Differentially Expressed Genes Genes Research Articles

Integrated mRNA and miRNA analysis reveals the regulatory network of oxidative stress and inflammation in Coilia nasus brains during air exposure and salinity mitigation

BackgroundAir exposure is an inevitable source of stress that leads to significant mortality in Coilia nasus. Our previous research demonstrated that adding 10‰ NaCl to aquatic water could enhance survival rates, albeit the molecular mechanisms involved in air exposure and salinity mitigation remained unclear. Conversely, salinity mitigation resulted in decreased plasma glucose levels and improved antioxidative activity. To shed light on this phenomenon, we characterized the transcriptomic changes in the C. nasus brain upon air exposure and salinity mitigation by integrated miRNA-mRNA analysis.ResultsThe plasma glucose level was elevated during air exposure, whereas it decreased during salinity mitigation. Antioxidant activity was suppressed during air exposure, but was enhanced during salinity mitigation. A total of 629 differentially expressed miRNAs (DEMs) and 791 differentially expressed genes (DEGs) were detected during air exposure, while 429 DEMs and 1016 DEGs were identified during salinity mitigation. GO analysis revealed that the target genes of DEMs and DEGs were enriched in biological process and cellular component during air exposure and salinity mitigation. KEGG analysis revealed that the target genes of DEMs and DEGs were enriched in metabolism. Integrated analysis showed that 24 and 36 predicted miRNA-mRNA regulatory pairs participating in regulating glucose metabolism, Ca2+ transport, inflammation, and oxidative stress. Interestingly, most of these miRNAs were novel miRNAs.ConclusionIn this study, substantial miRNA-mRNA regulation pairs were predicted via integrated analysis of small RNA sequencing and RNA-Seq. Based on predicted miRNA-mRNA regulation and potential function of DEGs, miRNA-mRNA regulatory network involved in glucose metabolism and Ca2+ transport, inflammation, and oxidative stress in C. nasus brain during air exposure and salinity mitigation. They regulated the increased/decreased plasma glucose and inhibited/promoted antioxidant activity during air exposure and salinity mitigation. Our findings would propose novel insights to the mechanisms underlying fish responses to air exposure and salinity mitigation.

Predictive Value of a Diagnostic Five-Gene Biomarker for Pediatric Sepsis.

Pediatric sepsis has a very high morbidity and mortality rate. The purpose of this study was to evaluate diagnostic biomarkers and immune cell infiltration in pediatric sepsis. Three datasets (GSE13904, GSE26378, and GSE26440) were downloaded from the gene expression omnibus (GEO) database. After identifying overlapping genes in differentially expressed genes (DEGs) and modular sepsis genes selected via a weighted gene co-expression network (WGCNA) in the GSE26378 dataset, pivotal genes were further identified by using LASSO regression and random forest analysis to construct a diagnostic model. Receiver operating characteristic curve (ROC) analysis was used to validate the efficacy of the diagnostic model for pediatric sepsis. Furthermore, we used qRT-PCR to detect the expression levels of pivotal genes and validate the diagnostic model's ability to diagnose pediatric sepsis in 65 actual clinical samples. Among 294 overlapping genes of DEGs and modular sepsis genes, five pivotal genes (STOM, MS4A4A, CD177, MMP8, and MCEMP1) were screened to construct a diagnostic model of pediatric sepsis. The expression of the five pivotal genes was higher in the sepsis group than in the normal group. The diagnostic model showed good diagnostic ability with AUCs of 1, 0.986, and 0.968. More importantly, the diagnostic model showed good diagnostic ability with AUCs of 0.937 in the 65 clinical samples and showed better efficacy compared to conventional inflammatory indicators such as procalcitonin (PCT), white blood cell (WBC) count, C-reactive protein (CRP), and neutrophil percentage (NEU%). We developed and tested a five-gene diagnostic model that can reliably identify pediatric sepsis and also suggest prospective candidate genes for peripheral blood diagnostic testing in pediatric sepsis patients.

The identification of a novel shared therapeutic target and drug across all insulin-sensitive tissues under insulin resistance.

