Prognostic prediction by liver tissue proteomic profiling in patients with colorectal liver metastases

To obtain proteomic profiles in patients with colorectal liver metastases (CRLM) and identify the relationship between profiles and the prognosis of CRLM patients. Prognosis prediction (favorable or unfavorable according to Fong's score) by a classification and regression tree algorithm of surf...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Future oncology (London, England) Ročník 13; číslo 10; s. 875 - 882
Hlavní autoři: Reyes, Adalgiza, Marti, Josep, Marfà, Santiago, Jiménez, Wladimiro, Reichenbach, Vedrana, Pelegrina, Amalia, Fondevila, Constantino, Garcia Valdecasas, Juan Carlos, Fuster, Josep
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Future Medicine Ltd 01.04.2017
Témata:
Adult
Aged
Aged, 80 and over
Arrays
biomarker
Biomarkers
Biomarkers, Tumor
Case-Control Studies
Chemotherapy
Colorectal cancer
Colorectal Neoplasms - mortality
Colorectal Neoplasms - pathology
Female
Genomics
Humans
Laboratories
Liver
liver metastases
Liver Neoplasms - metabolism
Liver Neoplasms - mortality
Liver Neoplasms - secondary
Male
Medical prognosis
Metastasis
Middle Aged
outcomes research
Patients
Prognosis
Proteins
Proteome
proteomic analysis
Proteomics
Proteomics - methods
Surgery
colorectal cancer
outcomes research
biomarker
prognosis
liver metastases
proteomic analysis
ISSN:1479-6694, 1744-8301
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstract To obtain proteomic profiles in patients with colorectal liver metastases (CRLM) and identify the relationship between profiles and the prognosis of CRLM patients. Prognosis prediction (favorable or unfavorable according to Fong's score) by a classification and regression tree algorithm of surface-enhanced laser desorption/ionization TOF-MS proteomic profiles from cryopreserved CRLM (patients) and normal liver tissue (controls). The protein peak 7371 showed the clearest differences between CRLM and control groups (94.1% sensitivity, 100% specificity, p < 0.001). The algorithm that best differentiated favorable and unfavorable groups combined 2970 and 2871 protein peaks (100% sensitivity, 90% specificity). Proteomic profiling in liver samples using classification and regression tree algorithms is a promising technique to differentiate healthy subjects from CRLM patients and to classify the severity of CRLM patients.
AbstractList To obtain proteomic profiles in patients with colorectal liver metastases (CRLM) and identify the relationship between profiles and the prognosis of CRLM patients. Prognosis prediction (favorable or unfavorable according to Fong's score) by a classification and regression tree algorithm of surface-enhanced laser desorption/ionization TOF-MS proteomic profiles from cryopreserved CRLM (patients) and normal liver tissue (controls). The protein peak 7371 m/z showed the clearest differences between CRLM and control groups (94.1% sensitivity, 100% specificity, p < 0.001). The algorithm that best differentiated favorable and unfavorable groups combined 2970 and 2871 m/z protein peaks (100% sensitivity, 90% specificity). Proteomic profiling in liver samples using classification and regression tree algorithms is a promising technique to differentiate healthy subjects from CRLM patients and to classify the severity of CRLM patients.
Aim: To obtain proteomic profiles in patients with colorectal liver metastases (CRLM) and identify the relationship between profiles and the prognosis of CRLM patients. Materials & methods: Prognosis prediction (favorable or unfavorable according to Fong's score) by a classification and regression tree algorithm of surface-enhanced laser desorption/ionization TOF–MS proteomic profiles from cryopreserved CRLM (patients) and normal liver tissue (controls). Results: The protein peak 7371 m/z showed the clearest differences between CRLM and control groups (94.1% sensitivity, 100% specificity, p < 0.001). The algorithm that best differentiated favorable and unfavorable groups combined 2970 and 2871 m/z protein peaks (100% sensitivity, 90% specificity). Conclusion: Proteomic profiling in liver samples using classification and regression tree algorithms is a promising technique to differentiate healthy subjects from CRLM patients and to classify the severity of CRLM patients.
