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Motivation Accurate identification of N4-methylcytosine (4mC) modifications in a genome wide can provide insights into their biological functions and mechanisms. Machine learning recently have become effective approaches for computational identification of 4mC sites in genome. Unfortunately, existing methods cannot achieve satisfactory performance, owing to the lack of effective DNA feature representations that are capable to capture the characteristics of 4mC modifications. Results In this work, we developed a new predictor named 4mcPred-IFL, aiming to identify 4mC sites. To represent and capture discriminative features, we proposed an iterative feature representation algorithm that enables to learn informative features from several sequential models in a supervised iterative mode. Our analysis results showed that the feature representations learnt by our algorithm can capture the discriminative distribution characteristics between 4mC sites and non-4mC sites, enlarging the decision margin between the positives and negatives in feature space. Additionally, by evaluating and comparing our predictor with the state-of-the-art predictors on benchmark datasets, we demonstrate that our predictor can identify 4mC sites more accurately. Availability and implementation The user-friendly webserver that implements the proposed 4mcPred-IFL is well established, and is freely accessible at http://server.malab.cn/4mcPred-IFL. Supplementary information Supplementary data are available at Bioinformatics online.

a b s t r a c tHepatocellular carcinoma (HCC) is the most common type of liver cancer. HDAC6 is a transcriptional regulator of the histone deacetylase family, subfamily 2. Previous studies have shown that HDAC6 plays critical roles in transcription regulation, cell cycle progression and developmental events. However, its biological roles in the development of HCC remain largely unexplored. In the present study, we found that mRNA and protein levels of HDAC6 were up-regulated in HCC tissues and cell lines. The proinflammatory cytokines, which were up-regulated in the human HCC microenvironment, increased HDAC6 expression through a proximal NF-kappaB binding site on the HDAC6 gene promoter. Furthermore, overexpression of HDAC6 could promote cell proliferation in HCC cell lines. In contrast, HDAC6 knockdown using small interfering RNA inhibited cell proliferation. At the molecular level, we demonstrated that HDAC6 could interact with p53 and attenuate its transcriptional activity through promotion of its degradation. Therefore, our results suggest a previously unknown HDAC6-p53 molecular network controlling HCC development.

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BackgroundConflicting evidence exists regarding the effects of platelet/lymphocyte ratio (PLR) and lymphocyte/monocyte ratio(LMR) on the prognosis of colorectal cancer (CRC) patients. This study aimed to evaluate the roles of the PLR and LMR in predicting the prognosis of CRC patients via meta-analysis.MethodsEligible studies were retrieved from the PubMed, Embase,andChina National Knowledge Infrastructure (CNKI) databases, supplemented by a manual search of references from retrieved articles. Pooled hazard ratios (HR) with 95% confidence intervals (95% CI) were calculated using the generic inverse variance and random-effect model to evaluate the association of PLR and LMR with prognostic variables in CRC, including overall survival (OS), cancer-specific survival (CSS) and disease-free survival (DFS).ResultsThirty-three studies containing 15,404 patients met criteria for inclusion. Pooled analysis suggested that elevated PLR was associated with poorer OS (pooled HR = 1.57, 95% CI: 1.41 – 1.75, p< 0.00001, I2=26%) and DFS (pooled HR = 1.58, 95% CI: 1.31 – 1.92, p< 0.00001, I2=66%). Conversely, high LMR correlated with more favorable OS (pooled HR = 0.59, 95% CI: 0.50 – 0.68, p< 0.00001, I2=44%), CSS (pooled HR = 0.54, 95% CI: 0.40 – 0.72, p< 0.00001, I2=11%) and DFS (pooled HR = 0.82, 95% CI: 0.71– 0.94,p=0.005, I2=29%).ConclusionsElevated PLR was associated with poor prognosis, while high LMR correlated with more favorable outcomes in CRC patients. Pretreatment PLR and LMR could serve as prognostic predictors in CRC patients.

G protein-coupled receptors (GPCRs) are the largest receptor superfamily. In this paper, we try to employ physical-chemical properties, which come from SVM-Prot, to represent GPCR. Random Forest was utilized as classifier for distinguishing them from other protein sequences. MEME suite was used to detect the most significant 10 conserved motifs of human GPCRs. In the testing datasets, the average accuracy was 91.61%, and the average AUC was 0.9282. MEME discovery analysis showed that many motifs aggregated in the seven hydrophobic helices transmembrane regions adapt to the characteristic of GPCRs. All of the above indicate that our machine-learning method can successfully distinguish GPCRs from non-GPCRs.

Persistent activation of Wnt/β-catenin signaling plays crucial roles in the development of human cancers, including hepatocellular carcinoma (HCC). Here, we performed a MicroRNA-based genetic screen, which revealed a novel diversion in β-catenin signaling triggered by MicroRNA-153 (miR-153). Overexpression of miR-153 was able to promote β-catenin transcriptional activity, leading to cell-cycle progression, proliferation and colony formation of HCC cells. Additionally, systemic administration of miR-153 antigomir suppressed hepatocellular carcinogenesis in a murine liver cancer model. At the molecular level, we found that miR-153 inhibited protein level of WWOX, a tumor suppressor and inhibitor of β-catenin signaling, through targeting its 3′-untranslated region. Therefore, our study highlights the importance of MicroRNA-153/WWOX/β-catenin regulatory axis in the HCC tumorigenesis.