In silico identification of oncogenic potential of fyn-related kinase in hepatocellular carcinoma

Identification of molecular characteristics induced by radiotherapy in rectal cancer based on microarray data

The present study aimed to reveal the molecular characteristics induced by radiotherapy in rectal cancer at the transcriptome level. Microarray data (ID, GSE26027) downloaded from the Gene Expression Omnibus database were re-analyzed to identify differentially expressed genes (DEGs) between rectal cancer tissues during and prior to radiotherapy. The DEGs were then inputted into the database for annotation, visualization and integrated discovery, an online tool to perform enrichment analyses, and into the search tool for the retrieval of interacting genes/proteins database to identify protein-protein interactions (PPIs). Subsequently, a PPI network was constructed, which was screened for densely connected modules. Furthermore, protein domain enrichment analysis was performed. In total, 690 DEGs, including 179 upregulated and 511 downregulated DEGs, were found in rectal cancer tissues during and prior to radiotherapy. The upregulated DEGs were significantly enriched in 'positive regulation of transport' and 'regulation of cardiac muscle contraction', while the downregulated DEGs were most markedly enriched in 'cell migration', 'cell-cell signaling', 'extracellular matrix organization' and 'blood vessel development', including prostaglandin-endoperoxide synthase 2, transforming growth factor β-induced, 68 kDa endothelin receptor type A, brain-derived neurotrophic factor, TIMP metallopeptidase inhibitor 1, and serpin family E member 1, which were the top 6 hub nodes in the PPI network. Furthermore, 2 protein domains were significantly enriched by PPI modules, including: The collagen triple helix repeat (CTHR) family members collagen type (COL) 5A2, COL9A3, COL6A3, COL21A1, COL5A3, COL11A1, COL7A1 and CTHR-containing-1; and the olfactory receptor family (OR) members OR7E24, OR7A17, OR6A2, OR1F1, OR10H3 and OR7A10. A total of 7 upregulated DEGs were characterized as tumor suppressor genes, and 8 downregulated DEGs were characterized as oncogenes. The biological processes or protein domains enriched by upregulated or downregulated DEGs may improve the understanding of molecular characteristics in response to radiotherapy.

Therapeutic effects of Euphorbia Pekinensis and Glycyrrhiza glabra on Hepatocellular Carcinoma Ascites Partially Via Regulating the Frk-Arhgdib-Inpp5d-Avpr2-Aqp4 Signal Axis

To clarify unknown rationalities of herbaceous compatibility of Euphorbia Pekinensis (DJ) and Glycyrrhiza glabra (GC) acting on hepatocellular carcinoma (HCC) ascites, peritoneum transcriptomics profiling of 15 subjects, including normal control (Con), HCC ascites mouse model (Mod), DJ-alone, DJ/GC-synergy and DJ/GC-antagonism treatment groups were performed on OneArray platform, followed by differentially expressed genes (DEGs) screening. DEGs between Mod and Con groups were considered as HCC ascites-related genes, and those among different drug treatment and Mod groups were identified as DJ/GC-combination-related genes. Then, an interaction network of HCC ascites-related gene-DJ/GC combination-related gene-known therapeutic target gene for ascites was constructed. Based on nodes’ degree, closeness, betweenness and k-coreness, the Frk-Arhgdib-Inpp5d-Avpr2-Aqp4 axis with highly network topological importance was demonstrated to be a candidate target of DJ/GC combination acting on HCC ascites. Importantly, both qPCR and western blot analyses verified this regulatory effects based on HCC ascites mice in vivo and M-1 collecting duct cells in vitro. Collectively, different combination designs of DJ and GC may lead to synergistic or antagonistic effects on HCC ascites partially via regulating the Frk-Arhgdib-Inpp5d-Avpr2-Aqp4 axis, implying that global gene expression profiling combined with network analysis can offer an effective way to understand pharmacological mechanisms of traditional Chinese medicine prescriptions.

Identification of genes associated with tongue cancer in patients with a history of tobacco and/or alcohol use

The present study aimed to identify genes associated with tongue cancer in patients with a history of tobacco and/or alcohol use. Microarray dataset GSE42023, including 10 tissue samples of tongue cancer from patients with a history of tobacco and/or alcohol use (habit group) and 11 tissue samples of non-habit-associated tongue cancer (non-habit group), were downloaded from the Gene Expression Omnibus database. Differentially-expressed genes (DEGs) between the habit and non-habit groups were identified using the Linear Models for Microarray Data software package. The enrichment functions and pathways of these genes were subsequently predicted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Transcription factors (TFs) and tumor-associated genes (TAGs) were selected from the DEGs using the Encyclopedia of DNA Elements database and the TAG database, respectively. Protein-protein interaction (PPI) networks for DEGs were constructed using Cytoscape. In addition, functional module analysis was performed using BioNet. This analysis identified 642 DEGs between the habit and non-habit groups, including 200 upregulated and 442 downregulated genes. The majority of upregulated DEGs were functionally enriched in the regulation of apoptosis and the calcium signaling pathway. The majority of downregulated DEGs were functionally enriched in fat cell differentiation and the adipocytokine signaling pathway. In addition, 31 TFs and 42 TAGs were identified from the DEGs. Furthermore, this analysis demonstrated that certain DEGs, including AKT serine/threonine kinase 1 (AKT1), E1A binding protein p300 (EP300), erb-b2 receptor tyrosine kinase 2 (ERBB2) and epiregulin (EREG), had high connectivity degrees in the PPI networks and/or functional modules. Overall, DEGs in a functional module, such as AKT1, EP300, ERBB2 and EREG, may serve important roles in the development of tongue cancer in patients with a history of tobacco and/or alcohol use. These DEGs are potential therapeutic targets for the treatment of tongue cancer in these groups.

Aberrant expression of cell cycle and material metabolism related genes contributes to hepatocellular carcinoma occurrence

This study aims to deepen our understanding of the molecular mechanism underlying the occurrence of hepatocellular carcinoma (HCC). We first downloaded a gene expression profile dataset GSE29721 (10 HCC and 10 control samples) from Gene Expression Omnibus database (http://www.ncbi.nlm.nih.gov/geo/). Differentially expressed genes (DEGs) were identified by the paired t-test using limma package. Pathway and functional enrichment analyses were performed with DAVID tools. Transcription factors were annotated with TRANSFAC database and tumor associated genes (TAGs) were annotated with TAG and TSGene databases. Protein-protein interaction (PPI) network was conducted using STRING online tool and function module was further identified with BioNet package. Totally, 527 up-regulated DEGs and 587 down-regulated DEGs were identified. GO functional and KEGG pathway enrichment analyses showed that the up-regulated DEGs were mainly related to cell division and cell cycle, while the down-regulated DEGs were largely related to material metabolism, especially secondary metabolism. Proteins encoded by DEGs CDK1, BUB1, CDC20, NCAPG, NDC80, CDCA8, MAD2L1, CCNB1, CCNA2 and BIRC5 were hub genes with high degrees in the PPI network; further module analysis detected a subnetwork consisting of 55 proteins, such as CYP2B6, ACAA1, BHMT and ALDH2. Taken together, aberrant expression of cell cycle related genes (e.g., CDK1, CCNA2, CCNB1, BUB1, MAD2L1 and CDC20) and material metabolism related genes (e.g., CYP2B6, ACAA1, BHMT and ALDH2) may contribute to HCC occurrence.

Understanding the cellular roles of Fyn-related kinase (FRK): implications in cancer biology

The non-receptor tyrosine kinase Fyn-related kinase (FRK) is a member of the BRK family kinases (BFKs) and is distantly related to the Src family kinases (SFKs). FRK was first discovered in 1993, and studies pursued thereafter attributed a potential tumour-suppressive function to the enzyme. In recent years, however, further functional characterization of the tyrosine kinase in diverse cancer types suggests that FRK may potentially play an oncogenic role as well. Specifically, while ectopic expression of FRK suppresses cell proliferation and migration in breast and brain cancers, knockdown or catalytic inhibition of FRK suppresses these cellular processes in pancreatic and liver cancer. Such functional paradox is therefore evidently exhibited in a tissue-specific context. This review sheds light on the recent developments emerged from investigations on FRK which include: (a) a review of the expression pattern of the protein in mammalian cells/tissues, (b) underlying genomic perturbations and (c) a mechanistic function of the enzyme across different cellular environments. Given its functional heterogeneity observed across different cancers, we also discuss the therapeutic significance of FRK.

