The Critical Biomarkers Identification of Insulin Signaling Involved in Initiating cAMP Signaling Mediated Salivary Secretion in Sjogren Syndrome: Transcriptome Sequencing in NOD Mice Model.

BACKGROUND: Sjogren's syndrome (SS) is an autoimmune disorder characterized by the destruction of exocrine glands, resulting in dry mouth and eyes. Currently, there is no effective treatment for SS, and the mechanisms associated with inadequate salivary secretion are poorly understood. METHODS: In this study, we used NOD mice model to monitor changes in mice's salivary secretion and water consumption. Tissue morphology of the submandibular glands was examined by H&E staining, and Immunohistochemical detected the expression of AQP5 (an essential protein in salivary secretion). Global gene expression profiling was performed on submandibular gland tissue of extracted NOD mice model using RNA-seq. Subsequently, a series of bioinformatics analyses of transcriptome sequencing was performed, including differentially expressed genes (DEGs) identification, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, PPI network construction, hub gene identification, and the validity of diagnostic indicators using the dataset GSE40611. Finally, IFN-γ was used to treat the cells, the submandibular gland tissue of NOD mice model was extracted, and RT-qPCR was applied to verify the expression of hub genes. RESULTS: We found that NOD mice model had reduced salivary secretion and increased water consumption. H&E staining suggests acinar destruction and basement membrane changes in glandular tissue. Immunohistochemistry detects a decrease in AQP5 immunostaining within acinar. In transcriptome sequencing, 42 overlapping DEGs were identified, and hub genes (REN, A2M, SNCA, KLK3, TTR, and AZGP1) were identified as initiating targets for insulin signaling. In addition, insulin signaling and cAMP signaling are potential pathways for regulating salivary secretion and constructing a regulatory relationship between target-cAMP signaling-salivary secretion. CONCLUSION: The new potential targets and signal axes for regulating salivary secretion provide a strategy for SS therapy in a clinical setting.

Screening and identification of potential biomarkers and therapeutic drugs in melanoma via integrated bioinformatics analysis.

Melanoma is a highly aggressive malignant skin tumor with a high rate of metastasis and mortality. In this study, a comprehensive bioinformatics analysis was used to clarify the hub genes and potential drugs. Download the GSE3189, GSE22301, and GSE35388 microarray datasets from the Gene Expression Omnibus (GEO), which contains a total of 33 normal samples and 67 melanoma samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) approach analyze DEGs based on the DAVID. Use STRING to construct protein-protein interaction network, and use MCODE and cytoHubba plug-ins in Cytoscape to perform module analysis and identified hub genes. Use Gene Expression Profile Interactive Analysis (GEPIA) to assess the prognosis of genes in tumors. Finally, use the Drug-Gene Interaction Database (DGIdb) to screen targeted drugs related to hub genes. A total of 140 overlapping DEGs were identified from the three microarray datasets, including 59 up-regulated DEGs and 81 down-regulated DEGs. GO enrichment analysis showed that these DEGs are mainly involved in the biological process such as positive regulation of gene expression, positive regulation of cell proliferation, positive regulation of MAP kinase activity, cell migration, and negative regulation of the apoptotic process. The cellular components are concentrated in the membrane, dendritic spine, the perinuclear region of cytoplasm, extracellular exosome, and membrane raft. Molecular functions include protein homodimerization activity, calmodulin-binding, transcription factor binding, protein binding, and cytoskeletal protein binding. KEGG pathway analysis shows that these DEGs are mainly related to protein digestion and absorption, PPAR signaling pathway, signaling pathways regulating stem cells' pluripotency, and Retinol metabolism. The 23 most closely related DEGs were identified from the PPI network and combined with the GEPIA prognostic analysis, CDH3, ESRP1, FGF2, GBP2, KCNN4, KIT, SEMA4D, and ZEB1 were selected as hub genes, which are considered to be associated with poor prognosis of melanoma closely related. Besides, ten related drugs that may have therapeutic effects on melanoma were also screened. These newly discovered genes and drugs provide new ideas for further research on melanoma.

Analysis and identification of novel biomarkers involved in neuroblastoma via integrated bioinformatics.

Neuroblastoma (NB) is the most common extracranial solid tumor in children. Under various treatments, some patients still have a poor prognosis. Hence, it is necessary to find new valid targets for NB therapy. In this study, a comprehensive bioinformatic analysis was used to identify differentially expressed genes (DEGs) between NB and control cells, and to select hub genes associated with NB. GSE66586 and GSE78061 datasets were downloaded from the Gene Expression Omnibus (GEO) database and DEGs were selected. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were applied to the selected DEGs. The STRING database and Cytoscape software were used to construct protein-protein interaction (PPI) networks and perform modular analysis of the DEGs. The R2 database was used for prognostic analysis. We identified a total of 238 DEGs from two microarray databases. GO enrichment analysis shows that these DEGs are mainly concentrated in the regulation of cell growth, cell migration, cell fate determination, and cell maturation. KEGG pathway analysis showed that these DEGs are mainly involved in focal adhesion, the TNF signaling pathway, cancer-related pathways, and signaling pathways regulating stem cell pluripotency. We identified the 15 most closely related DEGs from the PPI network, and performed R2 database prognostic analysis to select five hub genes - CTGF, EDN1, GATA2, LOX, and SERPINE1. This study distinguished hub genes and related signaling pathways that can potentially serve as diagnostic indicators and therapeutic biomarkers for NB, thereby improving understanding of the molecular mechanisms involved in NB.

