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      CCM signaling complex (CSC) couples both classic and non-classic Progesterone receptor signaling

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          Abstract

          Background

          Breast cancer, the most diagnosed cancer, remains the second leading cause of cancer death in the United States, and excessive Progesterone (PRG) or Mifepristone (MIF) exposure may be at an increased risk for developing breast cancer. PRG exerts its cellular responses through signaling cascades involving classic, non-classic, or combined responses by binding to either classic nuclear PRG receptors (nPRs) or non-classic membrane PRG receptors (mPRs). Currently, the intricate balance and switch mechanisms between these two signaling cascades remain elusive. Three genes, CCM1-3, form the CCM signaling complex (CSC) which mediates multiple signaling cascades.

          Methods

          Utilizing molecular, cellular, Omics, and systems biology approaches , we analyzed the relationship among the CSC, PRG, and nPRs/mPRs during breast cancer tumorigenesis.

          Results

          We discovered that the CSC plays an essential role in coupling both classic and non-classic PRG signaling pathways by mediating crosstalk between them, forming the CmPn (CSC-mPRs-PRG-nPRs) signaling network. We found that mPR-specific PRG actions (PRG + MIF) play an essential role in this CmPn network during breast cancer tumorigenesis. Additionally, we have identified 4 categories of candidate biomarkers (9 intrinsic, 2 PRG-inducible, 1 PRG-repressive, 1 mPR-specific PRG-repressive, and 2 mPR-responsive) for Luminal-A breast cancers during tumorigenesis and have confirmed the prognostic application of RPL13 and RPL38 as intrinsic biomarkers using a dual validation method.

          Conclusions

          We have discovered that the CSC plays an essential role in the CmPn signaling network for Luminal-A breast cancers with identification of two intrinsic biomarkers.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12964-022-00926-z.

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          Most cited references59

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          Kenneth Livak, Thomas D. Schmittgen (2001)
          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            Visualizing and interpreting cancer genomics data via the Xena platform

            Mary J. Goldman, Brian Craft, Mim Hastie … (2020)
            Article 0 comments Cited 1629 times – based on 0 reviews     Review now
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              The CRAPome: a Contaminant Repository for Affinity Purification Mass Spectrometry Data

              Dattatreya Mellacheruvu, Zachary Wright, Amber L Couzens … (2013)
              Affinity purification coupled with mass spectrometry (AP-MS) is now a widely used approach for the identification of protein-protein interactions. However, for any given protein of interest, determining which of the identified polypeptides represent bona fide interactors versus those that are background contaminants (e.g. proteins that interact with the solid-phase support, affinity reagent or epitope tag) is a challenging task. While the standard approach is to identify nonspecific interactions using one or more negative controls, most small-scale AP-MS studies do not capture a complete, accurate background protein set. Fortunately, negative controls are largely bait-independent. Hence, aggregating negative controls from multiple AP-MS studies can increase coverage and improve the characterization of background associated with a given experimental protocol. Here we present the Contaminant Repository for Affinity Purification (the CRAPome) and describe the use of this resource to score protein-protein interactions. The repository (currently available for Homo sapiens and Saccharomyces cerevisiae) and computational tools are freely available online at www.crapome.org.
              Article 0 comments Cited 712 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Jun Zhang: jun.zhang2000@gmail.com
                Journal
                Journal ID (nlm-ta): Cell Commun Signal
                Journal ID (iso-abbrev): Cell Commun Signal
                Title: Cell Communication and Signaling : CCS
                Publisher: BioMed Central (London )
                ISSN (Electronic): 1478-811X
                Publication date (Electronic): 15 August 2022
                Publication date PMC-release: 15 August 2022
                Publication date Collection: 2022
                Volume: 20
                Electronic Location Identifier: 120
                Affiliations
                [1 ]GRID grid.449768.0, Department of Molecular and Translational Medicine (MTM), , Texas Tech University Health Science Center El Paso, ; 5001 El Paso Drive, El Paso, TX 79905 USA
                [2 ]GRID grid.267324.6, ISNI 0000 0001 0668 0420, Department of Biological Sciences, , University of Texas at El Paso, ; El Paso, TX 79902 USA
                Article
                Publisher ID: 926
                DOI: 10.1186/s12964-022-00926-z
                PMC ID: 9377144
                PubMed ID: 35971177
                SO-VID: 77d5b471-9609-401c-a874-ca6dff64726d
                Copyright © © The Author(s) 2022
                License:

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                Date received : 7 April 2022
                Date accepted : 30 June 2022
                Categories
                Subject: Research
                Custom metadata
                issue-copyright-statement © The Author(s) 2022

                ScienceOpen disciplines: Cell biology
                Keywords: cerebral cavernous malformation (ccm),signaling complex (csc),progesterone (prg),mifepristone (mif),prognostic biomarkers,classic nuclear progesterone receptors (nprs),non-classic membrane progesterone receptors (mprs/paqrs),proteomics,rnaseq
                Data availability:
                ScienceOpen disciplines: Cell biology
                Keywords: cerebral cavernous malformation (ccm), signaling complex (csc), progesterone (prg), mifepristone (mif), prognostic biomarkers, classic nuclear progesterone receptors (nprs), non-classic membrane progesterone receptors (mprs/paqrs), proteomics, rnaseq

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