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were conducted. Osteoclast-specific up-regulated genes (OCSURGs) were identified by intersecting the osteoclast marker genes in the scRNA-seq and the up-regulated DEGs in the microarray and by the least absolute shrinkage and selection operator (LASSO) regression algorithm. The expression levels of OCSURGs were validated by an external dataset GSE175626. Then, single gene GSEA, protein–protein interaction (PPI) network, and gene-drug network of OCSURGs were performed.Result22 seurat clusters were acquired and annotated into 10 cell types based on the scRNA-seq data. TGCT had a larger population of osteoclasts compared to the control. A total of 159 osteoclast marker genes and 104 DEGs (including 61 up-regulated genes and 43 down-regulated genes) were screened from the scRNA-seq analysis and the microarray analysis. Three OCSURGs (MMP9, SPP1, and TYROBP) were finally identified. The AUC of the ROC curve in the training and testing datasets suggested a favourable diagnostic capability. The PPI network results illustrated the protein–protein interaction of each OCSURG. Drugs that potentially target the OCSURGs were predicted by the DGIdb database.ConclusionMMP9, SPP1, and TYROBP were identified as osteoclast-specific up-regulated genes of the tenosynovial giant cell tumour via bioinformatic analysis, which had a reasonable diagnostic efficiency and served as potential drug targets.

Study on Potential Differentially Expressed Genes in Idiopathic Pulmonary Fibrosis by Bioinformatics and Next-Generation Sequencing Data Analysis.

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next-generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analyses of DEGs were performed. Then, a protein-protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst databaseswereused to construct the targeted microRNAs (miRNAs), transcription factors (TFs), and small drug molecules. Finally, receiver operating characteristic (ROC) curve analysis was used to validate the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process, and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8, and EFHC2 were selected. Cyclothiazide and rotigotinethe are predicted small drug molecules for IPF treatment. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.

Transcriptomic Variation of Amphiprion Percula (Lacepède, 1802) in Response to Infection with Cryptocaryon Irritans Brown, 1951.

Cryptocaryon irritans (Brown 1951) frequently infect the Pomacentridae fishes causing severe economic losses. However, the anti-C. irritans' molecular mechanism in these fishes remains largely unknown. To address this issue, we conducted RNA-Seq for C. irrtians-infected gills of the clownfish Amphiprion percula (Lacepède 1802) at the early (day 1) and late (day 3) stages of infection. A total of 1655 differentially expressed genes (DEGs) were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs showed a vast genetic variation related to the following aspects: ECM-receptor interaction, P13K-Akt signalling, cytokine-cytokine receptor interaction, and endocytosis. During the early phase of infection, key genes involved in ATP production, energy homeostasis, and stress control were abruptly increased. In the late phase, however, acute response molecules of the peripheral nervous system (synaptic transmission and local immunity), metabolic system triggering glycogen synthesis, energy maintenance, and osmoregulation were found to be critical. The highest number of upregulated genes (URGs) recovered during the early phase was included under the 'biological process' category, which primarily functions as response to stimuli, signalling, and biological regulation. In the late phase, most of the URGs were related to gene regulation and immune system processes under 'molecular function' category. The immune-related URGs of early infection include major histocompatibility complex (MHC) class-II molecules apparently triggering CD4+ T-cell-activated Th responses, and that of late infection include MHC class-1 molecules for the possible culmination of CD8+ T-cell triggered cytotoxicity. The high level of genic single nucleotide polymorphisms (SNPs) identified during the late phase of infection is likely to influence their susceptibility to secondary infection. In summary, the identified DEGs and their related metabolic and immune-related pathways and the SNPs may provide new insights into coordinating the immunological events and improving resistance in Pomacentridae fishes against C. irritans.

