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Filippi A, Aurelian J, Mocanu MM. Analysis of the Gene Networks and Pathways Correlated with Tissue Differentiation in Prostate Cancer. Int J Mol Sci 2024; 25:3626. [PMID: 38612439 PMCID: PMC11011430 DOI: 10.3390/ijms25073626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 03/17/2024] [Accepted: 03/21/2024] [Indexed: 04/14/2024] Open
Abstract
Prostate cancer (PCa) is the most prevalent non-cutaneous cancer in men. Early PCa detection has been made possible by the adoption of screening methods based on the serum prostate-specific antigen and Gleason score (GS). The aim of this study was to correlate gene expression with the differentiation level of prostate adenocarcinomas, as indicated by GS. We used data from The Cancer Genome Atlas (TCGA) and included 497 prostate cancer patients, 52 of which also had normal tissue sample sequencing data. Gene ontology analysis revealed that higher GSs were associated with greater responses to DNA damage, telomere lengthening, and cell division. Positive correlation was found with transcription factor activator of the adenovirus gene E2 (E2F) and avian myelocytomatosis viral homolog (MYC) targets, G2M checkpoints, DNA repair, and mitotic spindles. Immune cell deconvolution revealed high M0 macrophage counts and an increase in M2 macrophages dependent on the GS. The molecular pathways most correlated with GSs were cell cycle, RNA transport, and calcium signaling (depleted). A combinatorial approach identified a set of eight genes able to differentiate by k-Nearest Neighbors (kNN) between normal tissues, low-Gleason tissues, and high-Gleason tissues with high accuracy. In conclusion, our study could be a step forward to better understanding the link between gene expression and PCa progression and aggressiveness.
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Affiliation(s)
- Alexandru Filippi
- Department of Biochemistry and Biophysics, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania;
| | - Justin Aurelian
- Department of Specific Disciplines, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania;
- Department of Urology, “Prof. Dr. Th. Burghele” Clinical Hospital, 050653 Bucharest, Romania
| | - Maria-Magdalena Mocanu
- Department of Biochemistry and Biophysics, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania;
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2
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An Y, Lu W, Li S, Lu X, Zhang Y, Han D, Su D, Jia J, Yuan J, Zhao B, Tu M, Li X, Wang X, Fang N, Ji S. Systematic review and integrated analysis of prognostic gene signatures for prostate cancer patients. Discov Oncol 2023; 14:234. [PMID: 38112859 PMCID: PMC10730790 DOI: 10.1007/s12672-023-00847-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 12/07/2023] [Indexed: 12/21/2023] Open
Abstract
Prostate cancer (PC) is one of the most common cancers in men and becoming the second leading cause of cancer fatalities. At present, the lack of effective strategies for prognosis of PC patients is still a problem to be solved. Therefore, it is significant to identify potential gene signatures for PC patients' prognosis. Here, we summarized 71 different prognostic gene signatures for PC and concluded 3 strategies for signature construction after extensive investigation. In addition, 14 genes frequently appeared in 71 different gene signatures, which enriched in mitotic and cell cycle. This review provides extensive understanding and integrated analysis of current prognostic signatures of PC, which may help researchers to construct gene signatures of PC and guide future clinical treatment.
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Affiliation(s)
- Yang An
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China.
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China.
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China.
| | - Wenyuan Lu
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Shijia Li
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Xiaoyan Lu
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Yuanyuan Zhang
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Dongcheng Han
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Dingyuan Su
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Jiaxin Jia
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Jiaxin Yuan
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Binbin Zhao
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Mengjie Tu
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Xinyu Li
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Xiaoqing Wang
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China
| | - Na Fang
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China.
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China.
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China.
| | - Shaoping Ji
- School of Basic Medical Sciences, Henan University, Kaifeng, 475004, China.
- Department of Biochemistry and Molecular Biology, Cell Signal Transduction Laboratory, School of Basic Medical Sciences, Henan University, Jinming Street, Kaifeng, 475004, Henan, China.
- Henan Provincial Engineering Center for Tumor Molecular Medicine, Kaifeng Key Laboratory of Cell Signal Transduction, Kaifeng, 475004, China.
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3
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Liu L, Li Y, Tang S, Yang B, Zhang Q, Xiao R, Hou X, Liu C, Ma L. Gleason Score-related MT1L as biomarker for prognosis in prostate adenocarcinoma and contribute to tumor progression in vitro. Int J Biol Markers 2023:3936155231156458. [PMID: 37192745 DOI: 10.1177/03936155231156458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
BACKGROUND The Gleason Score is well correlated with biological behavior and prognosis in prostate adenocarcinoma (PRAD). This study was derived to determine the clinical significance and function of Gleason-Score-related genes in PRAD. METHODS RNA-sequencing profiles and clinical data were extracted from the The Cancer Genome Atlas PRAD database. The Gleason-Score-related genes were screened out by the Jonckheere-Terpstra rank-based test. The "limma" R package was performed for differentially expressed genes. Next, a Kaplan-Meier survival analysis was performed. Correlation MT1L expression levels with tumor stage, non-tumor tissue stage, radiation therapy, and residual tumor were analyzed. Further, MT1L expression was detected in PRAD cell lines by reverse transcription-quantitative polymerase chain reaction assay. Overexpression of MT1L was constructed and used for cell count kit-8, flow cytometric assay, transwell assay, and wound-healing assay. RESULTS Survival analysis showed 15 Gleason-Score-related genes as prognostic biomarkers in PRAD. The high-frequency deletion of MT1L was verified in PRAD. Furthermore, MT1L expression was decreased in PRAD cell lines than RWPE-1 cells, and overexpression of MT1L repressed cell proliferation and migration, and induced apoptosis in PC-3 cells. CONCLUSION Gleason-Score-related MT1L may serve as a biomarker of poor prognostic biomarker in PRAD. In addition, MT1L plays a tumor suppressor in PRAD progression, which is beneficial for PRAD diagnosis and treatment research.
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Affiliation(s)
- Lei Liu
- Department of Urology, Peking University Third Hospital, Beijing, China
| | - Yaping Li
- Department of Medicine, Acornmed Biotechnology Co., Ltd, Beijing, China
| | - Shiying Tang
- Department of Urology, Peking University Third Hospital, Beijing, China
| | - Bin Yang
- Department of Urology, Peking University Third Hospital, Beijing, China
| | - Qiming Zhang
- Department of Urology, Peking University Third Hospital, Beijing, China
| | - Ruotao Xiao
- Department of Urology, Peking University Third Hospital, Beijing, China
| | - Xiaofei Hou
- Department of Urology, Peking University Third Hospital, Beijing, China
| | - Cheng Liu
- Department of Urology, Peking University Third Hospital, Beijing, China
| | - Lulin Ma
- Department of Urology, Peking University Third Hospital, Beijing, China
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4
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Li G, Lei J, Xu D, Yu W, Bai J, Wu G. Integrative analyses of ferroptosis and immune related biomarkers and the osteosarcoma associated mechanisms. Sci Rep 2023; 13:5770. [PMID: 37031292 PMCID: PMC10082853 DOI: 10.1038/s41598-023-33009-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 04/05/2023] [Indexed: 04/10/2023] Open
Abstract
Osteosarcoma (OS) is the most common primary malignant bone tumor with high metastatic potential and relapse risk. To study the regulatory mechanism of the OS microenvironment, a complex regulatory network involving the ferroptosis- and immune response-related genes remains to be established. In the present study, we determined the effect of a comprehensive evaluation system established on the basis of ferroptosis- and immune-related genes on the immune status, related biomarkers, prognosis, and the potential regulatory networks underlying OS based on the TARGET and Gene Expression Omnibus databases that contain information on OS patients by bioinformatics analyses. We first characterized individual ferroptosis scores and immune scores through gene set variation analysis (GSVA) against TARGET-OS datasets. We then identified differentially expressed genes by score groups. Weighted gene co-expression network analysis was performed to identify the most relevant ferroptosis-related and immune-related gene modules, which facilitated the identification of 327 ferroptosis gene and 306 immune gene candidates. A 4-gene (WAS, CORT, WNT16, and GLB1L2) signature was constructed and valuation using the least absolute shrinkage and selection operator-Cox regression models to effectively predict OS prognosis. The prediction efficiency was further validated by GSE39055. We stratified patients based on the prognostic scoring systems. Eight hub genes (namely CD3D, CD8A, CD3E, IL2, CD2, MYH6, MYH7, and MYL2) were identified, and TF-miRNA target regulatory networks were constructed. Furthermore, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, gene set enrichment analysis, and GSVA were used to determine the signature's potential pathways and biological functions, which showed that the hub genes were enriched in ferroptosis-associated biological functions and immune-associated molecular mechanisms. Thereafter, we investigated the proportion and infiltration extent of 22 infiltrating immune cells by using CIBERSORT, which revealed significant subgroup differences in CD8 + T cells, M0 macrophages, and M2 macrophages. In conclusion, we determined a new ferroptosis-related and immune-related gene signature for predicting OS patients' prognosis and further explored the ferroptosis and immunity interactions during OS development, which provides insights into the exploration of molecular mechanisms and targeted therapies in patients with OS.
