Systems Level Analysis and Identification of Pathways and Key Genes Associated with Delirium

Delirium is a complex pathophysiological process, and multiple contributing mechanisms have been identified. However, it is largely unclear how the genes associated with delirium contribute and which of them play key roles. In this study, the genes associated with delirium were retrieved from the Comparative Toxicogenomics Database (CTD) and integrated through a protein–protein interaction (PPI) network. Delirium-associated genes formed a highly interconnected PPI subnetwork, indicating a high tendency to interact and agglomerate. Using the Molecular Complex Detection (MCODE) algorithm, we identified the top two delirium-relevant network modules, M1 and M5, that have the most significant enrichments for the delirium-related gene sets. Functional enrichment analysis showed that genes related to neurotransmitter receptor activity were enriched in both modules. Moreover, analyses with genes located in human accelerated regions (HARs) provided evidence that HAR-Brain genes were overrepresented in the delirium-relevant network modules. We found that four of the HAR-Brain genes, namely APP, PLCB1, NPY, and HTR2A, in the M1 module were highly connected and appeared to exhibit hub properties, which might play vital roles in delirium development. Further understanding of the function of the identified modules and member genes could help to identify therapeutic intervention targets and diagnostic biomarkers for delirium.

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Proteomic Analysis of the Secretome and Exosomes of Feline Adipose-Derived Mesenchymal Stem Cells

Animals ◽

10.3390/ani11020295 ◽

2021 ◽

Vol 11 (2) ◽

pp. 295

Author(s):

Antonio J. Villatoro ◽

María del Carmen Martín-Astorga ◽

Cristina Alcoholado ◽

María del Mar Sánchez-Martín ◽

José Becerra

Keyword(s):

Stem Cells ◽

Mesenchymal Stem Cells ◽

Endocrine System ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Cell Junctions ◽

Bioactive Molecules ◽

Protein Protein Interaction ◽

Cell Signaling Pathways

Mesenchymal stem cells (MSCs) have been shown to have therapeutic efficacy in different complex pathologies in feline species. This effect is attributed to the secretion of a wide variety of bioactive molecules and extracellular vesicles, such as exosomes, with significant paracrine activity, encompassed under the concept of the secretome. However, at present, the exosomes from feline MSCs have not yet been studied in detail. The objective of this study is to analyze and compare the protein profiles of the secretome as a whole and its exosomal fraction from feline adipose-derived MSCs (fAd-MSCs). For this, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Protein–Protein Interaction Networks Functional Enrichment Analysis (STRING) were utilized. A total of 239 proteins were identified in the secretome, and 228 proteins specific to exosomes were identified, with a total of 133 common proteins. The proteins identified in the secretome were located in the extracellular regions and in the cytoplasm, while the exosomal proteins were located mainly in the membrane, cytoplasm and cytosol. Regarding function, in the secretome, proteins involved in different metabolic pathways, in pathways related to the immune system and the endocrine system and in the processing of proteins in the endoplasmic reticulum predominated. In contrast, proteins specific to exosomes were predominantly associated with endocytosis, cell junctions, platelet activation and other cell signaling pathways. The possible future use of the secretome, or some of its components, such as exosomes, would provide a non-cell-based therapeutic strategy for the treatment of different diseases that would avoid the drawbacks of cell therapy.

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Improved cancer biomarkers identification using network-constrained infinite latent feature selection

PLoS ONE ◽

10.1371/journal.pone.0246668 ◽

2021 ◽

Vol 16 (2) ◽

pp. e0246668

Author(s):

Lihua Cai ◽

Honglong Wu ◽

Ke Zhou

Keyword(s):

Feature Selection ◽

Expression Profiles ◽

Biological Significance ◽

Feature Selection Method ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Gene Sets ◽

Significant Gene

Identifying biomarkers that are associated with different types of cancer is an important goal in the field of bioinformatics. Different researcher groups have analyzed the expression profiles of many genes and found some certain genetic patterns that can promote the improvement of targeted therapies, but the significance of some genes is still ambiguous. More reliable and effective biomarkers identification methods are then needed to detect candidate cancer-related genes. In this paper, we proposed a novel method that combines the infinite latent feature selection (ILFS) method with the functional interaction (FIs) network to rank the biomarkers. We applied the proposed method to the expression data of five cancer types. The experiments indicated that our network-constrained ILFS (NCILFS) provides an improved prediction of the diagnosis of the samples and locates many more known oncogenes than the original ILFS and some other existing methods. We also performed functional enrichment analysis by inspecting the over-represented gene ontology (GO) biological process (BP) terms and applying the gene set enrichment analysis (GSEA) method on selected biomarkers for each feature selection method. The enrichments analysis reports show that our network-constraint ILFS can produce more biologically significant gene sets than other methods. The results suggest that network-constrained ILFS can identify cancer-related genes with a higher discriminative power and biological significance.

