KEGG Orthology Based Annotation System Research Articles

CIEC: Cross-tissue Immune Cell Type Enrichment and Expression Map Visualization for Cancer.

Single-cell transcriptome sequencing technology has been applied to decode the cell types and functional states of immune cells, revealing their tissue-specific gene expression patterns and functions in cancer immunity. Comprehensive assessments of immune cells within and across tissues will provide us with a deeper understanding of the tumor immune system in general. Here, we present Cross-tissue Immune cell type or state Enrichment analysis of gene lists for Cancer (CIEC), the first web-based application that integrates database and enrichment analysis to estimate the cross-tissue immune cell type or state. CIEC version 1.0 consists of 480 samples covering primary tumor, adjacent normal tissue, lymph node, metastasis tissue, and peripheral blood from 323 cancer patients. By applying integrative analysis, we constructed an immune cell-type/state map for each context and adopted our previously developed Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology Based Annotation System (KOBAS) algorithm to estimate the enrichment for context-specific immune cell type/state. In addition, CIEC also provides an easy-to-use online interface for users to comprehensively analyze the immune cell characteristics mapped across multiple tissues, including expression map, correlation, similar genes detection, signature score, and expression comparison. We believe that CIEC will be a valuable resource for exploring the intrinsic characteristics of immune cells in cancer patients and for potentially guiding novel cancer-immune biomarker development and immunotherapy strategies. CIEC is freely accessible at http://ciec.gene.ac/.

Construction and analysis of competitive endogenous RNA networks and prognostic models associated with ovarian cancer based on the exoRBase database.

To construct a competitive endogenous RNA (ceRNA) regulatory network in blood exosomes of patients with ovarian cancer (OC) using bioinformatics and explore its pathogenesis. The exoRbase2.0 database was used to download blood exosome gene sequencing data from patients OC and normal controls and the expression profiles of exosomal mRNA, long non-coding RNA (lncRNA), and circular RNA (circRNA) were detected independently using R language for differential expression analysis. TargetScan and miRanda databases were combined for the prediction and differential expression of mRNA-binding microRNAs (miRNA). The miRcode and starBase databases were used to predict miRNAs that bind to differentially expressed lncRNAs and circRNAs repectively. The relevant mRNA, circRNA, lncRNA and their corresponding miRNA prediction data were imported into Cytoscape software for visualization of the ceRNA network. The R language and KEGG Orthology-based Annotation System (KOBAS) were used to execute and illustrate the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Hub genes were identified using The CytoHubba plugin. Thirty-one differentially expressed mRNAs, 17 differentially expressed lncRNAs, and 24 differentially expressed circRNAs were screened. Cytoscape software was used to construct the ceRNA network with nine mRNA nodes, two lncRNA nodes, eight circRNA nodes, and 51 miRNA nodes. Both GO and KEGG were focused on the Spliceosome pathway, indicating that spliceosomes are closely linked with the development of OC, while heterogenous nuclear ribonucleoprotein K and RNA binding motif protein X-linked genes were the top 10 score Hub genes screened by Cytoscape software, including two lncRNAs, four mRNAs, and four circRNAs. In patients with OC, the expression of eukaryotic translation initiation factor 4 gamma 2 (EIF4G2), SERPINE 1 mRNA binding protein 1 (SERBP1), ribosomal protein L15 (RPL15) and human leukocyte antigen complex P5 (HCP5) was significantly higher whereas that of testis expressed transcript, Y-linked 15 and DEAD-box helicase 3 Y-linked genes was lower compared to normal controls Immunocorrelation scores revealed that SERBP1 was significantly and negatively correlated with endothelial cells and CD4+ T cells and positively correlated with natural killer (NK) cells and macrophages, respectively; RPL15 was significantly positively correlated with macrophages and endothelial cells and negatively correlated with CD8+ T cells and uncharacterized cells, respectively. EIF4G2 was significantly and negatively correlated with endothelial cells and CD4+ T cells, and positively correlated with uncharacterized cells, respectively. Based on the survival data and the significant correlation characteristics derived from the multifactorial Cox analysis (P < 0.05), the survival prediction curves demonstrated that the prognostic factors associated with 3-year survival in patients with OC were The prognostic factors associated with survival were Macrophage, RPL15. This study successfully constructs a ceRNA regulatory network in blood exosomes of OV patients, which provides the specific targets for diagnosis and treatment of OC.

