Transcription factor and microRNA regulation in androgen-dependent and -independent prostate cancer cells

Enhancer-FRL: Improved and Robust Identification of Enhancers and Their Activities Using Feature Representation Learning

Enhancers are crucial for precise regulation of gene expression, while enhancer identification and strength prediction are challenging because of their free distribution and tremendous number of similar fractions in the genome. Although several bioinformatics tools have been developed, shortfalls in these models remain, and their performances need further improvement. In the present study, a two-layer predictor called Enhancer-FRL was proposed for identifying enhancers (enhancers or nonenhancers) and their activities (strong and weak). More specifically, to build an efficient model, the feature representation learning scheme was applied to generate a 50D probabilistic vector based on 10 feature encodings and five machine learning algorithms. Subsequently, the multiview probabilistic features were integrated to construct the final prediction model. Compared with the single feature-based model, Enhancer-FRL showed significant performance improvement and model robustness. Performance assessment on the independent test dataset indicated that the proposed model outperformed state-of-the-art available toolkits. The webserver Enhancer-FRL is freely accessible at http://lab.malab.cn/∼wangchao/softwares/Enhancer-FRL/, The code and datasets can be downloaded at the webserver page or at the Github https://github.com/wangchao-malab/Enhancer-FRL/.

Androgen-Independent Prostate Cancer Is Sensitive to CDC42-PAK7 Kinase Inhibition

Prostate cancer is a common form of cancer in men, and androgen-deprivation therapy (ADT) is often used as a first-line treatment. However, some patients develop resistance to ADT, and their disease is called castration-resistant prostate cancer (CRPC). Identifying potential therapeutic targets for this aggressive subtype of prostate cancer is crucial. In this study, we show that statins can selectively inhibit the growth of these CRPC tumors that have lost their androgen receptor (AR) and have overexpressed the RNA-binding protein QKI. We found that the repression of microRNA-200 by QKI overexpression promotes the rise of AR-low mesenchymal-like CRPC cells. Using in silico drug/gene perturbation combined screening, we discovered that QKI-overexpressing cancer cells are selectively vulnerable to CDC42-PAK7 inhibition by statins. We also confirmed that PAK7 overexpression is present in prostate cancer that coexists with hyperlipidemia. Our results demonstrate a previously unseen mechanism of action for statins in these QKI-expressing AR-lost CRPCs. This may explain the clinical benefits of the drug and support the development of a biology-driven drug-repurposing clinical trial. This is an important finding that could help improve treatment options for patients with this aggressive form of prostate cancer.

Dysregulation of the miRNome unveils a crosstalk between obesity and prostate cancer: miR-107 asa personalized diagnostic and therapeutic tool

Prostate-specific antigen (PSA) is the gold-standard marker to screen prostate cancer (PCa) nowadays. Unfortunately, its lack of specificity and sensitivity makes the identification of novel tools to diagnose PCa an urgent medical need. In this context, microRNAs (miRNAs) have emerged as potential sources of non-invasive diagnostic biomarkers in several pathologies. Therefore, this study was aimed at assessing for the first time the dysregulation of the whole plasma miRNome in PCa patients and its putative implication in PCa from a personalized perspective (i.e., obesity condition). Plasma miRNome from a discovery cohort (18 controls and 19 PCa patients) was determined using an Affymetrix-miRNA array, showing that the expression of 104 miRNAs was significantly altered, wherein six exhibited a significant receiver operating characteristic (ROC) curve to distinguish between control and PCa patients (area under the curve [AUC] = 1). Then, a systematic validation using an independent cohort (135 controls and 160 PCa patients) demonstrated that miR-107 was the most profoundly altered miRNA in PCa (AUC = 0.75). Moreover, miR-107 levels significantly outperformed the ability of PSA to distinguish between control and PCa patients and correlated with relevant clinical parameters (i.e., PSA). These differences were more pronounced when considering only obese patients (BMI > 30). Interestingly, miR-107 levels were reduced in PCa tissues versus non-tumor tissues (n = 84) and in PCa cell lines versus non-tumor cells. In vitro miR-107 overexpression altered key aggressiveness features in PCa cells (i.e., proliferation, migration, and tumorospheres formation) and modulated the expression of important genes involved in PCa pathophysiology (i.e., lipid metabolism [i.e., FASN] and splicing process). Altogether, miR-107 might represent a novel and useful personalized diagnostic and prognostic biomarker and a potential therapeutic tool in PCa, especially in obese patients.

AOPM: Application of Antioxidant Protein Classification Model in Predicting the Composition of Antioxidant Drugs

Antioxidant proteins can not only balance the oxidative stress in the body, but are also an important component of antioxidant drugs. Accurate identification of antioxidant proteins is essential to help humans fight diseases and develop new drugs. In this paper, we developed a friendly method AOPM to identify antioxidant proteins. 188D and the Composition of k-spaced Amino Acid Pairs were adopted as the feature extraction method. In addition, the Max-Relevance-Max-Distance algorithm (MRMD) and random forest were the feature selection and classifier, respectively. We used 5-folds cross-validation and independent test dataset to evaluate our model. On the test dataset, AOPM presented a higher performance compared with the state-of-the-art methods. The sensitivity, specificity, accuracy, Matthew’s Correlation Coefficient and an Area Under the Curve reached 87.3, 94.2, 92.0%, 0.815 and 0.972, respectively. In addition, AOPM still has excellent performance in predicting the catalytic enzymes of antioxidant drugs. This work proved the feasibility of virtual drug screening based on sequence information and provided new ideas and solutions for drug development.

