NCBI Gene Expression Omnibus Research Articles

Single-cell sequencing reveals novel cellular heterogeneity in uterine leiomyomas.

What are the cellular composition and single-cell transcriptomic differences between myometrium and leiomyomas as defined by single-cell RNA sequencing? We discovered cellular heterogeneity in smooth muscle cells (SMCs), fibroblast and endothelial cell populations in both myometrium and leiomyoma tissues. Previous studies have shown the presence of SMCs, fibroblasts, endothelial cells and immune cells in myometrium and leiomyomas. However, there is no information on the cellular heterogeneity in these tissues and the transcriptomic differences at the single-cell level between these tissues. We collected five leiomyoma and five myometrium samples from a total of eight patients undergoing hysterectomy. We then performed single-cell RNA sequencing to generate a cell atlas for both tissues. We utilized our single-cell sequencing data to define cell types, compare cell types by tissue type (leiomyoma versus myometrium) and determine the transcriptional changes at a single-cell resolution between leiomyomas and myometrium. Additionally, we performed MED12-variant analysis at the single-cell level to determine the genotype heterogeneity within leiomyomas. We collected five MED12-variant positive leiomyomas and five myometrium samples from a total of eight patients. We then performed single-cell RNA sequencing on freshly isolated single-cell preparations. Histopathological assessment confirmed the identity of the samples. Sanger sequencing was performed to confirm the presence of the MED12 variant in leiomyomas. Our data revealed previously unknown heterogeneity in the SMC, fibroblast cell and endothelial cell populations of myometrium and leiomyomas. We discovered the presence of two different lymphatic endothelial cell populations specific to uterine leiomyomas. We showed that both myometrium and MED12-variant leiomyomas are relatively similar in cellular composition but differ in cellular transcriptomic profiles. We found that fibroblasts influence the leiomyoma microenvironment through their interactions with endothelial cells, immune cells and SMCs. Variant analysis at the single-cell level revealed the presence of both MED12 variants as well as the wild-type MED12 allele in SMCs of leiomyomatous tissue. These results indicate genotype heterogeneity of cellular composition within leiomyomas. The datasets are available in the NCBI Gene Expression Omnibus (GEO) using GSE162122. Our study focused on MED12-variant positive leiomyomas for single-cell RNA sequencing analyses. Leiomyomas carrying other genetic rearrangements may differ in their cellular composition and transcriptomic profiles. Our study provides a cellular atlas for myometrium and MED12-variant positive leiomyomas as defined by single-cell RNA sequencing. Our analysis provides significant insight into the differences between myometrium and leiomyomas at the single-cell level and reveals hitherto unknown genetic heterogeneity in multiple cell types within human leiomyomas. Our results will be important for future studies into the origin and growth of human leiomyomas. This work was supported by funding from the National Institute of Child Health and Human Development (HD098580 and HD088629). The authors declare no competing interests.

Identification of genetic molecular markers and immune infiltration characteristics of Alzheimer's disease through weighted gene co-expression network analysis

BackgroundAlzheimer's disease (AD) is a progressive neurodegenerative disease that leads to cognitive impairment and memory loss. Currently, the pathogenesis and underlying causative genes of AD remain unclear, and there exists no effective treatment for this disease. This study explored AD-related diagnostic and therapeutic biomarkers from the perspective of immune infiltration by analyzing public data from the NCBI Gene Expression Omnibus database.MethodIn this study, weighted gene co-expression network analysis (WGCNA) was conducted to identify modules and hub genes contributing to AD development. A protein–protein interaction network was constructed when the genes in the modules were enriched and examined by Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Furthermore, a gene network was established using topological WGCNA, from which five hub genes were selected. Logistic regression analysis and receiver operating characteristic curve analysis were performed to explore the clinical value of genes in AD diagnosis. The genes in the core module intersected with the hub genes, and four intersection genes (ATP2A2, ATP6V1D, CAP2, and SYNJ1) were selected. These four genes were enriched by gene set enrichment analysis (GSEA). Finally, an immune infiltration analysis was performed.ResultsThe GO/KEGG analysis suggested that genes in the core module played a role in the differentiation and growth of neural cells and in the transmission of neurotransmitters. The GSEA of core genes showed that these four genes were mainly enriched in immune/infection pathways (e.g., cholera infection and Helicobacter pylori infection pathways) and other metabolic pathways. An investigation of immune infiltration characteristics revealed that activated mast cells, regulatory T cells, plasma cells, neutrophils, T follicular helper cells, CD8 T cells, resting memory CD4 T cells, and M1 macrophages were the core immune cells contributing to AD progression. qRT-PCR analysis showed that the ATP6V1D is upregulated in AD.ConclusionThe results of enrichment and immuno-osmotic analyses indicated that immune pathways and immune cells played an important role in the occurrence and development of AD. The selected key genes were used as biomarkers related to the pathogenesis of AD to further explore the pathways and cells, which provided new perspectives on therapeutic targets in AD.

