Screening of cytokines-cytokine receptor-associated genes in childhood asthma based on bioinformatics.

Objective: To explore the hub genes and mechanisms in the pathological process of non-alcoholic steatohepatitis (NASH) by bioinformatics methods. Methods: Microarray datasets GSE89632 were downloaded from the Gene Expression Omnibus (GEO) database, which including 20 simple non-alcoholic fatty liver disease patients, 19 NASH patients and 24 healthy control individuals. The differentially expressed genes (DEGs) in patients with simple non-alcoholic fatty liver disease and NASH were compared with healthy control individuals respectively, and the intersection of the two groups of DEGs was taken. GO functional annotation and KEGG pathway enrichment analysis of DEGs were performed with DAVID 6.8 and KOBAS 3.0 separately. Protein-protein interaction network (PPI) was constructed by STRING database, then the mRNA hub genes were selected by Cytoscape software. The Attie Lab Diabetes database was used to verify the relative expression of hub genes mRNA in the liver of 4 groups of C57BL/6 mice (4-week-old normal group, 4-week-old obese group, 10-week-old normal group and 10-week-old obese group, 5 mice in each group). Spearman's correlation analysis was performed to analyze the correlation between hub gens and prognostic clinical parameters. Results: From the GSE89632 dataset, 365 common DEGs (115 up-regulated genes and 250 down-regulated genes) were identified in patients with simple non-alcoholic fatty liver disease and NASH patients compared with control individuals. GO analysis showed that DEGs were mainly enriched in biological processes such as inflammatory response and immune response. KEGG pathway analysis showed that up-regulated genes were mainly enriched in cholesterol metabolism, bile secretion and fat digestion and absorption signal pathways. Down-regulated genes were mainly enriched in interleukin-17 signaling pathway, tumor necrosis factor signaling pathway, advanced glycation end products and their receptors of diabetic complications. Seven key hub genes were identified by PPI analysis, which were FOS, EGR1, FOSB, JUNB, FOSL1, MYC and NR4A1.The mRNA relative expression levels of EGR1 and JUNB in the liver of 10-week-old obese mice were lower than those of normal mice (P<0.05).The relative expression levels of NR4A1 in the liver of obese mice at 4-and 10-week-old were lower than those of normal mice at the same age (P values<0.05). Spearman's correlation analysis showed that the expression of EGR1 was negatively correlated with the degree of hepatic steatosis (r=-0.785, P<0.001).The expression levels of FOSB, MYC and NR4A1 were negatively correlated with the level of alanine aminotransferase (r=-0.649, -0.597 and-0.580 respectively, all P values<0.001). Conclusion: EGR1, FOSB, MYC, JUNB and NR4A1 might be the hub genes in the pathological process of NASH and the inflammatory and immune response in hepatocytes, IL-17 signaling pathway and TNF signaling pathway might be the key molecular mechanisms in the occurrence and development of NASH.

Previous studies have shown that asthma is a risk factor for lung cancer, while the mechanisms involved remain unclear. We attempted to further explore the association between asthma and non-small cell lung cancer (NSCLC) via bioinformatics analysis. We obtained GSE143303 and GSE18842 from the GEO database. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) groups were downloaded from the TCGA database. Based on the results of differentially expressed genes (DEGs) between asthma and NSCLC, we determined common DEGs by constructing a Venn diagram. Enrichment analysis was used to explore the common pathways of asthma and NSCLC. A protein-protein interaction (PPI) network was constructed to screen hub genes. KM survival analysis was performed to screen prognostic genes in the LUAD and LUSC groups. A Cox model was constructed based on hub genes and validated internally and externally. Tumor Immune Estimation Resource (TIMER) was used to evaluate the association of prognostic gene models with the tumor microenvironment (TME) and immune cell infiltration. Nomogram model was constructed by combining prognostic genes and clinical features. 114 common DEGs were obtained based on asthma and NSCLC data, and enrichment analysis showed that significant enrichment pathways mainly focused on inflammatory pathways. Screening of 5 hub genes as a key prognostic gene model for asthma progression to LUAD, and internal and external validation led to consistent conclusions. In addition, the risk score of the 5 hub genes could be used as a tool to assess the TME and immune cell infiltration. The nomogram model constructed by combining the 5 hub genes with clinical features was accurate for LUAD. Five-hub genes enrich our understanding of the potential mechanisms by which asthma contributes to the increased risk of lung cancer.