To identify key and shared insulin resistance (IR) molecular signatures across all insulin-sensitive tissues (ISTs), and their potential targeted drugs. Three datasets from Gene Expression Omnibus (GEO) were acquired, in which the ISTs (fat, muscle, and liver) were from the same individual with obese mice. Integrated bioinformatics analysis was performed to obtain the differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was carried out to determine the "most significant trait-related genes" (MSTRGs). Enrichment analysis and PPI network were performed to find common features and novel hub genes in ISTs. The shared genes of DEGs and genes between DEGs and MSTRGs across four ISTs were identified as key IR therapeutic target. The Attie Lab diabetes database and obese rats were used to verify candidate genes. A medical drug-gene interaction network was conducted by using the Comparative Toxicogenomics Database (CTD) to find potential targeted drugs. The candidate drug was validated in Hepa1-6 cells. Lipid metabolic process, mitochondrion, and oxidoreductase activity as common features were enriched from ISTs under an obese context. Thirteen shared genes (Ubd, Lbp, Hp, Arntl, Cfd, Npas2, Thrsp., Tpx2, Pkp1, Sftpd, Mthfd2, Tnfaip2, and Vnn3) of DEGs across ISTs were obtained and confirmed. Among them, Ubd was the only shared gene between DEGs and MSTRGs across four ISTs. The expression of Ubd was significantly upregulated across four ISTs in obese rats, especially in the liver. The IR Hepa1-6 cell models treated with dexamethasone (Dex), palmitic acid (PA), and 2-deoxy-D-ribose (dRib) had elevated expression of Ubd. Knockdown of Ubd increased the level of p-Akt. A lowing Ubd expression drug, promethazine (PMZ) from CTD analysis rescued the decreased p-Akt level in IR Hepa1-6 cells. This study revealed Ubd, a novel and shared IR molecular signature across four ISTs, as an effective biomarker and provided new insight into the mechanisms of IR. PMZ was a candidate drug for IR which increased p-Akt level and thus improved IR by targeting Ubd and downregulation of Ubd expression. Both Ubd and PMZ merit further clinical translational investigation to improve IR.

Analysis of genes characterizing chronic thrombosis and associated pathways in chronic thromboembolic pulmonary hypertension.

In chronic thromboembolic pulmonary hypertension (CTEPH), fibrosis of thrombi in the lumen of blood vessels and obstruction of blood vessels are important factors in the progression of the disease. Therefore, it is important to explore the key genes that lead to chronic thrombosis in order to understand the development of CTEPH, and at the same time, it is beneficial to provide new directions for early identification, disease prevention, clinical diagnosis and treatment, and development of novel therapeutic agents. The GSE130391 dataset was downloaded from the Gene Expression Omnibus (GEO) public database, which includes the full gene expression profiles of patients with CTEPH and Idiopathic Pulmonary Arterial Hypertension (IPAH). Differentially Expressed Genes (DEGs) of CTEPH and IPAH were screened, and then Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) functional enrichment analyses were performed on the DEGs; Weighted Gene Co-Expression Network Analysis (WGCNA) to screen the key gene modules and take the intersection genes of DEGs and the key module genes in WGCNA; STRING database was used to construct the protein-protein interaction (PPI) network; and cytoHubba analysis was performed to identify the hub genes. A total of 924 DEGs were screened, and the MEturquoise module with the strongest correlation was selected to take the intersection with DEGs A total of 757 intersecting genes were screened. The top ten hub genes were analyzed by cytoHubba: IL-1B, CXCL8, CCL22, CCL5, CCL20, TNF, IL-12B, JUN, EP300, and CCL4. IL-1B, CXCL8, CCL22, CCL5, CCL20, TNF, IL-12B, JUN, EP300, and CCL4 have diagnostic and therapeutic value in CTEPH disease, especially playing a role in chronic thrombosis. The discovery of NF-κB, AP-1 transcription factors, and TNF signaling pathway through pivotal genes may be involved in the disease progression process.

Identification and validation of NAD+ metabolism-related biomarkers in patients with diabetic peripheral neuropathy.

The mechanism of Nicotinamide Adenine Dinucleotide (NAD+) metabolism-related genes (NMRGs) in diabetic peripheral neuropathy (DPN) is unclear. This study aimed to find new NMRGs biomarkers in DPN. DPN related datasets GSE95849 and GSE185011 were acquired from the Gene Expression Omnibus (GEO) database. 51 NMRGs were collected from a previous article. To explore NMRGs expression in DPN and control samples, differential expression analysis was completed in GSE95849 to obtain differentially expressed genes (DEGs), and the intersection of DEGs and NMRGs was regarded as DE-NMRGs. Next, a protein-protein interaction (PPI) network based on DE-NMRGs was constructed and biomarkers were screened by eight algorithms. Additionally, Gene Set Enrichment Analysis (GSEA) enrichment analysis was completed, biomarker-based column line graphs were constructed, lncRNA-miRNA-mRNA and competing endogenouse (ce) RNA networks were constructed, and drug prediction was completed. Finally, biomarkers expression validation was completed in GSE95849 and GSE185011. 5217 DEGs were obtained from GSE95849 and 21 overlapping genes of DEGs and NMRGs were DE-NMRGs. Functional enrichment analysis revealed that DE-NMRGs were associated with glycosyl compound metabolic process. The PPI network contained 93 protein-interaction pairs and 21 nodes, with strong interactions between NMNAT1 and NAMPT, NADK and NMNAT3, ENPP3 and NUDT12 as biomarkers based on 8 algorithms. Expression validation suggested that ENPP3 and NUDT12 were upregulated in DPN samples (P < 0.05). Moreover, an alignment diagram with good diagnostic efficacy based on ENPP3 and NUDT12 were identified was constructed. GSEA suggested that ENPP3 was enriched in Toll like receptor (TLR) pathway, NUDT12 was enriched in maturity onset diabetes of the young and insulin pathway. Furthermore, 18 potential miRNAs and 36 Transcription factors (TFs) were predicted and the miRNA-mRNA-TF networks were constructed, suggesting that ENPP3 might regulate hsa-miR-34a-5p by affecting MYNN. The ceRNA network suggested that XLOC_013024 might regulate hsa-let-7b-5p by affecting NUDT12. 15 drugs were predicted, with 8 drugs affecting NUDT12 such as resveratrol, and 13 drugs affecting ENPP3 such as troglitazone. ENPP3 and NUDT12 might play key roles in DPN, which provides reference for further research on DPN.

