Comprehensive analysis of clinical and biological value of ING family genes in liver cancer.

BACKGROUND: Liver cancer (LIHC) is a malignant tumor that occurs in the liver and has a high mortality in cancer. The ING family genes were identified as tumor suppressor genes. Dysregulated expression of these genes can lead to cell cycle arrest, senescence and/or apoptosis. ING family genes are promising targets for anticancer therapy. However, their role in LIHC is still not well understood. AIM: To have a better understanding of the important roles of ING family members in LIHC. METHODS: A series of bioinformatics approaches (including gene expression analysis, genetic alteration analysis, survival analysis, immune infiltration analysis, prediction of upstream microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) of ING1, and ING1-related gene functional enrichment analysis) was applied to study the expression profile, clinical relationship, prognostic significance and immune infiltration of ING in LIHC. The relationship between ING family genes expression and tumor associated immune checkpoints was investigated in LIHC. The molecular mechanism of ING1 mediated hepatocarcinogenesis was preliminarily discussed. RESULTS: mRNA/protein expression of different ING family genes in LIHC was analyzed in different databases, showing that ING family genes were highly expressed in LIHC. In 47 samples from 366 LIHC patients, the ING family genes were altered at a rate of 13%. By comprehensively analyzing the expression, clinical pathological parameters and prognostic value of ING family genes, ING1/5 was identified. ING1/5 was related to poor prognosis of LIHC, suggesting that they may play key roles in LIHC tumorigenesis and progression. One of the target miRNAs of ING1 was identified as hsa-miR-214-3p. Two upstream lncRNAs of hsa-miR-214-3p, U91328.1, and HCG17, were identified. At the same time, we found that the expression of ING family genes was correlated with immune cell infiltration and immune checkpoint genes. CONCLUSION: This study lays a foundation for further research on the potential mechanism and clinical value of ING family genes in the treatment and prognosis of LIHC.

Circulating tumor DNA in liquid biopsy: Current diagnostic limitation.

With the rapid development of science and technology, cell-free DNA (cfDNA) is rapidly becoming an important biomarker for tumor diagnosis, monitoring and prognosis, and this cfDNA-based liquid biopsy technology has great potential to become an important part of precision medicine. cfDNA is the total amount of free DNA in the systemic circulation, including DNA fragments derived from tumor cells and all other somatic cells. Tumor cells release fragments of DNA into the bloodstream, and this source of cfDNA is called circulating tumor DNA (ctDNA). cfDNA detection has become a major focus in the field of tumor research in recent years, which provides a new opportunity for non-invasive diagnosis and prognosis of cancer. In this paper, we discuss the limitations of the study on the origin and dynamics analysis of ctDNA, and how to solve these problems in the future. Although the future faces major challenges, it also contains great potential.

Preparation of Microporous Molding Activated Carbon Derived from Bamboo Pyrolysis Gasification Byproducts for Toluene Gas Adsorption.

The effective utilization of charcoal and tar byproducts is a challenge for pyrolysis gasification of bamboo. Herein, the bamboo tar was modified via polymerization and acted as a new adhesive for the preparation of excellent bamboo-charcoal-derived molding activated carbon (MBAC). As compared with pristine tar and other adhesives, the aromatization of tar with phenol increased its molecular weight, oxygenic functional groups, and thermal stability, leading to the decreased blocking impact of charcoal pore and improved bonding and pyrolytic crosslinking effect between charcoal particles. These further contribute to the high mechanical strength, specific surface area, pore volume, and amount of oxygenic functional groups for fabricated MBAC. Owing to the high microporous volume of MBAC, it exhibited 385 mg·g-1 toluene and 75.2% tetrachloride gas adsorption performances. Moreover, the pseudo-first-order, pseudo-second-order, and Bangham models were used to evaluate the kinetic data. The toluene adsorption process conforms to the Bangham kinetic model, suggesting that the diffusion mechanism of toluene adsorption mainly followed intraparticle diffusion.

