Autoantibodies are often detected in hepatocellular carcinoma (HCC), and these responses may represent recognition of tumor Ags that are associated with transformation events. The identities of these Ags, however, are less well known. Using serological analysis of recombinant cDNA expression libraries (SEREX) from four HCC patients, we identified 55 independent cDNA sequences potentially encoding HCC tumor Ags. Of these genes, 15 are novel. Two such proteins, HCA587 and HCA661, were predominantly detected in testis, but not in other normal tissues, except for a weak expression in normal pancreas. In addition to HCC, these two Ags can be found in cancers of other histological types. Therefore, they can be categorized as cancer-testis (CT) Ags. Two other Ags (HCA519 and HCA90) were highly overexpressed in HCC and also expressed in cancer cell lines of lung, prostate, and pancreas, but not in the respective normal tissues. Four other Ags were identified to be expressed in particular types of cancer cell lines (HCA520 in an ovarian cancer cell line, HCA59 and HCA67 in a colon cancer cell line, HCA58 in colon and ovarian cancer cell lines), but not in the normal tissue counterpart(s). In addition, abundant expression of complement inactivation factors was found in HCC. These results indicate a broad range expression of autoantigens in HCC patients. Our findings open an avenue for the study of autoantigens in the transformation, metastasis, and immune evasion in HCC.

Hepatocellular carcinoma (HCC) is well known for poor prognosis and short survival because of high recurrence rate even after curative surgery. Today there is no available biomarker or biochemical test to indicate HCC recurrence, and this study aims to identify protein markers that can discriminate postoperative patients with early recurrence (ER), i.e. disease relapsed within the first year. In this study, 103 hepatitis B-related HCC patients were recruited, and 68 of them were used for ER-related biomarker discovery study. Proteomic expression patterns of matched tumor and adjacent non-tumor tissues from these patients plus 16 normal liver tissues were delineated by the two-dimensional gel electrophoresis differential profiling method. Significant protein spots were evaluated by hierarchical clustering analysis. SSP4612 that yielded the highest receiver operating characteristic (ROC) curve value for the ER subgroup of HCC was subsequently identified by tandem mass spectrometry, and the corresponding expression patterns were further confirmed by quantitative PCR, Western blot, and immunohistochemistry. Correlation analysis with clinicopathological data was also examined. Proteomic profiling analysis revealed overexpression of mortalin (gene HSPA9) in HCC when compared with the non-tumor and normal liver tissues (area under the curve (AUC) ‫؍‬ 0.821). Furthermore, elevated mortalin level was also detected in the ER subgroup of HCC versus the recurrence-free state (where no cancer recurs for >1 year) (AUC ‫؍‬ 0.833, sensitivity ‫؍‬ 90.9%, specificity ‫؍‬ 71.4%). Metastatic HCC cell lines also exhibited higher levels of mortalin and HSPA9 mRNA. Patients diagnosed with advanced tumor stages and neoplastic metastasis are usually untreatable and very often survive briefly. Liver cancer is one of the prevalent and lethal malignancies and represents a tumor type of highly invasive and aggressive behavior with ability to adapt to new environment. Because of its asymptomatic features during malignant progression, liver cancer is often diagnosed at very late stage when conventional and effective treatment options become unavailable (1).Hepatocellular carcinoma (HCC) 1 is the most common liver malignancy, accounting for the third most common cause of cancer-related deaths worldwide, especially in parts of Asia and Africa with estimated Ͼ680,000 new cases and half million of deaths annually (2, 3). Recent epidemiological studies have also projected an alarming increase of HCC in both Japan and the United States. The only curative treatments for HCC are surgical resection or liver transplantation (4, 5). To date, conventional chemotherapeutic regimen is ineffective against HCC with a response rate between 5-10%, and no single drug or "cocktail" could prolong the patient's survival. Even though after curative surgery, the long-term prognosis of HCC still remains poor, that is largely attributable to the high tumor recurrence rate (about 56% within the first year after surgery) (6).The current paradigm of HCC development is in...

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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 non-malignant liver tissues. A retrospective cohort of 80 HBV-associated HCC was included and both the tumor and adjacent non-tumor 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 non-malignant tissues, which may also provide hints and guidance on how normal hepatocytes are transformed to malignant state during tumor progression.

GPC-3 and HCCR are useful tumor markers complementary to AFP for clinical diagnosis of HCC.

Hexokinase II is a key enzyme in the glycolytic pathway and possesses anti-apoptotic properties in tumor cells. The present study aimed to analyze the expression of hexokinase II and its clinical correlation with clinical factors in patients with hepatocellular carcinoma who treated surgically in China. Reverse transcription-polymerase chain reaction and real-time quantitative polymerase chain reaction were performed to determine hexokinase II mRNA expression in cancer tissues. Protein expression of hexokinase II was evaluated immunohistochemically. Correlation of hexokinase II expression with clinical data was analyzed by the χ(2) or Fisher exact test. Survival was estimated by the Kaplan-Meier method, compared by log-rank test and Cox regression model. A total of 97 specimens were analyzed. Fifty-four tumors showed strong expression of hexokinase II (55.67% expression rate). There were no statistical associations between hexokinase II expression and age, gender, tumor size, TNM stage, serum AFP level, and hepatitis virus infection. Kaplan-Meier curves showed an association between positive Hexokinase II expression and worse overall survival (P value = 0.043). Furthermore, patients expressing hexokinase II had a relatively higher risk for poor prognosis (hazard ratio = 2.049). These results suggest that hexokinase II is highly expressed in hepatocellular carcinoma and has prognostic significance. Hexokinase II represents a potential new therapeutic target in this malignancy.

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BackgroundBiomarkers 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 MethodsSerum 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.ResultsA 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 (AFP20) (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).ConclusionSerum 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.