Combining bioinformatics and machine learning algorithms to identify and analyze shared biomarkers and pathways in COVID-19 convalescence and diabetes mellitus.

Most patients who had coronavirus disease 2019 (COVID-19) fully recovered, but many others experienced acute sequelae or persistent symptoms. It is possible that acute COVID-19 recovery is just the beginning of a chronic condition. Even after COVID-19 recovery, it may lead to the exacerbation of hyperglycemia process or a new onset of diabetes mellitus (DM). In this study, we used a combination of bioinformatics and machine learning algorithms to investigate shared pathways and biomarkers in DM and COVID-19 convalescence. Gene transcriptome datasets of COVID-19 convalescence and diabetes mellitus from Gene Expression Omnibus (GEO) were integrated using bioinformatics methods and differentially expressed genes (DEGs) were found using the R programme. These genes were also subjected to Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to find potential pathways. The hub DEGs genes were then identified by combining protein-protein interaction (PPI) networks and machine learning algorithms. And transcription factors (TFs) and miRNAs were predicted for DM after COVID-19 convalescence. In addition, the inflammatory and immune status of diabetes after COVID-19 convalescence was assessed by single-sample gene set enrichment analysis (ssGSEA). In this study, we developed genetic diagnostic models for 6 core DEGs beteen type 1 DM (T1DM) and COVID-19 convalescence and 2 core DEGs between type 2 DM (T2DM) and COVID-19 convalescence and demonstrated statistically significant differences (p<0.05) and diagnostic validity in the validation set. Analysis of immune cell infiltration suggests that a variety of immune cells may be involved in the development of DM after COVID-19 convalescence. We identified a genetic diagnostic model for COVID-19 convalescence and DM containing 8 core DEGs and constructed a nomogram for the diagnosis of COVID-19 convalescence DM.

SR-18292 Induces Fetal Hemoglobin Synthesis and Reduces Disease Pathology in Sickle Cell Mice

Sickle cell disease (SCD) is a commonly inherited blood disorder, which affects millions around the world. Reactivation of fetal hemoglobin (HbF) synthesis in adult erythroid cells can diminish the severity of many clinical features of the disease. Up-regulation of peroxisome proliferator-activated receptor-γ coactivator-1α (PGC-1α) has been shown to induce HbF levels in human primary erythroid progenitor CD34 + cells. Here we reported the in vitro and in vivo effects of a small molecular compound, SR-18292, on HbF induction via modulating PGC-1α. To determine whether SR-18292 can induce HbF in human CD34 + cells, we performed RT-qPCR, flow cytometry, and western blot to measure the mRNA and protein levels of PGC-1α and γ-globin. We found that mRNA levels of PGC-1α and γ-globin were significantly increased in cells treated with SR-18292 while β-globin mRNA levels were unaffected. Protein levels of γ-globin and the ratio of F-cells were increased in treated cells. Erythroid cellular differentiation markers (CD71/CD235a) and terminal differentiation markers (CD49d/CD233) were used to examine the differentiation status. There was no significant difference in the population of CD71 +CD235a + cells among all groups. However, SR-18292 treatment increased the percentage of CD49d -CD233 + cells at 5 or 7.5 μM. These results suggest that SR-18292 at higher concentrations can promote erythroid terminal differentiation. To evaluate whether the HbF induction by SR-18292 can reduce the SCD disease pathology in SCD mice or not, we intraperitoneally administered SR-18292 to SCD mice. HbF induction was determined by flow cytometric analysis using an anti-HbF antibody. And it was shown that the HbF-high cell population was induced in SCD mice after 4 weeks of treatment. And we used transferrin receptor CD71 and erythroid-specific marker Ter119 to determine whether SR-18292 could affect erythroid differentiation. There was no significant difference in the CD71 +Ter119 + population after 4 weeks of SR-18192 treatment compared with untreated animals. The mRNA levels of γ-globin and PGC-1α were increased in treated mice. And the increase of PGC-1α and γ-globin protein levels were confirmed by western blot. Reticulocyte is a sign of premature RBC destruction (hemolysis), and the SCD mice usually have high reticulocyte counts (&amp;gt;50%). We measured the reticulocyte in the peripheral blood of SR-18292 treated and untreated SCD mice by flow-cytometric analyses and found that reticulocyte was significantly reduced in SR-18292 treated animals (from 55% to 20%) after 4 weeks of treatment. Complete blood cell counts (CBC) showed that SR-18292 treated mice had slightly elevated numbers of red blood cells (RBCs) and more hemoglobin, but lower counts of white blood cells, lymphocytes, and neutrophils. In addition, the RBC distribution width (RDW) was significantly reduced after SR-18292 treatment, indicating that the size of circulating RBCs was more uniform in treated mice. In addition, erythroid cell morphology was evaluated by blood smears with Wright-Giemsa stain. The number of sickled RBCs were reduced in SCD mice treated with SR-18292 for 4 weeks. This is consistent with our observation that RDW was reduced in SCD mice treated with SR-18292. Furthermore, SR-18292 treated mice had a statistically significant reduction in spleen size and weight in contrast to the spleens of untreated SCD mice. The indices of RBCs were improved in accompany with reduced hemolysis and a higher fraction of circulating mature erythrocytes in treated SCD mice. There were no observable adverse effects on other hematopoietic cell lineages. We also performed single-cell RNA-seqs in human CD34 + cells treated with SR-18292 and found that some known HbF repressors (such as BCL11A and ZBTB7A) were decreased in treated groups. Besides that, there were many differentially expressed genes (DEGs), which might be novel HbF regulators. We are verifying those DEGs genes by qPCR and using CRISPR/cas9 to examine their functions in globin regulation. In summary, our findings suggest that SR-18292 might be a promising therapeutic agent to treat SCD and modulating PGC-1α activity or the signaling pathways that it regulates might benefit therapeutically patients with SCD.