Acute promyelocytic leukemia with FIP1L1::RARA fusion gene: The clinical utility of transcriptome sequencing and bioinformatic analyses.

Acute promyelocytic leukemia (APL) is typically characterized by the presence of coagulopathy and the PML::RARA fusion gene. The FIP1L1::RARA has been reported as a novel fusion gene, but studies on its pathogenesis are limited. A FIP1L1::RARA fusion in a child finally diagnosed as APL was reported. RNA sequencing (RNA-seq) of six patients (three cases of acute lymphoblastic leukemia (ALL), one case of myelodysplastic syndrome (MDS), one case of acute megakaryoblastic leukemia (M7), and one case of APL with FIP1L1::RARA) were performed. Transcriptome analysis of six patients was performed by RNA-seq. The heat map was used for showing the RNA expression profile, the volcano plot for identifying differential expression genes (DEGs), and the KEGG Orthology-Based Annotation System (KOBAS) online biological information database for KEGG pathway enrichment analysis. Obvious differences between APL with FIP1L1::RARA and hematologic malignancies were identified. 1060 common differentially expressed genes (co-DEGs) were detected between APL with FIP1L1::RARA vs ALL and APL with FIP1L1::RARA vs myeloid neoplasms (MDS, M7), the up-regulated genes were mainly mapped into platelet activation, cancer, AMPK signaling pathway, PI3K-Akt signaling pathway, and MAPK signaling pathway. The down-regulated genes were significantly associated with TNF signaling pathway, Rap1 signaling pathway, Age-RAGE signaling pathway, and apoptosis. A FIP1L1::RARA fusion in a child finally diagnosed as APL was reported. RNA-seq may provide a new diagnostic method when RARA rearrangements fail to be identified by conventional methods. In the analysis of co-DEGs between case vs ALL and case vs myeloid neoplasms, the up-regulated and down-regulated genes were enriched in different signaling pathways. Further experimental studies are needed to identify pathogenesis and treatment for APL with FIP1L1::RARA.

Fatty acid synthase (Fasn) inhibits the expression levels of immune response genes via alteration of alternative splicing in islet cells

BackgroundIncreasing evidence has shown that fatty acid synthase (Fasn) is associated with diabetes mellitus (DM) and insulin resistance, however, it remains unclear how Fasn upregulation leads to dysregulation of energy homeostasis in islet cells. Consequently, uncovering the function of Fasn in islet cells. Consequently, uncovering the function of FASN in islet cells is immensely important for finding a treatment target. AimIn this study, we elucidated the biological function of Fasn on the target genes in a rat insulinoma INS-1 cell line. MethodsWe created a Fasn overexpressing rat insulinoma cell line (Fasn-OE), and performed bulk RNA-sequencing (RNA-seq) experiments on Fasn-OE and INS-1 (control) cells. We first identified differentially expressed genes (DEGs) using Bioconductor package edgeR, and then discovered enriched gene ontology terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways using the KEGG Orthology Based Annotation System (KOBAS) 2.0 web server. Furthermore, we identified alternative splicing events (ASEs) and regulated alternative splicing events (RASEs) by applying the ABLas pipeline. The reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used for validation of selected differentially expressed genes (DEGs) and Fasn-regulated alternative splicing genes (RASGs). ResultsIn this study we found that Fasn overexpression led to significant changes of gene expression profiles, including downregulations of mRNA levels of immune related genes, including Bst2, Ddit3, Isg15, Mx2, Oas1a, Oasl, and RT1-S3 in INS-1 cell line. Furthermore, Fasn positively regulated the expression of transcription factors such as Fat1 and Ncl diabetes-related genes. Importantly, Fasn overexpression to result in alternative splicing events including in a metabolism-associated ATP binding protein mRNA Abcc5. In Gene Ontology analysis, the downregulated genes in Fasn-OE cells were mainly enriched in inflammatory response and innate immune response. In Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, the downregulated genes were mainly enriched in TNF signaling pathway and cytokine-mediated signaling pathways. ConclusionsOur findings showed that upregulation of Fasn may play a critical role in islet cell immunmetabolism via modifications of immune/inflammatory related genes on transcription and alternative splicing level, which provide novel insights into characterizing the function of Fasn in islet cell immunity and for the development of chemo/immune therapies.

Fc fragment of immunoglobulin G receptor IIa (FCGR2A) as a new potential prognostic biomarker of esophageal squamous cell carcinoma.

Fc fragment of immunoglobulin G receptor IIa (FCGR2A) as a new potential prognostic biomarker of esophageal squamous cell carcinoma.