Exploring the pathogenesis of diabetic kidney disease by microarray data analysis.

Diabetic kidney disease (DKD) is a major complication of diabetes mellitus, and the leading contributor of end-stage renal disease. Hence, insights into the molecular pathogenesis of DKD are urgently needed. The purpose of this article is to reveal the molecular mechanisms underlying the pathogenesis of DKD. The microarray datasets of GSE30528 and GSE30529 were downloaded from the NCBI Gene Expression Omnibus (GEO) database to identify the common differentially expressed genes (DEGs) between the glomerular DKD (GDKD) and tubular DKD (TDKD), respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to analyze the function and pathways of the common DEGs. After constructing the protein–protein interaction (PPI) network and subnetwork analysis, three types of analyses were performed, namely, identification of hub genes, analysis of the coexpressed network, and exploration of transcription factors (TFs). Totally, 348 and 463 DEGs were identified in GDKD and TDKD, respectively. Then, 66 common DEGs (63 upregulated DEGs and three downregulated DEGs) were obtained in DKD patients. GO and KEGG pathway analyses revealed the importance of inflammation response, immune-related pathways, and extracellular matrix-related pathways, especially chemokines and cytokines, in DKD. Fifteen hub genes from the 66 common DEGs, namely, IL10RA, IRF8, LY86, C1QA, C1QB, CD53, CD1C, CTSS, CCR2, CD163, CCL5, CD48, RNASE6, CD52, and CD2 were identified. In summary, through the microarray data analysis, the common functions and hub genes greatly contribute to the elucidation of the molecular pathogenesis associated with DKD.

Development and validation of a prognostic model related to pyroptosis-related genes for esophageal squamous cell carcinoma using bioinformatics analysis.

BackgroundEsophageal squamous cell carcinoma (ESCC) is one of the most lethal malignant tumors worldwide, and a larger number of ESCC patients have unsatisfactory overall survival (OS) rates. While pyroptosis participates in the development of a variety of malignancies, the function of pyroptosis-related genes (PRGs) in ESCC is still obscure. The aim of this study was to construct the pyroptosis-related prognostic model for ESCC, which will be developed to stratify the risk hazards of ESCC patients and to provide theoretical evidence for individualized treatment.MethodsRNA-seq data of ESCC were download from the NCBI Gene Expression Omnibus (GEO) database. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to explore the potential biological functions or pathways. OS was considered as the primary prognosis outcome in this study. The riskscore was constructed by Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis. The pyroptosis-related prognostic model was constructed based on all independent prognostic factors and verified by C-index, Receiver operating characteristic (ROC) curves, and Calibration curves, and the role of the riskscore in ESCC immunotherapy was evaluated by the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm.ResultsThe current study found 31 differentially expressed PRGs (P<0.001), and functional enrichment analysis showed these PRGs were enriched in positive regulation of cytokine production, interleukin-1 beta production. Univariate and multivariate Cox regression analysis were applied to validate that the riskscore based on four prognostic PRGs (HMGB1, IL-18, NLRP7, and PLCG1) was an independent prognostic factor for ESCC, and the C-index of prognostic model related to the riskscore (C-index =0.705) was higher than that of tumor node metastasis (TNM) stage (0.620). The low-risk group showed a better efficacy of immune checkpoint inhibitors.ConclusionsThe riskscore related to PRGs was one of the independent prognostic factors for ESCC. Moreover, the prognostic model related to the riskscore could be used to predict the OS of ESCC patients effectively. However, there still were several limitations in this study, such as no external validation sample. In summary, our data provides a novel perspective in exploring the potential prognostic biomarkers of ESCC.

Gene module regulation in dilated cardiomyopathy and the role of Na/K-ATPase.