Predicting diagnostic gene biomarkers in patients with diabetic kidney disease based on weighted gene co expression network analysis and machine learning algorithms.

The present study was designed to identify potential diagnostic markers for diabetic kidney disease (DKD). Two publicly available gene expression profiles (GSE142153 and GSE30528 datasets) from human DKD and control samples were downloaded from the GEO database. Differentially expressed genes (DEGs) were screened between 23 DKD and 10 control samples using the gene data from GSE142153. Weighted gene co expression network analysis was used to find the modules related to DKD. The overlapping genes of DEGs and Turquoise modules were narrowed down and using the least absolute shrinkage and selection operator regression model and support vector machine-recursive feature elimination analysis to identify candidate biomarkers. The area under the receiver operating characteristic curve value was obtained and used to evaluate discriminatory ability using the gene data from GSE30528. A total of 110 DEGs were obtained: 64 genes were significantly upregulated and 46 genes were significantly downregulated. Weighted gene co expression network analysis found that the turquoise module had the strongest correlation with DKD (R = -0.58, P = 4 × 10-4). Thirty-eight overlapping genes of DEGs and turquoise modules were extracted. The identified DEGs were mainly involved in p53 signaling pathway, HIF-1 signaling pathway, JAK - STAT signaling pathway and FoxO signaling pathway between and the control. C-X-C motif chemokine ligand 3 was identified as diagnostic markers of DKD with an area under the receiver operating characteristic curve of 0.735 (95% CI 0.487-0.932). C-X-C motif chemokine ligand 3 was identified as diagnostic biomarkers of DKD and can provide new insights for future studies on the occurrence and the molecular mechanisms of DKD.

Identification of optimal feature genes in patients with thyroid associated ophthalmopathy and their relationship with immune infiltration: a bioinformatics analysis.

Thyroid associated ophthalmopathy (TAO) is an organ-specific autoimmune disease that has a significant impact on individuals and society. The etiology of TAO is complicated and poorly understood. Thus, the goal of this study was to use bioinformatics to look into the pathogenesis of TAO and to identify the optimum feature genes (OFGs) and immune infiltration patterns of TAO. Firstly, the GSE58331 microarray data set was utilized to find 366 differentially expressed genes (DEGs). To find important modular genes, the dataset was evaluated using weighted gene coexpression network analysis (WGCNA). Then, the overlap genes of major module genes and DEGs were further assessed by applying three machine learning techniques to find the OFGs. The CIBERSORT approach was utilized to examine immune cell infiltration in normal and TAO samples, as well as the link between optimum characteristic genes and immune cells. Finally, the related pathways of the OFGs were predicted using single gene set enrichment analysis (ssGSEA). KLB, TBC1D2B, LINC01140, SGCG, TMEM37, and LINC01697 were the six best feature genes that were employed to create a nomogram with high predictive performance. The immune cell infiltration investigation revealed that the development of TAO may include memory B cells, T cell follicular helper cells, resting NK cells, macrophages of type M0, macrophages of type M1, resting dendritic cells, active mast cells, and neutrophils. In addition, ssGSEA results found that these characteristic genes were closely associated with lipid metabolism pathways. In this research, we found that KLB, TBC1D2B, LINC01140, SGCG, TMEM37, and LINC01697 are intimately associated with the development and progression of TAO, as well as with lipid metabolism pathways.

The mechanism of dehydroandrographolide inhibiting metastasis in gastric cancer based on network pharmacology and bioinformatics.

