Systematic analysis of olfactory protein-protein interactions network of fruitfly, Drosophila melanogaster.

Olfaction is one of the physiological traits of insect behavior. Insects have evolved a sophisticated olfactory system and use a combined coding strategy to process general odor. Drosophila melanogaster is a powerful model to reveal the molecular and cellular mechanisms of odor detection. Identifying new olfactory targets through complex interactions will contribute to a better understanding of the functions, interactions, and signaling pathways of olfactory proteins. However, the mechanism of D. melanogaster olfaction is still unclear, and more olfactory proteins are required to be discovered. In this study, we tried to explore essential proteins in the olfactory system of D. melanogaster and conduct protein-protein interactions (PPIs) analysis. We constructed the PPIs network of the olfactory system of D. melanogaster, consisting of 863 proteins and 18,959 interactions. Various methods were used to perform functional enrichment analysis, topological analysis and cluster analysis. Our results confirmed that Class B scavenger receptors (SR-Bs), glutathione S-transferases (GSTs), and UDP-glycosyltransferases (UGTs) play an essential role in olfaction of D. melanogaster. The proteins obtained in this study can be used for subsequent functional identification in D. melanogaster olfactory.

Effect of hyperoside on cervical cancer cells and transcriptome analysis of differentially expressed genes.

BACKGROUND: Hyperoside (Hy) is a plant-derived quercetin 3-d-galactoside that exhibits inhibitory activities on various tumor types. The objective of the current study was to explore Hy effects on cervical cancer cell proliferation, and to perform a transcriptome analysis of differentially expressed genes. METHODS: Cervical cancer HeLa and C-33A cells were cultured and the effect of Hy treatment was determined using the Cell Counting Kit-8 (CCK-8) assay. After calculating the IC50 of Hy in HeLa and C-33A cells, the more sensitive to Hy treatment cell type was selected for RNA-Seq. Differentially expressed genes (DEGs) were identified by comparing gene expression between the Hy and control groups. Candidate genes were determined through DEG analysis, protein interaction network (PPI) construction, PPI module analysis, transcription factor (TF) prediction, TF-target network construction, and survival analysis. Finally, the key candidate genes were verified by RT-qPCR and western blot. RESULTS: Hy inhibited HeLa and C33A cell proliferation in a dose- and time-dependent manner, as determined by the CCK-8 assay. Treatment of C-33A cells with 2 mM Hy was selected for the subsequent experiments. Compared with the control group, 754 upregulated and 509 downregulated genes were identified after RNA-Seq. After functional enrichment, 74 gene ontology biological processes and 43 Kyoto Encyclopedia of Genes and Genomes pathways were obtained. According to the protein interaction network (PPI), PPI module analysis, TF-target network construction, and survival analysis, the key genes MYC, CNKN1A, PAX2, TFRC, ACOX2, UNC5B, APBA1, PRKACA, PEAR1, COL12A1, CACNA1G, PEAR1, and CCNA2 were detected. RT-qPCR was performed on the key genes, and Western blot was used to verify C-MYC and TFRC. C-MYC and TFRC expressions were lower and higher than the corresponding values in the control group, respectively, in accordance with the results from the RNA-Seq analysis. CONCLUSION: Hy inhibited HeLa and C-33A cell proliferation through C-MYC gene expression reduction in C-33A cells and TFRC regulation. The results of the current study provide a theoretical basis for Hy treatment of cervical cancer.

Development and Validation of the Diagnostic Model of 7 Gene in Endometriosis.

AIMS: To explore the diagnostic biomarkers for diagnosing endometriosis. BACKGROUND: Endometriosis is a benign, progressive, estrogen-dependent gynecological disorder that has highly variant prevalence. Therefore, it is essential to develop reliable diagnostic biomarkers for endometriosis diagnosis. OBJECTIVE: To explore the diagnostic biomarkers for endometriosis diagnosis. METHOD: Based on transcriptome data from GSE145701, we identified potential therapeutic targets through the intersection of endometriosis-related genes from weighted gene correlation network analysis (WGCNA) and differential expression analysis. Aprotein-protein interaction (PPI) was constructed. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were employed for functional enrichment analysis. The intersection of hub genes from topological analysis and module genes from module-based network analysis were selected as core targets, which were used for diagnostic model construction. Its robustness was validated using GSE7305 and GSE134056. Associations of core targets with immune characteristics and pathways were further evaluated. Molecular docking was employed to evaluate the docking affinity between core targets and drugs. Additionally, western blot and quantitative real-time polymerase chain reaction were also carried out to validate molecular docking results. RESULT: A diagnostic model was constructed using 7 core targets, which had a high diagnostic ability for endometriosis. CTSK was positively correlated with immune scores, while CDH2 was negatively correlated with immune scores. CTSK, HGF, and EPCAM were positively correlated with energy metabolism and inflammation-related pathways, while RUNX2, FN1, NCAM1, and CDH2 were positively correlated with epithelial-to-mesenchymal transition (EMT) and unfolded protein response (UPR). Moreover, FN1 had good docking affinity with Elagolix, Esmya, and Proellex. NCAM1 might be a promising target modulated by Elagolix. in vitro experiment revealed that the expression of FN1 in human normal endometrial cell lines (hEEC) gradually decreased with the increase of Esmya concentration, indicating that FN1 was a target for Esmya. CONCLUSION: These results may facilitate the in-depth understanding of the development of endometriosis, and guide early diagnostic as well as clinical treatments for patients with endometriosis.