Integrated Microarray to Identify the Hub miRNAs and Constructed miRNA-mRNA Network in Neuroblastoma Via Bioinformatics Analysis.

Neuroblastomas (NB) are childhood malignant tumors originating in the sympathetic nervous system. MicroRNAs (miRNAs) play an essential regulatory role in tumorigenesis and development. In this study, NB miRNA and mRNA expression profile data in the Gene Expression Omnibus database were used to screen for differentially expressed miRNAs (DEMs) and genes (DEGs). We used the miRTarBase and miRSystem databases to predict the target genes of the DEMs, and we selected target genes that overlapped with the DEGs as candidate genes for further study. Annotations, visualization, and the DAVID database were used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the candidate genes. Additionally, the protein-protein interaction (PPI) network and miRNA-mRNA regulatory network were constructed and visualized using the STRING database and Cytoscape, and the hub modules were analyzed for function and pathway enrichment using the DAVID database and BiNGO plug-in. 107 DEMs and 1139 DEGs were identified from the miRNA and mRNA chips, respectively. 4390 overlapping target genes were identified using the two databases, and 405 candidate genes which intersected with the DEGs were selected. These candidate genes were enriched in 363 GO terms and 24 KEGG pathways. By constructing a PPI network and a miRNA-mRNA regulatory network, three hub miRNAs (hsa-miR-30e-5p, hsa-miR-15a, and hsa-miR-16) were identified. The target genes of the hub miRNAs were significantly enriched in the following pathways: microRNAs in cancer, the PI3K-Akt signaling pathway, pathways in cancer, the p53 signaling pathway, and the cell cycle. In summary, our results have identified candidate genes and pathways related to the underlying molecular mechanism of NB. These findings provide a new perspective for NB research and treatment.

Comprehensive analysis and validation reveal potential MYCN regulatory biomarkers associated with neuroblastoma prognosis.

Neuroblastoma (NB) is an embryonic malignant tumor that occurs in the sympathetic nervous system. The treatment results of patients in the high-risk group are poor, and relapse and treatment failure can occur even with multiple combination treatments. The proto-oncogene MYCN is a BHLH Transcription Factor used as an independent prognostic factor for NB. The proportion of MYCN amplification in tumor tissues of high-risk patients reaches 40-50%. Hence, exploring new MYCN target genes is a meaningful approach in developing treatment for high-risk NB patients. The microarray datasets were obtained from Gene Expression Omnibus (GEO), and differentially expressed genes (DEGs) were identified. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and miRPathDB were used for enrichment analysis. STRING and Cytoscape were used to construct a protein-protein interaction (PPI) network and for modular analysis. The miRNet and NetworkAnalyst databases were used to predict and construct gene-miRNA and gene-TFs networks. The R2 database was used for expression, correlation, and prognostic analyses. The diagnostic value of the biomarkers was predicted by ROC analysis, and RT-qPCR was used to validate the identified hub genes. Finally, using specific MYCN siRNA and overexpressing plasmids, the correlation between the identified hub genes and MYCN was investigated. Our results showed that FBXO9, HECW2, MIB2, RNF19B, RNF213, TRIM36, and ZBTB16 are novel biomarkers that affect the prognosis of the NB patients. In addition, FBXO9, RNF19B, and TRIM36 were preliminarily confirmed as potential target genes of MYCN. Overall, FBXO9, HECW2, MIB2, RNF19B, RNF213, TRIM36, and ZBTB16 are expected to become novel biomarkers for the treatment of high-risk NB patients.Communicated by Ramaswamy H. Sarma.

Role of estrogen receptor signaling pathway-related genes in diffuse large B-cell lymphoma and identification of key targets via integrated bioinformatics analysis and experimental validation.

Diffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous malignancy. Epidemiologically, the incidence of DLBCL is higher in men, and the female sex is a favorable prognostic factor, which can be explained by estrogen. This study aimed to explore the potential targets of the estrogen receptor (ER) signaling pathway and provide a meaningful way to treat DLBCL patients. Datasets were obtained from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs). Representative gene sets estrogen receptor pathways, and growth regulatory pathways were identified based on Gene Set Enrichment Analysis (GSEA) analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for function and pathway analysis. STRING and Cytoscape were used to construct the interaction network, and the MCODE plug-in performed the module analysis. GEPIA, TCGA, and LOGpc databases were used for expression and predictive analysis. The Human Protein Atlas (HPA) database was used to analyze the protein expression levels, cBioPortal was used to explore genetic alterations, and ROC analysis and prognostic assessment were used to predict the diagnostic value of genes. Finally, BJAB cells were treated with ER inhibitor fulvestrant and specific shRNA, and the expression of hub genes was verified by RT-qPCR. We identified 81 overlapping DEGs and CDC6, CDC20, KIF20A, STIL, and TOP2A as novel biomarkers affecting the prognosis of DLBCL. In addition, the STAT and KRAS pathways are considered potential growth regulatory pathways. These results hold promise for new avenues for the treatment of DLBCL patients.