Gene expression profiling in bladder cancer to identify potential therapeutic targets.

4518 Background: Characterization of gene expression patterns in bladder cancer (BC) allows the identification of pathways involved in its pathogenesis, and may stimulate the development of novel therapies targeting these pathways. Methods: Between 2004 and 2005, cystoscopic bladder biopsies were obtained from 19 patients and 11 controls. These were subjected to whole transcript-based microarray analysis. Unsupervised hierarchical clustering was used to identify samples with similar expression profiles. Results: Hierarchical clustering defined signatures, which differentiated between cancer and normal, muscle-invasive or non-muscle invasive cancer and normal, g1 and g3. Pathways associated with cell cycle and proliferations were markedly upregulated in muscle-invasive and grade 3 cancers. Genes associated with the classical complement pathway were downregulated in non-muscle invasive cancer. Osteopontin was markedly overexpressed in invasive cancer as compared to normal tissue. Conclusions: This study contributes to a growing body of work on gene expression signatures in BC. The data support an important role for osteopontin in BC, and identify several pathways worthy of further investigation. [Table: see text]

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Characterization of the Community Structure of a Dechlorinating Mixed Culture and Comparisons of Gene Expression in Planktonic and Biofloc-Associated “Dehalococcoides” and Methanospirillum Species

Applied and Environmental Microbiology ◽

10.1128/aem.00445-08 ◽

2008 ◽

Vol 74 (21) ◽

pp. 6709-6719 ◽

Cited By ~ 39

Author(s):

Annette R. Rowe ◽

Brendan J. Lazar ◽

Robert M. Morris ◽

Ruth E. Richardson

Keyword(s):

Gene Expression ◽

16S Rrna ◽

16S Rrna Gene ◽

Population Distribution ◽

Enrichment Culture ◽

Expression Profiles ◽

Expression Patterns ◽

Rrna Gene ◽

Hydrogenase Gene

ABSTRACT This study sought to characterize bacterial and archaeal populations in a perchloroethene- and butyrate-fed enrichment culture containing hydrogen-consuming “Dehalococcoides ethenogenes” strain 195 and a Methanospirillum hungatei strain. Phylogenetic characterization of this microbial community was done via 16S rRNA gene clone library and gradient gel electrophoresis analyses. Fluorescence in situ hybridization was used to quantify populations of “Dehalococcoides” and Archaea and to examine the colocalization of these two groups within culture bioflocs. A technique for enrichment of planktonic and biofloc-associated biomass was developed and used to assess differences in population distribution and gene expression patterns following provision of substrate. On a per-milliliter-of-culture basis, most D. ethenogenes genes (the hydrogenase gene hupL; the highly expressed gene for an oxidoreductase of unknown function, fdhA; the RNA polymerase subunit gene rpoB; and the 16S rRNA gene) showed no statistical difference in expression between planktonic and biofloc enrichments at either time point studied (1 to 2 and 6 h postfeeding). Normalization of transcripts to ribosome (16S rRNA) levels supported that planktonic and biofloc-associated D. ethenogenes had similar gene expression profiles, with one notable exception; planktonic D. ethenogenes showed higher expression of tceA relative to biofloc-associated cells at 6 h postfeeding. These trends were compared to those for the hydrogen-consuming methanogen in the culture, M. hungatei. The vast majority of M. hungatei cells, ribosomes (16S rRNA), and transcripts of the hydrogenase gene mvrD and the housekeeping gene rpoE were observed in the biofloc enrichments. This suggests that, unlike the comparable activity of D. ethenogenes from both enrichments, planktonic M. hungatei is responsible for only a small fraction of the hydrogenotrophic methanogenesis in this culture.

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Comparative Gene Expression Analysis Reveals Similarities and Differences of Chronic Myeloid Leukemia Phases

Cancers ◽

10.3390/cancers14010256 ◽

2022 ◽

Vol 14 (1) ◽

pp. 256

Author(s):

Annemarie Schwarz ◽

Ingo Roeder ◽

Michael Seifert

Keyword(s):