Biomarkers associated with severity classification of asthma identified by comprehensive bioinformatics analysis

Using an integrated bioinformatics approach to find novel biomarkers that can predict asthma severity. From June 2022 to December 2022, this clinical medical study was conducted and completed in the Department of Allergy, Zhongnan Hospital of Wuhan University. The gene chip dataset GSE43696 was screened and downloaded from the high-throughput Gene Expression Omnibus (GEO) database, and the gene chip data preprocessing was completed using package "affy" in R and "rma" algorithm in turn. Use the the "edgeR" and "limma" packages to screen out the differentially expressed genes (DEGs) between normal controls, mild to moderate asthma patients and severe asthma patients, and then use the "clusterProfiler" package to perform GO enrichment analysis and KEGG pathway enrichment analysis of DEGs, finally use the STRING website to construct a protein-protein interaction (PPI) network of DEGs to further screen key genes. Using the R language "WGCNA" package, the weighted gene co-expression network analysis (WGCNA) was performed on the dataset GSE43696, and the modules significantly related to the severity of asthma were screened out, then the hub genes were obtained by intersecting the WGCNA analysis results with the DEGs screened by PPI. Datasets GSE43696 and GSE63142 were used to verify the expression of hub genes, and the diagnostic value was evaluated according to the ROC curve, then the potential function of hub genes in dataset GSE43696 was further clarified by gene set enrichment analysis (GSEA). The results showed that a total of 251 DEGs were screened, including 39 in the normal group and mild to moderate asthma group, 178 in the normal group and severe asthma group, and 34 in the mild to moderate asthma group and severe asthma group, mainly involved in biological processes such as response to toxic substance, response to oxidative stress, extracellular structure organization, extracellular matrix organization. Two modules significantly correlated with asthma severity were screened out (red module, P=7e-6, r=0.43; pink module, P=5e-8, r=-0.51), and finally six hub genes were obtained, including B3GNT6, CEACAM5, CCK, ERBB2, CSH1 and DPPA5. The comparison of gene expression levels and ROC curve analysis of datasets GSE43696 and GSE63142 further verified the six hub genes, which may associated with o-glycan biosynthesis, alpha linolenic acid metabolism, linoleic acid metabolism, pentose and glucoronate interconversions. In conclusion, through a variety of bioinformatics analysis methods, this study identified six hub genes significantly related to the severity of asthma, which potentially provided a new direction for the prediction and targeted therapy of asthma.

Comprehensive analyses of m6A RNA methylation patterns and related immune microenvironment in idiopathic pulmonary arterial hypertension.

Idiopathic pulmonary arterial hypertension (IPAH) is a life-threatening disease with a poor prognosis and high heritability, characterized by elevated pulmonary vascular resistance (PVR) and pulmonary artery pressure. N6-methyladenosine (m6A) RNA modification influences many RNA metabolism pathways. However, the position of m6A methylation regulators in IPAH remains unknown. Therefore, the study aims to disclose the function m6A regulators exert in the pathological mechanisms of IPAH and the immune microenvironment involved. The GSE117261 dataset was downloaded from the Gene Expression Omnibus (GEO) to screen the differentially expressed genes (DEGs) between normal and IPAH samples. Functional and pathway enrichment analyses of DEGs were then conducted by Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG). We also identified the differentially-expressed m6A (DEm6A) regulators between normal and IPAH samples. Key m6A regulators related to the prediction of IPAH were selected using the random forest model. The results showed that FMR1, RBM15, HNRNPA2B1 and IGFBP3 were upregulated in IPAH. In contrast, LRPPRC was downregulated. The single sample gene set enrichment analysis (ssGSEA) method was then adopted to estimate the immune microenvironment in distinct m6A clusters and m6A phenotype-related genes (PRGs) clusters, respectively. Furthermore, we calculated the m6A score via principal component analysis (PCA), and the Sankey diagram was selected to present the correlation among the m6A clusters, m6A PRGs clusters and m6A score. Finally, quantitative RT-PCR and Western blotting were used to validate the key genes in human pulmonary artery smooth muscle cells (HPASMCs) treated by human platelet-derived growth factor-BB (PDGF-BB). The relative mRNA and protein expression levels of FMR1 were significantly elevated, however, the relative mRNA and protein expression levels of LRPPRC were downregulated. Besides, the relative mRNA level of HNRNPA2B1 was increased. Generally, this bioinformatics analysis might provoke more insights into diagnosing and treating IPAH.

Transcriptome analysis of Fenneropenaeus merguiensis in response to Vibrio proteolyticus infection.

