|
1
|
Lorenzo G, Hughes TJR, Dominguez-Frojan P,
Reali A and Gomez H: Computer simulations suggest that prostate
enlargement due to benign prostatic hyperplasia mechanically
impedes prostate cancer growth. Proc Natl Acad Sci USA.
116:1152–1161. 2019.PubMed/NCBI View Article : Google Scholar
|
|
2
|
NIH-NIDDK. Prostate Enlargement (Benign
Prostatic Hyperplasia)|NIDDK [Internet]. National Institute of
Diabetes and Digestive and Kidney Diseases. 2019 [cited 2020 Jan
16]. Available from: https://www.niddk.nih.gov/health-information/urologic-diseases/prostate-problems/prostate-enlargement-benign-prostatic-hyperplasia.
|
|
3
|
Nicholson TM and Ricke WA: Androgens and
estrogens in benign prostatic hyperplasia: Past, present and
future. Differentiation. 82:184–199. 2011.PubMed/NCBI View Article : Google Scholar
|
|
4
|
Hata J, Satoh Y, Akaihata H, Hiraki H,
Ogawa S, Haga N, Ishibashi K, Aikawa K and Kojima Y: Molecular
classification of benign prostatic hyperplasia: A gene expression
profiling study in a rat model. Int J Urol. 23:599–612.
2016.PubMed/NCBI View Article : Google Scholar
|
|
5
|
Dai X, Fang X, Ma Y and Xianyu J: Benign
prostatic hyperplasia and the risk of prostate cancer and bladder
cancer: A meta-analysis of observational studies. Medicine
(Baltimore). 95(e3493)2016.PubMed/NCBI View Article : Google Scholar
|
|
6
|
Miah S and Catto J: BPH and prostate
cancer risk. Indian J Urol. 30:214–218. 2014.PubMed/NCBI View Article : Google Scholar
|
|
7
|
Chughtai B, Forde JC, Thomas DD, Laor L,
Hossack T, Woo HH, Te AE and Kaplan SA: Benign prostatic
hyperplasia. Nat Rev Dis Primers. 2(16031)2016.PubMed/NCBI View Article : Google Scholar
|
|
8
|
Kim EH, Larson JA and Andriole GL:
Management of benign prostatic hyperplasia. Annu Rev Med.
67:137–151. 2016.PubMed/NCBI View Article : Google Scholar
|
|
9
|
Barry MJ, Fowler FJ, O'Leary MP,
Bruskewitz RC, Holtgrewe HL and Mebust WK: Measuring
disease-specific health status in men with benign prostatic
hyperplasia. Measurement committee of the American urological
association. Med Care. 33 (Suppl 4):AS145–AS155. 1995.PubMed/NCBI
|
|
10
|
Rawla P: Epidemiology of prostate cancer.
World J Oncol. 10:63–89. 2019.PubMed/NCBI View Article : Google Scholar
|
|
11
|
Levitt JM and Slawin KM: Prostate-specific
antigen and prostate-specific antigen derivatives as predictors of
benign prostatic hyperplasia progression. Curr Urol Rep. 8:269–274.
2007.PubMed/NCBI View Article : Google Scholar
|
|
12
|
Nickel JC, Aaron L, Barkin J, Elterman D,
Nachabé M and Zorn KC: Canadian urological association guideline on
male lower urinary tract symptoms/benign prostatic hyperplasia
(MLUTS/BPH): 2018 update. Can Urol Assoc J. 12:303–312.
2018.PubMed/NCBI View Article : Google Scholar
|
|
13
|
Chen J, Zhang D, Yan W, Yang D and Shen B:
Translational bioinformatics for diagnostic and prognostic
prediction of prostate cancer in the next-generation sequencing
era. Biomed Res Int. 2013(901578)2013.PubMed/NCBI View Article : Google Scholar
|
|
14
|
Mobley RE and Bizzarro MJ: Central
line-associated bloodstream infections in the NICU: Successes and
controversies in the quest for zero. Semin Perinatol. 41:166–174.