NmRF: identification of multispecies RNA 2'-O-methylation modification sites from RNA sequences

2'-O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-methyltransferase and involves replacing the H on the 2'-hydroxyl group with a methyl group. The 2'-O-methylation modification site is detected in a variety of RNA types (miRNA, tRNA, mRNA, etc.), plays an important role in biological processes and is associated with different diseases. There are few functional mechanisms developed at present, and traditional high-throughput experiments are time-consuming and expensive to explore functional mechanisms. For a deeper understanding of relevant biological mechanisms, it is necessary to develop efficient and accurate recognition tools based on machine learning. Based on this, we constructed a predictor called NmRF based on optimal mixed features and random forest classifier to identify 2'-O-methylation modification sites. The predictor can identify modification sites of multiple species at the same time. To obtain a better prediction model, a two-step strategy is adopted; that is, the optimal hybrid feature set is obtained by combining the light gradient boosting algorithm and incremental feature selection strategy. In 10-fold cross-validation, the accuracies of Homo sapiens and Saccharomyces cerevisiae were 89.069 and 93.885%, and the AUC were 0.9498 and 0.9832, respectively. The rigorous 10-fold cross-validation and independent tests confirm that the proposed method is significantly better than existing tools. A user-friendly web server is accessible at http://lab.malab.cn/∼acy/NmRF.

The Development of Machine Learning Methods in discriminating Secretory Proteins of Malaria Parasite

Malaria caused by Plasmodium falciparum is one of the major infectious diseases in the world. It is essential to exploit an effective method to predict secretory proteins of malaria parasites to develop effective cures and treatment. Biochemical assays can provide details for accurate identification of the secretory proteins, but these methods are expensive and time-consuming. In this paper, we summarized the machine learning-based identification algorithms and compared the construction strategies between different computational methods. Also, we discussed the use of machine learning to improve the ability of algorithms to identify proteins secreted by malaria parasites.

Machine Learning Methods in Prediction of Protein Palmitoylation Sites: A Brief Review

Protein palmitoylation is a fundamental and reversible post-translational lipid modification that involves a series of biological processes. Although a large number of experimental studies have explored the molecular mechanism behind the palmitoylation process, the computational methods has attracted much attention for its good performance in predicting palmitoylation sites compared with expensive and time-consuming biochemical experiments. The prediction of protein palmitoylation sites is helpful to reveal its biological mechanism. Therefore, the research on the application of machine learning methods to predict palmitoylation sites has become a hot topic in bioinformatics and promoted the development in the related fields. In this review, we briefly introduced the recent development in predicting protein palmitoylation sites by using machine learningbased methods and discussed their benefits and drawbacks. The perspective of machine learning-based methods in predicting palmitoylation sites was also provided. We hope the review could provide a guide in related fields.

ORS-Pred: An optimized reduced scheme-based identifier for antioxidant proteins

Antioxidant proteins can terminate a chain of reactions caused by free radicals and protect cells from damage. To identify antioxidant proteins rapidly, a computational model was proposed based on the optimized recoding scheme, sequence information and machine learning methods. First, over 600 recoding schemes were collected to build a scheme set. Then, the original sequence was recoded as a reduced expression whose g-gap dipeptides (g = 0, 1, 2) were used as the features of proteins. Furthermore, a random forest method was used to evaluate the classification ability of the obtained dipeptide features. After going through all schemes, the best predictive performance scheme was chosen as the optimized reduction scheme. Finally, for the RF method, a grid search strategy was used to select a better parameter combination to identify antioxidant proteins. In the experiment, the present method correctly recognized 90.13-99.87% of the antioxidant samples. Other experimental results also proved that the present method was efficient to identify antioxidant proteins. Finally, we also developed a web server that was freely accessible to researchers.

RFhy-m2G: Identification of RNA N2-methylguanosine modification sites based on random forest and hybrid features

N2-methylguanosine is a post-transcriptional modification of RNA that is found in eukaryotes and archaea. The biological function of m2G modification discovered so far is to control and stabilize the three-dimensional structure of tRNA and the dynamic barrier of reverse transcription. To discover additional biological functions of m2G, it is necessary to develop time-saving and labor-saving calculation tools to identify m2G. In this paper, based on hybrid features and a random forest, a novel predictor, RFhy-m2G, was developed to identify the m2G modification sites for three species. The hybrid feature used by the predictor is used to fuse the three features of ENAC, PseDNC, and NPPS. These three features include primary sequence derivation properties, physicochemical properties, and position-specific properties. Since there are redundant features in hybrid features, MRMD2.0 is used for optimal feature selection. Through feature analysis, it is found that the optimal hybrid features obtained still contain three kinds of properties, and the hybrid features can more accurately identify m2G modification sites and improve prediction performance. Based on five-fold cross-validation and independent testing to evaluate the prediction model, the accuracies obtained were 0.9982 and 0.9417, respectively. The robustness of the predictor is demonstrated by comparisons with other predictors.

MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

Background:

Detecting DNA-binding proteins (DBPs) based on biological and chemical methods is time-consuming and expensive.

Objective:

In recent years, the rise of computational biology methods based on Machine Learning (ML) has greatly improved the detection efficiency of DBPs.

Method:

In this study, the Multiple Kernel-based Fuzzy SVM Model with Support Vector Data Description (MK-FSVM-SVDD) is proposed to predict DBPs. Firstly, sex features are extracted from the protein sequence. Secondly, multiple kernels are constructed via these sequence features. Then, multiple kernels are integrated by Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL). Next, fuzzy membership scores of training samples are calculated with Support Vector Data Description (SVDD). FSVM is trained and employed to detect new DBPs.