Altered Pathway Analyzer: A gene expression dataset analysis tool for identification and prioritization of differentially regulated and network rewired pathways

Gene connection rewiring is an essential feature of gene network dynamics. Apart from its normal functional role, it may also lead to dysregulated functional states by disturbing pathway homeostasis. Very few computational tools measure rewiring within gene co-expression and its corresponding regulatory networks in order to identify and prioritize altered pathways which may or may not be differentially regulated. We have developed Altered Pathway Analyzer (APA), a microarray dataset analysis tool for identification and prioritization of altered pathways, including those which are differentially regulated by TFs, by quantifying rewired sub-network topology. Moreover, APA also helps in re-prioritization of APA shortlisted altered pathways enriched with context-specific genes. We performed APA analysis of simulated datasets and p53 status NCI-60 cell line microarray data to demonstrate potential of APA for identification of several case-specific altered pathways. APA analysis reveals several altered pathways not detected by other tools evaluated by us. APA analysis of unrelated prostate cancer datasets identifies sample-specific as well as conserved altered biological processes, mainly associated with lipid metabolism, cellular differentiation and proliferation. APA is designed as a cross platform tool which may be transparently customized to perform pathway analysis in different gene expression datasets. APA is freely available at http://bioinfo.icgeb.res.in/APA.

Driver gene classification reveals a substantial overrepresentation of tumor suppressors among very large chromatin-regulating proteins

Compiling a comprehensive list of cancer driver genes is imperative for oncology diagnostics and drug development. While driver genes are typically discovered by analysis of tumor genomes, infrequently mutated driver genes often evade detection due to limited sample sizes. Here, we address sample size limitations by integrating tumor genomics data with a wide spectrum of gene-specific properties to search for rare drivers, functionally classify them, and detect features characteristic of driver genes. We show that our approach, CAnceR geNe similarity-based Annotator and Finder (CARNAF), enables detection of potentially novel drivers that eluded over a dozen pan-cancer/multi-tumor type studies. In particular, feature analysis reveals a highly concentrated pool of known and putative tumor suppressors among the <1% of genes that encode very large, chromatin-regulating proteins. Thus, our study highlights the need for deeper characterization of very large, epigenetic regulators in the context of cancer causality.

Potential molecular characteristics in situ in response to repetitive UVB irradiation

Background

To identify molecular characteristics in situ in response to repetitive UVB (ultraviolet-B) irradiation.

Methods

Microarray data from the Gene Expression Omnibus were re-analyzed to identify DEGs (differentially expressed genes) between UVB-irradiated and non-irradiated skin biopsies. Enrichment and annotation analyses were performed respectively using DAVID, and TSGene and TAG databases. PPIs (protein-protein interactions) were analyzed using STRING, and miRNAs (microRNAs) and TFs (transcription factors) were predicted separately by miRNA-related databases and ENCODE. Accordingly, the PPI network and regulatory networks were visualized using Cytoscape, and they were merged together to obtain an integrated network for mining densely connected modules.

Results

Altogether, 151 up- and 64 down-regulated genes were identified between UVB-irradiated and non-irradiated skin biopsies, among which down-regulated DNAJB4 and SLIT2 were annotated as tumor-suppressors and up-regulated KIT was annotated as an oncogene. The up-regulated DEGs were significantly enriched in biological processes related to pigmentation (DCT, SOX10, TYRP1, TYR, MLPH, KIT and GPR143), while the down-regulated DEGs were dramatically related to haemopoiesis and the immune system (GPR183, INHBA, PTPRC, PLEK, CD8A and IKZF1). Furthermore, many miRNAs were screened for the DEGs, including miR-206 and miR-496 targeting KIT, miR-184 targeting DCT, and highly significant miR-337-5p, miR-21 and miR-16. Additionally, TFs were identified for the DEGs, among which PAX5 and HNF4A targeted MLPH and GPR143, respectively, while BATF, SPI1 and EP300 jointly target GPR183, PTPRC and PLEK.

Conclusions

The pigmentation and immune system implicated by DEGs, miRNAs and TFs might be important molecular mechanisms in response to UVB irradiation.

Analysis of the molecular mechanism of osteosarcoma using a bioinformatics approach

The aim of this study was to explore the underlying molecular mechanism related to the process and progression of osteosarcoma (OS). The differentially expressed genes (DEGs) were downloaded from the Gene Expression Omnibus database. The pathway and gene ontology (GO) enrichment analysis, as well as transcription factor, tumor-associated gene and tumor suppressor gene analyses were performed to investigate the functions of DEGs. Next, the protein-protein interaction (PPI) network was constructed and module analysis was further assessed by cluster analysis with the overlapping neighborhood expansion (Cluster ONE) cytoscape plug-in. A total of 359 upregulated and 614 downregulated DEGs were identified to be differentially expressed between OS samples and normal controls. Pathways significantly enriched by DEGs included the focal adhesion and chromosome maintenance pathways. Significant GO terms were cell adhesion, cell cycle and nucleic acid metabolic processes. The upregulated PPI network was constructed with 170 nodes and the downregulated PPI network was constructed with 332 nodes. Breast-ovarian cancer gene 1 (BRCA1), melanocyte-stimulating hormone 2 (MSH2), cyclin D1 (CCND1) and integrin α5 (ITGA5) were identified to be hub proteins in PPI. In conclusion, the dysregulated genes played key roles in the progression of OS. Cell adhesion is a significant biological process in OS development, and the genes BRCA1, MSH2, CCND1 and ITGA5 may be potential targets in the therapy of OS.

Microarray data analysis to identify crucial genes regulated by CEBPB in human SNB19 glioma cells

Background

Glioma is one of the most common primary malignancies in the brain or spine. The transcription factor (TF) CCAAT/enhancer binding protein beta (CEBPB) is important for maintaining the tumor initiating capacity and invasion ability. To investigate the regulation mechanism of CEBPB in glioma, microarray data GSE47352 was analyzed.

Methods

GSE47352 was downloaded from Gene Expression Omnibus, including three samples of SNB19 human glioma cells transduced with non-target control small hairpin RNA (shRNA) lentiviral vectors for 72 h (normal glioma cells) and three samples of SNB19 human glioma cells transduced with CEBPB shRNA lentiviral vectors for 72 h (CEBPB-silenced glioma cells). The differentially expressed genes (DEGs) were screened using limma package and then annotated. Afterwards, the Database for Annotation, Visualization, and Integrated Discovery (DAVID) software was applied to perform enrichment analysis for the DEGs. Furthermore, the protein-protein interaction (PPI) network and transcriptional regulatory network were constructed using Cytoscape software.

Results

Total 529 DEGs were identified in the normal glioma cells compared with the CEBPB-silenced glioma cells, including 336 up-regulated and 193 down-regulated genes. The significantly enriched pathways included chemokine signaling pathway (which involved CCL2), focal adhesion (which involved THBS1 and THBS2), TGF-beta signaling pathway (which involved THBS1, THBS2, SMAD5, and SMAD6) and chronic myeloid leukemia (which involved TGFBR2 and CCND1). In the PPI network, CCND1 (degree = 29) and CCL2 (degree = 12) were hub nodes. Additionally, CEBPB and TCF12 might function in glioma through targeting others (CEBPB → TCF12, CEBPB → TGFBR2, and TCF12 → TGFBR2).

Conclusions

CEBPB might act in glioma by regulating CCL2, CCND1, THBS1, THBS2, SMAD5, SMAD6, TGFBR2, and TCF12.

Bioinformatics analysis of the molecular mechanism of diffuse intrinsic pontine glioma

The present study aimed to elucidate key molecular mechanisms in the progression of diffuse intrinsic pontine glioma (DIPG). The gene expression profile GSE50021, which consisted of 35 pediatric DIPG samples and 10 normal brain samples, was downloaded from the Gene Expression Omnibus database. The differentially-expressed genes (DEGs) in the pediatric DIPG samples were identified. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathways of DEGs were enriched and analyzed. The protein-protein interaction (PPI) network of the DEGs was constructed and functional modules of the PPI network were disclosed using ClusterONE. A total of 679 DEGs (454 up- and 225 downregulated) were identified in the pediatric DIPG samples. DEGs were significantly enriched in various GO terms, and KEGG and Reactome pathways. The PPI network of upregulated (153 nodes and 298 connections) and downregulated (71 nodes and 124 connections) DEGs, and two crucial modules, were obtained. Downregulated genes in module 2, such as cholecystokinin (CCK), gastrin (GAST), adenylate cyclase 2 (brain) (ADCY2) and 5-hydroxytryptamine (serotonin) receptor 7 (HTR7), were significantly enriched in the calcium signaling pathway, the neuroactive ligand-receptor interaction pathway and in GO terms, such as the G-protein coupled receptor (GPCR) signaling pathway, while upregulated genes in module 1 were not enriched in any pathways or GO terms. CCK and GAST associated with the GPCR signaling pathway, HTR7 enriched in the neuroactive ligand-receptor interaction, and ADCY2 and HTR7 involved in the calcium signaling pathway may be key mechanisms playing crucial roles in the development and progression of DIPG.