Dilated cardiomyopathy (DCM) is a major cause of cardiac death and heart transplantation. It has been known that black people have a higher incidence of heart failure and related diseases compared to white people. To identify the relationship between gene expression and cardiac function in DCM patients, we performed pathway analysis and weighted gene co-expression network analysis (WGCNA) using RNA-sequencing data (GSE141910) from the NCBI Gene Expression Omnibus (GEO) database and identified several gene modules that were significantly associated with the left ventricle ejection fraction (LVEF) and DCM phenotype. Genes included in these modules are enriched in three major categories of signaling pathways: fibrosis-related, small molecule transporting-related, and immune response-related. Through consensus analysis, we found that gene modules associated with LVEF in African Americans are almost identical as in Caucasians, suggesting that the two groups may have more common rather than disparate genetic regulations in the etiology of DCM. In addition to the identified modules, we found that the gene expression level of Na/K-ATPase, an important membrane ion transporter, has a strong correlation with the LVEF. These clinical results are consistent with our previous findings and suggest the clinical significance of Na/K-ATPase regulation in DCM.

ESRRG, ATP4A, and ATP4B as Diagnostic Biomarkers for Gastric Cancer: A Bioinformatic Analysis Based on Machine Learning.

Based on multiple bioinformatics methods and machine learning techniques, this study was designed to explore potential hub genes of gastric cancer with a diagnostic value. The novel biomarkers were detected through multiple databases of gastric cancer–related genes. The NCBI Gene Expression Omnibus (GEO) database was used to obtain gene expression files. Three hub genes (ESRRG, ATP4A, and ATP4B) were detected through a combination of weighted gene co-expression network analysis (WGCNA), gene–gene interaction network analysis, and supervised feature selection method. GEPIA2 was used to verify the differences in the expression levels of the hub genes in normal and cancer tissues in the RNA-seq levels of Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases. The objectivity of potential hub genes was also verified by immunohistochemistry in the Human Protein Atlas (HPA) database and transcription factor–hub gene regulatory network. Machine learning (ML) methods including data pre-processing, model selection and cross-validation, and performance evaluation were examined on the hub-gene expression profiles in five Gene Expression Omnibus datasets and verified on a GEO external validation (EV) dataset. Six supervised learning models (support vector machine, random forest, k-nearest neighbors, neural network, decision tree, and eXtreme Gradient Boosting) and one semi-supervised learning model (label spreading) were established to evaluate the diagnostic value of biomarkers. Among the six supervised models, the support vector machine (SVM) algorithm was the most effective one according to calculated performance metrics, including 0.93 and 0.99 area under the curve (AUC) scores on the test and external validation datasets, respectively. Furthermore, the semi-supervised model could also successfully learn and predict sample types, achieving a 0.986 AUC score on the EV dataset, even when 10% samples in the five GEO datasets were labeled. In conclusion, three hub genes (ATP4A, ATP4B, and ESRRG) closely related to gastric cancer were mined, based on which the ML diagnostic model of gastric cancer was conducted.

Abstract 2709: Transcriptomic meta-analysis of non-Hodgkin’s B-cell lymphomas reveals reliance on pathways associated with extracellular matrix

Abstract We report here the results of a transcriptomic meta-analysis of public data for B-cell Non-Hodgkin’s Lymphomas (BCNHL). In 2016, there were 461,000 cases of Non-Hodgkin’s lymphoma worldwide, resulting in 240,000 deaths that year. BCNHLs pose a significant disease burden worldwide, making up 85-90% of BCNHL cases. BCNHL subtypes include Burkitt’s lymphoma, marginal-zone B-cell lymphomas, follicular lymphoma, diffuse large B-cell lymphoma, and mantle cell lymphoma. There is a growing need to understand the mechanisms of BCNHL pathology. The scientific community has spent a great deal of time and effort to identify the hallmarks of cancer. Since cancers of different subtypes can be considered parallel systems, we have performed a meta-analysis including as many BCNHL subtypes as possible to determine common underlying mechanisms. We performed a transcriptomic meta-analysis of publicly available RNA-sequencing data for BCNHL, consisting of 322 relevant samples across seven distinct studies in the NCBI Gene Expression Omnibus (GEO). To our knowledge, no BCNHL meta-analysis of this magnitude has previously been performed. We found ~10,400 significant differentially expressed genes (FDR p-value &amp;lt;= 0.05) and 33 significantly modulated pathways (FDR p-value &amp;lt;= 0.05). We observed potential common mechanisms for BCNHL’s differentially expressed genes and signaling pathways. Our findings include a significant class of proteoglycans not previously associated with lymphomas as well as significant upregulation of extracellular matrix-associated proteins. This meta-analysis offers fresh insights into the inner workings and mechanisms of B-cell lymphomas that could give rise to improved diagnostics and/or therapeutics. Citation Format: Naomi Rapier-Sharman, Brett E. Pickett. Transcriptomic meta-analysis of non-Hodgkin’s B-cell lymphomas reveals reliance on pathways associated with extracellular matrix [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2709.