Results:

Our model is evaluated on several benchmark datasets. Compared with other methods, MKFSVM- SVDD achieves best Matthew's Correlation Coefficient (MCC) on PDB186 (0.7250) and PDB2272 (0.5476).

Conclusion:

We can conclude that MK-FSVM-SVDD is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

Bioluminescent Proteins Prediction with Voting Strategy

Background:

Bioluminescence is a unique and significant phenomenon in nature. Bioluminescence is important for the lifecycle of some organisms and is valuable in biomedical research, including for gene expression analysis and bioluminescence imaging technology. In recent years, researchers have identified a number of methods for predicting bioluminescent proteins (BLPs), which have increased in accuracy, but could be further improved.

Method:

In this study, a new bioluminescent proteins prediction method, based on a voting algorithm, is proposed. Four methods of feature extraction based on the amino acid sequence were used. 314 dimensional features in total were extracted from amino acid composition, physicochemical properties and k-spacer amino acid pair composition. In order to obtain the highest MCC value to establish the optimal prediction model, a voting algorithm was then used to build the model. To create the best performing model, the selection of base classifiers and vote counting rules are discussed.

Results:

The proposed model achieved 93.4% accuracy, 93.4% sensitivity and 91.7% specificity in the test set, which was better than any other method. A previous prediction of bioluminescent proteins in three lineages was also improved using the model building method, resulting in greatly improved accuracy.

An Efficient Multiple Kernel Support Vector Regression Model for Assessing Dry Weight of Hemodialysis Patients

Background:

Dry Weight (DW) is the lowest weight after dialysis, and patients with lower weight usually have symptoms of hypotension and shock. Several clinical-based approaches have been presented to assess the dry weight of hemodialysis patients. However, these traditional methods all depend on special instruments and professional technicians.

Objective:

In order to avoid this limitation, we need to find a machine-independent way to assess dry weight, therefore we collected some clinical influencing characteristic data and constructed a Machine Learning-based (ML) model to predict the dry weight of hemodialysis patients.

Methods::

In this paper, 476 hemodialysis patients' demographic data, anthropometric measurements, and Bioimpedance spectroscopy (BIS) were collected. Among them, these patients' age, sex, Body Mass Index (BMI), Blood Pressure (BP) and Heart Rate (HR) and Years of Dialysis (YD) were closely related to their dry weight. All these relevant data were used to enter the regression equation. Multiple Kernel Support Vector Regression-based on Maximizes the Average Similarity (MKSVRMAS) model was proposed to predict the dry weight of hemodialysis patients.

Result:

The experimental results show that dry weight is positively correlated with BMI and HR. And age, sex, systolic blood pressure, diastolic blood pressure and hemodialysis time are negatively correlated with dry weight. Moreover, the Root Mean Square Error (RMSE) of our model was 1.3817.

Conclusion:

Our proposed model could serve as a viable alternative for dry weight estimation of hemodialysis patients, thus providing a new way for clinical practice. Our proposed model could serve as a viable alternative of dry weight estimation for hemodialysis patients, thus providing a new way for the clinic.

PMDFI: Predicting miRNA-Disease Associations Based on High-Order Feature Interaction

MicroRNAs (miRNAs) are non-coding RNA molecules that make a significant contribution to diverse biological processes, and their mutations and dysregulations are closely related to the occurrence, development, and treatment of human diseases. Therefore, identification of potential miRNA–disease associations contributes to elucidating the pathogenesis of tumorigenesis and seeking the effective treatment method for diseases. Due to the expensive cost of traditional biological experiments of determining associations between miRNAs and diseases, increasing numbers of effective computational models are being used to compensate for this limitation. In this study, we propose a novel computational method, named PMDFI, which is an ensemble learning method to predict potential miRNA–disease associations based on high-order feature interactions. We initially use a stacked autoencoder to extract meaningful high-order features from the original similarity matrix, and then perform feature interactive learning, and finally utilize an integrated model composed of multiple random forests and logistic regression to make comprehensive predictions. The experimental results illustrate that PMDFI achieves excellent performance in predicting potential miRNA–disease associations, with the average area under the ROC curve scores of 0.9404 and 0.9415 in 5-fold and 10-fold cross-validation, respectively.

A Large-Scale Association Study Detects Novel Rare Variants, Risk Genes, Functional Elements, and Polygenic Architecture of Prostate Cancer Susceptibility

To identify rare variants associated with prostate cancer susceptibility and better characterize the mechanisms and cumulative disease risk associated with common risk variants, we conducted an integrated study of prostate cancer genetic etiology in two cohorts using custom genotyping microarrays, large imputation reference panels, and functional annotation approaches. Specifically, 11,984 men (6,196 prostate cancer cases and 5,788 controls) of European ancestry from Northern California Kaiser Permanente were genotyped and meta-analyzed with 196,269 men of European ancestry (7,917 prostate cancer cases and 188,352 controls) from the UK Biobank. Three novel loci, including two rare variants (European ancestry minor allele frequency < 0.01, at 3p21.31 and 8p12), were significant genome wide in a meta-analysis. Gene-based rare variant tests implicated a known prostate cancer gene (HOXB13), as well as a novel candidate gene (ILDR1), which encodes a receptor highly expressed in prostate tissue and is related to the B7/CD28 family of T-cell immune checkpoint markers. Haplotypic patterns of long-range linkage disequilibrium were observed for rare genetic variants at HOXB13 and other loci, reflecting their evolutionary history. In addition, a polygenic risk score (PRS) of 188 prostate cancer variants was strongly associated with risk (90th vs. 40th-60th percentile OR = 2.62, P = 2.55 × 10-191). Many of the 188 variants exhibited functional signatures of gene expression regulation or transcription factor binding, including a 6-fold difference in log-probability of androgen receptor binding at the variant rs2680708 (17q22). Rare variant and PRS associations, with concomitant functional interpretation of risk mechanisms, can help clarify the full genetic architecture of prostate cancer and other complex traits. SIGNIFICANCE: This study maps the biological relationships between diverse risk factors for prostate cancer, integrating different functional datasets to interpret and model genome-wide data from over 200,000 men with and without prostate cancer.See related commentary by Lachance, p. 1637.

