Optimizing molecular signatures for predicting prostate cancer recurrence.
about
Evaluation of protein biomarkers of prostate cancer aggressivenessMolecular pathways involved in prostate carcinogenesis: insights from public microarray datasetsAn accurate prostate cancer prognosticator using a seven-gene signature plus Gleason score and taking cell type heterogeneity into accountLoss of somatostatin receptor subtype 2 in prostate cancer is linked to an aggressive cancer phenotype, high tumor cell proliferation and predicts early metastatic and biochemical relapseDevelopment of multigene expression signature maps at the protein level from digitized immunohistochemistry slidesNetwork and data integration for biomarker signature discovery via network smoothed T-statistics.Derivation of cancer diagnostic and prognostic signatures from gene expression data.Meta-analysis of miRNA expression profiles for prostate cancer recurrence following radical prostatectomy.Analysis of normal-tumour tissue interaction in tumours: prediction of prostate cancer features from the molecular profile of adjacent normal cells.Elevated YKL40 is associated with advanced prostate cancer (PCa) and positively regulates invasion and migration of PCa cellsCancer progression modeling using static sample dataDiagnosis of prostate cancer using differentially expressed genes in stromaUrinary glycoprotein biomarker discovery for bladder cancer detection using LC/MS-MS and label-free quantification.Protein-network modeling of prostate cancer gene signatures reveals essential pathways in disease recurrence.CD147 expression predicts biochemical recurrence after prostatectomy independent of histologic and pathologic featuresMolecular diagnostic trends in urological cancer: biomarkers for non-invasive diagnosis.A candidate molecular biomarker panel for the detection of bladder cancer.Bladder cancer detection and monitoring: assessment of urine- and blood-based marker testsStromal responses among common carcinomas correlated with clinicopathologic featuresUrinary proteomic profiling for diagnostic bladder cancer biomarkers.MULTIPLEX URINARY TESTS FOR BLADDER CANCER DIAGNOSIS.Metabolomic profiling identifies biochemical pathways associated with castration-resistant prostate cancer.RB Loss Promotes Prostate Cancer Metastasis.A four gene signature predictive of recurrent prostate cancer.FOXA1 inhibits prostate cancer neuroendocrine differentiation.The role of ATP-binding cassette transporter genes in the progression of prostate cancer.Dynein axonemal heavy chain 8 promotes androgen receptor activity and associates with prostate cancer progression.Investigation of the molecular mechanisms underlying postoperative recurrence in prostate cancer by gene expression profiling.Linking prostate cancer cell AR heterogeneity to distinct castration and enzalutamide responsesA functional genomics screen reveals a strong synergistic effect between docetaxel and the mitotic gene DLGAP5 that is mediated by the androgen receptor
P2860
Q21261257-7EEB147E-2664-44C4-9149-EBEED1E5F870Q27990300-873404B1-4BF5-4BC4-A468-A00DD9018B32Q28484126-C37CF8F1-D2B7-48AB-AC0D-E7BA4455FA4DQ28540624-8353F8A0-4054-46B0-9530-A6FD60012C5DQ28731283-69FB49A6-3D82-41D3-813B-2CC1BCD2B48CQ30665910-40BE61AB-3DC1-4792-BD74-9F1FE66419C8Q33748879-B000E2B3-6084-44B8-9922-BEC625CD6637Q33834711-009BE1FC-7667-47F0-B8F0-68C77E3A5B02Q33867402-969B9B3D-F334-416B-907B-925BC4709526Q34052057-820BFD5A-FDD2-49D7-9DE4-BD7A20E082E6Q34334159-FAEAEBE9-470A-46D1-A446-8F4D6FB410F7Q34763431-2366629C-DCD0-432A-A008-C32CF950BAFBQ34987865-1DC7562A-A85E-40B4-BB36-F6C1302C4168Q35082989-34DB077B-3897-419E-8BE3-A4571FB9CFE0Q35713085-3137BD84-F0DB-4859-9377-A73B70A8963BQ36190604-705BDEB1-EB4D-40D2-9E5D-91BA911045F8Q36509712-D4FBB4B2-F6CF-4DDC-9833-26933C244CD4Q36770389-32D9F480-169E-4231-A35C-B226505CA815Q37185376-203526C1-22ED-4C75-B70E-AFC81C0DC663Q37610310-33E0AAFD-3733-42AF-88E8-8F910C14FE7FQ38188561-8B72CEBD-B3A5-4BCB-A7C2-FCBA3E95296BQ38615085-457D7C03-A41F-43E3-93B9-84E6429C7FA1Q38727367-EFE36AD6-93B3-41AE-8949-E66AF0965072Q38783018-13365980-B555-4E6E-ABE2-41ED72FDA9F9Q38893882-C5D8D396-8062-4B6A-A952-96178CB309C9Q40167077-26041916-63FF-48A8-ACFF-54FF92A19B36Q41263669-32AD62BB-E815-4341-86E1-A2FF4CB863D6Q49335670-121E1514-921B-45E8-AAF0-3715755E84BEQ57106289-3CD46415-24AD-4B0A-AFE6-BD482911C596Q57809711-4C56FB97-09F4-4019-96A5-3932AEE5D982
P2860
Optimizing molecular signatures for predicting prostate cancer recurrence.
description
2009 nî lūn-bûn
@nan
2009年の論文
@ja
2009年論文
@yue
2009年論文
@zh-hant
2009年論文
@zh-hk
2009年論文
@zh-mo
2009年論文
@zh-tw
2009年论文
@wuu
2009年论文
@zh
2009年论文
@zh-cn
name
Optimizing molecular signatures for predicting prostate cancer recurrence.
@ast
Optimizing molecular signatures for predicting prostate cancer recurrence.
@en
type
label
Optimizing molecular signatures for predicting prostate cancer recurrence.
@ast
Optimizing molecular signatures for predicting prostate cancer recurrence.
@en
prefLabel
Optimizing molecular signatures for predicting prostate cancer recurrence.
@ast
Optimizing molecular signatures for predicting prostate cancer recurrence.
@en
P2860
P356
P1433
P1476
Optimizing molecular signatures for predicting prostate cancer recurrence.
@en
P2093
Steve Goodison
P2860
P304
P356
10.1002/PROS.20961
P577
2009-07-01T00:00:00Z