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      The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples

      , , , , , , , ,
      Cancers
      MDPI AG

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          Abstract

          Prostate cancer (PCa), one of the most common malignancies in men, typically responds to initial treatment, but resistance to therapy often leads to metastases and death. The dehydrogenase/reductase 7 (DHRS7, SDR34C1) is an “orphan” enzyme without known physiological function. DHRS7 was previously found to be decreased in higher-stage PCa, and siRNA-mediated knockdown increased the aggressiveness of LNCaP cells. To further explore the role of DHRS7 in PCa, we analyzed the proteome of LNCaP cells following DHRS7 knockdown to assess potentially altered pathways. Although DHRS7 is able to inactivate 5α-dihydrotestosterone, DHRS7 knockdown did not affect androgen receptor (AR) target gene expression, and its effect on PCa cells seems to be androgen-independent. Importantly, proteome analyses revealed increased expression of epidermal growth factor receptor (EGFR), which was confirmed by RT-qPCR and Western blotting. Comparison of AR-positive LNCaP with AR-negative PC-3 and DU145 PCa cell lines revealed a negative correlation between DHRS7 and EGFR expression. Conversely, EGFR knockdown enhanced DHRS7 expression in these cells. Importantly, analysis of patient samples revealed a negative correlation between DHRS7 and EGFR expression, both at the mRNA and protein levels, and DHRS7 expression correlated positively with patient survival rates. These results suggest a protective role for DHRS7 in PCa.

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          Most cited references65

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          KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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            The PRIDE database and related tools and resources in 2019: improving support for quantification data

            Abstract The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world’s largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.
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              The Perseus computational platform for comprehensive analysis of (prote)omics data.

              A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.
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                Author and article information

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                Journal
                CANCCT
                Cancers
                Cancers
                MDPI AG
                2072-6694
                July 2022
                June 23 2022
                : 14
                : 13
                : 3074
                Article
                10.3390/cancers14133074
                2b1c6830-ad48-4699-bf71-88110df0ed37
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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