Application of Multilayer Network Models in Bioinformatics

Multilayer networks provide an efficient tool for studying complex systems, and with current, dramatic development of bioinformatics tools and accumulation of data, researchers have applied network concepts to all aspects of research problems in the field of biology. Addressing the combination of multilayer networks and bioinformatics, through summarizing the applications of multilayer network models in bioinformatics, this review classifies applications and presents a summary of the latest results. Among them, we classify the applications of multilayer networks according to the object of study. Furthermore, because of the systemic nature of biology, we classify the subjects into several hierarchical categories, such as cells, tissues, organs, and groups, according to the hierarchical nature of biological composition. On the basis of the complexity of biological systems, we selected brain research for a detailed explanation. We describe the application of multilayer networks and chronological networks in brain research to demonstrate the primary ideas associated with the application of multilayer networks in biological studies. Finally, we mention a quality assessment method focusing on multilayer and single-layer networks as an evaluation method emphasizing network studies.

Identification of long noncoding RNAs with machine learning methods: a review

Long noncoding RNAs (lncRNAs) are noncoding RNAs with a length greater than 200 nucleotides. Studies have shown that they play an important role in many life activities. Dozens of lncRNAs have been characterized to some extent, and they are reported to be related to the development of diseases in a variety of cells. However, the biological functions of most lncRNAs are currently still unclear. Therefore, accurately identifying and predicting lncRNAs would be helpful for research on their biological functions. Due to the disadvantages of high cost and high resource-intensiveness of experimental methods, scientists have developed numerous computational methods to identify and predict lncRNAs in recent years. In this paper, we systematically summarize the machine learning-based lncRNAs prediction tools from several perspectives, and discuss the challenges and prospects for the future work.

Advances in the Identification of Circular RNAs and Research Into circRNAs in Human Diseases

Circular RNAs (circRNAs) are a class of endogenous non-coding RNAs (ncRNAs) with a closed-loop structure that are mainly produced by variable processing of precursor mRNAs (pre-mRNAs). They are widely present in all eukaryotes and are very stable. Currently, circRNA studies have become a hotspot in RNA research. It has been reported that circRNAs constitute a significant proportion of transcript expression, and some are significantly more abundantly expressed than other transcripts. CircRNAs have regulatory roles in gene expression and critical biological functions in the development of organisms, such as acting as microRNA sponges or as endogenous RNAs and biomarkers. As such, they may have useful functions in the diagnosis and treatment of diseases. CircRNAs have been found to play an important role in the development of several diseases, including atherosclerosis, neurological disorders, diabetes, and cancer. In this paper, we review the status of circRNA research, describe circRNA-related databases and the identification of circRNAs, discuss the role of circRNAs in human diseases such as colon cancer, atherosclerosis, and gastric cancer, and identify remaining research questions related to circRNAs.

Signaling Pathways That Control Apoptosis in Prostate Cancer

Prostate cancer is the second most common malignancy and the fifth leading cancer-caused death in men worldwide. Therapies that target the androgen receptor axis induce apoptosis in normal prostates and provide temporary relief for advanced disease, yet prostate cancer that acquired androgen independence (so called castration-resistant prostate cancer, CRPC) invariably progresses to lethal disease. There is accumulating evidence that androgen receptor signaling do not regulate apoptosis and proliferation in prostate epithelial cells in a cell-autonomous fashion. Instead, androgen receptor activation in stroma compartments induces expression of unknown paracrine factors that maintain homeostasis of the prostate epithelium. This paradigm calls for new studies to identify paracrine factors and signaling pathways that control the survival of normal epithelial cells and to determine which apoptosis regulatory molecules are targeted by these pathways. This review summarizes the recent progress in understanding the mechanism of apoptosis induced by androgen ablation in prostate epithelial cells with emphasis on the roles of BCL-2 family proteins and "druggable" signaling pathways that control these proteins. A summary of the clinical trials of inhibitors of anti-apoptotic signaling pathways is also provided. Evidently, better knowledge of the apoptosis regulation in prostate epithelial cells is needed to understand mechanisms of androgen-independence and implement life-extending therapies for CRPC.

Recent Advances in Predicting Protein S-Nitrosylation Sites

Protein S-nitrosylation (SNO) is a process of covalent modification of nitric oxide (NO) and its derivatives and cysteine residues. SNO plays an essential role in reversible posttranslational modifications of proteins. The accurate prediction of SNO sites is crucial in revealing a certain biological mechanism of NO regulation and related drug development. Identification of the sites of SNO in proteins is currently a very hot topic. In this review, we briefly summarize recent advances in computationally identifying SNO sites. The challenges and future perspectives for identifying SNO sites are also discussed. We anticipate that this review will provide insights into research on SNO site prediction.