AIMTo obtain proteomic profiles in patients with colorectal liver metastases (CRLM) and identify the relationship between profiles and the prognosis of CRLM patients.MATERIALS & METHODSPrognosis prediction (favorable or unfavorable according to Fong's score) by a classification and regression tree algorithm of surface-enhanced laser desorption/ionization TOF-MS proteomic profiles from cryopreserved CRLM (patients) and normal liver tissue (controls).RESULTSThe protein peak 7371 m/z showed the clearest differences between CRLM and control groups (94.1% sensitivity, 100% specificity, p < 0.001). The algorithm that best differentiated favorable and unfavorable groups combined 2970 and 2871 m/z protein peaks (100% sensitivity, 90% specificity).CONCLUSIONProteomic profiling in liver samples using classification and regression tree algorithms is a promising technique to differentiate healthy subjects from CRLM patients and to classify the severity of CRLM patients.
To obtain proteomic profiles in patients with colorectal liver metastases (CRLM) and identify the relationship between profiles and the prognosis of CRLM patients. Prognosis prediction (favorable or unfavorable according to Fong's score) by a classification and regression tree algorithm of surface-enhanced laser desorption/ionization TOF-MS proteomic profiles from cryopreserved CRLM (patients) and normal liver tissue (controls). The protein peak 7371 showed the clearest differences between CRLM and control groups (94.1% sensitivity, 100% specificity, p < 0.001). The algorithm that best differentiated favorable and unfavorable groups combined 2970 and 2871 protein peaks (100% sensitivity, 90% specificity). Proteomic profiling in liver samples using classification and regression tree algorithms is a promising technique to differentiate healthy subjects from CRLM patients and to classify the severity of CRLM patients.
Author Pelegrina, Amalia
Reyes, Adalgiza
Garcia Valdecasas, Juan Carlos
Jiménez, Wladimiro
Marfà, Santiago
Fondevila, Constantino
Fuster, Josep
Marti, Josep
Reichenbach, Vedrana
AuthorAffiliation 1Liver Surgery & Transplantation Unit, Department of Surgery, ICMDM, Hospital Clinic, IDIBAPS, CIBERehd, Villarroel, 170, 08036, Barcelona, Spain
3Physiological Sciences Department I, University of Barcelona, Casanova, 143, 08036, Barcelona, Spain
2Biochemistry & Molecular Genetics Service, Hospital Clinic, IDIBAPS, CIBERehd, Villarroel, 170, 08036, Barcelona, Spain
AuthorAffiliation_xml – name: 1Liver Surgery & Transplantation Unit, Department of Surgery, ICMDM, Hospital Clinic, IDIBAPS, CIBERehd, Villarroel, 170, 08036, Barcelona, Spain
– name: 2Biochemistry & Molecular Genetics Service, Hospital Clinic, IDIBAPS, CIBERehd, Villarroel, 170, 08036, Barcelona, Spain
– name: 3Physiological Sciences Department I, University of Barcelona, Casanova, 143, 08036, Barcelona, Spain
Author_xml – sequence: 1
  givenname: Adalgiza
  surname: Reyes
  fullname: Reyes, Adalgiza
– sequence: 2
  givenname: Josep
  surname: Marti
  fullname: Marti, Josep
– sequence: 3
  givenname: Santiago
  surname: Marfà
  fullname: Marfà, Santiago
