s2fieldsofstudy: Biology Medicine

text:
Apamin suppresses biliary fibrosis and activation of hepatic stellate cells

Cholestatic liver disease is characterized by the progressive destruction of biliary epithelial cells (BECs) followed by fibrosis, cirrhosis and liver failure. Activated hepatic stellate cells (HSCs) and portal fibroblasts are the major cellular effectors of enhanced collagen deposition in biliary fibrosis. Apamin, an 18 amino acid peptide neurotoxin found in apitoxin (bee venom), is known to block Ca2+-activated K+ channels and prevent carbon tetrachloride-induced liver fibrosis. In the present study, we aimed to ascertain whether apamin inhibits biliary fibrosis and the proliferation of HSCs. Cholestatic liver fibrosis was established in mouse models with 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC) feeding. Cellular assays were performed on HSC-T6 cells (rat immortalized HSCs). DDC feeding led to increased hepatic damage and proinflammtory cytokine levels. Notably, apamin treatment resulted in decreased liver injury and proinflammatory cytokine levels. Moreover, apamin suppressed the deposition of collagen, proliferation of BECs and expression of fibrogenic genes in the DDC-fed mice. In HSCs, apamin suppressed activation of HSCs by inhibiting the Smad signaling pathway. These data suggest that apamin may be a potential therapeutic target in cholestatic liver disease.

Introduction
Hepatic fibrosis is caused by various types of chronic liver injury. This progressive pathological process is described as accumulation of extracellular matrix (ECM) proteins in and around injured liver tissues (1). Cholestasis results in intrahepatic accumulation of cytotoxic bile acids and hepatic inflammation, which is then followed by biliary fibrosis, cirrhosis and finally end-stage liver disease (2,3). Cholestatic liver disease such as primary biliary cirrhosis and primary sclerosing cholangitis is characterized by a progressive destruction of biliary epithelial cells (BECs) and inflammatory and autoimmune disorders (4,5).
Proliferating BECs have been shown to secrete transforming growth factor-β1 (TGF-β1) and platelet-derived growth factor (PDGF), which stimulate the activation and proliferation of hepatic stellate cells (HSCs) and portal fibroblasts (6,7). Activated HSCs and portal fibroblasts cause enhanced collagen deposition and are the major cellular effectors in liver fibrosis (1,8). This ultimately leads to excessive generation of ECM and accelerates the progression of fibrosis (9). Thus, the suppression of proliferating BECs and activated HSCs has been considered to be a therapeutic target for treating liver fibrosis.
Apamin is an 18 amino acid peptide neurotoxin found in apitoxin (bee venom) (10). It has long been known as a specifically selective blocker of Ca 2+ -activated K + (SK) channels (11). These channels play an important role in mediating the increase in transepithelial secretion due to increases in intracellular Ca 2+ (12). Moreover, apamin has been demonstrated to exhibit anti-inflammatory and anti-fibrotic activity in various cell types and mouse models (13,14). A previous study carried out by our group confirmed that apamin is an anti-fibrotic agent which acts through suppression of TGF-β1induced hepatocyte epithelial-mesenchymal transition (13). However, the effects of apamin in biliary cirrhosis and the molecular mechanism underlying HSC proliferation have not been explored.
In the present study, we fed mice with 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC), which induces sclerosing cholangitis and biliary fibrosis. We demonstrated that apamin inhibited DDC-induced liver fibrosis and mediated BEC proliferation and ductular reaction, which are repair responses to cholestatic injury. Moreover, apamin treatment caused the suppression of activated HSCs through the TGF-β1/Smad signaling pathway. Histopathology and immunohistochemistry. Hematoxylin and eosin (H&E), Masson's trichrome and immunohistochemical staining were performed according to a previously described procedure (15). Sections were stained with H&E and Masson's trichrome. For immunohistochemical analysis, sections were incubated with anti-fibroblast specific protein-1 (FSP-1) (ab41532; Abcam, Cambridge, UK) for 1 h at 37˚C, processed by an indirect immunoperoxidase technique using a commercial kit (Dako, Carpinteria, CA, USA). The slides were examined with an Eclipse 80i microscope (Nikon, Tokyo, Japan) and analyzed with iSolution DT software (IMT i-Solution, Vancouver, BC, Canada).
Immunocytochemistry. HSC-T6 cells were grown on chamber slides and were fixed with 3.7% paraformaldehyde for 10 min. Cells were permeated with 0.5% Triton X-100 for 10 min and then incubated with primary antibodies against SMA (A5228; Sigma) for 1 h at 37˚C. After washing, the cells were incubated with the secondary antibodies (Alexa Fluor 594) for 30 min at 37˚C. Cells were counterstained with Hoechst 33342. The cells were imaged using a Nikon A1+ confocal microscope.
Western blot analysis. Western blotting was performed as previously described (16). Primary antibodies used in this study were the following; anti-p-Smad2/3 (#8828), anti-Smad2/3 Statistical analysis. The experimental results are expressed as mean ± SE. ANOVA and paired or unpaired t-test were performed for statistical analysis as appropriate. p-value <0.05 was considered to indicate a statistically significant result. All experiments were performed at least three times.

Apamin ameliorates liver damage and inflammatory hepatic injury.
To investigate the effects of apamin treatment on liver fibrosis, a mouse model induced by DDC diet feeding was used. When challenged with the DDC diet for 4 weeks, the structure of the hepatic lobule was clear in the NC group (Fig. 1A) and there was a large amount of bile duct proliferation, accompanied by inflammatory cell infiltration in the DDC-fed group as shown by H&E staining (Fig. 1C). In addition, the above pathological changes were reduced in the apamin-treated group (Fig. 1D) compared with these changes noted in the DDC-fed group. Masson's trichrome staining indicated collagen deposition surrounding the proliferated bile duct in the DDC-fed group (Fig. 2C). In contrast, apamin treatment resulted in diminished fibrosis and collagen deposition (Fig. 2D). In cholangiopathies, inflammation and reactive proliferation of bile ducts are closely related with the development of biliary fibrosis (17). ELISA and western blot analyses indicated that expression levels of IL-6, IFN-γ, TNF-α and IL-1β were significantly higher in the DDC-fed group compared with these levels in the NC group (Fig. 3). However, apamin treatment attenuated inflammatory cytokine expression, including IL-6, IFN-γ, TNF-α and IL-1β compared with expression levels in the DDC-fed group. Taken together, these data confirm the anti-inflammatory and moderate antifibrotic effects of apamin on the DDC-fed mice.

