This document reviews literature on the expression of genes involved in sex steroid hormone metabolism and activity in normal, benign, and cancerous prostate tissues. It aims to determine if there is an association between gene expression and factors like age, BMI, and estrogen and testosterone levels. The literature search found articles studying expression of genes like the androgen receptor and enzymes involved in androgen and estrogen metabolism using techniques including immunohistochemistry, microarrays, and real-time PCR. Understanding epigenetic regulation of these genes may provide insights into prostate carcinogenesis.
Microarray data analysis of publicly available high grade prostate cancer data, functional annotation (using DAVID web service) and gene regulatory network construction using Prize-collecting Steiner Forest algorithm
Integrative analysis of transcriptomics and proteomics data with ArrayMining ...Natalio Krasnogor
These slides are part of a presentation I gave on March 2010 at the BioInformatics and Genome Research Open Club at the Weizmann Institute of Science, Israel.
In these slides my student and I describe two web-applications for microarray and gene/protein set analysis,
ArrayMining.net and TopoGSA. These use ensemble and consensus methods as well as the
possibility of modular combinations of different analysis techniques for an integrative view of
(microarray-based) gene sets, interlinking transcriptomics with proteomics data sources. This integrative process uses tools from different fields, e.g. statistics, optimisation and network
topological studies. As an example for these integrative techniques, we use a microarray
consensus-clustering approach based on Simulated Annealing, which is part of the ArrayMining.net
Class Discovery Analysis module, and show how this approach can be combined in a modular
fashion with a prior gene set analysis. The results reveal that improved cluster validity indices can be obtained by merging the two methods, and provide pointers to distinct sub-classes within pre-defined tumour categories for a breast cancer dataset by the Nottingham Queens Medical Centre.
In the second part of the talk, I show how results from a supervised
microarray feature selection analysis on ArrayMining.net can be investigated in further detail with
TopoGSA, a new web-tool for network topological analysis of gene/protein sets mapped on a
comprehensive human protein-protein interaction network. I discuss results from a TopoGSA
analysis of the complete set of genes currently known to be mutated in cancer.
Integrative analysis of transcriptomics and proteomics data with ArrayMining ...Enrico Glaab
Presentation: Integrative analysis of transcriptomics and proteomics data with ArrayMining and TopoGSA
Abstract:
The increasing availability of large-scale biological datasets has not only led to the development of many specialized bioinformatics analysis methods but also entails the opportunity and challenge to combine already available algorithms and datasets as building blocks in new meta-level approaches.
In our web-applications for microarray and gene/protein set analysis, ArrayMining.net and TopoGSA, we present various integrative methods, including ensemble and con¬sensus techniques as well as modular combinations of different analysis types, to ex¬tract new insights from experimental data. Apart from the combination of closely re¬lated datasets and algorithms, the major purpose of these tools is to integrate knowl¬edge extraction methods from widely different fields, e.g. statistics, optimisation and topological network analysis.
As an example for these integrative analysis techniques, we present a microarray con¬sensus-clustering approach based on Simulated Annealing, which is part of the ArrayMining.net Class Discovery Analysis module, and demonstrate how this ap¬proach can be combined in a modular fashion with a prior gene set analysis. The re¬sults reveal that improved cluster validity indices can be obtained by merging the two methods, and provide pointers to distinct sub-classes within pre-defined tumour categories for a breast cancer dataset by the Nottingham Queens Medical Centre.
In the second part of the talk, we show how results from a supervised microarray fea¬ture selection analysis on ArrayMining.net can be investigated in further detail with TopoGSA, a new web-tool for network topological analysis of gene/protein sets mapped on a comprehensive human protein-protein interaction network. Finally, we discuss results from a TopoGSA analysis of the complete set of genes currently known to be mutated in cancer (Futreal et al., 2004). The presented web-applications are freely available at www.infobiotics.net and the work have been published recently (Glaab, Garibaldi and Krasnogor, 2009, BMC Bioinformatics; Glaab, Baudot, Krasnogor and Valencia, 2010, Bioinformatics).
