Transcriptome and Exome Analyses of Hepatocellular Carcinoma Reveal Patterns to Predict Cancer Recurrence in Liver Transplant Patients

Hepatocellular carcinoma (HCC) is one of the most lethal human cancers. Liver transplantation has been an effective approach to treat liver cancer. However, significant numbers of patients with HCC experience cancer recurrence, and the selection of suitable candidates for liver transplant remains a...

Full description

Saved in:
Bibliographic Details
Published inHepatology communications Vol. 6; no. 4; pp. 710 - 727
Main Authors Liu, Silvia, Nalesnik, Michael A., Singhi, Aatur, Wood‐Trageser, Michelle A., Randhawa, Parmjeet, Ren, Bao‐Guo, Humar, Abhinav, Liu, Peng, Yu, Yan‐Ping, Tseng, George C., Michalopoulos, George, Luo, Jian‐Hua
Format Journal Article
LanguageEnglish
Published United States Wolters Kluwer Health Medical Research, Lippincott Williams & Wilkins 01.04.2022
John Wiley and Sons Inc
Wolters Kluwer Health/LWW
Subjects
Online AccessGet full text

Cover

Loading…
Abstract Hepatocellular carcinoma (HCC) is one of the most lethal human cancers. Liver transplantation has been an effective approach to treat liver cancer. However, significant numbers of patients with HCC experience cancer recurrence, and the selection of suitable candidates for liver transplant remains a challenge. We developed a model to predict the likelihood of HCC recurrence after liver transplantation based on transcriptome and whole‐exome sequencing analyses. We used a training cohort and a subsequent testing cohort based on liver transplantation performed before or after the first half of 2012. We found that the combination of transcriptome and mutation pathway analyses using a random forest machine learning correctly predicted HCC recurrence in 86.8% of the training set. The same algorithm yielded a correct prediction of HCC recurrence of 76.9% in the testing set. When the cohorts were combined, the prediction rate reached 84.4% in the leave‐one‐out cross‐validation analysis. When the transcriptome analysis was combined with Milan criteria using the k‐top scoring pairs (k‐TSP) method, the testing cohort prediction rate improved to 80.8%, whereas the training cohort and the combined cohort prediction rates were 79% and 84.4%, respectively. Application of the transcriptome/mutation pathways RF model on eight tumor nodules from 3 patients with HCC yielded 8/8 consistency, suggesting a robust prediction despite the heterogeneity of HCC. Conclusion: The genome prediction model may hold promise as an alternative in selecting patients with HCC for liver transplant. We have determined algorithms which predict with high accuracy the possibility of a hepatocellular carcinoma (HCC) reappearing to a new transplanted liver, after the original HCC‐containing liver resection. The algorithm is based on genomic analyses of the HCC, predicated on transcriptome expression, and gene mutations in selected pathways.
AbstractList Hepatocellular carcinoma (HCC) is one of the most lethal human cancers. Liver transplantation has been an effective approach to treat liver cancer. However, significant numbers of patients with HCC experience cancer recurrence, and the selection of suitable candidates for liver transplant remains a challenge. We developed a model to predict the likelihood of HCC recurrence after liver transplantation based on transcriptome and whole-exome sequencing analyses. We used a training cohort and a subsequent testing cohort based on liver transplantation performed before or after the first half of 2012. We found that the combination of transcriptome and mutation pathway analyses using a random forest machine learning correctly predicted HCC recurrence in 86.8% of the training set. The same algorithm yielded a correct prediction of HCC recurrence of 76.9% in the testing set. When the cohorts were combined, the prediction rate reached 84.4% in the leave-one-out cross-validation analysis. When the transcriptome analysis was combined with Milan criteria using the k-top scoring pairs (k-TSP) method, the testing cohort prediction rate improved to 80.8%, whereas the training cohort and the combined cohort prediction rates were 79% and 84.4%, respectively. Application of the transcriptome/mutation pathways RF model on eight tumor nodules from 3 patients with HCC yielded 8/8 consistency, suggesting a robust prediction despite the heterogeneity of HCC. Conclusion: The genome prediction model may hold promise as an alternative in selecting patients with HCC for liver transplant.
Hepatocellular carcinoma (HCC) is one of the most lethal human cancers. Liver transplantation has been an effective approach to treat liver cancer. However, significant numbers of patients with HCC experience cancer recurrence, and the selection of suitable candidates for liver transplant remains a challenge. We developed a model to predict the likelihood of HCC recurrence after liver transplantation based on transcriptome and whole‐exome sequencing analyses. We used a training cohort and a subsequent testing cohort based on liver transplantation performed before or after the first half of 2012. We found that the combination of transcriptome and mutation pathway analyses using a random forest machine learning correctly predicted HCC recurrence in 86.8% of the training set. The same algorithm yielded a correct prediction of HCC recurrence of 76.9% in the testing set. When the cohorts were combined, the prediction rate reached 84.4% in the leave‐one‐out cross‐validation analysis. When the transcriptome analysis was combined with Milan criteria using the k‐top scoring pairs (k‐TSP) method, the testing cohort prediction rate improved to 80.8%, whereas the training cohort and the combined cohort prediction rates were 79% and 84.4%, respectively. Application of the transcriptome/mutation pathways RF model on eight tumor nodules from 3 patients with HCC yielded 8/8 consistency, suggesting a robust prediction despite the heterogeneity of HCC. Conclusion: The genome prediction model may hold promise as an alternative in selecting patients with HCC for liver transplant. We have determined algorithms which predict with high accuracy the possibility of a hepatocellular carcinoma (HCC) reappearing to a new transplanted liver, after the original HCC‐containing liver resection. The algorithm is based on genomic analyses of the HCC, predicated on transcriptome expression, and gene mutations in selected pathways.
