scholarly journals Identification of significant genes with invasive promotion in non-functional pituitary adenoma via bioinformatical analysis

2021 ◽  
Author(s):  
An Shuo Wang ◽  
Hao Xu ◽  
Ming Hui Zeng ◽  
Fei Wang
Keyword(s):  
Pituitary Adenoma ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Pituitary Tumors ◽  
Receptor Interaction ◽  
Protein Protein Interaction ◽  
Normal Tissues ◽  
Non Invasive ◽  
Pathway Gene ◽  
Underlying Mechanisms

Abstract Background Non-functional pituitary adenoma (NFPA) is a disease with a high incidence, which accounts for a large part of pituitary tumors and plays a pivotal role. While invasive NFPAs which have not any endocrinology manifestations and space-occupying symptoms at early stages account for about 30 percent of NFPAs. The purpose of the present academic work was to identify significant genes with invasive promotion and their underlying mechanisms. Methods Gene expression profiles of GSE51618 was available from GEO database. There are 4 non-invasive NFPA tissues, 3 invasive NFPA tissues and 3 normal tissues in the profile datasets. Differentially expressed genes (DEGs) between non-invasive NFPA tissues and invasive NFPA tissues were picked out by GEO2R online tool. There were total of 226 up-regulated genes and 298 down-regulated genes. Next, we made use of the Database for Annotation, Visualization and Integrated Discovery (DAVID) to analyze Kyoto Encyclopedia of Gene and Genome (KEGG) pathway, gene ontology (GO) and Kaplan Meier Plotter. Then protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). There were total of 141 up-regulated genes and 171 down-regulated genes. Of PPI network analyzed by Molecular Complex Detection (MCODE) plug-in, all 141 up-regulated genes were selected. Results After reanalysis of GO, five genes (ATP2B3, ADCYAP1R1, PTGER2, FSHβ, HTR4) were found to significantly enrich in the cAMP signaling pathway, Neuroactive ligand-receptor interaction and Renin secretion via reanalysis of DAVID. Conclusions We have identified five significant up-regulated DEGs with invasive promotion in invasive NFPAs on the basis of integrated bioinformatical methods, which could be potential therapeutic targets for invasive NFPAs patients.

scholarly journals Transcriptome profiling of digital flexor tendons after injury in a chicken model

2020 ◽  
Vol 40 (6) ◽  
Author(s):  
Wei Feng Mao ◽  
Yin Xian Yu ◽  
Chen Chen ◽  
Ya Fang Wu
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Expression Profiles ◽  
Tendon Repair ◽  
Tendon Healing ◽  
Gene Expression Profiles ◽  
Repair Process ◽  
Receptor Interaction ◽  
Protein Protein Interaction ◽  
Chicken Model

Abstract Background: Modulation of tendon healing remains a challenge because of our limited understanding of the tendon repair process. Therefore, we performed the present study to provide a global perspective of the gene expression profiles of tendons after injury and identify the molecular signals driving the tendon repair process. Results: The gene expression profiles of flexor digitorum profundus tendons in a chicken model were assayed on day 3, weeks 1, 2, 4, and 6 after injury using the Affymetrix microarray system. Principal component analysis (PCA) and hierarchical cluster analysis of the differentially expressed genes showed three distinct clusters corresponding to different phases of the tendon healing period. Gene ontology (GO) analysis identified regulation of cell proliferation and cell adhesion as the most enriched biological processes. Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis revealed that the cytokine–cytokine receptor interaction and extracellular matrix (ECM)–receptor interaction pathways were the most impacted. Weighted gene co-expression network analysis (WGCNA) demonstrated four distinct patterns of gene expressions during tendon healing. Cell adhesion and ECM activities were mainly associated with genes with drastic increase in expression 6 weeks after injury. The protein–protein interaction (PPI) networks were constructed to identify the key signaling pathways and hub genes involved. Conclusions: The comprehensive analysis of the biological functions and interactions of the genes differentially expressed during tendon healing provides a valuable resource to understand the molecular mechanisms underlying tendon healing and to predict regulatory targets for the genetic engineering of tendon repair. Tendon healing, Adhesion, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, Weighted Gene Co-expression Network Analysis, Protein–protein Interaction

scholarly journals Identification of Prognostic Biomarkers of Potential Hub Genes in Urothelial Carcinoma and Function in Microenvironment