Integrative bioinformatics analysis identifies LINC01614 as a potential prognostic signature in esophageal cancer.

BackgroundEsophageal cancer (EC) is one of the most common gastrointestinal cancers and the incidence is on the increase in recent years. The aim of the present study was to assess novel long non-coding RNA (lncRNA) biomarkers for the prognosis of EC through the analysis of gene expression microarrays.MethodsThree datasets (GSE53622, GSE53624, and GSE53625) were downloaded from the Gene Expression Omnibus (GEO) database and EC patients’ clinical information were from The Cancer Genome Atlas (TCGA) databases. Differentially expressed genes (DEGs) were screened by comparing tumor tissues with normal tissues using limma R package. The Gene Expression Profiling Interactive Analysis 2 (GEPIA2) database was used to obtain the novel lncRNAs and their co-expression genes in EC and these were visualized with the Cytoscape software. The Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology Based Annotation System (KOBAS) database was used to analyze the functions enrichment of selected DEGs. Cell Counting Kit-8 (CCK8) and Transwell assays were used to further confirm the function of target lncRNAs.ResultsWe identified 24 differentially expressed (DE) lncRNAs and 659 DE mRNAs from the intersection of GEO and TCGA databases. And we found that only LINC01614 was concerned with a candidate prognostic signature in EC. “Extracellular matrix (ECM)-receptor interaction” and “PI3K-Akt signaling pathway” were observed, and we constructed a lncRNA-mRNA co-expression network for EC that includes LINC01614 and 64 mRNAs. The results of CCK8 and Transwell assays showed that suppression of LINC01614 inhibited EC cell proliferation and migration.ConclusionsOur study might provide LINC01614 as a novel lncRNA biomarker for diagnosis and prognosis in EC.

KIF4A enhanced cell proliferation and migration via Hippo signaling and predicted a poor prognosis in esophageal squamous cell carcinoma.

BackgroundIn this study, we aimed to explore and clarify the function of KIF4A in esophageal squamous cell carcinoma (ESCC).MethodsThe microarray data were extracted from the Gene Expression Omnibus (GEO) database. We then used the database for Annotation, Visualization, and Integrated Discovery (DAVID) to perform the gene ontology function (GO) and KEGG Orthology‐Based Annotation System (KOBAS) to perform Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of differentially expressed genes (DEGs). The six core candidate genes were identified using protein–protein interaction (PPI) network analysis and Cytoscape software. Among them, the expression of KIF4A were validated by UALCAN database from the Cancer Genome Atlas (TCGA) (P < 0.05). Western blotting, qRT‐PCR and IHC were used to detect the expression of KIF4A in tissues. Cell experiments (transwell migration assays, wound healing assay, CCK8 assay, and clone formation experiment) were utilized to verify the roles of KIF4A on the ESCC cells. Western blotting was used to explore the mechanism of KIF4A in ESCC.ResultsThe expression level of KIF4A was upregulated in ESCC samples compared to those in paracancerous tissues. Transwell migration and wound healing assay showed overexpression of KIF4A significantly promoted the migration of ESCC cells. CCK8 assay and clone formation experiment analysis showed that overexpression of KIF4A promoted proliferation of ESCC cells. Western blot detection found that KIF4A could affect the phosphorylation level of Hippo signaling pathway related proteins.ConclusionsIn summary, KIF4A promotes ESCC cell proliferation and migration by regulating the biological function of ESCC cells through the Hippo signaling pathway.Key pointsSignificant findings of the studyWe found that high KIF4A expression was associated with poor overall survival in esophageal squamous cell carcinoma. KIF4A expression also promoted the proliferation and migration of ESCC cells in vitro.What this study addsOur experimental results explain the role of KIF4A in ESCC, and provide a new biomolecular target for the treatment of ESCC.

Study on the differentially expressed genes and signaling pathways in dermatomyositis using integrated bioinformatics method.