Identification of Methicillin-Resistant Staphylococcus Aureus From Methicillin-Sensitive Staphylococcus Aureus and Molecular Characterization in Quanzhou, China

To distinguish Methicillin-Resistant Staphylococcus aureus (MRSA) from Methicillin-Sensitive Staphylococcus aureus (MSSA) in the protein sequences level, test the susceptibility to antibiotic of all Staphylococcus aureus isolates from Quanzhou hospitals, define the virulence factor and molecular characteristics of the MRSA isolates. MRSA and MSSA Pfam protein sequences were used to extract feature vectors of 188D, n-gram and 400D. Weka software was applied to classify the two Staphylococcus aureus and performance effect was evaluated. Antibiotic susceptibility testing of the 81 Staphylococcus aureus was performed by the Mérieux Microbial Analysis Instrument. The 65 MRSA isolates were characterized by Panton-Valentine leukocidin (PVL), X polymorphic region of Protein A (spa), multilocus sequence typing test (MLST), staphylococcus chromosomal cassette mec (SCCmec) typing. After comparing the results of Weka six classifiers, the highest correctly classified rates were 91.94, 70.16, and 62.90% from 188D, n-gram and 400D, respectively. Antimicrobial susceptibility test of the 81 Staphylococcus aureus: Penicillin-resistant rate was 100%. No resistance to teicoplanin, linezolid, and vancomycin. The resistance rate of the MRSA isolates to clindamycin, erythromycin and tetracycline was higher than that of the MSSAs. Among the 65 MRSA isolates, the positive rate of PVL gene was 47.7% (31/65). Seventeen sequence types (STs) were identified among the 65 isolates, and ST59 was the most prevalent. SCCmec type III and IV were observed at 24.6 and 72.3%, respectively. Two isolates did not be typed. Twenty-one spa types were identified, spa t437 (34/65, 52.3%) was the most predominant type. MRSA major clone type of molecular typing was CC59-ST59-spa t437-IV (28/65, 43.1%). Overall, 188D feature vectors can be applied to successfully distinguish MRSA from MSSA. In Quanzhou, the detection rate of PVL virulence factor was high, suggesting a high pathogenic risk of MRSA infection. The cross-infection of CA-MRSA and HA-MRSA was presented, the molecular characteristics were increasingly blurred, HA-MRSA with typical CA-MRSA molecular characteristics has become an important cause of healthcare-related infections. CC59-ST59-spa t437-IV was the main clone type in Quanzhou, which was rare in other parts of mainland China.

A prognosis-related based method for miRNA selection on liver hepatocellular carcinoma prediction

Hepatocellular carcinoma (HCC) is considered as the sixth most common cancer in the world, and it is also considered as one of the causes of death. Moreover, the poor prognosis of recurrence of HCC after surgery and metastasis is also a big problem for human health. If the disease can be diagnosed earlier, the survival rate of the patients will be improved significantly. In the early stage of hepatocellular carcinoma, the expression of miRNAs is likely to become abnormal. In our work, the expression profile of miRNAs of human HCC in cancer tissue is compared with their adjacent tissue samples collected from tumor cancer genomic Atlas (TCGA) platform, then the genes with significant difference are selected by Limma test. Selected genes are referred to predict miRNAs related to the prognosis of HCC patients. Finally, miRNAs regulated by target genes are selected by our method, and the experimental results demonstrated that our method is more efficient than biology wet experimental method with lower cost.

Screening of Prospective Plant Compounds as H1R and CL1R Inhibitors and Its Antiallergic Efficacy through Molecular Docking Approach

Allergens have the ability to enter the body and cause illness. Leukotriene is the widespread allergen which could stimulate mast cells to discharge histamine which causes allergy symptoms. An effective strategy for treating leukotriene-induced allergy is to find the inhibitors of leukotriene or histamine activity from phytochemicals. For this purpose, a library of 8,500 phytochemicals was generated using MOE software. The structures of histamine-1 receptor and cysteinyl leukotriene receptor-1 were predicted by the homology modeling method through the SWISS model. The phytochemicals were docked with predicted structures of histamine-1 and cysteinyl leukotriene receptor-1 in MOE software to determine the binding affinity of the phytochemicals against the targets. Moreover, chemoinformatics properties and ADMET of phytochemicals were assessed to find the drug likeness behavior of compounds. Compound ID 10054216 has the lowest $S$ -score value for H-1 receptor that is -18.9186 kcal/mol which is lower than the value of standard -15.167 kcal/mol. The other compounds 393471, 71448939, 10722577, and 442614 also showed good $S$ -score values than the standard. Moreover, compound ID 11843082 has the lowest $S$ -score value for CL1R that is -15.481 kcal/mol which is lower than the value of standard -12.453 kcal/mol. The other compounds 72284, 5282102, 66559251, and 102506430 also showed good $S$ -score values than the standard. In this research article, we performed molecular docking to find the best inhibitors against H1R and CL1R and their antiallergic efficacy. This in silico knowledge will be helpful in near future for the design of novel, safe, and less costing H-1 receptor and CL1R inhibitors with the aim to improve human life quality.

iBLP: An XGBoost-Based Predictor for Identifying Bioluminescent Proteins

Bioluminescent proteins (BLPs) are a class of proteins that widely distributed in many living organisms with various mechanisms of light emission including bioluminescence and chemiluminescence from luminous organisms. Bioluminescence has been commonly used in various analytical research methods of cellular processes, such as gene expression analysis, drug discovery, cellular imaging, and toxicity determination. However, the identification of bioluminescent proteins is challenging as they share poor sequence similarities among them. In this paper, we briefly reviewed the development of the computational identification of BLPs and subsequently proposed a novel predicting framework for identifying BLPs based on eXtreme gradient boosting algorithm (XGBoost) and using sequence-derived features. To train the models, we collected BLP data from bacteria, eukaryote, and archaea. Then, for getting more effective prediction models, we examined the performances of different feature extraction methods and their combinations as well as classification algorithms. Finally, based on the optimal model, a novel predictor named iBLP was constructed to identify BLPs. The robustness of iBLP has been proved by experiments on training and independent datasets. Comparison with other published method further demonstrated that the proposed method is powerful and could provide good performance for BLP identification. The webserver and software package for BLP identification are freely available at http://lin-group.cn/server/iBLP.