2021 ◽  
Author(s):  
Wei Chu ◽  
Bing Zhang ◽  
Haifeng Gong ◽  
Qianqian Zhao ◽  
Jun Chen ◽  
...  
Keyword(s):  
T Cells ◽  
Urothelial Carcinoma ◽  
Checkpoint Inhibitors ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Database Analysis ◽  
Normal Tissues

Abstract Background: Urothelial carcinoma (UC) is the most common histological type of urinary system. In the past decades, despite the advances in UC diagnosis and therapy, there are still challenges to improve the overall survival (OS) of UC patients. PD-L1 inhibitor and PD-1 inhibitor have been approved for treating invasive UC, however, only about 20% of patients with metastatic UC show clinical benefits from immune checkpoint inhibitors. Therefor, bioinformatics tools were utilized to screen prognostic-related biomarkers, and analyze their relationship with immunocyte in UC, hoping to provide new ideas for the clinical treatment of UC patients.Methods: Three gene expression profiles (i.e. GSE32548, GSE32894 and GSE48075) were selected from GEO, and divide them into invasive and superficial UC group for study. NetworkAnalyst tool was used to construct gene regulatory network of DEGs, while DAVID and Metascape were utilized to perform GO/KEGG enrichment analysis of DEGs. The hub genes were screened by STRING and cytoscape, and the ONCOMINE, GEPIA, UALCAN, cBioPortal and HPA databases were used to analyze the expression differences at the DNA, RNA, protein levels and prognostic of UC. TIMER was used to analyze the relationship between hub genes and immunocyte infiltration.Results: In total, 63 DEGs were identified from the GEO database of UC, of which 31 and 32 were up-and down-regulated. GO/KEGG pathway analysis identified DEGs were mainly enriched in the collagen catabolic process, extracellular matrix (ECM) organization, ECM structural constituent and ECM-receptor interaction. Nine hub genes (i.e. COL1A1, COL1A2, COL3A1, COL5A2, MMP9, POSTN, SPP1, VCAN and THBS2) upregulated in invasive UC compared with superficial UC were identified. cBioportal database analysis showed that 35% of UC patients presented genetic variants in the hub genes, of which amplification and deletion mutations were the most common. ONCOMINE and UALCAN database analysis showed that the mRNA expression of all hub genes in invasive UC was significantly higher than that in superficial UC and normal tissues. HPA database analysis showed that there was up-regulation of COL3A1, SPP1, POSTN and VCAN protein in UC tissues than in normal tissues. GEPIA showed that COL1A2, COL3A1, THBS2, and VCAN were positively correlated with the OS rate among patients with UC (P < 0.05). UALCAN showed that UC patients with high expression of COL1A1, COL1A2, COL5A2 and POSTN had a poorer prognosis (P < 0.05). TRRUST database analysis indicated that there was a significant correlation between the expression of the hub genes and the infiltration of CD4+T cells, CD8+T cells, macrophages, neutrophils and dendritic cells. Conclusion: Hub genes played important roles in pathogenesis and treatment prognosis of UC and they can provides new biomolecular predictions for immunotherapy and prognosis judgment of UC.

scholarly journals Identify CRNDE and LINC00152 as the key lncRNAs in age-related degeneration of articular cartilage through comprehensive and integrative analysis

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7024 ◽  
Author(s):  
Pengfei Hu ◽  
Fangfang Sun ◽  
Jisheng Ran ◽  
Lidong Wu
Keyword(s):  
Gene Expression ◽  
Articular Cartilage ◽  
Developmental Stages ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Receptor Interaction ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Age Related ◽  
Pathway Analyses