Dermatomyositis is a common connective tissue disease. The occurrence and development of dermatomyositis is a result of multiple factors, but its exact pathogenesis has not been fully elucidated. Here, we used biological information method to explore and predict the major disease related genes of dermatomyositis and to find the underlying pathogenic molecular mechanism.The gene expression data of GDS1956, GDS2153, GDS2855, and GDS3417 including 94 specimens, 66 cases of dermatomyositis specimens and 28 cases of normal specimens, were obtained from the Gene Expression Omnibus database. The 4 microarray gene data groups were combined to get differentially expressed genes (DEGs). The gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were operated by the database for annotation, visualization and integrated discovery and KEGG orthology based annotation system databases, separately. The protein–protein interaction networks of the DEGs were built from the STRING website. A total of 4097 DEGs were extracted from the 4 Gene Expression Omnibus datasets, of which 2213 genes were upregulated, and 1884 genes were downregulated. Gene ontology analysis indicated that the biological functions of DEGs focused primarily on response to virus, type I interferon signaling pathway and negative regulation of viral genome replication. The main cellular components include extracellular space, cytoplasm, and blood microparticle. The molecular functions include protein binding, double-stranded RNA binding and MHC class I protein binding. KEGG pathway analysis showed that these DEGs were mainly involved in the toll-like receptor signaling pathway, cytosolic DNA-sensing pathway, RIG-I-like receptor signaling pathway, complement and coagulation cascades, arginine and proline metabolism, phagosome signaling pathway. The following 13 closely related genes, XAF1, NT5E, UGCG, GBP2, TLR3, DDX58, STAT1, GBP1, PLSCR1, OAS3, SP100, IGK, and RSAD2, were key nodes from the protein–protein interaction network.This research suggests that exploring for DEGs and pathways in dermatomyositis using integrated bioinformatics methods could help us realize the molecular mechanism underlying the development of dermatomyositis, be of actual implication for the early detection and prophylaxis of dermatomyositis and afford reliable goals for the curing of dermatomyositis.

Systematic Understanding of the Mechanisms of Flos Chrysanthemi Indici-mediated Effects on Hypertension via Computational Target Fishing.

Hypertension-induced stroke and coronary artery disease are significant causes of global morbidity and mortality. Metabolic hypertension has recently become the leading cause of hypertension. Flos Chrysanthemi Indici (CIF) has a long history as a treatment of hypertension as part of traditional Chinese medicine. However, its mechanisms of activity remain largely unknown. This study was aimed to uncover the potential anti-hypertensive mechanisms of CIF based on network pharmacology. In this research, a systems pharmacology approach integrating the measurement of active compounds, target fishing, gene screening, Gene Ontology (GO) pathway analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology Based Annotation System (KOBAS) database analysis, and compound-target network construction were performed to explore the anti-hypertensive mechanisms of CIF. These studies revealed that 12 bioactive compounds in CIF had good druggability, 5 of which were flavonoids. After screening, 8 of those 12 bioactive compounds interacted with 118 hypertensionrelated target genes, which were mapped to 218 signal pathways. Network analysis showed that these targets were associated with improving insulin resistance, improving vascular function, inhibiting renninangiotensin- aldosterone system (RAAS), inhibiting the sympathetic nervous system (SNS) and regulating other physiological processes. In summary, CIF is predicted to target multiple proteins and pathways to form a network that exerts systematic pharmacological effects in order to regulate blood pressure and metabolic disorder.

Identification of key candidate genes and pathways in anaplastic thyroid cancer by bioinformatics analysis.

Anaplastic thyroid carcinoma (ATC) is a refractory and poor prognosis tumor Present study aimed to investigate the underlying biological functions and pathways involved in the development of ATC and to identify potential hub genes and candidate biomarkers of ATC. Bioinformatics analyses were performed to identify the differentially expressed genes (DEGs) between ATC tissue samples and adjacent normal tissue samples. Protein-protein interaction (PPI) networks of the DEGs were constructed using Search Tool for the Retrieval of Interacting Genes online tool and Cytoscape software and divided into sub-networks using the Molecular Complex Detection (MCODE) plug-in. DEGs in each module was analyzed by enrichment analysis of the KEGG Orthology Based Annotation System (KOBAS) web software version 3.0. Eventually, the hub genes from bioinformatics analysis were verified by qRT-PCR assay in different ATC cell lines. Thirty hub genes were selected and three modules were built by the Cytoscape software from the PPI network. Seven genes (CDK1, CCNB2, BUB1B, CDC20, RRM2, CHEK1 and CDC45) were screened from thirty hub genes. Enrichment analysis showed that these hub genes were primarily accumulated in 'cell cycle', 'p53 signaling pathway', 'viral carcinogenesis', 'pyrimidine metabolism' and 'ubiquitin mediated proteolysis'. The results of qRT-PCR indicated that seven hub genes were unregulated in three ATC cell lines compared with normal thyroid gland cell. These findings suggest that CDK1, CCNB2, BUB1B, CDC20, RRM2, CHEK1 and CDC45 may serve as novel diagnosis biomarkers and potential therapeutic target for ATC.

