Search Tool For The Retrieval Of Interacting Genes/Proteins Research Articles

Identification of key genes and validation of key gene aquaporin 1 on Wilms' tumor metastasis.

Wilms' tumor (WT) is one of the most common solid tumors in children with unsatisfactory prognosis, but few molecular prognostic markers have been discovered for it. Many genes are associated with the occurrence and prognosis of WT. This study aimed to explore the key genes and potential molecular mechanisms through bioinformatics and to verify the effects of aquaporin 1 (AQP1) on WT metastasis. Differentially expressed genes (DEGs) were generated from WT gene expression data sets from the Gene Expression Omnibus (GEO) database. Gene functional enrichment analysis was carried out with the Database for Annotation, Visualization and Integrated Discovery (DAVID). A protein-protein interaction network (PPI) was constructed and visualized by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and Cytoscape software. Minimal Common Oncology Data Elements (MCODE) was used to detect the important modules in the PPI network, and the important nodes (genes) in the PPI module were sorted by CytoHubba. RT-qPCR was performed to validate the expression of the key genes in WT. Wound healing and Transwell assays were used to detect the cell migration and invasion abilities of AQP1-overexpressing cells. Phalloidin-iFlour 488 was used to stain the cytoskeleton to observe how AQP1 overexpression affects cytoskeletal microfilament structure. A total of 73 co-expressed DEGs were chosen for further investigation. The importance of homeostasis and transmembrane transport of ions and water were highlighted by functional analysis. Gene regulatory network and PPI network were predicted. MCODE plug identified two important modules. Finally, top five key genes were identified using CytoHubba, including Renin (REN), nephrosis 2 (NPHS2), Solute Carrier Family 12 Member 3 (SLC12A3), Solute Carrier Family 12 Member 1 (SLC12A1) and AQP1. The five key genes were mainly enriched in cell volume and ion homeostasis. RT-qPCR confirmed the expression of the five key genes in WT. AQP1 was validated to be expressed at significantly lower levels in WT than in normal tissue. AQP1 overexpression significantly reduced the migratory and invasive capacity of Wit-49 cells, as evidenced by reducing the scratch healing rate and the number of perforated control cells by Wit-49 cells. AQP1 overexpression also reduced the expression of biomarkers of epithelial-mesenchymal transformation, decreased levels of vimentin and N-cadherin and increased expression of E-cadherin, resulting in decreased formation of conspicuous lamellipodial protrusions, characteristic of diminished WT cell invasion and migration. Our study reveals the key genes of WT. These key genes may provide novel insight for the mechanism and diagnosis of WT. AQP1 overexpression inhibited invasion, migration, EMT, and cytoskeletal rearrangement of WT cells, indicating that AQP1 plays a role in the pathogenesis of WT.

Functional analysis of tumor-derived immunoglobulin lambda and its interacting proteins in cervical cancer

BackgroundImmunoglobulin lambda (Igλ) has been reported to be expressed in many normal and tumor tissues and cells. However, the function and clinical significance of tumor-derived Igλ remain unclear.MethodsThe differential expressions of Immunoglobulin Lambda Constants (IGLCs) in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) were examined with The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Human Protein Atlas (HPA) databases. The effects of IGLCs on patient clinical phenotypes and prognosis were explored via bioinformatics analyses based on the TCGA databases. We used the bioinformatics analyses based on the TCGA and GTEx databases to elucidate the correlations among IGLC expressions, immunomodulator expressions, tumor stemness, and infiltration scores of tumor infiltrating immune cells. Co-immunoprecipitation (Co-IP) and silver staining combined with liquid chromatography-tandem mass spectrometry (LC–MS/MS) were used to obtain potential tumor-derived Igλ-interacting proteins. Functional annotation of candidate proteins identified by LC–MS/MS was performed in Database for Annotation, Visualization and Integrated Discovery (DAVID). The bioinformatics analyses of 7 IGLCs in CESC and normal cervical tissues were performed based on TCGA, GTEx, and Gene Expression Profiling Interactive Analysis 2 (GEPIA2) databases. Protein–protein interaction (PPI) network was analyzed based on tumor-derived Igλ-interacting proteins in Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. Immunohistochemistry (IHC) was used to validate the expressions of IGLCs in CESC.ResultsWe found that the expressions of the majority of IGLCs (IGLC1, IGLC2, IGLC3, IGLC4, IGLC5, IGLC6, and IGLC7) were upregulated in CESC tissues, compared with those in normal cervical tissues. The expressions of IGLC5 and IGLC7 had significant difference in different pathologic metastasis (M), one of tumor, node, and metastasis (TNM) staging system, categories of CESC. Except for disease-free interval (DFI), 4 IGLC (IGLC1, IGLC2, IGLC3, and IGLC7) expression levels were positively associated with patient overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) respectively in CESC tissues. 5 IGLC (IGLC1, IGLC2, IGLC3, IGLC6, and IGLC7) expressions were positively correlated with the expressions of a majority of immunomodulators respectively in CESC tissues. Tumor stemness was negatively correlated with the expressions of 4 IGLCs (IGLC1, IGLC2, IGLC3, and IGLC7) respectively in CESC tissues. Except for IGLC4, IGLC5, and IGLC7, 4 IGLC (IGLC1, IGLC2, IGLC3, and IGLC6) expressions were positively correlated with infiltration scores of 6 tumor-infiltrating immune cells (B cell, T cell CD4, T cell CD8, neutrophil, macrophage, and DC). After analyses of the above bioinformatics data of tumor-derived Igλ, Co-IP and LC–MS/MS were used to confirm that 4 proteins (RPL7, RPS3, H1-5, and H1-6) might interact with tumor-derived Igλ in cervical cancer cells. Functional analyses of these candidate proteins showed that they interacted with many proteins and were involved in various cellular biological processes. Finally, IHC was used to further confirm the above bioinformatics results, it was indicated that the expression level of Igλ in cervical adenocarcinoma and cervical squamous cell carcinoma was higher than that in normal cervical tissue.ConclusionThis study comprehensively investigated the functions of tumor-derived Igλ and its interacting proteins based on bioinformatics analysis and the potential value of Igλ as a prognostic and therapeutic marker for CESC, providing new direction and evidence for CESC therapy.