Hepatocellular carcinoma (HCC) is one of the most lethal human cancers. Liver transplantation has been an effective approach to treat liver cancer. However, significant numbers of patients with HCC experience cancer recurrence, and the selection of suitable candidates for liver transplant remains a challenge. We developed a model to predict the likelihood of HCC recurrence after liver transplantation based on transcriptome and whole‐exome sequencing analyses. We used a training cohort and a subsequent testing cohort based on liver transplantation performed before or after the first half of 2012. We found that the combination of transcriptome and mutation pathway analyses using a random forest machine learning correctly predicted HCC recurrence in 86.8% of the training set. The same algorithm yielded a correct prediction of HCC recurrence of 76.9% in the testing set. When the cohorts were combined, the prediction rate reached 84.4% in the leave‐one‐out cross‐validation analysis. When the transcriptome analysis was combined with Milan criteria using the k ‐top scoring pairs ( k ‐TSP) method, the testing cohort prediction rate improved to 80.8%, whereas the training cohort and the combined cohort prediction rates were 79% and 84.4%, respectively. Application of the transcriptome/mutation pathways RF model on eight tumor nodules from 3 patients with HCC yielded 8/8 consistency, suggesting a robust prediction despite the heterogeneity of HCC. Conclusion: The genome prediction model may hold promise as an alternative in selecting patients with HCC for liver transplant. We have determined algorithms which predict with high accuracy the possibility of a hepatocellular carcinoma (HCC) reappearing to a new transplanted liver, after the original HCC‐containing liver resection. The algorithm is based on genomic analyses of the HCC, predicated on transcriptome expression, and gene mutations in selected pathways.
Hepatocellular carcinoma (HCC) is one of the most lethal human cancers. Liver transplantation has been an effective approach to treat liver cancer. However, significant numbers of patients with HCC experience cancer recurrence, and the selection of suitable candidates for liver transplant remains a challenge. We developed a model to predict the likelihood of HCC recurrence after liver transplantation based on transcriptome and whole-exome sequencing analyses. We used a training cohort and a subsequent testing cohort based on liver transplantation performed before or after the first half of 2012. We found that the combination of transcriptome and mutation pathway analyses using a random forest machine learning correctly predicted HCC recurrence in 86.8% of the training set. The same algorithm yielded a correct prediction of HCC recurrence of 76.9% in the testing set. When the cohorts were combined, the prediction rate reached 84.4% in the leave-one-out cross-validation analysis. When the transcriptome analysis was combined with Milan criteria using the k -top scoring pairs ( k -TSP) method, the testing cohort prediction rate improved to 80.8%, whereas the training cohort and the combined cohort prediction rates were 79% and 84.4%, respectively. Application of the transcriptome/mutation pathways RF model on eight tumor nodules from 3 patients with HCC yielded 8/8 consistency, suggesting a robust prediction despite the heterogeneity of HCC. Conclusion: The genome prediction model may hold promise as an alternative in selecting patients with HCC for liver transplant. We have determined algorithms which predict with high accuracy the possibility of a hepatocellular carcinoma (HCC) reappearing to a new transplanted liver, after the original HCC-containing liver resection. The algorithm is based on genomic analyses of the HCC, predicated on transcriptome expression, and gene mutations in selected pathways.
Author Nalesnik, Michael A.
Tseng, George C.
Luo, Jian‐Hua
Michalopoulos, George
Randhawa, Parmjeet
Liu, Silvia
Wood‐Trageser, Michelle A.
Ren, Bao‐Guo
Humar, Abhinav
Liu, Peng
Yu, Yan‐Ping
Singhi, Aatur
AuthorAffiliation 2 Department of Surgery University of Pittsburgh School of Medicine Pittsburgh PA USA
3 Department of Biostatistics University of Pittsburgh School of Public Health Pittsburgh PA USA
1 Department of Pathology University of Pittsburgh School of Medicine Pittsburgh PA USA
AuthorAffiliation_xml – name: 2 Department of Surgery University of Pittsburgh School of Medicine Pittsburgh PA USA
– name: 3 Department of Biostatistics University of Pittsburgh School of Public Health Pittsburgh PA USA
– name: 1 Department of Pathology University of Pittsburgh School of Medicine Pittsburgh PA USA
Author_xml – sequence: 1
  givenname: Silvia
  surname: Liu
  fullname: Liu, Silvia
  organization: University of Pittsburgh School of Medicine
– sequence: 2
  givenname: Michael A.
  surname: Nalesnik
  fullname: Nalesnik, Michael A.
  organization: University of Pittsburgh School of Medicine
– sequence: 3
  givenname: Aatur
  surname: Singhi
  fullname: Singhi, Aatur
  organization: University of Pittsburgh School of Medicine
– sequence: 4
  givenname: Michelle A.
  orcidid: 0000-0002-1880-4926
  surname: Wood‐Trageser
  fullname: Wood‐Trageser, Michelle A.
  organization: University of Pittsburgh School of Medicine
– sequence: 5
  givenname: Parmjeet
  surname: Randhawa
  fullname: Randhawa, Parmjeet
  organization: University of Pittsburgh School of Medicine
– sequence: 6
  givenname: Bao‐Guo
  surname: Ren
  fullname: Ren, Bao‐Guo
  organization: University of Pittsburgh School of Medicine
– sequence: 7
  givenname: Abhinav
  surname: Humar
  fullname: Humar, Abhinav
  organization: University of Pittsburgh School of Medicine
– sequence: 8
  givenname: Peng
  surname: Liu
  fullname: Liu, Peng
  organization: University of Pittsburgh School of Public Health
– sequence: 9
  givenname: Yan‐Ping
  surname: Yu
  fullname: Yu, Yan‐Ping
  organization: University of Pittsburgh School of Medicine
– sequence: 10
  givenname: George C.
  surname: Tseng
  fullname: Tseng, George C.