Identification of differentially expressed genes regulated by methylation in colon cancer based on bioinformatics analysis.

BACKGROUNDDNA methylation, acknowledged as a key modification in the field of epigenetics, regulates gene expression at the transcriptional level. Aberrant methylation in DNA regulatory regions could upregulate oncogenes and downregulate tumor suppressor genes without changing the sequences. However, studies of methylation in the control of gene expression are still inadequate. In the present research, we performed bioinformatics analysis to clarify the function of methylation and supply candidate methylation-related biomarkers and drivers for colon cancer.AIMTo identify and analyze methylation-regulated differentially expressed genes (MeDEGs) in colon cancer by bioinformatics analysis.METHODSWe downloaded RNA expression profiles, Illumina Human Methylation 450K BeadChip data, and clinical data of colon cancer from The Cancer Genome Atlas project. MeDEGs were identified by analyzing the gene expression and methylation levels using the edgeR and limma package in R software. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed in the DAVID database and KEGG Orthology-Based Annotation System 3.0, respectively. We then conducted Kaplan–Meier survival analysis to explore the relationship between methylation and expression and prognosis. Gene set enrichment analysis (GSEA) and investigation of protein-protein interactions (PPI) were performed to clarify the function of prognosis-related genes.RESULTSA total of 5 up-regulated and 81 down-regulated genes were identified as MeDEGs. GO and KEGG pathway analyses indicated that MeDEGs were enriched in multiple cancer-related terms. Furthermore, Kaplan–Meier survival analysis showed that the prognosis was negatively associated with the methylation status of glial cell-derived neurotrophic factor (GDNF) and reelin (RELN). In PPI networks, GDNF and RELN interact with neural cell adhesion molecule 1. Besides, GDNF can interact with GDNF family receptor alpha (GFRA1), GFRA2, GFRA3, and RET. RELN can interact with RAFAH1B1, disabled homolog 1, very low-density lipoprotein receptor, lipoprotein receptor-related protein 8, and NMDA 2B. Based on GSEA, hypermethylation of GDNF and RELN were both significantly associated with pathways including “RNA degradation,” “ribosome,” “mismatch repair,” “cell cycle” and “base excision repair.”CONCLUSIONAberrant DNA methylation plays an important role in colon cancer progression. MeDEGs that are associated with the overall survival of patients may be potential targets in tumor diagnosis and treatment.

Weighted gene co‑expression network analysis for identifying hub genes in association with prognosis in Wilms tumor.

Wilms tumor (WT) is the most common type of renal malignancy in children. Survival rates are low and high-risk WT generally still carries a poor prognosis. To better elucidate the pathogenesis and tumorigenic pathways of high-risk WT, the present study presents an integrated analysis of RNA expression profiles of high-risk WT to identify predictive molecular biomarkers, for the improvement of therapeutic decision-making. mRNA sequence data from high-risk WT and adjacent normal samples were downloaded from The Cancer Genome Atlas to screen for differentially expressed genes (DEGs) using R software. From 132 Wilms tumor samples and six normal samples, 2,089 downregulated and 941 upregulated DEGs were identified. In order to identify hub DEGs that regulate target genes, weighted gene co-expression network analysis (WGCNA) was used to identify 11 free-scale gene co-expressed clusters. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were annotated using KEGG Orthology Based Annotation System annotation of different module genes. The Search Tool for the Retrieval of Interacting Genes was used to construct a protein-protein interaction network for the identified DEGs, and the hub genes of WGCNA modules were identified using the Cytohubb plugin with Cytoscape software. Survival analysis was subsequently performed to highlight hub genes with a clinical signature. The present results suggest that epidermal growth factor, cyclin dependent kinase 1, endothelin receptor type A, nerve growth factor receptor, opa-interacting protein 5, NDC80 kinetochore complex component and cell division cycle associated 8 are essential to high-risk WT pathogenesis, and they are closely associated with clinical prognosis.

Plasma Long Non-Coding RNA Expression Profiles in Patients with Rheumatoid Arthritis.