Network pharmacology -based study on the mechanism of traditional Chinese medicine in the treatment of glioblastoma multiforme

BackgroundGlioblastoma multiforme (GBM) is one of the most common primary malignant brain tumors. Yi Qi Qu Yu Jie Du Fang (YYQQJDF) is a traditional Chinese medicine (TCM) prescription for GBM. The present study aimed to use a network pharmacology method to analyze the underlying mechanism of YQQYJDF in treating GBM.MethodsGBM sample data, active ingredients and potential targets of YQQYJDF were obtained from databases. R language was used to screen differentially expressed genes (DEGs) between GBM tissues and normal tissues, and to perform enrichment analysis and weighted gene coexpression network analysis (WGCNA). The Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database was used to perform a protein‒protein interaction (PPI) analysis. A Venn diagram was used to obtain the core target genes of YQQYJDF for GBM treatment. Molecular docking was used to verify the binding between the active ingredient molecules and the proteins corresponding to the core target genes. Cell proliferation assays and invasion assays were used to verify the effect of active ingredients on the proliferation and invasion of glioma cells.ResultsA total of 73 potential targets of YQQYJDF in the treatment of GBM were obtained. Enrichment analyses showed that the biological processes and molecular functions involved in these target genes were related to the activation of the G protein-coupled receptor (GPCR) signaling pathway and the regulation of hypoxia. The neuroactive ligand‒receptor pathway, the cellular senescence pathway, the calcium signaling pathway, the cell cycle pathway and the p53 signaling pathway might play important roles. Combining the results of WGCNA and PPI analysis, five core target genes and their corresponding four core active ingredients were screened. Molecular docking indicated that the core active ingredient molecules and the proteins corresponding to the core target genes had strong binding affinities. Cell proliferation and invasion assays showed that the core active ingredients of YQQYJDF significantly inhibited the proliferation and invasion of glioma cells (P < 0.01).ConclusionsThe present study predicted the possible active ingredients and targets of YQQYJDF in treating GBM, and analyzed its possible mechanism. These results may provide a basis and ideas for further research.

Prognostic significance of TM4SF1 and DDR1 expression in epithelial ovarian cancer.

Transmembrane 4 L6 family member 1 (TM4SF1) and discoidin domain receptor 1 (DDR1) are expressed in numerous types of cancer, but their expression in epithelial ovarian cancer and the association between their expression and patient prognosis are unclear. The present study aimed to explore the expression of TM4SF1 and DDR1 and their relationship with prognosis in epithelial ovarian cancer. Firstly, the Oncomine and Gene Expression Profiling Interactive Analysis (GEPIA) platforms were used to compare the expression levels of TM4SF1 and DDR1 in ovarian cancer and normal ovarian tissue, and Kaplan-Meier plotter was used to analyze the association between gene expression and patient prognosis. The proteins interacting with TM4SF1 and DDR1 were analyzed using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), and enrichment analysis of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways was conducted for the interacting proteins. Furthermore, immunohistochemical staining was performed to detect the expression of TM4SF1 and DDR1 protein in epithelial ovarian cancer tissue and to analyze the association between expression and prognosis. The Oncomine and GEPIA analyses showed that the expression levels of TM4SF1 and DDR1 were significantly higher in epithelial ovarian cancer than in normal ovarian tissue, and the analysis of clinical samples revealed that TM4SF1 and DDR1 were coexpressed in some cases. STRING analysis indicated that the TM4SF1 and DDR1 proteins interact with each other. The overall survival and progression-free survival of patients whose epithelial ovarian cancer coexpressed TM4SF1 and DDR1 were significantly shorter than those of patients lacking TM4SF1 and DDR1 coexpression. Multivariate analysis indicated that TM4SF1 and DDR1 protein coexpression was an independent prognostic factor. In summary, TM4SF1 and DDR1 proteins were coexpressed in some epithelial ovarian cancer tissues and appear to be adverse prognostic factors for epithelial ovarian cancer. In addition, TM4SF1 and DDR1 may have an interactive or mutual regulatory mechanism.