Identification of potential core genes and pathways predicting pathogenesis in head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is the most common subtype of head and neck cancer; however, its pathogenesis and potential therapeutic targets remain largely unknown. In the present study, we analyzed three gene expression profiles and screened differentially expressed genes (DEGs) between HNSCC and normal tissues. The DEGs were subjected to gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), protein-protein interaction (PPI), and survival analyses, while the connectivity map (CMap) database was used to predict candidate small molecules that may reverse the biological state of HNSCC. Finally, we measured the expression of the most relevant core gene in vitro and examined the effect of the top predicted potential drug against the proliferation of HNSCC cell lines. Among the 208 DEGs and ten hub genes identified, CDK1 and CDC45 were associated with unfavorable HNSCC prognosis, and three potential small molecule drugs for treating HNSCC were identified. Increased CDK1 expression was confirmed in HNSCC cells, and menadione, the top predicted potential drug, exerted significant inhibitory effects against HNSCC cell proliferation and markedly reversed CDK1 expression. Together, the findings of the present study suggest that the ten hub genes and pathways identified may be closely related to HNSCC pathogenesis. In particular, CDK1 and CDC45 overexpression could be reliable biomarkers for predicting unfavorable prognosis in patients with HNSCC, while the new candidate small molecules identified by CMap analysis provide new avenues for the development of potential drugs to treat HNSCC.

An integrative bioinformatics analysis for identifying hub genes associated with infection of lung samples in patients infected with SARS-CoV-2.

BACKGROUND: At the end of 2019, the world witnessed the emergence and ravages of a viral infection induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Also known as the coronavirus disease 2019 (COVID-19), it has been identified as a public health emergency of international concern (PHEIC) by the World Health Organization (WHO) because of its severity. METHODS: The gene data of 51 samples were extracted from the GSE150316 and GSE147507 data set and then processed by means of the programming language R, through which the differentially expressed genes (DEGs) that meet the standards were screened. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on the selected DEGs to understand the functions and approaches of DEGs. The online tool STRING was employed to construct a protein-protein interaction (PPI) network of DEGs and, in turn, to identify hub genes. RESULTS: A total of 52 intersection genes were obtained through DEG identification. Through the GO analysis, we realized that the biological processes (BPs) that have the deepest impact on the human body after SARS-CoV-2 infection are various immune responses. By using STRING to construct a PPI network, 10 hub genes were identified, including IFIH1, DDX58, ISG15, EGR1, OASL, SAMD9, SAMD9L, XAF1, IFITM1, and TNFSF10. CONCLUSION: The results of this study will hopefully provide guidance for future studies on the pathophysiological mechanism of SARS-CoV-2 infection.

Identification of differentially expressed metastatic genes and their signatures to predict the overall survival of uveal melanoma patients by bioinformatics analysis.