Genome-Wide Analysis of LysM-Containing Gene Family in Wheat: Structural and Phylogenetic Analysis during Development and Defense

The lysin motif (LysM) family comprise a number of defense proteins that play important roles in plant immunity. The LysM family includes LysM-containing receptor-like proteins (LYP) and LysM-containing receptor-like kinase (LYK). LysM generally recognizes the chitin and peptidoglycan derived from bacteria and fungi. Approximately 4000 proteins with the lysin motif (Pfam PF01476) are found in prokaryotes and eukaryotes. Our study identified 57 LysM genes and 60 LysM proteins in wheat and renamed these genes and proteins based on chromosome distribution. According to the phylogenetic and gene structure of intron-exon distribution analysis, the 60 LysM proteins were classified into seven groups. Gene duplication events had occurred among the LysM family members during the evolution process, resulting in an increase in the LysM gene family. Synteny analysis suggested the characteristics of evolution of the LysM family in wheat and other species. Systematic analysis of these species provided a foundation of LysM genes in crop defense. A comprehensive analysis of the expression and cis-elements of LysM gene family members suggested that they play an essential role in defending against plant pathogens. The present study provides an overview of the LysM family in the wheat genome as well as information on systematic, phylogenetic, gene duplication, and intron-exon distribution analyses that will be helpful for future functional analysis of this important protein family, especially in Gramineae species.

An Information Gain-based Method for Evaluating the Classification Power of Features Towards Identifying Enhancers

Background:

Enhancers are cis-regulatory elements that enhance gene expression on DNA sequences. Since most of enhancers are located far from transcription start sites, it is difficult to identify them. As other regulatory elements, the regions around enhancers contain a variety of features, which can help in enhancer recognition.

Objective:

The classification power of features differs significantly, the performances of existing methods that use one or a few features for identifying enhancer vary greatly. Therefore, evaluating the classification power of each feature can improve the predictive performance of enhancers.

Methods:

We present an evaluation method based on Information Gain (IG) that captures the entropy change of enhancer recognition according to features. To validate the performance of our method, experiments using the Single Feature Prediction Accuracy (SFPA) were conducted on each feature.

Results:

The average IG values of the sequence feature, transcriptional feature and epigenetic feature are 0.068, 0.213, and 0.299, respectively. Through SFPA, the average AUC values of the sequence feature, transcriptional feature and epigenetic feature are 0.534, 0.605, and 0.647, respectively. The verification results are consistent with our evaluation results.

Conclusion:

This IG-based method can effectively evaluate the classification power of features for identifying enhancers. Compared with sequence features, epigenetic features are more effective for recognizing enhancers.

Identifying Antioxidant Proteins by Using Amino Acid Composition and Protein-Protein Interactions

Excessive oxidative stress responses can threaten our health, and thus it is essential to produce antioxidant proteins to regulate the body’s oxidative responses. The low number of antioxidant proteins makes it difficult to extract their representative features. Our experimental method did not use structural information but instead studied antioxidant proteins from a sequenced perspective while focusing on the impact of data imbalance on sensitivity, thus greatly improving the model’s sensitivity for antioxidant protein recognition. We developed a method based on the Composition of k-spaced Amino Acid Pairs (CKSAAP) and the Conjoint Triad (CT) features derived from the amino acid composition and protein-protein interactions. SMOTE and the Max-Relevance-Max-Distance algorithm (MRMD) were utilized to unbalance the training data and select the optimal feature subset, respectively. The test set used 10-fold crossing validation and a random forest algorithm for classification according to the selected feature subset. The sensitivity was 0.792, the specificity was 0.808, and the average accuracy was 0.8.

Discrimination of Thermophilic Proteins and Non-thermophilic Proteins Using Feature Dimension Reduction

Thermophilicity is a very important property of proteins, as it sometimes determines denaturation and cell death. Thus, methods for predicting thermophilic proteins and non-thermophilic proteins are of interest and can contribute to the design and engineering of proteins. In this article, we describe the use of feature dimension reduction technology and LIBSVM to identify thermophilic proteins. The highest accuracy obtained by cross-validation was 96.02% with 119 parameters. When using only 16 features, we obtained an accuracy of 93.33%. We discuss the importance of the different characteristics in identification and report a comparison of the performance of support vector machine to that of other methods.

iGlu_AdaBoost: Identification of Lysine Glutarylation Using the AdaBoost Classifier

