• Cellular retinoic acid binding protein 2
• Expression profiling data
• Hub genes
• Lung adenocarcinoma
• Matrix metallopeptidase 12
• Topoisomerase II alpha

• Drosophila
• loss of heterozygosity (LOH)
• mitotic recombination
• pyridoxal 5′ phosphate (PLP)
• vitamin B6
• warts

The aim of this study was to identify potential key genes, proteins, and associated interaction networks for the development of lung cancer in nonsmoking women through a bioinformatics approach.

We used the GSE19804 dataset, which includes 60 lung cancer and corresponding paracancerous tissue samples from nonsmoking women, to perform the work. The GSE19804 microarray was downloaded from the GEO database and differentially expressed genes were identified using the limma package analysis in R software, with the screening criteria of p value < 0.01 and ∣log₂ fold change (FC) | >2.

A total of 169 DEGs including 130 upregulated genes and 39 downregulated were selected. Gene Ontology and KEGG pathway analysis were performed using the DAVID website, and protein-protein interaction (PPI) networks were constructed and the hub gene module was screened through STING and Cytoscape.

We obtained five key genes such as GREM1, MMP11, SPP1, FOSB, and IL33 which were strongly associated with lung cancer in nonsmoking women, which improved understanding and could serve as new therapeutic targets, but their functionality needs further experimental verification.

• Fluorescent Dyes/metabolism
• Gene Expression Regulation
• Nucleic Acids/genetics
• Oligonucleotide Array Sequence Analysis/methods
• Polymorphism, Single Nucleotide/genetics
• Proteins/metabolism
• RNA, Messenger/genetics
• RNA, Messenger/metabolism
• Subcellular Fractions/metabolism
• Transcriptome/genetics

• Automated, miniaturized instruments
• Gene Expression Measurement Module (GEMM)
• Gene expression
• High-throughput biological instruments
• Omics
• astrobiology
• biotechnology
• space biology

• Automation
• Bacteria/genetics
• Bacteria/isolation & purification
• Exobiology/instrumentation
• Exobiology/methods
• Gene Expression Profiling/instrumentation
• Gene Expression Profiling/methods
• High-Throughput Nucleotide Sequencing
• Oligonucleotide Array Sequence Analysis
• Sensitivity and Specificity
• Synechococcus/genetics
• Synechococcus/isolation & purification

• Differentially expressed genes
• Drug targets
• Microarray
• R
• Type 2 diabetes mellitus

• Computational Biology/methods
• Databases, Genetic
• Diabetes Mellitus, Type 2/genetics
• Drug Evaluation, Preclinical/methods
• Gene Expression Profiling
• Gene Expression Regulation
• Gene Regulatory Networks/drug effects
• Humans
• Oligonucleotide Array Sequence Analysis
• Protein Interaction Mapping/methods
• Protein Interaction Maps

• Biomarkers
• Cancer stem cells
• Epidermal growth factor receptor (EGFR)
• Lung cancer
• Molecular histology
• Personalised medicine

• Humans
• Lung Neoplasms/drug therapy
• Lung Neoplasms/genetics
• Lung Neoplasms/metabolism
• Lung Neoplasms/pathology
• Molecular Targeted Therapy
• Neoplastic Stem Cells/drug effects
• Neoplastic Stem Cells/metabolism
• Neoplastic Stem Cells/pathology
• Precision Medicine/methods
• Prognosis
• Signal Transduction

• 20465 Cancer Research UK

• Acute myeloid leukemia
• DNMT3A
• Leukemogenesis
• Myelodysplastic syndrome
• Oncogenes

• Algorithms
• Computational Biology
• Gene Expression Profiling
• Humans
• Oligonucleotide Array Sequence Analysis
• Software

• Carcinoid Tumor/genetics
• Gene Expression Profiling
• Genome-Wide Association Study
• Humans
• Ileal Neoplasms/genetics

• CpG island
• DNA methylation
• bisulfite sequencing
• differential methylation hybridization
• methylated CpG island recovery assay
• methylated DNA immunoprecipitation
• methylation profiling
• methylation-sensitive SNP chip analysis
• next-generation DNA sequencing

• spermatogenesis
• male germ cells
• development
• tiling microarray
• expression
• profiling

• Animals
• Cell Separation/methods
• Computational Biology/methods
• DNA, Complementary/chemical synthesis
• Gene Expression Profiling/methods
• Male
• Mice
• Nucleic Acid Amplification Techniques/methods
• Nucleic Acid Hybridization/methods
• Oligonucleotide Array Sequence Analysis/methods
• RNA, Messenger/isolation & purification
• Spermatogonia/metabolism
• Staining and Labeling/methods
• Transcriptome/genetics