Gene Expression ◽

Chronic Myeloid Leukemia ◽

Signaling Pathway ◽

Myeloid Leukemia ◽

Expression Profiles ◽

Expression Patterns ◽

Chronic Phase ◽

Receptor Interaction ◽

Similarities And Differences

Chronic myeloid leukemia (CML) is a slowly progressing blood cancer that primarily affects elderly people. Without successful treatment, CML progressively develops from the chronic phase through the accelerated phase to the blast crisis, and ultimately to death. Nowadays, the availability of targeted tyrosine kinase inhibitor (TKI) therapies has led to long-term disease control for the vast majority of patients. Nevertheless, there are still patients that do not respond well enough to TKI therapies and available targeted therapies are also less efficient for patients in accelerated phase or blast crises. Thus, a more detailed characterization of molecular alterations that distinguish the different CML phases is still very important. We performed an in-depth bioinformatics analysis of publicly available gene expression profiles of the three CML phases. Pairwise comparisons revealed many differentially expressed genes that formed a characteristic gene expression signature, which clearly distinguished the three CML phases. Signaling pathway expression patterns were very similar between the three phases but differed strongly in the number of affected genes, which increased with the phase. Still, significant alterations of MAPK, VEGF, PI3K-Akt, adherens junction and cytokine receptor interaction signaling distinguished specific phases. Our study also suggests that one can consider the phase-wise CML development as a three rather than a two-step process. This is in accordance with the phase-specific expression behavior of 24 potential major regulators that we predicted by a network-based approach. Several of these genes are known to be involved in the accumulation of additional mutations, alterations of immune responses, deregulation of signaling pathways or may have an impact on treatment response and survival. Importantly, some of these genes have already been reported in relation to CML (e.g., AURKB, AZU1, HLA-B, HLA-DMB, PF4) and others have been found to play important roles in different leukemias (e.g., CDCA3, RPL18A, PRG3, TLX3). In addition, increased expression of BCL2 in the accelerated and blast phase indicates that venetoclax could be a potential treatment option. Moreover, a characteristic signaling pathway signature with increased expression of cytokine and ECM receptor interaction pathway genes distinguished imatinib-resistant patients from each individual CML phase. Overall, our comparative analysis contributes to an in-depth molecular characterization of similarities and differences of the CML phases and provides hints for the identification of patients that may not profit from an imatinib therapy, which could support the development of additional treatment strategies.

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Identifying Transcriptomic Correlates of Histology using Deep Learning

10.1101/2020.08.07.241331 ◽

2020 ◽

Cited By ~ 1

Author(s):

Liviu Badea ◽

Emil Stănescu

Keyword(s):

Gene Expression ◽

Deep Learning ◽

Expression Profiles ◽

Expression Patterns ◽

Gene Expression Profiles ◽

Specific Gene ◽

Genome Wide ◽

Gene Perturbations ◽

Whole Slide Images

AbstractLinking phenotypes to specific gene expression profiles is an extremely important problem in biology, which has been approached mainly by correlation methods or, more fundamentally, by studying the effects of gene perturbations. However, genome-wide perturbations involve extensive experimental efforts, which may be prohibitive for certain organisms. On the other hand, the characterization of the various phenotypes frequently requires an expert’s subjective interpretation, such as a histopathologist’s description of tissue slide images in terms of complex visual features (e.g. ‘acinar structures’). In this paper, we use Deep Learning to eliminate the inherent subjective nature of these visual histological features and link them to genomic data, thus establishing a more precisely quantifiable correlation between transcriptomes and phenotypes. Using a dataset of whole slide images with matching gene expression data from 39 normal tissue types, we first developed a Deep Learning tissue classifier with an accuracy of 94%. Then we searched for genes whose expression correlates with features inferred by the classifier and demonstrate that Deep Learning can automatically derive visual (phenotypical) features that are well correlated with the transcriptome and therefore biologically interpretable. As we are particularly concerned with interpretability and explainability of the inferred histological models, we also develop visualizations of the inferred features and compare them with gene expression patterns determined by immunohistochemistry. This can be viewed as a first step toward bridging the gap between the level of genes and the cellular organization of tissues.

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Identifying transcriptomic correlates of histology using deep learning

PLoS ONE ◽

10.1371/journal.pone.0242858 ◽

2020 ◽

Vol 15 (11) ◽

pp. e0242858

Author(s):

Liviu Badea ◽

Emil Stănescu

Keyword(s):

Gene Expression ◽

Deep Learning ◽

Expression Profiles ◽

Expression Patterns ◽

Gene Expression Profiles ◽

Specific Gene ◽

Genome Wide ◽

Gene Perturbations ◽

Whole Slide Images

Linking phenotypes to specific gene expression profiles is an extremely important problem in biology, which has been approached mainly by correlation methods or, more fundamentally, by studying the effects of gene perturbations. However, genome-wide perturbations involve extensive experimental efforts, which may be prohibitive for certain organisms. On the other hand, the characterization of the various phenotypes frequently requires an expert’s subjective interpretation, such as a histopathologist’s description of tissue slide images in terms of complex visual features (e.g. ‘acinar structures’). In this paper, we use Deep Learning to eliminate the inherent subjective nature of these visual histological features and link them to genomic data, thus establishing a more precisely quantifiable correlation between transcriptomes and phenotypes. Using a dataset of whole slide images with matching gene expression data from 39 normal tissue types, we first developed a Deep Learning tissue classifier with an accuracy of 94%. Then we searched for genes whose expression correlates with features inferred by the classifier and demonstrate that Deep Learning can automatically derive visual (phenotypical) features that are well correlated with the transcriptome and therefore biologically interpretable. As we are particularly concerned with interpretability and explainability of the inferred histological models, we also develop visualizations of the inferred features and compare them with gene expression patterns determined by immunohistochemistry. This can be viewed as a first step toward bridging the gap between the level of genes and the cellular organization of tissues.

