Dataset Information

GENT2: an updated gene expression database for normal and tumor tissues.

ABSTRACT: BACKGROUND:Gene Expression database of Normal and Tumor tissues 2 (GENT2) is an updated version of GENT, which has provided a user-friendly search platform for gene expression patterns across different normal and tumor tissues compiled from public gene expression data sets. RESULTS:We refactored GENT2 with recent technologies such as Apache Lucene indexing for fast search and Google Web Toolkit (GWT) framework for a user-friendly web interface. Now, GENT2 contains more than 68,000 samples and has several new useful functions. First, GENT2 now provides gene expression across 72 different tissues compared to 57 in GENT. Second, with increasing importance of tumor subtypes, GENT2 provides an option to study the differential expression and its prognostic significance based on tumor subtypes. Third, whenever available, GENT2 provides prognostic information of a gene of interest. Fourth, GENT2 provides a meta-analysis of survival information to provide users more reliable prognostic value of a gene of interest. CONCLUSIONS:In conclusion, with these significant improvements, GENT2 will continue to be a useful tool to a wide range of researchers. GENT2 is freely available at http://gent2.appex.kr .

SUBMITTER: Park SJ

PROVIDER: S-EPMC6624177 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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GENT2: an updated gene expression database for normal and tumor tissues.

Park Seung-Jin SJ Yoon Byoung-Ha BH Kim Seon-Kyu SK Kim Seon-Young SY

BMC medical genomics 20190711 Suppl 5

<h4>Background</h4>Gene Expression database of Normal and Tumor tissues 2 (GENT2) is an updated version of GENT, which has provided a user-friendly search platform for gene expression patterns across different normal and tumor tissues compiled from public gene expression data sets.<h4>Results</h4>We refactored GENT2 with recent technologies such as Apache Lucene indexing for fast search and Google Web Toolkit (GWT) framework for a user-friendly web interface. Now, GENT2 contains more than 68,000 ...[more]

PMID: 31296229

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Project description:BACKGROUND: Precise classification of cancer types is critically important for early cancer diagnosis and treatment. Numerous efforts have been made to use gene expression profiles to improve precision of tumor classification. However, reliable cancer-related signals are generally lacking. METHOD: Using recent datasets on colon and prostate cancer, a data transformation procedure from single gene expression to pair-wise gene expression ratio is proposed. Making use of the internal consistency of each expression profiling dataset this transformation improves the signal to noise ratio of the dataset and uncovers new relevant cancer-related signals (features). The efficiency in using the transformed dataset to perform normal/tumor classification was investigated using feature partitioning with informative features (gene annotation) as discriminating axes (single gene expression or pair-wise gene expression ratio). Classification results were compared to the original datasets for up to 10-feature model classifiers. RESULTS: 82 and 262 genes that have high correlation to tissue phenotype were selected from the colon and prostate datasets respectively. Remarkably, data transformation of the highly noisy expression data successfully led to lower the coefficient of variation (CV) for the within-class samples as well as improved the correlation with tissue phenotypes. The transformed dataset exhibited lower CV when compared to that of single gene expression. In the colon cancer set, the minimum CV decreased from 45.3% to 16.5%. In prostate cancer, comparable CV was achieved with and without transformation. This improvement in CV, coupled with the improved correlation between the pair-wise gene expression ratio and tissue phenotypes, yielded higher classification efficiency, especially with the colon dataset - from 87.1% to 93.5%. Over 90% of the top ten discriminating axes in both datasets showed significant improvement after data transformation. The high classification efficiency achieved suggested that there exist some cancer-related signals in the form of pair-wise gene expression ratio. CONCLUSION: The results from this study indicated that: 1) in the case when the pair-wise expression ratio transformation achieves lower CV and higher correlation to tissue phenotypes, a better classification of tissue type will follow. 2) the comparable classification accuracy achieved after data transformation suggested that pair-wise gene expression ratio between some pairs of genes can identify reliable markers for cancer.

Project description:BackgroundHepatocellular carcinoma (HCC) is the leading cause of cancer mortality. Chemical and virus induction are two major risk factors, however, the potential molecular mechanisms of their differences remain elusive. In this study, to identify the similarities and differences between chemical and virus induced HCC models, we compared the gene expression profiles between DEN and HBx mice models, as well as the differences among tumor, para-tumor and normal tissues.MethodsWe sequenced both gene and microRNA (miRNA) expression for HCC tumor tissues, para-tumor and normal liver tissues from DEN model mice (30-week-old) and downloaded the corresponding microarray expression data of HBx model from GEO database. Then differentially expressed genes (DEGs), miRNAs and transcription factors (TFs) were detected by R packages and performed functional enrichment analysis. To explore the gene regulatory network in HCC models, miRNA and TF regulatory networks were constructed by target prediction.ResultsFor model comparison, although DEGs between tumor and normal tissues in DEN and HBx models only had a small part of overlapping, they shared common pathways including lipid metabolism, oxidation-reduction process and immune process. For tissue comparisons in each model, genes in oxidation-reduction process were down-regulated in tumor tissues and genes in inflammatory response showed the highest expression level in para-tumor tissues. Genes highly expressed in both tumor and para-tumor tissues in two models mainly participated in immune and inflammatory response. Genes expressed in HBx model were also involved in cell proliferation and cell migration etc. Network analysis revealed that several miRNAs such as miR-381-3p, miR-142a-3p, miR-214-3p and TFs such as Egr1, Atf3 and Klf4 were the core regulators in HCC.ConclusionsThrough the comparative analyses, we found that para-tumor tissue is a highly inflammatory tissue while the tumor tissue is specific with both inflammatory and cancer signaling pathways. The DEN and HBx mice models have different gene expression pattern but shared pathways. This work will help to elucidate the molecular mechanisms underlying different HCC models.

Dataset Information

GENT2: an updated gene expression database for normal and tumor tissues.

Publications

GENT2: an updated gene expression database for normal and tumor tissues.

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