  organization: University of Pittsburgh School of Public Health
– sequence: 11
  givenname: George
  orcidid: 0000-0001-9922-6920
  surname: Michalopoulos
  fullname: Michalopoulos, George
  email: luoj@upmc.edu
  organization: University of Pittsburgh School of Medicine
– sequence: 12
  givenname: Jian‐Hua
  surname: Luo
  fullname: Luo, Jian‐Hua
  email: luoj@upmc.edu
  organization: University of Pittsburgh School of Medicine
BackLink https://www.ncbi.nlm.nih.gov/pubmed/34725972$$D View this record in MEDLINE/PubMed
BookMark eNp1ksFuGyEQhldVqiZNc-gLVEi9pAcnLLALXCpFllNHslSrSqXeEAvjBGsNW9h16xfIc5e10yipVC6Mhm9-BuZ_Wxz54KEo3pf4osSYXN5Dxy5KwepXxQlhvJyQiv04ehYfF2cprTHGpSRlKfGb4pgyTirJyUnxcBu1Tya6rg8bQNpbNPs9Rldet7sECYUVmkOn-2CgbYdWRzTV0TgfNhp9gy3oFi1130P0CfUBLSNYZ_oMeQMxE2aIEXKMnEcLt825_ZVdq30_VjrwfXpXvF7pNsHZ435afL-e3U7nk8XXLzfTq8XEVFjWk6oG29gGmOS1aSg3FaOG1FjQFTXY4kZSyonlvBEEpOFCa2YbTKiVXHDG6Glxc9C1Qa9VF91Gx50K2ql9IsQ7pWPvTAsKV6wWK0E5z4UlqxqSF62wgBKkFTJrfT5odUOzAWvyO6JuX4i-PPHuXt2FrRKSiYqPAuePAjH8HCD1auPS-MvaQxiSyiMiFMuMZ_TjP-g6DDGPKFM1I7UQvCaZ-nSgTAwpRVg9NVNiNZpFjWZRo1ky--F590_kX2tk4PIA_HIt7P6vpOazJdtL_gFqXss7
CitedBy_id crossref_primary_10_3233_THC_230123
crossref_primary_10_7554_eLife_87607_3
crossref_primary_10_3389_ti_2023_11358
crossref_primary_10_3748_wjg_v29_i5_780
crossref_primary_10_1097_MD_0000000000035892
crossref_primary_10_1097_HC9_0000000000000412
crossref_primary_10_7554_eLife_87607
crossref_primary_10_1097_MOT_0000000000001123
ContentType Journal Article
Copyright 2021 The Authors. published by Wiley Periodicals LLC on behalf of American Association for the Study of Liver Diseases.
2021 The Authors. Hepatology Communications published by Wiley Periodicals LLC on behalf of American Association for the Study of Liver Diseases.
2022. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: 2021 The Authors. published by Wiley Periodicals LLC on behalf of American Association for the Study of Liver Diseases.
– notice: 2021 The Authors. Hepatology Communications published by Wiley Periodicals LLC on behalf of American Association for the Study of Liver Diseases.
– notice: 2022. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID 24P
WIN
CGR
CUY
CVF
ECM
EIF
NPM
AAYXX
CITATION
3V.
7X7
7XB
8FI
8FJ
8FK
ABUWG
AFKRA
AZQEC
BENPR
CCPQU
DWQXO
FYUFA
GHDGH
K9.
M0S
PIMPY
PQEST
PQQKQ
PQUKI
PRINS
7X8
5PM
DOA
DOI 10.1002/hep4.1846
DatabaseName Wiley Online Library (Open Access Collection)
Wiley Online Library (Open Access Collection)
Medline
MEDLINE
MEDLINE (Ovid)
MEDLINE
MEDLINE
PubMed
CrossRef
ProQuest Central (Corporate)
ProQuest - Health & Medical Complete保健、医学与药学数据库
ProQuest Central (purchase pre-March 2016)
Hospital Premium Collection
Hospital Premium Collection (Alumni Edition)
ProQuest Central (Alumni) (purchase pre-March 2016)
ProQuest Central (Alumni)
ProQuest Central
ProQuest Central Essentials
ProQuest Central
ProQuest One Community College
ProQuest Central
Health Research Premium Collection
Health Research Premium Collection (Alumni)
ProQuest Health & Medical Complete (Alumni)
Health & Medical Collection (Alumni Edition)
Access via ProQuest (Open Access)
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Academic
ProQuest One Academic UKI Edition
ProQuest Central China
MEDLINE - Academic
PubMed Central (Full Participant titles)
Directory of Open Access Journals
DatabaseTitle MEDLINE
Medline Complete
MEDLINE with Full Text
PubMed
MEDLINE (Ovid)
CrossRef
Publicly Available Content Database
ProQuest Central Essentials
ProQuest One Academic Eastern Edition
ProQuest Health & Medical Complete (Alumni)
ProQuest Central (Alumni Edition)
ProQuest One Community College
ProQuest Hospital Collection
Health Research Premium Collection (Alumni)
ProQuest Central China
ProQuest Hospital Collection (Alumni)
ProQuest Central
ProQuest Health & Medical Complete
Health Research Premium Collection
ProQuest One Academic UKI Edition
Health and Medicine Complete (Alumni Edition)
ProQuest Central Korea
ProQuest One Academic
ProQuest Central (Alumni)
MEDLINE - Academic
DatabaseTitleList MEDLINE



Publicly Available Content Database
CrossRef
Database_xml – sequence: 1
  dbid: DOA
  name: Directory of Open Access Journals
  url: https://www.doaj.org/
  sourceTypes: Open Website
– sequence: 2
  dbid: 24P
  name: Wiley-Blackwell Open Access Collection
  url: https://authorservices.wiley.com/open-science/open-access/browse-journals.html
  sourceTypes: Publisher
– sequence: 3
  dbid: NPM
  name: PubMed
  url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 4
  dbid: EIF
  name: MEDLINE
  url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search
  sourceTypes: Index Database