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Differential gene expression profile between progressive and de novo muscle invasive bladder cancer and its prognostic implication

Scientific Reports ◽

10.1038/s41598-021-85137-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Raquel Carrasco ◽

Laura Izquierdo ◽

Antoine G. van der Heijden ◽

Juan José Lozano ◽

Marco Franco ◽

...

Keyword(s):

Gene Expression ◽

Bladder Cancer ◽

Tumor Progression ◽

Gene Expression Profile ◽

De Novo ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Prognostic Biomarkers ◽

Muscle Invasive Bladder Cancer ◽

Muscle Invasive

AbstractThis study aimed to ascertain gene expression profile differences between progressive muscle-invasive bladder cancer (MIBC) and de novo MIBC, and to identify prognostic biomarkers to improve patients’ treatment. Retrospective multicenter study in which 212 MIBC patients who underwent radical cystectomy between 2000 and 2019 were included. Gene expression profiles were determined in 26 samples using Illumina microarrays. The expression levels of 94 genes were studied by quantitative PCR in an independent set of 186 MIBC patients. In a median follow-up of 16 months, 46.7% patients developed tumor progression after cystectomy. In our series, progressive MIBC patients show a worse tumor progression (p = 0.024) and cancer-specific survival (CSS) (p = 0.049) than the de novo group. A total of 480 genes were found to be differently expressed between both groups. Differential expression of 24 out of the 94 selected genes was found in an independent cohort. RBPMC2 and DSC3 were found as independent prognostic biomarkers of tumor progression and CALD1 and LCOR were identified as prognostic biomarkers of CSS between both groups. In conclusion, progressive and de novo MIBC patients show different clinical outcome and gene expression profiles. Gene expression patterns may contribute to predict high-risk of progression to distant metastasis or CSS.

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Faculty Opinions recommendation of Comprehensive immune transcriptomic analysis in bladder cancer reveals subtype specific immune gene expression patterns of prognostic relevance.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.732026813.793548288 ◽

2018 ◽

Author(s):

Ashish Kamat

Keyword(s):

Gene Expression ◽

Bladder Cancer ◽

Expression Patterns ◽

Transcriptomic Analysis ◽

Gene Expression Patterns ◽

Immune Gene ◽

Immune Gene Expression ◽

Prognostic Relevance

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Comprehensive Comparison of Amnion Stromal Cells and Chorion Stromal Cells by RNA-Seq

International Journal of Molecular Sciences ◽

10.3390/ijms22041901 ◽

2021 ◽

Vol 22 (4) ◽

pp. 1901

Author(s):

Brielle Jones ◽

Chaoyang Li ◽

Min Sung Park ◽

Anne Lerch ◽

Vimal Jacob ◽

...

Keyword(s):

Gene Expression ◽

Stromal Cells ◽

Expression Profiles ◽

Expression Patterns ◽

Principal Component ◽

Differentially Expressed ◽

Inflammatory Factor ◽

Next Generation Sequencing Technology ◽

Angiogenic Potential ◽

Anti Inflammatory

Mesenchymal stromal cells derived from the fetal placenta, composed of an amnion membrane, chorion membrane, and umbilical cord, have emerged as promising sources for regenerative medicine. Here, we used next-generation sequencing technology to comprehensively compare amniotic stromal cells (ASCs) with chorionic stromal cells (CSCs) at the molecular and signaling levels. Principal component analysis showed a clear dichotomy of gene expression profiles between ASCs and CSCs. Unsupervised hierarchical clustering confirmed that the biological repeats of ASCs and CSCs were able to respectively group together. Supervised analysis identified differentially expressed genes, such as LMO3, HOXA11, and HOXA13, and differentially expressed isoforms, such as CXCL6 and HGF. Gene Ontology (GO) analysis showed that the GO terms of the extracellular matrix, angiogenesis, and cell adhesion were significantly enriched in CSCs. We further explored the factors associated with inflammation and angiogenesis using a multiplex assay. In comparison with ASCs, CSCs secreted higher levels of angiogenic factors, including angiogenin, VEGFA, HGF, and bFGF. The results of a tube formation assay proved that CSCs exhibited a strong angiogenic function. However, ASCs secreted two-fold more of an anti-inflammatory factor, TSG-6, than CSCs. In conclusion, our study demonstrated the differential gene expression patterns between ASCs and CSCs. CSCs have superior angiogenic potential, whereas ASCs exhibit increased anti-inflammatory properties.

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