Identification of Secondary Breast Cancer in Vital Organs through the Integration of Machine Learning and Microarrays

Breast cancer includes genetic and environmental factors and is the most prevalent malignancy in women contributing to the pathogenesis and progression of cancer. Breast cancer prognosis metastasizes towards bones, the liver, brain, and lungs, and is the main cause of death in patients. Furthermore, the selection of features and classification is significant in microarray data analysis, which suffers from huge time consumption. To address these issues, this research uniquely integrates machine learning and microarrays to identify secondary breast cancer in vital organs. This work firstly imputes the missing values using K-nearest neighbors and improves the recursive feature elimination with cross-validation (RFECV) using the random forest method. Secondly, the class imbalance is handled by employing K-means synthetic object oversampling technique (SMOTE) to balance minority class and prevent noise. We successfully identified the 16 most essential Entrez gene ids responsible for predicting metastatic locations in the bones, brain, liver, and lungs. Extensive experiments are conducted on NCBI Gene Expression Omnibus GSE14020 and GSE54323 datasets. The proposed methods have handled class imbalance, prevented noise, and appropriately reduced time consumption. Reliable results were obtained on four classification models: decision tree; K-nearest neighbors; random forest; and support vector machine. Results are presented having considered confusion matrices, accuracy, ROC-AUC and PR-AUC, and F1-score.

Weighted Gene Co-expression Network Analysis Identifies Specific Modules and Hub Genes Related to Subacute Ruminal Acidosis.

Weighted gene co-expression network analysis (WGCNA) was used to understand the pathogenesis of subacute ruminal acidosis (SARA) and identify potential genes related to the disease. Microarray data from dataset GSE143765, which included 22 cows with and nine cows without SARA, were downloaded from the NCBI Gene Expression Omnibus (GEO). Results of WGCNA identified highly correlated modules of sample genes, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses allowed further biological insights into SARA-related modules. The protein-protein interaction (PPI) network, modules from the PPI network, and cistron annotation enrichment of modules were also analyzed. A total of 14,590 DEGs were used for the WGCNA. Construction of a protein-protein network identified DCXR, MMP15, and MMP17 as hub genes. Functional annotation showed that DCXR mainly exhibited L-xylulose reductase (NADP+) activity, glucose metabolic process, xylulose metabolic process, and carbonyl reductase (NADPH) activity, which are involved in the pentose and glucuronate interconversion pathways. MMP15 and MMP17 mainly have a collagen catabolic process. Overall, the results of this study aid the clarification of the biological and metabolic processes associated with SARA at the molecular level. The data highlight potential mechanisms for the future development of intervention strategies to reduce or alleviate the risk of SARA.

RETRACTED ARTICLE: Calcium-dependent transcriptional changes in human pancreatic islet cells reveal functional diversity in islet cell subtypes

Aims/hypothesisPancreatic islets depend on cytosolic calcium (Ca2+) to trigger the secretion of glucoregulatory hormones and trigger transcriptional regulation of genes important for islet response to stimuli. To date, there has not been an attempt to profile Ca2+-regulated gene expression in all islet cell types. Our aim was to construct a large single-cell transcriptomic dataset from human islets exposed to conditions that would acutely induce or inhibit intracellular Ca2+ signalling, while preserving biological heterogeneity.MethodsWe exposed intact human islets from three donors to the following conditions: (1) 2.8 mmol/l glucose; (2) 16 mmol/l glucose and 40 mmol/l KCl to maximally stimulate Ca2+ signalling; and (3) 16 mmol/l glucose, 40 mmol/l KCl and 5 mmol/l EGTA (Ca2+ chelator) to inhibit Ca2+ signalling, for 1 h. We sequenced 68,650 cells from all islet cell types, and further subsetted the cells to form an endocrine cell-specific dataset of 59,373 cells expressing INS, GCG, SST or PPY. We compared transcriptomes across conditions to determine the differentially expressed Ca2+-regulated genes in each endocrine cell type, and in each endocrine cell subcluster of alpha and beta cells.ResultsBased on the number of Ca2+-regulated genes, we found that each alpha and beta cell cluster had a different magnitude of Ca2+ response. We also showed that polyhormonal clusters expressing both INS and GCG, or both INS and SST, are defined by Ca2+-regulated genes specific to each cluster. Finally, we identified the gene PCDH7 from the beta cell clusters that had the highest number of Ca2+-regulated genes, and showed that cells expressing cell surface PCDH7 protein have enhanced glucose-stimulated insulin secretory function.Conclusions/interpretationHere we use our large-scale, multi-condition, single-cell dataset to show that human islets have cell-type-specific Ca2+-regulated gene expression profiles, some of them specific to subpopulations. In our dataset, we identify PCDH7 as a novel marker of beta cells having an increased number of Ca2+-regulated genes and enhanced insulin secretory function.Data availabilityA searchable and user-friendly format of the data in this study, specifically designed for rapid mining of single-cell RNA sequencing data, is available at https://lynnlab.shinyapps.io/Human_Islet_Atlas/. The raw data files are available at NCBI Gene Expression Omnibus (GSE196715).Graphical abstract