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Co-expression network analysis identifies specific hub genes in association with developmental neuronal remodeling in Drosophila melanogaster

10.7287/peerj.preprints.27586v1 ◽

2019 ◽

Author(s):

Yunze Liu ◽

Xiaojie Sun ◽

Aijun Qu

Keyword(s):

Drosophila Melanogaster ◽

Interaction Network ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

Protein Protein Interaction ◽

Neuronal Remodeling ◽

Gene Modules

As an evolutionarily conserved mechanism, developmental neuronal remodeling is needed for the proper wiring of the nervous system and is critical for understanding the neurodevelopment mechanisms. Previous studies have shown that during metamorphosis lots of Drosophila melanogaster mushroom body neurons experience stereotypic remodeling. However, the related regulators and downstream executors of pathways are yet unclear, especially studies of transcriptional gene co-expression analysis of nervous systems remain insufficient. In this study, we develop a weighted gene co-expression network (WGCNA) to classify gene modules associated with neuronal remodeling. Moreover, functional and pathway enrichment analysis with protein-protein network construction is applied to detect high informative hub genes in the targeted gene module. Thus, we select a total of five hub genes that play critical roles in neuronal remodeling and identify them with functional enrichment analysis and protein-protein interaction network. Overall, this study provides insight into the underlying molecular mechanism of developmental neuronal remodeling in Drosophila melanogaster.

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Graph analytics for phenome-genome associations inference

10.1101/682229 ◽

2019 ◽

Author(s):

Davide Cirillo ◽

Dario Garcia-Gasulla ◽

Ulises Cortés ◽

Alfonso Valencia

Keyword(s):

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Specific Gene ◽

Proteus Syndrome ◽

Supporting Evidence ◽

Human Phenotype ◽

Biological Ontologies ◽

Statistical Framework ◽

Gene Sets

AbstractMotivationBiological ontologies, such as the Human Phenotype Ontology (HPO) and the Gene Ontology (GO), are extensively used in biomedical research to find enrichment in the annotations of specific gene sets. However, the interpretation of the encoded information would greatly benefit from methods that effectively interoperate between multiple ontologies providing molecular details of disease-related features.ResultsIn this work, we present a statistical framework based on graph theory to infer direct associations between HPO and GO terms that do not share co-annotated genes. The method enables to map genotypic features to phenotypic features thus providing a valid tool for bridging functional and pathological annotations. We validated the results by (a) supporting evidence of known drug-target associations (PanDrugs), protein-protein physical and functional interactions (BioGRID and STRING), and common pathways (Reactome); (b) comparing relationships inferred from early ontology releases with knowledge contained in the latest versions.ApplicationsWe applied our method to improve the interpretation of molecular processes involved in pathological conditions, illustrating the applicability of our predictions with a number of biological examples. In particular, we applied our method to expand the list of relevant genes from standard functional enrichment analysis of high-throughput experimental results in the context of comorbidities between Alzheimer’s disease, Lung Cancer and Glioblastoma. Moreover, we analyzed pathways linked to predicted phenotype-genotype associations getting insights into the molecular actors of cellular senescence in Proteus syndrome.Availabilityhttps://github.com/dariogarcia/phenotype-genotype_graph_characterization

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Apolipoprotein B Is Associated With the Microenvironment of Cholangiocarcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.654689 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xiaofeng Xu ◽

Diyu Chen ◽

Xiaode Feng ◽

Jiating Hu ◽

Jiangzhen Ge ◽

...

Keyword(s):

Immune Cells ◽

Apolipoprotein B ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Abundance Ratio ◽