Scripps Future Of Genome Medicine 2013 - Gholson LyonGholson Lyon
Talk delivered at Scripps Future Of Genome Medicine 2013 by Gholson Lyon, from Cold Spring Harbor Laboratory and Utah Foundation for Biomedical Research.
Microarray data analysis of publicly available high grade prostate cancer data, functional annotation (using DAVID web service) and gene regulatory network construction using Prize-collecting Steiner Forest algorithm
Integrative analysis of transcriptomics and proteomics data with ArrayMining ...Natalio Krasnogor
These slides are part of a presentation I gave on March 2010 at the BioInformatics and Genome Research Open Club at the Weizmann Institute of Science, Israel.
In these slides my student and I describe two web-applications for microarray and gene/protein set analysis,
ArrayMining.net and TopoGSA. These use ensemble and consensus methods as well as the
possibility of modular combinations of different analysis techniques for an integrative view of
(microarray-based) gene sets, interlinking transcriptomics with proteomics data sources. This integrative process uses tools from different fields, e.g. statistics, optimisation and network
topological studies. As an example for these integrative techniques, we use a microarray
consensus-clustering approach based on Simulated Annealing, which is part of the ArrayMining.net
Class Discovery Analysis module, and show how this approach can be combined in a modular
fashion with a prior gene set analysis. The results reveal that improved cluster validity indices can be obtained by merging the two methods, and provide pointers to distinct sub-classes within pre-defined tumour categories for a breast cancer dataset by the Nottingham Queens Medical Centre.
In the second part of the talk, I show how results from a supervised
microarray feature selection analysis on ArrayMining.net can be investigated in further detail with
TopoGSA, a new web-tool for network topological analysis of gene/protein sets mapped on a
comprehensive human protein-protein interaction network. I discuss results from a TopoGSA
analysis of the complete set of genes currently known to be mutated in cancer.
Integrative analysis of transcriptomics and proteomics data with ArrayMining ...Enrico Glaab
Presentation: Integrative analysis of transcriptomics and proteomics data with ArrayMining and TopoGSA
Abstract:
The increasing availability of large-scale biological datasets has not only led to the development of many specialized bioinformatics analysis methods but also entails the opportunity and challenge to combine already available algorithms and datasets as building blocks in new meta-level approaches.
In our web-applications for microarray and gene/protein set analysis, ArrayMining.net and TopoGSA, we present various integrative methods, including ensemble and con¬sensus techniques as well as modular combinations of different analysis types, to ex¬tract new insights from experimental data. Apart from the combination of closely re¬lated datasets and algorithms, the major purpose of these tools is to integrate knowl¬edge extraction methods from widely different fields, e.g. statistics, optimisation and topological network analysis.
As an example for these integrative analysis techniques, we present a microarray con¬sensus-clustering approach based on Simulated Annealing, which is part of the ArrayMining.net Class Discovery Analysis module, and demonstrate how this ap¬proach can be combined in a modular fashion with a prior gene set analysis. The re¬sults reveal that improved cluster validity indices can be obtained by merging the two methods, and provide pointers to distinct sub-classes within pre-defined tumour categories for a breast cancer dataset by the Nottingham Queens Medical Centre.
In the second part of the talk, we show how results from a supervised microarray fea¬ture selection analysis on ArrayMining.net can be investigated in further detail with TopoGSA, a new web-tool for network topological analysis of gene/protein sets mapped on a comprehensive human protein-protein interaction network. Finally, we discuss results from a TopoGSA analysis of the complete set of genes currently known to be mutated in cancer (Futreal et al., 2004). The presented web-applications are freely available at www.infobiotics.net and the work have been published recently (Glaab, Garibaldi and Krasnogor, 2009, BMC Bioinformatics; Glaab, Baudot, Krasnogor and Valencia, 2010, Bioinformatics).