Background Osteoarthritis (OA) is one of the most important age-related degenerative diseases, and the leading cause of disability and chronic pain in the aging population. Recent studies have identified several lncRNA-associated functions involved in the development of OA. Because age is a key risk factor for OA, we investigated the differential expression of age-related lncRNAs in each stage of OA. Methods Two gene expression profiles were downloaded from the GEO database and differentially expressed genes (DEGs) were identified across each of the different developmental stages of OA. Next, gene ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to annotate the function of the DEGs. Finally, a lncRNA-targeted DEG network was used to identify hub-lncRNAs. Results A total of 174 age-related DEGs were identified. GO analyses confirmed that age-related degradation was strongly associated with cell adhesion, endodermal cell differentiation and collagen fibril organization. Significantly enriched KEGG pathways associated with these DEGs included the PI3K–Akt signaling pathway, focal adhesion, and ECM–receptor interaction. Further analyses via a protein–protein interaction (PPI) network identified two hub lncRNAs, CRNDE and LINC00152, involved in the process of age-related degeneration of articular cartilage. Our findings suggest that lncRNAs may play active roles in the development of OA. Investigation of the gene expression profiles in different development stages may supply a new target for OA treatment.

scholarly journals Identification of Key Genes and Pathways for Enchondromas by Bioinformatics Analysis

Dose-Response ◽  
2020 ◽  
Vol 18 (1) ◽  
pp. 155932582090753
Author(s):  
Tianlong Wu ◽  
Honghai Cao ◽  
Lei Liu ◽  
Kan Peng
Keyword(s):  
Gene Expression ◽  
Endoplasmic Reticulum ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Omnibus ◽  
Protein Processing ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Exact Etiology

Background: The risk of malignant transformation of enchondromas (EC) toward central chondrosarcoma is increased up to 35%, while the exact etiology of EC is unknown. The purpose of this research was to authenticate gene signatures during EC and reveal their potential mechanisms in occurrence and development of EC. Methods: The gene expression profiles was acquired from Gene Expression Omnibus database (no. GSE22855). The gene ontology (GO), protein–protein interaction (PPI) network and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were utilized to identify differentially expressed genes (DEGs). Results: Finally, 242 DEGs were appraisal, containing 200 overregulated genes and 42 downregulated genes. The outcomes of GO analysis indicated that upregulated DEGs were mainly enriched in several biological processes containing response to hypoxia, calcium ion, and negative regulation extrinsic apoptotic signaling pathway. Furthermore, the upregulated DEGs were enriched in extracellular matrix (ECM)–receptor interaction, protein processing in endoplasmic reticulum and ribosome, which was analyzed by KEGG pathway. From the PPI network, the top 10 hub genes were identified, which were related to significant pathways containing ribosome, protein processing in endoplasmic reticulum, and ECM-receptor interaction. Conclusion: In conclusion, the present study may be helpful for understanding the diagnostic biomarkers of EC.

scholarly journals Identification of significant alteration genes, pathways and TFs induced by LPS in ARDS via bioinformatical analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Weina Lu ◽  
Ran Ji
Keyword(s):  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Receptor Interaction ◽  
Integrated Analysis ◽  
Pathway Enrichment Analysis ◽  
Signal Pathways ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction

Abstract Background and aims Acute respiratory distress syndrome (ARDS) or acute lung injury (ALI) is one of the most common acute thoracopathy with complicated pathogenesis in ICU. The study is to explore the differentially expressed genes (DEGs) in the lung tissue and underlying altering mechanisms in ARDS. Methods Gene expression profiles of GSE2411 and GSE130936 were available from GEO database, both of them included in GPL339. Then, an integrated analysis of these genes was performed, including gene ontology (GO) and KEGG pathway enrichment analysis in DAVID database, protein–protein interaction (PPI) network construction evaluated by the online database STRING, Transcription Factors (TFs) forecasting based on the Cytoscape plugin iRegulon, and their expression in varied organs in The Human Protein Atlas. Results A total of 39 differential expressed genes were screened from the two datasets, including 39 up-regulated genes and 0 down-regulated genes. The up-regulated genes were mainly enriched in the biological process, such as immune system process, innate immune response, inflammatory response, and also involved in some signal pathways, including cytokine–cytokine receptor interaction, Salmonella infection, Legionellosis, Chemokine, and Toll-like receptor signal pathway with an integrated analysis. GBP2, IFIT2 and IFIT3 were identified as hub genes in the lung by PPI network analysis with MCODE plug-in, as well as GO and KEGG re-enrichment. All of the three hub genes were regulated by the predictive common TFs, including STAT1, E2F1, IRF1, IRF2, and IRF9. Conclusions This study implied that hub gene GBP2, IFIT2 and IFIT3, which might be regulated by STAT1, E2F1, IRF1, IRF2, or IRF9, played significant roles in ARDS. They could be potential diagnostic or therapeutic targets for ARDS patients.

scholarly journals Chamber-enriched gene expression profiles in failing human hearts with reduced ejection fraction

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Luo ◽  
Jun Yin ◽  
Denise Dwyer ◽  
Tracy Yamawaki ◽  
Hong Zhou ◽  
...  
Keyword(s):  
Heart Failure ◽  
Ejection Fraction ◽  
Human Genetics ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Left Heart ◽  
Reduced Ejection Fraction ◽  
Cell Type Composition ◽  
Type Composition ◽  
Underlying Mechanisms

AbstractHeart failure with reduced ejection fraction (HFrEF) constitutes 50% of HF hospitalizations and is characterized by high rates of mortality. To explore the underlying mechanisms of HFrEF etiology and progression, we studied the molecular and cellular differences in four chambers of non-failing (NF, n = 10) and HFrEF (n = 12) human hearts. We identified 333 genes enriched within NF heart subregions and often associated with cardiovascular disease GWAS variants. Expression analysis of HFrEF tissues revealed extensive disease-associated transcriptional and signaling alterations in left atrium (LA) and left ventricle (LV). Common left heart HFrEF pathologies included mitochondrial dysfunction, cardiac hypertrophy and fibrosis. Oxidative stress and cardiac necrosis pathways were prominent within LV, whereas TGF-beta signaling was evident within LA. Cell type composition was estimated by deconvolution and revealed that HFrEF samples had smaller percentage of cardiomyocytes within the left heart, higher representation of fibroblasts within LA and perivascular cells within the left heart relative to NF samples. We identified essential modules associated with HFrEF pathology and linked transcriptome discoveries with human genetics findings. This study contributes to a growing body of knowledge describing chamber-specific transcriptomics and revealed genes and pathways that are associated with heart failure pathophysiology, which may aid in therapeutic target discovery.

scholarly journals Identification and Validation of an Immune-Related eRNA Prognostic Signature for Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Shenglan Cai ◽  
Xingwang Hu ◽  
Ruochan Chen ◽  
Yiya Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cells ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Roc Curves ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Immune Genes ◽  
Normal Tissues