Cuproptosis-related gene SLC31A1 expression correlates with the prognosis and tumor immune microenvironment in glioma

Cuproptosis is a newly discovered form of cell death. It is regulated by a string of genes. The genes are identified to influence the tumor progression, but in glioma, the cuproptosis-related genes are little studied. The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) were used to screen for SLC31A1 gene expression in glioma and healthy tissue samples. The results were validated using the Gene Expression Omnibus (GEO) and quantitative real-time polymerase chain reaction (qPCR). The Human Protein Atlas (HPA) and the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) were used to validate our results at the protein level. Multivariable analysis and Kaplan–Meier survival curves were used to examine the relationship among SLC31A1 gene expression, clinical parameters, and survival rates. The online Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) was used to find the genes and proteins that correlate to SLC31A1. The immune infiltration analysis was performed using the Tumor Immune Estimation Resource (TIMER) databases. Small interfering RNA was used to knock down the SLC31A1 expression, and the cell proliferation, apoptosis, and migration were analyzed using cell counting kit-8, flow cytometry, and transwell. The glioma patients have higher SLC31A1 expression levels, which increase as the World Health Organization (WHO) grade escalates. The survival analysis illustrates that the SLC31A1 gene expression negatively correlates with overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS). The immune infiltration analysis shows the SLC31A1 gene positively correlates with T helper 2 (Th2) cells, macrophages, and M2-type macrophages and negatively correlates with plasmacytoid dendritic cells (pDCs), natural killer (NK) CD56bright cells, and CD8 T cells. The in vitro KD experiment shows the SLC31A1 knockdown depressed the glioma cell proliferation and migration and promoted the apoptosis rate. The SLC31A1 gene expression can shorten the survival time of glioma patients. In vitro study shows that SLC31A1 can promote cell proliferation, and migration, and depress the cell apoptosis of glioma cells. It also can promote the formation of a tumor-suppressive microenvironment.

Exploring the Potential of Exome Sequencing in Idiopathic Azoospermia: A Genetic Burden and Network Analysis Study

The purpose of this study was to investigate the linkage of the association of azoospermia phenotype with genetic alterations, involved in genome instability. Male infertility is a multifactorial pathology, and genetic alterations might be the underlying factors in majority of cases of severe male infertility. The recent emergence of next-generation sequencing offers an opportunity to analyze many genes and their interactions at once, and whole-exome sequencing (WES) together with whole-genome sequencing (WGS) was recently suggested for implementation of diagnosis workup in severe infertility cases. However, the reports on WES in conjunction with burden tests and gene network analysis are scarce or lacking in cases of severe male infertility. WES was performed on 21 nonobstructive azoospermia patients. DNA samples were sequenced using the Twist Comprehensive Exome Panel. Genetic burden test was performed with Testing Rare vAriants using Public Data. Protein interactions were investigated with ConsensusPathDB and Cytoscape. For single nucleotide variants and copy number variations (CNV) analysis, samples were analyzed with the Illumina’s BaseSpace Variant Interpreter. Genetic variant burden was found elevated in 1,473 genes out of 30,000 known testis expressed genes. Three hundred and two genes with increased loss-of-function (LoF) variant set were present in more than one sample. Overrepresentation analysis with pathway-based set of genes with high variant burden demonstrated 26 pathways. Overrepresentation analysis with protein complex-based gene sets obtained 14 sets, showing the involvement in cell proliferation and DNA repair. Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) network analysis with Cytoscape identified two clusters: (1) genes, involved in DNA binding/condensation and repair processes and (2) genes with the role in ribosome biosynthesis and gene expression processes. Increased loss of function germline variant burden and sumoylation may have critical significance in spermatogenesis. These parameters may be used for focused diagnosis in nonobstructive azoospermia patients. This may have both general significance for the decreased organism functionality but in particular is critical in spermatogenesis.

Molecular Mechanism of Qingzaojiufei Decoction in the Treatment of Pulmonary Fibrosis based on Network Pharmacology and Molecular Docking.

In recent years, pulmonary fibrosis (PF) has increased in incidence and prevalence. Qingzaojiufei decoction (QD) is a herbal formula that is used for the treatment of PF. In this research, network pharmacology and molecular docking methods were used to explore the major chemical components and potential mechanisms of QD in the treatment of PF. The principal components and corresponding protein targets of QD were used to screen on Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Traditional Chinese Medicine Integrated Database (TCMID) and high-throughput experiment-and reference-guided database (HERB), Cytoscape 3.7.2 was used to construct the drug-component-target network. PF targets were collected by GeneCards and Online Mendelian Inheritance in Man (OMIM) databases. The protein-protein interaction (PPI) network was constructed by importing compound-disease intersection targets into the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and visualized by Cytoscape3.7.2. We further performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the intersecting targets. In the last, we validated the core targets and active compounds by molecular docking. The key compounds of quercetin, (-)-epigallocatechin-3-gallate, and kaempferol of QD were obtained. The key targets of AKT1, TNF, and IL6 of QD were obtained. The molecular docking results show that quercetin, (-)-epigallocatechin-3-gallate and kaempferol work well with AKT1, TNF and IL6. This research shows the multiple active components and molecular mechanism of QD in the treatment of PF and offers resources and suggestions for future studies.