• Z01 HD008726-08 Intramural NIH HHS

Wilms tumor (WT) has been a model to study kidney embryogenesis and tumorigenesis and, although associated with hereditary, cancer predisposition syndromes, the majority of tumors occur sporadically. To analyze genetic changes in WT we have defined copy number changes and loss of heterozygosity in 56 Wilms tumors using high resolution oligonucleotide arrays at a average resolution of ∼12 Kb. Consistent deletions were seen on chromosomes 1p, 4q, 7p, 9q, 11p, 11q, 14q, 16q, and 21q. High frequency gains were seen for 1q and lower frequency gains were seen on 7q and chromosomes 8, 12 and 18. The high resolution provided by the SNP mapping arrays has defined minimal regions of deletion for many of these LOH events. Analysis of CNAs by tumor stage show relatively stable karyotypes in stage 1 tumors and more complex aCGH profiles in tumors from stages 3–5.

Analyses of Expressed Sequence Tags (ESTs) databases suggest that most human genes have multiple alternative splice variants. The alternative splicing of pre-mRNA is tightly regulated during development and in different tissue types. Changes in splicing patterns have been described in disease states. Recently, we used whole-transcriptome shotgun pryrosequencing to characterize 4 malignant pleural mesothelioma (MPM) tumors, 1 lung adenocarcinoma and 1 normal lung. We hypothesized that alternative splicing profiles might be detected in the sequencing data for the expressed genes in these samples.

We developed a software pipeline to map the transcriptome read sequences of the 4 MPM samples and 1 normal lung sample onto known exon junction sequences in the comprehensive AceView database of expressed sequences and to count how many reads map to each junction. 13,274,187 transcriptome reads generated by the Roche/454 sequencing platform for 5 samples were compared with 151,486 exon junctions from the AceView database. The exon junction expression index (EJEI) was calculated for each exon junction in each sample to measure the differential expression of alternative splicing events. Top ten exon junctions with the largest EJEI difference between the 4 mesothelioma and the normal lung sample were then examined for differential expression using Quantitative Real Time PCR (qRT-PCR) in the 5 sequenced samples. Two of the differentially expressed exon junctions (ACTG2.aAug05 and CDK4.aAug05) were further examined with qRT-PCR in additional 18 MPM and 18 normal lung specimens.

We found 70,953 exon junctions covered by at least one sequence read in at least one of the 5 samples. All 10 identified most differentially expressed exon junctions were validated as present by RT-PCR, and 8 were differentially expressed exactly as predicted by the sequence analysis. The differential expression of the AceView exon junctions for the ACTG2 and CDK4 genes were also observed to be statistically significant in an additional 18 MPM and 18 normal lung samples examined using qRT-PCR. The differential expression of these two junctions was shown to successfully classify these mesothelioma and normal lung specimens with high sensitivity (89% and 78%, respectively).

Whole-transcriptome shotgun sequencing, combined with a downstream bioinformatics pipeline, provides powerful tools for the identification of differentially expressed exon junctions resulting from alternative splice variants. The alternatively spliced genes discovered in the study could serve as useful diagnostic markers as well as potential therapeutic targets for MPM.

The development and maintenance of adequate shared infrastructures is considered a major goal for academic centers promoting translational research programs. Among infrastructures favoring translational research, centralized facilities characterized by shared, multidisciplinary use of expensive laboratory instrumentation, or by complex computer hardware and software and/or by high professional skills are necessary to maintain or improve institutional scientific competitiveness. The success or failure of a shared resource program also depends on the choice of appropriate institutional policies and requires an effective institutional governance regarding decisions on staffing, existence and composition of advisory committees, policies and of defined mechanisms of reporting, budgeting and financial support of each resource. Shared Resources represent a widely diffused model to sustain cancer research; in fact, web sites from an impressive number of research Institutes and Universities in the U.S. contain pages dedicated to the SR that have been established in each Center, making a complete view of the situation impossible. However, a nation-wide overview of how Cancer Centers develop SR programs is available on the web site for NCI-designated Cancer Centers in the U.S., while in Europe, information is available for individual Cancer centers. This article will briefly summarize the institutional policies, the organizational needs, the characteristics, scientific aims, and future developments of SRs necessary to develop effective translational research programs in oncology.

In fact, the physical build-up of SRs per se is not sufficient for the successful translation of biomedical research. Appropriate policies to improve the academic culture in collaboration, the availability of educational programs for translational investigators, the existence of administrative facilitations for translational research and an efficient organization supporting clinical trial recruitment and management represent essential tools, providing solutions to overcome existing barriers in the development of translational research in biomedical research centers.