Effect of apamin on BEC proliferation in DDC-fed mice.
We next determined the effect of apamin on ductular reaction in the DDC-fed mice by immunofluorescence of CK19 expression. Chronic DDC feeding in mice was previously demonstrated to result in cholangitis and immune responses against BECs with the destruction of bile ducts and ductules (18). CK19 is regarded as a hallmark of bile epithelial cells (19). Immunofluorescence staining showed that CK19 was highly expressed in the BECs in bile ductules in enlarged portal tracts (Fig. 4). The DDC-fed group had significantly increased expression of CK19 compared with the NC group. In contrast, apamin treatment significantly reduced biliary activation and proliferation as evidenced by CK19 staining, indicating a defect in the ductular reaction. In addition, immunofluorescence staining of PCNA showed that apamin treatment suppressed the proliferation of BECs compared with the DDC-fed group. These results indicate that apamin may inhibit cholestatic liver fibrosis by suppressing BEC proliferation and ductular reaction induced by the DDC diet.
Apamin inhibits ECM deposition in the livers of DDC-fed mice. To investigate the anti-fibrotic effect of apamin on ECM  deposition in the DDC-fed mice, we used western blot analysis, immunohistochemistry and immunofluorescence assay to determine the effects of this compound on ECM molecules. Liver fibrosis induced by DDC was confirmed by induction of fibrogenic genes, FSP-1, α-smooth muscle actin (α-SMA) and collagen I expression. Expression of α-SMA was strongly expressed in the myofibroblasts and HSCs around the proliferated bile duct in the DDC-fed group and clearly with the apamin treatment (Fig. 5). Moreover, expression of collagen I in the DDC-fed group was significantly increased, especially in the portal tracts. Compared to the DDC group, apamin treatment inhibited collagen I expression. During tissue remodeling in liver fibrosis, FSP-1 is considered as a marker of fibroblasts in the fibrotic liver. DDC feeding increased the number of cells positive for FSP-1 expression (Fig. 6C). In contrast, apamin treatment resulted in a reduction in FSP-1-positive cells (Fig. 6D). Furthermore, western blot results showed that the expression levels of TGF-β1, collagen I, and fibronectin were significantly higher in the DDC-fed group, whereas apamin treatment markedly decreased the protein level of TGF-β1, collagen I, and fibronectin compared with the DDC-fed group (Fig. 6F). Taken together, the data suggest that apamin may protect liver fibrosis during DDC feeding by suppressing fibrotic gene expression.
Apamin inhibits activation of HSCs through the Smad signaling pathway. TGF-β1 is a Smad family member, and it  is known to stimulate the activation of HSCs (20). We next investigated whether the inhibitory effect of apamin on the activation of HSCs by TGF-β1 is through the Smad signaling pathway. α-SMA and collagen I expression was increased  by TGF-β1 and was decreased by apamin treatment in the HSC-T6 cells (Fig. 7A). Also, immunofluorescence showed that TGF-β1 induced activation of HSC-T6 cells through increased α-SMA expression (Fig. 7C). However, apamin treatment markedly reduced the expression of α-SMA in the TGF-β1-induced HSC-T6 cells. Western blot analysis showed that phosphorylation of Smad2/3 and Smad4 were stimulated by 2 ng/ml TGF-β1 (Fig. 7B). Apamin treatment abrogated the activation of p-Smad2/3 and Smad4 induced by TGF-β1. These results indicated that apamin may attenuate TGF-β1-activated HSCs by inhibiting the Smad signaling pathway.

Discussion
Liver fibrosis is a wound healing process that results in increased levels of ECM protein. Bee venom and its components are known to have anti-inflammatory and anti-fibrotic effects on liver fibrosis (21)(22)(23)(24). Recent studies have shown that melittin inhibited cholangitis and biliary fibrosis in a xenobiotic-induced mouse model (15). Apamin comprises 2-3% of the dry weigh of bee venom (25). Moreover, apamin is a specific blocker of SK channels. It has been reported that apamin-sensitive K + channel is present in biliary cells and contributes to secretion in response to increased intracellular Ca 2+ (11,26). Hence, we investigated the effects of apamin on the pathogenesis of DDC-induced biliary fibrosis and the suppression of activated HSCs.
The biliary system is lined by BECs, which make up ~5% of all liver cells and function as important bile modi-fiers (4,27). Hepatic fibrosis induced by DDC feeding is related to increased biliary pyophyrin secretion and the activation of BECs with development of bile duct injury, leading to pericholangitis and ductular reaction resulting in portal-portal fibrosis (28). Proliferating BECs can secrete a variety of profibrogenic cytokines, which promote the activation and proliferation of HSCs and also promote the synthesis of ECM, leading to the initiation and development of cholestatic hepatic fibrosis (6). Moreover, CK19 is particularly important for the proliferation of BECs during the ductular reaction, which is generally considered an attempt by the liver to restore bile flow between the lobules and the terminal bile ducts (29). In the present study, apamin significantly inhibited bile duct proliferation through a decrease in CK7, CK19 and PCNA expression in the DDC-fed mice.
TGF-β1 is a major fibrogenic mediator involved in the activation and transdifferentiation of HSCs (30). TGF-β1 binds to the TGF-β1 type II receptor (TβRII) and phosphorylates Smad2/3, and then p-Smad2/3 interacts with Smad4 to trans-activate target genes in the nucleus (31). Smad activation is critical for the induction of many TGF-β-responsive genes including collagen I, fibronectin and α-SMA (32). In the present study, TGF-β1, collagen I, fibronectin and α-SMA levels were significantly increased in the DDC-fed mouse group as determined by western blot analysis and immunofluorescence (Figs. 5 and 6F). Treatment with apamin resulted in a significant reduction in TGF-β1, collagen I, fibronectin and α-SMA levels. Moreover, apamin treatment also inhibited α-SMA expression through the Smad signaling pathway in Figure 7. Apamin inhibits the activation of hepatic stellate cells (HSCs) through the Smad signaling pathway. Cells were pre-cultured in a serum-free medium in the presence or absence of apamin for 24 h. The cells were lysed, and proteins were analyzed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). Western blot analysis was performed with antibodies specific for (A) α-smooth muscle actin (α-SMA) and collagen I, and (B) p-Smad2/3, Smad2/3 and Smad4. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was used as an internal control. (C) Immunocytochemistry of α-SMA expression. Scale bar, 50 µm. * p<0.05 vs. normal control; † p<0.05 vs. transforming growth factor-β1 (TGF-β1)-treated cells.
the TGF-β1-induced HSCs (Fig. 7). These findings suggest that the HSCs are transformed into myofibroblasts and secrete TGF-β1, thus stimulating the production of ECM in the DDC-fed mouse model (15).
The principal finding of this study is the anti-fibrotic effects of apamin. Apamin suppressed the proliferation of BECs and activation of HSCs. In the present study, apamin significantly inhibited bile duct proliferation and reduced ECM deposition in the DDC-fed mice. Furthermore, apamin suppressed the protein expression of p-Smad2/3 and Smad4 induced by TGF-β1 in the HSCs. These results suggest that apamin inhibits the proliferation of BECs and activation of HSCs by suppressing the TGF-β1 signaling pathway in hepatic fibrosis.


extfieldsofstudy:
Biology
Computer Science

text:
Optimal planning of eye movements

The capability of directing gaze to relevant parts in the environment is crucial for our survival. Computational models based on ideal-observer theory have provided quantitative accounts of human gaze selection in a range of visual search tasks. According to these models, gaze is directed to the position in a visual scene, at which uncertainty about task relevant properties will be reduced maximally with the next look. However, in tasks going beyond a single action, delayed rewards can play a crucial role thereby necessitating planning. Here we investigate whether humans are capable of planning more than the next single eye movement. We found evidence that our subjects’ behavior was better explained by an ideal planner compared to the ideal observer. In particular, the location of the first fixation differed depending on the stimulus and the time available for the search. Overall, our results are the first evidence that our visual system is capable of planning.