Integrated Navigation Algorithm Based on Multiple Fading Factors Kalman Filter.

An integrated navigation algorithm based on a multiple fading factors Kalman filter (MFKF) is proposed to solve the problems that the Kalman filtering (KF) algorithm easily brings about diffusion when the model becomes a mismatched or noisy, and the MFKF accuracy is reduced when the fading factor is overused. Based on the innovation covariance theory, the algorithm designs an improved basis for judging filtering anomalies and makes the timing of the introduction of the fading factor more reasonable by switching the filtering state. Different from the traditional basis of filter abnormality judgment, the improved judgment basis adopts a recursive way to continuously update the estimated value of the innovation covariance to improve the estimation accuracy of the innovation covariance, and an empirical reserve factor for the judgment basis is introduced to adapt to practical engineering applications. By establishing an inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation model, the results show that the average positioning accuracy of the proposed algorithm is improved by 26.52% and 7.48%, respectively, compared with the KF and MFKF, and shows better robustness and self-adaptability.

Current and Future Perspectives of Cell-Free DNA in Liquid Biopsy.

A liquid biopsy is a minimally invasive or non-invasive method to analyze a range of tumor material in blood or other body fluids, including circulating tumor cells (CTCs), cell-free DNA (cfDNA), messenger RNA (mRNA), microRNA (miRNA), and exosomes, which is a very promising technology. Among these cancer biomarkers, plasma cfDNA is the most widely used in clinical practice. Compared with a tissue biopsy of traditional cancer diagnosis, in assessing tumor heterogeneity, a liquid biopsy is more reliable because all tumor sites release cfDNA into the blood. Therefore, a cfDNA liquid biopsy is less invasive and comprehensive. Moreover, the development of next-generation sequencing technology makes cfDNA sequencing more sensitive than a tissue biopsy, with higher clinical applicability and wider application. In this publication, we aim to review the latest perspectives of cfDNA liquid biopsy clinical significance and application in cancer diagnosis, treatment, and prognosis. We introduce the sequencing techniques and challenges of cfDNA detection, analysis, and clinical applications, and discuss future research directions.

Finding new cancer epigenetic and genetic biomarkers from cell-free DNA by combining SALP-seq and machine learning.

The effective non-invasive diagnosis and prognosis are critical for cancer treatment. The plasma cell-free DNA (cfDNA) provides a good material for cancer liquid biopsy and its worth in this field is increasingly explored. Here we describe a new pipeline for effectively finding new cfDNA-based biomarkers for cancers by combining SALP-seq and machine learning. Using the pipeline, 30 cfDNA samples from 26 esophageal cancer (ESCA) patients and 4 healthy people were analyzed as an example. As a result, 103 epigenetic markers (including 54 genome-wide and 49 promoter markers) and 37 genetic markers were identified for this cancer. These markers provide new biomarkers for ESCA diagnosis, prognosis and therapy. Importantly, these markers, especially epigenetic markers, not only shed important new insights on the regulatory mechanisms of this cancer, but also could be used to classify the cfDNA samples. We therefore developed a new pipeline for effectively finding new cfDNA-based biomarkers for cancers by combining SALP-seq and machine learning. In this study, we also discovered new clinical worth of cfDNA distinct from other reported characters.

DRAMP 2.0, an updated data repository of antimicrobial peptides.

Data Repository of Antimicrobial Peptides (DRAMP, http://dramp.cpu-bioinfor.org/ ) is an open-access comprehensive database containing general, patent and clinical antimicrobial peptides (AMPs). Currently DRAMP has been updated to version 2.0, it contains a total of 19,899 entries (newly added 2,550 entries), including 5,084 general entries, 14,739 patent entries, and 76 clinical entries. The update covers new entries, structures, annotations, classifications and downloads. Compared with APD and CAMP, DRAMP contains 14,040 (70.56% in DRAMP) non-overlapping sequences. In order to facilitate users to trace original references, PubMed_ID of references have been contained in activity information. The data of DRAMP can be downloaded by dataset and activity, and the website source code is also available on dedicatedly designed download webpage. Although thousands of AMPs have been reported, only a few parts have entered clinical stage. In the paper, we described several AMPs in clinical trials, including their properties, indications and clinicaltrials.gov identifiers. Finally, we provide the applications of DRAMP in the development of AMPs.