– sequence: 4
  givenname: Wladimiro
  surname: Jiménez
  fullname: Jiménez, Wladimiro
– sequence: 5
  givenname: Vedrana
  surname: Reichenbach
  fullname: Reichenbach, Vedrana
– sequence: 6
  givenname: Amalia
  surname: Pelegrina
  fullname: Pelegrina, Amalia
– sequence: 7
  givenname: Constantino
  surname: Fondevila
  fullname: Fondevila, Constantino
– sequence: 8
  givenname: Juan Carlos
  surname: Garcia Valdecasas
  fullname: Garcia Valdecasas, Juan Carlos
– sequence: 9
  givenname: Josep
  surname: Fuster
  fullname: Fuster, Josep
BackLink https://www.ncbi.nlm.nih.gov/pubmed/28088872$$D View this record in MEDLINE/PubMed
BookMark eNp1kc1rFTEUxYNU7Icu3cpAN92M5nOSLEtprVDQha5DJu-mpswkr0mm5f33ZnxPF4XChXtDfudyOecUHcUUAaGPBH-mlMgvPsWeYjL0mA_kDTohkvNeMUyO2syl7odB82N0WsoDxlwygd-hY6qwUkrSE-R_5HQfU6nBddsMm-BqSLEbd90UniB3NZSyQPtKFdL8F0o-TCHedyF2W1sDxFq651B_dy5NKYOrdjqIZ6i2tILyHr31dirw4dDP0K-b659Xt_3d96_fri7vesckqb2QgnjChHZWCe21tpjINqgN1YozsaEjk3b0HhrHBzsKzfggrRcYM8ocO0MX-73tzMcFSjVzKA6myUZISzFEDUQQrClv6PkL9CEtObbrDKVSNJAx0qhPB2oZZ9iYbQ6zzTvzz8EG9HvA5VRKBv8fIdisCZmWkFkTMmtCjWcveBeqXU2v2YbpVZXeq_xSlwzFNdsdmP1rXmMLEV7R_gEkKKh3
CitedBy_id crossref_primary_10_2217_fon_2017_0103
crossref_primary_10_2217_fon_2017_0035
crossref_primary_10_1186_s12911_025_03012_9
Cites_doi 10.1002/(SICI)1097-0142(19960401)77:7<1254::AID-CNCR5>3.0.CO;2-I
10.3322/caac.21254
10.1007/s00384-011-1368-4
10.1016/j.jss.2013.04.065
10.1007/s12094-006-0062-4
10.1097/00000658-199909000-00004
10.1007/s12094-011-0722-x
10.1016/S0025-7753(08)72265-6
10.1515/CCLM.2007.351
10.1371/journal.pone.0027718
10.1371/journal.pone.0109254
10.4251/wjgo.v5.i12.207
10.3109/13813455.2010.495130
10.3748/wjg.15.2587
10.1007/s00595-014-1108-9
10.1055/s-0033-1351785
10.1038/ncponc1187
10.1016/S0140-6736(10)60353-4
10.1097/00042737-200507000-00006
10.4240/wjgs.v5.i5.138
10.1002/hep.26608
10.1513/pats.200608-156JG
10.1016/j.bpg.2007.10.002
10.1007/s12094-012-0990-0
10.1016/j.soc.2007.12.005
ContentType Journal Article
Copyright Future Medicine Ltd
Copyright Future Medicine Ltd Apr 2017
Copyright_xml – notice: Future Medicine Ltd
– notice: Copyright Future Medicine Ltd Apr 2017
DBID AAYXX
CITATION
CGR
CUY
CVF
ECM
EIF
NPM
3V.
7X7
7XB
88E
8AO
8FI
8FJ
8FK
ABUWG
AFKRA
BENPR
CCPQU
EHMNL
FYUFA
GHDGH
K9.
M0S
M1P
PHGZM
PHGZT
PJZUB
PKEHL
PPXIY
PQEST
PQQKQ
PQUKI
PRINS
7X8
DOI 10.2217/fon-2016-0461
DatabaseName CrossRef
Medline
MEDLINE
MEDLINE (Ovid)
MEDLINE
MEDLINE
PubMed
ProQuest Central (Corporate)
Health & Medical Collection
ProQuest Central (purchase pre-March 2016)
Medical Database (Alumni Edition)
ProQuest Pharma Collection
Hospital Premium Collection
Hospital Premium Collection (Alumni Edition)
ProQuest Central (Alumni) (purchase pre-March 2016)
ProQuest Central (Alumni)
ProQuest Central UK/Ireland
ProQuest Central
ProQuest One Community College
UK & Ireland Database
Health Research Premium Collection
Health Research Premium Collection (Alumni)
ProQuest Health & Medical Complete (Alumni)
Health & Medical Collection (Alumni Edition)
PML(ProQuest Medical Library)
ProQuest Central Premium
ProQuest One Academic
ProQuest Health & Medical Research Collection