Network Pharmacology-Based Strategy to Investigate the Mechanisms of Lenvatinib in the Treatment of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a complex and refractory malignant tumor, ranking the third cause of cancer-related deaths worldwide. Lenvatinib is currently employed to treat advanced, unresectable HCC as a first-line drug. The purpose of this study was to explore the pharmacological mechanisms of lenvatinib acting on HCC through the analysis of differential expressed genes based on network pharmacology. The target genes of lenvatinib were collected from PubChem, SwissTargetPrediction, PharmMapper, and BATMAN-TCM online public databases. In addition, related gene targets for HCC were obtained using NCBI Gene Expression Omnibus (NCBI-GEO) database. Afterward, the protein-protein interaction (PPI) network was established to visualize and understand the interaction relationships of overlapping gene targets from both lenvatinib and HCC. Furthermore, according to the data obtained, Gene Ontology (GO) analysis indicated that these intersectant genes were mainly enriched in response to xenobiotic stimulus, gland development, ion channel complex, membrane raft, and steroid binding. Besides, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis suggested that the therapeutic effects of lenvatinib on HCC probably involved bile secretion, MAPK signaling pathway, cGMP-PKG signaling pathway, PI3K-Akt signaling pathway, and Ras signaling pathway. Moreover, a total of six key differential genes, namely, ALB, CCND1, ESR1, AR, CCNA2, and AURKA, were identified as most significant targets associated with lenvatinib treating HCC and further verified by molecular docking, which demonstrated that lenvatinib had a strong binding efficiency with these six key gene-encoded proteins. Taken together, this study systematically provided new insights for researchers to determine the intervention mechanisms of lenvatinib in HCC therapy.

Identifying Novel Molecular Genetic Markers in Attention Deficit Hyperactivity Disorder and Glioblastoma Multiforme

Attention deficit hyperactivity disorder (ADHD) and glioblastoma multiforme (GBM) are two neurological disorders with high rates of incidence and mortality respectively, both capable of inflicting long‐term physiological damage. Although prior research has indicated that brain tumor survivors exhibit increases in susceptibility to ADHD, the underlying genetic components still remain relatively unknown. The primary purpose of this investigation was to identify genetic associations between ADHD and GBM by single‐nucleotide polymorphisms (SNPs) of target genes. NCBI’s Gene Expression Omnibus was accessed, and genes of interest were selected by differential gene expression analysis in GBM patients. Sequence data of ADHD and GBM patients were obtained from the Sequence Read Archive (SRA), followed by the construction of analysis pipelines to map the sequences against human chromosome 12, followed by indexing and variant calling. This study highlights 18 variants (including 7 reported SNPs) that contribute to the genetic overlap of ADHD and GBM and may provide a useful tool for precision medicine.

Network-Based Analysis Reveals Gene Signature in Tip Cells and Stalk Cells.

Angiogenesis occurs during various physiological or pathological processes such as wound healing and tumor growth. Differentiation of vascular endothelial cells into tip cells and stalk cells initiates the formation of new blood vessels. Tip cells and stalk cells are endothelial cells with different biological characteristics and functions. The aim of this study was to determine the mechanisms of angiogenesis by exploring differences in gene expression of tip cells and stalk cells. Raw data were retrieved from NCBI Gene Expression Omnibus (GSE19284). Data were reanalyzed using bioinformatics methods that employ robust statistical methods, including identification of differentially expressed genes (DEGs) between the stalk and tip cells, Weighted Gene Correlation Network Analysis (WGCNA), gene ontology and pathway enrichment analysis using DAVID tools, integration of Protein-Protein Interaction (PPI) networks and screening of hub genes. DEGs of stalk and tip cells were grouped as dataset A. Gene modules associated with differentiation of stalk and tip cells screened by WGCNA were named dataset B. Further, we retrieved existing markers of angiogenesis from previous experimental studies on tip and stalk cells which we called dataset C. Intersection of datasets A, B and C was used as a candidate gene. Subsequently, we verified the results applying Quantitative Polymerase Chain Reaction (Q-PCR) to our clinical specimen. In general, the Q-PCR results coincide with the majority of the expression profile. We identified five candidate genes, including ESM1, CXCR4, JAG1, FLT1 and PTK2, and two pathways, including Rap1 signaling pathway and PI3K-Akt signaling pathway in vascular endothelial cells that differentiate into tip cells and stalk cells using bioinformatics analysis. Bioinformatics approaches provide new avenues for basic research in different fields such as angiogenesis. The findings of this study provide new perspectives and a basis for the study of molecular mechanisms of vascular endothelial cell differentiation into stalk and tip cells. Genes and pathways identified in this study are potential biomarkers and therapeutic targets for angiogenesis in the tumor.