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Affiliation(s)
- Guibin Li
- Department of Orthopaedics, Jilin Province FAW General Hospital, Changchun, Jilin, China
| | - Jie Lei
- Department of Hospital affairs, Jilin Province FAW General Hospital, Changchun, Jilin, China
| | - Dexin Xu
- Department of Orthopaedics, Jilin Province FAW General Hospital, Changchun, Jilin, China
| | - Wenchang Yu
- Department of Drug management, Jilin Province FAW General Hospital, Changchun, Jilin, China
| | - Jinping Bai
- Chronic disease outpatient service, Jilin Province FAW General Hospital, Changchun, Jilin, China
| | - Ge Wu
- Department of Clinical Pharmacy, Jilin Province FAW General Hospital, Changchun, Jilin, China.
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5
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Graham MK, Chikarmane R, Wang R, Vaghasia A, Gupta A, Zheng Q, Wodu B, Pan X, Castagna N, Liu J, Meyers J, Skaist A, Wheelan S, Simons BW, Bieberich C, Nelson WG, DeWeese TL, De Marzo AM, Yegnasubramanian S. Single-cell atlas of epithelial and stromal cell heterogeneity by lobe and strain in the mouse prostate. Prostate 2023; 83:286-303. [PMID: 36373171 DOI: 10.1002/pros.24460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 10/11/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND Evaluating the complex interplay of cell types in the tissue microenvironment is critical to understanding the origin and progression of diseases in the prostate and potential opportunities for intervention. Mouse models are an essential tool to investigate the molecular and cell-type-specific contributions of prostate disease at an organismal level. While there are well-documented differences in the extent, timing, and nature of disease development in various genetically engineered and exposure-based mouse models in different mouse strains and prostate lobes within each mouse strain, the underlying molecular phenotypic differences in cell types across mouse strains and prostate lobes are incompletely understood. METHODS In this study, we used single-cell RNA-sequencing (scRNA-seq) methods to assess the single-cell transcriptomes of 6-month-old mouse prostates from two commonly used mouse strains, friend virus B/NIH jackson (FVB/NJ) (N = 2) and C57BL/6J (N = 3). For each mouse, the lobes of the prostate were dissected (anterior, dorsal, lateral, and ventral), and individual scRNA-seq libraries were generated. In situ and pathological analyses were used to explore the spatial and anatomical distributions of novel cell types and molecular markers defining these cell types. RESULTS Data dimensionality reduction and clustering analysis of scRNA-seq data revealed that basal and luminal cells possessed strain-specific transcriptomic differences, with luminal cells also displaying marked lobe-specific differences. Gene set enrichment analysis comparing luminal cells by strain showed enrichment of proto-Oncogene targets in FVB/NJ mice. Additionally, three rare populations of epithelial cells clustered independently of strain and lobe: one population of luminal cells expressing Foxi1 and components of the vacuolar ATPase proton pump (Atp6v0d2 and Atp6v1g3), another population expressing Psca and other stem cell-associated genes (Ly6a/Sca-1, Tacstd2/Trop-2), and a neuroendocrine population expressing Chga, Chgb, and Syp. In contrast, stromal cell clusters, including fibroblasts, smooth muscle cells, endothelial cells, pericytes, and immune cell types, were conserved across strain and lobe, clustering largely by cell type and not by strain or lobe. One notable exception to this was the identification of two distinct fibroblast populations that we term subglandular fibroblasts and interstitial fibroblasts based on their strikingly distinct spatial distribution in the mouse prostate. CONCLUSIONS Altogether, these data provide a practical reference of the transcriptional profiles of mouse prostate from two commonly used mouse strains and across all four prostate lobes.
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Affiliation(s)
- Mindy K Graham
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland, USA
- School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Roshan Chikarmane
- School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Rulin Wang
- School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Ajay Vaghasia
- School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Anuj Gupta
- School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Qizhi Zheng
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Bulouere Wodu
- School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Xin Pan
- Department of Neurology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Nicole Castagna
- School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jianyong Liu
- School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Jennifer Meyers
- School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Alyza Skaist
- School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Sarah Wheelan
- School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Brian W Simons
- Center for Comparative Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - Charles Bieberich
- Department of Biological Sciences, University of Maryland at Baltimore County, Baltimore, Maryland, USA
| | - William G Nelson
- School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Theodore L DeWeese
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland, USA
- School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
| | - Angelo M De Marzo
- School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Srinivasan Yegnasubramanian
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland, USA
- School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
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6
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Transcripts of the Prostate Cancer-Associated Gene ANO7 Are Retained in the Nuclei of Prostatic Epithelial Cells. Int J Mol Sci 2023; 24:ijms24021052. [PMID: 36674564 PMCID: PMC9865797 DOI: 10.3390/ijms24021052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/02/2023] [Accepted: 01/03/2023] [Indexed: 01/06/2023] Open
Abstract
Prostate cancer affects millions of men globally. The prostate cancer-associated gene ANO7 is downregulated in advanced prostate cancer, whereas benign tissue and low-grade cancer display varying expression levels. In this study, we assess the spatial correlation between ANO7 mRNA and protein using fluorescent in situ hybridization and immunohistochemistry for the detection of mRNA and protein in parallel sections of tissue microarrays prepared from radical prostatectomy samples. We show that ANO7 mRNA and protein expression correlate in prostate tissue. Furthermore, we show that ANO7 mRNA is enriched in the nuclei of the luminal cells at 89% in benign ducts and low-grade cancer, and at 78% in high-grade cancer. The nuclear enrichment of ANO7 mRNA was validated in prostate cancer cell lines 22Rv1 and MDA PCa 2b using droplet digital polymerase chain reaction (ddPCR) on RNA isolated from nuclear and cytoplasmic fractions of the cells. The nuclear enrichment of ANO7 mRNA was compared to the nuclearly-enriched lncRNA MALAT1, confirming the surprisingly high nuclear retention of ANO7 mRNA. ANO7 has been suggested to be used as a diagnostic marker and a target for immunotherapy, but a full comprehension of its role in prostate cancer progression is currently lacking. Our results contribute to a better understanding of the dynamics of ANO7 expression in prostatic tissue.
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7
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Bogdanova NV, Radmanesh H, Ramachandran D, Knoechelmann AC, Christiansen H, Derlin T, von Klot CAJ, Merten R, Henkenberens C. The Prognostic Value of Liquid Biopsies for Benefit of Salvage Radiotherapy in Relapsed Oligometastatic Prostate Cancer. Cancers (Basel) 2022; 14:cancers14174095. [PMID: 36077632 PMCID: PMC9454496 DOI: 10.3390/cancers14174095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 08/15/2022] [Accepted: 08/22/2022] [Indexed: 11/28/2022] Open
Abstract
Simple Summary Around 30% of patients with oligometastatic prostate cancer relapse will benefit from local PET/CT-guided ablative radiotherapy (RT) with improved progression-free and ADT (Androgene Deprivation Therapy)-free survivals. Therefore, there is an urgent need for predictive testing for therapeutic benefits prior to initiation. Various tests have already been established on tumor specimens for the prediction of prostate cancer’s behavior or therapy outcome. However, in imaging-proven relapse tumor tissue from the local recurrence or metastases is often not available. Hence, there is a need for a liquid biopsy-based testing. We aimed to assess the prognostic value of CTCs- associated mRNA and blood-derived RNA for the benefit of PSMA PET-guided salvage RT in oligometastatic prostate cancer relapses. Significant correlations were found between the relative transcript levels of several investigated genes and clinicopathological parameters. Furthermore, distinct “transcriptional signatures” were found in patients with temporary and long-term benefits from RT. Abstract To assess the prognostic value of “liquid biopsies” for the benefit of salvage RT in oligometastatic prostate cancer relapse, we enrolled 44 patients in the study between the years 2016 and 2020. All the patients were diagnosed as having an oligometastatic prostate cancer relapse on prostate-specific membrane antigen (PSMA)-targeted PET-CT and underwent irradiation at the Department of Radiotherapy at the Hannover Medical School. Tumor cells and total RNA, enriched from the liquid biopsies of patients, were processed for the subsequent quantification analysis of relative transcript levels in real-time PCR. In total, 54 gene transcripts known or suggested to be associated with prostate cancer or treatment outcome were prioritized for analysis. We found significant correlations between the relative transcript levels of several investigated genes and the Gleason score, PSA (prostate-specific antigen) value, or UICC stage (tumor node metastasis -TNM classification of malignant tumors from Union for International Cancer Control). Furthermore, a significant association of MTCO2, FOXM1, SREBF1, HOXB7, FDXR, and MTRNR transcript profiles was found with a temporary and/or long-term benefit from RT. Further studies on larger patients cohorts are necessary to prove our preliminary findings for establishing liquid biopsy tests as a predictive examination method prior to salvage RT.