Lysine glutarylation is a newly reported post-translational modification (PTM) that plays significant roles in regulating metabolic and mitochondrial processes. Accurate identification of protein glutarylation is the primary task to better investigate molecular functions and various applications. Due to the common disadvantages of the time-consuming and expensive nature of traditional biological sequencing techniques as well as the explosive growth of protein data, building precise computational models to rapidly diagnose glutarylation is a popular and feasible solution. In this work, we proposed a novel AdaBoost-based predictor called iGlu_AdaBoost to distinguish glutarylation and non-glutarylation sequences. Here, the top 37 features were chosen from a total of 1768 combined features using Chi2 following incremental feature selection (IFS) to build the model, including 188D, the composition of k-spaced amino acid pairs (CKSAAP), and enhanced amino acid composition (EAAC). With the help of the hybrid-sampling method SMOTE-Tomek, the AdaBoost algorithm was performed with satisfactory recall, specificity, and AUC values of 87.48%, 72.49%, and 0.89 over 10-fold cross validation as well as 72.73%, 71.92%, and 0.63 over independent test, respectively. Further feature analysis inferred that positively charged amino acids RK play critical roles in glutarylation recognition. Our model presented the well generalization ability and consistency of the prediction results of positive and negative samples, which is comparable to four published tools. The proposed predictor is an efficient tool to find potential glutarylation sites and provides helpful suggestions for further research on glutarylation mechanisms and concerned disease treatments.

A Method for Identifying Vesicle Transport Proteins Based on LibSVM and MRMD

With the development of computer technology, many machine learning algorithms have been applied to the field of biology, forming the discipline of bioinformatics. Protein function prediction is a classic research topic in this subject area. Though many scholars have made achievements in identifying protein by different algorithms, they often extract a large number of feature types and use very complex classification methods to obtain little improvement in the classification effect, and this process is very time-consuming. In this research, we attempt to utilize as few features as possible to classify vesicular transportation proteins and to simultaneously obtain a comparative satisfactory classification result. We adopt CTDC which is a submethod of the method of composition, transition, and distribution (CTD) to extract only 39 features from each sequence, and LibSVM is used as the classification method. We use the SMOTE method to deal with the problem of dataset imbalance. There are 11619 protein sequences in our dataset. We selected 4428 sequences to train our classification model and selected other 1832 sequences from our dataset to test the classification effect and finally achieved an accuracy of 71.77%. After dimension reduction by MRMD, the accuracy is 72.16%.

Identification of Proteins of Tobacco Mosaic Virus by Using a Method of Feature Extraction

Tobacco mosaic virus, TMV for short, is widely distributed in the global tobacco industry and has a significant impact on tobacco production. It can reduce the amount of tobacco grown by 50-70%. In this research of study, we aimed to identify tobacco mosaic virus proteins and healthy tobacco leaf proteins by using machine learning approaches. The experiment's results showed that the support vector machine algorithm achieved high accuracy in different feature extraction methods. And 188-dimensions feature extraction method improved the classification accuracy. In that the support vector machine algorithm and 188-dimensions feature extraction method were finally selected as the final experimental methods. In the 10-fold cross-validation processes, the SVM combined with 188-dimensions achieved 93.5% accuracy on the training set and 92.7% accuracy on the independent validation set. Besides, the evaluation index of the results of experiments indicate that the method developed by us is valid and robust.

Predicting Preference of Transcription Factors for Methylated DNA Using Sequence Information

Transcription factors play key roles in cell-fate decisions by regulating 3D genome conformation and gene expression. The traditional view is that methylation of DNA hinders transcription factors binding to them, but recent research has shown that many transcription factors prefer to bind to methylated DNA. Therefore, identifying such transcription factors and understanding their functions is a stepping-stone for studying methylation-mediated biological processes. In this paper, a two-step discriminated method was proposed to recognize transcription factors and their preference for methylated DNA based only on sequences information. In the first step, the proposed model was used to discriminate transcription factors from non-transcription factors. The areas under the curve (AUCs) are 0.9183 and 0.9116, respectively, for the 5-fold cross-validation test and independent dataset test. Subsequently, for the classification of transcription factors that prefer methylated DNA and transcription factors that prefer non-methylated DNA, our model could produce the AUCs of 0.7744 and 0.7356, respectively, for the 5-fold cross-validation test and independent dataset test. Based on the proposed model, a user-friendly web server called TFPred was built, which can be freely accessed at http://lin-group.cn/server/TFPred/.

Prediction of N7-methylguanosine sites in human RNA based on optimal sequence features

N-7 methylguanosine (m7G) modification is a ubiquitous post-transcriptional RNA modification which is vital for maintaining RNA function and protein translation. Developing computational tools will help us to easily predict the m7G sites in RNA sequence. In this work, we designed a sequence-based method to identify the modification site in human RNA sequences. At first, several kinds of sequence features were extracted to code m7G and non-m7G samples. Subsequently, we used mRMR, F-score, and Relief to obtain the optimal subset of features which could produce the maximum prediction accuracy. In 10-fold cross-validation, results showed that the highest accuracy is 94.67% achieved by support vector machine (SVM) for identifying m7G sites in human genome. In addition, we examined the performances of other algorithms and found that the SVM-based model outperformed others. The results indicated that the predictor could be a useful tool for studying m7G. A prediction model is available at https://github.com/MapFM/m7g_model.git.