– sequence: 5
  dbid: 7X7
  name: Health & Medical Complete (ProQuest Database)
  url: https://search.proquest.com/healthcomplete
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Medicine
DocumentTitleAlternate Liu, Nalesnik, et al
EISSN 2471-254X
EndPage 727
ExternalDocumentID oai_doaj_org_article_05468f8377744145b22223508e1e9d89
10_1002_hep4_1846
34725972
HEP41846
Genre article
Journal Article
Research Support, N.I.H., Extramural
GrantInformation_xml – fundername: Pittsburgh Liver Research center
  funderid: NIH/NIDDK P30DK120531
– fundername: National Institutes of Health
  funderid: 1R56CA229262‐01; UL1TR001857
– fundername: NCI NIH HHS
  grantid: R56 CA229262
– fundername: NIDDK NIH HHS
  grantid: P30 DK120531
– fundername: NCATS NIH HHS
  grantid: UL1 TR001857
– fundername: ;
  grantid: 1R56CA229262‐01; UL1TR001857
– fundername: Pittsburgh Liver Research center
  grantid: NIH/NIDDK P30DK120531
GroupedDBID 0R~
1OC
24P
53G
7X7
8FI
8FJ
AAAAV
AAHHS
AAIQE
ABUWG
ACCFJ
ACILI
ACXJB
ACXQS
ADBBV
ADGGA
ADHPY
ADKYN
ADPDF
ADZMN
ADZOD
AECAP
AEEZP
AEQDE
AFKRA
AFUWQ
AHQNM
AIWBW
AJAOE
AJBDE
AJNWD
AJZMW
ALIPV
ALMA_UNASSIGNED_HOLDINGS
ALUQN
AMNEI
AOHHW
AOIJS
AVUZU
BCNDV
BENPR
BPHCQ
BVXVI
CCPQU
DIWNM
EBS
EEVPB
EJD
FCALG
FYUFA
GQDEL
GROUPED_DOAJ
HMCUK
HYE
IAO
IHR
INH
M~E
OK1
OVD
OVDNE
PIMPY
PQQKQ
PROAC
RPM
TEORI
TSPGW
UKHRP
WIN
CGR
CUY
CVF
ECM
EIF
ITC
NPM
AAYXX
CITATION
3V.
7XB
8FK
AAHPQ
AFDTB
AZQEC
DWQXO
K9.
PQEST
PQUKI
PRINS
7X8
5PM
ID FETCH-LOGICAL-c5096-56edbdbe4976cb37c543c26083f3c0d0b93372d77b82e9c78aa4db023d9787443
IEDL.DBID RPM
ISSN 2471-254X
IngestDate Tue Oct 22 15:15:04 EDT 2024
Tue Sep 17 20:56:04 EDT 2024
Sat Oct 26 01:32:55 EDT 2024
Mon Nov 04 11:14:08 EST 2024
Wed Oct 16 15:16:39 EDT 2024
Sat Sep 28 08:19:22 EDT 2024
Sat Aug 24 00:56:27 EDT 2024
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 4
Language English
License Attribution-NonCommercial-NoDerivs
2021 The Authors. Hepatology Communications published by Wiley Periodicals LLC on behalf of American Association for the Study of Liver Diseases.
This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c5096-56edbdbe4976cb37c543c26083f3c0d0b93372d77b82e9c78aa4db023d9787443
Notes These authors contributed equally to this work.
Supported by the National Institutes of Health (1R56CA229262‐01 and UL1TR001857) and Pittsburgh Liver Research Center (NIH/NIDDK P30DK120531).
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ORCID 0000-0001-9922-6920
0000-0002-1880-4926
OpenAccessLink https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8948579/
PMID 34725972
PQID 2642688762
PQPubID 4370311
PageCount 18
ParticipantIDs doaj_primary_oai_doaj_org_article_05468f8377744145b22223508e1e9d89
pubmedcentral_primary_oai_pubmedcentral_nih_gov_8948579
proquest_miscellaneous_2592309894
proquest_journals_2642688762
crossref_primary_10_1002_hep4_1846
pubmed_primary_34725972
wiley_primary_10_1002_hep4_1846_HEP41846
PublicationCentury 2000
PublicationDate April 2022
PublicationDateYYYYMMDD 2022-04-01
PublicationDate_xml – month: 04
  year: 2022
  text: April 2022
PublicationDecade 2020
PublicationPlace United States
PublicationPlace_xml – name: United States
– name: New York
– name: Hoboken
PublicationTitle Hepatology communications
PublicationTitleAlternate Hepatol Commun
PublicationYear 2022
Publisher Wolters Kluwer Health Medical Research, Lippincott Williams & Wilkins
John Wiley and Sons Inc
Wolters Kluwer Health/LWW
Publisher_xml – name: Wolters Kluwer Health Medical Research, Lippincott Williams & Wilkins
– name: John Wiley and Sons Inc
– name: Wolters Kluwer Health/LWW
References 2004; 22
2009; 25
2010; 10
2000; 28
2000; 25
2018; 1092
2020; 40
2015; 31
1997; 26
2012; 180
2019; 37
2002; 33
2016; 32
2003; 13
2019; 16
2011; 54
2016; 2016
2004
2011; 12
2000; 232
1997; 9
2018; 24
2004; 10
1995; 20
2018; 7
2010; 20
2009; 31
2017; 36
2010; 28
2016; 316
2006; 44
2020; 70
2015; 22
2006; 25
1967; 5
2015; 21
1999; 36
2016; 936
2016; 64
2020; 26
2020; 48
1999; 30
2014; 184
2014; 30
2007; 45
2016; 48
2016; 8
2014; 11
References_xml – volume: 13
  start-page: 2498
  year: 2003
  end-page: 2504
  article-title: Cytoscape: a software environment for integrated models of biomolecular interaction networks
  publication-title: Genome Res
– volume: 48
  start-page: D498
  year: 2020
  end-page: D503
  article-title: The reactome pathway knowledgebase
  publication-title: Nucleic Acids Res
– volume: 21
  start-page: 599
  year: 2015
  end-page: 606
  article-title: Combinations of biomarkers and Milan criteria for predicting hepatocellular carcinoma recurrence after liver transplantation
  publication-title: Liver Transpl
– volume: 22