Olanzapine manipulates neuroactive signals and may onset metabolic disturbances

Olanzapine is one of the most prescribed atypical antipsychotics to treat psychiatric illness and is associated with weight gain and metabolic disturbance. The present study investigated the olanzapine-regulated metabolic pathways using functional enrichent analysis including binding affinity with G-protein-coupled receptors (GPCRs). Proteins modulated by olanzapine were retrieved from SwissTargetPrediction, DIGEP-Pred, and BindingDB and then enriched in Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) to assess molecular function, biological process, and cellular components including Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. We used homology modeling to improve the 3D structure for GPCR synapse proteins including dopamine, serotonin, muscarinic, and histamine receptors which were then optimized using molecular dynamics (MD) simulations. The protein-olanzapine binding mechanisms for different GPCR binders were evaluated using molecular docking; later refined by MD simulations. Binding mechanism of olanzapine with D2, 5HT1A, 5HT2A, 5HT2B, 5HT2C, M1, and M2 receptors were created using homology modeling and optimized using MD simulations. In target identification, it was observed that olanzapine majority targeted G-protein coupled receptors. Further, enrichment analysis identified around 76% of the total genes regulated in molecular function, biological process, and cellular components were common including KEGG pathways. Moreover, it was observed that olanzapine had a major potency over the neurotransmitter synapse including neuroactive signals . Olanzapine-induced weight gain and metabolic alterations could be due to the deregulation of multiple synapses like dopamine, serotonin, muscarinic, and histamine at the feeding center followed by cGMP-PKG, cAMP, and PI3K-Akt signaling pathways. HIGHLIGHTS Olanzapine is used in the management of psychiatric illnesses. Olanzapine causes disturbance in lipids and glucosehomeostasis and manipulates energy expenditure. Olanzapine-induced weight gain may occur due to the deregulation of the multiple synapse and cGMP-PKG, cAMP, and PI3K-Akt signaling pathway Communicated by Ramaswamy H. Sarma

Effect of PTGES3 on the Prognosis and Immune Regulation in Lung Adenocarcinoma.

PTGES3 is upregulated in multiple cancer types and promotes tumorigenesis and progression. However, the clinical outcome and immune regulation of PTGES3 in lung adenocarcinoma (LUAD) are not fully understood. This study aimed to explore the expression level and prognostic value of PTGES3 and its correlation with potential immunotherapy in LUAD. All data were obtained from several databases, including the Cancer Genome Atlas database. Firstly, gene and protein expression of PTGES3 were analyzed using Tumor Immune Estimation Resource (TIMER), R software, Clinical Proteomic Tumor Analysis Consortium (CPTAC), and Human Protein Atlas (HPA). Thereafter, survival analysis was conducted using the R software, Gene Expression Profiling Interactive Analysis 2 (GEPIA2), and Kaplan-Meier Plotter. In addition, gene alteration and mutation analyses were conducted using the cBio Cancer Genomics Portal (cBioPortal) and Catalog of Somatic Mutations in Cancer (COSMIC) databases. The molecular mechanisms associated with PTGES3 were assessed via Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), GeneMANIA, GEPIA2, and R software. Lastly, the role of PTGES3 in immune regulation in LUAD was investigated using TIMER, Tumor-Immune System Interaction Database (TISIDB), and SangerBox. The gene and protein expression of PTGES3 were elevated in LUAD tissues and compared to the normal tissues, and the high expression of PTGES3 was correlated with cancer stage and tumor grade. Survival analysis revealed that overexpression of PTGES3 was associated with poor prognosis of LUAD patients. Moreover, gene alteration and mutation analysis revealed the occurrence of several types of PTGES3 gene alterations in LUAD. Moreover, co-expression analysis and cross-analysis revealed that three genes, including CACYBP, HNRNPC, and TCP1, were correlated and interacted with PTGES3. Functional analysis of these genes revealed that PTGES3 was primarily enriched in oocyte meiosis, progesterone-mediated oocyte maturation, and arachidonic acid metabolism pathways. Furthermore, we found that PTGES3 participated in a complex immune regulation network in LUAD. The current study indicated the crucial role of PTGES3 in LUAD prognosis and immune regulation. Altogether, our results suggested that PTGES3 could serve as a promising therapeutic and prognosis biomarker for the LUAD.

GNPNAT1 promotes the stemness of breast cancer and serves as a potential prognostic biomarker.