Background Pulmonary arterial hypertension (PAH) is a disease or pathophysiological syndrome which has a low survival rate with abnormally elevated pulmonary artery pressure caused by known or unknown reasons. In addition, the pathogenesis of PAH is not fully understood. Therefore, it has become an urgent matter to search for clinical molecular markers of PAH, study the pathogenesis of PAH, and contribute to the development of new science-based PAH diagnosis and targeted treatment methods. Methods In this study, the Gene Expression Omnibus (GEO) database was used to downloaded a microarray dataset about PAH, and the differentially expressed genes (DEGs) between PAH and normal control were screened out. Moreover, we performed the functional enrichment analyses and protein-protein interaction (PPI) network analyses of the DEGs. In addition, the prediction of miRNA and transcriptional factor (TF) of hub genes and construction miRNA-TF-hub gene network were performed. Besides, the ROC curve was used to evaluate the diagnostic value of hub genes. Finally, the potential drug targets for the 5 identified hub genes were screened out. Results 69 DEGs were identified between PAH samples and normal samples. GO and KEGG pathway analyses revealed that these DEGs were mostly enriched in the inflammatory response and cytokine-cytokine receptor interaction, respectively. The miRNA-hub genes network was conducted subsequently with 131 miRNAs, 7 TFs, and 5 hub genes (CCL5, CXCL12, VCAM1, CXCR1, and SPP1) which screened out via constructing the PPI network. 17 drugs interacted with 5 hub genes were identified. Conclusions Through bioinformatic analysis of microarray data sets, 5 hub genes (CCL5, CXCL12, VCAM1, CXCR1, and SPP1) were identified from DEGs between control samples and PAH samples. Studies showed that the five hub genes might play an important role in the development of PAH. These 5 hub genes might be potential biomarkers for diagnosis or targets for the treatment of PAH. In addition, our work also indicated that paying more attention on studies based on these 5 hub genes might help to understand the molecular mechanism of the development of PAH.

The aim of the present study was to identify potential key candidate genes and mechanisms associated with rheumatoid arthritis (RA). Gene expression data from GSE55235, GSE55457 and GSE1919 datasets were downloaded from the Gene Expression Omnibus database. These datasets comprised 78 tissue samples collectively, including 25 healthy synovial membrane samples and 28 RA synovial membrane samples, whilst the 25 osteoarthritis (OA) samples were not included in the analysis. The differentially expressed genes (DEGs) between the two types of samples were identified with the Linear Models for Microarray Analysis package in R. Gene Ontology (GO) functional term and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analyses were also performed. In addition, Protein-Protein Interaction (PPI) network and module analyses were visualized using Cytoscape, and subsequent hub gene identification as well as GO and KEGG enrichment analyses of the modules was performed. Finally, reverse transcription-quantitative PCR (RT-qPCR) was used to validate the expression of the DEGs identified by GO and KEGG analysis in vitro. The analysis identified 491 DEGs, including 289 upregulated and 202 downregulated genes, which were mainly enriched in the following pathways: ‘Cytokine-cytokine receptor interaction’, ‘Rheumatoid arthritis’, ‘Chemokine signaling pathway’, ‘Intestinal immune network for IgA production’ and ‘Primary immunodeficiency’. The top 10 hub genes identified from the PPI network were IL-6, protein tyrosine phosphatase receptor type C, VEGFA, CD86, EGFR, C-X-C chemokine receptor type 4, matrix metalloproteinase 9, CC-chemokine receptor type (CCR)7, CCR5 and selectin L. KEGG signaling pathway enrichment analysis of the top two modules identified from the PPI network revealed that the genes in Module 1 were mainly enriched in the ‘Cytokine-cytokine receptor interaction’ and ‘Chemokine signaling pathway’, whereas analysis of Module 2 revealed that the genes were mainly enriched in ‘Primary immunodeficiency’ and ‘Cytokine-cytokine receptor interaction’. Finally, the results of the RT-qPCR and western blot analysis demonstrated that the expression levels of inflammation and NF-κB signaling pathway-related mRNAs were significantly upregulated following lipopolysaccharide stimulation. In conclusion, the findings of the present study identified key genes and signaling pathways associated with RA, which may improve the current understanding of the molecular mechanisms underlying its development and progression. The identified hub genes may also be used as potential targets for RA diagnosis and treatment.