2017.PubMed/NCBI View Article : Google Scholar
|
|
15
|
Tacklind J, Fink HA, Macdonald R, Rutks I
and Wilt TJ: Finasteride for benign prostatic hyperplasia. Cochrane
Database Syst Rev. 6(CD006015)2010.PubMed/NCBI View Article : Google Scholar
|
|
16
|
Michel MC and Vrydag W: Alpha1-, alpha2-
and beta-adrenoceptors in the urinary bladder, urethra and
prostate. Br J Pharmacol. 147 (Suppl 2):S88–S119. 2006.PubMed/NCBI View Article : Google Scholar
|
|
17
|
Yadav SS, Li J, Lavery HJ, Yadav KK and
Tewari AK: Next-generation sequencing technology in prostate cancer
diagnosis, prognosis, and personalized treatment. Urol Oncol.
33:267.e1–e13. 2015.PubMed/NCBI View Article : Google Scholar
|
|
18
|
Yu ZJ, Yan HL, Xu FH, Chao HC, Deng LH, Xu
XD, Huang JB and Zeng T: Efficacy and side effects of drugs
commonly used for the treatment of lower urinary tract symptoms
associated with benign prostatic hyperplasia. Front Pharmacol.
11(658)2020.PubMed/NCBI View Article : Google Scholar
|
|
19
|
Davis S and Meltzer PS: GEOquery: A bridge
between the gene expression omnibus (GEO) and BioConductor.
Bioinformatics. 23:1846–1847. 2007.PubMed/NCBI View Article : Google Scholar
|
|
20
|
Smyth GK: Limma: Linear models for
microarray data. In: Gentleman R, Carey VJ, Huber W, Irizarry RA
and Dudoit S (eds): Bioinformatics and Computational Biology
Solutions Using R and Bioconductor. Statistics for Biology and
Health, Springer, New York, NY, pp397-420, 2005.
|
|
21
|
Huang DW, Sherman BT and Lempicki RA:
Bioinformatics enrichment tools: Paths toward the comprehensive
functional analysis of large gene lists. Nucleic Acids Res.
37:1–13. 2009.PubMed/NCBI View Article : Google Scholar
|
|
22
|
Warde-Farley D, Donaldson SL, Comes O,
Zuberi K, Badrawi R, Chao P, Franz M, Grouios C, Kazi F, Lopes CT,
et al: The GeneMANIA prediction server: Biological network
integration for gene prioritization and predicting gene function.
Nucleic Acids Res. 38:W214–W220. 2010.PubMed/NCBI View Article : Google Scholar
|
|
23
|
Uhlén M, Fagerberg L, Hallström BM,
Lindskog C, Oksvold P, Mardinoglu A, Sivertsson Å, Kampf C,
Sjöstedt E, Asplund A, et al: Proteomics. Tissue-based map of the
human proteome. Science. 347(1260419)2015.PubMed/NCBI View Article : Google Scholar
|
|
24
|
Gaulton A, Bellis LJ, Bento AP, Chambers
J, Davies M, Hersey A, Light Y, McGlinchey S, Michalovich D,
Al-Lazikani B and Overington JP: ChEMBL: A large-scale bioactivity
database for drug discovery. Nucleic Acids Res. 40:D1100–D1107.
2012.PubMed/NCBI View Article : Google Scholar
|
|
25
|
Kim S, Thiessen PA, Bolton EE, Chen J, Fu
G, Gindulyte A, Han L, He J, He S, Shoemaker BA, et al: PubChem
substance and compound databases. Nucleic Acids Res.
44:D1202–D1213. 2016.PubMed/NCBI View Article : Google Scholar
|
|
26
|
Wishart DS, Knox C, Guo AC, Cheng D,
Shrivastava S, Tzur D, Gautam B and Hassanali M: DrugBank: A
knowledgebase for drugs, drug actions and drug targets. Nucleic
Acids Res. 36:D901–D906. 2008.PubMed/NCBI View Article : Google Scholar
|
|
27
|
Trott O and Olson AJ: AutoDock Vina:
Improving the speed and accuracy of docking with a new scoring
function, efficient optimization, and multithreading. J Comput
Chem. 31:455–461. 2010.PubMed/NCBI View Article : Google Scholar
|
|
28
|
Salentin S, Schreiber S, Haupt VJ, Adasme
MF and Schroeder M: PLIP: Fully automated protein-ligand
interaction profiler. Nucleic Acids Res. 43:W443–W447.
2015.PubMed/NCBI View Article : Google Scholar
|
|
29
|
Schena M, Shalon D, Davis RW and Brown PO:
Quantitative monitoring of gene expression patterns with a
complementary DNA microarray. Science. 270:467–470. 1995.PubMed/NCBI View Article : Google Scholar
|
|
30
|
Draghici S: Data Analysis Tools for DNA
Microarrays. Chapman and Hall/CRC Press, Boca Raton (FL), 2003.