Identification of differentially expressed genes in salivary adenoid cystic carcinoma cells associated with metastasis

INTRODUCTION

Salivary adenoid cystic carcinoma (SACC) is a frequent type of salivary gland cancer which is characterized by slow growth but high incidence of distant metastasis. We aimed to identify therapeutic targets which are associated with metastasis of SACC.

MATERIAL AND METHODS

Total RNA was isolated from a low metastatic SACC cell line (ACC-2) and a highly metastatic SACC cell line (ACC-M), which was screened from ACC-2 by combination of in vivo selection and cloning in vitro. Then the total RNA was subjected to microarray analysis. Differentially expressed genes (DEGs) were screened from ACC-M compared with ACC-2, followed by Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Function annotation for DEGs also was performed. A protein-protein interaction network (PPI) was constructed for DEGs.

RESULTS

A total of 1128 DEGs were identified from ACC-M cells compared with ACC-2 cells. Both up- and down-regulated DEGs were enriched in different functions in biological process (BP), cellular component (CC) and molecular function (MF). Additionally, down-regulated DEGs were mainly enriched in "Apoptosis" and "Cytokine-cytokine receptor interaction" pathways which involved IFN-α1, NTRK1 and TGF-β1. In the PPI network, PIK3CA, PTPN11 and PIK3R1 had a number of nodes greater than 10.

CONCLUSIONS

Transforming growth factor β1 might play a pivotal role during lung metastasis of SACC and be selected as a candidate target for treatment of metastatic SACC. IFNA1, NTRK1 and PIK3CA were also associated with tumor metastasis.

Identification of candidate genes for myeloma-induced osteocyte death based on microarray data

Background

The study was aimed to investigate the molecular mechanisms of osteocyte death in multiple myeloma (MM) patients.

Methods

GSE27372 was downloaded from Gene Expression Omnibus, including three HOB-01 (osteocyte cell line) control samples and three HOB-01 samples co-cultured with JJN3 (human MM cell line). After the differentially expressed genes (DEGs) were identified by Student’s t test method, enrichment analyses were performed for them using DAVID software. Using TRANSFAC, TSGene, and tumor-associated gene (TAG) databases, functional annotation was conducted for the DEGs. Additionally, protein-protein interaction (PPI) network and sub-network analyses were performed using STRING database and Cytoscape software.

Results

Total 393 DEGs were identified, including 22 transcription factors (e.g., KLF4 and IRF8) and 37 TAGs. Enrichment analysis suggested that EGF, S1PR1, and NPY1R were enriched in the function of circulatory system development. EGF (degree = 31) and EGR1 (degree = 19) had high degrees and interactions in the PPI network. In the sub-network, S1PR1, C3AR1, and NPY1R could interact with each other.

Conclusions

These DEGs might participate in the osteocyte apoptosis induced by myeloma cells. These findings might provide a theoretical basis for a better understanding of the osteolysis in MM patients.

Hsa-miR-137, hsa-miR-520e and hsa-miR-590-3p perform crucial roles in Lynch syndrome

The aim of the present study was to identify the differentially expressed microRNAs (DEMs) between Lynch syndrome (LS) and the normal colonic (N-C) control samples, predict the target genes (TGs) and analyze the potential functions of the DEMs and TGs. The miRNA expression dataset GSE30454, which included data of 13 LS and 20 N-C tissue samples, was downloaded from the Gene Expression Omnibus. The classical t-test in Linear Models for Microarray Data package was used for DEM identification. TG prediction was performed using 5 databases. The regulatory network of the DEMs and their TGs was constructed using Cytoscape. Functional and pathway enrichment analysis was performed. The transcription factors (TFs), tumor-associated genes (TAG) and tumor suppressor genes (TSGs) were then identified. Three key DEMs hsa-miR-137, hsa-miR-520e, and hsa-miR-590-3p were identified. Hsa-miR-520e and hsa-miR-137 had 4 common TGs, including SNF related kinase, metal-regulatory transcription factor 1 (MTF1), round spermatid basic protein 1 and YTH N6-methyladenosine RNA binding protein 3; hsa-miR-590-3p and hsa-miR-137 had 14 common TGs, including NCK adaptor protein 1 (NCK1), EPH receptor A7, and stress-associated endoplasmic reticulum protein 1; hsa-miR-590-3p and hsa-miR-520e had 12 common TGs, including Krüppel-like factor (KLF) 13, twinfilin actin binding protein 1, and nuclear factor I B. Through the functional and pathway enrichments analysis, MTF1 was involved in regulation of gene expression and metabolic processes, and sequence-specific DNA binding TF activity. KLF13 was involved in regulation of gene expression and regulation of cellular metabolic processes. NCK1 was enriched in the axon guidance pathway. In addition, the functional and pathway enrichment analysis showed certain TGs, such as hypoxia-inducible factor 1α, AKT serine/threonine kinase 2, and rapamycin-insensitive companion of mammalian target of rapamycin, participated in the mTOR signaling pathway. The 3 key DEMs hsa-miR-137, hsa-miR-520e, and hsa-miR-590-3p may have important roles in the process of LS.

Predicting associations between microRNAs and target genes in breast cancer by bioinformatics analyses

Breast cancer is the leading type of cancer among females. However, the association between microRNAs (miRNAs) and target genes in breast tumorigenesis is poorly studied. The original data set GSE26659 was downloaded from the Gene Expression Omnibus, and then the differentially expressed miRNAs among 77 breast cancer patients and 17 controls were identified using the Limma package in R software. Furthermore, breast cancer-related differentially expressed miRNAs were selected from a human miRNA disease database and their target genes were selected from five miRNA databases. Then, functional analysis was performed for the target genes followed by construction of a miRNA-target gene network. A total of 34 differentially expressed miRNAs were identified, including 13 breast cancer-related miRNAs. Moreover, the target genes of the 13 miRNAs were significantly enriched in regulation of transcription (P=7.43E-09) and pathways related to cancer (P=3.33E-11). Finally, eight upregulated miRNAs (including hsa-miR-425) and five downregulated miRNAs (including hsa-miR-143, hsa-miR-145 and hsa-miR-125b) were identified in the miRNA-target gene network. In conclusion, using bioinformatics approaches, we demonstrate that the changes in regulation of transcription and cancer pathways may play significant roles in the process of breast cancerogenesis. Differentially expressed miRNAs and their target genes may be new targets for breast cancer therapy.

Analysis of the transcriptional regulation of cancer-related genes by aberrant DNA methylation of the cis-regulation sites in the promoter region during hepatocyte carcinogenesis caused by arsenic

Liver is the major organ for arsenic methylation metabolism and may be the potential target of arsenic-induced cancer. In this study, normal human liver cell was treated with arsenic trioxide, and detected using DNA methylation microarray. Some oncogenes, tumor suppressor genes, transcription factors (TF), and tumor-associated genes (TAG) that have aberrant DNA methylation have been identified. However, simple functional studies of genes adjacent to aberrant methylation sites cannot well reflect the regulatory relationship between DNA methylation and gene transcription during the pathogenesis of arsenic-induced liver cancer, whereas a further analysis of the cis-regulatory elements and their trans-acting factors adjacent to DNA methylation can more precisely reflect the relationship between them. MYC and MAX (MYC associated factor X) were found to participating cell cycle through a bioinformatics analysis. Additionally, it was found that the hypomethylation of cis-regulatory sites in the MYC promoter region and the hypermethylation of cis-regulatory sites in the MAX promoter region result in the up-regulation of MYC mRNA expression and the down-regulation of MAX mRNA, which increased the hepatocyte carcinogenesis tendency.

Identification of key genes in glioblastoma-associated stromal cells using bioinformatics analysis

The aim of the present study was to identify key genes and pathways in glioblastoma-associated stromal cells (GASCs) using bioinformatics. The expression profile of microarray GSE24100 was obtained from the Gene Expression Omnibus database, which included the expression profile of 4 GASC samples and 3 control stromal cell samples. Differentially expressed genes (DEGs) were identified using limma software in R language, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis of DEGs were performed using the Database for Annotation, Visualization and Integrated Discovery software. In addition, a protein-protein interaction (PPI) network was constructed. Subsequently, a sub-network was constructed to obtain additional information on genes identified in the PPI network using CFinder software. In total, 502 DEGs were identified in GASCs, including 331 upregulated genes and 171 downregulated genes. Cyclin-dependent kinase 1 (CDK1), cyclin A2, mitotic checkpoint serine/threonine kinase (BUB1), cell division cycle 20 (CDC20), polo-like kinase 1 (PLK1), and transcription factor breast cancer 1, early onset (BRCA1) were identified from the PPI network, and sub-networks revealed these genes as hub genes that were involved in significant pathways, including mitotic, cell cycle and p53 signaling pathways. In conclusion, CDK1, BUB1, CDC20, PLK1 and BRCA1 may be key genes that are involved in significant pathways associated with glioblastoma. This information may lead to the identification of the mechanism of glioblastoma tumorigenesis.

