Screening serum hepatocellular carcinoma-associated proteins by SELDI-based protein spectrum analysis

AIM:To find out potential serum hepatocellular carcinoma (HCC)-associated proteins with low molecular weight and low abundance by SELDI-based serum protein spectra analysis, that will have much application in the diagnosis or differentiated diagnosis of HCC, as well as giving a better understanding of the mechanism of hepato-carcinogenesis. METHODS:Total serum samples were collected with informed consent from 81 HCC patients with HBV(+)/ cirrhosis(+), 36 cirrhosis patients and 43 chronic hepatitis B patients. Serum protein fingerprint profiles were first generated by selected WCX2 protein chip capture integrating with SELDI-TOF-MS, then normalized and aligned by Ciphergen SELDI Software 3.1.1 with Biomarker Wizard. Comparative analysis of the intensity of corresponding protein fingerprint peaks in normalized protein spectra, some protein peaks with significant difference between HCC and cirrhosis or chronic hepatitis B were found. RESULTS:One hundred and twenty-eight serum protein peaks between 2000 and 30 000Da were identified under the condition of signal-to-noise > 5 and minimum threshold for cluster > 20%. Eighty-seven of these proteins were showed significant differences in intensity between HCC and cirrhosis (P < 0.05). Of the above differential proteins, 45 proteins had changes greater than two-fold, including 15 upregulated proteins and 30 downregulated proteins in HCC serum. Between HCC and chronic hepatitis B, 9 of 52 differential proteins (P < 0.05) had intensities of more than two-fold, including 2 upregulated proteins and 7 downregulated proteins in HCC serum. Between cirrhosis and chronic hepatitis B, 28 of 79 significant differential proteins (P < 0.05) changes greater than two-fold in intensity, including 17 upregulated proteins and 11 downregulated proteins in cirrhosis serum. For the analysis of these leading differential proteins in subtraction difference mode among three diseases, the five common downregulated proteins in HCC serum (M/Z 2870, 3941, 2688, 3165, 5483) and two common upregulated proteins (M/Z 3588, 2017) in HCC and cirrhosis serum were screened.CONCLUSION:Because the interference of unspecific secreted proteins from hepatitis B and cirrhosis could be eliminated partly in HCC serum under subtraction difference analysis, these seven common differential proteins have the obvious advantage of specificity for evaluating the pathological state of HCC and might become novel candidate biomarkers in the diagnosis of HCC.

Cell survival curve for primary hepatic carcinoma cells and relationship between SF2 of hepatic carcinoma cells and radiosensitivity

AIM: To establish the cell survival curve for primary hepatic carcinoma cells and to study the relationship between SF2 of primary hepatic carcinoma cells and radiosensitivity.METHODS: Hepatic carcinoma cells were cultured in vitro using 39 samples of hepatic carcinoma at stagesⅡ-Ⅳ. Twenty-nine samples were cultured successfully in the fifth generation cells. After these cells were radiated with different dosages, the cell survival ratio and SF2were calculated by clonogenic assay and SF2 model respectively. The relationship between SF2 and the clinical pathological feature was analyzed.RESULTS: Twenty-nine of thirty-nine samples were successfully cultured. After X-ray radiation of the fifth generation cells with 0, 2, 4, 6, 8 Gy, the cell survival rate was 41%, 36.5%, 31.0%, 26.8%, and 19%,respectively. There was a negative correlation between cell survival and irradiation dosage (r = -0.973, P＜0.05).SF2 ranged 0.28-0.78 and correlated with the clinical stage and pathological grade of hepatic carcinoma(P＜0.05). There was a positive correlation between SF2and D0.5 (r = 0.773, P＜0.05).CONCLUSION: SF2 correlates with the clinical stage and pathological grade of hepatic carcinoma and is a marker for predicting the radiosensitivity of hepatic carcinomas.

Energy-information trade-off induces continuous and discontinuous phase transitions in lateral predictive coding

Lateral predictive coding is a recurrent neural network that creates energy-efficient internal representations by exploiting statistical regularity in sensory inputs.Here,we analytically investigate the trade-off between information robustness and energy in a linear model of lateral predictive coding and numerically minimize a free energy quantity.We observed several phase transitions in the synaptic weight matrix,particularly a continuous transition that breaks reciprocity and permutation symmetry and builds cyclic dominance and a discontinuous transition with the associated sudden emergence of tight balance between excitatory and inhibitory interactions.The optimal network follows an ideal gas law over an extended temperature range and saturates the efficiency upper bound of energy use.These results provide theoretical insights into the emergence and evolution of complex internal models in predictive processing systems.

Lateral predictive coding revisited:internal model,symmetry breaking,and response time

Predictive coding is a promising theoretical framework in neuroscience for understanding information transmission and perception.It posits that the brain perceives the external world through internal models and updates these models under the guidance of prediction errors.Previous studies on predictive coding emphasized top-down feedback interactions in hierarchical multilayered networks but largely ignored lateral recurrent interactions.We perform analytical and numerical investigations in this work on the effects of single-layer lateral interactions.We consider a simple predictive response dynamics and run it on the MNIST dataset of hand-written digits.We find that learning will generally break the interaction symmetry between peer neurons,and that high input correlation between two neurons does not necessarily bring strong direct interactions between them.The optimized network responds to familiar input signals much faster than to novel or random inputs,and it significantly reduces the correlations between the output states of pairs of neurons.

Cycle-tree guided attack of random K-core: Spin glass model and efficient message-passing algorithm

The K-core of a graph is the maximal subgraph within which each vertex is connected to at least K other vertices. It is a fundamental network concept for understanding threshold cascading processes with a discontinuous percolation transition. A minimum attack set contains the smallest number of vertices whose removal induces complete collapse of the K-core. Here we tackle this prototypical optimal initial-condition problem from the spin-glass perspective of cycle-tree maximum packing and propose a cycle-tree guided attack(CTGA) message-passing algorithm. The good performance and time efficiency of CTGA are verified on the regular random and Erd?s-Rényi random graph ensembles. Our central idea of transforming a long-range correlated dynamical process to static structural patterns may also be instructive to other hard optimization and control problems.