ProQuest One Academic Middle East (New)
ProQuest One Health & Nursing
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Academic (retired)
ProQuest One Academic UKI Edition
ProQuest Central China
MEDLINE - Academic
DatabaseTitle CrossRef
MEDLINE
Medline Complete
MEDLINE with Full Text
PubMed
MEDLINE (Ovid)
ProQuest One Academic Middle East (New)
ProQuest Health & Medical Complete (Alumni)
ProQuest Central (Alumni Edition)
ProQuest One Community College
ProQuest One Health & Nursing
ProQuest Pharma Collection
ProQuest Central China
ProQuest Central
ProQuest Health & Medical Research Collection
Health Research Premium Collection
Health and Medicine Complete (Alumni Edition)
Health & Medical Research Collection
ProQuest Central (New)
ProQuest Medical Library (Alumni)
ProQuest One Academic Eastern Edition
ProQuest Hospital Collection
Health Research Premium Collection (Alumni)
ProQuest Hospital Collection (Alumni)
ProQuest Health & Medical Complete
ProQuest Medical Library
UK & Ireland Database
ProQuest One Academic UKI Edition
ProQuest One Academic
ProQuest One Academic (New)
ProQuest Central (Alumni)
MEDLINE - Academic
DatabaseTitleList MEDLINE
ProQuest One Academic Middle East (New)
MEDLINE - Academic
Database_xml – sequence: 1
  dbid: NPM
  name: PubMed
  url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 2
  dbid: BENPR
  name: ProQuest Central
  url: https://www.proquest.com/central
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Medicine
EISSN 1744-8301
EndPage 882
ExternalDocumentID 28088872
10_2217_fon_2016_0461
Genre Journal Article
GroupedDBID -
0R
29H
3V.
53G
5GY
70G
7X7
88E
8AO
8FI
8FJ
AAWTL
ABUWG
ACGFS
ADBBV
AENEX
AFKRA
AHMBA
ALMA_UNASSIGNED_HOLDINGS
BBAFP
BENPR
BPHCQ
BVXVI
CS3
DU5
EBS
EHMNL
EJD
F5P
FYUFA
H13
HZ
IAO
IEA
IHR
M1P
MV1
NTCAX
O9-
P2P
PQQKQ
PRINS
PROAC
PSQYO
RFM
---
0R~
4.4
AAWFG
AAYXX
ABJNI
ABNDM
ACWKX
AFFHD
AFFYO
CCPQU
CITATION
HMCUK
HZ~
ITC
K-O
M4Z
OVD
PHGZM
PHGZT
PJZUB
PPXIY
RPM
TDBHL
TEORI
TFL
TFMDE
TMEDX
UKHRP
ALIPV
CGR
CUY
CVF
ECM
EIF
NPM
7XB
8FK
K9.
PKEHL
PQEST
PQUKI
7X8
AQTUD
ID FETCH-LOGICAL-c371t-5751f1359ca859f99a01759f8d298435d2b37abffe51f46ab593467af500323c3
IEDL.DBID 7X7
ISICitedReferencesCount 3
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000395763600005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 1479-6694
IngestDate Sun Nov 09 14:42:02 EST 2025
Sat Nov 29 14:46:58 EST 2025
Thu Jan 02 23:02:02 EST 2025
Sat Nov 29 06:21:12 EST 2025
Tue Nov 18 22:34:28 EST 2025
Tue Jan 05 21:42:19 EST 2021
IsPeerReviewed true
IsScholarly true
Issue 10
Keywords colorectal cancer
outcomes research
biomarker
prognosis
liver metastases
proteomic analysis
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c371t-5751f1359ca859f99a01759f8d298435d2b37abffe51f46ab593467af500323c3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
PMID 28088872
PQID 2275861331
PQPubID 54587
PageCount 8
ParticipantIDs proquest_miscellaneous_1861510924
proquest_journals_2275861331
pubmed_primary_28088872
crossref_primary_10_2217_fon_2016_0461
crossref_citationtrail_10_2217_fon_2016_0461
futurescience_futuremedicine_10_2217_fon_2016_0461