Gene expression profiling analysis of ovarian cancer

As a gynecological oncology, ovarian cancer has high incidence and mortality. To study the mechanisms of ovarian cancer, the present study analyzed the GSE37582 microarray. GSE37582 was downloaded from Gene Expression Omnibus and included data from 74 ovarian cancer cases and 47 healthy controls. The differentially-expressed genes (DEGs) were screened using linear models for microarray data package in R and were further screened for functional annotation. Next, Gene Ontology and pathway enrichment analysis of the DEGs was conducted. The interaction associations of the proteins encoded by the DEGs were searched using the Search Tool for the Retrieval of Interacting Genes, and the protein-protein interaction (PPI) network was visualized by Cytoscape. Moreover, module analysis of the PPI network was performed using the BioNet analysis tool in R. A total of 284 DEGs were screened, consisting of 145 upregulated genes and 139 downregulated genes. In particular, downregulated FBJ murine osteosarcoma viral oncogene homolog (FOS) was an oncogene, while downregulated cyclin-dependent kinase inhibitor 1A (CDKN1A) was a tumor suppressor gene and upregulated cluster of differentiation 44 (CD44) was classed as an 'other' gene. The enriched functions included collagen catabolic process, stress-activated mitogen-activated protein kinases cascade and insulin receptor signaling pathway. Meanwhile, FOS (degree, 15), CD44 (degree, 9), B-cell CLL/lymphoma 2 (BCL2; degree, 7), CDKN1A (degree, 7) and matrix metallopeptidase 3 (MMP3; degree, 6) had higher connectivity degrees in the PPI network for the DEGs. These genes may be involved in ovarian cancer by interacting with other genes in the module of the PPI network (e.g., BCL2-FOS, BCL2-CDKN1A, FOS-CDKN1A, FOS-CD44, MMP3-MMP7 and MMP7-CD44). Overall, BCL2, FOS, CDKN1A, CD44, MMP3 and MMP7 may be correlated with ovarian cancer.

CoReCG: a comprehensive database of genes associated with colon-rectal cancer

Cancer of large intestine is commonly referred as colorectal cancer, which is also the third most frequently prevailing neoplasm across the globe. Though, much of work is being carried out to understand the mechanism of carcinogenesis and advancement of this disease but, fewer studies has been performed to collate the scattered information of alterations in tumorigenic cells like genes, mutations, expression changes, epigenetic alteration or post translation modification, genetic heterogeneity. Earlier findings were mostly focused on understanding etiology of colorectal carcinogenesis but less emphasis were given for the comprehensive review of the existing findings of individual studies which can provide better diagnostics based on the suggested markers in discrete studies.

Colon Rectal Cancer Gene Database (CoReCG), contains 2056 colon-rectal cancer genes information involved in distinct colorectal cancer stages sourced from published literature with an effective knowledge based information retrieval system. Additionally, interactive web interface enriched with various browsing sections, augmented with advance search facility for querying the database is provided for user friendly browsing, online tools for sequence similarity searches and knowledge based schema ensures a researcher friendly information retrieval mechanism.

Colorectal cancer gene database (CoReCG) is expected to be a single point source for identification of colorectal cancer-related genes, thereby helping with the improvement of classification, diagnosis and treatment of human cancers.

Database URL: lms.snu.edu.in/corecg

Subtypes of HPV-Positive Head and Neck Cancers Are Associated with HPV Characteristics, Copy Number Alterations, PIK3CA Mutation, and Pathway Signatures

PURPOSE

There is substantial heterogeneity within human papillomavirus (HPV)-associated head and neck cancer (HNC) tumors that predispose them to different outcomes; however, the molecular heterogeneity in this subgroup is poorly characterized due to various historical reasons.

EXPERIMENTAL DESIGN

We performed unsupervised gene expression clustering on deeply annotated (transcriptome and genome) HPV(+) HNC samples from two cohorts (84 total primary tumors), including 18 HPV(-) HNC samples, to discover subtypes and characterize the differences between subgroups in terms of their HPV characteristics, pathway activity, whole-genome somatic copy number alterations, and mutation frequencies.

RESULTS

We identified two distinct HPV(+) subtypes (namely HPV-KRT and HPV-IMU). HPV-KRT is characterized by elevated expression of genes in keratinocyte differentiation and oxidation-reduction process, whereas HPV-IMU has strong immune response and mesenchymal differentiation. The differences in expression are likely connected to the differences in HPV characteristics and genomic changes. HPV-KRT has more genic viral integration, lower E2/E4/E5 expression levels, and higher ratio of spliced to full-length HPV oncogene E6 than HPV-IMU; the subgroups also show differences in copy number alterations and mutations, in particular the loss of chr16q in HPV-IMU and gain of chr3q and PIK3CA mutation in HPV-KRT.

CONCLUSIONS

Our characterization of two subtypes of HPV(+) HNC tumors provides valuable molecular level information that point to two main carcinogenic paths. Together, these results shed light on stratifications of the HPV(+) HNCs and will help to guide personalized care for HPV(+) HNC patients. Clin Cancer Res; 22(18); 4735-45. ©2016 AACR.

The potential role of PHF6 as an oncogene: a genotranscriptomic/proteomic meta-analysis

Epigenetic complexes control various pathways within the cells. Their abnormalities can be involved in the initiation and the progression of different types of cancer. Nucleosome remodeling and deacetylase (NuRD) is an epigenetic complex that comprises several subunits such as PHF6. Although PHF6 is reported as a tumor suppressor in some of the hematopoietic malignancies, its function is still challenging in other cancers. Our study aimed at investigating the role of PHF6 in different types of cancer. We conducted a meta-analysis of PHF6 in human cancers at genomic, transcriptomic, and proteomic levels. For this purpose, we acquired the data from several databases, and tried to statistically integrate and analyze the data in order to find the potential role of PHF6 in different tumors. The results demonstrated that although PHF6 has been previously known as a tumor suppressor gene, it was remarkably overexpressed in many cancer types such as breast and colorectal cancers. Notably, PHF6 was under-expressed in a few types of cancer, including esophageal tumors. Moreover, the results indicated that although the mutation rate of PHF6 is relatively low, it is mutated in some tumor types.  In addition, our data for 40 epigenetic genes showed that missense and nonsense mutations were associated with overexpression and under-expression, respectively. Our results suggest that PHF6 may function as an oncogenic factor in several types of cancer. We also hypothesize that PHF6 may also play its role in a tissue-specific manner. Our findings suggest further investigations regarding the exact role of PHF6 in tumor types.

Identification of colorectal cancer-restricted microRNAs and their target genes based on high-throughput sequencing data

To identify potential key microRNAs (miRNAs) and their target genes for colorectal cancer (CRC). High-throughput sequencing data of miRNA expression and gene expression (ID: GSE46622) were downloaded from Gene Expression Omnibus, including matched colon tumor, normal colon epithelium, and liver metastasis tissues from eight CRC patients. Paired t-test and NOISeq separately were utilized to identify differentially expressed miRNAs (DE-miRNAs) and genes. Then, target genes with differential expression and opposite expression trends were identified for DE-miRNAs. Combined with tumor suppressor gene, tumor-associated gene, and TRANSFAC databases, CRC-restricted miRNAs were screened out based on miRNA-target pairs. Compared with normal tissues, there were 56 up- and 37 downregulated miRNAs in metastasis tissues, as well as eight up- and 30 downregulated miRNAs in tumor tissues. miRNA-1 was downregulated in tumor and metastasis tissues, while its target oncogenes TWIST1 and GATA4 were upregulated. Besides, miRNA-let-7f-1-3p was downregulated in tumor tissues, which also targeted TWIST1. In addition, miRNA-133b and miRNA-4458 were downregulated in tumor tissues, while their common target gene DUSP9 was upregulated. Conversely, miRNA-450-b-3p was upregulated in metastasis tissues, while its target tumor suppressor gene CEACAM7 showed downregulation. The identified CRC-restricted miRNAs might be implicated in cancer progression via their target genes, suggesting their potential usage in CRC treatment.

Time course analysis based on gene expression profile and identification of target molecules for colorectal cancer

BACKGROUND

The study aimed to investigate the expression changes of genes in colorectal cancer (CRC) and screen the potential molecular targets.

METHODS

The GSE37178 of mRNA expression profile including the CRC samples extracted by surgical resection and the paired normal samples was downloaded from Gene Expression Omnibus database. The genes whose expressions were changed at four different time points were screened and clustered using Mfuzz package. Then DAVID was used to perform the functional and pathway enrichment analysis for genes in different clusters. The protein-protein interaction (PPI) networks were constructed for genes in the clusters according to the STRING database. Furthermore, the related-transcription factors (TFs) and microRNAs (miRNAs) were obtained based on the resources in databases and then were combined with the PPI networks in each cluster to construct the integrated network containing genes, TFs and miRNAs.