Recently, long non-coding RNAs (lncRNAs) have attracted substantial attention owing to their unforeseen critical roles in a wide range of biological processes. The aim of our study was to provide an overview of lncRNA expression profiles in plasma of RA patients. The Agilent LncRNA + mRNA Human Gene Expression Microarray V4.0 was employed to determine differentially expressed (DE) lncRNAs and mRNAs in plasma of four female newly diagnosed and DMARD-naïve RA patients and four female age-matched healthy controls. The KOBAS (KEGG Orthology Based Annotation System) software was applied to determine the gene ontology (GO) terms and pathway in which the DE mRNAs were enriched. Furthermore, a lncRNA-mRNA co-expression network was constructed according to the correlation between DE lncRNAs and mRNAs. Compared with healthy controls, a total of 289 DE lncRNAs (169 up-regulated and 120 down-regulated) and 468 DE mRNAs (280 up-regulated and 188 down-regulated) were found in the plasma of patients with RA. Bioinformatics analysis indicated that the DE mRNAs might be involved in the pathogenesis of RA mainly through platelets. In addition, a co-expression network composed of 229 network nodes and 340 connections between 116 lncRNAs and 113 mRNAs was constructed. We characterized the plasma lncRNA expression profiles in RA patients for the first time. Our results could shed new light on the pathogenesis of RA and help identify lncRNAs as novel diagnostic biomarkers and therapeutic targets for RA.

Identification and prediction of novel non-coding and coding RNA-associated competing endogenous RNA networks in colorectal cancer.

AIMTo identify and predict the competing endogenous RNA (ceRNA) networks in colorectal cancer (CRC) by bioinformatics analysis.METHODSIn the present study, we obtained CRC tissue and normal tissue gene expression profiles from The Cancer Genome Atlas project. Differentially expressed (DE) genes (DEGs) were identified. Then, upregulated and downregulated miRNA-centered ceRNA networks were constructed by analyzing the DEGs using multiple bioinformatics approaches. DEmRNAs in the ceRNA networks were identified in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways using KEGG Orthology Based Annotation System 3.0. The interactions between proteins were analyzed using the STRING database. Kaplan-Meier survival analysis was conducted for DEGs and real time quantitative polymerase chain reaction (RT-qPCR) was also performed to validate the prognosis-associated lncRNAs in CRC cell lines.RESULTSEighty-one DElncRNAs, 20 DEmiRNAs, and 54 DEmRNAs were identified to construct the ceRNA networks of CRC. The KEGG pathway analysis indicated that nine out of top ten pathways were related with cancer and the most significant pathway was “colorectal cancer”. Kaplan-Meier survival analysis showed that the overall survival was positively associated with five DEGs (IGF2-AS, POU6F2-AS2, hsa-miR-32, hsa-miR-141, and SERPINE1) and it was negatively related to three DEGs (LINC00488, hsa-miR-375, and PHLPP2). Based on the STRING protein database, it was found that SERPINE1 and PHLPP2 interact with AKT1. Besides, SERPINE1 can interact with VEGFA, VTN, TGFB1, PLAU, PLAUR, PLG, and PLAT. PHLPP2 can interact with AKT2 and AKT3. RT-qPCR revealed that the expression of IGF2-AS, POU6F2-AS2, and LINC00488 in CRC cell lines was consistent with the in silico results.CONCLUSIONCeRNA networks play an important role in CRC. Multiple DEGs are related with clinical prognosis, suggesting that they may be potential targets in tumor diagnosis and treatment.

Identification of key genes and associated pathways in KIT/PDGFRA wild‑type gastrointestinal stromal tumors through bioinformatics analysis.

