|
1
|
Chen W, Zheng R, Baade PD, Zhang S, Zeng
H, Bray F, Jemal A, Yu XQ and He J: Cancer statistics in china,
2015. CA Cancer J Clin. 66:115–132. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
2
|
Brockman JA, Alanee S, Vickers AJ,
Scardino PT, Wood DP, Kibel AS, Lin DW, Bianco FJ Jr, Rabah DM,
Klein EA, et al: Nomogram predicting prostate cancer-specific
mortality for men with biochemical recurrence after radical
prostatectomy. Eur Urol. 67:1160–1167. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
3
|
Torrecilla Lopez J, Hervas A, Zapatero A,
Gómez Caamaño A, Macías V, Herruzo I, Maldonado X, Iturriaga Gómez
A, Casas F and Segundo González San C: Uroncor consensus statement:
Management of biochemical recurrence after radical radiotherapy for
prostate cancer: From biochemical failure to castration resistance.
Rep Pract Oncol Radiother. 20:259–272. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Algarra R, Hevia M, Tienza A, Merino I,
Velis JM, Zudaire J, Robles JE and Pascual I: Survival analysis of
patients with biochemical relapse after radical prostatectomy
treated with androgen deprivation: Castration-resistance
influential factors. Can Urol Assoc J. 8:E333–E341. 2014.
View Article : Google Scholar : PubMed/NCBI
|
|
5
|
Epstein JI, Zelefsky MJ, Sjoberg DD,
Nelson JB, Egevad L, Magi-Galluzzi C, Vickers AJ, Parwani AV,
Reuter VE, Fine SW, et al: A contemporary prostate cancer grading
system: A validated alternative to the gleason score. Eur Urol.
69:428–435. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
6
|
Huang TB, Dong CP, Zhou GC, Lu SM, Luan Y,
Gu X, Liu L and Ding XF: A potential panel of four-long noncoding
RNA signature in prostate cancer predicts biochemical
recurrence-free survival and disease-free survival. Int Urol
Nephrol. 49:825–835. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Zhang H, Cheng S, Wang A, Ma H, Yao B, Qi
C, Liu R, Qi S and Xu Y: Expression of RABEX-5 and its clinical
significance in prostate cancer. J Exp Clin Cancer Res. 33:312014.
View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Chiang YT, Wang K, Fazli L, Qi RZ, Gleave
ME, Collins CC, Gout PW and Wang Y: GATA2 as a potential
metastasis-driving gene in prostate cancer. Oncotarget. 5:451–461.
2014. View Article : Google Scholar : PubMed/NCBI
|
|
9
|
Zhang L, Guo F, Gao X and Wu Y: Golgi
phosphoprotein 3 expression predicts poor prognosis in patients
with prostate cancer undergoing radical prostatectomy. Mol Med Rep.
12:1298–1304. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
10
|
Amaro A, Esposito AI, Gallina A, Nees M,
Angelini G, Albini A and Pfeffer U: Validation of proposed prostate
cancer biomarkers with gene expression data: A long road to travel.
Cancer Metastasis Rev. 33:657–671. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Samra Bou E, Klein B, Commes T and Moreaux
J: Development of gene expression-based risk score in
cytogenetically normal acute myeloid leukemia patients. Oncotarget.
3:824–832. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Ito H, Mo Q, Qin LX, Viale A, Maithel SK,
Maker AV, Shia J, Kingham P, Allen P, DeMatteo RP, et al: Gene
expression profiles accurately predict outcome following liver
resection in patients with metastatic colorectal cancer. PloS One.
8:e816802013. View Article : Google Scholar : PubMed/NCBI
|
|
13
|
Chang W, Gao X, Han Y, Du Y, Liu Q, Wang
L, Tan X, Zhang Q, Liu Y, Zhu Y, et al: Gene expression
profiling-derived immunohistochemistry signature with high
prognostic value in colorectal carcinoma. Gut. 63:1457–1467. 2014.
View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Salazar R, Roepman P, Capella G, Moreno V,
Simon I, Dreezen C, Lopez-Doriga A, Santos C, Marijnen C, Westerga
J, et al: Gene expression signature to improve prognosis prediction
of stage II and III colorectal cancer. J Clin Oncol. 29:17–24.
2011. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Samra Bou E, Klein B, Commes T and Moreaux
J: Identification of a 20-gene expression-based risk score as a
predictor of clinical outcome in chronic lymphocytic leukemia
patients. Biomed Res Int. 2014:4231742014.PubMed/NCBI
|
|
16
|
Kim SK, Kim SY, Kim JH, Roh SA, Cho DH,
Kim YS and Kim JC: A nineteen gene-based risk score classifier
predicts prognosis of colorectal cancer patients. Mol Oncol.
8:1653–1666. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
17
|
Zhang ZL, Zhao LJ, Chai L, Zhou SH, Wang
F, Wei Y, Xu YP and Zhao P: Seven LncRNA-mRNA based risk score
predicts the survival of head and neck squamous cell carcinoma. Sci
Rep. 7:3092017. View Article : Google Scholar : PubMed/NCBI
|
|
18
|
Ross-Adams H, Lamb AD, Dunning MJ, Halim
S, Lindberg J, Massie CM, Egevad LA, Russell R, Ramos-Montoya A,
Vowler SL, et al: Integration of copy number and transcriptomics
provides risk stratification in prostate cancer: A discovery and
validation cohort study. EBioMedicine. 2:1133–1144. 2015.