RESULTS

As a result, 314 genes were clustered into four groups. Genes in cluster 1 and cluster 2 showed a decreasing trend, while genes in cluster 3 and cluster 4 presented an increasing trend. Then 18 TFs (e.g., TCF4, MEF2C and FOS) and 18 miRNAs (e.g., miR-382, miR-217, miR-1184, miR-326 and miR-330-5p) were identified and three integrated networks for cluster 1, 3, and 4 were constructed.

CONCLUSIONS

The results implied that expression of PITX2, VSNL1, TCF4, MEF2C and FOS are time-related and associated with CRC development, accompanied by several miRNAs including miR-382, miR-217, miR-21, miR-1184, miR-326 and miR-330-5p. All of them might be used as potential diagnostic or therapeutic target molecules for CRC.

The identification of age-associated cancer markers by an integrative analysis of dynamic DNA methylation changes

As one of the most widely studied epigenetic modifications, DNA methylation has an important influence on human traits and cancers. Dynamic variations in DNA methylation have been reported in malignant neoplasm and aging; however, the mechanisms remain poorly understood. By constructing an age-associated and cancer-related weighted network (ACWN) based on the correlation of the methylation level and the protein-protein interaction, we found that DNA methylation changes associated with age were closely related to the occurrence of cancer. Additional analysis of 102 module genes mined from the ACWN revealed discrimination based on two main patterns. One pattern involved methylation levels that increased with aging and were higher in cancer patients compared with normal controls (HH pattern). The other pattern involved methylation levels that decreased with aging and were lower in cancer compared with normal (LL pattern). Upon incorporation with gene expression levels, 25 genes were filtered based on negative regulation by DNA methylation. These genes were regarded as potential cancer risk markers that were influenced by age in the process of carcinogenesis. Our results will facilitate further studies regarding the impact of the epigenetic effects of aging on diseases and will aid in the development of tailored cancer preventive strategies.

Bioinformatics analysis of hepatitis C virus genotype 2a-induced human hepatocellular carcinoma in Huh7 cells

Hepatocellular carcinoma (HCC) is a liver cancer that could be induced by hepatitis C virus genotype 2a Japanese fulminant hepatitis-1 (JFH-1) strain. The aim of this study was to investigate the molecular mechanisms of HCC. The microarray data GSE20948 includes 14 JFH-1- and 14 mock (equal volume of medium [control])-infected Huh7 samples. The data were downloaded from the Gene Expression Omnibus. After data processing, soft cluster analyses were performed to identify co-regulated genes with similar temporal expression patterns. Functional and pathway enrichment analyses, as well as functional annotation analysis, were performed. Subsequently, combined networks of protein–protein interaction network, microRNA regulatory network, and transcriptional regulatory network were constructed. Hub nodes, modules, and five clusters of co-regulated genes were also identified. In total, 173 up and 207 down co-regulated genes were separately identified in JFH-1-infected Huh7 cells compared with those of control cells. Functional enrichment analysis indicated that up co-regulated genes were related to skeletal system morphogenesis and neuron differentiation and down co-regulated genes were related to steroid/cholesterol/sterol metabolisms. Hub genes (such as IRF1, GBP1, ICAM1, Foxa1, DHCR7, HMGCS2, and MSMO1) were identified. Transcription factors IRF1 and Foxa1 were the targets of miR-130a, miR-17-5p, and miR-20a. PPARGC1A was targeted by miR-29 family, and MSMO1 was the target of miR-23 family. Hub nodes (such as IRF1, GBP1, ICAM1, Foxa1, DHCR7, HMGCS2, and MSMO1) and microRNAs might be used as candidate biomarkers of JFH-1-infected HCC.

Role of NSC319726 in ovarian cancer based on the bioinformatics analyses

Aim

This study aimed to explore the molecular mechanisms of NSC319726 in ovarian cancer by bioinformatics analyses.

Methods

Gene expression profile GSE35972 was downloaded from the Gene Expression Omnibus database. The data set contains six samples, including three samples of TOV112D cells untreated and three samples of TOV112D cells treated with NSC319726. The differentially expressed genes (DEGs) between untreated and treated samples were analyzed using the limma package. Kyoto Encyclopedia of Genes and Genomes pathway analysis was performed using the signaling pathway impact analysis package, followed by the functional annotation of DEGs and protein–protein interaction network construction. Finally, the subnetwork was identified, and Gene Ontology functional enrichment analysis was performed on the DEGs enriched in the subnetwork.

Results

A total of 120 upregulated and 126 downregulated DEGs were identified. Six Kyoto Encyclopedia of Genes and Genomes pathways were significantly perturbed by DEGs, and the pathway of oocyte meiosis was identified as the most perturbed one. Oocyte meiosis was enriched by eight downregulated DEGs, such as ribosomal protein S6 kinase, 90 kDa, and polypeptide 6 (RPS6KA6). After functional annotation, eight transcription factors were upregulated (such as B-cell CLL/lymphoma 6 [BCL6]), and three transcription factors were downregulated. Seven tumor suppressor genes, such as forkhead box O3 (FOXO3), were upregulated. Additionally, in the protein–protein interaction network and subnetwork, cyclin B1 (CCNB1) and cell division cycle 20 (CDC20) were hub genes, which were also involved in the functions related to mitotic cell cycle.

Conclusion

NSC319726 may play an efficient role against ovarian cancer via targeting genes, such as RPS6KA6, BCL6, FOXO3, CCNB1, and CDC20, which are involved in oocyte meiosis pathway.

Identification of genes and signaling pathways associated with squamous cell carcinoma by bioinformatics analysis

The present study aimed to investigate the genes and signaling pathways associated with squamous cell carcinoma (SCC) by bioinformatics analysis. For this purpose, the GSE2503 was downloaded from the Gene Expression Omnibus database, and the differentially expressed genes (DEGs) between 6 normal skin and 5 SCC samples were analyzed using the Linear Models for Microarray Data package. Gene Ontology (GO) and pathway enrichment analysis of DEGs were performed, followed by functional annotation and construction of a protein-protein interaction (PPI) network. Subnetwork modules were subsequently identified and analyzed. A total of 181 DEGs, including 95 upregulated and 86 downregulated DEGs, were identified, in addition to 20 GO biological processes terms enriched by upregulated DEGs and 14 enriched by downregulated DEGs. The upregulated DEGs were enriched in 18 pathways, and the downregulated DEGs were enriched in 7 pathways. Following functional annotation, three upregulated transcription factors (TFs), including hypoxia inducible factor 1, alpha subunit (HIF1A), and six downregulated TFs were identified. In the PPI network and subnetwork, matrix metallopeptidase 1 (MMP1), also known as interstitial collagenase, and interleukin 8 (IL8) were the hub genes with the highest degree of connectivity (degree =8). Integrin alpha (ITGA)6 and 2 were enriched in several pathways, including focal adhesion and extracellular matrix-receptor interaction. DEGs of SCC were primarily enriched in pathways associated with cancer and cell adhesion. Therefore, DEGs such as IL8, MMP1, HIF1A, ITGA6 and ITGA2 may be potential targets for the diagnosis and treatment of SCC.

Identification of genes and pathways associated with pancreatic ductal adenocarcinoma by bioinformatics analyses

This study aimed to explore the underlying genes and pathways associated with pancreatic ductal adenocarcinoma (PDAC) by bioinformatics analyses. Gene expression profile GSE43795 was downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) between six PDAC and five non-neoplastic pancreatic tissue samples were analyzed using the limma package. Gene ontology (GO) and pathway enrichment analyses of DEGs were performed, followed by functional annotation and protein-protein interaction (PPI) network construction. Finally, the sub-network was identified and pathway enrichment analysis was performed on the contained DEGs. A total of 374 downregulated and 559 upregulated DEGs were identified. The downregulated DEGs were enriched in GO terms associated with digestion and transport and pathways related to metabolism, while the upregulated DEGs were enriched in GO terms associated with the cell cycle and mitosis and pathways associated with the occurrence of cancer including the cell cycle pathway. Following functional annotation, the oncogene pituitary tumor-transforming 1 (PTTG1) was upregulated. In the PPI network and sub-network, cell division cycle 20 (CDC20) and BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) were hub genes with high connectivity degrees. Additionally, DEGs in the sub-network including cyclin B1 (CCNB1) were mainly enriched in the cell cycle and p53 signaling pathways. In conclusion, the cell cycle and p53 signaling pathways may play significant roles in PDAC, and DEGs including CDC20, BUB1B, CCNB1 and PTTG1 may be potential targets for PDAC diagnosis and treatment.