  start-page: 2790
  year: 2004
  end-page: 2799
  article-title: Gene expression alterations in prostate cancer predicting tumor aggression and preceding development of malignancy
  publication-title: J Clin Oncol
– volume: 9
  start-page: 1545
  year: 1997
  end-page: 1588
  article-title: Shape quantization and recognition with randomized trees
  publication-title: Neural Comput
– volume: 25
  start-page: 25
  year: 2000
  end-page: 29
  article-title: Gene ontology: tool for the unification of biology
  publication-title: Gene Ontology Consortium. Nat Genet
– volume: 12
  start-page: 375
  year: 2011
  article-title: Top scoring pairs for feature selection in machine learning and applications to cancer outcome prediction
  publication-title: BMC Bioinformatics
– volume: 232
  start-page: 490
  year: 2000
  end-page: 500
  article-title: Long‐term survival after liver transplantation in 4,000 consecutive patients at a single center
  publication-title: Ann Surg
– volume: 37
  start-page: 907
  year: 2019
  end-page: 915
  article-title: Graph‐based genome alignment and genotyping with HISAT2 and HISAT‐genotype
  publication-title: Nat Biotechnol
– volume: 16
  start-page: 589
  year: 2019
  end-page: 604
  article-title: A global view of hepatocellular carcinoma: trends, risk, prevention and management
  publication-title: Nat Rev Gastroenterol Hepatol
– volume: 11
  year: 2014
  article-title: Genomic predictors for recurrence patterns of hepatocellular carcinoma: model derivation and validation
  publication-title: PLoS Med
– volume: 28
  start-page: 27
  year: 2000
  end-page: 30
  article-title: KEGG: Kyoto Encyclopedia of Genes and Genomes
  publication-title: Nucleic Acids Res
– volume: 8
  start-page: 474
  year: 2016
  end-page: 485
  article-title: ggfortify: Unified Interface to Visualize Statistical Results of Popular R Packages
  publication-title: R J
– volume: 24
  start-page: 3626
  year: 2018
  end-page: 3636
  article-title: Expansion of the hepatocellular carcinoma Milan criteria in liver transplantation: future directions
  publication-title: World J Gastroenterol
– volume: 64
  start-page: 2077
  year: 2016
  end-page: 2088
  article-title: The extended Toronto criteria for liver transplantation in patients with hepatocellular carcinoma: a prospective validation study
  publication-title: Hepatology
– volume: 31
  start-page: 273
  year: 2015
  end-page: 274
  article-title: switchBox: an R package for k‐Top scoring pairs classifier development
  publication-title: Bioinformatics
– volume: 10
  start-page: 534
  year: 2004
  end-page: 540
  article-title: Recurrence of hepatocellular carcinoma after liver transplant: patterns and prognosis
  publication-title: Liver Transpl
– volume: 12
  start-page: 77
  year: 2011
  article-title: pROC: an open‐source package for R and S+ to analyze and compare ROC curves
  publication-title: BMC Bioinformatics
– volume: 36
  start-page: 105
  year: 1999
  end-page: 139
  article-title: An empirical comparison of voting classification algorithms: bagging, boosting, and variants
  publication-title: Mach Learn
– volume: 36
  start-page: 3629
  year: 2017
  end-page: 3639
  article-title: Oncogenic activity of amplified miniature chromosome maintenance 8 in human malignancies
  publication-title: Oncogene
– volume: 48
  start-page: 500
  year: 2016
  end-page: 509
  article-title: Whole‐genome mutational landscape and characterization of noncoding and structural mutations in liver cancer
  publication-title: Nat Genet
– volume: 26
  start-page: 444
  year: 1997
  end-page: 450
  article-title: The prediction of risk of recurrence and time to recurrence of hepatocellular carcinoma after orthotopic liver transplantation: a pilot study
  publication-title: Hepatology
– volume: 44
  start-page: 1012
  year: 2006
  end-page: 1024
  article-title: Transcriptomic and genomic analysis of human hepatocellular carcinomas and hepatoblastomas
  publication-title: Hepatology
– volume: 54
  start-page: 1227
  year: 2011
  end-page: 1236
  article-title: Genome‐wide copy number analyses identified novel cancer genes in hepatocellular carcinoma
  publication-title: Hepatology
– volume: 180
  start-page: 1495
  year: 2012
  end-page: 1508
  article-title: Gene deletions and amplifications in human hepatocellular carcinomas: correlation with hepatocyte growth regulation
  publication-title: Am J Pathol
– volume: 28
  start-page: 511
  year: 2010
  end-page: 515
  article-title: Transcript assembly and quantification by RNA‐Seq reveals unannotated transcripts and isoform switching during cell differentiation
  publication-title: Nat Biotechnol