View Article : Google Scholar : PubMed/NCBI
|
|
19
|
Kim KH, Yang SS, Yoon YS, Lim SB, Yu CS
and Kim JC: Validation of the seventh edition of the american joint
committee on cancer tumor-node-metastasis (AJCC TNM) staging in
patients with stage II and stage III colorectal carcinoma: Analysis
of 2511 cases from a medical centre in korea. Colorectal Dis.
13:e220–e226. 2011. View Article : Google Scholar : PubMed/NCBI
|
|
20
|
Edgar R, Domrachev M and Lash AE: Gene
expression omnibus: NCBI gene expression and hybridization array
data repository. Nucleic Acids Res. 30:207–210. 2002. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Katz S, Irizarry RA, Lin X, Tripputi M and
Porter MW: A summarization approach for affymetrix genechip data
using a reference training set from a large, biologically diverse
database. BMC Bioinformatics. 7:4642006. View Article : Google Scholar : PubMed/NCBI
|
|
22
|
Ishwaran H and Kogalur UB: Consistency of
random survival forests. Stat Probab Lett. 80:1056–1064. 2010.
View Article : Google Scholar : PubMed/NCBI
|
|
23
|
Ishwaran H, Gerds TA, Kogalur UB, Moore
RD, Gange SJ and Lau BM: Random survival forests for competing
risks. Biostatistics. 15:757–773. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
24
|
Gautier L, Cope L, Bolstad BM and Irizarry
RA: affy-analysis of affymetrix genechip data at the probe level.
Bioinformatics. 20:307–315. 2004. View Article : Google Scholar : PubMed/NCBI
|
|
25
|
Liu Z and Pounds S: An R package that
automatically collects and archives details for reproducible
computing. BMC Bioinformatics. 15:1382014. View Article : Google Scholar : PubMed/NCBI
|
|
26
|
Charlop-Powers Z and Brady SF: Phylogeo:
An R package for geographic analysis and visualization of
microbiome data. Bioinformatics. 31:2909–2911. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
27
|
Robin X, Turck N, Hainard A, Tiberti N,
Lisacek F, Sanchez JC and Müller M: pROC: An open-source package
for R and S+ to analyze and compare ROC curves. BMC Bioinformatics.
12:772011. View Article : Google Scholar : PubMed/NCBI
|
|
28
|
Kim R: Anesthetic technique and cancer
recurrence in oncologic surgery: Unraveling the puzzle. Cancer
Metastasis Rev. 36:159–177. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
29
|
Chen J, Miao Z, Xue B, Shan Y, Weng G and
Shen B: Long non-coding RNAs in urologic malignancies: Functional
roles and clinical translation. J Cancer. 7:1842–1855. 2016.
View Article : Google Scholar : PubMed/NCBI
|
|
30
|
Nimse SB, Sonawane MD, Son KS and Kim T:
Biomarker detection technologies and future directions. Analyst.
141:740–755. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
31
|
Tian X, Zhu X, Yan T, Yu C, Shen C, Hong
J, Chen H and Fang JY: Differentially expressed lncrnas in gastric
cancer patients: A potential biomarker for gastric cancer
prognosis. J Cancer. 8:2575–2586. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
32
|
Bensalah K, Lotan Y, Karam JA and Shariat
SF: New circulating biomarkers for prostate cancer. Prostate Cancer
Prostatic Dis. 11:112–120. 2008. View Article : Google Scholar : PubMed/NCBI
|
|
33
|
Gupta D and Lis CG: Pretreatment serum
albumin as a predictor of cancer survival: A systematic review of
the epidemiological literature. Nutr J. 9:692010. View Article : Google Scholar : PubMed/NCBI
|
|
34
|
Kaushik AK, Shojaie A, Panzitt K, Sonavane
R, Venghatakrishnan H, Manikkam M, Zaslavsky A, Putluri V, Vasu VT,
Zhang Y, et al: Inhibition of the hexosamine biosynthetic pathway
promotes castration-resistant prostate cancer. Net Commun.
7:116122016. View Article : Google Scholar
|
|
35
|
Tang H, Wu Y, Qin Y, Wang H, Jia Y, Yang
S, Luo S and Wang Q: Predictive significance of HMGCS2 for
prognosis in resected Chinese esophageal squamous cell carcinoma
patients. OncoTargets Ther. 10:2553–2560. 2017. View Article : Google Scholar
|
|
36
|
Saraon P, Cretu D, Musrap N, Karagiannis
GS, Batruch I, Drabovich AP, van der Kwast T, Mizokami A, Morrissey
C, Jarvi K and Diamandis EP: Quantitative proteomics reveals that
enzymes of the ketogenic pathway are associated with prostate
cancer progression. Mol Cell Proteomics. 12:1589–1601. 2013.
View Article : Google Scholar : PubMed/NCBI
|
|
37
|
Su SG, Yang M, Zhang MF, Peng QZ, Li MY,
Liu LP and Bao SY: miR-107-mediated decrease of HMGCS2 indicates
poor outcomes and promotes cell migration in hepatocellular
carcinoma. Int J Biochem Cell Biol. 91:53–59. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
38
|
Sun LN, Zhi Z, Chen LY, Zhou Q, Li XM, Gan
WJ, Chen S, Yang M, Liu Y, Shen T, et al: SIRT1 suppresses
colorectal cancer metastasis by transcriptional repression of
miR-15b-5p. Cancer Lett. 409:104–115. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
39
|
Edqvist PH, Huvila J, Forsstrom B, Talve
L, Carpén O, Salvesen HB, Krakstad C, Grénman S, Johannesson H,
Ljungqvist O, et al: Loss of ASRGL1 expression is an independent
biomarker for disease-specific survival in endometrioid endometrial
carcinoma. Gynecol Oncol. 137:529–537. 2015. View Article : Google Scholar : PubMed/NCBI
|