Scripps Future Of Genome Medicine 2013 - Gholson LyonGholson Lyon
Talk delivered at Scripps Future Of Genome Medicine 2013 by Gholson Lyon, from Cold Spring Harbor Laboratory and Utah Foundation for Biomedical Research.
Creative Biolabs is skilled in NGS-based cancer research. We support one-stop cancer research services based on a variety of sequencing technologies. Our whole gene sequencing (WGS) and whole exome sequencing (WES) platforms enable us to comprehensively analyze and identify known and unknown cancer gene mutations. Besides, we also provide high-quality target sequencing services, allowing us to analyze several or even hundreds of target gene mutations economically and effectively. Our services will facilitate our customers’ cancer research including cancer diagnosis and treatment.
https://www.creative-biolabs.com/suprecision/genetic-testing-for-cancer.htm
Demonstration of alternate scanning LCMS for simultanous acquisition of precursor and product ions without precursor mass selection (ie Multiplex LCMS).
Cell and gene therapy for Parkinson’s disease - part 2Parkinson's UK
Presentation by Prof Deniz Kirik, MD, PhD at the Parkinson's UK Research Conference, November 2010 in York.
With introduction by Dr Oliver Bandmann.
Part 1: http://www.slideshare.net/ParkinsonsResearchUK/cell-and-gene-therapy-for-parkinsons-disease-part-1
Creative Biolabs is skilled in NGS-based cancer research. We support one-stop cancer research services based on a variety of sequencing technologies. Our whole gene sequencing (WGS) and whole exome sequencing (WES) platforms enable us to comprehensively analyze and identify known and unknown cancer gene mutations. Besides, we also provide high-quality target sequencing services, allowing us to analyze several or even hundreds of target gene mutations economically and effectively. Our services will facilitate our customers’ cancer research including cancer diagnosis and treatment.
https://www.creative-biolabs.com/suprecision/genetic-testing-for-cancer.htm
Demonstration of alternate scanning LCMS for simultanous acquisition of precursor and product ions without precursor mass selection (ie Multiplex LCMS).
Cell and gene therapy for Parkinson’s disease - part 2Parkinson's UK
Presentation by Prof Deniz Kirik, MD, PhD at the Parkinson's UK Research Conference, November 2010 in York.
With introduction by Dr Oliver Bandmann.
Part 1: http://www.slideshare.net/ParkinsonsResearchUK/cell-and-gene-therapy-for-parkinsons-disease-part-1
American Public Health Association (APHA) Annual meeting Medical Care Section: Expectant Management among Early-Stage Prostate Cancer Patients: The American College of Surgeons Special Study
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
Report Back from SGO 2024: What’s the Latest in Cervical Cancer?bkling
Are you curious about what’s new in cervical cancer research or unsure what the findings mean? Join Dr. Emily Ko, a gynecologic oncologist at Penn Medicine, to learn about the latest updates from the Society of Gynecologic Oncology (SGO) 2024 Annual Meeting on Women’s Cancer. Dr. Ko will discuss what the research presented at the conference means for you and answer your questions about the new developments.
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Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
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Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
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Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
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Poster Slide Final
1. A Review of the Expression of Genes Involved in Sex Steroid Hormone
Metabolism and Activity in Prostate Tissue: A Need for Epigenetic
Information
Jamie Ritchey1, MPH; Wilfried Karmaus1, MD, Dr.med, MPH; Tara Sabo-Attwood1,2, PhD; Susan E. Steck1,3, PhD, MPH, RD; Hongmei Zhang1, PhD;
1Department of Epidemiology and Biostatistics, 2Department of Environmental Health Sciences, 3Cancer Prevention and Control Program, University of South
Carolina, 800 Sumter Street, Columbia, SC 29208.