IntroductionEssential thrombocytosis (ET) is a group of myeloproliferative neoplasms characterized by abnormal proliferation of platelet and megakaryocytes. Research on potential key genes and novel regulatory markers in essential thrombocythemia (ET) is still limited.MethodsDownloading array profiles from the Gene Expression Omnibus database, we identified the differentially expressed genes (DEGs) through comprehensive bioinformatic analysis. GO, and REACTOME pathway enrichment analysis was used to predict the potential functions of DEGs. Besides, constructing a protein–protein interaction (PPI) network through the STRING database, we validated the expression level of hub genes in an independent cohort of ET, and the transcription factors (TFs) were detected in the regulatory networks of TFs and DEGs. And the candidate drugs that are targeting hub genes were identified using the DGIdb database.ResultsWe identified 63 overlap DEGs that included 21 common up-regulated and 42 common down-regulated genes from two datasets. Functional enrichment analysis shows that the DEGs are mainly enriched in the immune system and inflammatory processes. Through PPI network analysis, ACTB, PTPRC, ACTR2, FYB, STAT1, ETS1, IL7R, IKZF1, FGL2, and CTSS were selected as hub genes. Interestingly, we found that the dysregulated hub genes are also aberrantly expressed in a bone marrow cohort of ET. Moreover, we found that the expression of CTSS, FGL2, IKZF1, STAT1, FYB, ACTR2, PTPRC, and ACTB genes were significantly under-expressed in ET (P<0.05), which is consistent with our bioinformatics analysis. The ROC curve analysis also shows that these hub genes have good diagnostic value. Besides, we identified 4 TFs (SPI1, IRF4, SRF, and AR) as master transcriptional regulators that were associated with regulating the DEGs in ET. Cyclophosphamide, prednisone, fluorouracil, ruxolitinib, and lenalidomide were predicted as potential candidate drugs for the treatment of ET.DiscussionThese dysregulated genes and predicted key regulators had a significant relationship with the occurrence of ET with affecting the immune system and inflammation of the processes. Some of the immunomodulatory drugs have potential value by targeting ACTB, PTPRC, IL7R, and IKZF1 genes in the treatment of ET.

BackgroundBecause of the complex mechanisms of injury, conventional surgical treatment and early blood pressure control does not significantly reduce mortality or improve patient prognosis in cases of intracerebral hemorrhage (ICH). We aimed to identify the hub genes associated with intracerebral hemorrhage, to act as therapeutic targets, and to identify potential small-molecule compounds for treating ICH.MethodsThe GSE24265 dataset, consisting of data from four perihematomal brain tissues and seven contralateral brain tissues, was downloaded from the Gene Expression Omnibus (GEO) database and screened for differentially expressed genes (DEGs) in ICH, with a fold change (FC) value of (|log2FC|) > 2 and a P-value of <0.05 set as cut-offs. The functional annotation of DEGs was performed using Gene Ontology (GO) resources, and the cell signaling pathway analysis of DEGs was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG), with a P-value of <0.05 set as the cut-off. We constructed a protein-protein interaction (PPI) network to clarify the interrelationships between the different DEGs and to select the hub genes with significant interactions. Next, the DEGs were analyzed using the CMap tool to identify small-molecule compounds with potential therapeutic effects. Finally, we verified the expression levels of the hub genes by RT-qPCR on the rat ICH model.ResultA total of 59 up-regulated genes and eight down-regulated genes associated with ICH were identified. The biological functions of DEGs associated with ICH are mainly involved in the inflammatory response, chemokine activity, and immune response. The KEGG analysis identified several pathways significantly associated with ICH, including but not limited to HIF-1, TNF, toll-like receptor, cytokine-cytokine receptor interaction, and chemokine molecules. A PPI network consisting of 57 nodes and 373 edges was constructed using STRING, and 10 hub genes were identified with Cytoscape software. These hub genes are closely related to secondary brain injury induced by ICH. RT-qPCR results showed that the expression of ten hub genes was significantly increased in the rat model of ICH. In addition, a CMap analysis of three small-molecule compounds revealed their therapeutic potential.ConclusionIn this study we obtained ten hub genes, such as IL6, TLR2, CXCL1, TIMP1, PLAUR, SERPINE1, SELE, CCL4, CCL20, and CD163, which play an important role in the pathology of ICH. At the same time, the ten hub genes obtained through PPI network analysis were verified in the rat model of ICH. In addition, we obtained three small molecule compounds that will have therapeutic effects on ICH, including Hecogenin, Lidocaine, and NU-1025.