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Affiliation(s)
- Natalia V. Bogdanova
- Department of Radiation Oncology, Hannover Medical School, 30625 Hannover, Germany
| | - Hoda Radmanesh
- Department of Radiation Oncology, Hannover Medical School, 30625 Hannover, Germany
| | - Dhanya Ramachandran
- Gynecology Research Unit, Clinics of Obstetrics and Gynaecology, Hannover Medical School, 30625 Hannover, Germany
| | | | - Hans Christiansen
- Department of Radiation Oncology, Hannover Medical School, 30625 Hannover, Germany
| | - Thorsten Derlin
- Department of Nuclear Medicine, Hannover Medical School, 30625 Hannover, Germany
| | | | - Roland Merten
- Department of Radiation Oncology, Hannover Medical School, 30625 Hannover, Germany
- Correspondence: ; Tel.: +49-(0)-511-532-3590
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8
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Mou Z, Spencer J, Knight B, John J, McCullagh P, McGrath JS, Harries LW. Gene expression analysis reveals a 5-gene signature for progression-free survival in prostate cancer. Front Oncol 2022; 12:914078. [PMID: 36033512 PMCID: PMC9413154 DOI: 10.3389/fonc.2022.914078] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 07/14/2022] [Indexed: 11/13/2022] Open
Abstract
Prostate cancer (PCa) is the second most common male cancer worldwide, but effective biomarkers for the presence or progression risk of disease are currently elusive. In a series of nine matched histologically confirmed PCa and benign samples, we carried out an integrated transcriptome-wide gene expression analysis, including differential gene expression analysis and weighted gene co-expression network analysis (WGCNA), which identified a set of potential gene markers highly associated with tumour status (malignant vs. benign). We then used these genes to establish a minimal progression-free survival (PFS)-associated gene signature (GS) (PCBP1, PABPN1, PTPRF, DANCR, and MYC) using least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox regression analyses from The Cancer Genome Atlas prostate adenocarcinoma (TCGA-PRAD) dataset. Our signature was able to predict PFS over 1, 3, and 5 years in TCGA-PRAD dataset, with area under the curve (AUC) of 0.64–0.78, and our signature remained as a prognostic factor independent of age, Gleason score, and pathological T and N stages. A nomogram combining the signature and Gleason score demonstrated improved predictive capability for PFS (AUC: 0.71–0.85) and was superior to the Cambridge Prognostic Group (CPG) model alone and some conventionally used clinicopathological factors in predicting PFS. In conclusion, we have identified and validated a novel five-gene signature and established a nomogram that effectively predicted PFS in patients with PCa. Findings may improve current prognosis tools for PFS and contribute to clinical decision-making in PCa treatment.
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Affiliation(s)
- Zhuofan Mou
- Institute of Biomedical and Clinical Sciences, University of Exeter Medical School, Devon, United Kingdom
| | - Jack Spencer
- Translational Research Exchange at Exeter, Living Systems Institute, University of Exeter, Exeter, United Kingdom
| | - Bridget Knight
- National Institute for Health and Care Research (NIHR) Exeter Clinical Research Facility, Royal Devon and Exeter National Health Service (NHS) Foundation Trust, Royal Devon and Exeter Hospital, Exeter, United Kingdom
| | - Joseph John
- Institute of Biomedical and Clinical Sciences, University of Exeter Medical School, Devon, United Kingdom
- Exeter Surgical Health Services Research Unit, Royal Devon and Exeter National Health Service (NHS) Foundation Trust, Exeter, United Kingdom
| | - Paul McCullagh
- Department of Pathology, Royal Devon and Exeter National Health Service (NHS) Foundation Trust, Exeter, United Kingdom
| | - John S. McGrath
- Institute of Biomedical and Clinical Sciences, University of Exeter Medical School, Devon, United Kingdom
- Exeter Surgical Health Services Research Unit, Royal Devon and Exeter National Health Service (NHS) Foundation Trust, Exeter, United Kingdom
| | - Lorna W. Harries
- Institute of Biomedical and Clinical Sciences, University of Exeter Medical School, Devon, United Kingdom
- *Correspondence: Lorna W. Harries,
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9
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Xin S, Sun X, Jin L, Li W, Liu X, Zhou L, Ye L. The Prognostic Signature and Therapeutic Value of Phagocytic Regulatory Factors in Prostate Adenocarcinoma (PRAD). Front Genet 2022; 13:877278. [PMID: 35706452 PMCID: PMC9190300 DOI: 10.3389/fgene.2022.877278] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 04/19/2022] [Indexed: 01/03/2023] Open
Abstract
There is growing evidence that phagocytosis regulatory factors (PRFs) play important roles in tumor progression, and therefore, identifying and characterizing these factors is crucial for understanding the mechanisms of cellular phagocytosis in tumorigenesis. Our research aimed to comprehensively characterize PRFs in prostate adenocarcinoma (PRAD) and to screen and determine important PRFs in PRAD which may help to inform tumor prognostic and therapeutic signatures based on these key PRFs. Here, we first systematically described the expression of PRFs in PRAD and evaluated their expression patterns and their prognostic value. We then analyzed prognostic phagocytic factors by Cox and Lasso analysis and constructed a phagocytic factor-mediated risk score. We then divided the samples into two groups with significant differences in overall survival (OS) based on the risk score. Then, we performed correlation analysis between the risk score and clinical features, immune infiltration levels, immune characteristics, immune checkpoint expression, IC50 of several classical sensitive drugs, and immunotherapy efficacy. Finally, the Human Protein Atlas (HPA) database was used to determine the protein expression of 18 PRF characteristic genes. The aforementioned results confirmed that multilayer alterations of PRFs were associated with the prognosis of patients with PRAD and the degree of macrophage infiltration. These findings may provide us with potential new therapies for PRAD.
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Affiliation(s)
- Shiyong Xin
- Department of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xianchao Sun
- Department of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Liang Jin
- Department of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Weiyi Li
- Department of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xiang Liu
- Department of Urology, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Liqing Zhou
- Department of Rheumatology and Immunology, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, China
| | - Lin Ye
- Department of Urology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China
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10
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Wahlström G, Heron S, Knuuttila M, Kaikkonen E, Tulonen N, Metsälä O, Löf C, Ettala O, Boström PJ, Taimen P, Poutanen M, Schleutker J. The variant rs77559646 associated with aggressive prostate cancer disrupts ANO7 mRNA splicing and protein expression. Hum Mol Genet 2022; 31:2063-2077. [PMID: 35043958 PMCID: PMC9239746 DOI: 10.1093/hmg/ddac012] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 12/17/2021] [Accepted: 01/10/2022] [Indexed: 12/05/2022] Open
Abstract
Prostate cancer is among the most common cancers in men, with a large fraction of the individual risk attributable to heritable factors. A majority of the diagnosed cases does not lead to a lethal disease, and hence biological markers that can distinguish between indolent and fatal forms of the disease are of great importance for guiding treatment decisions. Although over 300 genetic variants are known to be associated with prostate cancer risk, few have been associated with the risk of an aggressive disease. One such variant is rs77559646 located in ANO7. This variant has a dual function. It constitutes a missense mutation in the short isoform of ANO7 and a splice region mutation in full-length ANO7. In this study, we have analyzed the impact of the variant allele of rs77559646 on ANO7 mRNA splicing using a minigene splicing assay and by performing splicing analysis with the tools IRFinder (intron retention finder), rMATS (replicate multivariate analysis of transcript splicing) and LeafCutter on RNA sequencing data from prostate tissue of six rs77559646 variant allele carriers and 43 non-carriers. The results revealed a severe disruption of ANO7 mRNA splicing in rs77559646 variant allele carriers. Immunohistochemical analysis of prostate samples from patients homozygous for the rs77559646 variant allele demonstrated a loss of apically localized ANO7 protein. Our study is the first to provide a mechanistic explanation for the impact of a prostate cancer risk SNP on ANO7 protein production. Furthermore, the rs77559646 variant is the first known germline loss-of-function mutation described for ANO7. We suggest that loss of ANO7 contributes to prostate cancer progression.