Gastrointestinal stromal tumors (GISTs) are the most common type of mesenchymal tumor in the gastrointestinal tract. The present study aimed to identify the potential candidate biomarkers that may be involved in the pathogenesis and progression of v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog (KIT)/platelet-derived growth factor receptor α (PDGFRA) wild-type GISTs. A joint bioinformatics analysis was performed to identify the differentially expressed genes (DEGs) in wild-type GIST samples compared with KIT/PDGFRA mutant GIST samples. Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs was conducted using Database for Annotation, Visualization and Integrated Discovery and KEGG Orthology-Based Annotation System (KOBAS) online tools, respectively. Protein-protein interaction (PPI) networks of the DEGs were constructed using Search Tool for the Retrieval of Interacting Genes online tool and Cytoscape, and divided into sub-networks using the Molecular Complex Detection (MCODE) plug-in. Furthermore, enrichment analysis of DEGs in the modules was analyzed with KOBAS. In total, 546 DEGs were identified, including 238 upregulated genes primarily enriched in ‘cell adhesion’, ‘biological adhesion’, ‘cell-cell signaling’, ‘PI3K-Akt signaling pathway’ and ‘ECM-receptor interaction’, while the 308 downregulated genes were predominantly involved in ‘inflammatory response’, ‘sterol metabolic process’ and ‘fatty acid metabolic process’, ‘small GTPase mediated signal transduction’, ‘cAMP signaling pathway’ and ‘proteoglycans in cancer’. A total of 25 hub genes were obtained and four modules were mined from the PPI network, and sub-networks also revealed these genes were primarily involved in significant pathways, including ‘PI3K-Akt signaling pathway’, ‘proteoglycans in cancer’, ‘pathways in cancer’, ‘Rap1 signaling pathway’, ‘ECM-receptor interaction’, ‘phospholipase D signaling pathway’, ‘ras signaling pathway’ and ‘cGMP-PKG signaling pathway’. These results suggested that several key hub DEGs may serve as potential candidate biomarkers for wild-type GISTs, including phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit γ, insulin like growth factor 1 receptor, hepatocyte growth factor, thrombospondin 1, Erb-B2 receptor tyrosine kinase 2 and matrix metallopeptidase 2. However, further experiments are required to confirm these results.

Screening potential biomarkers for colorectal cancer based on circular RNA chips.

The aim of the present study was to screen colorectal cancer (CRC) tissue and adjacent tissue for differences in circular RNA (circRNA) expression, to analyze the related miRNAs and messenger RNAs (mRNAs), and to investigate the circRNA expression in CRC and its function. The circRNA expression profile was generated using CapitalBio microarray technology. The differentially expressed circRNAs were identified with GeneSpring 12.5 software. Subsequently, the related mRNAs of the differentially expressed circRNAs were annotated with the molecule annotation system (MAS) 3.0, and the diseases, pathways and functional enrichment analysis of these mRNAs were performed using the KEGG orthology-based annotation system (KOBAS) 3.0. In addition, the target miRNAs of differentially expressed circRNAs were screened using the miRanda algorithm. The circRNA/miRNA network was constructed for the top 8 most significant differentially expressed circRNAs with Cytoscape software 3.4.0. A total of 10,245 differentially expressed circRNAs were identified, including 6,264 upregulated ones, and 3,981 downregulated ones. The related mRNAs were enriched in 462 KEGG diseases, 411 FunDO, 669 NHGRI GWAS catalog, and 845 OMIM; and 1,334 Reactomes, 281 KEGG pathways, 117 PANTHER and 193 BioCyc; and 11,606 Gene Ontology (GO) terms. A total of 133 circRNA/miRNA pairs were involved in the circRNA/miRNA network. hsa_circ_0126897_CBC1 may be a potential biomarker for CRC, and the cell cycle was closely associated with the occurrence and development of CRC.

Comparative transcriptome analysis provides new insights into erect and prostrate growth in bermudagrass (Cynodon dactylon L.)

Bermudagrass (Cynodon dactylon L.) is a prominent warm-season turf and forage grass species with multiple applications. In most C. dactylon cultivars and accessions, erect-growing stems (shoot) and prostrate-growing stems (stolon) often coexist. These two types of stems are both formed through tillering but grow in two directions with different tiller angles. Elucidating the mechanism of tiller angle regulation in bermudagrass could provide important clues to breed cultivars with different plant architectural features for diverse usage. In this study, we compared the stem internode transcriptome of two bermudagrass wild accessions with extremely different tiller angles and stem growth directions. A total of 2088 and 12,141 unigenes were preferentially expressed in prostrate-growing wild accession C792 and erect-growing wild accession C793, respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology-based Annotation System (KOBAS) analyses further indicated that light- and gravity-responsive genes were enriched in accession C792, whereas lignin synthesis-related genes were enriched in accession C793, which well explains the difference in lignification of vascular bundles and mechanical tissues in the two accessions. These results not only expand our understanding of the genetic control of tiller angle and stem growth direction in bermudagrass but also provide insight for future molecular breeding of C. dactylon and other turfgrass species with different plant architectures.

Identification of the anticancer effects of a novel proteasome inhibitor, ixazomib, on colorectal cancer using a combined method of microarray and bioinformatics analysis.