Sjögren's syndrome (SS), a systemic autoimmune disorder, is characterized by dry mouth and eyes. However, SS pathogenesis is poorly understood. We performed bioinformatics analysis to investigate the potential targets and molecular pathogenesis of SS. Gene expression profiles (GSE157159) and methylation data (GSE110007) associated with SS patients were obtained from the Gene Expression Omnibus (GEO) database. Differentially methylated positions (DMPs) and differentially expressed genes (DEGs) were identified by the R package limma. The potential biological functions of DEGs were determined using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Key DMPs were selected by overlap and the shrunken centroid algorithm, and corresponding genes were identified as hub genes, with their diagnostic value assessed by receiver operating characteristic (ROC) curves. The potential molecular mechanisms of hub genes were analyzed by protein-protein interaction (PPI) networks and single-gene gene set enrichment analysis (GSEA). Peripheral blood mononuclear cells (PBMCs) were collected from control and SS patients atThe Affiliated Hospital of Southwest Medical Universityand Dazhou Central Hospital. The mRNA levels of hub genes were verified by quantitative real-time polymerase chain reaction (qRT-PCR). We identified 788 DMPs and 2457 DEGs between the two groups. Functional enrichment analysis suggested that the DEGs were significantly enriched in T cell activation, leukocyte cell-cell adhesion, and cytokine-cytokine receptor interaction. TSS200, TSS1500, and 1stExon were identified as highly enriched areas of differentially methylated promoter CpG islands (DMCIs). In total, 61 differentially methylated genes (DMGs) were identified by the overlap of 2457 DEGs and 507 genes related to DMPs (DMPGs), of which 21 genes located near TSS200, TSS1500, and 1stExon were selected. Then, three key DMPs and the corresponding hub genes (RUNX3, HLA-DPA1, and CD6) were screened by the shrunken centroid algorithm and calculated to have areas under the ROC curve of 1.000, 0.931, and 0.986, respectively, indicating good diagnostic value. The GSEA results suggested that all three hub genes were highly associated with the immune response. Finally, positive mRNA expression of the three hub genes in clinical SS samples was verified by qRT-PCR, consistent with the GSE157159 data. The identification of three hub genes provides novel insight into molecular mechanisms and therapeutic targets for SS. Key Points • Hub genes were screened by DNA methylation and transcriptome analyses. • The relative expression of hub genes in peripheral blood samples was verified by qRT-PCR. • HLA-DPA1 was correlated with the pathogenic mechanism of SS.

Background Cold exposure is linked to numerous diseases, yet the changes in key genes and pathways in mice under cold exposure remain unexplored. Understanding these alterations could offer insights into the mechanisms of cold resistance and contribute valuable ideas for treating cold-related diseases. Methods The dataset GSE148361 was obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the “limma” package in R software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed on DEGs. The STRING (Search Tool for the Retrieval of Interacting Genes) database was used to construct a protein-protein interaction (PPI) network. Additionally, gene set enrichment analysis (GSEA) was conducted to identify pathways associated with key genes. miRNAs and upstream transcription factors (TFs) were predicted using the miRNet database. Results A total of 208 DEGs were identified, with 137 upregulated and 71 downregulated. In biological processes, DEGs were enriched in nucleotide and purine-containing compound metabolism. For cellular components, DEGs were involved in condensed chromosomes and mitochondrial protein complexes. In molecular functions, proton transmembrane transporter activity was enriched. KEGG pathway analysis showed significant enrichment in biosynthesis of unsaturated fatty acids, fatty acids, and pyruvate metabolism. From the PPI network, 12 hub genes were identified using MCODE. Four hub genes (Col3a1, fi203, Rtp4, Vcan) demonstrated similar trends in a validation set (GSE110420) and were significantly differentially expressed. GSEA analysis indicated that these four genes were enriched in pathways such as ECM-receptor interaction and cytokinecytokine receptor interaction. The hub gene network included 93 miRNAs and one TF Conclusion This study identified four hub genes as potential diagnostic biomarkers for cold exposure, providing insights for further research on the effects of cold on gene expression and disease.