Currently, statistical techniques for analysis of microarray-generated data sets have deficiencies due to limited understanding of errors inherent in the data. A generalized likelihood ratio (GLR) test based on an error model has been recently proposed to identify differentially expressed genes from microarray experiments. However, the use of different error structures under the GLR test has not been evaluated, nor has this method been compared to commonly used statistical tests such as the parametric t-test. The concomitant effects of varying data signal-to-noise ratio and replication number on the performance of statistical tests also remain largely unexplored. In this study, we compared the effects of different underlying statistical error structures on the GLR test’s power in identifying differentially expressed genes in microarray data. We evaluated such variants of the GLR test as well as the one sample t-test based on simulated data by means of receiver operating characteristic (ROC) curves. Further, we used bootstrapping of ROC curves to assess statistical significance of differences between the areas under the curves. Our results showed that i) the GLR tests outperformed the t-test for detecting differential gene expression, ii) the identity of the underlying error structure was important in determining the GLR tests’ performance, and iii) signal-to-noise ratio was a more important contributor than sample replication in identifying statistically significant differential gene expression.

• Comparative Genomic Hybridization/instrumentation
• Comparative Genomic Hybridization/methods
• Genome
• Humans
• Loss of Heterozygosity
• Neoplasms/genetics
• Oligonucleotide Array Sequence Analysis/instrumentation
• Oligonucleotide Array Sequence Analysis/methods
• Ploidies

• Amyotrophic Lateral Sclerosis/genetics
• Animals
• Gene Expression
• Genetic Markers/genetics
• Genetic Predisposition to Disease
• Genomics/methods
• Humans
• RNA/genetics

• ZIA AG000933 Intramural NIH HHS
• ZIA AG000933-05 Intramural NIH HHS
• R21 NS051738-01A1 NINDS NIH HHS

Ribosomal proteins are encoded by a gene family, members of which are overexpressed in human cancers. Many of them have been found, using oligonucleotide microarray hybridization, to be differentially expressed in the faeces of patients with various stages of col-orectal cancer (CRC). The gene encoding ribosomal protein L19 (RPL19), a prognostic marker for human prostate cancer, is differentially expressed in CRC patients. Measurement of faecal RPL19 mRNA might improve prognostic prediction for CRC patients. Using quantitative real-time reverse transcription PCR, levels of RPL19 mRNA were detected in samples of colonic tissues from 44 CRC patients, in the faeces of 54 CRC patients and 15 controls, and in 11 colonic cell lines. Seven of 24 patients with late-stage CRC (Dukes' stages C and D) expressed over 2-fold more RPL19 in colonic tumour tissues than in corresponding normal tissues (P= 0.038). The mean faecal RPL19 mRNA levels of late-staged patients were higher than those of controls (P= 0.003) and early-staged patients (P= 0.008). Patients with both high serum levels of carcinoembryonic antigen (CEA; >5 ng/mL) and high-faecal RPL19 mRNA (≥0.0069) had higher risk (odds ratio, 8.0; P= 0.015) and lower overall 48-month survival (33.8 ± 13.7%, P= 0.013). Oligonucleotide microarray hybridization analysis of faecal molecules identified gene transcripts differentially present in faeces. In conclusion, faecal RPL19 expression is associated with advanced tumour stages and addictive to serum CEA in predicting prognosis of CRC patients.

• Biomarkers, Tumor/metabolism
• Breast Neoplasms/diagnosis
• Breast Neoplasms/genetics
• Breast Neoplasms/metabolism
• Cell Proliferation
• Computational Biology/methods
• Female
• Gene Expression Profiling
• Gene Expression Regulation, Neoplastic
• Humans
• Prognosis
• Software

• Antineoplastic Agents/therapeutic use
• Chromatin Assembly and Disassembly
• DNA Methylation
• Epigenesis, Genetic/drug effects
• Epigenesis, Genetic/physiology
• Genes, Neoplasm
• Genome/physiology
• Histones/metabolism
• Humans
• Models, Biological
• Neoplasms/drug therapy
• Neoplasms/genetics
• Nucleic Acid Conformation

• P01-CA101956-01 NCI NIH HHS
• P30-CA16058 NCI NIH HHS
• T32 CA009338-28 NCI NIH HHS

• Peptide Hydrolases/chemistry
• Peptide Hydrolases/metabolism
• Peptides/chemistry
• Peptides/metabolism
• Phosphotransferases/chemistry
• Phosphotransferases/metabolism
• Protein Array Analysis/instrumentation
• Protein Array Analysis/methods
• Protein Array Analysis/trends

• E18330/2 Biotechnology and Biological Sciences Research Council