Glucosamine‑phosphate N‑acetyltransferase 1 (GNPNAT1) is a member of the acetyltransferase superfamily, related to general control non‑depressible 5 (GCN5). It has been documented that GNPNAT1 expression is increased in lung cancer, whereas its involvement in breast cancer (BC) remains to be further investigated. The present study aimed to evaluate the expression levels of GNPNAT1 in BC and its effect on BC stem cells (BCSCs). The Cancer Genome Atlas (TCGA) database was used for the analysis of the expression of GNPNAT1 and its clinical significance. Cox regression and logistic regression analyses were used to evaluate prognosis‑related factors. The GNPNAT1‑binding protein network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) application. The biological signaling pathways implicated in GNPNAT1 were investigated through function enrichment analysis including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analysis. The single‑sample GSEA method was used to investigate the connection between the level of immune infiltration and GNPNAT1 expression in BC. GNPNAT1 expression was upregulated in patients with BC and was significantly associated with a poor prognosis. GNPNAT1 and its co‑expressed genes were mostly enriched in nuclear transport, Golgi vesicle transport, ubiquitin‑like protein transferase activity and ribonucleoprotein complex binding, as determined using functional enrichment analysis. GNPNAT1 expression was positively associated with Th2 cells and T‑helper cells, and negatively associated with plasmacytoid dendritic cells, CD8+ T‑cells and cytotoxic cells. Additionally, the GNPNAT1 expression levels were considerably increased in BCSCs. GNPNAT1 knockdown markedly decreased the stemness ability of SKBR3 and Hs578T cells, including the production of CSC markers and mammosphere or clone formation, while GNPNAT1 overexpression increased the stemness level. Hence, the findings of the present study demonstrate that GNPNAT1 may be exploited as a novel prognostic biomarker and therapeutic target for BC.

Identification of Hub Genes and Upstream Regulatory Factors Based on Cell Adhesion in Triple-negative Breast Cancer by Integrated Bioinformatical Analysis.

Triple-negative breast cancer (TNBC) is characterized by metastasis and invasion, as well as poor prognosis, with chemotherapy being the main treatment option. Cell adhesion regulates tumorigenesis and new blood vessel formation. Thus, accurately identifying effective targets for TNBC and cell adhesion is challenging. Herein, we screened for differentially expressed genes between TNBC and normal cancer-free tissues to identify genes contributing to TNBC. Microarray data were obtained using a comprehensive gene-expression database. We used Database for Annotation, Visualization and Integrated Discovery, Kyoto Encyclopedia of Genes and Genomes and Functional Enrichment (FunRich) to perform Gene Ontology functional enrichment and predict signal pathways. The protein interaction network was predicted using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape v. 3.8.2. for visualization of results. TargetScan, miRanda, miRDB, miRWalk and RNA22 were used to predict miRNAs regulating key genes, and long non-coding RNAs (lncRNAs) regulating miRNAs were predicted using StarBase V2.0 from a comprehensive gene-expression database. Differentially expressed genes were mainly concentrated in the biological process of cell-cell adhesion. The protein-protein interaction network identified eight hub genes: Fibronectin 1 (FN1), Rac family small GTPase 1 (RAC1), heat-shock protein 90 alpha family class B member 1 (HSP90AB1), tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein zeta (YWHAZ), heat-shock protein family A member 8 (HSPA8), IQ motif containing GTPase-activating protein 1 (IQGAP1), CD44 molecule (CD44), and catenin beta 1 (CTNNB1). miRNAs related to TNBC occurrence and development were hsa-miR-142-5p, hsa-miR-144, hsa-miR-28-5p, hsa-miR-548d-3p, hsa-miR-587, hsa-miR-641, and hsa-miR-708. StarBase v2.0 predicted 12 lncRNAs, namely NEAT1, XIST, OIP5-AS1, MALAT1, AL035425.3, NORAD, AL391069.4, AC118758.3, AC026362.1, AC009065.4, AC016876.2, and AC093010.3, as upstream molecules that regulate miRNAs and which may regulate TNBC. Overall, mRNA-miRNA-lncRNA interactions appear to play a role in TNBC development.

Analysis of Inflammation-Related Genes in Patients with Stanford Type A Aortic Dissection.

Background: Aortic dissection (AD) is a life-threatening cardiovascular disease. Pathophysiologically, it has been shown that aortic wall inflammation promotes the occurrence and development of aortic dissection. Thus, the aim of the current research was to determine the inflammation-related biomarkers in AD. Methods: In this study, we conducted differentially expressed genes (DEGs) analysis using the GSE153434 dataset containing 10 type A aortic dissection (TAAD) and 10 normal samples downloaded from the Gene Expression Omnibus (GEO) database. The intersection of DEGs and inflammation-related genes was identified as differential expressed inflammation-related genes (DEIRGs). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed for DEIRGs. We then constructed the protein-protein interaction (PPI) network using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and identified hub genes using the Cytoscape plugin MCODE. Finally, least absolute shrinkage and selection operator (LASSO) logistic regression was used to construct a diagnostic model. Results: A total of 1728 DEGs were identified between the TAAD and normal samples. Thereafter, 61 DEIRGs are obtained by taking the intersection of DEGs and inflammation-related genes. The GO indicated that DEIRGs were mainly enriched in response to lipopolysaccharide, in response to molecules of bacterial origin, secretory granule membrane, external side of plasma, receptor ligand activity, and signaling receptor activator activity. KEGG analysis indicated that DEIRGs were mainly enriched in cytokine-cytokine receptor interaction, TNF signaling pathway, and proteoglycans in cancer. We identified MYC, SELL, HIF1A, EDN1, SERPINE1, CCL20, IL1R1, NOD2, TLR2, CD69, PLAUR, MMP14, and HBEGF as hub genes using the MCODE plug-in. The ROC indicated these genes had a good diagnostic performance for TAAD. Conclusion: In conclusion, our study identified 13 hub genes in the TAAD. This study will be of significance for the future development of a preventive therapy of TAAD.