BackgroundHypoxic-ischemic encephalopathy (HIE) is a leading cause of neonatal brain injury and neurodevelopmental disorders. Pyroptosis, an inflammatory programmed cell death, may offer new therapeutic targets for HIE by modulating cytokine expression and related pathways. This study aims to identify HIE-associated pyroptosis genes and explore potential drugs and molecular mechanisms.MethodsThe gene microarray data of hypoxic-ischemic brain damage (HIBD) were obtained from the Gene Expression Omnibus (GEO) database. The Limma package was used to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was performed to find significant expression modules. GO and KEGG analyses were carried out for the pathway enrichment of DEGs, as well as protein–protein interaction (PPI) network analysis were subsequently conducted. Cytohubba software was employed to identify hub genes among DEGs. A random forest (RF) model assessed the pyroptosis-related genes, examining their diagnostic performance. Potential therapeutic drugs or compounds targeting the hub genes were screened through DSigDB, and their binding scores and affinities were evaluated by molecular docking.Results96 DEGs with HIBD were identified in our result, including 89 up-regulated genes and 7 down-regulated genes. GO and KEGG results indicated that these DEGs were mostly enriched in Cytokine-cytokine receptor interaction, IL-17 signaling pathway and TNF signaling pathway. Using Cytoscape software and WGCNA-related modules, we identified three hub genes—Tnf, IL1B, and Tlr2—which were further validated in other transcriptomic datasets, all showing significant differential expression. Random forest analysis demonstrated that these three hub genes had AUC values > 0.75, indicating strong diagnostic performance. Immune infiltration analysis revealed that, compared to the control group, the HIBD group exhibited higher levels of innate immune cells (e.g., macrophages, M0 cells, and dendritic cells) and adaptive immune cells (e.g., CD8 naïve T cells, CD4 follicular helper T cells, and Th1 cells). The ssGSEA algorithm results indicated differences in 25 types of immune cells and 10 immune functions. The hub genes were also validated finally.ConclusionTnf, Il1b and Tlr2 may be potential hub pyroptosis-related genes for HIBD. The results of this study could improve the understanding of the mechanisms underlying pyroptosis in HIBD.

Lupus erythematosus is an autoimmune disease that causes damage to multiple organs ranging from skin lesions to systemic manifestations. Cutaneous lupus erythematosus (CLE) is a common type of lupus erythematosus (LE), but its molecular mechanisms are currently unknown. The study aimed to explore changes in the gene expression profiles and identify key genes involved in CLE, hoping to uncover its molecular mechanism and identify new targets for CLE. We analyzed the microarray dataset (GSE109248) derived from the Gene Expression Omnibus (GEO) database, which was a transcriptome profiling of CLE cutaneous lesions. The differentially expressed genes (DEGs) were identified, and the functional annotation of DEGs was performed with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Protein-protein interaction (PPI) network was also constructed to identify hub genes involved in CLE. A total of 755 up-regulated DEGs and 405 down-regulated DEGs were identified. GO enrichment analysis showed that defense response to virus, immune response, and type I interferon signaling pathway were the most significant enrichment items in DEGs. The KEGG pathway analysis identified 51 significant enrichment pathways, which mainly included systemic lupus erythematosus, osteoclast differentiation, cytokine-cytokine receptor interaction, and primary immunodeficiency. Based on the PPI network, the study identified the top 10 hub genes involved in CLE, which were CXCL10, CCR7, FPR3, PPARGC1A, MMP9, IRF7, IL2RG, SOCS1, ISG15, and GSTM3. By comparison between subtypes, the results showed that ACLE had the least DEGs, while CCLE showed the most gene and functional changes. The identified hub genes and functional pathways found in this study may expand our understanding on the underlying pathogenesis of CLE and provide new insights into potential biomarkers or targets for the diagnosis and treatment of CLE. Key Points • The bioinformatics analysis based on CLE patients and healthy controls was performed and 1160 DEGs were identified • The 1160 DEGs were mainly enriched in biological processes related to immune responses, including innate immune response, type I interferon signaling pathway, interferon-γ-mediated signaling pathway, positive regulation of T cell proliferation, regulation of immune response, antigen processing, and presentation via MHC class Ib and so on • KEGG pathway enrichment analysis indicated that DEGs were mainly enriched in several immune-related diseases and virus infection, including systemic lupus erythematosus, primary immunodeficiency, herpes simplex infection, measles, influenza A, and so on • The hub genes such as CXCL10, IRF7, MMP9, CCR7, and SOCS1 may become new markers or targets for the diagnosis and treatment of CLE.

