Upregulation of ATP Binding Cassette Subfamily C Member 5 facilitates Prostate Cancer progression and Enzalutamide resistance via the CDK1-mediated AR Ser81 Phosphorylation Pathway

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Abstract

The treatment of advanced prostate cancer, castration-resistant prostate cancer, remains challenging. The mechanisms of action of ATP binding cassette subfamily C member 5 (ABCC5) in prostate cancer and its relationship with drug resistance are still unclear. Expression and prognostic analyses of ABCC5 were performed through bioinformatic methods and immunohistochemistry analyses in multiple public databases as well as in our own prostate cancer cohort. The biological function of ABCC5 in prostate cancer cells was evaluated by in vitro and in vivo cell proliferation and migration and invasion assays. The regulation of CDK1 by ABCC5 was determined via RT-qPCR, western blots, and immunofluorescence. ABCC5 was significantly overexpressed in prostate cancer and positively associated with unfavorable clinicopathological features and prognosis. Upregulation of ABCC5 could enhance the cell proliferation, migration, and invasion of prostate cancer in vitro and in vivo. Mechanistically, ABCC5 exerts a protumor effect by binding to and inhibiting the protein degradation of CDK1, which promotes the phosphorylation of AR at Ser81 by CDK1 and activates the transcriptional activity of AR on target genes. Moreover, the addition of a CDK1 inhibitor or knockdown of CDK1 significantly improved the efficacy of enzalutamide on prostate cancer cells. The ABCC5-CDK1-AR regulatory pathway could be a potential therapeutic target for advanced prostate cancer, especially castration-resistant prostate cancer (CRPC), to enhance the therapeutic effect of enzalutamide.

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Cancer statistics, 2020

Rebecca Siegel, Kimberly D Miller, Ahmedin Jemal (2020)

Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on population-based cancer occurrence. Incidence data (through 2016) were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2017) were collected by the National Center for Health Statistics. In 2020, 1,806,590 new cancer cases and 606,520 cancer deaths are projected to occur in the United States. The cancer death rate rose until 1991, then fell continuously through 2017, resulting in an overall decline of 29% that translates into an estimated 2.9 million fewer cancer deaths than would have occurred if peak rates had persisted. This progress is driven by long-term declines in death rates for the 4 leading cancers (lung, colorectal, breast, prostate); however, over the past decade (2008-2017), reductions slowed for female breast and colorectal cancers, and halted for prostate cancer. In contrast, declines accelerated for lung cancer, from 3% annually during 2008 through 2013 to 5% during 2013 through 2017 in men and from 2% to almost 4% in women, spurring the largest ever single-year drop in overall cancer mortality of 2.2% from 2016 to 2017. Yet lung cancer still caused more deaths in 2017 than breast, prostate, colorectal, and brain cancers combined. Recent mortality declines were also dramatic for melanoma of the skin in the wake of US Food and Drug Administration approval of new therapies for metastatic disease, escalating to 7% annually during 2013 through 2017 from 1% during 2006 through 2010 in men and women aged 50 to 64 years and from 2% to 3% in those aged 20 to 49 years; annual declines of 5% to 6% in individuals aged 65 years and older are particularly striking because rates in this age group were increasing prior to 2013. It is also notable that long-term rapid increases in liver cancer mortality have attenuated in women and stabilized in men. In summary, slowing momentum for some cancers amenable to early detection is juxtaposed with notable gains for other common cancers.

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WGCNA: an R package for weighted correlation network analysis

Peter Langfelder, Steve Horvath (2008)

Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .

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TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells.

Taiwen Li, Jingyu Fan, Binbin Wang … (2017)

Recent clinical successes of cancer immunotherapy necessitate the investigation of the interaction between malignant cells and the host immune system. However, elucidation of complex tumor-immune interactions presents major computational and experimental challenges. Here, we present Tumor Immune Estimation Resource (TIMER; cistrome.shinyapps.io/timer) to comprehensively investigate molecular characterization of tumor-immune interactions. Levels of six tumor-infiltrating immune subsets are precalculated for 10,897 tumors from 32 cancer types. TIMER provides 6 major analytic modules that allow users to interactively explore the associations between immune infiltrates and a wide spectrum of factors, including gene expression, clinical outcomes, somatic mutations, and somatic copy number alterations. TIMER provides a user-friendly web interface for dynamic analysis and visualization of these associations, which will be of broad utilities to cancer researchers. Cancer Res; 77(21); e108-10. ©2017 AACR.

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Author and article information

Journal

Journal ID (nlm-ta): Int J Biol Sci

Journal ID (iso-abbrev): Int J Biol Sci

Journal ID (publisher-id): ijbs

Title: International Journal of Biological Sciences

Publisher: Ivyspring International Publisher (Sydney )

ISSN (Electronic): 1449-2288

Publication date Collection: 2021

Publication date (Electronic): 12 April 2021

Volume: 17

Issue: 7

Pages: 1613-1628

Affiliations

Institute of Urology, Peking University. Department of Urology, Peking University First Hospital. National Urological Cancer Center of China, Beijing, China.

Author notes

✉ Corresponding authors: Yanqing Gong, Institute of Urology, Peking University. Department of Urology, Peking University First Hospital. National Urological Cancer Center of China, Beijing, China. Phone: +86 13488658965; E-mail: yqgong@ 123456bjmu.edu.cn ; Xuesong Li, Institute of Urology, Peking University. Department of Urology, Peking University First Hospital. National Urological Cancer Center of China, Beijing, China. Phone: +86 15801399116; E-mail: pineneedle@ 123456sina.com ; Liqun Zhou, Institute of Urology, Peking University. Department of Urology, Peking University First Hospital. National Urological Cancer Center of China, Beijing, China. Phone: +86 13601239241; E-mail: zhoulqmail@ 123456sina.com .

#These authors contributed equally to this work.

Competing Interests: The authors have declared that no competing interest exists.

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Publisher ID: ijbsv17p1613

DOI: 10.7150/ijbs.59559

PMC ID: 8120459

PubMed ID: 33994848

SO-VID: bfeebe2f-cdb4-4aba-aa3a-72b3cca05a99

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This is an open access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.

Upregulation of ATP Binding Cassette Subfamily C Member 5 facilitates Prostate Cancer progression and Enzalutamide resistance via the CDK1-mediated AR Ser81 Phosphorylation Pathway

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Cancer Metabolism

Most cited references 49

Cancer statistics, 2020

WGCNA: an R package for weighted correlation network analysis

TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells.

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