ProviderPackageCode RFM
NTCAX
70G
PublicationCentury 2000
PublicationDate 2017-April-01
2017-04-00
2017-Apr
20170401
PublicationDateYYYYMMDD 2017-04-01
PublicationDate_xml – month: 04
  year: 2017
  text: 2017-April-01
  day: 01
PublicationDecade 2010
PublicationPlace England
PublicationPlace_xml – name: England
– name: London
PublicationTitle Future oncology (London, England)
PublicationTitleAlternate Future Oncol
PublicationYear 2017
Publisher Future Medicine Ltd
Publisher_xml – name: Future Medicine Ltd
References e_1_3_4_4_1
e_1_3_4_3_1
e_1_3_4_2_1
Li SY (e_1_3_4_15_1) 2010; 9
e_1_3_4_9_1
e_1_3_4_8_1
e_1_3_4_7_1
e_1_3_4_20_1
e_1_3_4_6_1
e_1_3_4_5_1
e_1_3_4_23_1
e_1_3_4_24_1
e_1_3_4_21_1
e_1_3_4_27_1
e_1_3_4_28_1
e_1_3_4_25_1
e_1_3_4_26_1
Gonzalez-Gonzalez M (e_1_3_4_22_1) 2013; 10
e_1_3_4_12_1
e_1_3_4_13_1
e_1_3_4_10_1
e_1_3_4_11_1
e_1_3_4_16_1
e_1_3_4_17_1
e_1_3_4_14_1
e_1_3_4_18_1
e_1_3_4_19_1
28650211 - Future Oncol. 2017 Jun;13(13):1133-1134
References_xml – ident: e_1_3_4_8_1
  doi: 10.1002/(SICI)1097-0142(19960401)77:7<1254::AID-CNCR5>3.0.CO;2-I
– ident: e_1_3_4_2_1
  doi: 10.3322/caac.21254
– ident: e_1_3_4_16_1
  doi: 10.1007/s00384-011-1368-4
– ident: e_1_3_4_18_1
  doi: 10.1016/j.jss.2013.04.065
– ident: e_1_3_4_23_1
  doi: 10.1007/s12094-006-0062-4
– ident: e_1_3_4_7_1
  doi: 10.1097/00000658-199909000-00004
– ident: e_1_3_4_6_1
  doi: 10.1007/s12094-011-0722-x
– volume: 9
  start-page: 149
  issue: 2
  year: 2010
  ident: e_1_3_4_15_1
  article-title: Proteomic analysis of differentially expressed proteins involving in liver metastasis of human colorectal carcinoma
  publication-title: Hepatobiliary Pancreat. Dis. Int.
– ident: e_1_3_4_24_1
  doi: 10.1016/S0025-7753(08)72265-6
– ident: e_1_3_4_28_1
  doi: 10.1515/CCLM.2007.351
– ident: e_1_3_4_26_1
  doi: 10.1371/journal.pone.0027718
– ident: e_1_3_4_21_1
  doi: 10.1371/journal.pone.0109254
– ident: e_1_3_4_27_1
  doi: 10.4251/wjgo.v5.i12.207
– ident: e_1_3_4_25_1
  doi: 10.3109/13813455.2010.495130
– ident: e_1_3_4_20_1
  doi: 10.3748/wjg.15.2587
– ident: e_1_3_4_9_1
  doi: 10.1007/s00595-014-1108-9
– ident: e_1_3_4_5_1
  doi: 10.1055/s-0033-1351785
– ident: e_1_3_4_12_1
  doi: 10.1038/ncponc1187
– ident: e_1_3_4_3_1
  doi: 10.1016/S0140-6736(10)60353-4
– ident: e_1_3_4_14_1
  doi: 10.1097/00042737-200507000-00006
– ident: e_1_3_4_4_1
  doi: 10.4240/wjgs.v5.i5.138
– ident: e_1_3_4_17_1
  doi: 10.1002/hep.26608
– ident: e_1_3_4_13_1
  doi: 10.1513/pats.200608-156JG
– ident: e_1_3_4_11_1
  doi: 10.1016/j.bpg.2007.10.002
– volume: 10
  start-page: 19
  issue: 1
  year: 2013
  ident: e_1_3_4_22_1
  article-title: Genomics and proteomics approaches for biomarker discovery in sporadic colorectal cancer with metastasis
  publication-title: Cancer Genomics Proteomics
– ident: e_1_3_4_19_1
  doi: 10.1007/s12094-012-0990-0
– ident: e_1_3_4_10_1
  doi: 10.1016/j.soc.2007.12.005
– reference: 28650211 - Future Oncol. 2017 Jun;13(13):1133-1134
SSID ssj0047350
Score 2.1289473
Snippet To obtain proteomic profiles in patients with colorectal liver metastases (CRLM) and identify the relationship between profiles and the prognosis of CRLM...