Renal cell carcinoma (RCC) is the most common form of kidney cancer, caused by renal epithelial cells. RCC remains to be a challenging public health problem worldwide. Metastases that are resistant to radiotherapy and chemotherapy are the major cause of death from cancer. However, the underlying molecular mechanism regulating the metastasis of RCC is poorly known. Publicly available databases of RCC were obtained from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using GEO2R analysis, whereas the Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed by Gene Set Enrichment Analysis (GSEA) and Metascape. Protein-protein interaction (PPI) network of DEGs was analyzed by STRING online database, and Cytoscape software was used for visualizing PPI network. Survival analysis of hub genes was conducted using GEPIA online database. The expression levels of hub genes were investigated from The Human Protein Atlas online database and GEPIA online database. Finally, the comparative toxicogenomics database (CTD; http://ctdbase.org) was used to identify hub genes associated with tumor or metastasis. We identified 229 DEGs comprising 135 downregulated genes and 94 upregulated genes. Functional analysis revealed that these DEGs were associates with cell recognition, regulation of immune, negative regulation of adaptive immune response, and other functions. And these DEGs mainly related to P53 signaling pathway, cytokine-cytokine receptor interaction, Natural killer cell mediated cytotoxicity, and other pathways are involved. Ten genes were identified as hub genes through module analyses in the PPI network. Finally, survival analysis of 10 hub genes was conducted, which showed that the MMP2 (matrix metallo peptidase 2), DCN, COL4A1, CASR (calcium sensing receptor), GPR4 (G protein-coupled receptor 4), UTS2 (urotensin 2), and LDLR (low density lipoprotein receptor) genes were significant for survival. In this study, the DEGs between RCC and metastatic RCC were analyzed, which assist us in systematically understanding the pathogeny underlying metastasis of RCC. The MMP2, DCN, COL4A1, CASR, GPR4, UTS2, and LDLR genes might be used as potential targets to improve diagnosis and immunotherapy biomarkers for RCC.

The aim of this study was to explore the key genes, microRNA (miRNA), and the pathogenesis of oral squamous cell carcinoma (OSCC) at the molecular level through the analysis of bioinformatics, which could provide a theoretical basis for the screening of drug targets. Data of OSCC were obtained from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified via GEO2R analysis. Next, protein-protein interaction (PPI) network of DEGs was constructed through Search Tool for the Retrieval of Interacting Gene and visualized via Cytoscape, whereas the hub genes were screened out with Cytoscape. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed by Database for Annotation, Visualization and Integrated Discovery. The miRNA, which might regulate hub genes, were screened out with TargetScan and GO and KEGG analysis of miRNA was performed by DNA Intelligent Analysis-miRPath. Survival analyses of DEGs were conducted via the Kaplan-Meier plotter. Finally, the relationships between gene products and tumors were analyzed by Comparative Toxicogenomics Database. A total of 121 differential genes were identified. One hundred thirty-five GO terms and 56 pathways were obtained, which were mainly related to PI3K-Akt signals pathway, FoxO signaling pathway, Wnt signaling pathway, cell cycle, p53 signaling pathway, cellular senescence, and other pathways; 10 genes were identified as hub genes through modules analyses in the PPI network. Finally, a survival analysis of 10 hub genes was conducted, which showed that the low expression of matrix metalloproteinase (MMP)1, MMP3, and C-X-C motif chemokine ligand (CXCL)1 and the high expression of CXCL9 and CXCL10 resulted in a significantly poor 5-year overall survival rate in patients with OSCC. In this study, the DEGs of OSCC was analyzed, which assists us in a systematic understanding of the pathogenicity underlying occurrence and development of OSCC. The MMP1, MMP3, CXCL1, CXCL9, and CXCL10 genes might be used as potential targets to improve diagnosis and as immunotherapy biomarkers for OSCC.