– volume: 184
  start-page: 2840
  year: 2014
  end-page: 2849
  article-title: Novel fusion transcripts associate with progressive prostate cancer
  publication-title: Am J Pathol
– volume: 316
  start-page: 533
  year: 2016
  end-page: 534
  article-title: Logistic regression: relating patient characteristics to outcomes
  publication-title: JAMA
– volume: 5
  start-page: 790
  year: 1967
  end-page: 803
  article-title: Homotransplantation of the liver
  publication-title: Transplantation
– volume: 40
  start-page: 1064
  issue: 6
  year: 2020
  end-page: 1076
  article-title: Pten‐NOLC1 fusion promotes cancers involving MET and EGFR signalings
  publication-title: Oncogene
– volume: 20
  start-page: 1297
  year: 2010
  end-page: 1303
  article-title: The Genome Analysis Toolkit: a MapReduce framework for analyzing next‐generation DNA sequencing data
  publication-title: Genome Res
– volume: 21
  start-page: 11185
  year: 2015
  end-page: 11198
  article-title: Managements of recurrent hepatocellular carcinoma after liver transplantation: a systematic review
  publication-title: World J Gastroenterol
– volume: 32
  start-page: 2847
  year: 2016
  end-page: 2849
  article-title: Complex heatmaps reveal patterns and correlations in multidimensional genomic data
  publication-title: Bioinformatics
– volume: 30
  start-page: 2114
  year: 2014
  end-page: 2120
  article-title: Trimmomatic: a flexible trimmer for Illumina sequence data
  publication-title: Bioinformatics
– volume: 25
  start-page: 1754
  year: 2009
  end-page: 1760
  article-title: Fast and accurate short read alignment with Burrows‐Wheeler transform
  publication-title: Bioinformatics
– volume: 45
  start-page: 42
  year: 2007
  end-page: 52
  article-title: Transcriptome classification of HCC is related to gene alterations and to new therapeutic targets
  publication-title: Hepatology
– start-page: 1
  year: 2004
  end-page: 526
  article-title: Discriminant analysis and statistical pattern recognition
  publication-title: Appl Probab Stat
– volume: 10
  start-page: 59
  year: 2010
  end-page: 64
  article-title: A census of amplified and overexpressed human cancer genes
  publication-title: Nat Rev Cancer
– volume: 936
  start-page: 93
  year: 2016
  end-page: 106
  article-title: Circulating tumor cells: when a solid tumor meets a fluid microenvironment
  publication-title: Adv Exp Med Biol
– volume: 2016
  year: 2016
  article-title: The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins
  publication-title: Database (Oxford)
– volume: 70
  start-page: 7
  year: 2020
  end-page: 30
  article-title: Cancer statistics, 2020
  publication-title: CA Cancer J Clin
– volume: 22
  start-page: 2286
  year: 2015
  end-page: 2294
  article-title: Benefit of treating hepatocellular carcinoma recurrence after liver transplantation and analysis of prognostic factors for survival in a large Euro‐American series
  publication-title: Ann Surg Oncol
– volume: 25
  start-page: 1090
  year: 2006
  end-page: 1098
  article-title: MCM7 amplification and overexpression are associated with prostate cancer progression
  publication-title: Oncogene
– volume: 33
  start-page: 25
  year: 2002
  end-page: 35
  article-title: Gene expression analysis of prostate cancers
  publication-title: Mol Carcinog
– volume: 26
  start-page: 823
  year: 2020
  end-page: 831
  article-title: Role of molecular biomarkers in liver transplantation for hepatocellular carcinoma
  publication-title: Liver Transpl
– volume: 7
  start-page: 1576
  year: 2018
  article-title: gganatogram: An R package for modular visualisation of anatograms and tissues based on ggplot2
  publication-title: F1000Res
– volume: 1092
  start-page: 209
  year: 2018
  end-page: 233
  article-title: Biomechanics of the circulating tumor cell microenvironment
  publication-title: Adv Exp Med Biol
– volume: 20
  start-page: 273
  year: 1995
  end-page: 297
  article-title: Support‐vector networks
  publication-title: Mach Learn
– volume: 31
  start-page: 227
  year: 2009
  end-page: 233
  article-title: HCC heterogeneity: molecular pathogenesis and clinical implications
  publication-title: Cell Oncol
– volume: 30
  start-page: 1130
  year: 1999
  end-page: 1137
  article-title: Evaluation of efficacy of liver transplantation in alcoholic cirrhosis using matched and simulated controls: 5‐year survival. Multi‐centre group
  publication-title: J Hepatol
SSID ssj0001921190
Score 2.282013
Snippet Hepatocellular carcinoma (HCC) is one of the most lethal human cancers. Liver transplantation has been an effective approach to treat liver cancer. However,...