In recent years, due to the destruction of the culture environment and serious ecological pressure, especially in the process of culture, residual bait, faeces and fishery drug abuse will lead to the accumulation of harmful metabolites such as ammonia nitrogen and nitrite, and biological denitrification is the most economical and effective method to remove the single. Therefore, in this study, a nitrite removal strain XA19 was isolated and screened from a shrimp biofloc culture pond. This strain was identified as a clade of Vibrio proteolyticus because the homology between XA19 and V. proteolyticus WDVP was as high as 99.86% by using 16S rDNA gene sequence analysis and NCBI database comparison. Scanning electron microscopy images showed that V. proteolyticus is short-rod-shaped with a curved body and no budding spores, pods and flagella. Antimicrobial susceptibility test proved that V. proteolyticus was resistant to ampicillin, oxacillin, penicillin, vancomycin and clindamycin. In the median lethal concentration 50 (LC50 ) test, at 7-day post-infection (dpi), LC50 of V. proteolyticus for Fenneropenaeus merguiensis was 1.69 × 104 CFU/mL. Transcriptome sequencing analysis was carried out on hepatopancreas of F. merguiensis at 24 and 48 hpi. A total of 176 differentially expressed genes (DEGs) were screened at 24 hpi, including 104 up-regulated DEGs and 72 down-regulated DEGs, and a total of 52 DEGs were screened at 48 hpi, including 32 up-regulated DEGs and 20 down-regulated DEGs. In the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs, many immune-related signalling pathways were significantly enriched, including Hippo signalling pathway, phagosome, Toll and Imd signalling pathways and Wnt signalling pathway. In addition, some pathways related to Warburg effect were also enriched, including Glycolysis/Gluconeogenesis, Biosynthesis of amino acids, amino sugar and nucleotide sugar metabolism and so on. In this study, the toxicity and drug sensitivity of V. proteolyticus were systematically studied, and the immune response of hepatopancreas of F. merguiensis to V. proteolyticus infection was preliminarily revealed from the molecular level. The results may provide a reference for the prevention and control of V. proteolyticus.

Identification of Potential Biomarkers of Septic Shock Based on Pathway and Transcriptome Analyses of Immune-Related Genes.

Immunoregulation is crucial to septic shock (SS) but has not been clearly explained. Our aim was to explore potential biomarkers for SS by pathway and transcriptional analyses of immune-related genes to improve early detection. GSE57065 and GSE95233 microarray data were used to screen differentially expressed genes (DEGs) in SS. Gene Ontology and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses of DEGs were performed, and correlations between immune cell and pathway enrichment scores were analyzed. The predictive value of candidate genes was evaluated by receiver operating characteristic (ROC) curves. GSE66099, GSE4607, and GSE13904 datasets were used for external validation. Blood samples from six patients and six controls were collected for validation by qRT-PCR and western blotting. In total, 550 DEGs in SS were identified; these genes were involved in the immune response, inflammation, and infection. Immune-related pathways and levels of infiltration of CD4 + TCM, CD8 + T cells, and preadipocytes differed between SS cases and controls. Seventeen genes were identified as potential biomarkers of SS (areas under ROC curves >0.9). The downregulation of CD8A, CD247, CD3G, LCK, and HLA-DRA in SS was experimentally confirmed. We identified several immune-related biomarkers in SS that may improve early identification of disease risk.

Identification of common core ion channel genes in epilepsy and Alzheimer's disease.

Although available literature indicates that the incidence of dementia in the epilepsy population and the risk of seizures in the Alzheimer's disease (AD) population are high, the specific genetic risk factors and the interaction mechanism are unclear, rendering rational genetic interpretation rather challenging. Our work aims to identify the common core ion channel genes in epilepsy and AD. In this study, we first integrated gene expression omnibus datasets (GSE48350 and GSE6834) on AD and epilepsy to identify differentially expressed genes (DEGs), performing Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. The related protein-protein interaction (PPI) network was constructed for DEGs, and the hub gene was evaluated. A total of 2800 and 35 genes were identified in GSE48350 and GSE6834, and 12 DEGs were significantly differentially expressed between the datasets. KEGG pathway analysis showed that DEGs were primarily enriched in glutamatergic synapse and dopaminergic synapse pathways. SCN2A, GRIA1, and KCNJ9 were the hub genes with high connectivity. The findings suggest that the three genes, SCN2A, GRIA1, and KCNJ9, may serve as potential targets for treating AD comorbid with epilepsy.

MRNA expression profile reveals differentially expressed genes in splenocytes of experimental autoimmune encephalomyelitis model.