Available from: https://trove.nla.gov.au/version/46530985.
|
|
31
|
Raza K: Reconstruction, topological and
gene ontology enrichment analysis of cancerous gene regulatory
network modules. Curr Bioinform. 11:243–258. 2016.
|
|
32
|
Raza K and Hasan AN: A comprehensive
evaluation of machine learning techniques for cancer class
prediction based on microarray data. Int J Bioinform Res Appl.
11:397–416. 2015.PubMed/NCBI View Article : Google Scholar
|
|
33
|
Raza K: Analysis of microarray data using
artificial intelligence based techniques. In: Handbook of Research
on Computational Intelligence Applications in Bioinformatics,
pp216-239, 2016.
|
|
34
|
Cross NA, Chandrasekharan S, Jokonya N,
Fowles A, Hamdy FC, Buttle DJ and Eaton CL: The expression and
regulation of ADAMTS-1, -4, -5, -9, and -15, and TIMP-3 by TGFbeta1
in prostate cells: Relevance to the accumulation of versican.
Prostate. 63:269–275. 2005.PubMed/NCBI View Article : Google Scholar
|
|
35
|
Burger MJ, Tebay MA, Keith PA, Samaratunga
HM, Clements J, Lavin MF and Gardiner RA: Expression analysis of
delta-catenin and prostate-specific membrane antigen: Their
potential as diagnostic markers for prostate cancer. Int J Cancer.
100:228–237. 2002.PubMed/NCBI View Article : Google Scholar
|
|
36
|
Cohen P, Peehl DM, Baker B, Liu F, Hintz
RL and Rosenfeld RG: Insulin-like growth factor axis abnormalities
in prostatic stromal cells from patients with benign prostatic
hyperplasia. J Clin Endocrinol Metab. 79:1410–1415. 1994.PubMed/NCBI View Article : Google Scholar
|
|
37
|
Piñero J, Ramírez-Anguita JM,
Saüch-Pitarch J, Ronzano F, Centeno E, Sanz F and Furlong LI: The
DisGeNET knowledge platform for disease genomics: 2019 update.
Nucleic Acids Res. 48:D845–D855. 2020.PubMed/NCBI View Article : Google Scholar
|
|
38
|
Gustavsson H, Tesan T, Jennbacken K, Kuno
K, Damber JE and Welén K: ADAMTS1 alters blood vessel morphology
and TSP1 levels in LNCaP and LNCaP-19 prostate tumors. BMC Cancer.
10(288)2010.PubMed/NCBI View Article : Google Scholar
|
|
39
|
Chen Y, Wang J, Fraig MM, Metcalf J,
Turner WR, Bissada NK, Watson DK and Schweinfest CW: Defects of DNA
mismatch repair in human prostate cancer. Cancer Res. 61:4112–4121.
2001.PubMed/NCBI
|
|
40
|
Armenia J, Wankowicz SAM, Liu D, Gao J,
Kundra R, Reznik E, Chatila WK, Chakravarty D, Han GC, Coleman I,
et al: The long tail of oncogenic drivers in prostate cancer. Nat
Genet. 50:645–651. 2018.PubMed/NCBI View Article : Google Scholar
|
|
41
|
Morgenbesser SD, McLaren RP, Richards B,
Zhang M, Akmaev VR, Winter SF, Mineva ND, Kaplan-Lefko PJ, Foster
BA, Cook BP, et al: Identification of genes potentially involved in
the acquisition of androgen-independent and metastatic tumor growth
in an autochthonous genetically engineered mouse prostate cancer
model. Prostate. 67:83–106. 2007.PubMed/NCBI View Article : Google Scholar
|
|
42
|
Maresh EL, Mah V, Alavi M, Horvath S,
Bagryanova L, Liebeskind ES, Knutzen LA, Zhou Y, Chia D, Liu AY and
Goodglick L: Differential expression of anterior gradient gene AGR2
in prostate cancer. BMC Cancer. 10(680)2010.PubMed/NCBI View Article : Google Scholar
|
|
43
|
Bu H, Bormann S, Schäfer G, Horninger W,
Massoner P, Neeb A, Lakshmanan VK, Maddalo D, Nestl A, Sültmann H,
et al: The anterior gradient 2 (AGR2) gene is overexpressed in
prostate cancer and may be useful as a urine sediment marker for
prostate cancer detection. Prostate. 71:575–587. 2011.PubMed/NCBI View Article : Google Scholar
|
|
44
|
Neeb A, Hefele S, Bormann S, Parson W,
Adams F, Wolf P, Miernik A, Schoenthaler M, Kroenig M, Wilhelm K,
et al: Splice variant transcripts of the anterior gradient 2 gene
as a marker of prostate cancer. Oncotarget. 5:8681–8689.