SourceID doaj
pubmedcentral
proquest
crossref
pubmed
wiley
SourceType Open Website
Open Access Repository
Aggregation Database
Index Database
Publisher
StartPage 710
SubjectTerms Biomarkers
Carcinoma, Hepatocellular - diagnosis
Cell division
Discriminant analysis
Exome - genetics
Genomes
Hepatitis B
Hepatitis C
Humans
Hyperplasia
Liver cancer
Liver Neoplasms - diagnosis
Liver Transplantation
Liver transplants
Mutation
Neoplasm Recurrence, Local - diagnosis
Original
Retrospective Studies
Transcriptome - genetics
Tumors
Whole Exome Sequencing
SummonAdditionalLinks – databaseName: Directory of Open Access Journals
  dbid: DOA
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQD4gLgvIKtJVBHLiEem0ndo5QbbVCFK0QlXqL_Ipa1DrVbor6C_q7mbGzq10VxIVbFNuR45nxfJPMfCbkfVCemUZ0JTNg5FIxW2oXfMk9so-HzvCAhcIn3-rZqfxyVp1tHPWFOWGZHjgv3CFAilp3EEYBTpETWVmOHg1gRZiExutcuseajWDqZ8YtE3B1Kyohxg_Pw7X8COFMveWAEk__n8Dl_RzJTeyanM_xE_J4RI30U57tU_IgxF3y8GT8L_6M3CWXkzaA_ipQEz2d3uJV5hwJS9p3dAaeZ-jxSz2mntIjPEUo9leGfg-_AC7SeaLajEs69HS-wGcP0AmUYgE9XOJxcoFeRPoVczlopkW_BMngSCyrXD4np8fTH0ezcjxhoXRI-1JWdfDW2yABlDgrlKukcBDhaNEJxzyzjRCKe6Ws5qFxShsjvQU37yH4BHmIF2Qn9jG8IhR9IWt8ZTuppLa8AShmFOykllmpuSnIu9Wyt9eZSKPNlMm8Rdm0KJuCfEaBrDsg93W6ARrRjhrR_ksjCrK3Emc7GuSyBdzHa41bf0HerpvBlHDVTQz9DfSpAO0yZKQvyMss_fVMhFTQrGC02tKLralut8SL80TXrZGAR8G0PiQN-vvbt7PpXOLF6_-xDG_II46lGinLaI_sDIubsA8AarAHyVZ-A8xRFro
  priority: 102
  providerName: Directory of Open Access Journals
– databaseName: ProQuest - Health & Medical Complete保健、医学与药学数据库
  dbid: 7X7
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9QwELagSIgLojwDLTKIA5dQr-3EzglBtdUKUbRCVNpb5FfaSjReNinqL-jvZsbJblnxuFmxHdkej-fzePyZkDdBeWYq0eTMgJJLxWyuXfA598g-HhrDA14UPv5Szk7kp0WxGB1u3RhWuV4T00Lto0Mf-QEYbl5q1N33yx85vhqFp6vjExq3yZ0JZyWGdKmFuvGxVMhfxtaEQowfnIWlfAebmnLLDCW2_r9BzD8jJX9HsMkEHT0g90fsSD8Mwt4lt0L7kNw9Hk_HH5HrZHjSMhAvAjWtp9MrTA3MI6GjsaEzsD99RH89BqDSQ3xLqI0Xhn4NPwE00nki3Gw72kc6X-G_eygEU2MFJVxic3KBnrf0M0Z00IEc_TvIB2vi5cruMTk5mn47nOXjOwu5Q_KXvCiDt94GCdDEWaFcIYWDfY4WjXDMM1sJobhXymoeKqe0MdJbMPYetqBKSvGE7LSxDc8IRYvIKl_YRiqpLa8AkBkF66llVmpuMvJ6Pez1cqDTqAfiZF6jbGqUTUY-okA2BZABO32Iq9N6VKgaoGapG9heA36VE1lYjkgH4GaYhMrrKiN7a3HWo1p29c0kysirTTYoFI66aUO8hDIFYF6GvPQZeTpIf9MSIRVkK6ittubFVlO3c9rzs0TarZGGR0Gz3qYZ9O_e17PpXGLi-f978ILc43gVI0UR7ZGdfnUZ9gEg9fZl0oJfZSAQsA
  priority: 102
  providerName: ProQuest
– databaseName: Wiley Online Library (Open Access Collection)
  dbid: 24P
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjZ1Nb9QwEIatUiTEBVE-AwUZxIFLaGo7sSNOUG21QhStEJX2ZvkrbSXqVJsU8Qv6uzvjZNOuAIlbFI8jb8bjee21nxDyLkhfmJo3eWEgyIUsbK5c8DnzSB8PjWEBDwoffavmx-LLslxukY_rszADH2JacMPISOM1Brix3d4NNPQ0XIgPMD-p7pC7SIxBcD4Ti5sFlhrhZbjGwmAAzmEitFyThQq2N9XeyEcJ2_83rfnnlsnbUjblosOH5MEoIumnwes7ZCvER-Te0fg3-WNylTJQGg_a80BN9HT2G68GBEnoaNvQOSSivsWFe9yJSg_wo0KxPTf0e_gF6pEuEnkzdrRv6WKFz-7BCPrICixcwjq5QM8i_YpbO-hASf8JjsKaeMqye0KOD2c_Dub5-MGF3CEFJi-r4K23QYBGcZZLVwruYMKjeMNd4Qtbcy6Zl9IqFmonlTHCW8j6HuaiUgj-lGzHNobnhGJqLGpf2kZIoSyrQZkZCQOrLaxQzGTk7fq164uBq6EHgjLT6BuNvsnIZ3TIZIAo7HSjXZ3oMbI0aM5KNTDPBiEr9kVpGUoe0J1hP9Re1RnZXbtTj_HZaZCBrFKYCTLyZiqGyMK3bmJoL8GmBPFbIKA-I88G708t4UJCsYTacqNfbDR1sySenSZ6t0Iej4RmvU896N-_Xs9nC4EXL_7f9CW5z_B8RtpatEu2-9VleAWqqbevU3RcA6HoErQ
  priority: 102
  providerName: Wiley-Blackwell
Title Transcriptome and Exome Analyses of Hepatocellular Carcinoma Reveal Patterns to Predict Cancer Recurrence in Liver Transplant Patients
URI https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fhep4.1846
https://www.ncbi.nlm.nih.gov/pubmed/34725972
https://www.proquest.com/docview/2642688762