Identification of genes associated with melanoma metastasis

The aims of the study were to identify the differentially expressed genes (DEGs) between primary melanomas and metastasis melanomas (MMs), and to investigate the mechanisms of MMs. The microarray data GSE8401 including 31 primary melanomas and 52 MMs were downloaded from Gene Expression Omnibus. DEGs were identified using the Linear Models for Microarray Data package. The functional and pathway enrichment analyses were performed for DEGs. Identification of transcription factors, tumor-associated genes (TAGs), and tumor suppressor genes (TSGs) were performed with the TRANSFAC, TAG, and TSGene databases, respectively. A protein-protein interaction network was constructed using Search Tool for the Retrieval of Interacting Genes. The modules construction and analysis was performed using Molecular Complex Detection and Gene Cluster with Literature Profiles, respectively. In total, 1004 upregulated and 1008 downregulated DEGs were identified. The upregulated DEGs, such as CDK1, BRCA1, MAD2L1, and PCNA, were significantly enriched in cell cycles, DNA replication, and mismatch repair. The downregulated DEGs, such as COLIAL, COL4A5, COL18A1, and LAMC2, were enriched in cell adhesion and extracellular matrix-receptor interaction. BRCA1 was identified as a transcription factor and TSG, and COL18A1 and LAMC2 were identified as a TSG and TAG, respectively. The upregulated DEGs had higher degrees in the protein-protein interaction network and module, such as PCNA, CDK1, and MAD2L1, and the heat map showed they were clustered in the functions of cell cycle and division. These results may demonstrate the potential roles of DEGs such as CDK1, BRCA1, COL18A1, and LAMC2 in the mechanism of MM.

Gene Network Rewiring to Study Melanoma Stage Progression and Elements Essential for Driving Melanoma

Metastatic melanoma patients have a poor prognosis, mainly attributable to the underlying heterogeneity in melanoma driver genes and altered gene expression profiles. These characteristics of melanoma also make the development of drugs and identification of novel drug targets for metastatic melanoma a daunting task. Systems biology offers an alternative approach to re-explore the genes or gene sets that display dysregulated behaviour without being differentially expressed. In this study, we have performed systems biology studies to enhance our knowledge about the conserved property of disease genes or gene sets among mutually exclusive datasets representing melanoma progression. We meta-analysed 642 microarray samples to generate melanoma reconstructed networks representing four different stages of melanoma progression to extract genes with altered molecular circuitry wiring as compared to a normal cellular state. Intriguingly, a majority of the melanoma network-rewired genes are not differentially expressed and the disease genes involved in melanoma progression consistently modulate its activity by rewiring network connections. We found that the shortlisted disease genes in the study show strong and abnormal network connectivity, which enhances with the disease progression. Moreover, the deviated network properties of the disease gene sets allow ranking/prioritization of different enriched, dysregulated and conserved pathway terms in metastatic melanoma, in agreement with previous findings. Our analysis also reveals presence of distinct network hubs in different stages of metastasizing tumor for the same set of pathways in the statistically conserved gene sets. The study results are also presented as a freely available database at http://bioinfo.icgeb.res.in/m3db/. The web-based database resource consists of results from the analysis presented here, integrated with cytoscape web and user-friendly tools for visualization, retrieval and further analysis.

Gene expression profile analysis of ventilator-associated pneumonia

Based on the gene expression profile of patients with ventilator-associated pneumonia (VAP) and patients not affected by the disease, the present study aimed to enhance the current understanding of VAP development using bioinformatics methods. The expression profile GSE30385 was downloaded from the Gene Expression Omnibus database. The Linear Models for Microarray Data package in R language was used to screen and identify differentially expressed genes (DEGs), which were grouped as up- and down-regulated genes. The up- and downregulated genes were functionally enriched using the Database for Annotation, Visualization and Integrated Discovery system and then annotated according to TRANSFAC, Tumor Suppressor Gene and Tumor Associated Gene databases. Subsequently, the protein-protein interaction (PPI) network was constructed, followed by module analysis using CFinder software. A total of 69 DEGs, including 33 up- and 36 downregulated genes were screened out in patients with VAP. Upregulated genes were mainly enriched in functions and pathways associated with the immune response (including the genes ELANE and LTF) and the mitogen-activated protein kinase (MAPK) signaling pathway (including MAPK14). The PPI network comprised 64 PPI pairs and 44 nodes. The top two modules were enriched in different pathways, including the MAPK signaling pathway. Genes including ELANE, LTF and MAPK14 may have important roles in the development of VAP via altering the immune response and the MAPK signaling pathway.

Bioinformatics analyses of differentially expressed genes associated with bisphosphonate-related osteonecrosis of the jaw in patients with multiple myeloma

PURPOSE

This study aimed to explore the molecular mechanisms associated with bisphosphonate (BP)-related osteonecrosis of the jaw (ONJ) in patients with multiple myeloma (MM).

METHODS

The gene expression profile GSE7116 was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) from eleven patients with ONJ resulting from MM treated with BPs (ONJBPs) and ten MM patients without ONJ treated with BPs (MMBPs) were analyzed. Gene ontology (GO) and pathway enrichment analyses of DEGs were performed, followed by functional annotation and protein-protein interaction network construction. Finally, sub-network modules were constructed and analyzed.

RESULTS

A total of 166 up- and 473 down-regulated DEGs were identified. The up-regulated DEGs were enriched in pathways related to cancer, and the down-regulated DEGs were enriched in pathways related to the immune system. Moreover, the GO terms enriched by the up-regulated DEGs were associated with misfolded proteins, and the down-regulated DEGs were associated with immune responses. After functional annotation, 16 transcription factors were identified, including X-box binding protein 1 (XBP1). In protein-protein interaction network analysis, tumor necrosis factor (TNF) and interleukin 1, beta (IL1B) had higher connectivity degrees. Among the constructed sub-network modules, module 1 was the best one, and DEAD (Asp-Glu-Ala-Asp) box helicase 5 (DDX5) was a hub gene. The DEGs in module 1 were mainly enriched in GO terms related to RNA splicing.

CONCLUSION

DEGs of ONJ were mainly enriched in pathways related to the immune system and RNA splicing. DEGs such as TNF, ILB1, DDX5, and XBP1 may be the potential targets of ONJ treatment.

How do oncoprotein mutations rewire protein-protein interaction networks?

The acquisition of mutations that activate oncogenes or inactivate tumor suppressors is a primary feature of most cancers. Mutations that directly alter protein sequence and structure drive the development of tumors through aberrant expression and modification of proteins, in many cases directly impacting components of signal transduction pathways and cellular architecture. Cancer-associated mutations may have direct or indirect effects on proteins and their interactions and while the effects of mutations on signaling pathways have been widely studied, how mutations alter underlying protein-protein interaction networks is much less well understood. Systematic mapping of oncoprotein protein interactions using proteomics techniques as well as computational network analyses is revealing how oncoprotein mutations perturb protein-protein interaction networks and drive the cancer phenotype.

Effects of β-catenin on differentially expressed genes in multiple myeloma

This study aimed to identify the differentially expressed genes after silencing of β-catenin in multiple myeloma transduced with β-catenin shRNA. The DNA microarray dataset GSE17385 was downloaded from Gene Expression Omnibus, including 3 samples of MM1.S (human multiple myeloma cell lines) cells transduced with control shRNA and 3 samples of MM1.S cells transduced with β-catenin shRNA. Then the differentially expressed genes (DEGs) were screened by using Limma. Their underlying functions were analyzed by employing Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. Moreover, DEGs annotation was conducted based on the databases of tumor associated genes, tumor suppressed genes and the transcriptional regulation from patterns to profiles. Furthermore, the protein-protein interaction (PPI) relationship was obtained from STRING and the protein-protein interaction network and the functional modules were visualized by Cytoscape. Then, the pathway enrichment for the DEGs in the functional module was performed. A total of 301 DEGs, including 124 up-regulated and 117 down-regulated DEGs, were screened. Functional enrichment showed that CCNB1 and CDK1 were significantly related to the function of cell proliferation. FOS and JUN were related to innate immune response-activating signal transduction. Pathway enrichment analysis indicated that CCNB1 and CDK1 were most significantly enriched in the pathway of cell cycle. Besides, FOS and JUN were significantly enriched in the Toll-like receptor signaling pathway. FOXM1 was identified as a transcription factor. Moreover, there existed interactions among CCNB1, FOXM1 and CDK1 in PPI network. The expression of FOS, JUN, CCNB1, FOXM1 and CDK1 may be affected by β-catenin in multiple myeloma.

Identification of the key genes connected with plasma cells of multiple myeloma using expression profiles

Objective

To uncover the potential regulatory mechanisms of the relevant genes that contribute to the prognosis and prevention of multiple myeloma (MM).

Methods

Microarray data (GSE13591) were downloaded, including five plasma cell samples from normal donors and 133 plasma cell samples from MM patients. Differentially expressed genes (DEGs) were identified by Student’s t-test. Functional enrichment analysis was performed for DEGs using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Transcription factors and tumor-associated genes were also explored by mapping genes in the TRANSFAC, the tumor suppressor gene (TSGene), and tumor-associated gene (TAG) databases. A protein–protein interaction (PPI) network and PPI subnetworks were constructed by Cytoscape software using the Search Tool for the Retrieval of Interacting Genes (STRING) database.