Functional Enrichment ◽

Score System ◽

Protein Protein Interaction ◽

New Ideas ◽

Differential Expressed Genes

BackgroundCholangiocarcinoma (CCA) is a kind of devastating malignancy, which is correlated with the extremely high mortality. Due to the occult pathogenesis of CCA, most patients are diagnosed in the advanced stage. However, the efficacy of chemotherapy and immunotherapy is limited for these patients. The cause for this phenomenon is unclear, the recent researches indicate that it could be related to predisposing genetic factors and tumor microenvironment (TME) changes. The TME is created by the tumor and dominated by tumor-induced interactions. And the tumor prognosis could be influenced by the extent of infiltrating immune cells and stromal cells in TME.Materials and methodsThe abundance ratio of immune cells for each sample was obtained via the CIBERSORT algorithm, and we used ESTIMATE score system to calculate the immune and stromal scores in CCA. The CCA cases in TCGA database were categorized into high and low score groups according to their immune/stromal scores. And then, we identified the differential expressed genes (DEGs) in two groups. Functional enrichment analysis and protein‐protein interaction networks were carried out for DEGs. Interestingly, we found out that apolipoprotein B (APOB) is the most down-regulated among these genes. Then we performed the immunohistochemistry staining of APOB in a CCA tumor microarray which contained 100 CCA cases, APOB was down-regulated in CCA samples. Thus, we evaluated the APOB function in the TME of CCA through TIMER.Results and ConclusionThe results demonstrate that the infiltration degree of immune cells in CCA could be influenced by the expression of APOB, and the APOB expression could be mediated by DNA methylation. Our study not only indicates APOB is a potential target for CCA immunotherapy but also provides new ideas for researchers to explore the immunotherapy of various tumors.

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Co-expression network analysis identifies specific hub genes in association with developmental neuronal remodeling in Drosophila melanogaster

10.7287/peerj.preprints.27586 ◽

2019 ◽

Author(s):

Yunze Liu ◽

Xiaojie Sun ◽

Aijun Qu

Keyword(s):

Drosophila Melanogaster ◽

Interaction Network ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

Protein Protein Interaction ◽

Neuronal Remodeling ◽

Gene Modules

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Gene Clusters Based on OLIG2 and CD276 Could Distinguish Molecular Profiling in Glioblastoma

10.21203/rs.3.rs-599543/v1 ◽

2021 ◽

Author(s):

Minjie Fu ◽

Jinsen Zhang ◽

Weifeng Li ◽

Shan He ◽

Jingwen Zhang ◽

...

Keyword(s):

Gene Cluster ◽

Efficient Method ◽

Enrichment Analysis ◽

Gene Clusters ◽

Functional Enrichment Analysis ◽

Molecular Classification ◽

Functional Enrichment ◽

Protein Protein Interaction ◽

Dna Elements

Abstract BackgroundThe molecular classification of glioblastoma (GBM) based on transcriptomic analysis could provide precise treatment and prognosis. However, current subtyping (Classic, Mesenchymal, Neural, Proneural) is a time-consuming and cost-intensive process, which hinders its clinical application. A simple and efficient method for classification was imperative.MethodsRandom forest algorithm was applied to conduct a gene cluster featured with hub genes, OLIG2 and CD276. Functional enrichment analysis and Protein-protein interaction were performed using the genes in this gene cluster. The classification efficiency of the gene cluster was validated by WGCNA and LASSO algorithms, and tested in GSE84010 and Gravandeel’s GBM datasets. ResultsThe gene cluster (n = 26) could distinguish mesenchymal and proneural excellently (AUC = 0.92), which could be validated by multiple algorithms (WGCNA, LASSO) and datasets (GSE84010 and Gravandeel’s GBM dataset). The gene cluster could be functionally enriched in DNA elements and T cell associated pathways. Additionally, five genes in the signature could predict the prognosis well (p = 0.0051 for training cohort, p = 0.065 for test cohort). ConclusionsThis study proved the accuracy and efficiency of random forest classifier for GBM subtyping and provided a convenient and efficient method for subtyping Proneural and Mesenchymal GBM.

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Role of COL3A1 and POSTN on Pathologic Stages of Esophageal Cancer

Technology in Cancer Research & Treatment ◽

10.1177/1533033820977489 ◽

2020 ◽

Vol 19 ◽

pp. 153303382097748

Author(s):

Shao-wei Zhang ◽

Nan Zhang ◽

Na Wang

Keyword(s):

Gene Expression ◽

Esophageal Cancer ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Expression Omnibus ◽

Functional Enrichment ◽

Protein Protein Interaction ◽

And Control ◽

The Impact

Background: Esophageal cancer (EC) is a primary malignant tumor originating from the esophageal of the epithelium. Surgical resection is a potential treatment for EC, but this is only appropriate for patients who have locally resectable lesions suitable for surgery. However, most patients with EC are at a late stage when diagnosed. Therefore, there is an urgent need to further explore the pathogenesis of EC to enable early diagnosis and treatment. Methods: Our study downloaded 2 expression spectrum datasets (GSE92396 and GSE100942) in the Gene Expression Omnibus (GEO) database. GEO2 R was used to identify the Differentially expressed genes (DEGs) between the samples of EC and control. Using the DAVID tool to make the Functional enrichment analysis. Constructing A protein–protein interaction (PPI) network. Identifying the Hub genes. The impact of hub gene expression on overall survival and their expression based on immunohistochemistry were analyzed. Associated microRNAs were also predicted. Results: There were 36 common DEGs identified. The analysis of GO and KEGG results shown that the variations were predominantly concentrated in the extracellular matrix (ECM), ECM organization, DNA binding, platelet activation, and ECM-receptor interactions. COL3A1 and POSTN had high expression in EC tissues which was compared with their expression in healthy tissues. Analysis of pathologic stages showed that when COL3A1 and POSTN were highly expressed, the stage of the pathologic of EC patients was relatively high (P < 0.005). Conclusions: COL3A1 and POSTN may play an important role in the advancement and occurrence of EC. These genes could provide some novel ideas and basis for the diagnosis and targeted treatment of EC.