Intrahepatic inflammatory IgA+PD-L1high monocytes in hepatocellular carcinoma development and immunotherapy

BackgroundIgA neutralizes pathogens to prevent infection at mucosal sites. However, emerging evidence shows that IgA contributes to aggravating inflammation or dismantling antitumor immunity in human diseased liver. The aim of...

Esophageal squamous cell carcinoma: Integrated bioinformatics analysis for differential gene expression with identification of hub genes and lncRNA

BackgroundEsophageal squamous cell carcinoma (ESCC) is a typical Gastro-Intestinal (GI) tract neoplasm. This study was conducted to know the Differential Expressed Genes (DEGs) profile of ESCC along with hub gene screening, lncRNA identification, and drug-genes interactions. MethodsGSE161533, GSE20347, GSE45670 microarray datasets were retrieved from the NCBI Gene Expression Omnibus (GEO) database. GEO2R was used for the DEGs identification, whereas GO (Gene Ontology) and KEGG enrichment analysis were performed in DAVID. PPI network constructed using STRING and visualized with Cytoscape app with the help of MCODE. The top ten connectivity genes were selected as hub genes—further survival analysis was performed in the Kaplan-Meier plotter. Moreover, Boxplot, pathological stage plots were constructed using GEPIA (Gene Expression Profiling Interactive Analysis). The methylation heatmap assembled in the DiseaseMeth version 2.0. lncRNA (Long non-coding RNA) was identified comparing the list of genes in HUGO, and Gene-drug interactions were accumulated from the DgiDB platform. ResultsThis experiment showed 16 upregulated, and 59 downregulated DEGs shared among the three datasets. Biological process analysis showed significant terms such as extracellular matrix disassembly and collagen catabolism. The extracellular region was detected as the most crucial cellular compartment. Notably, metalloen dopeptidease and serine-type endopeptidase activity showed significant molecular functions term. In contrast, transcriptional misregulation was a highly substantial KEGG pathway. Kaplan-Meier plotter showed higher expression of CXCL8, SPP1, MMP13, CXCL1, and TOP2A have a significant impact on the overall survival of the patients. Nine out of ten hub genes have significantly different expression levels than normal and cancer tissues. HYMAI was the only lncRNA commonly expressed upregulated among the three datasets. Drug-gene interaction showed multiple genes have no drug options exist till now.

326 Integrated Analysis of Genetic Databases Identifies miRNA Associated With Poor Survival In Melanoma

OBJECTIVES/GOALS: Despite advances in precision medicine and understanding of the molecular pathways, melanoma remains the deadliest skin cancer, warranting identification of novel biomarkers. In this study, we performed bioinformatic analysis of melanoma patient tumors to identify novel dysregulated gene and micro-RNA (miRNA) targets responsible for survival. METHODS/STUDY POPULATION: Genetic sequencing data for 594 patient samples of melanoma and normal skin tissue from 10 databases were accessed using the NCBI Gene Expression Omnibus. Genes and miRNA that were significantly dysregulated (adjusted p-value &lt; 0.05, log fold change &gt; ± 2) in melanoma compared to normal skin were identified using the GEO2R program. Dataset expression profiles were cross-referenced to identify genetic elements dysregulated in at least 50% of datasets and filtered for association with poor survival using R2 Genomics Analysis and Visualization Platform. DAVID 6.8 provided pathway analysis of dysregulated genes. miRTarBase linked genes associated with poor survival and dysregulated miRNA from our database analysis. RESULTS/ANTICIPATED RESULTS: Bioinformatic analysis revealed consistent differential regulation of 205 genes (down=177 and up=28) and 38 miRNA across datasets with fold change &gt;2 (bonf. p&lt;0.05). Pathway analysis indicated that PPAR, phosphatidylinositol signaling, Rap1 signaling, and p53 signaling pathways were enriched by downregulated genes while the NF-kB pathway was enriched up regulated genes. Survival analysis of the differentially regulated genes identified 11 downregulated (ACSL1, CEBPA, CES4A, CRIP1, GATA3, HLADQB2, PTGS1, PYCARD, PPARG, PKP3, RSSF6) and 3 upregulated (DUXAP10, SLC2A3 and PRAME) hub genes to be associated with poor overall survival. Out of the 13 miRNA associated with hub genes, five miRNA (hsa-miR-125b-5p, hsa-miR-130b-3p, hsa-miR-26n-5p, hsa-miR-30b, hsa-miR-30c) were linked to multiple hub genes. DISCUSSION/SIGNIFICANCE: Our analysis identified 14 hub genes (regulators of PPARA, adipocyte differentiation, and transcriptional pathways) as well as miRNAs hsa-miR-30c (regulator of PTGS1 and SLC2A3) and hsa-let-7i-5p (regulator of ASCL1) as potential therapeutic targets. Further studies for validation of the targets are needed for clinical translation in melanoma.