1 tasks, which rely on acquiring visual information for survival such as gathering 2 food, avoiding predators, making tools, and social interaction. As we can only 3 perceive a small proportion of our surroundings at any moment in time due to 4 the spatial distribution of our retinal receptor cells 1 , we are constantly forced to 5 bring task relevant parts of the visual scene into focus using eye movements 2 . 6 Thus, vision is a sequential process of active decisions. These decisions have 7 been characterized in terms of optimizing performance in the ongoing task 3-7 , 8 maximizing knowledge about the environment 8-10 , or targeting gaze towards 9 locations that are most salient 11 .

10
To understand the requirements of perceptual tasks, ideal-observer analy-11 sis 12,13 has been very successful based on the idea that visual perception is 12 inference of latent causes based on sensory signals 14,15 . In this framework, the 13 goal of the visual system is to use sensory data D to infer unknown properties of 14 the state s of the environment. For example, s could be indicating whether there 15 is a predator hiding behind a bush, and by directing gaze to the bush visual 16 data D about the latent variable describing the true state s of the environment 17 is obtained. This information can be incorporated into what is known about s 18 using Bayes' theorem P (s|D) = P (D|s)P (s)/P (D). Hence, the ideal observer 19 combines prior knowledge P (s) and sensory information P (D|s) to form an up- 20 dated posterior belief about environmental states relevant to the specific task. 21 The ideal-observer paradigm has been used successfully to understand how hu-22 mans choose locations for the next saccade. Specifically, human eye movements 23 use the current posterior and target the location where they expect uncertainty 24 about task relevant variables to be reduced most after having acquired new data 25 from that location in situations such as visual search 3 , face recognition 5 , and 26 temporal event detection 6 .

27
A limitation of ideal-observer theory is that performing sensory inference by 28 itself does not prescribe an action, i.e. information about s in the end needs 29 to be used to decide for an appropriate action, e.g. whether to flee. The costs 30 and benefits for the potential outcomes of the action can be very different, 31 e.g., not to flee if a predator is present is more costly than an unnecessary 32 flight. Bayesian decision theory provides such an answer by using the costs 33 and benefits of different outcomes with the respective uncertainties of the as-34 sociated outcomes. Hence, different potential outcomes of s are weighted with 35 a utility function U (a, s) to determine the action with highest expected util-36 ity: a = arg max a � s U (a, s)P (s|D)ds. Thus, it may be better to flee, even 37 when one is not absolutely certain that a predator is hiding behind a bush, be-38 cause the consequences may be particularly harmful. Interestingly, within this 39 framework, the optimal action targets the location where the next fixation will 40 reduce uncertainty the most and not the location that currently looks like the 41 most probable target location. Indeed, both explicit monetary rewards 16 and 42 implicit behavioral costs 6 in experimental settings have been shown to influence 43 eye movement choices. 44 However, Bayesian decision theory is limited to a particular subset of visual 45 tasks, namely tasks that do not involve planning. Repeatedly taking the action 46 with the maximum immediate utility in general may fail in tasks with longer 47 action sequences and delayed rewards depending on the specific task structure. 48 In these cases, an ideal planner based on the more powerful framework of belief 49 MDPs, which contains the ideal observer and the Bayesian decision maker as 50 special cases, is needed to find the optimal strategy. A Markov Decision Process 51 (MDP) 17,18 is a tuple (S, A, T, R, γ), where S is a set of states, A is a set 52 of actions, T = P (s � |s, a) contains the probabilities of transitioning from one 53 state to another, R represents the reward, and finally, γ denotes the discount 54 factor. In a belief MDP only partial information about the current state s is 55 available, therefore a probability distribution over states is kept as a belief state 56 b(s) = P (s | D) 19   Targets were only visible in close proximity to the current fixation location (i.e., inside the search area). (b) Procedure for a single trial. Subjects fixated a fixation cross either shown on the left or the right side, respectively. The shape appeared 750 ms prior to the start of the search. The search time was initiated by the participants' gaze crossing the dotted line. The line, however, was not visible to the subjects. Depending on the condition (short or long) subjects were able to perform one or two fixations inside the shape. (c) Raw gaze data is shown for a trial with short search time and initial fixation on the right side (upper panel) and for a trial with long search time and initial fixation on the left side (lower panel). Shapes were mirrored in a counterbalanced design to ensure equal orientation with respect to the initial fixation cross.
a belief state b(s) is denoted by the action-value function Q: where V * (b(s � )) is the expected future reward gained from the next belief state 59 b(s � ). Essentially, what this means is that the value of an action based on the 60 current belief is a combination of the immediate reward and the long term ex-61 pected reward, weighted by how likely the next belief is under the action. Thus, 62 as the belief about the state of task relevant quantities depends on uncertain 63 observations, actions are influenced both by obtaining rewards and obtaining 64 more evidence about the state of the environment.

65
In the present study we devised a task that allows probing whether ideal-66 observer models are sufficient to describe human eye movement strategies. For 67 our visual search task, we derived computational models based on ideal-observer 68 theory as well as on the framework of belief MDPs. Using these models, we 69 specifically created our stimuli such that the two models led either to differ-70 ent behavioral sequences or to the same behavioral sequences. The rationale 71 for this was to not only show the differences between ideal planer and ideal 72 observer but to also demonstrate that the solutions of both may lead to the 73 same action sequence, depending on the structure of the specific task. Using 74 this experimental paradigm we are able to test whether human eye movement 75 strategies follow the computational principles underlying ideal-observer theory 76 and sequential Bayesian decision making or whether the strategies are planned 77 and future rewards need to be considered (belief MDP).

79
Visual search as planning under uncertainty. To develop a computa-80 tional model of visual search as optimal planning under uncertainty it is first 81 necessary to specify the relevant quantities describing the task, i.e. the state 82 representation. In our visual search task (Fig. 1), a suitable candidate for 83 a state representation is the target location and the current location of gaze. 84 However, in general, the exact location of the target is unknown. Therefore, we 85 formalize the probability distribution of the target as a belief state. The action 86 space comprises potential fixation locations and with each action we receive in-87 formation about the target, update our belief and transition to the next belief 88 state. The reward function is an intuitive mapping between the belief state, 89 which comprises the knowledge about the location of a potential target, and the 90 probability of finding the target.