AIM: To identify metastatic genes and miRNAs and to investigate the metastatic mechanism of uveal melanoma (UVM). METHODS: GSE27831, GSE39717, and GSE73652 gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database, and the limma R package was used to identify differentially expressed genes (DEGs). Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the DAVID online tool. A comprehensive list of interacting DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and Cytoscape software. The Cytoscape MCODE plug-in was used to identify clustered sub-networks and modules of hub genes from the protein-protein interaction network. GEPIA online software was used for survival analysis of UVM patients (n=80) from the The Cancer Genome Atlas (TCGA) cohort. OncomiR online software was used to find that the miRNAs were associated with UVM prognosis from the TCGA cohort. TargetScan Human 7.2 software was then used to identify the miRNAs targeting the genes. RESULTS: There were 1600 up-regulated genes and 1399 down-regulated genes. The up-regulated genes were mainly involved in protein translation in the cytosol, whereas the down-regulated genes were correlated with extracellular matrix organization and cell adhesion in the extracellular space. Among the 2999 DEGs, five genes, Znf391, Mrps11, Htra3, Sulf2, and Smarcd3 were potential predictors of UVM prognosis. Otherwise, three miRNAs, hsa-miR-509-3-5p, hsa-miR-513a-5p, and hsa-miR-1269a were associated with UVM prognosis. CONCLUSION: After analyzing the metastasis-related enriched terms and signaling pathways, the up-regulated DEGs are mainly involved in protein synthesis and cell proliferation by ribosome and mitogen-activated protein kinase (MAPK) pathways. However, the down-regulated DEGs are mainly involved in processes that reduced cell-cell adhesion and promoted cell migration in the extracellular matrix through PI3K-Akt signaling pathway, focal adhesion, and extracellular matrix-receptor interactions. Bioinformatics and interaction analysis may provide new insights on the events leading up to the development and progression of UVM.

Expression profiles analysis identifies a novel three-mRNA signature to predict overall survival in oral squamous cell carcinoma.

Oral squamous cell carcinoma (OSCC) remains to be a challenging public health problem worldwide. However, the underlying molecular mechanism regulating the carcinogenesis of OSCC is poorly known. Gene expression profiles of GSE13601, GSE30784, GSE37991 and The Cancer Genome Atlas (TCGA) head and neck cancer were downloaded from gene expression omnibus (GEO) and TCGA database respectively. R software and bioconductor packages were used to compare and identify the differentially expressed genes (DEGs) between OSCC tissues and normal controls. The common DEGs were then subjected to gene ontology (GO) enrichment analysis, ingenuity pathway analysis (IPA), protein-protein interaction (PPI) network analysis as well as survival analysis. A total of 76 up- and 102 down-regulated DEGs were identified. Functional analysis revealed that these DEGs were associates with increased oncostatin M signaling, cell diapedesis and extravasation as well as reduced calcium signaling and loss of adherens junctions and tight junctions. A set of robust prognostic signatures including PLAU, CLDN8 and CDKN2A were identified from DEGs and could predict overall survival in OSCC patients from TCGA cohort. This three-gene signature was further successfully validated as a prognostic marker for overall survival prediction in another independent cohort GSE41613. In conclusion, our study has identified a registry of novel genes and pathways that play important roles in regulating the initiation and development of OSCC. A set of robust molecular signature is identified for prognostic prediction, which will provide useful guidance for therapeutic applications.

Expression profile analysis identifies a two-gene signature for prediction of head and neck squamous cell carcinoma patient survival.

AIM: The aim of this study is to identify a gene prognostic signature for the head-and-neck squamous cell carcinoma (HNSCC). HNSCC is one of the most common malignancies worldwide; however, the molecular mechanisms underlying the malignancy are unclear. MATERIALS AND METHODS: We analyzed the gene expression profiles of GSE2379, GSE53819, and GSE59102 derived from the gene expression omnibus, and the cancer genome atlas (TCGA) HNSC databases. The R software was used to identify the differentially expressed genes (DEGs) between HNSCC tissues and normal controls. Gene ontology, Kyoto Encyclopedia of Genes and Genomes pathway, protein-protein interactions network, and survival analyses of common DEGs were also performed. RESULTS: A total of 52 upregulated and 31 downregulated DEGs were identified. Functional analyses demonstrated that these DEGs were mainly enriched in extracellular matrix-receptor interaction, focal adhesion, tyrosine metabolism, and cytokine-cytokine receptor interaction. According to the survival analyses, PLAU and SERPINE1 could predict the overall survival of HNSCC patients from the TCGA cohort. Multivariable Cox regression analyses showed that the PLAU and SERPINE1 were independent prognostic factors for HNSCC patients. The prediction power of this two-gene signature was evaluated through receiver operating characteristic curve analysis and achieved a better prognostic value than PLAU (area under curve 0.613 [95% confidence interval 0.569-0.656] vs. 0.577 [0.533-0.621]; P = 0.008) or SERPINE1 (0.613 [0.569-0.656] vs. 0.586 [0.541-0.629]; P = 0.043) when considered alone. CONCLUSIONS: The study has identified a set of novel genes and pathways that play significant roles in the carcinogenesis and progression of HNSCC. This two-gene signature may prove to be a useful therapeutic target for HNSCC.