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Affiliation(s)
- Gudrun Wahlström
- Cancer Research Unit, Institute of Biomedicine, University of Turku, 20520 Turku, Finland
- FICAN West Cancer Centre, University of Turku and Turku University Hospital, 20520 Turku, Finland
| | - Samuel Heron
- Cancer Research Unit, Institute of Biomedicine, University of Turku, 20520 Turku, Finland
- FICAN West Cancer Centre, University of Turku and Turku University Hospital, 20520 Turku, Finland
| | - Matias Knuuttila
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, 20520 Turku, Finland
- FICAN West Cancer Centre, University of Turku and Turku University Hospital, 20520 Turku, Finland
- Turku Center for Disease Modeling (TCDM), University of Turku, 20520 Turku, Finland
| | - Elina Kaikkonen
- Cancer Research Unit, Institute of Biomedicine, University of Turku, 20520 Turku, Finland
- FICAN West Cancer Centre, University of Turku and Turku University Hospital, 20520 Turku, Finland
| | - Nea Tulonen
- Cancer Research Unit, Institute of Biomedicine, University of Turku, 20520 Turku, Finland
- FICAN West Cancer Centre, University of Turku and Turku University Hospital, 20520 Turku, Finland
| | - Olli Metsälä
- Cancer Research Unit, Institute of Biomedicine, University of Turku, 20520 Turku, Finland
- FICAN West Cancer Centre, University of Turku and Turku University Hospital, 20520 Turku, Finland
| | - Christoffer Löf
- Cancer Research Unit, Institute of Biomedicine, University of Turku, 20520 Turku, Finland
- FICAN West Cancer Centre, University of Turku and Turku University Hospital, 20520 Turku, Finland
| | - Otto Ettala
- Department of Urology, Turku University Hospital, 20520 Turku, Finland
| | - Peter J Boström
- Department of Urology, Turku University Hospital, 20520 Turku, Finland
| | - Pekka Taimen
- Cancer Research Unit, Institute of Biomedicine, University of Turku, 20520 Turku, Finland
- FICAN West Cancer Centre, University of Turku and Turku University Hospital, 20520 Turku, Finland
- Department of Pathology, Turku University Hospital, 20520 Turku, Finland
| | - Matti Poutanen
- Research Centre for Integrative Physiology and Pharmacology, Institute of Biomedicine, University of Turku, 20520 Turku, Finland
- FICAN West Cancer Centre, University of Turku and Turku University Hospital, 20520 Turku, Finland
- Turku Center for Disease Modeling (TCDM), University of Turku, 20520 Turku, Finland
| | - Johanna Schleutker
- To whom correspondence should be addressed at: Institute of Biomedicine, University of Turku, Kiinamyllynkatu 10, 20520 Turku, Finland. Tel: +358 294502726; Fax: +358 294505040;
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11
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Rintala TJ, Federico A, Latonen L, Greco D, Fortino V. A systematic comparison of data- and knowledge-driven approaches to disease subtype discovery. Brief Bioinform 2021; 22:6350885. [PMID: 34396389 PMCID: PMC8575038 DOI: 10.1093/bib/bbab314] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 07/05/2021] [Accepted: 07/20/2021] [Indexed: 12/14/2022] Open
Abstract
Typical clustering analysis for large-scale genomics data combines two unsupervised learning techniques: dimensionality reduction and clustering (DR-CL) methods. It has been demonstrated that transforming gene expression to pathway-level information can improve the robustness and interpretability of disease grouping results. This approach, referred to as biological knowledge-driven clustering (BK-CL) approach, is often neglected, due to a lack of tools enabling systematic comparisons with more established DR-based methods. Moreover, classic clustering metrics based on group separability tend to favor the DR-CL paradigm, which may increase the risk of identifying less actionable disease subtypes that have ambiguous biological and clinical explanations. Hence, there is a need for developing metrics that assess biological and clinical relevance. To facilitate the systematic analysis of BK-CL methods, we propose a computational protocol for quantitative analysis of clustering results derived from both DR-CL and BK-CL methods. Moreover, we propose a new BK-CL method that combines prior knowledge of disease relevant genes, network diffusion algorithms and gene set enrichment analysis to generate robust pathway-level information. Benchmarking studies were conducted to compare the grouping results from different DR-CL and BK-CL approaches with respect to standard clustering evaluation metrics, concordance with known subtypes, association with clinical outcomes and disease modules in co-expression networks of genes. No single approach dominated every metric, showing the importance multi-objective evaluation in clustering analysis. However, we demonstrated that, on gene expression data sets derived from TCGA samples, the BK-CL approach can find groupings that provide significant prognostic value in both breast and prostate cancers.
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Affiliation(s)
- Teemu J Rintala
- Institute of Biomedicine University of Eastern Finland, Yliopistonranta 1 E, 70210 Kuopio, Finland
| | - Antonio Federico
- Faculty of Medicine and Health Technology Tampere University, Kalevantie, 4 33100 Tampere, Finland.,BioMediTech Institute Tampere University, Kalevantie 4, 33100 Tampere, Finland
| | - Leena Latonen
- Institute of Biomedicine University of Eastern Finland, Yliopistonranta 1 E, 70210 Kuopio, Finland
| | - Dario Greco
- Faculty of Medicine and Health Technology Tampere University, Kalevantie, 4 33100 Tampere, Finland.,BioMediTech Institute Tampere University, Kalevantie 4, 33100 Tampere, Finland.,Institute of Biotechnology University of Helsinki, Viikinkaari 5d, 00014 Helsinki, Finland
| | - Vittorio Fortino
- Institute of Biomedicine University of Eastern Finland, Yliopistonranta 1 E, 70210 Kuopio, Finland
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12
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Nguyen HTN, Xue H, Firlej V, Ponty Y, Gallopin M, Gautheret D. Reference-free transcriptome signatures for prostate cancer prognosis. BMC Cancer 2021; 21:394. [PMID: 33845808 PMCID: PMC8040209 DOI: 10.1186/s12885-021-08021-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 03/09/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND RNA-seq data are increasingly used to derive prognostic signatures for cancer outcome prediction. A limitation of current predictors is their reliance on reference gene annotations, which amounts to ignoring large numbers of non-canonical RNAs produced in disease tissues. A recently introduced kind of transcriptome classifier operates entirely in a reference-free manner, relying on k-mers extracted from patient RNA-seq data. METHODS In this paper, we set out to compare conventional and reference-free signatures in risk and relapse prediction of prostate cancer. To compare the two approaches as fairly as possible, we set up a common procedure that takes as input either a k-mer count matrix or a gene expression matrix, extracts a signature and evaluates this signature in an independent dataset. RESULTS We find that both gene-based and k-mer based classifiers had similarly high performances for risk prediction and a markedly lower performance for relapse prediction. Interestingly, the reference-free signatures included a set of sequences mapping to novel lncRNAs or variable regions of cancer driver genes that were not part of gene-based signatures. CONCLUSIONS Reference-free classifiers are thus a promising strategy for the identification of novel prognostic RNA biomarkers.
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Affiliation(s)
- Ha T N Nguyen
- Institute for Integrative Biology of the Cell, UMR 9198, CEA, CNRS, Université Paris-Saclay, Gif-Sur-Yvette, France
| | - Haoliang Xue
- Institute for Integrative Biology of the Cell, UMR 9198, CEA, CNRS, Université Paris-Saclay, Gif-Sur-Yvette, France
| | - Virginie Firlej
- Institute of Biology, Université Paris Est Creteil, Creteil, Creteil, France
| | - Yann Ponty
- LIX CNRS UMR 7161, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
| | - Melina Gallopin
- Institute for Integrative Biology of the Cell, UMR 9198, CEA, CNRS, Université Paris-Saclay, Gif-Sur-Yvette, France
| | - Daniel Gautheret
- Institute for Integrative Biology of the Cell, UMR 9198, CEA, CNRS, Université Paris-Saclay, Gif-Sur-Yvette, France.
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13
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Molecular Expression of Some Oncogenes and Predisposing Behaviors Contributing to the Aggressiveness of Prostate Cancer. Rep Biochem Mol Biol 2021; 10:60-68. [PMID: 34277869 DOI: 10.52547/rbmb.10.1.60] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 09/24/2020] [Indexed: 11/18/2022]
Abstract
Background Prostate cancer is the second most common cancer in men in Iran. It can be treated in the early stages of the disease; therefore, early diagnosis can be lifesaving. The aim of this study was to investigate the molecular expression of some oncogenes and predisposing behaviors contributing to the aggressiveness of prostate cancer. Methods In this case-control study, prostate cancer specimens were collected from both patients and healthy volunteers. Several factors such as age, family history, smoking, and stage of the disease, were investigated based on the criteria of this study. Real-time PCR was used to measure the expression of four oncogenes. Statistical analysis of our data was carried out using SPSS software version 22. Results The X2 test showed that there was a difference in the incidence of prostate cancer in different age groups (X2= 9.30; p= 0.026). Although data analysis by the X2 test showed that family history had a significant effect on prostate cancer (X2= 14.43; p= 0.001), smoking did not show a significant effect on the incidence of this disorder (X2= 4.67; p= 0.097). The T2N1M0 stage is the most common form of prostate cancer in patients with family history of prostate cancer and the habit of smoking. Also, the expression of KRAS1P, GLB1L2, SChLAP1 and PACSIN3 oncogenes reduced in prostate cancer samples compared to the control group. Conclusion Overall, functional interpretation of gene expression in the prostate tissue can affect tumor progression. Yet, further practical studies are required to reveal the accurate underlying mechanisms.
