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Identification of prognostic biomarker in predicting hepatocarcinogenesis from cirrhotic liver using protein and gene signatures.
Yim, Sun Young; Hae, Nahm Ji; Shin, Ji-Hyun; Jeong, Yun Seong; Kang, Sang-Hee; Park, Young Nyun; Um, Soon Ho; Lee, Ju-Seog.
Afiliação
  • Yim SY; Department of Internal Medicine, University College of Medicine, Republic of Korea.
  • Hae NJ; Department of Pathology, Yonsei University College of Medicine, Republic of Korea.
  • Shin JH; Department of Systems Biology, Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Jeong YS; Department of Systems Biology, Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Kang SH; Department of Surgery, Korea University College of Medicine, Seoul, Republic of Korea.
  • Park YN; Department of Pathology, Yonsei University College of Medicine, Republic of Korea.
  • Um SH; Department of Internal Medicine, University College of Medicine, Republic of Korea.
  • Lee JS; Department of Systems Biology, Department of Cancer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. Electronic address: jlee@mdanderson.org.
Exp Mol Pathol ; 111: 104319, 2019 12.
Article em En | MEDLINE | ID: mdl-31676327
ABSTRACT

INTRODUCTION:

Cirrhosis primes the liver for hepatocellular carcinoma (HCC) development. However, biomarkers that predict HCC in cirrhosis patients are lacking. Thus, we aimed to identify a biomarker directly from protein analysis and relate it with transcriptomic data to validate in larger cohorts. MATERIAL AND

METHOD:

Forty-six patients who underwent hepatectomy for HCC that arose from cirrhotic liver were enrolled. Reverse-phase protein array and microarray data of these patients were analyzed. Clinical validation was performed in two independent cohorts and functional validation using cell and tissue microarray (TMA).

RESULTS:

Systematic analysis performed after selecting 20 proteins from 201 proteins with AUROC >70 effectively categorized patients into high (n = 20) or low (n = 26) risk HCC groups. Proteome-derived late recurrence (PDLR)-gene signature comprising 298 genes that significantly differed between high and low risk groups predicted HCC well in a cohort of 216 cirrhosis patients and also de novo HCC recurrence in a cohort of 259 patients who underwent hepatectomy. Among 20 proteins that were selected for analysis, caveolin-1 (CAV1) was the most dominant protein that categorized the patients into high and low risk groups (P < .001). In a multivariate analysis, compared with other clinical variables, the PDLR-gene signature remained as a significant predictor of HCC (HR 1.904, P = .01). In vitro experiments revealed that compared with mock-transduced immortalized liver cells, CAV1-transduced cells showed significantly increased proliferation (P < .001) and colony formation in soft agar (P < .033). TMA with immunohistochemistry showed that tissues with CAV1 expression were more likely to develop HCC than tissues without CAV1 expression (P = .047).

CONCLUSION:

CAV1 expression predicts HCC development, making it a potential biomarker and target for preventive therapy.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Biomarcadores Tumorais / Carcinoma Hepatocelular / Proliferação de Células / Caveolina 1 / Cirrose Hepática / Neoplasias Hepáticas / Recidiva Local de Neoplasia Tipo de estudo: Diagnostic_studies / Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Exp Mol Pathol Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Biomarcadores Tumorais / Carcinoma Hepatocelular / Proliferação de Células / Caveolina 1 / Cirrose Hepática / Neoplasias Hepáticas / Recidiva Local de Neoplasia Tipo de estudo: Diagnostic_studies / Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: Exp Mol Pathol Ano de publicação: 2019 Tipo de documento: Article