Patients with lymphoma are at high risk of developing venous thromboembolism (VTE). The purpose of the present study was to identify the target gene associated with VTE for patients with lymphoma. Microarray data was downloaded from the gene expression omnibus database (GSE17078), which comprised the control group, 27 normal blood outgrowth endothelial cell (BOEC) samples, and the case group, 3 BOEC samples of venous thrombosis with protein C deficiency. Differentially expressed genes (DEGs) were identified by the Limma package of R. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses were performed via the database for annotation, visualization and integrated discovery. Differentially coexpressed pairs were identified by the DCGL package of R. The subsequent protein-protein interaction (PPI) networks and gene coexpression networks were constructed by the Search Tool for the Retrieval of Interacting Genes/Proteins database, and were visualized by Cytoscape software. A total of 110 DEGs were obtained, including 73 upregulated and 37 downregulated genes. GO and KEGG pathway enrichment analyses identified 132 significant GO terms and 9 significant KEGG pathways. In total, 97 PPI pairs for PPI network and 309 differential coexpression pairs for the gene coexpression network were obtained. Additionally, the connective tissue growth factor (CTGF) gene was closely connected with other genes in the two networks. A total of 2 KEGG pathways were associated with VTE and CTGF may be the target gene of VTE in patients with lymphoma. The present study may identify the molecular mechanism of VTE, but additional clinical study is required to validate the results.

Diffuse large B-cell lymphoma (DLBCL), the most frequently occurring type of lymphoid malignancy, has been demonstrated to be associated with mutations of Ten‑Eleven Translocation (TET). In order to explore the association between DLBCL and TET mutations, the present study analyzed the gene expression and methylation profiles in human DLBCL biopsy tissues with wildtype and mutated TET2. The microarray dataset GSE37365, containing two subseries: the genome‑wide gene expression dataset GSE37362 and the DNA methylation microarray dataset GSE37363, was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified using the limma package of R. Furthermore, differentially methylated sites and differentially methylated regions were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed via GO stats and GSEABase packages respectively. Finally, the Pathview package was used to construct the network of enriched pathways. A total of 198 DEGs (106 up‑ and 92 downregulated) were obtained. A total of 602 shared differentially methylated genes (DMGs) were identified according to differentially methylated levels. A total of 12 overlapping genes were identified in DEGs and DMGs. It was observed that 9 of the 12 overlapped genes were downregulated and hypermethylated, with 24 GO terms and one KEGG pathway significantly enriched. The results of the present study demonstrated that the genes cryptochrome circadian clock 1, zinc finger protein (ZNF) interacting with K protein 1, ZNF134, ZNF256 and ZNF615, which were hypermethylated and downregulated in DLBCL patients with TET2 mutations, were the key genes in the association between DLBCL and TET mutations. These genes may act as potential biomarkers for the diagnosis of DLBCL in the future.

The purpose of this study was to construct a new typing model for diffuse large B-cell lymphoma (DLBCL) patients based on the B-cell receptor (BCR) and explore its potential molecular mechanism.

The purpose of the present study was to explore the effect of propranolol on angiosarcoma, and the potential target genes involved in the processes of proliferation and differentiation of angiosarcoma tumor cells. The mRNA expression profile (GSE42534) was downloaded from the Gene Expressed Omnibus database, including three samples without propranolol treatment (control), three samples with propranolol treatment for 4 h and three samples with propranolol treatment for 24 h. The differentially expressed genes (DEGs) in angiosarcoma tumor cells with or without propranolol treatment were obtained via the limma package of R and designated DEGs-4 h and DEGs-24 h. The DEGs-24 h group was divided into two sets. Set 1 contained the DEGs also contained in the DEGs-4 h group. Set 2 contained the remainder of the DEGs. Functional and pathway enrichment analysis of sets 1 and 2 was performed. The protein-protein interaction (PPI) networks of sets 1 and 2 were constructed, termed PPI 1 and PPI 2, and visualized using Cytoscape software. Modules of the two PPI networks were analyzed, and their topological structures were simulated using the tYNA platform. A total of 543 and 2,025 DEGs were identified in angiosarcoma tumor cells treated with propranolol for 4 and 24 h, respectively, compared with the control group. A total of 401 DEGs were involved in DEGs-4 h and DEGs-24 h, including metallothionein 1, heme oxygenase 1, WW domain-binding protein 2 and sequestosome 1. Certain significantly enriched gene ontology (GO) terms and pathways of sets 1 and 2 were identified, containing 28 overlapping GO terms. Furthermore, 121 nodes and 700 associated pairs were involved in PPI 1, whereas 1,324 nodes and 11,839 associated pairs were involved in PPI 2. A total of 45 and 593 potential target genes were obtained according to the node degrees of PPI 1 and PPI 2. The results of the present study indicated that a number of potential target genes, including AXL receptor tyrosine kinase, coatomer subunit α, DR1-associated protein 1 and ERBB receptor feedback inhibitor 1 may be involved in the effect of propranolol on angiosarcoma.

Liver cancer is one of the most common cancers worldwide with high morbidity and mortality. Its molecular mechanism hasn't been fully understood though many studies have been conducted and thus further researches are still needed to improve the prognosis of liver cancer. Firstly, differentially expressed genes (DEGs) between six Mdr2-knockout (Mdr2-KO) mutant mice samples (3-month-old and 12-month-old) and six control mice samples were identified. Then, the enriched GO terms and KEGG pathways of those DEGs were obtained using the Database for Annotation, Visualization and Integrated Discovery (DAVID, http://david.abcc.ncifcrf.gov/). Finally, protein-protein interactions (PPI) network of those DEGs were constructed using STRING database ( http://www.string-db.org/) and visualized by Cytoscape software, at the same time, genes with high degree were selected out. Several novel biomarkers that might play important roles in liver cancer were identified through the analysis of gene microarray in GEO. Also, some genes such as Tyrobp, Ctss and pathways such as Pathways in cancer, ECM-receptor interaction that had been researched previously were further confirmed in this study. Through the bioinformatics analysis of the gene microarray in GEO, we found some novel biomarkers of liver cancer and further confirmed some known biomarkers.