Novel 3D Structure Based Model for Activity Prediction and Design of Antimicrobial Peptides.

The emergence and worldwide spread of multi-drug resistant bacteria makes an urgent challenge for the development of novel antibacterial agents. A perspective weapon to fight against severe infections caused by drug-resistant microorganisms is antimicrobial peptides (AMPs). AMPs are a diverse class of naturally occurring molecules that are produced as a first line of defense by all multi-cellular organisms. Limited by the number of experimental determinate 3D structure, most of the prediction or classification methods of AMPs were based on 2D descriptors, including sequence, amino acid composition, peptide net charge, hydrophobicity, amphiphilic, etc. Due to the rapid development of structural simulation methods, predicted models of proteins (or peptides) have been successfully applied in structure based drug design, for example as targets of virtual ligand screening. Here, we establish the activity prediction model based on the predicted 3D structure of AMPs molecule. To our knowledge, it is the first report of prediction method based on 3D descriptors of AMPs. Novel AMPs were designed by using the model, and their antibacterial effect was measured by in vitro experiments.

Computational resources and tools for antimicrobial peptides.

Antimicrobial peptides (AMPs), as evolutionarily conserved components of innate immune system, protect against pathogens including bacteria, fungi, viruses, and parasites. In general, AMPs are relatively small peptides (<10 kDa) with cationic nature and amphipathic structure and have modes of action different from traditional antibiotics. Up to now, there are more than 19 000 AMPs that have been reported, including those isolated from nature sources or by synthesis. They have been considered to be promising substitutes of conventional antibiotics in the quest to address the increasing occurrence of antibiotic resistance. However, most AMPs have modest direct antimicrobial activity, and their mechanisms of action, as well as their structure-activity relationships, are still poorly understood. Computational strategies are invaluable assets to provide insight into the activity of AMPs and thus exploit their potential as a new generation of antimicrobials. This article reviews the advances of AMP databases and computational tools for the prediction and design of new active AMPs. Copyright © 2016 European Peptide Society and John Wiley & Sons, Ltd.

[Relationship between expression of multidrug-resistant genes in nasopharyngeal carcinoma tissue and sensitivity to chemotherapy].

OBJECTIVE: To study the relationship between multidrug-resistant (MDR) expression in nasopharyngeal carcinoma (NPC) and its sensitivity to chemotherapy. METHODS: The specimens of 23 NPC cases were studied by immunohistochemistry with monoclonal antibody of P-glycoprotein (P-gp), multidrug resistance relation protein (MRP), lung-resistance related protein (LRP), topoisomerase II (Topo II), thymidylate synthase (TS), glutathione-S-transferase (GST-pi). Among them, 20 specimens were taken from primary NPC lesion which were treated with two course of cisplatin (DDP) and 5-fluorouracil (5-FU), 3 specimens were taken from cervical lymph-node of recurrent NPC patients who were treated by radical dissection. RESULTS: Various MDR parameters were expressed differently in 22 cases except for 1 clear cell carcinoma case. The difference was statistically significant (P < 0.05). However, there were no significant difference of MDR expression either among various carcinoma pathomorphology cell groups or among different clinical stage groups. Expression of LRP and TS were found in 10 and 14 cases respectively and the chemotherapy responders rates were 20% (2/10) and 28.5% (4/14) respectively. While the chemotherapy responders rates were 70% (7/10) and 5/6 in cases without expression. There was significant difference (P < 0.001, and P < 0.05). CONCLUSION: The NPC patients with LRP and TS expression may be less sensitive to chemotherapy with DDP + 5-FU.