91
How should the actor decide where to look next according to this frame-92 work? A policy π is a sequence of actions and the optimal policy π * comprises 93 actions a = arg max a Q(b(s), a) that maximize the expected reward. In tasks 94 comprising sequences of actions, the optimal strategy, the ideal planner, incor-95 porates rewards associated with future actions (V * (b(s � )) into action selection. 96 As a result, the sequence of actions that leads to the maximum total reward is 97 chosen: 98 π * ideal planner = argmax a0,a1,...,an E[r 0 + γr 1 + · · · + γ n r n ] , where γ is the discount factor, which controls how much future rewards influence 99 the current action selection.

100
Ideal observer as special case of the ideal planner. If we are only in-101 terested in the optimal next action (γ = 0) or if there is only a single action to 102 perform equation (1) simplifies to: where is the posterior over relevant quantities in the task 104 and R(s, a) is the cost or reward function. Therefore, if reduced to the next 105 action alone, the ideal planner reduces to the ideal observer with an action 106 b Ideal Observer = Ideal Planner S1 S2 S3 S4  For the long search interval (right side, two fixations), the ideal observer and the ideal planner differ with respect to the scanpath. While the ideal observer's next fixation is chosen to maximize the immediate reward (better performance after the first fixation, bottom row), the ideal planner's scanpath is chosen to maximize performance after two fixations. Computational complexity (depicted as decision trees) is higher for the ideal planner as in the condition with long search intervals all two-fixation sequences are evaluated in order to maximize performance. (b) Shapes used in our visual search experiment. For each shape the optimal policy is shown for the ideal observer (pink) and the ideal planner (green). Whether these models lead to different strategies depends on the particular shape. Scanpaths are the same for Shapes S1 and S3, but differ for S2 and S4. selected to maximize task success after the next action. For sequences of actions, 107 the sequential application of the ideal-observer paradigm leads to the action 108 sequence: where a 0 , . . . , a n is the sequence of actions that yields the maximum expected 110 return r t for each time step t. Whether π ideal observer and π * ideal planner lead to 111 the same action sequence depends on the specific nature of the task. However, 112 in general: as can be seen in Fig. 2. Ideal-observer approaches only lead to optimal actions 114 if future rewards do not play a role, for example, if only a single action is 115 concerned.  The scanpaths suggested by the best fitting models for the ideal planner and the ideal observer are shown in the center and the right column, respectively. Again, solid lines depict the strategy for the long search interval, dashed lines for the short search interval. Global means of the human data are also shown for reference (red, green, and blue). (b) Actual and predicted spatial relation of first saccades for all four shapes. Graphs are centered at the fixation location in the short search interval condition. Arrows depict the displacement of the first fixation location in the long search interval relative to the short interval. Arrow color corresponds to the data source. For the ideal observer, the first fixation location is the same for both conditions (indicated by the square centered at (0,0)). (c) Difference in BIC between all tested models. The lower bound corresponds to a model directly estimating the mean fixation locations for each shape and condition from the data (3 × 4 means).
Surprisingly, all of the reviewed computational models for eye movements are 117 myopic, i.e. they choose actions that maximize the immediate reward 3,20,16,5-7 . 118 In practice, the problem of delayed rewards is circumvented by either inves-119 tigating only single saccades or by choosing tasks where both policies lead to 120 equivalent solutions. To our knowledge, there exist neither computational mod-121 els nor empirical data investigating whether humans are capable of planning 122 eye movements. The execution of eye movement sequences has been subject 123 to psychological research and results have shown that the latency of the first 124 saccade was higher for longer sequences of saccades 21 . Also, discrimination per-125 formance was enhanced at multiple locations within an instructed sequence of 126 saccades 22 . Further, if an eye movement plan was interrupted by additional in-127 formation midway the execution of the second saccade was delayed 23 . Although 128 these results indicate that a scanpath of at least two saccades is internally pre-129 pared before execution, no light is shed on whether multiple future fixation 130 locations are jointly chosen to maximize performance in a task.

131
Behavioral and model results. The mean fixation location for each par-132 ticipant separately for all shapes and conditions is shown in Fig. 3a. Also, 133 fixation sequences for the best fit of the ideal observer (right column) and the 134 ideal planner (center column) are depicted. Visual inspection suggests, that the 135 behavioral data is closer resembled by the results of the ideal planner. To test 136 whether eye movements were planned, we compared the first fixation location 137 in the short condition to the first fixation location in the long condition for all 138 shapes. If subjects were capable of performing planning, we expected a differ-139 ence in the first fixation location for Shape S2 and S4. We used Hotelling's 140 T-test to compare the bivariate landing positions of the first saccade between 141 the two search intervals (Supplementary Table 1). Indeed, mean target loca-142 tions for the first saccade were different in Shape S2 and S4. No significant 143 differences, however, were found in shapes S1 and S3. This behavior was well 144 predicted by our ideal planner, but not by the ideal observer. In addition, the 145 direction of the spatial difference of the first fixation location between the search 146 interval conditions followed the course suggested by our ideal planner (Fig. 3b). 147 Bounded actor extensions. We extended both the ideal observer as well 148 as the ideal planner to yield a more realistic model for human visual search 149 behavior, i.e. a bounded actor (see Materials). We added additive costs for 150 longer saccade amplitude (as they lead to longer scanpath duration 24 and higher 151 endpoint variability 25 , which humans have been shown to minimize 26 ), used 152 foveated versions of the shapes to account for the decline of visual acuity in 153 peripheral vision 27 , and accounted for the often reported fact, that human sac-154 cades undershoot their target 28,29 . We used the sum of squared errors between 155 our model prediction and our data to compute the BIC for each model. Figure 156 3c shows the difference in BIC of all models compared to the best model. The 157 lower bound was derived by computing the mean fixation locations directly from 158 the data (3 × 4 parameters). The difference in BIC values between two models 159 is an approximation for the log Bayes factor and a difference ΔBIC > 4.6 is 160 considered to be decisive 30 . Results clearly favor the ideal planner over the 161 ideal observer (ΔBIC = 138). Crucially, the ideal planner without any param-162 eter fitting still provided a better description of our human data than the ideal 163 observer with all extensions (ΔBIC = 27). Further, all model extensions did 164 not only improve our model fit for the ideal planner but were favored by model 165 selection, suggesting that they are needed for describing the eye movement data 166 in our experiment (ΔBIC = 11 between ideal planner with all extensions and 167 ideal planner without undershot).

168
Parameter estimates for the saccadic undershot were similar for the ideal 169 observer (4.14 %) and the ideal planner ( 5.07 %). The influence of the costs for 170 longer saccades was higher for the ideal observer (1.2 DP / Deg) compared to 171 the ideal planner (0.55 DP/Deg). The unit of the costs is detection performance 172 (DP) per degree (Deg) and states, how much performance subjects were willing 173 to give up to shorten saccade amplitudes by one visual degree. We also estimated 174 the radius of the circular gaze contingent search shape centered at the current 175 fixation. Parameter estimation yielded values very close to the true radius 176 and did not improve model quality for neither the ideal planner nor the ideal 177 observer.