Experimental autoimmune encephalomyelitis (EAE) is a mouse model that can be used to investigate aetiology, pathogenesis, and treatment approaches for multiple sclerosis (MS). A novel integrated bioinformatics approach was used to understand the involvement of differentially expressed genes (DEGs) in the spleen of EAE mice through data mining of existing microarray and RNA-seq datasets. We screened differentially expressed mRNAs using mRNA expression profile data of EAE spleens takenfrom Gene Expression Omnibus (GEO). Functional and pathway enrichment analyses of DEGs were performed by Database for Annotation, Visualization, and Integrated Discovery (DAVID). Subsequently, the DEGs-encoded protein-protein interaction (PPI) network was constructed. The 784 DEGs in GSE99300 A.SW PP-EAE mice spleen mRNA profiles, 859 DEGs in GSE151701 EAE mice spleen mRNA profiles, and 646 DEGs in GSE99300 SJL/J PP-EAE mice spleen mRNA profiles were explored. Functional enrichment of 55 common DEGs among 3 sub-datasets revealed several immune-related terms, such as neutrophil extravasation, leucocyte migration, antimicrobial humoral immune response mediated by an antimicrobial peptide, toll-like receptor 4 bindings, IL-17 signalling pathway, and TGF-beta signalling pathway. In the screening of 10 hub genes, including MPO, ELANE, CTSG, LTF, LCN2, SELP, CAMP, S100A9, ITGA2B, and PRTN3, and in choosing and validating the 5 DEGs, including ANK1, MBOAT2, SLC25A21, SLC43A1, and SOX6, the results showed that SLC43A1 and SOX6 were significantly decreased in EAE mice spleen. Thus this study offers a list of genes expressed in the spleen that might play a key role in the pathogenesis of EAE.

Affecting mechanism of Chlorella sorokiniana meal replacing fish meal on growth and immunity of Litopenaeus vannamei based on transcriptome analysis

The purpose of this study was to analyze the affecting mechanism of Chlorella sorokiniana meal replacing fish meal on the growth performance and immune function of Pacific white shrimp, Litopenaeus vannamei by transcriptomics analysis. In a control diet containing 560 g % /kg fish meal, chlorella was used to replace 0 %, 20 %, 40 %, 60 %, 80 % and 100 % of dietary fish meal to form six iso-nitrogenous and iso-lipidic diets (C0, C20, C40, C60, C80, C100). The six diets were fed to white shrimp (1.37 ± 0.02 g) for 8 weeks. Compared to the control group, the final body weight (FBW), specific growth rate (SGR) and feed efficiency (FE) of the C20 group were significantly improved (P < 0.05). There was no significant difference in growth performance between the C40 group and the C0 group (P > 0.05). When the replacement level of fish meal by chlorella was ≥ 60 %, the FBW, SGR and FE of shrimp were significantly decreased (P < 0.05). In the analysis of the hepatopancreatic transcriptome, the amount of differentially expressed genes (DEGs) in C20, C40, C60, C80 and C100 groups were 322 (176 up and 146 down), 482 (246 up and 236 down), 804 (487 up and 317 down), 1080 (684 up and 396 down) and 1262 (827 up and 435 down), respectively, when compared to the C0 group. GO Term and KEGG pathway enrichment analysis of DEGs showed that DEGs between the C20 and C0 groups were significantly enriched in chitin binding and inositol metabolism processes. Several immune-related signaling pathways were also enriched, including the C-type lectin receptor pathway, complement and coagulation cascades, the B-cell receptor pathway, and most of these genes were up-regulated in the C20 group. Compared to the C0 group, DEGs in C40, C60, C80 and C100 groups were significantly enriched in lysosomal, protein digestion, and absorption pathways. In addition, in the C80 and C100 groups, the DEGs were also significantly enriched in lipid catabolism process. The high replacement of fish meal with chlorella meal affected the absorption and utilization of nutrients by regulating the signaling pathways related to protein digestion and absorption, and aggravated lipid catabolism of the body, thereby affecting the growth of shrimp. The high replacement of fish meal might also affect the homeostasis of shrimp cells and reduce immunity through the lysosomal signaling pathway. In summary, the low replacement of fish meal (20 %) with chlorella meal significantly improved the growth performance of Litopenaeus vannamei, which might be realized through activating the C-type lectin receptor signaling pathway to regulate immune function of the body, but the high substitution of fish meal (≥ 60 %) reduced the growth performance and immunity by affecting the lysosomal signaling pathway.

Identification and Validation of the Diagnostic Markers for Inflammatory Bowel Disease by Bioinformatics Analysis and Machine Learning.