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14
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Sopyllo K, Erickson AM, Mirtti T. Grading Evolution and Contemporary Prognostic Biomarkers of Clinically Significant Prostate Cancer. Cancers (Basel) 2021; 13:cancers13040628. [PMID: 33562508 PMCID: PMC7914622 DOI: 10.3390/cancers13040628] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Revised: 01/26/2021] [Accepted: 01/28/2021] [Indexed: 12/20/2022] Open
Abstract
Simple Summary Prostate cancer treatment decisions are based on clinical stage and histological diagnosis, including Gleason grading assessed by a pathologist, in biopsies. Prior to staging and grading, serum or blood prostate-specific antigen (PSA) levels are measured and often trigger diagnostic examinations. However, PSA is best suited as a marker of cancer relapse after initial treatment. In this review, we first narratively describe the evolution of histological grading, the current status of Gleason pattern-based diagnostics and glance into future methodology of risk assessment by histological examination. In the second part, we systematically review the biomarkers that have been shown, independent from clinical characteristics, to correlate with clinically relevant end-points, i.e., occurrence of metastases, disease-specific mortality and overall survival after initial treatment of localized prostate cancer. Abstract Gleason grading remains the strongest prognostic parameter in localized prostate adenocarcinoma. We have here outlined the evolution and contemporary practices in pathological evaluation of prostate tissue samples for Gleason score and Grade group. The state of more observer-independent grading methods with the aid of artificial intelligence is also reviewed. Additionally, we conducted a systematic review of biomarkers that hold promise in adding independent prognostic or predictive value on top of clinical parameters, Grade group and PSA. We especially focused on hard end points during the follow-up, i.e., occurrence of metastasis, disease-specific mortality and overall mortality. In peripheral blood, biopsy-detected prostate cancer or in surgical specimens, we can conclude that there are more than sixty biomarkers that have been shown to have independent prognostic significance when adjusted to conventional risk assessment or grouping. Our search brought up some known putative markers and panels, as expected. Also, the synthesis in the systematic review indicated markers that ought to be further studied as part of prospective trials and in well characterized patient cohorts in order to increase the resolution of the current clinico-pathological prognostic factors.
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Affiliation(s)
- Konrad Sopyllo
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland;
| | - Andrew M. Erickson
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford OX3 9DU, UK;
| | - Tuomas Mirtti
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, 00014 Helsinki, Finland;
- Department of Pathology, HUS Diagnostic Centre, Helsinki University Hospital, 00029 Helsinki, Finland
- Correspondence:
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15
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Thomas PB, Jeffery P, Gahete MD, Whiteside E, Walpole C, Maugham M, Jovanovic L, Gunter J, Williams E, Nelson C, Herington A, Luque RM, Veedu R, Chopin LK, Seim I. The long non-coding RNA GHSROS reprograms prostate cancer cell lines toward a more aggressive phenotype. PeerJ 2021; 9:e10280. [PMID: 33585078 PMCID: PMC7860111 DOI: 10.7717/peerj.10280] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2019] [Accepted: 10/09/2020] [Indexed: 12/27/2022] Open
Abstract
It is now appreciated that long non-coding RNAs (lncRNAs) are important players in orchestrating cancer progression. In this study we characterized GHSROS, a human lncRNA gene on the opposite DNA strand (antisense) to the ghrelin receptor gene, in prostate cancer. The lncRNA was upregulated by prostate tumors from different clinical datasets. Transcriptome data revealed that GHSROS alters the expression of cancer-associated genes. Functional analyses in vitro showed that GHSROS mediates tumor growth, migration and survival, and resistance to the cytotoxic drug docetaxel. Increased cellular proliferation of GHSROS-overexpressing PC3, DU145, and LNCaP prostate cancer cell lines in vitro was recapitulated in a subcutaneous xenograft model. Conversely, in vitro antisense oligonucleotide inhibition of the lncRNA reciprocally regulated cell growth and migration, and gene expression. Notably, GHSROS modulates the expression of PPP2R2C, the loss of which may drive androgen receptor pathway-independent prostate tumor progression in a subset of prostate cancers. Collectively, our findings suggest that GHSROS can reprogram prostate cancer cells toward a more aggressive phenotype and that this lncRNA may represent a potential therapeutic target.
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Affiliation(s)
- Patrick B. Thomas
- Ghrelin Research Group, Translational Research Institute, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- Comparative and Endocrine Biology Laboratory, Translational Research Institute, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- Australian Prostate Cancer Research Centre - Queensland, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Penny Jeffery
- Ghrelin Research Group, Translational Research Institute, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- Comparative and Endocrine Biology Laboratory, Translational Research Institute, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- Australian Prostate Cancer Research Centre - Queensland, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Manuel D. Gahete
- Maimonides Institute of Biomedical Research of Cordoba (IMIBIC), Cordoba, Spain
- Department of Cell Biology, Physiology and Immunology, University of Cordoba, Cordoba, Spain
- Hospital Universitario Reina Sofía (HURS), Cordoba, Spain
- Campus de Excelencia Internacional Agroalimentario (ceiA3), Cordoba, Spain
- CIBER de la Fisiopatología de la Obesidad y Nutrición (CIBERobn), Cordoba, Spain
| | - Eliza Whiteside
- Centre for Health Research, University of Southern Queensland, Toowoomba, Queensland, Australia
- Institute for Life Sciences and the Environment, University of Southern Queensland, Toowoomba, Queensland, Australia
| | - Carina Walpole
- Ghrelin Research Group, Translational Research Institute, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- Australian Prostate Cancer Research Centre - Queensland, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Michelle Maugham
- Ghrelin Research Group, Translational Research Institute, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- Comparative and Endocrine Biology Laboratory, Translational Research Institute, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- Australian Prostate Cancer Research Centre - Queensland, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Lidija Jovanovic
- Australian Prostate Cancer Research Centre - Queensland, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Jennifer Gunter
- Australian Prostate Cancer Research Centre - Queensland, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Elizabeth Williams
- Australian Prostate Cancer Research Centre - Queensland, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Colleen Nelson
- Australian Prostate Cancer Research Centre - Queensland, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Adrian Herington
- Ghrelin Research Group, Translational Research Institute, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- Australian Prostate Cancer Research Centre - Queensland, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Raul M. Luque
- Maimonides Institute of Biomedical Research of Cordoba (IMIBIC), Cordoba, Spain
- Department of Cell Biology, Physiology and Immunology, University of Cordoba, Cordoba, Spain
- Hospital Universitario Reina Sofía (HURS), Cordoba, Spain
- Campus de Excelencia Internacional Agroalimentario (ceiA3), Cordoba, Spain
- CIBER de la Fisiopatología de la Obesidad y Nutrición (CIBERobn), Cordoba, Spain
| | - Rakesh Veedu
- Centre for Comparative Genomics, Murdoch University, Perth, Western Australia, Australia
| | - Lisa K. Chopin
- Ghrelin Research Group, Translational Research Institute, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- Comparative and Endocrine Biology Laboratory, Translational Research Institute, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- Australian Prostate Cancer Research Centre - Queensland, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Inge Seim
- Ghrelin Research Group, Translational Research Institute, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- Comparative and Endocrine Biology Laboratory, Translational Research Institute, School of Biomedical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
- Australian Prostate Cancer Research Centre - Queensland, Queensland University of Technology, Brisbane, Queensland, Australia
- Integrative Biology Laboratory, College of Life Sciences, Nanjing Normal University, Nanjing, China
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16
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Labbate CV, Paner GP, Eggener SE. Should Grade Group 1 (GG1) be called cancer? World J Urol 2021; 40:15-19. [PMID: 33432506 DOI: 10.1007/s00345-020-03583-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 12/24/2020] [Indexed: 11/25/2022] Open
Abstract
INTRODUCTION ISUP Grade Group 1 prostate cancer is the lowest histologic grade of prostate cancer with a clinically indolent course. Removal of the term 'cancer' has been proposed and has historical precedent both in urothelial and thyroid carcinoma. METHODS Evidence-based review identifying arguments for and against Grade Group 1 being referred to as cancer. RESULTS Grade Group 1 has histologic evidence of tissue microinvasion and 0.3-3% rate of extraprostatic extension. Genomic evaluation suggests overlap of a minority of Grade Group 1 cancers with those of Grade Group 2. Conversely, Grade Group 1 tumors appear to have distinct genetic and genomic profiles from Grade Group 3 or higher tumors. Grade Group 1 has no documented ability for regional or distant metastasis and long-term follow up after treatment or active surveillance is safe with excellent oncologic outcomes. DISCUSSION Grade Group 1 prostate cancer, while showing evidence of neoplasia on histology has a remarkably indolent natural history more akin to non-neoplastic precursor lesions. Consideration should be given to renaming Grade Group 1 prostate cancer, which has the potential to minimize overtreatment, treatment-related side effects, patient anxiety, and financial burden on the healthcare system.