The molecular mechanism of Xiaoxuming decoction in the treatment of ischemic stroke based on network pharmacology and molecular docking

AbstractXiaoxuming decoction is a traditional Chinese medicine that has been widely used in the clinical treatment of ischemic stroke (IS). This study employed network pharmacology to identify the bioactive molecules and therapeutic mechanism of Xiaoxuming decoction against IS. First, the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) was utilized to obtain the ingredients and potential target proteins related to IS in the Xiaoxuming decoction. Subsequently, known target proteins related to IS were collected from online Mendelian inheritance in man (OMIM), Disgenet, and Gencards databases. The mechanism of Xiaoxuming decoction against IS was identified by enrichment analysis of gene ontology (GO) and Kyoto Encyclopedia of genes and genomes (KEGG). Additionally, protein–protein interaction data were obtained from the search tool for the retrieval of interacting genes/proteins (STRING). The hub gene was further screened out from the gene expression omnibus (GEO) and verified by molecular docking. The study identified a total of 183 candidate molecules and 140 targets related to IS. These candidate targets regulate biological processes including inflammation, autophagy, oxidative stress, and vascular reaction. Our findings provide a comprehensive demonstration of the active compounds, key targets, main signaling pathways, and underlying molecular mechanisms of Xiaoxuming decoction in treating IS.

Exploration of the shared genes and signaling pathways between lung adenocarcinoma and idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF), a type of interstitial lung disease (ILD), is a chronic disease with an unknown etiology. The occurrence of lung cancer (LC) is one of the main causes of death in patients with IPF. However, the pathogenesis driving these malignant transformations remains unclear; therefore, this study aimed to identify the shared genes and functional pathways associated with both disease conditions. Data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. To identify overlapping genes in both diseases, the "limma" package in R software and weighted gene coexpression network analysis (WGCNA) were used. Venn diagrams were used to obtain the shared genes. The diagnostic value of the shared genes was assessed using receiver operating characteristic (ROC) curve analysis. Gene Ontology (GO) term enrichment was performed on the shared genes between lung adenocarcinoma (LUAD) and IPF, and the genes were also functionally enriched using Metascape. A protein-protein interaction (PPI) network was created using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. Finally, the link between shared genes and common antineoplastic medicines was investigated using the CellMiner database. The coexpression modules associated with LUAD and IPF were discovered using WGCNA, and 148 genes were found to overlap. In addition, 74 upregulated and 130 downregulated overlapping genes were obtained via differential gene analysis. Functional analysis of the genes revealed that these genes are primarily engaged in extracellular matrix (ECM) pathways. Furthermore, COL1A2, POSTN, COL5A1, CXCL13, CYP24A1, CXCL14, and BMP2 were identified as potential biomarkers in patients with LUAD secondary to IPF showing good diagnostic values. ECM-related mechanisms may be the underlying link between LC and IPF. A total of 7 shared genes were identified as potential diagnostic markers and therapeutic targets for LUAD and IPF.

Identification of genes encoding targets associated with adverse events in multiple myeloma.

1556 Background: Despite novel therapeutic options, understanding the toxicity profile of anti multiple myeloma (MM) drug regimens (AMDR) is vital. Data suggests adverse event (AE) profile is orchestrated by certain associated genes. The purpose of this study is to explore the interplay of genes, regimens, AEs and provide genetic evidence for a linkage between regimens and the presence of AEs. Methods: We examined a group of AMDRs retrieved from the FDA Adverse Event Reporting System. Drug-associated genes were obtained from the Drug Gene Interaction Database, DGIdb. Then, genes were imported to the Online Mendelian Inheritance in Man (OMIM) to search for the existing clinical synopses (features) in the peer reviewed biomedical material, or phenotypes when no data was available. Clinical synopses were then linked to MedDRA. Gene gene interaction was also determined through the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). We then constructed a detailed large MM pharmacogenetic network to illustrate the linkage among drugs, genes and MM via various expert integrated resources. AMDR responses were then assessed at the DNA and protein levels by modularizing genetic causation into different scenarios i) a direct drug effect on gene expression; ii) the secondary or tertiary effect of drug(s) on subsequent drug metabolism or disposition altering gene expression pathways; iii) modulation of another biological network that disturbs drug effect on gene expression resulting in exacerbating AE occurrence. Results: We found multiple genes including CTLA4, NFKB1, PSMB8, CCND1 and HLAA were complicit/associated with different AEs including cardiac or hepatobiliary disorders or other serious events. Our results suggested the expression of CTLA4 was influenced by multiple AMDRs from combinations of bortezomib (V), lenalidomide (R), melphalan (M), or dexamethasone (d). When AMDRs involve these agents, the occurrence of pericardial disorders due to the treatment exhibited high confidence. We also found carfilzomib (K) combinations (Kd or KRd) were associated with PSMB8 which was likely to be the genetic factor concomitant with heart failures, cardiac arrhythmias, or hepatobiliary toxicities. Furthermore, our findings suggested NFKB1, thalidomide associated gene interacting with PSMB8, may present the same toxicity events in hepatobiliary disorders. Conclusions: Understanding the genomic basis of specific drug-AE combinations could reveal molecular mechanisms underlying drug-gene-AE associations and recognize the causality of AEs. When considering alternative agents in MM, our study offers insights for predicting treatment responses such as PSMB8 which could potentially cause an off target effect of K on cardiomyocyte 20S beyond MM cells. Our plan is to integrate this novel computational biology approach with translational medicine to further investigate our observation.