The aim of the present study was to identify the target genes of cediranib and the associated signaling pathways in alveolar soft part sarcoma (ASPS). A microarray dataset (GSE32569) was obtained from the Gene Expression Omnibus database. The R software package was used for data normalization and screening of differentially expressed genes (DEGs). The Database for Annotation, Visualization and Integrated Discovery was used to perform Gene Ontology analysis. Gene Set Enrichment Analysis was performed to obtain the up- and downregulated pathways in ASPS. The Distant Regulatory Elements of co-regulated genes database was used to identify the transcription factors (TFs) that were enriched in the signaling pathways. A protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins database and was visualized using Cytoscape software. A total of 71 DEGs, including 59 upregulated genes and 12 downregulated genes, were identified. Gene sets associated with ASPS were enriched primarily in four signaling pathways: The phenylalanine metabolism pathway, the mitogen-activated protein kinase (MAPK) signaling pathway, the taste transduction pathway and the intestinal immune network for the production of immunoglobulin A. Furthermore, 107 TFs were identified to be enriched in the MAPK signaling pathway. Certain genes, including those coding for Fms-like tyrosine kinase 1, kinase insert domain receptor, E-selectin and platelet-derived growth factor receptor D, that were associated with other genes in the PPI network, were identified. The present study identified certain potential target genes and the associated signaling pathways of cediranib action in ASPS, which may be helpful in understanding the efficacy of cediranib and the development of new targets for cediranib.

The present study aimed to identify the differentially expressed genes (DEGs) regulated by microRNA (miRNA)-221 and miRNA-222 that are associated with the resistance of breast cancer to fulvestrant. The GSE19777 transcription profile was downloaded from the Gene Expression Omnibus database, and includes data from three samples of antisense miRNA-221-transfected fulvestrant-resistant MCF7-FR breast cancer cells, three samples of antisense miRNA-222-transfected fulvestrant-resistant MCF7-FR cells and three samples of control inhibitor (green fluorescent protein)-treated fulvestrant-resistant MCF7-FR cells. The linear models for microarray data package in R/Bioconductor was employed to screen for DEGs in the miRNA-transfected cells, and the pheatmap package in R was used to perform two-way clustering. Pathway enrichment was conducted using the Gene Set Enrichment Analysis tool. Furthermore, a miRNA-messenger (m) RNA regulatory network depicting interactions between miRNA-targeted upregulated DEGs was constructed and visualized using Cytoscape. In total, 492 and 404 DEGs were identified for the antisense miRNA-221-transfected MCF7-FR cells and the antisense miRNA-222-transfected MCF7-FR cells, respectively. Genes of the pentose phosphate pathway (PPP) were significantly enriched in the antisense miRNA-221-transfected MCF7-FR cells. In addition, components of the Wnt signaling pathway and cell adhesion molecules (CAMs) were significantly enriched in the antisense miRNA-222-transfected MCF7-FR cells. In the miRNA-mRNA regulatory network, miRNA-222 was demonstrated to target protocadherin 10 (PCDH10). The results of the present study suggested that the PPP and Wnt signaling pathways, as well as CAMs and PCDH10, may be associated with the resistance of breast cancer to fulvestrant.

Breast cancer (BC) is the most common type of malignancy in females worldwide, however, its underlying mechanisms remain poorly understood. The present study aimed to investigate the mechanisms behind the development and progression of BC and identify potential biomarkers for it. The chromatin immunoprecipitation-DNA sequencing (ChIP-Seq) dataset GSM1642516 and gene expression dataset GSE34925 were downloaded from the Gene Expression Omnibus database. Affy and oligo packages were used for the background correction and normalization of the gene expression dataset. Based on Limma package and the criteria of a fold change >1.41 or <0.71, and a false discovery rate adjusted P-value <0.05, differentially-expressed genes (DEGs) in euchromatic histone lysine methyltransferase 2 (G9A) -knockout (KO) breast samples compared with control samples were identified. The Database for Annotation, Visualization and Integrated Analysis was used for the functional enrichment analysis of the DEGs. Bowtie 2 and model-based analysis of ChIP-Seq version 14 (macs14) were used for the mapping of raw reads and the identification of G9A binding sites (peaks), respectively. In addition, overlapping genes between the DEGs and genes in the peaks located in -3000 to 3000 bp centered in the transcription start sites (conpeaks) were screened out and microRNAs (miRNAs) believed to regulate those overlaps were identified through the TargetScan database. A total of 217 DEGs were identified in G9A-KO samples, which were mainly involved in the biological processes and pathways associated with the inflammatory response and cancer progression. A total of 10,422 peaks, containing 1,210 conpeaks involving 1,138 genes, were identified. Among the 1,138 genes, 15 were overlapped with the DEGs, and 35 miRNAs were identified to regulate those overlaps. Insulin-induced gene 1 was regulated by 9 genes in the miRNA-gene regulation network, which may indicate its importance in the progression of BC. The present study identified potential biomarkers of BC that may be useful in the diagnosis and treatment of patients with the disease.