Gastric cancer (GC) is the most aggressive malignant tumor of the digestive tract. However, there is still a lack of effective treatment methods in clinical practice. Studies have shown that dehydroandrographolide (DA) has been shown to have anti-cancer activity in a variety of cancers, but it has not been reported in GC. Firstly, we obtained data on DA target genes, GC-related genes, and differentially expressed genes (DEGs) from the PharmMapper, GeneCards, and GEO databases, respectively. Then, the STRING database was used to construct the protein-protein interaction network of intersection genes, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses of intersection genes were performed. Finally, 8 hub target genes were identified by analyzing their expression and prognostic survival, and molecular docking between the hub genes and DA was performed. In this study, 293 DA drug target genes, 11,366 GC-related genes, and 3184 DEGs were identified. Gene Ontology and KEGG analysis showed that the intersection genes of DA targets and GC-related genes were mainly related to cancer pathways involving apoptosis and cell adhesion. The intersection genes of DEGs, DA targets, and GC-related genes were also mainly related to cancer pathways involving chemical carcinogenesis, and drug metabolism. The molecular docking results showed that the 8 hub target genes had an apparent affinity for DA, which could be used as potential targets for DA treatment of GC. The results of this study show that the molecular mechanism by which DA inhibits GC metastasis involves multiple target genes. It may play an essential role in inhibiting the invasion and metastasis of GC by regulating the expression and polymorphism of hub target genes, such as MMP9, MMP12, CTSB, ESRRG, GSTA1, ADHIC, CA2, and AKR1C2.

Osteoarthritis related epigenetic variations in miRNA expression and DNA methylation

Osteoarthritis (OA) is chronic arthritis characterized by articular cartilage degradation. However, a comprehensive regulatory network for OA-related microRNAs and DNA methylation modifications has yet to be established. Thus, we aimed to identify epigenetic changes in microRNAs and DNA methylation and establish the regulatory network between miRNAs and DNA methylation. The mRNA, miRNA, and DNA methylation expression profiles of healthy or osteoarthritis articular cartilage samples were downloaded from Gene Expression Omnibus (GEO) database, including GSE169077, GSE175961, and GSE162484. The differentially expressed genes (DEGs), differentially expressed miRNAs (DEMs), and differentially methylated genes (DMGs) were analyzed by the online tool GEO2R. DAVID and STRING databases were applied for functional enrichment analysis and protein-protein interaction (PPI) network. Potential therapeutic compounds for the treatment of OA were identified by Connectivity map (CMap) analysis. A total of 1424 up-regulated DEGs, 1558 down-regulated DEGs, 5 DEMs with high expression, 6 DEMs with low expression, 1436 hypermethylated genes, and 455 hypomethylated genes were selected. A total of 136 up-regulated and 65 downregulated genes were identified by overlapping DEGs and DEMs predicted target genes which were enriched in apoptosis and circadian rhythm. A total of 39 hypomethylated and 117 hypermethylated genes were obtained by overlapping DEGs and DMGs, which were associated with ECM receptor interactions and cellular metabolic processes, cell connectivity, and transcription. Moreover, The PPI network showed COL5A1, COL6A1, LAMA4, T3GAL6A, and TP53 were the most connective proteins. After overlapping of DEGs, DMGs and DEMs predicted targeted genes, 4 up-regulated genes and 11 down-regulated genes were enriched in the Axon guidance pathway. The top ten genes ranked by PPI network connectivity degree in the up-regulated and downregulated overlapping genes of DEGs and DMGs were further analyzed by the CMap database, and nine chemicals were predicted as potential drugs for the treatment of OA. In conclusion, TP53, COL5A1, COL6A1, LAMA4, and ST3GAL6 may play important roles in OA genesis and development.

#3839 THERAPEUTIC TARGETS OF ARSENIC TRIOXIDE IN LUPUS NEPHRITIS – DATA FROM BIOINFORMATICS ANALYSIS AND IN VITRO STUDIES

Abstract Background and Aims Patients with systemic lupus erythematosus (SLE) are at an increased risk of developing lupus nephritis (LN), which results in high morbidity and mortality. There is preliminary data showing the efficacy of low dose of Arsenic Trioxide (ATO) in reducing renal flares amongst LN patients, but the underlying mechanism for such benefit remains unclear. Method We performed bioinformatic analysis and network pharmacology to elucidate the potential targets from differentially expressed genes (DEGs) of human SLE and LN peripheral blood mononuclear cells (PBMCs) datasets and pharmacological databases. The relationship between the potential targets and immune cells was further analyzed. Results Twelve intersection genes of DEGs in SLE, predicting targets of ATO and immune related genes were identified. KEGG pathway analysis suggested that the mechanisms of ATO in SLE were associated with IL-17 signalling pathway (p = 1.67E-18), TNF signalling pathway (p = 5.77E-11) and NOD-like receptor signalling pathway (p = 1.93E-09). Ten types of immune cells showed significant difference with intersection genes expression in SLE using ssGSEA approach. MMP9 was the most significantly associated with immune cells and showed positive correlations with macrophages and neutrophils. A significant TF-MMP9-miRNA regulatory network was constructed using cytoscape software. Pilot data from our in vitro studies suggested that ATO might downregulate MMP9 expression in PBMCs obtained from LN patients during disease remission (n = 3). Conclusion ATO interacts with immune cells in LN patients via different inflammatory pathways and may downregulate MMP9 expression in PBMCs.