179
It has been unclear whether sequences of human eye movements are planned 180 ahead in time. Prior studies indicate that multiple saccadic targets are jointly 181 prepared as a scanpath and that cueing new targets during execution of eye 182 movements results in longer execution times [21][22][23] . However, to our knowledge 183 there has been no experimental evidence that eye movements are chosen by 184 considering more than one step ahead into the future. Instead, the ideal-observer 185 paradigm, that models human eye movements as sequential Bayesian decisions 186 has been the predominant approach.

187
In our study we tested whether the implicit assumptions that accompany the 188 ideal observer are justified. Therefore, we contrasted the ideal observer with the 189 more general ideal planner that was formalized as a Markov Decision Process 18 190 with partially observable states 19 . We formalized policies for the ideal observer, 191 only considering the immediate reward for action selection, and for the ideal 192 planner, which also considers future rewards. Next, we derived the specific cir-193 cumstances under which the models produce different policies. Ultimately, we 194 used these insights to manufacture stimuli that maximized the behavioral differ-195 ences elicited by the different cognitive strategies and also obtained stimuli that 196 show very similar strategies. Thus, the resulting stimuli were highly suitable for 197 examining which cognitive strategy was adopted by our subjects. 198 We developed a visual search task where we expected different behavioral 199 sequences depending on the cognitive strategy of our subjects. In particular, 200 we investigated whether subjects adjust their scanpath during visual search 201 dependent on the duration of the search interval. Therefore, we controlled the 202 length of the saccadic sequence. The short search interval allowed subjects to 203 execute a single saccade, while in the long search interval subjects were able to 204 fixate two locations. 205 Our results suggest that eye movements are indeed planned. Subjects' scan-206 path was very well predicted by the ideal planner while showing severe deviations 207 from the scanpath proposed by the ideal observer. Crucially, this was the case 208 even if the sequence required planning. We found fixation locations to be dif-209 ferent depending on the duration of the search interval. This difference is only 210 expected under the ideal planner and can not be explain by the ideal observer. 211 Finally, model comparison favored the ideal planner and its extensions over the 212 ideal observer by a large margin. Furthermore, extending our ideal planner 213 model to a bounded planner, we found evidence that subjects traded off task 214 performance and saccade amplitude. Including additive costs for saccades with 215 great amplitude into the ideal planner and accounting for saccadic undershot 216 was best capable of explaining our data further.

217
Finding and executing near optimal gaze sequences is crucial for many ex-218 tended sequential every-day tasks 31,32 . The capability of humans to plan be-219 havioral sequences gives further insights into why we can solve so many tasks 220 with ease, which are extremely difficult from a computational perspective. In 221 many visuomotor tasks coordinated action sequences are needed rather than 222 single isolated actions 33 . This leads to delayed rewards and thus a complex pol-223 icy is required rather than an action that directly maximizes the performance 224 after the next single gaze switch. Additionally, our findings have implications 225 for future models of human eye movements. While numerous influential past 226 models have not taken planning into consideration 3,5,6,20 , our results indicate 227 that in the case of visual search humans are capable of including future states 228 into the selection of a suitable scan path.

229
The broader significance of the present results beyond the understanding of 230 eye movements lies in the fact that human behavior in our experiment was best 231 described by a computational model of a bounded probabilistic planning under 232 perceptual uncertainty algorithm. In this framework, sensory measurements 233 and goal directed actions are inseparably intertwined 34,35 . So far, the predom-234 inant approach to probabilistic models in perception has been the ideal ob-235 server 12,13 , which can be formalized in the Bayesian framework 14,15 as inferring 236 latent causes in the environment giving rise to sensory observations. Models of 237 eye movements selection have so far used ideal observers 3,5,6 without planning. 238 Probabilistic, Bayesian formulations of optimality in perceptual tasks 36,37 , cog-239 nitive tasks 38,39 , reasoning 40 , motorcontrol 41 , learning 42 , and planning 43 have 240 lead to a better understanding of human behavior and the quest to unravel, how 241 the brain could implement these computations [44][45][46] , which are known in general 242 to be intractable 47   Task. In our task subjects searched for a hidden target within irregularly bounded shapes 257 (Fig. 1a). Using a gaze contingent paradigm the hidden target only became visible if a 258 fixation landed close enough (||p Fix − p Tar || < 6.5�). The search area was made explicit by 259 showing the shape's texture for all points closer than 6.5� to the fixation location. Targets 260 within that area became visible to the participant after a delay of 130 ms. This was done 261 to prevent participants from sliding over the image and instead encourage them to perform 262 distinct fixations. Texture was chosen to reinforce the feeling of looking through the shape 263 (subjects were told to imagine wearing x-ray goggles).

264
A single trial was as follows (Fig. 1b): Participants fixated a fixation cross that was 265 randomly presented either on the left or the right side of the screen. After 1 s the shape was  Materials. Our computational models enabled us to specifically select shapes that facilitate 279 testing our hypothesis. In particular, we identified stimuli that triggered different policies 280 for the ideal-observer model and the ideal-planner model. First, multiple candidates shapes 281 were generated using the following approach: Five points were drawn uniformly in a bounded 282 area (23.24�× 23.24�). Next, a B-spline was fitted to the random points. Finally, the shapes 283 bounded by the splines using the fitted parameters were filled with a texture (white noise). We 284 applied both models to identify shapes that lead to different policies. Overall, four different 285 shapes were used in the experiment (see Fig. 2b). We chose two shapes where optimal behavior 286 requires planning (S2 and S4) and two where it does not (S1 and S3), i.e. where the sequence 287 of eye movements from the ideal observer and the ideal planner coincide. In each category 288 we selected two shapes by visual inspection ensuring that they were similar with respect to 289 the area covered. For display during the experiment the shapes were upscaled with a factor 290 of 1.5 and centered on the monitor such that the center of the shapes bounding box matched 291 the center of the screen.

292
The target was a circular grating stimulus (0.87�in diameter). Contrast was set in a way 293 that it was easily detected if it was within the visible search radius of the current fixation.

294
The target's position was generated by randomly choosing a location within the shape.  If necessary the search time was adjusted (between 500ms and 580ms, for the long search 301 interval). Participants were encouraged to ask questions if anything was unclear. After train-302 ing, participants answered ten questions from a checklist to ensure that they understood the 303 task properly (e.g., when does the search interval start and how many targets can be found 304 at most). Incorrect answers were documented and the correct answers were discussed. Af-305 ter successfully finishing the training, four blocks each containing 100 trials were performed. 306 Thereby, the order of the blocks was either SSLL (two blocks with short search time followed 307 by two blocks with long search time) or LLSS. Participants were randomly assigned to one of 308 the two orders. Eye tracking calibration was renewed before each block.  Model. Here we derive expressions that implement the general mechanisms of equation (2) 336 and (4) for our visual search task. According to our experimental design participants directed 337 their gaze to suitable locations within a shape in order to decide if a target was present.

