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Expression of hepatitis C cirrhosis and hepatic cytocytic cell marker genes in hepatocellular carcinoma (HCC) may help predict the prognosis of patients with root-and-branch excision, according to new research. "This study determined that the marker genes of the hepatocyst cyst cell source in the liver were better than the standard measure when predicting the clinical prognosis of patients with advanced liver disease," Dr. Scott L. Friedman of the Icahn School of Medicine at Mount Sinai in New York told Reuters Health by email.
to deduce the marker gene, Dr. Friedman and his colleagues compared the composition of astrocytes, immune cells and liver transcriptions, they explained in a July 20 online report in the journal Gut.
team continued to test 122 hepatitis astrocyte marker genes, including genes that encode extracellular matrix proteins and those associated with developmental factors.
this is related to the degree of fibrosis in the data sets of humans, mice, and rats.
they also developed a prognostic indicator that combines bilirubin, platelet counts and hepatocytic cell marker gene expression, using data from 216 patients with cirrhosis cines, 30% of whom developed HCC.
this indicator was validated in 82 patients.
using the cut-off value of the derivation concentration, 12% was considered a high risk, significantly reducing the overall survival rate (risk ratio, 3.9), but did not increase the recurrence rate of liver cancer.
in addition, the marker genome improved the model that predicted the rate of death and recurrence of liver cancer.
"the result of the prognostic indicator C-statistics for the total lifetime of major outcomes is 0.70, a threshold that is generally considered clinically useful," the researchers said. Further developments
necessary, but Dr. Friedman concluded that the findings "provide a more accurate tool than currently used to determine clinical efficacy and highlight the important role of hepatic astrocytes in predicting liver function and the risk of disease deterioration."
" Source: Decoding Medicine.