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Affiliation(s)
- Craig V Labbate
- Section of Urology, Department of Surgery, University of Chicago Medicine, Chicago, IL, USA
| | - Gladell P Paner
- Department of Pathology, University of Chicago Medicine, Chicago, IL, USA
| | - Scott E Eggener
- Section of Urology, Department of Surgery, University of Chicago Medicine, Chicago, IL, USA.
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17
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Kheirkhah S, Javanzad M, Hoseinzadeh M, Hekmati Azar Mehrabani Z, Mohammadzadeh N, Monfaredan A. Monitoring prostate cancer (PCa) with appraise the gene expression of PRUNE2, NCAPD3 and ASPA and their connection with age, family history and tumor stage. GENE REPORTS 2020. [DOI: 10.1016/j.genrep.2020.100840] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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18
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Bioinformatics analysis of the genes involved in the extension of prostate cancer to adjacent lymph nodes by supervised and unsupervised machine learning methods: The role of SPAG1 and PLEKHF2. Genomics 2020; 112:3871-3882. [PMID: 32619574 DOI: 10.1016/j.ygeno.2020.06.035] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 06/11/2020] [Accepted: 06/22/2020] [Indexed: 12/12/2022]
Abstract
The present study aimed to identify the genes associated with the involvement of adjunct lymph nodes of patients with prostate cancer (PCa) and to provide valuable information for the identification of potential diagnostic biomarkers and pathological genes in PCa metastasis. The most important candidate genes were identified through several machine learning approaches including K-means clustering, neural network, Naïve Bayesian classifications and PCA with or without downsampling. In total, 21 genes associated with lymph nodes involvement were identified. Among them, nine genes have been identified in metastatic prostate cancer, six have been found in the other metastatic cancers and four in other local cancers. The amplification of the candidate genes was evaluated in the other PCa datasets. Besides, we identified a validated set of genes involved in the PCa metastasis. The amplification of SPAG1 and PLEKHF2 genes were associated with decreased survival in patients with PCa.
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19
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Kudryavtseva AV, Lukyanova EN, Kharitonov SL, Nyushko KM, Krasheninnikov AA, Pudova EA, Guvatova ZG, Alekseev BY, Kiseleva MV, Kaprin AD, Dmitriev AA, Snezhkina AV, Krasnov GS. Bioinformatic identification of differentially expressed genes associated with prognosis of locally advanced lymph node-positive prostate cancer. J Bioinform Comput Biol 2020; 17:1950003. [PMID: 30866732 DOI: 10.1142/s0219720019500033] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Prostate cancer (PCa) is one of the primary causes of cancer-related mortality in men worldwide. Patients with locally advanced PCa with metastases in regional lymph nodes are usually marked as a high-risk group. One of the chief concerns for this group is to make an informed decision about the necessity of conducting adjuvant androgen deprivation therapy after radical surgical treatment. During the oncogenic transformation and progression of the disease, the expression of many genes is altered. Some of these genes can serve as markers for diagnosis, predicting the prognosis or effectiveness of drug therapy, as well as possible therapeutic targets. We undertook bioinformatic analysis of the RNA-seq data deposited in The Cancer Genome Atlas consortium database to identify possible prognostic markers. We compared the groups with favorable and unfavorable prognosis for the cohort of patients with PCa showing lymph node metastasis (pT2N1M0, pT3N1M0, and pT4N1M0) and for the most common molecular type carrying the fusion transcript TMPRSS2-ERG. For the entire cohort, we revealed at least six potential markers (IDO1, UGT2B15, IFNG, MUC6, CXCL11, and GBP1). Most of these genes are involved in the positive regulation of immune response. For the TMPRSS2-ERG subtype, we also identified six genes, the expression of which may be associated with prognosis: TOB1, GALNT7, INAFM1, APELA, RAC3, and NNMT. The identified genes, after additional studies and validation in the extended cohort, could serve as a prognostic marker of locally advanced lymph node-positive PCa.
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Affiliation(s)
- Anna V Kudryavtseva
- * Laboratory of Postgenomic Research, Engelhardt Institute of Molecular Biology Russian Academy of Sciences, Vavilova 32, Moscow 119991, Russian Federation
| | - Elena N Lukyanova
- * Laboratory of Postgenomic Research, Engelhardt Institute of Molecular Biology Russian Academy of Sciences, Vavilova 32, Moscow 119991, Russian Federation
| | - Sergey L Kharitonov
- * Laboratory of Postgenomic Research, Engelhardt Institute of Molecular Biology Russian Academy of Sciences, Vavilova 32, Moscow 119991, Russian Federation
| | - Kirill M Nyushko
- † Federal State Budgetary Institution, National Medical Research Radiological Center of the Ministry of Health of the Russian Federation, 4 Korolev Str., Obninsk 249036, Russian Federation
| | - Alexey A Krasheninnikov
- † Federal State Budgetary Institution, National Medical Research Radiological Center of the Ministry of Health of the Russian Federation, 4 Korolev Str., Obninsk 249036, Russian Federation
| | - Elena A Pudova
- * Laboratory of Postgenomic Research, Engelhardt Institute of Molecular Biology Russian Academy of Sciences, Vavilova 32, Moscow 119991, Russian Federation
| | - Zulfiya G Guvatova
- * Laboratory of Postgenomic Research, Engelhardt Institute of Molecular Biology Russian Academy of Sciences, Vavilova 32, Moscow 119991, Russian Federation
| | - Boris Y Alekseev
- † Federal State Budgetary Institution, National Medical Research Radiological Center of the Ministry of Health of the Russian Federation, 4 Korolev Str., Obninsk 249036, Russian Federation
| | - Marina V Kiseleva
- † Federal State Budgetary Institution, National Medical Research Radiological Center of the Ministry of Health of the Russian Federation, 4 Korolev Str., Obninsk 249036, Russian Federation
| | - Andrey D Kaprin
- † Federal State Budgetary Institution, National Medical Research Radiological Center of the Ministry of Health of the Russian Federation, 4 Korolev Str., Obninsk 249036, Russian Federation
| | - Alexey A Dmitriev
- * Laboratory of Postgenomic Research, Engelhardt Institute of Molecular Biology Russian Academy of Sciences, Vavilova 32, Moscow 119991, Russian Federation
| | - Anastasiya V Snezhkina
- * Laboratory of Postgenomic Research, Engelhardt Institute of Molecular Biology Russian Academy of Sciences, Vavilova 32, Moscow 119991, Russian Federation
| | - George S Krasnov
- * Laboratory of Postgenomic Research, Engelhardt Institute of Molecular Biology Russian Academy of Sciences, Vavilova 32, Moscow 119991, Russian Federation
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20
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Cheng B, He Q, Cheng Y, Yang H, Pei L, Deng Q, Long H, Zhu L, Jiang R. A Three-Gene Classifier Associated With MicroRNA-Mediated Regulation Predicts Prostate Cancer Recurrence After Radical Prostatectomy. Front Genet 2020; 10:1402. [PMID: 32117427 PMCID: PMC7011265 DOI: 10.3389/fgene.2019.01402] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 12/23/2019] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND AND OBJECTIVE After radical prostatectomy (RP), prostate cancer (PCa) patients may experience biochemical recurrence (BCR) and clinical recurrence, which remains a dominant issue in PCa treatment. The purpose of this study was to identify a protein-coding gene classifier associated with microRNA (miRNA)-mediated regulation to provide a comprehensive prognostic index to predict PCa recurrence after RP. METHODS Candidate classifiers were constructed using two machine-learning algorithms (a least absolute shrinkage and selector operation [LASSO]-based classifier and a decision tree-based classifier) based on a discovery cohort (n = 156) from The Cancer Genome Atlas (TCGA) database. After selecting the LASSO-based classifier based on the prediction accuracy, both an internal validation cohort (n = 333) and an external validation cohort (n = 100) were used to examined the classifier using survival analysis, time-dependent receiver operating characteristic (ROC) curve analysis, and univariate and multivariate Cox proportional hazards regression analyses. Functional enrichment analysis of co-expressed genes was carried out to explore the underlying moleculer mechanisms of the genes included in the classifier. RESULTS We constructed a three-gene classifier that included FAM72B, GNE, and TRIM46, and we identified four upstream prognostic miRNAs (hsa-miR-133a-3p, hsa-miR-222-3p, hsa-miR-1301-3p, and hsa-miR-30c-2-3p). The classifier exhibited a remarkable ability (area under the curve [AUC] = 0.927) to distinguish PCa patients with high and low Gleason scores in the discovery cohort. Furthermore, it was significantly associated with clinical recurrence (p < 0.0001, log rank statistic = 20.7, AUC = 0.733) and could serve as an independent prognostic factor of recurrence-free survival (hazard ratio: 1.708, 95% CI: 1.180-2.472, p < 0.001). Additionally, it was a predictor of BCR according to BCR-free survival analysis (p = 0.0338, log rank statistic = 4.51). CONCLUSIONS The three-gene classifier associated with miRNA-mediated regulation may serve as a novel prognostic biomarker for PCa patients after RP.