338
Depending on the condition, the action sequence in our task comprised one (short condition) 339 or two (long condition) fixation locations. Formally, the greedy policy of the ideal observer 340 (equation (4)) leads to the sequence of fixation locations (x 0 , y 0 ), (x 1 , y 1 ), . . . , (xn, yn) that 341 maximizes the quality of the decision after each step. In the case of two fixations this leads 342 to: where xn, yn are the coordinates of nth fixation location and P (correct|xn, yn) denotes the 344 probability of deciding correctly whether a target is present after the nth fixation.

345
The non greedy policy of the ideal planner can be derived from equation (2) in a similar 346 fashion. Again, we consider the case of two fixations (LI). Here, the next fixation location is 347 determined by maximizing the reward simultaneously using the next two fixation locations: 348 π ideal planner := argmax Thereby, (x 0 , y 0 ) is the next location and (x 1 , y 1 ) is the location thereafter. By jointly op-349 timizing the entire sequence of fixation locations the ideal planner is always equal or better 350 compared to the ideal observer. Intuitively, π ideal observer and π ideal planner yield the same 351 action sequence if the sequence only contains a single action, i.e. a single fixation. Also, the 352 first fixation location of ideal observer is the same for both conditions. Crucially, this is not 353 the case for π ideal planner . By jointly maximization the reward over the whole action sequence, 354 even the first fixation location can differ between the conditions.

355
Next, we derive the probability of a correct decision given a sequence of fixation locations 356 since both proposed policies depend on the performance in the task, i.e., the detection proba- where P T (x, y) is the probability that the target is located at (x, y) and P O (x, y|xn, yn) is the 360 probability that the location (x, y) is covered by the search given that the saccade was targeted where the threshold is equal to the radius of the search area (6.5�).

365
Model extensions. To take into account known cognitive and biological constraints we need 366 to incorporate several well known characteristics of the human visual system. We introduced 367 costs on the saccade amplitude thus favoring smaller eye movements. As was shown by


extfieldsofstudy: Biology Medicine

text:
G2/M checkpoint plays a vital role at the early stage of HCC by analysis of key pathways and genes

The present study was designed to explore the molecular mechanism at the early stage of hepatocarcinoma (HCC) and identify the candidate genes and pathways changed significantly. We downloaded the gene expression file dataset GSE6764 from GEO, adopted the Robust Multi-array Average (RMA) algorithm to preprocess the raw file. 797 differentially expressed genes (DEGs) were screened out based on the SAM method using R language. Ingenuity Pathway Analysis (IPA) was used to perform canonical pathway analysis in order to calculate the most significantly changed pathways and predict the upstream regulators. In order to confirm the results from the DEGs which based on the individual gene level, the gene set enrichment analysis (GSEA) was done from the gene set level and the leading edge analysis was performed to find out the most appeared genes in several gene sets. The PPI network was built using GeneMANIA and the key genes were calculated using cytoHubba plugin based on cytoscape 3.4.0. We found that the Cell Cycle: G2/M DNA damage checkpoint regulation is the top-ranked pathways at the early stage of HCC by IPA. The high expression of several genes including CCNB1, CDC25B, XPO1, GMPS, KPNA2 and MELK is correlated with high risk, poor prognosis and shorter overall survival time in HCC patients by use of Kaplan-Meier Survival analysis. Taken together, our study showed that the G2/M checkpoint plays a vital role at the early HCC and the genes participate in the process may serve as biomarkers for the diagnosis and prognosis.

INTRODUCTION
Hepatocellular carcinoma (HCC) is the fifth most common cause of cancer and responsible for a third of the cancer-related deaths worldwide. The occurrence of HCC comprises many changes such as gene mutations, chromosomal aberrations and molecular pathways which always accompanied by cell cycle dysregulation, evasion of apoptosis [1]. So far, the best therapeutic approach for the HCC patients is liver transplantation which can eliminate HCC. However, recurrence rates remain high. Methods for early HCC detection are often evaluated on specificity and sensitivity [2] and many guidelines have been established for the early liver cancer diagnosis [3].
To identify potentially useful biomarkers and targets for the early diagnosis of HCC, the molecular mechanism of the cancer has been studied intensely especially the onset of HCC [4][5][6][7][8]. SPRTN could decrease DNA replication stress in DNA replication and G2/Mcheckpoint regulation and the mutation of SPRTN could cause early onset of hepatocellular carcinoma [9]. In order to determine candidate genes and the most significant pathways associated with the early stage of HCC, we performed the individual and gene set level analysis by www.impactjournals.com/oncotarget/ Oncotarget, 2017, Vol. 8, (No. 44), pp: 76305-76317 Research Paper www.impactjournals.com/oncotarget use of a series of bioinformatics approaches. Especially, the differential expressed genes (DEGs) were screened out using the SAM method and the pathways enrichment was performed using Ingenuity Pathway Analysis (IPA). Furthermore, in order to avoid the drawback of individual gene analysis, GSEA was performed to verify the former result. Then, we built the PPI network from DEGs to identify the key genes using cytoHubba plugin. And then the co-expression network was built from the key genes by use of the geneMANIA plugin based on Cytoscape.

Microarray analysis and data pre-processing
In order to guarantee the quality of every chip before the next analysis, we performed quality control (QC) for every raw file. The results of QC plot and box plot before and after normalization were shown in Figure 1.

Identification of DEGs
A total of 981probes were screened out at the delta = 2.44 with the FDR<0.1% (Figure 2A) (Supplementary Table 1). The minimum FDR value was reserved if several probes corresponded the same gene. At last, 797 DEGs between the early HCC and normal controls were screened out using SAM, including 421 up-regulated and 376 down-regulated genes (Supplementary Table 1). All of these DEGs are classed into 14 types according to IPA as shown in Figure 2B.
According to the classification, enzymes, TFs, transporter, kinase composed most of the DEGs.

Canonical pathway analysis
We compared the early HCC group with the control group using IPA tool. 78 canonical pathways were identified with a p-value<0.05 and the top 26 pathways associated with the onset of HCC are shown in

The upstream regulator analysis
The upstream regulator analysis was performed by IPA and 7 transcription factors (TFs) were predicted to be activated and 6TFs be inhibited as shown in Table 1. The 7 predicted activated TFs and their target genes are shown in Figure 4. The DEGs regulated by FOXO1 participate in cell cycle mainly.   Red represents the up-regulated genes, the green represents the down-regulated genes and the grey represents the no overlap genes with dataset. The significance (-log p value) of every pathway is indicated in parenthesis.