OBJECTIVE RESULTS
• To review the literature for gene expression of Figure 1. A plausible gene expression network
enzymes and receptors involved in sex steroid Table 1. Literature search summary
hormone metabolism and activity in normal, benign,
for genes and receptors involved in Estrogen (E)
and cancerous prostate tissues and Testosterone (T) metabolism in the prostate Gene &
Chromosomal location
Total articles
retrieved
Laboratory
methods†
[3, 11, 19-20] Androgen Metabolism
AR 26 IHC, Microarrays, IS, ISV, real-time PCR
Ch Xq12
• To determine if there is an association with gene HSD17B2
Ch 16q24.1-q24.2
4 IHC, ISH, Microarray, real-time PCR
expression and age, body mass index, estrogen and HSD17B3
Ch 9q22
4 IHC, microarray, real-time PCR
testosterone levels, and cancer treatments SRD5A1
Ch 5p15.31
6 IHC, ISH, Microarray
SRD5A2 10 IHC, ISH, Microarray, real-time PCR
Ch 2p23.1
INTRODUCTION CYP3A4
Ch 7q21.1-22
CYP3A5
5
6
IHC, real-time PCR
IHC, real-time PCR, meta-analysis
• Age, race, and family history are the only confirmed Ch 7q21.1-22
CYP3A7 3 IHC, real-time PCR
risk factors for prostate cancer [1]
Ch 7q21.1-22
CYP3A43 1 ICH, real-time PCR
Ch 7q21.1
AKR1C3 6 Array, real-time PCR, Northern blot, review
Ch 10p15-14
• Observational, clinical, and laboratory evidence AKR1C2
Ch 10p15-14
5 Array, real-time PCR, review
indicate that sex steroid hormones are important to HSD3B1
Ch 1p13-11
2 Array, real-time PCR
the development and progression of prostate cancer
HSD3B2 3 Array, review
Ch 1p13.1
UGT2B15 3 Array, microarray
[2-14] Ch 4q13
UGT2B17 3 IHC, ISH, real-time PCR
Ch 4q13
Estrogen Metabolism
• Epidemiology focusing on sex steroid hormone risk
ESR1 9 IHC, ISH, real-time PCR
Ch 6q24-27
ESR2 10 IHC, ISH, microarray, real-time PCR
and prostate cancer is inconclusive [2-14] Ch 14q31-22
CYP19A1 9 Avidin-biotin, IHC, ISH, microarray, real-time PCR
Ch 15q21
HSD17B1 2 IHC, real-time PCR
• Most epidemiologic studies have focused on gene
Ch 17q11-21
HSD17B4 5 IHC, ISH, microarray, real-time PCR, meta-analysis
Ch 5q2
single nucleotide polymorphisms (SNPs) of hormone HSD17B7
Ch 1q23
1 IHC
metabolic enzymes and serum hormone levels [2-3] SULT1A1
Ch 16p21.1
3 IHC, Western blot
SULT1A3 2 IHC, Western blot
RATIONALE
Ch 16p11.2
SULT2B1a 3 Northern blot, immunoblot, IHC
Ch 19 q13.3
• Gene expression may provide etiologic clues
SULT2Bb 3 Northern blot, immunoblot, IHC
Ch 19 q13.3
HSD17B12 1 IHC, ISH
regarding how sex steroid metabolism is associated Ch 11p11
CYP1A1 4 IHC, real-time PCR
with prostate cancer not detected at the DNA Ch 15q24.1
CYP1A2 4 IHC, real-time PCR
sequence or serum level [130]
Ch 15q24.1
CYP1B1 6 IHC, ISH, real-time PCR
Ch 2p22.2
COMT 2 IHC, real-time PCR
Ch 22q11.21
• The expression of genes involved in sex steroid GSTT1
Ch 22q11.23
1 Microarray, real-time PCR
metabolism in prostate tissues may differ by different
GSTM1 3 IHC, microarray, real-time PCR
Ch 1p13.1
GSTP1 10 IHC, IS, microarray, real-time PCR
levels of age, body mass index, race, estrogen and Ch 11q13.2
†Laboratory method abbreviations: Immunohistochemistry (IHC), Real-time polymerase chain reaction (real-time PCR), Immunostaining (IS), In situ
testosterone levels, and/or cancer treatments hybridization (ISH), Immunostaining with optimized IHC criteria and video image analysis (ISV)
administered • A total of 85 studies were identified
• To guide our searches, we constructed a figure of a