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Affiliation(s)
- Bo Cheng
- Department of Urology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Qidan He
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yong Cheng
- Department of Urology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Haifan Yang
- Department of Urology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Lijun Pei
- Department of Urology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Qingfu Deng
- Department of Urology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Hao Long
- Department of Urology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Likun Zhu
- Department of Urology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Rui Jiang
- Department of Urology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
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21
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Pudova EA, Lukyanova EN, Nyushko KM, Mikhaylenko DS, Zaretsky AR, Snezhkina AV, Savvateeva MV, Kobelyatskaya AA, Melnikova NV, Volchenko NN, Efremov GD, Klimina KM, Belova AA, Kiseleva MV, Kaprin AD, Alekseev BY, Krasnov GS, Kudryavtseva AV. Differentially Expressed Genes Associated With Prognosis in Locally Advanced Lymph Node-Negative Prostate Cancer. Front Genet 2019; 10:730. [PMID: 31447885 PMCID: PMC6697060 DOI: 10.3389/fgene.2019.00730] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Accepted: 07/11/2019] [Indexed: 12/14/2022] Open
Abstract
Older age is one of the main risk factors for cancer development. The incidence of prostate cancer, as a multifactorial disease, also depends upon demographic factors, race, and genetic predisposition. Prostate cancer most frequently occurs in men over 60 years of age, indicating a clear association between older age and disease onset. Carcinogenesis is followed by the deregulation of many genes, and some of these changes could serve as biomarkers for diagnosis, prognosis, prediction of drug therapy efficacy, as well as possible therapeutic targets. We have performed a bioinformatic analysis of a The Cancer Genome Atlas (TCGA) data and RNA-Seq profiling of a Russian patient cohort to reveal prognostic markers of locally advanced lymph node-negative prostate cancer (lymph node-negative LAPC). We also aimed to identify markers of the most common molecular subtype of prostate cancer carrying a fusion transcript TMPRSS2-ERG. We have found several genes that were differently expressed between the favorable and unfavorable prognosis groups and involved in the enriched KEGG pathways based on the TCGA (B4GALNT4, PTK6, and CHAT) and Russian patient cohort data (AKR1C1 and AKR1C3). Additionally, we revealed such genes for the TMPRSS2-ERG prostate cancer molecular subtype (B4GALNT4, ASRGL1, MYBPC1, RGS11, SLC6A14, GALNT13, and ST6GALNAC1). Obtained results contribute to a better understanding of the molecular mechanisms behind prostate cancer progression and could be used for further development of the LAPC prognosis marker panel.
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Affiliation(s)
- Elena A Pudova
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
| | - Elena N Lukyanova
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
| | - Kirill M Nyushko
- National Medical Research Radiological Center, Ministry of Health of the Russian Federation, Moscow, Russia
| | - Dmitry S Mikhaylenko
- National Medical Research Radiological Center, Ministry of Health of the Russian Federation, Moscow, Russia.,Federal State Autonomous Educational Institution of Higher Education, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation, Moscow, Russia
| | - Andrew R Zaretsky
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
| | | | - Maria V Savvateeva
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
| | | | - Nataliya V Melnikova
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
| | - Nadezhda N Volchenko
- National Medical Research Radiological Center, Ministry of Health of the Russian Federation, Moscow, Russia
| | - Gennady D Efremov
- National Medical Research Radiological Center, Ministry of Health of the Russian Federation, Moscow, Russia
| | - Kseniya M Klimina
- Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia
| | - Anastasiya A Belova
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
| | - Marina V Kiseleva
- National Medical Research Radiological Center, Ministry of Health of the Russian Federation, Moscow, Russia
| | - Andrey D Kaprin
- National Medical Research Radiological Center, Ministry of Health of the Russian Federation, Moscow, Russia
| | - Boris Y Alekseev
- National Medical Research Radiological Center, Ministry of Health of the Russian Federation, Moscow, Russia
| | - George S Krasnov
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
| | - Anna V Kudryavtseva
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
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22
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Johnston WL, Catton CN, Swallow CJ. Unbiased data mining identifies cell cycle transcripts that predict non-indolent Gleason score 7 prostate cancer. BMC Urol 2019; 19:4. [PMID: 30616540 PMCID: PMC6322345 DOI: 10.1186/s12894-018-0433-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2018] [Accepted: 12/20/2018] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Patients with newly diagnosed non-metastatic prostate adenocarcinoma are typically classified as at low, intermediate, or high risk of disease progression using blood prostate-specific antigen concentration, tumour T category, and tumour pathological Gleason score. Classification is used to both predict clinical outcome and to inform initial management. However, significant heterogeneity is observed in outcome, particularly within the intermediate risk group, and there is an urgent need for additional markers to more accurately hone risk prediction. Recently developed web-based visualization and analysis tools have facilitated rapid interrogation of large transcriptome datasets, and querying broadly across multiple large datasets should identify predictors that are widely applicable. METHODS We used camcAPP, cBioPortal, CRN, and NIH NCI GDC Data Portal to data mine publicly available large prostate cancer datasets. A test set of biomarkers was developed by identifying transcripts that had: 1) altered abundance in prostate cancer, 2) altered expression in patients with Gleason score 7 tumours and biochemical recurrence, 3) correlation of expression with time until biochemical recurrence across three datasets (Cambridge, Stockholm, MSKCC). Transcripts that met these criteria were then examined in a validation dataset (TCGA-PRAD) using univariate and multivariable models to predict biochemical recurrence in patients with Gleason score 7 tumours. RESULTS Twenty transcripts met the test criteria, and 12 were validated in TCGA-PRAD Gleason score 7 patients. Ten of these transcripts remained prognostic in Gleason score 3 + 4 = 7, a sub-group of Gleason score 7 patients typically considered at a lower risk for poor outcome and often not targeted for aggressive management. All transcripts positively associated with recurrence encode or regulate mitosis and cell cycle-related proteins. The top performer was BUB1, one of four key MIR145-3P microRNA targets upregulated in hormone-sensitive as well as castration-resistant PCa. SRD5A2 converts testosterone to its more active form and was negatively associated with biochemical recurrence. CONCLUSIONS Unbiased mining of large patient datasets identified 12 transcripts that independently predicted disease recurrence risk in Gleason score 7 prostate cancer. The mitosis and cell cycle proteins identified are also implicated in progression to castration-resistant prostate cancer, revealing a pivotal role for loss of cell cycle control in the latter.
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Affiliation(s)
- Wendy L Johnston
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
| | - Charles N Catton
- Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.,Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada
| | - Carol J Swallow
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada.,Department of Surgery, University of Toronto, Toronto, ON, Canada.,Institute of Medical Science, University of Toronto, Toronto, ON, Canada.,Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada
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23
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Gerhauser C, Favero F, Risch T, Simon R, Feuerbach L, Assenov Y, Heckmann D, Sidiropoulos N, Waszak SM, Hübschmann D, Urbanucci A, Girma EG, Kuryshev V, Klimczak LJ, Saini N, Stütz AM, Weichenhan D, Böttcher LM, Toth R, Hendriksen JD, Koop C, Lutsik P, Matzk S, Warnatz HJ, Amstislavskiy V, Feuerstein C, Raeder B, Bogatyrova O, Schmitz EM, Hube-Magg C, Kluth M, Huland H, Graefen M, Lawerenz C, Henry GH, Yamaguchi TN, Malewska A, Meiners J, Schilling D, Reisinger E, Eils R, Schlesner M, Strand DW, Bristow RG, Boutros PC, von Kalle C, Gordenin D, Sültmann H, Brors B, Sauter G, Plass C, Yaspo ML, Korbel JO, Schlomm T, Weischenfeldt J. Molecular Evolution of Early-Onset Prostate Cancer Identifies Molecular Risk Markers and Clinical Trajectories. Cancer Cell 2018; 34:996-1011.e8. [PMID: 30537516 PMCID: PMC7444093 DOI: 10.1016/j.ccell.2018.10.016] [Citation(s) in RCA: 162] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2018] [Revised: 08/17/2018] [Accepted: 10/29/2018] [Indexed: 12/28/2022]
Abstract
Identifying the earliest somatic changes in prostate cancer can give important insights into tumor evolution and aids in stratifying high- from low-risk disease. We integrated whole genome, transcriptome and methylome analysis of early-onset prostate cancers (diagnosis ≤55 years). Characterization across 292 prostate cancer genomes revealed age-related genomic alterations and a clock-like enzymatic-driven mutational process contributing to the earliest mutations in prostate cancer patients. Our integrative analysis identified four molecular subgroups, including a particularly aggressive subgroup with recurrent duplications associated with increased expression of ESRP1, which we validate in 12,000 tissue microarray tumors. Finally, we combined the patterns of molecular co-occurrence and risk-based subgroup information to deconvolve the molecular and clinical trajectories of prostate cancer from single patient samples.