Network Pharmacology Integrated with Molecular Docking Elucidates the Mechanism of Wuwei Yuganzi San for the Treatment of Coronary Heart Disease

Introduction: The aim of this study was to investigate the pharmacological mechanism of Wuwei Yuganzi San (WYS) in treating coronary heart disease (CHD) using network pharmacology and molecular docking. Methods: The main active components, related targets, and the target genes related to WYS were investigated by the databases Traditional Chinese Medicine Systems Pharmacology and related articles. Information on the target genes of CHD was acquired through the OMIM database and GeneCards database, and the NCBI Gene Expression Omnibus DataSets (GSE71226) were used to acquire target genes of CHD. A Venn diagram was used to show the common targets of WYS and CHD. The compound-target-disease network was built up by Cytoscape 3.7.2, and the protein–protein interaction (PPI) network was acquired through the STRING database. ClusterProfiler and Pathview packages in RStudio software were used to conduct gene ontology enrichment analysis and KEGG pathway enrichment analysis to reveal the underlying mechanism. Finally, AutoDock Vina software was used to assess the binding affinity of significant ingredients and hub genes. Results: Thirty-four key ingredients of WYS in CHD were screened, which related to 59 targets in CHD. According to the results of enrichment analysis, 59 items in the biological process, 15 items in the molecular function, 10 items in the cellular component, and 52 signaling pathways were associated with efficacy. These processes and pathways were essential for cell survival and were related to several crucial factors of CHD, including a disintegrin and metalloprotease 17 (ADAM17), aldo-keto reductase family 1 member C2 (AKR1C2), albumin (ALB), protein kinase B (AKT1), and alcohol dehydrogenase 1C (ADH1C). Based on the outcomes of the PPI network, we selected ADAM17, AKR1C2, ALB, AKT1, ADH1C, and putative ingredients (sennoside D_qt, quercetin, and procyanidin B-5,3'- O-gallate) to perform molecular docking validation. From the molecular docking outcomes, some vital targets of CHD (including ADAM17, AKR1C2, ALB, AKT1, and ADH1C) could be related to form a stable combination with the putative ingredients of WYS. Conclusions: The network pharmacology and molecular docking study elucidated basically the mechanism of WYS in the treatment of CHD.

Knockdown of PDX1 enhances the osteogenic differentiation of ADSCs partly via activation of the PI3K/Akt signaling pathway

BackgroundOsteoporosis (OP) is a systemic bone disease manifested as low bone mass, destruction of bone microstructure, increased bone fragility and fracture risk. The purpose of this study was to explore the role and mechanism of PDX1 for osteogenic differentiation of adipose derived stem cells (ADSCs).MethodsGSE37329 dataset was retrieved from NCBI Gene Expression Omnibus (GEO) database and performed bioinformatic analyses. ADSCs were incubated with normal medium, osteogenic induction medium (OIM) and OIM+si-PDX1. Then, alkaline phosphatase (ALP) staining and Alizarin Red Staining (ARS) were performed to assess the role of PDX1 for osteogenesis of ADSCs. PI3K inhibitor, LY294002 was then added to further explore the mechanism of PDX1 for osteogenic differentiation of ADSCs. Western blot assay was used to assess the osteogenic-related markers. Graphpad software was used to perform statistically analysis.ResultsA total of 285 DEGs were obtained from analysis of the dataset GSE37329, of which 145 were upregulated and 140 were downregulated genes. These differentially expressed genes mainly enriched in cell differentiation and PI3K/Akt signaling pathway. Moreover, PDX1 was decreased in osteogenic induced ADSCs. Knockdown of PDX1 significantly increased osteogenic differentiation capacity and p-PI3K and p-Akt protein levels. Administration with LY294002 could partially reversed the promotion effects of si-PDX1.ConclusionIn conclusion, knockdown of PDX1 promotes osteogenic differentiation of ADSCs through the PI3K/Akt signaling pathway.