METHODS plausible gene network for E and T expression [3,11, • Most studies focused on the expression of one or
• A pathway-oriented approach guided the literature 19-20]. a few genes, rather than the complete pathway,
searches, conducted in Pub Med, limited to English
ignoring compensatory or alternate response
language and human prostate only • Past studies have only included portions of
pathways, or the T or E pathways alone, and often do • Studies comparing normal, benign, and cancerous
STRENGTHS not include preliminary pathways (the dashed line prostate tissue showed limited consistency, with
• Used a pathway approach to guide searches from androstenedione to DHT indicates that this one exception, the down-regulation of GSTP1 in
reaction is preliminary) [3,11,19-20]. cancerous tissue
• Omitted cell studies since results may not be
consistent with tissue studies for methodological
reasons
SUMMARY
• GSTP1 was consistently down regulated or not expressed in prostate cancer, which coincides with previous
• Reviewed references cited in publications found research indicating that GSTP1 is methylated in prostate cancer tissue [16,95,99-109,111-118,130,136]
during searches to improve completeness
• For all other genes, studies were either scant or inconsistent, and conflicting expression results may be due
LIMITATIONS to limitations, including: tissue collection, laboratory methods, or gene functionality
• Information for many genes was scant
• Few studies were found examining the association of gene expression with age, race, body mass index,
• Only publications in English language were estrogen, testosterone, and cancer treatments
considered
• Future studies should focus not only on gene expression, but also epigenetic mechanisms such as
• Only includes citations from PubMed methylation to assess prostate cancer risk
REFERENCES
1. American Cancer Society. Cancer Facts & Figures 2008. Atlanta: American Cancer Society, 2008.
2. Chu, L.W., J.K. Reichardt, and A.W. Hsing, Androgens and the molecular epidemiology of prostate cancer. Curr Opin Endocrinol Diabetes Obes, 2008. 15(3): p. 261-70. 65. Neslund-Dudas, C., et al., SRD5A2 and HSD3B2 polymorphisms are associated with prostate cancer risk and aggressiveness. Prostate, 2007. 67(15): p. 1654-63.
130. Sharma, S., T.K. Kelly, and P.A. Jones, Epigenetics in cancer. Carcinogenesis, 2010. 31(1): p. 27-36.
3. Chokkalingam, A.P., et al., Molecular epidemiology of prostate cancer: hormone-related genetic loci. Front Biosci, 2007. 12: p. 3436-60. 66. Febbo, P.G., et al., Androgen mediated regulation and functional implications of fkbp51 expression in prostate cancer. J Urol, 2005. 173(5): p. 1772-7.
131. Muhonen, P. and H. Holthofer, Epigenetic and microRNA-mediated regulation in diabetes. Nephrol Dial Transplant, 2009. 24(4): p. 1088-96.
4. Pollard, M., P.H. Luckert, and M.A. Schmidt, Induction of prostate adenocarcinomas in Lobund Wistar rats by testosterone. Prostate, 1982. 3(6): p. 563-8. 67. Barbier, O., et al., Cellular localization of uridine diphosphoglucuronosyltransferase 2B enzymes in the human prostate by in situ hybridization and immunohistochemistry. J Clin Endocrinol Metab, 2000. 85(12): p. 4819-26.
132. Lim, S., et al., Epigenetic regulation of cancer growth by histone demethylases. Int J Cancer, 2010. 127(9): p. 1991-8.
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