Astrocyte-derived complement C3 is activated in patients with tuberous sclerosis complex and mediates immune injury: an integrated bioinformatics analysis.

Tuberous sclerosis complex (TSC) is a genetic disorder associated with multiple neurological manifestations. Cortical tubers (CT) are recognized as the hallmark brain lesions of TSC and contribute to neurological and psychiatric symptoms. To understand the molecular mechanism of neuropsychiatric features of TSC, the differentially expressed genes (DEGs) in CT from patients with TSC and those in normal cortex (NC) from participants acting as healthy controls were investigated. The dataset of GSE16969, which had already been published and described (https://onlinelibrary.wiley.com/doi/10.1111/j.1750-3639.2009.00341.x), was downloaded from the Gene Expression Omnibus (GEO), including samples of 4 CT and 4 NC. The R package "limma" was used to screen DEGs in CT and NC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analyses of the DEGs were conducted using the R package "clusterProfiler". The online software Ingenuity Pathway Analysis (IPA) was used to explore activation/inaction of canonical pathways. The hub gene was selected based on the protein-protein interaction (PPI) network constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and Cytoscape software. Subsequently, the hub genes at messenger (mRNA) and transcriptional levels were tested. We also explored immune cell-type enrichment using the online database xCell, and assessed the correlation between cell types and C3 expression. Then, we verified the source of C3 by constructing TSC2 knockout cells in the U87 astrocyte cell line. The human neuronal cell line SH-SY5Y was used to examine the effects of excessive complement C3 levels. A total of 455 DEGs were identified. A large number of pathways were involved in the immune response process based on the results of GO, KEGG, and IPA. C3 was identified as a hub gene. Complement C3 was also upregulated in the human CT and peripheral blood. Furthermore, based on the enrichment of functions and signaling pathways, complement C3 played a critical role in immune injury in CT of TSC. In the in vitro experiments, we found that excessive complement C3 was derived from TSC2 knockout U87 cells, and there was an increased intracellular reactive oxygen species (ROS) level in SH-SY5Y cells. Complement C3 is activated in patients with TSC and can mediate immune injury.

Risk factors and predictors of urogenous sepsis after percutaneous nephrolithotomy for idiopathic calcium oxalate nephrolithiasis.

The aim of this study was to use bioinformatics approaches to screen and identify the key genes of idiopathic calcium oxalate nephrolithiasis, and explore its potential molecular mechanism. The GSE73680 kidney stone data set was downloaded from the Gene Expression Omnibus (GEO). R software (The R Foundation for Statistical Computing) was used to screen differentially expressed genes. GeneMANIA and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) databases were used to analyze related genes interacting with crucial genes, and a protein-protein interaction (PPI) network was constructed. The differential genes were then subjected to the Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database. The clinical data of 156 patients who received percutaneous nephrolithotomy (PCNL) therapy at our facility between January 2013 and December 2017 were retrospectively analyzed. The various parameters associated with postoperative urogenous sepsis were identified using multivariable logistic regression analysis. The study discovered one differentially expressed gene was nucleotide-binding oligomerization domain-containing protein 2 (NOD2). GO and KEGG analysis showed that NOD2 might affect the occurrence of idiopathic calcium oxalate kidney stones by affecting inflammation, receptor expression, immune environment, necrosis, apoptosis, and other pathways. The clinical parameter of patients who participated in the study, including preoperative urinary white blood cell (WBC) count, preoperative urinary nitrite, stone diameter, operation time, WBC count, and WBC D values, were statistically different between the systemic inflammatory response syndrome (SIRS) group and the urosepsis group. According to multivariate logistic regression analysis, the preoperative urine nitrite, calculus diameter, blood WBC, and NOD2 expression 3 hours after surgery were all independently associated with the urosepsis development. Preoperative urinary nitrite positive status, postoperative WBC count ≥2.98×109/L 3 hours after operation, stone diameter >6 cm, and low expression of NOD2 in renal papillary tissue are more likely to cause the urinary source of idiopathic calcium oxalate nephrolithiasis after PCNL urogenous sepsis. These parameters also offer a viable treatment paradigm for the perioperative management of PCNL in treating idiopathic calcium oxalate kidney stones.