Gene set enrichment analysis confirmed the enrichment of G2/M checkpoint at the early stage of HCC
The results from IPA showed that G2/M checkpoint regulation was the most significantly changed biological process which relates to cell proliferation closely. So we selected 14 gene sets related to the G2/M checkpoint from all 15142 gene sets in GSEA to confirm the enrichment of G2/M checkpoint or related process (Supplementary Table 2). We set the number of permutations was 1000, the permutation type was gene-set, the max and min size of gene sets selected was 500 and 10 respectively with the other parameters were default. As a result, 13/14 genes sets were up-regulated in HCC with 7 gene sets were significantly enriched at FDR<25% and one gene set was enriched at nominal p-value<0.05. 1/14 gene sets was upregulated in the control group (BHATI_G2M_ARREST_ BY_2METHOXYESTRADIOL_DN). The enrichment plot of 8 up-regulated gene sets are shown in Figure 5.

Leading edge analysis
In order to determine which genes appeared frequently in 8 genes sets associated with G2/M checkpoint and explore the genes that have the highest impact on G2/M checkpoint, 8 gene sets were dedicated to perform the leading edge analysis as shown in Figure 6. Three terms from GO overlapped mostly. CCNA2 appeared in 7 gene sets, CDC25B appeared in 6 gene sets, and NEK2,NBN,CCNB1,CDC7,ATM,XPO1, MRE11A,CENPF,TAOK3 appeared in 3 gene sets.

PPI network construction and analysis from all DEGs
From the 797 DEGs, a network with 721 nodes and 30900 edges was constructed using GeneMANIA plugin. And eleven scoring methods including the newly developed algorithms MCC were performed by use of cytoHubba plugin. At last, 15 genes were screened out according to local-based method MCC and global-based method bottleneck and stress. The co-expression network from the 15 top-ranked genes was constructed as shown in Figure  7. 14 out 15 genes were up regulated and only C8A down regulated. 6 out of 20 top related genes were DEGs and were all up regulated. Most of these genes related to cell cycle.

Kaplan-Meier survival analysis
In order to find the relationship between the key genes and survival of the HCC patients, we performed the Kaplan-Meier Survival analysis. The data showed

DISCUSSION
In order to explore the most significantly dysregulated pathways and key genes which play roles at the early stage of HCC and gain an insight into the onset of HCC which  ADAR,APOBEC3B,CLIC4,HLA-F,IFI27,IFI44,IFI6,IFIT2,ISG15  ,OASL,PARP12,PLAC8,PNP,STAT1,STAT2,TAP1,TDRD7,TLR4   could be applied to the early diagnosis and therapy, a series of bioinformatics methods were performed. According to our studies, the Cell Cycle: G2/M checkpoint regulation was the most dysregulated pathways with 11 DEGs are all up regulated. As the second checkpoint within the cell cycle, G2/M checkpoint prevents cells with damaged DNA from entering the M phase so that these DNAs could be repaired. This kind of regulation is critical to prevent cells from going through malignant transformation. The deficiency of p53 in most human cancers make G1 checkpoint defective. The S-phase checkpoint slows rather than arrest of the cell cycle. So the cancer cell with damaged DNA could accelerate through the cell cycle and arrest at the G2 checkpoint. All the above makes the G2 checkpoint an attractive therapeutic target for anticancer therapy [10]. It has been demonstrated that Polo-like kinase (PLK) may be an early diagnostic marker for the development of HCC by regulating the G2/M checkpoint [11]. lncRNA16 is a promising biomarker for early diagnosis of lung cancer by promoting the G2/M transition by regulating the transcription of cyclin B2. As a promising antitumor agent, Isocorydine(ICD) could induce G2/M cycle arrest of HCC through activation of GADD45A-p21 pathway [12]. So, we inferred the dysregulation of this pathway is very important to the onset of HCC.
In order to confirm the result, GSEA was performed from the gene set level. GSEA is a computational method which determines whether an a priori defined set of genes shows statically significant between two biological states at the level of gene sets instead of an individual gene. GSEA can make up for the deficiency of traditional strategies which focused on the DEGs. The result of 13 gene sets associated with G2/M checkpoint upregulated in HCC and 1 upregulated in control confirmed that the G2/M checkpoint changed significantly. In order to determine the genes which contributed most to the enrichment result of 13 gene sets, the leading edge analysis was performed. 11 genes including CCNA2 (DEG), CDC25B, NEK2 (DEG), NBN, CCNB1(DEG), CDC7, ATM, XPO1(DEG), MRE11A, CENPF(DEG), TAOK3 appeared most often in several gene sets. The transcriptional factor FOXO1 was predicted active and the target genes regulated by it were associated with cell cycle most. The crosstalk of genes participated in the G2/M checkpoint is shown in Figure 9. from the molecular activity predictor(MAP).
As a plugin in cytoscape, cytoHubba provides an effective method to identify important nodes in biological networks. It could accomplish the computation of eleven methods in one stop shopping way including four local-    except C8A are upregulated and most of them participate in the cell cycle. From the analysis of co-expression network, we can find that the co-expressed genes which belong to DEGs upreguated too. Notably, XPO1 and CENPF is also screened out in the leading edge analysis. SR (serine/arginine-rich domain) proteins play a critical role in many process including nuclear export of mature mRNA, polymerase II transcription and nonsensemediated mRNA decay. SRPK1 (Serine/threonine-protein kinase) can phosphorylate SR proteins through its PKc superfamily kinase domain. But, SRPK1 displays pleiotropic effects in various cancers and regulates different cellular properties which might be related to preferential activation of different downstream signaling pathways [13][14][15][16][17][18][19][20]. Expression of SRPK1 was significantly upregulated frequently in HCC cell lines and HCC samples compared with the normal tissue sample both at the mRNA and protein level [21]. In the study of HCC, SRPK1 may be located downstream of AKT and activated AKT may induce the autophosphorylation of SRPK1 which lead to the phosphorylation of downstream splicing factors [20]. SRPK1 may be associated with FAK signaling, MAPK signaling, Wnt/β-catenin signaling and angiogenesis [13,22]. More research implied that SRPK1 may be a novel target for cancer diagnosis and therapy. But the detailed roles and mechanisms of SRPK1 in cancer especially in HCC are not clear.
KPNA2 (karyopherin alpha 2) may participate in carcinogenesis by regulating the translocation of some cargo proteins which are involved in cancer. It was demonstrated that KPNA2 promotes cell proliferation and tumorigenicity in epithelial ovarian carcinoma by regulating c-Myc and FOXO3a [23]. The knockdown of KPNA2 could inhibit proliferation of several cancer cells including liver and lung and KPNA2 may be a useful prognostic biomarker to monitor cancer prognosis [24,25].
To date, over 230 kinds of proteins were verified as the cargo of XPO1(Chromosome region maintenance 1,CRM1) including p53, p21, IkB, ribosomal subunits and so on [26]. XPO1 plays a vital role in nucleo-cytoplasmic transport through RanGTP dependent mechanism. For most typical tumor suppressive proteins such as p21, they play different functions according to their subcellular localization which elucidate that XPO1 plays an vital role in the process of cancer and may lead to a new method for cancer therapy which associated with cell cycle arrest and induction of apoptosis [27][28][29].
As glutamine amidotransferases involved in de novo purine biosynthesis, GMPS (GMP synthetase) was shown to have a striking role in cell proliferation. Under genomic stress, GMPS plays a vital role in the relay of p53 stabilization by TRIM21-GMPS-USP7 molecular cascade. The guanine nucleotides is essential for nucleotide formation, energy storage and nuclear transport which could be provided by guanine biosynthesis pathway in cancer cells [30,31]. Repression of GMPS by p53 through p21 is a functionally relevant part of the p53mediated process in inhibiting tumor cell growth in liver cancer [32].
It is supposed that MELK (maternal embryonic leucine zipper kinase) plays critical roles in many aspects including cell cycle, cell proliferation, embryogenesis and oncogenesis due to its overexpression in many kinds of cancers. MELK is associated with early HCC recurrence and poor patients' survival but the mechanism has not been elucidated [33]. MELK knockdown or deletion in GC (gastric cancer) and ovarian cancer cells activates G2/M arrest and enhances apoptosis [34,35].
Previous studies have revealed that amplified CENPF (centromere protein F) may play a role as common cancerdriver genes in human cancers. CENPF contains many leucine zipper motifs and is regulated in a cell cycledependent manner [36]. It amplified and overexpressed not only in HCC but also in many other types of human cancer including breast cancer, colorectal cancer, prostate cancer [37,38]. Silence of CENPF arrests HCC cells at the G2/M transition with the accumulation of MPF (mature promoting factor) and CCNB1/CDC2 complex [39]. In consistence with these studies, the present study found that CENPF is identified as a key gene not only in DEGs but also in leading edge analysis which based on gene set level.
As the single down regulated one in all the key genes, C8A (complement component alpha) involves in the complement system and participates in the formation of MAC (membrane attack complex) combined with other complement proteins such as C5b, C6, C7, C8 and C9. In addition, the expression of several other complement components or subunits are all down regulated ,including C1S, C2, C5, C6, C7, C8B, C8G, C9. Obviously, it is not coincidence. That is to say, the activity of complement system down regulated at the early stage of HCC. The transcription of C8A and C5 is regulated by HNF1α (hepatocyte nuclear factor 1 alpha) both are essential components of MAC [40]. The relationship between C8A and cancer is not clear yet.
G2/M checkpoint provides an opportunity for DNA repair by increasing the time for repair and by transcriptionally inducing gene expression and stopping the proliferation of damaged cells [41]. To the best of our knowledge, G2/M checkpoint transition activated at the early stage of HCC was provided for the first time through the microarray analysis. The negligent G2/M checkpoint enhanced the possibility for DSB (double strand breakage). And the unrepaired DSBs before mitosis will pose a higher risk for genomic instability and tumor cell development [42]. So, the defect of G2/M checkpoint may play a critical role at the onset of HCC. www.impactjournals.com/oncotarget