PurposeThe study aimed to explore the anticancer effects of a novel proteasome inhibitor, ixazomib, on colorectal cancer (CRC) using a combined method of microarray and bioinformatics analysis.Materials and methodsCell proliferation was tested by Cell Counting Kit-8 (CCK-8) assay for SW620 cells treated with different concentrations of ixazomib and different treatment times. The microarray analysis was conducted for six samples, including three samples of SW620 cells untreated with ixazomib and three samples of SW620 cells treated with ixazomib. The differentially expressed genes (DEGs) between untreated and treated samples were identified by the Linear Models for Microarray data (LIMMA) package in R language. The Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed for the DEGs using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and KEGG Orthology-Based Annotation System (KOBAS) online tool. The protein–protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and module analysis was performed for the PPI network.ResultsIxazomib could inhibit the proliferation of SW620 cells in a dose-dependent and time-dependent manner. A total of 743 DEGs, including 203 upregulated DEGs such as HSPA6 and 540 downregulated DEGs such as APCDD1, were identified. Eighty-three GO terms were enriched for DEGs, which were mainly related to protein folding, apoptotic process, transcription factor activity, and proteasome. Thirty-seven KEGG pathways were perturbed, including pathway of apoptosis and cell cycle. Forty-six hub genes, such as TP53, JUN, and ITGA2, were screened out, and three modules with important functions were mined from the PPI network.ConclusionThe novel proteasome inhibitor ixazomib significantly inhibited the proliferation of human CRC SW620 cells. It exerted anticancer effects through targeting the expression of DEGs, such as HSPA6, APCDD1, TP53, and JUN, and affecting the signaling pathways including apoptosis and cell cycle pathway, which demonstrated the promising potential of ixazomib for CRC therapy.

Identification of genes associated with castration‑resistant prostate cancer by gene expression profile analysis.

Prostate cancer (CaP) is a serious and common genital tumor. Generally, men with metastatic CaP can easily develop castration-resistant prostate cancer (CRPC). However, the pathogenesis and tumorigenic pathways of CRPC remain to be elucidated. The present study performed a comprehensive analysis on the gene expression profile of CRPC in order to determine the pathogenesis and tumorigenic of CRPC. The GSE33316 microarray, which consisted of 5 non-castrated samples and 5 castrated samples, was downloaded from the gene expression omnibus database. Subsequently, 201 upregulated and 161 downregulated differentially expressed genes (DEGs) were identified using the limma package in R and those genes were classified and annotated by plugin Mcode of Cytoscape. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using Database for Annotation, Visualization and Integrated Discovery and KEGG Orthology Based Annotation System 2.0 online tools to investigate the function of different gene modules. The BiNGO tool was used to visualize the level of enriched GO terms. Protein-protein interaction network was constructed using STRING and analyzed with Cytoscape. In conclusion, the present study determined that aldo-keto reductase 3, cyclin B2, regulator of G protein signaling 2, nuclear factor of activated T-cells and protein kinase C a may have important roles in the development of CRPC.

Genome-wide expression profiling and bioinformatics analysis of deregulated genes in human gastric cancer tissue after gastroscopy.

The aim of this study was to analyze the gene expression profile and biological processes enriched in gastric cancer. We collected five human advanced gastric cancer tissues by gastroscopy and five peritumor normal tissues as controls and examined the gene expression changes by microarray. KEGG Orthology Based Annotation System annotation was used to identify pathways and biological processes regulated by the deregulated genes. Protein-protein interaction network analysis identified protein complex and functional modules. We also selected 14 genes for further verification by real-time quantitative Polymerase Chain Reaction (PCR). Human gene expression profile analysis showed that 2028 deregulated genes were detected in gastric cancer compared with the control group (at least a 2.0-fold change and P < 0.05), among which there were 689 upregulated and 1339 downregulated genes. Interestingly, we identified some important genes, such as CXCL17, OTX1 and CCDC125, which have not previously been reported in gastric cancer. Real-time quantitative PCR results verified that CXCL8, OTX1, CEBPB, FOSL1, FOXS1, ARFRP1 and IRF9 were upregulated in gastric cancer and CCDC125, PPP1R36, SOX2, JUN and MIA2 were downregulated. Moreover, bioinformatics analysis demonstrated that the biological processes of inflammatory response, angiogenesis, cell migration and pathways of chemokine signaling pathway, TNF signaling pathway were enriched. We also selected the top 30 significant Gene Ontology terms and select pathways for a brief summary. We performed a global analysis of the mRNA landscape in gastric cancer. Our results may stimulate a deeper understanding of the disease, and lead to the development of potential therapies and the identification of novel biomarkers.