2014.PubMed/NCBI View Article : Google Scholar
|
|
45
|
Paschke L, Rucinski M, Ziolkowska A,
Zemleduch T, Malendowicz W, Kwias Z and Malendowicz LK: ZFP91-a
newly described gene potentially involved in prostate pathology.
Pathol Oncol Res. 20:453–459. 2014.PubMed/NCBI View Article : Google Scholar
|
|
46
|
Jiang H, Zhang L, Liu J, Chen Z, Na R,
Ding G, Zhang H and Ding Q: Knockdown of zinc finger protein
X-linked inhibits prostate cancer cell proliferation and induces
apoptosis by activating caspase-3 and caspase-9. Cancer Gene Ther.
19:684–689. 2012.PubMed/NCBI View Article : Google Scholar
|
|
47
|
Vanaja DK, Cheville JC, Iturria SJ and
Young CY: Transcriptional silencing of zinc finger protein 185
identified by expression profiling is associated with prostate
cancer progression. Cancer Res. 63:3877–3882. 2003.PubMed/NCBI
|
|
48
|
Abildgaard MO, Borre M, Mortensen MM,
Ulhøi BP, Tørring N, Wild P, Kristensen H, Mansilla F, Ottosen PD,
Dyrskjøt L, et al: Downregulation of zinc finger protein 132 in
prostate cancer is associated with aberrant promoter
hypermethylation and poor prognosis. Int J Cancer. 130:885–895.
2012.PubMed/NCBI View Article : Google Scholar
|
|
49
|
Jiao L, Li Y, Shen D, Xu C, Wang L, Huang
G, Chen L, Yang Y, Yang C, Yu Y and Sun Y: The prostate
cancer-up-regulated Myc-associated zinc-finger protein (MAZ)
modulates proliferation and metastasis through reciprocal
regulation of androgen receptor. Med Oncol. 30(570)2013.PubMed/NCBI View Article : Google Scholar
|
|
50
|
Rahman MT: Zinc and benign prostatic
hyperplasia (BPH) & prostate cancer (PCa) association. Med Res
Arch. 4:1–16. 2016.
|
|
51
|
Dunn TA, Fedor HL, De Marzo AM and Luo J:
Molecular profiling of indolent human prostate cancer: Tackling
technical challenges to achieve high-fidelity genome-wide data.
Asian J Androl. 14:385–392. 2012.PubMed/NCBI View Article : Google Scholar
|
|
52
|
O'Malley KJ, Eisermann K, Pascal LE,
Parwani AV, Majima T, Graham L, Hrebinko K, Acquafondata M, Stewart
NA, Nelson JB, et al: Proteomic analysis of patient tissue reveals
PSA protein in the stroma of benign prostatic hyperplasia.
Prostate. 74:892–900. 2014.PubMed/NCBI View Article : Google Scholar
|
|
53
|
Adeola HA, Calder B, Soares NC, Kaestner
L, Blackburn JM and Zerbini LF: In silico verification and parallel
reaction monitoring prevalidation of potential prostate cancer
biomarkers. Future Oncol. 12:43–57. 2015.PubMed/NCBI View Article : Google Scholar
|
|
54
|
Sacca PA, Mazza ON, Scorticati C,
Vitagliano G, Casas G and Calvo JC: Human periprostatic adipose
tissue: Secretome from patients with prostate cancer or benign
prostate hyperplasia. Cancer Genomics Proteomics. 16:29–58.