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract which is mediated by the inappropriate immune responses. This study was aimed to identify novel diagnostic biomarkers for diagnosis of IBD and explore the relationship between the diagnostic biomarkers and infiltrated immune cells. GSE38713, GSE53306, and GSE75214 downloaded from the Gene Expression Omnibus (GEO) database were split into training and testing sets. Differentially expressed genes (DEGs) were screened using the "limma" package. Gene Ontology (GO) and KEGG pathway enrichment analysis of DEGs were performed by clusterProfiler package. The LASSO regression and support vector machine recursive feature elimination (SVM-RFE) algorithms were conducted to identify novel diagnostic biomarkers. The receiver operating characteristic (ROC) curve was applied to evaluate the diagnostic value of the candidate biomarkers. The relationship of the candidate biomarkers and infiltrating immune cells in IBD were evaluated by CIBERSOTR. Quantitative Real-Time PCR (qRT-PCR) was applied to measure the expression level of the biomarkers in IBD. A total of 289 dysregulated genes were identified as DEGs in IBD. These DEGs were significantly enriched in chemokine signaling pathway and cytokine-cytokine receptor interaction. RHOU was identified as a critical diagnostic gene in IBD, which was confirmed using ROC curve and qRT-PCR assays. Immune cell infiltration analysis showed that RHOU was correlated with macrophages M2, dendritic cells resting, mast cells resting, T cells CD4 memory resting, macrophages M0, and mast cells activated. Our results imply that RHOU may be a potential diagnostic biomarker for IBD.

Search for key genes, key signaling pathways, and immune cell infiltration in uterine fibroids by bioinformatics analysis.

Uterine fibroids grow in the myometrium and are benign tumors. The etiology and molecular mechanism are not fully understood. Here, we hope to study the potential pathogenesis of uterine fibroids by bioinformatics. Our aim is to search for the key genes, signaling pathways and immune infiltration about the development of uterine fibroids. The GSE593 expression profile was downloaded from the Gene Expression Omnibus database, which contains 10 samples, including 5 uterine fibroids samples and 5 normal controls. Bioinformatics methods were used to find differentially expressed genes (DEGs) in tissues and further analyze the DEGs. R (version 4.2.1) software was used for Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) pathway enrichment analysis of DEGs in uterine leiomyoma tissues and normal control. STRING database was used to generate protein-protein interaction (PPI) networks of key genes. Then, CIBERSORT was used to assess the infiltration of immune cells in uterine fibroids. A total of 834 DEGs were identified, of which 465 were up-regulated and 369 were down-regulated. GO andKEGG pathway analysis showed that the DEGs were mainly concentrated in extracellular matrix and cytokine related signaling pathways. We identified 30 key genes in DEGs from the PPI network. There were some differences in infiltration immunity between the 2 tissues. This study indicated that screening key genes, signaling pathways and immune infiltration by comprehensive bioinformatics analysis is helpful to understand the molecular mechanism of uterine fibroids and provide new insights into understanding the molecular mechanism.

Effect of interfering TOR signaling pathway on the biosynthesis of terpenoids in Salvia miltiorrhiza Bge

ABSTRACT The TOR (Target of Rapamycin) signaling pathway, which takes TOR kinase as the core, regulates the absorption, distribution, and recycling of nutrients by integrating metabolic network and other signaling pathways, thus participating in the plant growth-defense trade-off. While terpenoids play an important role in plant growth, development, stress response, and signal transduction. The effect of the TOR signaling pathway on terpenoid biosynthesis in plants has yet to be studied in detail. In this study, the tissue culture seedlings of Salvia miltiorrhiza were treated with the TOR inhibitor AZD8055. The results show that the roots of the control group had begun to grow on the 8th day, while the seedlings treated with AZD8055 had no rooting signs. Combined with the expression changes of genes related to the TOR signaling pathway in the first 8 days, samples on the 3rd, 6th, and 8th days were selected for RNA-Seq analysis. Through RNA-Seq analysis, a total of 50,689 unigenes were obtained from the samples of these three periods, of which 4088 unigenes showed differential expression. The function enrichment and time-series analysis of differentially expressed genes (DEGs) showed that the main influence of the TOR signal pathway on plant growth-related processes was gradually transmitted with treatment time after TOR was inhibited. Pathway enrichment analysis of DEGs showed that the genes in the biosynthesis of terpenoids, such as diterpenoid and carotenoid biosynthetic pathways, could be regulated. Compared with other stages, DEGs related to terpenoid biosynthesis were mainly regulated in the S2 stage. In addition, the genes involved in terpenoid skeleton biosynthesis was also considerably enriched in the S2 stage, according to the results of gene set enrichment analysis (GSEA) of unigenes. Inhibition of the TOR signaling pathway may affect the biosynthesis of terpenoid signaling molecules, inhibit gibberellin’s biosynthesis, and promote abscisic acid’s biosynthesis. This study has discussed the effect of interfering with the TOR pathway on terpenoid biosynthesis in S. miltiorrhiza from the perspective of omics and provides new insight into the interaction between the terpenoid biosynthesis pathway and the growth-defense trade-off of medicinal plants.