Expression and clinical significance of PD-1 in UCEC and its Impact on tumor.

Programmed death 1 (PD-1) plays an important role in the immune escape, occurrence and development of tumors by inhibiting the function of immune cells. However, its prognostic value in uterine corpus endometrial carcinoma (UCEC) and its impact on the tumor microenvironment remain to be further explored. Transcriptional datasets were retrieved from the GEPIA, TIMER and TCGA databases. "edgeR" package was used for the identification of differentially expressed genes (DEGs) between two groups of patients (PD1-high and PD1-low group). Gene set enrichment analysis (GSEA) was performed to identify underlying pathways between betweenPD1-high and PD1-low groups functioning in UCEC. Gene Correlation Analysis was used to further confirm the associations of PD1 expression with T-cell-related genes. Cytoscape software was used to identify hub genes in DEGs. Kaplan-Meier analysis was performed to validate the prognostic value of hub genes. Mutational characteristics of UCEC patients according to PD1 levels were depicted and analyzed. We found that the transcriptional expression of the PD1 gene in UCEC tumor tissues markedly increased in cohorts from the GEPIA and TCGA databases. PD1 expression was negatively correlated with gene signatures associated with the T-cell receptor signaling pathway and primary immunodeficiency. GESA confirmed that PD1 expression was negatively correlated with gene signatures associated with the T-cell receptor signaling pathway. T-cell receptor complex-related genes, ZAP70, TRAC, CD3D, CD3E, CD8A, TRBC2, TRBV28 and CD247, showed significant positive associations with PD1 expression. The results of the Kaplan-Meier OS analysis indicated that PD1, TIGIT, FASLG, ICOS and TNFRSF9 are the protective factor for patients with UCEC. The top 5 genes of mutations in the low expression group, included PTEN (56%), PIK3CA (43%), TP53 (41%), TTN (39%), and ARID1A (37%). The genes with a higher proportion of mutations in the PD1-high group are PTEN (67%), TTN (62%), PIK3CA (53%), ARID1A (52%), and MUC16 (12%). The prognosis of UCEC patients with PD1 overexpression phenotype is worse than that of the PD1 low group, which is due to the involvement of the PD1 gene in the T-cell receptor signaling pathway. This study provides a further theoretical basis and reference for targeted therapy against PD1.

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.

Bioinformatic analysis and experimental validation of the potential gene in the airway inflammation of steroid-resistant asthma

Steroid-resistant asthma is a troublesome clinical problem in public health. The pathogenesis of steroid-resistant asthma is complex and remains to be explored. In our work, the online Gene Expression Omnibus microarray dataset GSE7368 was used to explore differentially expressed genes (DEGs) between steroid-resistant asthma patients and steroid-sensitive asthma patients. Tissue-specific gene expression of DEGs was analyzed using BioGPS. The enrichment analyses were performed using GO, KEGG, and GSEA analysis. The protein–protein interaction network and key gene cluster were constructed using STRING, Cytoscape, MCODE, and Cytohubba. A steroid-resistant neutrophilic asthma mouse model was established using lipopolysaccharide (LPS) and ovalbumin (OVA). An LPS-stimulated J744A.1 macrophage model was prepared to validate the underlying mechanism of the interesting DEG gene using the quantitative reverse transcription-polymerase chain reaction (qRT-PCR). A total of 66 DEGs were identified, most of which were present in the hematologic/immune system. Enrichment analysis displayed that the enriched pathways were the IL-17 signaling pathway, MAPK signal pathway, Toll-like receptor signaling pathway, and so on. DUSP2, as one of the top upregulated DEGs, has not been clearly demonstrated in steroid-resistant asthma. In our study, we observed that the salubrinal administration (DUSP2 inhibitor) reversed neutrophilic airway inflammation and cytokine responses (IL-17A, TNF-α) in a steroid-resistant asthma mouse model. We also found that salubrinal treatment reduced inflammatory cytokines (CXCL10 and IL-1β) in LPS-stimulated J744A.1 macrophages. DUSP2 may be a candidate target for the therapy of steroid-resistant asthma.