https://search.proquest.com/docview/2592309894
https://pubmed.ncbi.nlm.nih.gov/PMC8948579
https://doaj.org/article/05468f8377744145b22223508e1e9d89
Volume 6
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3db9MwED-tQ0K8IL4JjMogHnhJmzpOnTxuVacK0SmaKKp4ieKPsKI1qdoM8Rfwd3PnJNUq2MteEit2Esd39v3OOf8M8NFKE-RJWPhBjp1cyED5sbbG54bYx22Rc0sLhecX49lCfF5GyyOIurUwLmhfq9WgvF4PytWVi63crPWwixMbpvNJTJQmMhn2oIcKestF_9lAlhFauY5FKODDK7sRA_RkaLuiUEgE_JIfmCHH1v8_iPlvpORtBOtM0PkTeNxiR3ba1PEpHNnyGTyct3_Hn8MfZ3jcMFCtLctLw6a_KdUwj9gdqwo2Q_tTVzRfTwGobEJ7CZXVOmeX9heCRpY6ws1yx-qKpVt6do2FUDW2WEI7Nidt2apkXyiigzXk6NcoH7qTFlfuXsDifPp1MvPbfRZ8TeQvfjS2RhllBUITrUKpIxFq9HPisAh1YAKVhKHkRkoVc5toGee5MAqNvUEXVAoRvoTjsirta2BkEYPERKoQUsSKJwjIconjqQqUiHnuwYeu2bNNQ6eRNcTJPCMxZSQmD85IIPsCxIDtLlTbH1mrBxlCzXFcoHuN-FWMRKQ4IR2Em3ZkExMnHpx04szabrnLEP3xcUwGwIP3-2zsUNTqeWmrGywTIeYNiJfeg1eN9Pc16bTHA3mgFwdVPcxBHXak3a3OevDJadDdX5_NpqmgxJt7v-QtPOK0SsMFGJ3Acb29se8QO9WqDz0uUjzKpezDg9Nvi-8LPJ9NL9LLvpuP6Lve9BfAeR_J
link.rule.ids 230,315,730,783,787,867,888,2109,11574,12068,21400,27936,27937,31731,31732,33756,33757,43322,43817,46064,46488,50826,50935,53804,53806,74073,74630
linkProvider National Library of Medicine
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9QwELagSMAF8SZQwCAOXEJdx4mdE4JqqwC71Qq10t4sv0Ir0WTZpIhfwO9mxsluWfG4WbEd2R6P57M9_oaQV0F6ZsqsTpkBJReS2VS54FPukX081IYHfCg8OyqqE_FxkS_GA7dudKtcr4lxofatwzPyPTDcvFCou2-X31KMGoW3q2MIjavkGvJwYQQDuZCXZywl8pexNaEQ43unYSnewKam2DJDka3_bxDzT0_J3xFsNEGHt8mtETvSd4Ow75AroblLrs_G2_F75Gc0PHEZaM8DNY2nkx-YGphHQkfbmlZgf_oWz-vRAZUeYCyhpj039HP4DqCRziPhZtPRvqXzFf67h0IwNVZQwkU2JxfoWUOn6NFBB3L0ryAfrImPK7v75ORwcnxQpWOchdQh-UuaF8Fbb4MAaOJsJl0uMgf7HJXVmWOe2TLLJPdSWsVD6aQyRngLxt7DFlQKkT0gO03bhEeEokVkpc9tLaRQlpcAyIyE9dQyKxQ3CXm5Hna9HOg09ECczDXKRqNsEvIeBbIpgAzY8UO7-qJHhdIANQtVw_Ya8KvYF7nliHQAbob9UHpVJmR3LU49qmWnLydRQl5sskGhcNRNE9oLKJMD5mXIS5-Qh4P0Ny3JhIRsCbXl1rzYaup2TnN2Gkm7FdLwSGjW6ziD_t17XU3mAhOP_9-D5-RGdTyb6umHo09PyE2OzzKiR9Eu2elXF-EpgKXePosa8QtGSxOX
linkToPdf http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1Lb9QwELagSBUXRHkGWjCIA5ewruPEzqkqZVcLtNUKUWlvll-hlWiybFLEL-B3M-Nkt6x43KzYjuzMjOcbZ_yZkFdBembKrEqZASMXktlUueBT7pF9PFSGBzwofHJaTM_Eh3k-H_Kf2iGtcrUmxoXaNw73yEfguHmh0HZH1ZAWMXs3OVh8S_EGKfzTOlyncZPckhCloM7LubzebymRy4ytyIUYH52HhXgDAU6x4ZIic__f4OafWZO_o9nojiZ3yZ0BR9LDXvA75Eao75Htk-FP-X3yMzqhuCQ0l4Ga2tPxDyz1LCShpU1Fp-CLugb37jEZlR7hvUJ1c2nop_AdACSdRfLNuqVdQ2dLfHcHjUBNltDCRWYnF-hFTY8xu4P2ROlfQVbYEw9atg_I2WT8-WiaDncupA6JYNK8CN56GwTAFGcz6XKROYh5VFZljnlmyyyT3EtpFQ-lk8oY4S04fg_hqBQie0i26qYOjwlF78hKn9tKSKEsLwGcGQlrq2VWKG4S8nL12fWip9bQPYky1ygbjbJJyFsUyLoBsmHHB83yix6MSwPsLFQFoTZgWbEvcssR9QD0DPuh9KpMyO5KnHow0VZfK1RCXqyrwbjwq5s6NFfQJgf8y5CjPiGPeumvR5IJCdUSessNvdgY6mZNfXEeCbwVUvJIGNbrqEH_nr2ejmcCC0_-P4PnZBuMQR-_P_34lNzmeEIjJhftkq1ueRX2ADd19lk0iF89bhfP
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=Transcriptome+and+Exome+Analyses+of+Hepatocellular+Carcinoma+Reveal+Patterns+to+Predict+Cancer+Recurrence+in+Liver+Transplant+Patients&rft.jtitle=Hepatology+communications&rft.au=Silvia+Liu&rft.au=Michael+A.+Nalesnik&rft.au=Aatur+Singhi&rft.au=Michelle+A.+Wood%E2%80%90Trageser&rft.date=2022-04-01&rft.pub=Wolters+Kluwer+Health%2FLWW&rft.eissn=2471-254X&rft.volume=6&rft.issue=4&rft.spage=710&rft.epage=727&rft_id=info:doi/10.1002%2Fhep4.1846&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_05468f8377744145b22223508e1e9d89
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2471-254X&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2471-254X&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2471-254X&client=summon