Data source
The gene expression dataset GSE6764 were downloaded from the Gene Expression Omnibus (GEO) database by Wurmbach E et.al (https://www.ncbi.nlm. nih.gov/geo/). 75 tissue samples obtained from patients undergoing resection or liver transplantation were divided into 8 groups from pre-neoplastic lesions to HCC and normal liver were used as control (http://www.ncbi.nlm. nih.gov/geo/). We combined very early and early HCC to the case group including 18 tissue samples altogether, and the control group, with 10 normal liver tissue. All tissue samples are hybridized on the human U133 plus 2.0 array (Affymetrix).

Microarray data analysis and identification of differentially expressed genes (DEGs) using SAM
Robust Multi-array Average (RMA) algorithm including background correction, normalization and summarization was performed to convert the .CEL raw file to expression data which based on R language [43]. The simpleaffy package was utilized to perform the quality control. Once the signal value for each probe set calculated in every microarray, the t-test based significance analysis of microarrays (SAM) which make use of permutations to simulate for every gene a situation in which there is no difference between the two groups was utilized to determine the DEGs. SAM method adjusts the p-value to false discovery rate (FDR) to reduce the false-positive through multiple testing. A <0.1% False discovery rate (FDR) cut-off was used for all differential expression calculations [44].

Ingenuity pathway analysis
Ingenuity Pathway Analysis (IPA) is a functional analysis tool (Ingenuity Systems, Mountain view, CA, USA). We use IPA to identify the most significant pathways (including 302 metabolic pathways and 360 signaling pathways) and construct molecular interaction networks from the DEGs. In brief, we uploaded the DEGs list file containing gene symbols, FC, p-values to IPA and performed the core analysis. In general settings, the node types, data sources, confidence, species, tissues & cell lines and mutation were specified.

The IPA upstream transcriptional regulator analysis
In order to explain the biological activities due to the DEGs, we identified the cascade of upstream transcriptional regulators with p-value of overlap <0.05 and the absolute activation z-score>2.

Gene set enrichment analysis (GSEA) and leading edge analysis
GSEA is a kind of gene enrichment method considering the full list of genes different from single gene method [45]. In GSEA, genes are ranked by their correlation with phenotype and every enrichment gene set will get an enrichment score (ES). In this study, 2000 gene permutations were used to generate a null distribution for ES, then each pathway will attain a normalization enrichment score(NES). Gene sets with considered significantly enriched with a relatively relax p-value and FDR<0.25. A leading edge analysis was performed to elucidate key genes associated with the early stage of HCC, especially the G2/M checkpoint regulation [46].

Construction of PPI network from all DEGs and the screening of key genes
In order to comprehend the specific molecular mechanism of early HCC, we constructed the PPI network based on GeneMANIA plugin and calculated the key DEGs using cytoHubba plugin [47,48]. At last, we built the co-expression network of top-ranked genes from all DEGs and performed the visualization and analysis by use of Cytoscape 3.4.0(http://cytoscape.org/).

Kaplan-Meier survival analysis
SurvExpress(http://bioinformatica.mty.itesm. mx:8080/Biomatec/SurvivaX.jsp) was employed to perform the survival analysis in the datasets TCGA-liver cancer containing 422 samples provided by SruvExpress using the key genes as an input. For the duplicated genes, all probe sets/records will be averaged per sample using the original (Quantile-Normalized) data. The maximum risk groups were selected for the cox survival analysis. This method uses an optimization algorithm from the ordered PI to produce risk groups as described in the tutorial provided in SurvExpress website [49].

CONCLUSION
The combinatorial effect of the GSEA, DEGs, and leading edge analysis output shed a light on the elucidating of key pathways and genes which genetically dysregulated at the early stage of HCC. The study unveiled that the G2/M checkpoint plays a vital role at the onset of HCC. And the genes SRPK1, XPO1, GMPS, MELK, DUT, TCERG1, RAD21, CENPF, PTTG1, EZH2, ANLN, KPNA2, RACGAP1, ADAR, C8A could be considered as critical genes for this process. These findings contributed to a better understanding of the onset of HCC. Further studies were required to elucidate the mechanism of the process.