Bioinformatics methods were used to analyze the key genes and related signal paths of sarcoidosis. RNA-seq of sarcoidosis were downloaded from the gene expression omnibus (GEO) database (GSE42826 and GSE42830) and differentially expressed genes (DEGs) were extracted from the two chip datasets. We uesd the gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) way to analyse DEGs, the cytoscape 3.8.2 softwarethe was uesd to visualize the DEGs. The ggpubr package was used to draw the volcano map of DEGs. Ggplot package to draw bubble charts for GO, KEGG, DO analysis. PPI analysis was used to identified hub genes, hub genes were verified in the GSE19314 dataset. 64 DEGs were obtained in the GSE42826 and GSE42830 datasets, of which 17 genes were down-regulated genes and 47 genes were up-regulated genes. GO analysis indicated that DEGs were mainly enriched in external stimuli, defense responses, and responses to biological stimuli in other biological processes, KEGG analysis showed that DEGs mainly affect NOD-like receptor signaling pathways, programmed pell death-ligand 1(PD-L1) expression, programmeddeath-1(PD-1) checkpoint pathways in cancer and cytoplasmic deoxyribonucleic acid (DNA) sensing pathways. The results of DO analysis showed that DEGs were associated with bacterial infectious diseases, hepatitis, primary bacterial infectious diseases. 8 hub genes, including C-X-C motif chemokine ligand 10 (CXCL10), interferon induced protein 44 (IFI44) and interferon induced protein with tetratricopeptide repeats 3 (IFIT3), were all significantly up-regulated in sarcoidosis group. Further analysis showed that sarcoidosis was sensitive to pinellia, radix isatidis, ephedra and phellodendri. This work showed that 10 hub genes may become relevant targets for diagnosis and treatment of patients with sarcoidosis and provided a new idea for the pathogenesis and treatment of sarcoidosis.

BackgroundPulpitis is an inflammatory disease, the grade of which is classified according to the level of inflammation. Traditional methods of evaluating the status of dental pulp tissue in clinical practice have limitations. The rapid and accurate diagnosis of pulpitis is essential for determining the appropriate treatment. By integrating different datasets from the Gene Expression Omnibus (GEO) database, we analysed a merged expression matrix of pulpitis, aiming to identify biological pathways and diagnostic biomarkers of pulpitis.MethodsBy integrating two datasets (GSE77459 and GSE92681) in the GEO database using the sva and limma packages of R, differentially expressed genes (DEGs) of pulpitis were identified. Then, the DEGs were analysed to identify biological pathways of dental pulp inflammation with Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene Set Enrichment Analysis (GSEA). Protein–protein interaction (PPI) networks and modules were constructed to identify hub genes with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape.ResultsA total of 470 DEGs comprising 394 upregulated and 76 downregulated genes were found in pulpitis tissue. GO analysis revealed that the DEGs were enriched in biological processes related to inflammation, and the enriched pathways in the KEGG pathway analysis were cytokine-cytokine receptor interaction, chemokine signalling pathway and NF-κB signalling pathway. The GSEA results provided further functional annotations, including complement system, IL6/JAK/STAT3 signalling pathway and inflammatory response pathways. According to the degrees of nodes in the PPI network, 10 hub genes were identified, and 8 diagnostic biomarker candidates were screened: PTPRC, CD86, CCL2, IL6, TLR8, MMP9, CXCL8 and ICAM1.ConclusionsWith bioinformatics analysis of merged datasets, biomarker candidates of pulpitis were screened and the findings may be as reference to develop a new method of pulpitis diagnosis.

Keratoconus is one of the most common causes leading to visual impairment in young adult population. The pathogenesis of keratoconus remains poorly understood. The aim of this study was to identify the potential key genes and pathways associated with keratoconus and to further analyze its molecular mechanism. Two RNA-sequencing datasets of keratoconus and paired normal corneal tissues from the Gene Expression Omnibus database were obtained. Differentially expressed genes (DEGs) were identified, and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted. The protein-protein interaction (PPI) network of the DEGs was established, and the hub genes and significant gene modules of PPI were further constructed. Lastly, the GO and KEGG analyses of the hub gene were performed. In total, 548 common DEGs were identified. GO enrichment analysis showed that the DEGs were primarily associated with regulation of cell adhesion, the response to molecule of bacterial origin, lipopolysaccharide and biotic stimulus, collagen-containing extracellular matrix, extracellular matrix, and structure organization. KEGG pathway analysis showed that these DEGs were mainly involved in the TNF signaling pathway, IL-17 signaling pathway, Rheumatoid arthritis, Cytokine-cytokine receptor interaction. The PPI network was constructed with 146 nodes and 276 edges, and 3 significant modules are selected. Finally, top 10 hub genes were identified from the PPI network. The results revealed that extracellular matrix remodeling and immune inflammatory response could be the key links of keratoconus, TNF, IL6, IL1A, IL1B, CCL3, MMP3, MMP9, MMP1, and TGFB1 may be potential crucial genes, and TNF signaling pathway and IL-17 signaling pathway were the potential pathways accounting for pathogenesis and development of keratoconus.