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Bioinformatics analysis of prognostic value genes in colorectal cancer microenvironment

10.21203/rs.2.16191/v1 ◽

2019 ◽

Author(s):

Taohua Yue ◽

Jing Zhu ◽

Xin Wang ◽

Yisheng Pan ◽

Yucun Liu ◽

...

Keyword(s):

Colorectal Cancer ◽

Large Scale ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Previous Literature ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Gastrointestinal Malignancies ◽

Protein Protein Interaction ◽

Cancer Genome Atlas

Abstract Colorectal cancer (CRC) is one of the most deadly gastrointestinal malignancies. The openness of the Cancer Genome Atlas (TCGA) allows us to perform correlation analysis between large-scale transcriptome data and overall survival (OS) of multiple malignancies. Previous literature reports that the infiltration of immune cells and stromal cells in the tumor microenvironment (TME) significantly associate with the prognosis of cancers. Based on the ESTIMATE algorithm, the immune and stromal components in TME can be quantified by immune and stromal scores. To determine the effects of immune and stromal cell associated genes on CRC prognosis, we divided the CRC cases into high- and low-groups based on the immune/stromal scores and identified 999 differentially expressed genes (DEGs). Heatmaps, functional enrichment analysis and protein‐protein interaction (PPIs) networks further indicated that 999 DEGs mainly participated in stromal composition and immune response. Finally, we obtained 56 genes that were significantly associated with CRC prognosis from 999 DEGs and identified the PPIs networks. The role of 41 genes in CRC has been reported in previous literature, and the other 15 genes have never been reported. Therefore, we found 15 novel TME genes associated with CRC prognosis waiting for more researches.

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Integrative Analysis of four miRNA prognostic signatures of prostate cancer

10.21203/rs.3.rs-34448/v1 ◽

2020 ◽

Author(s):

Chen Chi ◽

Xianwu Chen ◽

Liping Yao ◽

Min Li ◽

Lanting Xiang ◽

...

Keyword(s):

Prostate Cancer ◽

Overall Survival ◽

Target Genes ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Protein Protein Interaction ◽

Cellular Processes ◽

Relationship Of

Abstract Background Prostate cancer (PCa) is the most common urological cancer among men, having a poor prognosis, which is hard to accurately evaluate based on the present methods. MicroRNAs (miRNAs), a class of internal non-coding small RNA, can involve in the regulation of tumor biological function. So far, many researchers have tried to explore the relationship of malignant progress of PCa with miRNA, while there are just limited studies conducting the comprehensive analysis of miRNA in PCa clinical significance. Methods The data of miRNA and mRNA expressions in PCa were downloaded from TCGA database, and were performed the overall survival (OS) analysis using Survival package of R software to harvest the differentially expressed miRNAs (DEMs) and differentially expressed genes (DEGs). The bioinformatics tools such as TargetScan, miRDB, and miRanda were also conducted to forecast the desired target genes related with prognostic DEMs. In addition, both GO and KEGG analyses were used to uncover the fundamental signaling pathways and cellular processes in PCa as well as the protein-protein interaction (PPI) network was constructed through STRING and Cytoscape software. Results Firstly, 4 DEMs (miR-19a-3p, miR-144-3p, miR-223-5p, and miR-483-3p) were found having significantly associated with overall survival in PCa. Based on the criteria with FDR < 0.05 and |log2FC| > 1, 33 genes were screened out as DEGs. Besides, the functional enrichment analysis revealed that these DEGs of 4 miRNAs may participate in cancer-related pathways like FoxO and PI3K-Akt signaling pathway. Lastly, the low expression of CD177 may be potentially associated with poor survival of patients in PCa. Conclusion This study systematically analyzed multiple PCa prognostic DEMs (miR-19a-3p, miR-144-3p, miR-223-5p, and miR-483-3p), and verified a novel DEG signature (CD177) that can be used to effectively assess the prognosis of PCa patients.

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