Specific Gut Microbial Environment in Lard Diet-Induced Prostate Cancer Development and Progression.

Lard diet (LD) is a risk factor for prostate cancer (PCa) development and progression. Two immunocompetent mouse models fed with isocaloric specific fat diets (LD) enriched in saturated and monounsaturated fatty acid (SMFA), showed significanftly enhanced PCa progression with weight gain compared with a fish oil diet (FOD). High gut microbial divergency resulted from difference in diets, and the abundance of several bacterial species, such as in the orders Clostridiales and Lactobacillales, was markedly altered in the feces of LD- or FOD-fed mice. The proportion of the order Lactobacillales in the gut was negatively involved in SMFA-induced body weight gain and PCa progression. We found the modulation of lipid metabolism and cholesterol biosynthesis pathways with three and seven commonly up- and downregulated genes in PCa tissues, and some of them correlated with the abundance of the order Lactobacillales in mouse gut. The expression of sphingosine 1-phosphate receptor 2, which is associated with the order Lactobacillales and cancer progression in mouse models, was inversely associated with aggressive phenotype and weight gain in patients with PCa using the NCBI Gene Expression Omnibus database. Therefore, SMFA may promote PCa progression with the abundance of specific gut microbial species and overexpression of lipogenic genes in PCa. Therapeutics with alteration of gut microbiota and candidate genes involved in diet-induced PCa progression may be attractive in PCa.

Identification of Crucial Genes and Key Functions in Type 2 Diabetic Hearts by Bioinformatic Analysis.

Type 2 diabetes (T2D) patients with SARS-CoV-2 infection hospitalized develop an acute cardiovascular syndrome. It is urgent to elucidate underlying mechanisms associated with the acute cardiac injury in T2D hearts. We performed bioinformatic analysis on the expression profiles of public datasets to identify the pathogenic and prognostic genes in T2D hearts. Cardiac RNA-sequencing datasets from db/db or BKS mice (GSE161931) were updated to NCBI-Gene Expression Omnibus (NCBI-GEO), and used for the transcriptomics analyses with public datasets from NCBI-GEO of autopsy heart specimens with COVID-19 (5/6 with T2D, GSE150316), or dead healthy persons (GSE133054). Differentially expressed genes (DEGs) and overlapping homologous DEGs among the three datasets were identified using DESeq2. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes analyses were conducted for event enrichment through clusterProfile. The protein-protein interaction (PPI) network of DEGs was established and visualized by Cytoscape. The transcriptions and functions of crucial genes were further validated in db/db hearts. In total, 542 up-regulated and 485 down-regulated DEGs in mice, and 811 up-regulated and 1399 down-regulated DEGs in human were identified, respectively. There were 74 overlapping homologous DEGs among all datasets. Mitochondria inner membrane and serine-type endopeptidase activity were further identified as the top-10 GO events for overlapping DEGs. Cardiac CAPNS1 (calpain small subunit 1) was the unique crucial gene shared by both enriched events. Its transcriptional level significantly increased in T2D mice, but surprisingly decreased in T2D patients with SARS-CoV-2 infection. PPI network was constructed with 30 interactions in overlapping DEGs, including CAPNS1. The substrates Junctophilin2 (Jp2), Tnni3, and Mybpc3 in cardiac calpain/CAPNS1 pathway showed less transcriptional change, although Capns1 increased in transcription in db/db mice. Instead, cytoplasmic JP2 significantly reduced and its hydrolyzed product JP2NT exhibited nuclear translocation in myocardium. This study suggests CAPNS1 is a crucial gene in T2D hearts. Its transcriptional upregulation leads to calpain/CAPNS1-associated JP2 hydrolysis and JP2NT nuclear translocation. Therefore, attenuated cardiac CAPNS1 transcription in T2D patients with SARS-CoV-2 infection highlights a novel target in adverse prognostics and comprehensive therapy. CAPNS1 can also be explored for the molecular signaling involving the onset, progression and prognostic in T2D patients with SARS-CoV-2 infection.