Predicting prognosis in hepatocellular carcinoma after curative surgery with common clinicopathologic parameters

<p>Abstract</p> <p>Background</p> <p>Surgical resection is one important curative treatment for hepatocellular carcinoma (HCC), but the prognosis following surgery differs substantially and such large variation is mainly unexplained. A review of the literature yields a number of clinicopathologic parameters associated with HCC prognosis. However, the results are not consistent due to lack of systemic approach to establish a prediction model incorporating all these parameters.</p> <p>Methods</p> <p>We conducted a retrospective analysis on the common clinicopathologic parameters from a cohort of 572 ethnic Chinese HCC patients who received curative surgery. The cases were randomly divided into training (n = 272) and validation (n = 300) sets. Each parameter was individually tested and the significant parameters were entered into a linear classifier for model building, and the prediction accuracy was assessed in the validation set</p> <p>Results</p> <p>Our findings based on the training set data reveal 6 common clinicopathologic parameters (tumor size, number of tumor nodules, tumor stage, venous infiltration status, and serum α-fetoprotein and total albumin levels) that were significantly associated with the overall HCC survival and disease-free survival (time to recurrence). We next built a linear classifier model by multivariate Cox regression to predict prognostic outcomes of HCC patients after curative surgery This analysis detected a considerable fraction of variance in HCC prognosis and the area under the ROC curve was about 70%. We further evaluated the model using two other protocols; leave-one-out procedure (n = 264) and independent validation (n = 300). Both were found to have excellent prediction power. The predicted score could separate patients into distinct groups with respect to survival (p-value = 1.8e-12) and disease free survival (p-value = 3.2e-7).</p> <p>Conclusion</p> <p>This described model will provide valuable guidance on prognosis after curative surgery for HCC in clinical practice. The adaptive nature allows easy accommodation for future new biomarker inputs, and it may serve as the foundation for future modeling and prediction for HCC prognosis after surgical treatment.</p

Enhanced detection of early hepatocellular carcinoma by serum SELDI-TOF proteomic signature combined with alpha-fetoprotein marker.

BACKGROUND: Biomarkers for accurate diagnosis of early hepatocellular carcinoma (HCC) are limited in number and clinical validation. We applied SELDI-TOF-MS ProteinChip technology to identify serum profile for distinguishing HCC and liver cirrhosis (LC) and to compare the accuracy of SELDI-TOF-MS profile and alpha-fetoprotein (AFP) level in HCC diagnosis. PATIENTS AND METHODS: Serum samples were obtained from 120 HCC and 120 LC patients for biomarker discovery and validation studies. ProteinChip technology was employed for generating SELDI-TOF proteomic features and analyzing serum proteins/peptides. RESULTS: A diagnostic model was established by CART algorithm, which is based on 5 proteomic peaks with m/z values at 3324, 3994, 4665, 4795, and 5152. In the training set, the CART algorithm could differentiate HCC from LC subjects with a sensitivity and specificity of 98% and 95%, respectively. The results were assessed in blind validation using separate cohorts of 60 HCC and 60 LC patients, with an accuracy of 83% for HCC and 92% for LC patients. The diagnostic odd ratio (DOR) indicated that SELDI-TOF proteomic signature could achieve better diagnostic performance than serum AFP level at a cutoff of 20 ng/mL (AFP(20)) (92.72 vs 9.11), particularly superior for early-stage HCC (87% vs 54%). Importantly, a combined use of both tests could enhance the detection of HCC (sensitivity, 95%; specificity, 98%; DOR, 931). CONCLUSION: Serum SELDI-TOF proteomic signature, alone or in combination with AFP marker, promises to be a good tool for early diagnosis and/screening of HCC in at-risk population with liver cirrhosis

microRNA-122 as a regulator of mitochondrial metabolic gene network in hepatocellular carcinoma.

Tumorigenesis involves multistep genetic alterations. To elucidate the microRNA (miRNA)-gene interaction network in carcinogenesis, we examined their genome-wide expression profiles in 96 pairs of tumor/non-tumor tissues from hepatocellular carcinoma (HCC). Comprehensive analysis of the coordinate expression of miRNAs and mRNAs reveals that miR-122 is under-expressed in HCC and that increased expression of miR-122 seed-matched genes leads to a loss of mitochondrial metabolic function. Furthermore, the miR-122 secondary targets, which decrease in expression, are good prognostic markers for HCC. Transcriptome profiling data from additional 180 HCC and 40 liver cirrhotic patients in the same cohort were used to confirm the anti-correlation of miR-122 primary and secondary target gene sets. The HCC findings can be recapitulated in mouse liver by silencing miR-122 with antagomir treatment followed by gene-expression microarray analysis. In vitro miR-122 data further provided a direct link between induction of miR-122-controlled genes and impairment of mitochondrial metabolism. In conclusion, miR-122 regulates mitochondrial metabolism and its loss may be detrimental to sustaining critical liver function and contribute to morbidity and mortality of liver cancer patients