Aim: To obtain proteomic profiles in patients with colorectal liver metastases (CRLM) and identify the relationship between profiles and the prognosis of CRLM...
AIMTo obtain proteomic profiles in patients with colorectal liver metastases (CRLM) and identify the relationship between profiles and the prognosis of CRLM...
SourceID proquest
pubmed
crossref
futurescience
SourceType Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage 875
SubjectTerms Adult
Aged
Aged, 80 and over
Arrays
biomarker
Biomarkers
Biomarkers, Tumor
Case-Control Studies
Chemotherapy
Colorectal cancer
Colorectal Neoplasms - mortality
Colorectal Neoplasms - pathology
Female
Genomics
Humans
Laboratories
Liver
liver metastases
Liver Neoplasms - metabolism
Liver Neoplasms - mortality
Liver Neoplasms - secondary
Male
Medical prognosis
Metastasis
Middle Aged
outcomes research
Patients
Prognosis
Proteins
Proteome
proteomic analysis
Proteomics
Proteomics - methods
Surgery
Title Prognostic prediction by liver tissue proteomic profiling in patients with colorectal liver metastases
URI http://dx.doi.org/10.2217/fon-2016-0461
https://www.ncbi.nlm.nih.gov/pubmed/28088872
https://www.proquest.com/docview/2275861331
https://www.proquest.com/docview/1861510924
Volume 13
WOSCitedRecordID wos000395763600005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVPQU
  databaseName: Health & Medical Collection
  customDbUrl:
  eissn: 1744-8301
  dateEnd: 20201231
  omitProxy: false
  ssIdentifier: ssj0047350
  issn: 1479-6694
  databaseCode: 7X7
  dateStart: 20050201
  isFulltext: true
  titleUrlDefault: https://search.proquest.com/healthcomplete
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: ProQuest Central
  customDbUrl:
  eissn: 1744-8301
  dateEnd: 20201231
  omitProxy: false
  ssIdentifier: ssj0047350
  issn: 1479-6694
  databaseCode: BENPR
  dateStart: 20050201
  isFulltext: true
  titleUrlDefault: https://www.proquest.com/central
  providerName: ProQuest
– providerCode: PRVPQU
  databaseName: UK & Ireland Database
  customDbUrl:
  eissn: 1744-8301
  dateEnd: 20201231
  omitProxy: false
  ssIdentifier: ssj0047350
  issn: 1479-6694
  databaseCode: EHMNL
  dateStart: 20050201
  isFulltext: true
  titleUrlDefault: https://search.proquest.com/ukireland
  providerName: ProQuest