Results

A total of 63 DEGs (42 downregulated, 21 upregulated) were identified. Functional enrichment analysis showed that HLA-DRB1 and VCAM1 might be involved in the positive regulation of immune system processes, and HLA-DRB1 might be related to the intestinal immune network for IgA production pathway. The genes CEBPD, JUND, and ATF3 were identified as transcription factors. The top ten nodal genes in the PPI network were revealed including HLA-DRB1, VCAM1, and TFRC. In addition, genes in the PPI subnetwork, such as HLA-DRB1 and VCAM1, were enriched in the cell adhesion molecules pathway, whereas CD4 and TFRC were both enriched in the hematopoietic cell pathway.

Conclusion

Several crucial genes correlated to MM were identified, including CD4, HLA-DRB1, TFRC, and VCAM1, which might exert their roles in MM progression via immune-mediated pathways. There might be certain regulatory correlations between HLA-DRB1, CD4, and TFRC.

Molecular mechanisms for alcoholic hepatitis based on analysis of gene expression profile

BACKGROUND

Alcoholic hepatitis (AH) is an acute manifestation of alcoholic liver disease with high mortality rates.

OBJECTIVES

Our aim was to study the molecular mechanisms of AH.

MATERIALS AND METHODS

The differentially expressed genes (DEGs) in liver between AH and control cases were identified by analyzing the GSE28619 microarray data using t-test. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) enrichment analyses were performed using DAVID online tool. The protein-protein interaction (PPI) network was constructed using Search Tool for the Retrieval of Interacting Genes (STRING) and the subnetwork was identified by BioNet. Both PPI network and subnetwork were visualized using the Cytoscape software.

RESULTS

Total 908 DEGs (551 up- and 357 down-regulated DEGs) were obtained. The up-regulated DEGs were significantly enriched in 15 pathways and 112 GO biological processes. The down-regulated DEGs were significantly enriched in 22 pathways and 84 GO biological processes. The PPI network with 608 nodes and 2878 interactions was constructed and the subnetwork with 53 nodes and 131 interactions was also identified. The hub DEGs (TSPO, PPIB, NME1 and NME2) were extracted in this subnetwork.

CONCLUSIONS

TSPO might contribute to the liver damage and AH progression induced by mitochondrial dysfunction through oxidative stress of liver. TSPO interacted with PPIB might play important roles in liver damage in AH. The interaction between NME1 and NME2 might contribute to the transformation from AH to hepatocellular carcinoma.

Identification of the Interaction Network of Hub Genes for Melanoma Treated with Vemurafenib Based on Microarray Data

Aims and background

The objective of this study was to identify possible biomarkers and to explore the mechanisms of suppression of vemurafenib on melanoma progression.

Methods

GSE42872 affymetrix microarray data were downloaded from the Gene Expression Omnibus database for further analysis. Differentially expressed genes (DEGs) between vehicle-treated samples and vemurafenibtreated samples were identified. Gene ontology and pathway enrichment analysis of DEGs were performed, followed by protein-protein interaction (PPI) network construction. Furthermore, the functional modules of the PPI network were screened using BioNet analysis tool. Finally, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed for DEGs in the module.

Results

In total, 794 upregulated transcripts corresponding to 214 genes and 977 downregulated transcripts corresponding to 325 genes were screened. The downregulated DEGs were significantly enriched in pathways such as cell cycle, DNA replication, and p53 signaling pathway. Upregulated DEGs were significantly enriched in phosphatidylinositol signaling system and inositol phosphate metabolism. Significantly enriched functions of downregulated DEGs were mitotic cell cycle, nuclear division, DNA metabolic process, cell cycle, and mitosis. Upregulated DEGs were mainly enriched in single multicellular organism process and multicellular organismal process. Moreover, cell division cycle 6, checkpoint kinase 1 (CHEK1), E2F transcription factor 1 (E2F1), epidermal growth factor receptor (EGFR), and phosphoinositide-3-kinase, regulatory subunit 1-α (PIK3R1) of the module were remarkably enriched in pathways such as cell cycle, apoptosis, focal adhesion, and DNA replication.

Conclusions

Cell division cycle 6, CHEK1, E2F1, EGFR, and PIK3R1 of the module and their relative pathways, cell cycle, and focal adhesion might play important roles of suppression of vemurafenib on melanoma progression.

Identification of stage-specific biomarkers in lung adenocarcinoma based on RNA-seq data

Tumorigenesis is a multistep process that attributes to the sequential accumulation of abnormal expression in key oncogenes or tumor suppressors. We aimed to identify stage-specific biomarkers to distinguish lung adenocarcinoma (LAC) stages in cancer progression. RNA-sequencing data of LAC and matched adjacent non-cancer tissues were downloaded from the Cancer Genome Atlas, including 29 pairs of samples from LAC at stage I, 14 from LAC at stage II, 13 from LAC at stage III, and 1 from LAC at stage IV. Differentially expressed genes (DEGs) were analyzed for each case at different stages of LAC. DEGs were further annotated based on transcription factor data information, tumor-associated gene database, and protein-protein interaction database. Functional annotation was performed for genes in PPI network by DAVID online tool. Our analysis identified 11 high-frequency DEGs in the stage I, 29 in the stage II, and 90 in the stage III of LAC. Among them, eight genes were significantly correlated with LAC stages and identified as biomarkers in LAC progression. ANGPTL5, C7orf16, EDN3, LOC150622, HOXA11AS, IL1F5, and USH1G significantly distinguished stage III from stages I and II. GJB6 was significantly enriched in the gap junction trafficking pathway, while C7orf16 and EDN3 were enriched in Wnt signaling pathway, cell cycle, and G protein-coupled receptor (GPCR) signaling. Up-regulated GJB6 especially in LAC stage II and down-regulated C7orf16 and EDN3 specifically in stage III were identified as biomarkers for distinguishing cancer stage in tumor progression through dysregulating gap junction, Wnt signaling, and GPCR signaling pathways.

Prediction of cancer proteins by integrating protein interaction, domain frequency, and domain interaction data using machine learning algorithms

Many proteins are known to be associated with cancer diseases. It is quite often that their precise functional role in disease pathogenesis remains unclear. A strategy to gain a better understanding of the function of these proteins is to make use of a combination of different aspects of proteomics data types. In this study, we extended Aragues's method by employing the protein-protein interaction (PPI) data, domain-domain interaction (DDI) data, weighted domain frequency score (DFS), and cancer linker degree (CLD) data to predict cancer proteins. Performances were benchmarked based on three kinds of experiments as follows: (I) using individual algorithm, (II) combining algorithms, and (III) combining the same classification types of algorithms. When compared with Aragues's method, our proposed methods, that is, machine learning algorithm and voting with the majority, are significantly superior in all seven performance measures. We demonstrated the accuracy of the proposed method on two independent datasets. The best algorithm can achieve a hit ratio of 89.4% and 72.8% for lung cancer dataset and lung cancer microarray study, respectively. It is anticipated that the current research could help understand disease mechanisms and diagnosis.

Crucial microRNAs and genes of human primary breast cancer explored by microRNA-mRNA integrated analysis

This study aimed to screen potential microRNAs (miRNAs) and genes related to human primary breast cancer. The gene and miRNA expression profile data of GSE19783 was obtained from Gene Expression Omnibus. The matched messenger RNA (mRNA) and miRNA expression profiles of 100 human primary breast cancer samples were chosen for further analysis. The miRNA-gene regulatory modules were screened via iterative multiplicative updating algorithm. The potential functions of genes in modules were predicted by functional and pathway enrichment analysis; meanwhile, the potential functions of miRNAs were predicted by functional enrichment analysis. Furthermore, miRNA-miRNA functional synergistic network and miRNA-miRNA co-regulatory network were constructed. Totally, 16 miRNA-gene modules were screened, containing 222 miRNA-gene interactions. The genes in these modules were mainly related to breast cancer. Genes in module 6 (e.g., SFRP1) were enriched in cell junction assembly; genes in module 8 and 12 (e.g., ESR1 and ERBB4) were significantly implicated in mammary gland alveolus and lobule development. Meanwhile, genes in module 12 (e.g., ERBB4) were enriched in the pathway of endocytosis. Besides, several miRNAs (e.g., miR-375) were enriched in inflammatory cell apoptotic process; some other miRNAs (e.g., miR-139-5p and miR-9) were enriched in response to vitamin D. Additionally, miR-139-5p with several other miRNAs (e.g., miR-9) co-regulated SFRP1; miR-375, miR-592, and miR-135a co-regulated ESR1 and ERBB4. Some miRNAs (e.g., miR-139-5p and miR-9) and their target gene SFRP1, as well as several other miRNAs (e.g., miR-375, miR-592, and miR-135a) and their target genes (e.g., ESR1 and ERBB4), might be crucial in the pathogenesis of primary breast cancer.