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Affiliation(s)
- Clarissa Gerhauser
- Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Francesco Favero
- Finsen Laboratory, Rigshospitalet, DK-2200, Copenhagen, Denmark; Biotech Research & Innovation Centre (BRIC), University of Copenhagen, DK-2200, Copenhagen, Denmark
| | - Thomas Risch
- Max Planck Institute for Molecular Genetics, Otto Warburg Laboratory Gene Regulation and Systems Biology of Cancer, Ihnestrasse 63-73, 14195 Berlin, Germany
| | - Ronald Simon
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Lars Feuerbach
- Division Applied Bioinformatics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany
| | - Yassen Assenov
- Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Doreen Heckmann
- Division of Cancer Genome Research, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; German Cancer Consortium (DKTK), 69120 Heidelberg, Germany
| | - Nikos Sidiropoulos
- Finsen Laboratory, Rigshospitalet, DK-2200, Copenhagen, Denmark; Biotech Research & Innovation Centre (BRIC), University of Copenhagen, DK-2200, Copenhagen, Denmark
| | - Sebastian M Waszak
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69120 Heidelberg, Germany
| | - Daniel Hübschmann
- Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Department for Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology and Bioquant, University of Heidelberg, Heidelberg 69120, Germany; Department of Pediatric Immunology, Hematology and Oncology, University Hospital, Heidelberg 69120, Germany
| | - Alfonso Urbanucci
- Centre for Molecular Medicine Norway, Nordic European Molecular Biology Laboratory Partnership, Forskningsparken, University of Oslo, 0316 Oslo, Norway; Institute for Cancer Genetics and Informatics, Oslo University Hospital, 0316 Oslo, Norway; Department of Core Facilities, Institute for Cancer Research, Oslo University Hospital, 0316 Oslo, Norway
| | - Etsehiwot G Girma
- Finsen Laboratory, Rigshospitalet, DK-2200, Copenhagen, Denmark; Biotech Research & Innovation Centre (BRIC), University of Copenhagen, DK-2200, Copenhagen, Denmark
| | - Vladimir Kuryshev
- Division of Cancer Genome Research, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; German Cancer Consortium (DKTK), 69120 Heidelberg, Germany
| | - Leszek J Klimczak
- Integrative Bioinformatics Support Group, National Institute of Environmental Health Sciences, Durham, 27709 NC, USA
| | - Natalie Saini
- Genome Integrity and Structural Biology Laboratory, National Institute of Environmental Health Sciences, Durham, 27709 NC, USA
| | - Adrian M Stütz
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69120 Heidelberg, Germany
| | - Dieter Weichenhan
- Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Lisa-Marie Böttcher
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Reka Toth
- Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Josephine D Hendriksen
- Finsen Laboratory, Rigshospitalet, DK-2200, Copenhagen, Denmark; Biotech Research & Innovation Centre (BRIC), University of Copenhagen, DK-2200, Copenhagen, Denmark
| | - Christina Koop
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Pavlo Lutsik
- Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Sören Matzk
- Max Planck Institute for Molecular Genetics, Otto Warburg Laboratory Gene Regulation and Systems Biology of Cancer, Ihnestrasse 63-73, 14195 Berlin, Germany
| | - Hans-Jörg Warnatz
- Max Planck Institute for Molecular Genetics, Otto Warburg Laboratory Gene Regulation and Systems Biology of Cancer, Ihnestrasse 63-73, 14195 Berlin, Germany
| | - Vyacheslav Amstislavskiy
- Max Planck Institute for Molecular Genetics, Otto Warburg Laboratory Gene Regulation and Systems Biology of Cancer, Ihnestrasse 63-73, 14195 Berlin, Germany
| | - Clarissa Feuerstein
- Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Faculty of Biosciences, Heidelberg University, 69120 Heidelberg, Germany
| | - Benjamin Raeder
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69120 Heidelberg, Germany
| | - Olga Bogatyrova
- Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | | | - Claudia Hube-Magg
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Martina Kluth
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Hartwig Huland
- Martini-Clinic Prostate Cancer Center at the University Medical Center Hamburg-Eppendorf, Martinistrasse 52, D-20246 Hamburg, Germany
| | - Markus Graefen
- Martini-Clinic Prostate Cancer Center at the University Medical Center Hamburg-Eppendorf, Martinistrasse 52, D-20246 Hamburg, Germany
| | - Chris Lawerenz
- Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Gervaise H Henry
- Department of Urology, UT Southwestern Medical Center, Dallas, TX 75390-9110, USA
| | - Takafumi N Yamaguchi
- Informatics & Biocomputing Program, Ontario Institute for Cancer Research, Toronto, Canada
| | - Alicia Malewska
- Department of Urology, UT Southwestern Medical Center, Dallas, TX 75390-9110, USA
| | - Jan Meiners
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Daniela Schilling
- Division of Cancer Genome Research, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; NCT Trial Center, National Center for Tumor Diseases and German Cancer Research Center, 69120 Heidelberg, Germany
| | - Eva Reisinger
- Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Roland Eils
- Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Department for Bioinformatics and Functional Genomics, Institute of Pharmacy and Molecular Biotechnology and Bioquant, University of Heidelberg, Heidelberg 69120, Germany
| | - Matthias Schlesner
- Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; Bioinformatics and Omics Data Analytics (B240), German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Douglas W Strand
- Department of Urology, UT Southwestern Medical Center, Dallas, TX 75390-9110, USA
| | - Robert G Bristow
- Manchester Cancer Research Centre, University of Manchester, 555 Wilmslow Road, Manchester, UK
| | - Paul C Boutros
- Ontario Institute for Cancer Research, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada
| | - Christof von Kalle
- German Cancer Consortium (DKTK), 69120 Heidelberg, Germany; Division of Translational Oncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Dmitry Gordenin
- Genome Integrity and Structural Biology Laboratory, National Institute of Environmental Health Sciences, Durham, 27709 NC, USA
| | - Holger Sültmann
- Division of Cancer Genome Research, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; German Cancer Consortium (DKTK), 69120 Heidelberg, Germany
| | - Benedikt Brors
- Division Applied Bioinformatics, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; National Center for Tumor Diseases (NCT), 69120 Heidelberg, Germany; German Cancer Consortium (DKTK), 69120 Heidelberg, Germany
| | - Guido Sauter
- Department of Pathology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Christoph Plass
- Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; German Cancer Consortium (DKTK), 69120 Heidelberg, Germany
| | - Marie-Laure Yaspo
- Max Planck Institute for Molecular Genetics, Otto Warburg Laboratory Gene Regulation and Systems Biology of Cancer, Ihnestrasse 63-73, 14195 Berlin, Germany
| | - Jan O Korbel
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69120 Heidelberg, Germany.
| | - Thorsten Schlomm
- Martini-Clinic Prostate Cancer Center at the University Medical Center Hamburg-Eppendorf, Martinistrasse 52, D-20246 Hamburg, Germany; Charité Universitätsmedizin Berlin, Charitéplatz 1, D-10117 Berlin, Germany.
| | - Joachim Weischenfeldt
- Finsen Laboratory, Rigshospitalet, DK-2200, Copenhagen, Denmark; Biotech Research & Innovation Centre (BRIC), University of Copenhagen, DK-2200, Copenhagen, Denmark; European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69120 Heidelberg, Germany; Charité Universitätsmedizin Berlin, Charitéplatz 1, D-10117 Berlin, Germany.
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24
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Inamura K. Prostatic cancers: understanding their molecular pathology and the 2016 WHO classification. Oncotarget 2018; 9:14723-14737. [PMID: 29581876 PMCID: PMC5865702 DOI: 10.18632/oncotarget.24515] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2017] [Accepted: 02/12/2018] [Indexed: 12/12/2022] Open
Abstract
Accumulating evidence suggests that prostatic cancers represent a group of histologically and molecularly heterogeneous diseases with variable clinical courses. In accordance with the increased knowledge of their clinicopathologies and genetics, the World Health Organization (WHO) classification of prostatic cancers has been revised. Additionally, recent data on their comprehensive molecular characterization have increased our understanding of the genomic basis of prostatic cancers and enabled us to classify them into subtypes with distinct molecular pathologies and clinical features. Our increased understanding of the molecular pathologies of prostatic cancers has permitted their evolution from a poorly understood, heterogeneous group of diseases with variable clinical courses to characteristic molecular subtypes that allow the implementation of personalized therapies and better patient management. This review provides perspectives on the new 2016 WHO classification of prostatic cancers as well as recent knowledge of their molecular pathologies. The WHO classification of prostatic cancers will require additional revisions to allow for reliable and clinically meaningful cancer diagnoses as a better understanding of their molecular characteristics is obtained.
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Affiliation(s)
- Kentaro Inamura
- Division of Pathology, The Cancer Institute; Department of Pathology, The Cancer Institute Hospital, Japanese Foundation for Cancer Research, 3-8-31 Ariake, Koto-ku, Tokyo 135-8550, Japan
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