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3daxQxEB9sKyKIH_XrtJYI0ieXusnuJnkSlSt9aI9DFO4tJNkEhLp73l4F_3tnsrmTPpwvfdnPZDfwm69kJjMA72qnS-2sLkJEbqqskIWTdSxQWVSV90LFNm0UvpCzmVos9DwvuA05rHIjE5OgbntPa-SnnKNli7pHlB-XvwqqGkXe1VxCYw8OqGw20blcbCdcVFU3bYispC6aRldjjk2OVvhp7DskkLKh0Mbyhk56MCbzyApot-GZFNDZo9sO_TE8zKYn-zTSyhO4E7pDuHeZnetPIc5XPYXd4Wu2XNFjAo25P-yKgjfYOmHEUmYH2svMxnrfqPvYj47lBK0Do5VdRrmwSZbi_8bOP8PaoiE6hOEZfD-bfvtyXuQyDIUXslwX5JmJpai1t6rWUWuLXIwXquVaobXVciekdTEGbFc11tVaoPi1sUaJwYUXz2G_67vwEliQ0XnvrOOtroJudFvT_EZJV1ei5c0E3m-AMD7nKKdSGVcG5yqEm0HcDOFmCLcJnGybL8fkHLsa8huomvFuE76wq9PRBk6TGXsw_7CcwNvta2RJ8rPYLvTXgykVmYkfcGY7gRcjyWyHxxWKdSX5q_9__DXc52Q_pBChI9hfr67DG7jrfyPUq-NE5emojuHg83Q2_4rn6fnl7OIvZuIKtQ
linkProvider ProQuest
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VgigS4k27UMBIwImoxE7i-IAQAqpW3a56KNLejO3YElKbbDdbqv4pfiMzcbKoh-XWA7cktvPyNy97HgBvcqtSZY1KfEBqyoyQiZV5SFBYZJlzogxVFyg8lpNJOZ2qozX4PcTCkFvlwBM7Rl01jtbIdzhHzRZlj0g_zc4SqhpFu6tDCY0IiwN_eYEmW_tx_yvO71vOd78df9lL-qoCiRMyXSS00RBSkStnylwFpQyCEg_KiqsSlYeKWyGNDcFjv6wwNlcCuYkJORIAF07gfW_ATeTjkow9OV0aeFTFtwvAzKRKikJlMacnR61_JzQ1AjItyJUyvSID78bkIb3AW63odgJv9_7_9qsewL1etWafIy08hDVfP4Lbh73zwGMIR_OG3Aqxmc3mdJlAyewlOyHnFLboMMi6zBUUq81iPXOU7exnzfoEtC2jlWtGub5JVuDz4uBTvzCoaLe-fQLfr-Urn8J63dR-C5iXwTpnjeWVyrwqVJWT_VZKm2ei4sUI3g8Tr12fg51KgZxotMUIJxpxogknmnAygnfL7rOYfGRVR34FRTqeDe4ZqwZtD_DRPeNq9V_sjOD1shlZDu0jmdo3561OS1KDP6DlPoLNCNHl6_ESxVYp-bN_3_wVbOwdH471eH9y8BzucNKVOneobVhfzM_9C7jlfuG0z192FMbgx3Xj9A8LpmFA
linkToPdf http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VLaqQqvKGhQJGAk5ES-w87EOFEO2qVberPYDUm7EdW0IqybLZtupf669jJo9FPSy3HrglsZ2XP883Y49nAN6lVsXKGhX5gKMpMSKPbJ6GCMkiSZwTMhTNRuFJPp3K01M124Drfi8MuVX2MrER1EXlaI58xDlqtsg9Ih6Fzi1itj_-PP8dUQYpWmnt02m0EDn2V5dovtV7R_vY1-85Hx98-3oYdRkGIifyeBnRokOIRaqckakKShkEKB7IgiuJikTBrciNDcFjvSQzNlUCJYsJKQ4GLpzA-96BTcrqlA5g8-DwZDrpeYBy-jbbMZNcRVmmkjbCJ0cbYBSqEuEZZ-RYGd9gxO02lEhHf-vV3ob-xvf_5x_3AHY6pZt9aUfJQ9jw5SPYOuncCh5DmC0qcjjEYjZf0GWCK7NX7IzcVtiyQSdrYlrQLm7WZjpH1mc_S9aFpq0ZzWkzigJOLILPaxv_8kuDKnjt6yfw_Va-8ikMyqr0z4H5PFjnrLG8UIlXmSpSsuxkbtNEFDwbwsceBNp10dkpSciZRiuNMKMRM5owowkzQ_iwqj5vw5Ksq8hvIEq3Z73jxrpGuz2UdCfSav0XR0N4uypGYUQrTKb01XmtY0kK8ie06YfwrIXr6vW4REKTOX_x75u_gS2Ep54cTY9fwj1OSlTjJ7ULg-Xi3L-Cu-4Ce33xuhtuDH7cNlD_ALjBa10
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Prognostic+prediction+by+liver+tissue+proteomic+profiling+in+patients+with+colorectal+liver+metastases&rft.jtitle=Future+oncology+%28London%2C+England%29&rft.au=Reyes%2C+Adalgiza&rft.au=Marti%2C+Josep&rft.au=Marf%C3%A0%2C+Santiago&rft.au=Jim%C3%A9nez%2C+Wladimiro&rft.date=2017-04-01&rft.pub=Future+Medicine+Ltd&rft.issn=1479-6694&rft.eissn=1744-8301&rft.volume=13&rft.issue=10&rft.spage=875&rft_id=info:doi/10.2217%2Ffon-2016-0461&rft.externalDBID=HAS_PDF_LINK
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1479-6694&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1479-6694&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1479-6694&client=summon