Identifying microRNAs involved in cancer pathway using support vector machines

Since Ambros' discovery of small non-protein coding RNAs in the early 1990s, the past two decades have seen an upsurge in the number of reports of predicted microRNAs (miR), which have been implicated in various functions. The correlation of miRs with cancer has spurred the usage of this class of non-coding RNAs in various cancer therapies, although most of them are at trial stages. However, the experimental identification of a miR to be associated with cancer is still an elaborate, time-consuming process. To aid this process of miR association, we undertook an in-silico study involving the identification of global signatures in experimentally validated microRNAs associated with cancer. Subsequently, a support vector machine based two-step binary classifier system has been trained and modeled from the features extracted from the above study. A total of 60 distinguishing features were selected and ranked to form the feature set for classification - 26 of these extracted from the miR sequence itself, and the remainder from the thermodynamics of folding and the hybridized miRNA-mRNA structure. The two step classifier model - miRSEQ and miRINT had reasonably good performance measures with fairly high values of Matthew's correlation coefficient (MCC) values ranging from 0.72 to 0.82 (availability: https://sites.google.com/site/sumitslab/tools).

Transcription factor and microRNA-regulated network motifs for cancer and signal transduction networks

Abstract

Conclusions

To validate the role of network motifs in cancer formation, several examples are presented which demonstrated the effectiveness of the present approach. A web-based platform has been set up which can be accessed at: http://ppi.bioinfo.asia.edu.tw/pathway/. It is very likely that our results can supply very specific CMS missing information for certain cancer types, it is an indispensable tool for cancer biology research.

The influence of mtDNA deletion on lung cancer cells under the conditions of hypoxia and irradiation

PURPOSE

This study was to evaluate the influence of mtDNA deletion on the lung cancer cells under the conditions of hypoxia or irradiation.

METHOD

The treatment conditions of lung cancer cell lines with (A549) and without mtDNA (ρ0A549: obtained by inducing from A549) included 2 h of hypoxia and 4 Gy irradiation (group 1: without treatment; group 2: 2 h of hypoxia; group 3: 4 Gy irradiation; group 4: 2 h of hypoxia plus 4 Gy irradiation). The Human OneArray™ microarray was used to hybridize with the Cy5-labeled aRNA in microarray sample preparation. Differentially expressed genes (DEGs) between the lung cancer cells with and without mtDNA were identified using NOISeq package in R. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using the online tool of DAVID.

RESULT

In the KEGG pathway analysis of down-regulated DEGs, nineteen pathways were simultaneously enriched in the four groups, which were mainly metabolism- and biosynthesis-related pathways. Nine lung cancer-related pathways were enriched in group 4, and more cancer-associated DEGs, such as MYC, MAX, and E2F1 were found in group 4 than in the other groups.

CONCLUSION

The mtDNA deletion could inhibit the biosynthesis and metabolism of lung cancer cells and promote the effect of hypoxia and radiation on lung cancer cells. MYC might be the key gene of the cooperation of hypoxia and radiation and MYC, MAX, and E2F1 might play roles in hypoxia- and radiation-induced cell death in lung cancer cells without mtDNA.

Modulation of age- and cancer-associated DNA methylation change in the healthy colon by aspirin and lifestyle

BACKGROUND

Aberrant DNA methylation in gene promoters is associated with aging and cancer, but the circumstances determining methylation change are unknown. We investigated the impact of lifestyle modulators of colorectal cancer (CRC) risk on the stability of gene promoter methylation in the colonic mucosa.

METHODS

We measured genome-wide promoter CpG methylation in normal colon biopsies (n = 1092) from a female screening cohort, investigated the interaction of lifestyle factors with age-dependent increase in methylation with log-linear multivariable regression, and related their modifying effect to hypermethylation in CRC. All statistical tests were two-sided.

RESULTS

Of 20025 promoter-associated CpGs analyzed, 1713 showed statistically significant age-dependent methylation gains. Fewer CpGs acquired methylation in users of aspirin (≥ 2 years) and hormonal replacement therapy (HRT age ≥ 50 years) compared with nonusers (43 vs 1355; 1 vs1377, respectively), whereas more CpGs were affected in smokers (≥ 20 years) and individuals with a body mass index (BMI) of 25 kg/m(2) and greater compared with control groups (180 vs 39; 554 vs 144, respectively). Fifty percent of the CpGs showing age-dependent methylation were found hypermethylated in CRC (odds ratio [OR] = 20; 95% confidence interval [CI] = 18 to 23; P < 2 × 10(-16)). These loci gained methylation with a higher median rate compared with age-only methylated sites (P = 2 × 10(-76)) and were enriched for polycomb regions (OR = 3.67). Importantly, aspirin (P < .001) and HRT use (P < .001) reduced the methylation rate at these cancer-related genes, whereas smoking (P < .001) and high BMI (P = .004) increased it.

CONCLUSIONS

Lifestyle, including aspirin use, modulates age-associated DNA methylation change in the colonic epithelium and thereby impacts the evolution of cancer methylomes.

FRK inhibits migration and invasion of human glioma cells by promoting N-cadherin/β-catenin complex formation

Fyn-related kinase (FRK), a member of Src-related tyrosine kinases, is recently reported to function as a potent tumor suppressor in several cancer types. Our previous study has also shown that FRK over-expression inhibited the migration and invasion of glioma cells. However, the mechanism of FRK effect on glioma cell migration and invasion, a feature of human malignant gliomas, is still not clear. In this study, we found that FRK over-expression increased the protein level of N-cadherin, but not E-cadherin. Meanwhile, FRK over-expression promoted β-catenin translocation to the plasma membrane, where it formed complex with N-cadherin, while decreased β-catenin level in the nuclear fraction. In addition, down-regulation of N-cadherin by siRNA promoted the migration and invasion of glioma U251 and U87 cells and abolished the inhibitory effect of FRK on glioma cell migration and invasion. In summary, these results indicate that FRK inhibits migration and invasion of human glioma cells by promoting N-cadherin/β-catenin complex formation.

On the retention of gene duplicates prone to dominant deleterious mutations

Recent studies have shown that gene families from different functional categories have been preferentially expanded either by small scale duplication (SSD) or by whole-genome duplication (WGD). In particular, gene families prone to dominant deleterious mutations and implicated in cancers and other genetic diseases in human have been greatly expanded through two rounds of WGD dating back from early vertebrates. Here, we strengthen this intriguing observation, showing that human oncogenes involved in different primary tumors have retained many WGD duplicates compared to other human genes. In order to rationalize this evolutionary outcome, we propose a consistent population genetics model to analyze the retention of SSD and WGD duplicates taking into account their propensity to acquire dominant deleterious mutations. We solve a deterministic haploid model including initial duplicated loci, their retention through sub-functionalization or their neutral loss-of-function or deleterious gain-of-function at one locus. Extensions to diploid genotypes are presented and population size effects are analyzed using stochastic simulations. The only difference between the SSD and WGD scenarios is the initial number of individuals with duplicated loci. While SSD duplicates need to spread through the entire population from a single individual to reach fixation, WGD duplicates are de facto fixed in the small initial post-WGD population arising through the ploidy incompatibility between post-WGD individuals and the rest of the pre-WGD population. WGD duplicates prone to dominant deleterious mutations are then shown to be indirectly selected through purifying selection in post-WGD species, whereas SSD duplicates typically require positive selection. These results highlight the long-term evolution mechanisms behind the surprising accumulation of WGD duplicates prone to dominant deleterious mutations and are shown to be consistent with cancer genome data on the prevalence of human oncogenes with WGD duplicates.

Non-receptor protein tyrosine kinases signaling pathways in normal and cancer cells

Protein tyrosine kinases (PTKs) are enzymes that transfer phosphate groups to tyrosine residues on protein substrates. Phosphorylation of proteins causes changes in their function and/or enzymatic activity resulting in specific biological responses. There are two classes of PTKs: the transmembrane receptor PTKs and the cytoplasmic non-receptor PTKs (NRTKs). NRTKs are involved in transduction of signals originating from extracellular clues, which often interact with transmembrane receptors. Thus, they are important components of signaling pathways which regulate fundamental cellular functions such as cell differentiation, apoptosis, survival, and proliferation. The activity of NRTKs is tightly regulated, and de-regulation and/or overexpression of NRTKs has been implicated in malignant transformation and carcinogenesis. Research on NRTKs has shed light on the mechanisms of a number of cellular processes including those involved in carcinogenesis. Not surprisingly, several tyrosine kinase inhibitors are in use as treatment for a number of malignancies, and more are under investigation. This review deals with the structure, function, and signaling pathways of nine main families of NRTKs in normal and cancer cells.