Integrated analysis and clinical correlation analysis of hub genes, immune infiltration, and potential therapeutic agents related to lupus nephritis

Lupus nephritis (LN) is the most common complication of systemic lupus erythematosus (SLE). This study aimed to explore biomarkers, mechanisms, and potential novel agents regarding LN through bioinformatic analysis. Four expression profiles were downloaded from the Gene Expression Omnibus (GEO) database and differentially expressed genes (DEGs) were acquired. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGGs) pathway enrichment analyses of DEGs were performed using the R software. The protein-protein interaction (PPI) network was developed using the STRING database. Additionally, five algorithms were used to screen out the hub genes. Expression of the hub genes were validated using Nephroseq v5. CIBERSORT was used to evaluate the infiltration of immune cells. Finally, The Drug-Gene Interaction Database was used to predict potential targeted drugs. FOS and IGF1 were identified as hub genes, with excellent specificity and sensitivity diagnosis of LN. FOS was also related to renal injury. LN patients had lower activated and resting dendritic cells (DCs) and higher M1 macrophages and activated NK cells than healthy control (HC). FOS had a positive correlation with activated mast cells and a negative correlation with resting mast cells. IGF1 had a positive correlation with activated DCs and a negative correlation with monocytes. The targeted drugs were dusigitumab and xentuzumab target for IGF1. We analyzed the transcriptomic signature of LN along with the landscape of the immune cell. FOS and IGF1 are promising biomarkers for diagnosing and evaluating the progression of LN. The drug-gene interaction analyses provide a list of candidate drugs for the precise treatment of LN.

Peripheral Blood Gene Expression Profile of Infants with Atopic Dermatitis

To enhance the understanding of molecular mechanisms and mine previously unidentified biomarkers of pediatric atopic dermatitis, PBMC gene expression profiles were generated by RNA sequencing in infants with atopic dermatitis and age-matched controls. A total of 178 significantly differentially expressed genes (DEGs) (115 upregulations and 63 downregulations) were seen, compared with those in healthy controls. The DEGs identified included IL1β, TNF, TREM1, IL18R1, and IL18RAP. DEGs were validated by real-time RT- qPCR in a larger number of samples from PBMCs of infants with atopic dermatitis aged <12 months. Using the DAVID (Database for Annotation, Visualization and Integrated Discovery) database, functional and pathway enrichment analyses of DEGs were performed. Gene ontology enrichment analysis showed that DEGs were associated with immune responses, inflammatory responses, regulation of immune responses, and platelet activation. Pathway analysis indicated that DEGs were enriched in cytokine‒cytokine receptor interaction, immunoregulatory interactions between lymphoid and nonlymphoid cells, hematopoietic cell lineage, phosphoinositide 3-kinase‒protein kinase B signaling pathway, NK cell‒mediated cytotoxicity, and platelet activation. Furthermore, the protein‒protein interaction network was predicted using the STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database and visualized with Cytoscape software. Finally, on the basis of the protein‒protein interaction network, 18 hub genes were selected, and two significant modules were obtained. In conclusion, this study sheds light on the molecular mechanisms of pediatric atopic dermatitis and may provide diagnostic biomarkers and therapeutic targets.

Identification of potential biomarkers for colorectal cancer by clinical database analysis and Kaplan-Meier curves analysis.