2019.PubMed/NCBI View Article : Google Scholar
|
|
55
|
Hsing AW, Chen C, Chokkalingam AP, Gao YT,
Dightman DA, Nguyen HT, Deng J, Cheng J, Sesterhenn IA, Mostofi FK,
et al: Polymorphic markers in the SRD5A2 gene and prostate cancer
risk: A population-based case-control study. Cancer Epidemiol
Biomarkers Prev. 10:1077–102. 2001.PubMed/NCBI
|
|
56
|
Choi SY, Kim HJ, Cheong HS and Myung SC:
The association of 5-alpha reductase type 2 (SRD5A2) gene
polymorphisms with prostate cancer in a Korean population. Korean J
Urol. 56:19–30. 2015.PubMed/NCBI View Article : Google Scholar
|
|
57
|
Choubey VK, Sankhwar SN, Carlus SJ, Singh
AN, Dalela D, Thangaraj K and Rajender S: SRD5A2 gene polymorphisms
and the risk of benign prostatic hyperplasia but not prostate
cancer. Asian Pac J Cancer Prev. 16:1033–1036. 2015.PubMed/NCBI View Article : Google Scholar
|
|
58
|
Zeng XT, Su XJ, Li S, Weng H, Liu TZ and
Wang XH: Association between SRD5A2 rs523349 and rs9282858
polymorphisms and risk of benign prostatic hyperplasia: A
meta-analysis. Front Physiol. 8(688)2017.PubMed/NCBI View Article : Google Scholar
|
|
59
|
Lodewijk L, Willems SM, Dreijerink KMA, de
Keizer B, van Diest PJ, Schepers A, Morreau H, Bonenkamp HJ, Van
Engen-van Grunsven IACH, Kruijff S, et al: The theranostic target
prostate-specific membrane antigen is expressed in medullary
thyroid cancer. Hum Pathol. 81:245–254. 2018.PubMed/NCBI View Article : Google Scholar
|
|
60
|
Sácha P, Zámecník J, Barinka C, Hlouchová
K, Vícha A, Mlcochová P, Hilgert I, Eckschlager T and Konvalinka J:
Expression of glutamate carboxypeptidase II in human brain.
Neuroscience. 144:1361–1372. 2007.PubMed/NCBI View Article : Google Scholar
|
|
61
|
O'Keefe DS, Bacich DJ and Heston WD:
Comparative analysis of prostate-specific membrane antigen (PSMA)
versus a prostate-specific membrane antigen-like gene. Prostate.
58:200–210. 2004.PubMed/NCBI View Article : Google Scholar
|
|
62
|
Kinoshita Y, Kuratsukuri K, Landas S,
Imaida K, Rovito PM Jr, Wang CY and Haas GP: Expression of
prostate-specific membrane antigen in normal and malignant human
tissues. World J Surg. 30:628–636. 2006.PubMed/NCBI View Article : Google Scholar
|
|
63
|
Kumar R, Verma V, Sarswat A, Maikhuri JP,
Jain A, Jain RK, Sharma VL, Dalela D and Gupta G: Selective
estrogen receptor modulators regulate stromal proliferation in
human benign prostatic hyperplasia by multiple beneficial
mechanisms-action of two new agents. Invest New Drugs. 30:582–593.
2012.PubMed/NCBI View Article : Google Scholar
|
|
64
|
Huang DW, Sherman BT, Tan Q, Collins JR,
Alvord WG, Roayaei J, Stephens R, Baseler MW, Lane HC and Lempicki
RA: The DAVID gene functional classification tool: A novel
biological module-centric algorithm to functionally analyze large
gene lists. Genome Biol. 8(R183)2007.PubMed/NCBI View Article : Google Scholar
|
|
65
|
Raza K: Protein features identification
for machine learning-based prediction of protein-protein
interactions. In: International Conference on Information,
Communication and Computing Technology, pp305-317, 2017.
|
|
66
|
Raza K and Parveen R: Soft computing
approach for modeling genetic regulatory networks. In: Advances in
Computing and Information Technology. Advances in Intelligent
Systems and Computing. Meghanathan N, Nagamalai D and Chaki N
(eds). Springer, Berlin, Heidelberg, pp1-11, 2013.
|
|
67
|
Raza K and Parveen R: Reconstruction of
gene regulatory network of colon cancer using information theoretic
approach. In: Confluence 2013: The Next Generation Information
Technology Summit (4th International Conference), pp461-466,
2013.
|
|
68
|
Kryuchkova-Mostacci N and Robinson-Rechavi
M: A benchmark of gene expression tissue-specificity metrics. Brief
Bioinform. 18:205–214. 2017.PubMed/NCBI View Article : Google Scholar
|