Autoantibodies identify primary Sjögren’s syndrome in patients lacking serum IgG specific for Ro/SS-A and La/SS-B

ObjectiveIdentify autoantibodies in anti-Ro/SS-A negative primary Sjögren’s syndrome (SS).MethodsThis is a proof-of-concept, case-control study of SS, healthy (HC) and other disease (OD) controls. A discovery dataset of plasma samples (n=30...

Integrative analysis of gene expression and DNA methylation to identify biomarkers of non-genital warts induced by low-risk human papillomaviruses infection

BackgroundHuman papillomaviruses have been shown to dysregulate the gene expression and DNA methylation profiles of their host cells over the course of infection. However, there is a lack of information on the impact of low-risk HPV infection and wart formation on host cell's expression and methylation patterns. Therefore, the objective of this study is to analyse the genome and methylome of common warts using an integrative approach. MethodsIn the present study, gene expression (GSE136347) and methylation (GSE213888) datasets of common warts were obtained from the GEO database. Identification of the differentially expressed and differentially methylated genes was carried out using the RnBeads R package and the edgeR Bioconductor package. Next, functional annotation of the identified genes was obtained using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Network construction and analyses of the gene-gene, protein-protein, and signaling interactions of the differentially expressed and differentially methylated genes was performed using the GeneMANIA web interface, the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and the Signaling Network Open Resource 2.0 (SIGNOR 2.0), respectively. Lastly, significant hub genes were identified using the Cytoscape application CytoHubba. ResultsA total of 276 genes were identified as differentially expressed and differentially methylated in common warts, with 52% being upregulated and hypermethylated. Functional enrichment analysis identified extracellular components as the most enriched annotations, while network analyses identified ELN, ITGB1, TIMP1, MMP2, LGALS3, COL1A1 and ANPEP as significant hub genes. ConclusionsTo the best knowledge of the authors, this is the first integrative study to be carried out on non-genital warts induced by low-risk HPV types. Future studies are required to re-validate the findings in larger populations using alternative approaches.

Abstract 3158: Checkpoint Kinase I, Cyclin E1, and Exonuclease I genes play role in the survival of lung adenocarcinoma patients

Abstract According to the SEER program of NCI, lung, and bronchus cancer are the second most common cancer with an estimated 236,740 diagnoses in 2022. Fifty-four percent of the patients are expected to die of the disease in the United States alone. The five-year overall survival rate for these cancers is 22.9 percent. Lung adenocarcinoma (LUAD) accounts for forty percent of all lung cancer cases in the United States. Genetic biomarker-based early detection and precision treatment of LUAD patients can play a critical role in the reduction of the mortality rate. The purpose of this investigation was to use an integrated bioinformatics approach to identify differentially expressed Hub genes in LUAD with &amp;gt; 10 connections in the genetic interaction network(s). The higher number of genetic interactions potentially suggests their important role in patient survival. Out of 23,483 genes from seven published cancer studies and databases, we identified 107 significantly altered (up or down-regulated) genes that are common to all data sources. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) showed that many of the identified 107 genes were involved in DNA replication, DNA repair, ATP binding, and cancer pathways. A protein-protein interaction network was mapped with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). This analysis revealed 1116 protein-to-protein interactions (PPI) among the 107 genes. We selected 25 genes, which exhibited &amp;gt;30 PPI interactions each which showed Cox coefficients for gene expression, where a positive coefficient corresponds to a worse survival rate with higher levels of gene expression, while a negative coefficient corresponds to a better prognosis with lower levels of gene expression; it also provided a False Discovery Rate (FDR) with a corrected p-value. The Kaplan-Meier survival curves for genes that had an FDR of below 0.05 were graphed inside of the OncoLnc tool. Patients were subclassified into the low- and high-Hub gene expression level groups if the median expression was in the bottom 25 percentile or top 25 percentile. Hub genes isolated from Cytoscape were entered into OncoLnc to collect data on the relationship between LUAD patients (n=492) survival and gene expression, which resulted in the identification of three genes, Checkpoint Kinase 1, Cyclin E1, and Exonuclease 1. All three genes showed a significant correlation between increased differential expression in LUAD and worsened patient survival. These signature Hub genes (Checkpoint Kinase 1, Cyclin E1, and Exonuclease 1) and associated genetic networks can potentially be developed as early diagnostic markers or targets in precision gene therapies for improved prognosis of LUAD patients. Citation Format: Prateek Gupta, Leya Joykutty, Diaaidden Alwadi, Ana Ruas, Deodutta Roy, Quentin Felty, Alok Deoraj. Checkpoint Kinase I, Cyclin E1, and Exonuclease I genes play role in the survival of lung adenocarcinoma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3158.