Establishment of a N1-methyladenosine-related risk signature for breast carcinoma by bioinformatics analysis and experimental validation

ObjectivesBreast carcinoma (BRCA) has resulted in a huge health burden globally. N1-methyladenosine (m1A) RNA methylation has been proven to play key roles in tumorigenesis. Nevertheless, the function of m1A RNA methylation-related genes in BRCA is indistinct.MethodsThe RNA sequencing (RNA-seq), copy-number variation (CNV), single-nucleotide variant (SNV), and clinical data of BRCA were acquired via The Cancer Genome Atlas (TCGA) database. In addition, the GSE20685 dataset, the external validation set, was acquired from the Gene Expression Omnibus (GEO) database. 10 m1A RNA methylation regulators were obtained from the previous literature, and further analyzed through differential expression analysis by rank-sum test, mutation by SNV data, and mutual correlation by Pearson Correlation Analysis. Furthermore, the differentially expressed m1A-related genes were selected through overlapping m1A-related module genes obtained by weighted gene co-expression network analysis (WGCNA), differentially expressed genes (DEGs) in BRCA and DEGs between high- and low- m1A score subgroups. The m1A-related model genes in the risk signature were derived by univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. In addition, a nomogram was built through univariate and multivariate Cox analyses. After that, the immune infiltration between the high- and low-risk groups was investigated through ESTIMATE and CIBERSORT. Finally, the expression trends of model genes in clinical BRCA samples were further confirmed by quantitative real-time PCR (RT‒qPCR).ResultsEighty-five differentially expressed m1A-related genes were obtained. Among them, six genes were selected as prognostic biomarkers to build the risk model. The validation results of the risk model showed that its prediction was reliable. In addition, Cox independent prognosis analysis revealed that age, risk score, and stage were independent prognostic factors for BRCA. Moreover, 13 types of immune cells were different between the high- and low-risk groups and the immune checkpoint molecules TIGIT, IDO1, LAG3, ICOS, PDCD1LG2, PDCD1, CD27, and CD274 were significantly different between the two risk groups. Ultimately, RT-qPCR results confirmed that the model genes MEOX1, COL17A1, FREM1, TNN, and SLIT3 were significantly up-regulated in BRCA tissues versus normal tissues.ConclusionsAn m1A RNA methylation regulator-related prognostic model was constructed, and a nomogram based on the prognostic model was constructed to provide a theoretical reference for individual counseling and clinical preventive intervention in BRCA.

Identification and verification of ferroptosis-related genes in gastric intestinal metaplasia.

Background: Gastric intestinal metaplasia (IM) is the key link of gastric precancerous lesions. Ferroptosis is a novel form of programmed cell death. However, its impact on IM is unclear. The focus of this study is to identify and verify ferroptosis-related genes (FRGs) that may be involved in IM by bioinformatics analysis. Materials and methods: Differentially expressed genes (DEGs) were obtained from microarray dataset GSE60427 and GSE78523 downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed ferroptosis-related genes (DEFRGs) were obtained from overlapping genes of DEGs and FRGs got from FerrDb. DAVID database was used for functional enrichment analysis. Protein-protein interaction (PPI) analysis and Cytoscape software were used to screen hub gene. In addition, we built a receiver operating characteristic (ROC) curve and verified the relative mRNA expression by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Finally, the CIBERSORT algorithm was used to analyze the immune infiltration in IM. Results: First, a total of 17 DEFRGs were identified. Second, a gene module identified by Cytoscape software was considered as hub gene: PTGS2, HMOX1, IFNG, and NOS2. Third, ROC analysis showed that HMOX1 and NOS2 had good diagnostic characteristics. qRT-PCR experiments confirmed the differential expression of HMOX1 in IM and normal gastric tissues. Finally, immunoassay showed that the proportion of T cells regulatory (Tregs) and macrophages M0 in IM was relatively higher, while the proportion of T cells CD4 memory activated and dendritic cells activated was lower. Conclusion: We found significant associations between FRGs and IM, and HMOX1 may be diagnostic biomarkers and therapeutic targets for IM. These results may enhance our understanding of IM and may contribute to its treatment.

Screening of key pathogenic genes of type 1 diabetes in children.

The etiology of type 1 diabetes mellitus (T1DM) in pediatric populations remains poorly understood. The key to precise prevention and treatment of T1DM in identifying crucial pathogenic genes. These key pathogenic genes can serve as biological markers for early diagnosis and classification, as well as therapeutic targets. However, there is currently a lack of relevant research on screening key pathogenic genes based on sequencing data and efficient algorithms. The transcriptome sequencing results of peripheral blood mononuclear cells (PBMCs) of children with T1DM (GSE156035) were downloaded from the Gene Expression Omnibus (GEO) database. The data set contained 20 T1DM samples and 20 control samples. Differentially expressed genes (DEGs) in children with T1DM were selected based on fold change (FC) >1.5 times and adjusted P value <0.05. The weighted gene co-expression network was constructed. Hub genes were screened as modular membership (MM) >0.8 and gene significance (GS) >0.5. Intersection genes of DEGs and hub genes were defined as key pathogenic genes. The diagnostic efficacy of key pathogenic genes was evaluated using receiver operator characteristic (ROC) curves. A total of 293 DEGs were selected. Compared with the control group, 94 genes were down-regulated and 199 genes were up-regulated in the treatment group. Black modules (Cor =0.52, P=2e-12) were positively correlated with diabetic traits, whereas brown modules (Cor =-0.51, P=5e-12) and pink modules (Cor =-0.53, P=5e-13) were negatively correlated with diabetic traits. The black module contained 15 hub genes, the pink gene module contained 9 hub genes, and the brown module contained 52 hub genes. The intersection of hub genes and DEGs contained 2 genes, CCL25 and EGFR. The expression of CCL25 and EGFR was low in control samples and high in the test group (P<0.001). The areas under ROC curves (AUCs) of CCL25 and EGFR were 0.852 and 0.867, respectively (P<0.05). Weighted correlation network analysis (WGCNA) was used to identify the key pathogenic genes of T1DM in children, including CCL25 and EGFR, which have good diagnostic efficacy for T1DM in children.