BackgroundEnhancer RNAs (eRNAs) are intergenic long non-coding RNAs (lncRNAs) that participate in the progression of malignancies by targeting tumor-related genes and immune checkpoints. However, the potential role of eRNAs in hepatocellular carcinoma (HCC) is unclear. In this study, we aimed to construct an immune-related eRNA prognostic model that could be used to prospectively assess the prognosis of patients with HCC.MethodsGene expression profiles of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA). The eRNAs co-expressed from immune genes were identified as immune-related eRNAs. Cox regression analyses were applied in a training cohort to construct an immune-related eRNA signature (IReRS), that was subsequently used to analyze a testing cohort and combination of the two cohorts. Kaplan-Meier and receiver operating characteristic (ROC) curves were used to validate the predictive effect in the three cohorts. Gene Set Enrishment Analysis (GSEA) computation was used to identify an IReRS-related signaling pathway. A web-based cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) computation was used to evaluate the relationship between the IReRS and infiltrating immune cells.ResultsA total of sixty-four immune-related eRNAs (IReRNAs) was identified in HCC, and 14 IReRNAs were associated with overall survival (OS). Five IReRNAs were used for constructing an immune-related eRNA signature (IReRS), which was shown to correlate with poor survival and to be an independent prognostic biomarker for HCC. The GSEA results showed that the IReRS was correlated to cancer-related and immune-related pathways. Moreover, we found that IReRS was correlated to infiltrating immune cells, including CD8+ T cells and M0 macrophages. Finally, differential expressions of the five risk IReRNAs in tumor tissues vs. adjacent normal tissues and their prognostic values were verified, in which the AL445524.1 may function as an oncogene that affects prognosis partly by regulating CD4-CLTA4 related genes.ConclusionOur results suggest that the IReRS could serve as a biomarker for predicting prognosis in patients with HCC. Additionally, it may be correlated to the tumor immune microenvironment and could also be used as a biomarker in immunotherapy for HCC.

High-throughput single-cell RNA-seq data imputation and characterization with surrogate-assisted automated deep learning

2021 ◽  
Author(s):  
Xiangtao Li ◽  
Shaochuan Li ◽  
Lei Huang ◽  
Shixiong Zhang ◽  
Ka-chun Wong
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Single Cell ◽  
Deep Neural Networks ◽  
Expression Profiles ◽  
Marker Gene ◽  
Gene Expression Profiles ◽  
Underlying Mechanisms ◽  
Cell Data ◽  
Gene Expression Levels

Abstract Single-cell RNA sequencing (scRNA-seq) technologies have been heavily developed to probe gene expression profiles at single-cell resolution. Deep imputation methods have been proposed to address the related computational challenges (e.g. the gene sparsity in single-cell data). In particular, the neural architectures of those deep imputation models have been proven to be critical for performance. However, deep imputation architectures are difficult to design and tune for those without rich knowledge of deep neural networks and scRNA-seq. Therefore, Surrogate-assisted Evolutionary Deep Imputation Model (SEDIM) is proposed to automatically design the architectures of deep neural networks for imputing gene expression levels in scRNA-seq data without any manual tuning. Moreover, the proposed SEDIM constructs an offline surrogate model, which can accelerate the computational efficiency of the architectural search. Comprehensive studies show that SEDIM significantly improves the imputation and clustering performance compared with other benchmark methods. In addition, we also extensively explore the performance of SEDIM in other contexts and platforms including mass cytometry and metabolic profiling in a comprehensive manner. Marker gene detection, gene ontology enrichment and pathological analysis are conducted to provide novel insights into cell-type identification and the underlying mechanisms. The source code is available at https://github.com/li-shaochuan/SEDIM.

Cancer classification of single-cell gene expression data by neural network

2019 ◽  
Author(s):  
Bong-Hyun Kim ◽  
Kijin Yu ◽  
Peter C W Lee
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Classification ◽  
Supplementary Information ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Normal Tissues

Abstract Motivation Cancer classification based on gene expression profiles has provided insight on the causes of cancer and cancer treatment. Recently, machine learning-based approaches have been attempted in downstream cancer analysis to address the large differences in gene expression values, as determined by single-cell RNA sequencing (scRNA-seq). Results We designed cancer classifiers that can identify 21 types of cancers and normal tissues based on bulk RNA-seq as well as scRNA-seq data. Training was performed with 7398 cancer samples and 640 normal samples from 21 tumors and normal tissues in TCGA based on the 300 most significant genes expressed in each cancer. Then, we compared neural network (NN), support vector machine (SVM), k-nearest neighbors (kNN) and random forest (RF) methods. The NN performed consistently better than other methods. We further applied our approach to scRNA-seq transformed by kNN smoothing and found that our model successfully classified cancer types and normal samples. Availability and implementation Cancer classification by neural network. Supplementary information Supplementary data are available at Bioinformatics online.

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