Proteomic expression signature distinguishes cancerous and nonmalignant tissues in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is an aggressive liver cancer but clinically validated biomarkers that can predict natural history of malignant progression are lacking. The present study explored the proteome-wide patterns of HCC to identify biomarker signature that could distinguish cancerous and no...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Journal of proteome research Ročník 8; číslo 3; s. 1293
Hlavní autoři: Lee, Nikki P, Chen, Lei, Lin, Marie C, Tsang, Felice H, Yeung, Chun, Poon, Ronnie T, Peng, Jirun, Leng, Xisheng, Beretta, Laura, Sun, Stella, Day, Philip J, Luk, John M
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 06.03.2009
Témata:
Biomarkers, Tumor - metabolism
Carcinoma, Hepatocellular - complications
Carcinoma, Hepatocellular - metabolism
Cell Transformation, Neoplastic - metabolism
Hepatitis B - complications
Hepatitis B - metabolism
Hepatitis C - complications
Hepatitis C - metabolism
Humans
Liver Neoplasms - complications
Liver Neoplasms - metabolism
Proteome - metabolism
Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
ISSN:1535-3893
On-line přístup:Zjistit podrobnosti o přístupu
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Hepatocellular carcinoma (HCC) is an aggressive liver cancer but clinically validated biomarkers that can predict natural history of malignant progression are lacking. The present study explored the proteome-wide patterns of HCC to identify biomarker signature that could distinguish cancerous and nonmalignant liver tissues. A retrospective cohort of 80 HBV-associated HCC was included and both the tumor and adjacent nontumor tissues were subjected to proteome-wide expression profiling by 2-DE method. The subjects were randomly divided into the training (n = 55) and validation (n = 25) subsets, and the data analyzed by classification-and-regression tree algorithm. Protein markers were characterized by MALDI-ToF/MS and confirmed by immunohistochemistry, Western blotting and qPCR assays. Proteomic expression signature composed of six biomarkers (haptoglobin, cytochrome b5, progesterone receptor membrane component 1, heat shock 27 kDa protein 1, lysosomal proteinase cathepsin B, keratin I) was developed as a classifier model for predicting HCC. We further evaluated the model using both leave-one-out procedure and independent validation, and the overall sensitivity and specificity for HCC both are 92.5%, respectively. Clinical correlation analysis revealed that these biomarkers were significantly associated with serum AFP, total protein levels and the Ishak's score. The described model using biomarker signatures could accurately distinguish HCC from nonmalignant tissues, which may also provide hints on how normal hepatocytes are transformed to malignant state during tumor progression.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1535-3893
DOI:10.1021/pr800637z