Construction of the Interaction Network of Hub Genes in the Progression of Barrett's Esophagus to Esophageal Adenocarcinoma.

Esophageal adenocarcinoma (EAC) is one of the histologic types of esophageal cancer with a poor prognosis. The majority of EAC originate from Barrett's esophagus (BE). There are few studies focusing on the dynamic progression of BE to EAC. R software was used to analyze differentially expressed genes (DEGs) based on RNA-seq data of 94 normal esophageal squamous epithelial (NE) tissues, 113 BE tissues and 147 EAC tissues. The overlapping genes of DEGs between BE and EAC were analyzed by Venn diagram tool. The hub genes were selected by Cytoscape software based on the protein-protein interaction network of the overlapping genes using STRING database. The functional analysis of hub genes was performed by R software and the protein expression was identified by immunohistochemistry. In the present study, we found a large degree of genetic similarity between BE and EAC, and further identified seven hub genes (including COL1A1, TGFBI, MMP1, COL4A1, NID2, MMP12, CXCL1) which were all progressively upregulated in the progression of NE-BE-EAC. We have preliminarily uncovered the probable molecular mechanisms of these hub genes in disease development and constructed the ceRNA regulatory network of hub genes. More importantly, we explored the possibility of hub genes as biomarkers in the disease progression of NE-BE-EAC. For example, TGFBI can be used as biomarkers to predict the prognosis of EAC patients. COL1A1, NID2 and COL4A1 can be used as biomarkers to predict the response to immune checkpoint blockade (ICB) therapy. We also constructed a disease progression risk model for NE-BE-EAC based on CXCL1, MMP1 and TGFBI. Finally, the results of drug sensitivity analysis based on hub genes showed that drugs such as PI3K inhibitor TGX221, bleomycin, PKC inhibitor Midostaurin, Bcr-Abl inhibitor Dasatinib, HSP90 inhibitor 17-AAG, and Docetaxel may be potential candidates to inhibit the progression of BE to EAC. This study is based on a large number of clinical samples with high credibility, which is useful for revealing the probable carcinogenic mechanism of BE to EAC and developing new clinical treatment strategies.

Comprehensive analysis of scRNA-Seq and bulk RNA-Seq reveals dynamic changes in the tumor immune microenvironment of bladder cancer and establishes a prognostic model

BackgroundThe prognostic management of bladder cancer (BLCA) remains a great challenge for clinicians. Recently, bulk RNA-seq sequencing data have been used as a prognostic marker for many cancers but do not accurately detect core cellular and molecular functions in tumor cells. In the current study, bulk RNA-seq and single-cell RNA sequencing (scRNA-seq) data were combined to construct a prognostic model of BLCA.MethodsBLCA scRNA-seq data were downloaded from Gene Expression Omnibus (GEO) database. Bulk RNA-seq data were obtained from the UCSC Xena. The R package "Seurat" was used for scRNA-seq data processing, and the uniform manifold approximation and projection (UMAP) were utilized for downscaling and cluster identification. The FindAllMarkers function was used to identify marker genes for each cluster. The limma package was used to obtain differentially expressed genes (DEGs) affecting overall survival (OS) in BLCA patients. Weighted gene correlation network analysis (WGCNA) was used to identify BLCA key modules. The intersection of marker genes of core cells and genes of BLCA key modules and DEGs was used to construct a prognostic model by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) analyses. Differences in clinicopathological characteristics, immune microenvironment, immune checkpoints, and chemotherapeutic drug sensitivity between the high and low-risk groups were also investigated.ResultsscRNA-seq data were analyzed to identify 19 cell subpopulations and 7 core cell types. The ssGSEA showed that all 7 core cell types were significantly downregulated in tumor samples of BLCA. We identified 474 marker genes from the scRNA-seq dataset, 1556 DEGs from the Bulk RNA-seq dataset, and 2334 genes associated with a key module identified by WGCNA. After performing intersection, univariate Cox, and LASSO analysis, we obtained a prognostic model based on the expression levels of 3 signature genes, namely MAP1B, PCOLCE2, and ELN. The feasibility of the model was validated by an internal training set and two external validation sets. Moreover, patients with high-risk scores are predisposed to experience poor OS, a larger prevalence of stage III-IV, a greater TMB, a higher infiltration of immune cells, and a lesser likelihood of responding favorably to immunotherapy.ConclusionBy integrating scRNA-seq and bulk RNA-seq data, we constructed a novel prognostic model to predict the survival of BLCA patients. The risk score is a promising independent prognostic factor that is closely correlated with the immune microenvironment and clinicopathological characteristics.