This study aimed to explore critical genes as potential biomarkers for the diagnosis and prognosis of colorectal cancer (CRC) for clinical utility. To identify and screen candidate genes involved in CRC carcinogenesis and disease progression, we downloaded microarray datasets GSE89076, GSE73360, and GSE32323 from the GEO database identified differentially expressed genes (DEGs), and performed a functional enrichment analysis. A protein-protein interaction network was constructed, and correlated module analysis was performed using STRING and Cytoscape. The Kaplan-Meier survival curve shows the survival of the hub genes. The expression of cyclin-dependent kinase (CDK1), cyclin B1 (CCNB1), and PCNA in tissues and changes in tumor grade were analyzed. A total of 329 DEGs were identified, including 264 upregulated and 65 downregulated genes. The functions and pathways of DEGs include the mitotic cell cycle, poly(A) RNA binding replication, ATP binding, DNA replication, ribosome biogenesis in eukaryotes, and RNA transport. Forty-seven Hub genes were identified, and biological process analysis showed that these genes were mainly enriched in cell cycle and DNA replication. Patients with mutations in CDK1, PCNA, and CCNB1 had poorer survival rates. CDK1, PCNA, and CCNB1 were significantly overexpressed in the tumor tissues. The expression of CDK1 and CCNB1 gradually decreased with increasing tumor grade. CDK1, CCNB1, and PCNA can be used as potential markers for the diagnosis and prognosis of CRC. These genes are overexpressed in colon cancer tissues and are associated with low survival rates in CRC patients.

Transcriptome Analysis of the Immune Process of Golden Pompano (Trachinotus ovatus) Infected with Streptococcus agalactiae

The golden pompano (Trachinotus ovatus) is one of the most economically valuable marine fishes in South China. Streptococcus agalactiae, an infectious Gram-positive bacterium that is highly destructive for golden pompano culture, has recently caused massive losses to the golden pompano industry. This study aimed to investigate the dynamic immune response of golden pompano to S. agalactiae infection, using RNA-seq analysis at two different time points after infection. Abundances of differentially expressed genes (DEGs) gradually increased in the liver and spleen 48–120 h post-infection, whereas those in the head kidney were lower at 120 h than at 48 h. Pathway enrichment analysis of DEGs revealed that genes related to the complement system were continuously transcribed between 48 and 120 h. Metabolic and immune-regulation-related pathways were highly enriched in the liver 48 h after infection. Transcriptome analysis was verified using quantitative PCR for eight genes with similar expression trends. This study revealed the inflammatory response of golden pompano after S. agalactiae infection, including inflammation-related chemokines and signaling pathways. Our findings provide a theoretical basis for studying S. agalactiae resistance in golden pompano and provide a reliable resource for the genetic breeding of fish.

Identification and comprehensive analysis of circRNA-miRNA-mRNA regulatory networks in osteoarthritis.

Osteoarthritis (OA) is a common orthopedic degenerative disease, leading to high disability in activities of daily living. There remains an urgent need to identify the underlying mechanisms and identify new therapeutic targets in OA diagnosis and treatment. Circular RNAs (circRNAs) play a role in the development of multiple diseases. Many studies have reported that circRNAs regulate microRNAs (miRNAs) through an endogenous competitive mechanism. However, it remains unclear if an interplay between circRNAs, miRNAs, and target genes plays a deeper regulatory role in OA. Four datasets were downloaded from the GEO database, and differentially expressed circRNAs (DECs), differentially expressed miRNAs (DEMs), and differentially expressed genes (DEGs) were identified. Functional annotation and pathway enrichment analysis of DEGs and DECs were carried out to determine the main associated mechanism in OA. A protein-protein network (PPI) was constructed to analyze the function of, and to screen out, hub DEGs in OA. Based on the artificial intelligence prediction of protein crystal structures of two hub DEGs, TOP2A and PLK1, digitoxin and oxytetracycline were found to have the strongest affinity, respectively, with molecular docking. Subsequently, overlapping DEMs and miRNAs targeted by DECs obtained target DEMs (DETMs). Intersection of DEGs and genes targeted by DEMs obtained target DEGs (DETGs). Thus, a circRNA-miRNA-mRNA regulatory network was constructed from 16 circRNAs, 32 miRNAs, and 97 mRNAs. Three hub DECs have the largest number of regulated miRNAs and were verified through in vitro experiments. In addition, the expression level of 16 DECs was validated by RT-PCR. In conclusion, we constructed a circRNA-miRNA-mRNA regulatory network in OA and three new hub DECs, hsa_circ_0027914, hsa_circ_0101125, and hsa_circ_0102564, were identified as novel biomarkers for OA.