Identification of Human Enzymes Using Amino Acid Composition and the Composition of k-Spaced Amino Acid Pairs

Enzymes are proteins that can efficiently catalyze specific biochemical reactions, and they are widely present in the human body. Developing an efficient method to identify human enzymes is vital to select enzymes from the vast number of human proteins and to investigate their functions. Nevertheless, only a limited amount of research has been conducted on the classification of human enzymes and nonenzymes. In this work, we developed a support vector machine- (SVM-) based predictor to classify human enzymes using the amino acid composition (AAC), the composition of k-spaced amino acid pairs (CKSAAP), and selected informative amino acid pairs through the use of a feature selection technique. A training dataset including 1117 human enzymes and 2099 nonenzymes and a test dataset including 684 human enzymes and 1270 nonenzymes were constructed to train and test the proposed model. The results of jackknife cross-validation showed that the overall accuracy was 76.46% for the training set and 76.21% for the test set, which are higher than the 72.6% achieved in previous research. Furthermore, various feature extraction methods and mainstream classifiers were compared in this task, and informative feature parameters of k-spaced amino acid pairs were selected and compared. The results suggest that our classifier can be used in human enzyme identification effectively and efficiently and can help to understand their functions and develop new drugs.

Comprehensive Analysis of Differentially Expressed lncRNAs in Gastric Cancer

Gastric cancer (GC) is the fourth most common malignant tumor. The mechanism underlying GC occurrence and development remains unclear. Previous studies have indicated that long non-coding RNAs (lncRNAs) are significantly associated with gastric cancer, but a systematic understanding of the role of lncRNAs in gastric cancer is lacking. In recent years, with the development of next-generation sequencing technology, tens of thousands of lncRNAs have been discovered. However, a large number of unannotated lncRNAs remain unidentified in different tissues, including potential gastric cancer-related lncRNAs. In this study, RNA sequencing (RNA-seq) data from 16 samples of eight gastric cancer patients were obtained and analyzed. A total of 1,854 previously unannotated lncRNAs were identified by ab initio assembly, and 520 differentially expressed lncRNAs were validated in the TCGA expression dataset. Methylation and copy number variation (CNV) array data from the same sample were integrated in the analysis. Changes in DNA methylation levels and CNVs may be responsible for the differential expression of 91 lncRNAs. Differentially expressed lncRNAs were enriched in coexpressed clusters of genes related to functions such as cell signaling, cell cycle, immune response, metabolic processes, angiogenesis, and regulation of retinoic acid (RA) receptors. Finally, a differentially expressed lncRNA, AC004510.3, was identified as a potential biomarker for the prediction of the overall survival of gastric cancer patients.

PSBP-SVM: A Machine Learning-Based Computational Identifier for Predicting Polystyrene Binding Peptides

Polystyrene binding peptides (PSBPs) play a key role in the immobilization process. The correct identification of PSBPs is the first step of all related works. In this paper, we proposed a novel support vector machine-based bioinformatic identification model. This model contains four machine learning steps, including feature extraction, feature selection, model training and optimization. In a five-fold cross validation test, this model achieves 90.38, 84.62, 87.50, and 0.90% SN, SP, ACC, and AUC, respectively. The performance of this model outperforms the state-of-the-art identifier in terms of the SN and ACC with a smaller feature set. Furthermore, we constructed a web server that includes the proposed model, which is freely accessible at http://server.malab.cn/PSBP-SVM/index.jsp.

Predicting Bacteriophage Enzymes and Hydrolases by Using Combined Features

Bacteriophage is a type of virus that could infect the host bacteria. They have been applied in the treatment of pathogenic bacterial infection. Phage enzymes and hydrolases play the most important role in the destruction of bacterial cells. Correctly identifying the hydrolases coded by phage is not only beneficial to their function study, but also conducive to antibacteria drug discovery. Thus, this work aims to recognize the enzymes and hydrolases in phage. A combination of different features was used to represent samples of phage and hydrolase. A feature selection technique called analysis of variance was developed to optimize features. The classification was performed by using support vector machine (SVM). The prediction process includes two steps. The first step is to identify phage enzymes. The second step is to determine whether a phage enzyme is hydrolase or not. The jackknife cross-validated results showed that our method could produce overall accuracies of 85.1 and 94.3%, respectively, for the two predictions, demonstrating that the proposed method is promising.

Computational Detection of Breast Cancer Invasiveness with DNA Methylation Biomarkers

Breast cancer is the most common female malignancy. It has high mortality, primarily due to metastasis and recurrence. Patients with invasive and noninvasive breast cancer require different treatments, so there is an urgent need for predictive tools to guide clinical decision making and avoid overtreatment of noninvasive breast cancer and undertreatment of invasive cases. Here, we divided the sample set based on the genome-wide methylation distance to make full use of metastatic cancer data. Specifically, we implemented two differential methylation analysis methods to identify specific CpG sites. After effective dimensionality reduction, we constructed a methylation-based classifier using the Random Forest algorithm to categorize the primary breast cancer. We took advantage of breast cancer (BRCA) HM450 DNA methylation data and accompanying clinical data from The Cancer Genome Atlas (TCGA) database to validate the performance of the classifier. Overall, this study demonstrates DNA methylation as a potential biomarker to predict breast tumor invasiveness and as a possible parameter that could be included in the studies aiming to predict breast cancer aggressiveness. However, more comparative studies are needed to assess its usability in the clinic. Towards this, we developed a website based on these algorithms to facilitate its use in studies and predictions of breast cancer invasiveness.

Region-specific microRNA signatures in the human epididymis

The epithelium of the human epididymis maintains an appropriate luminal environment for sperm maturation that is essential for male fertility. Regional expression of small noncoding RNAs such as microRNAs contributes to segment-specific gene expression and differentiated functions. MicroRNA profiles were reported in human epididymal tissues but not specifically in the epithelial cells derived from those regions. Here, we reveal miRNA signatures of primary cultures of caput, corpus, and cauda epididymis epithelial cells and of the tissues from which they were derived. We identify 324 epithelial cell-derived microRNAs and 259 tissue-derived microRNAs in the epididymis, some of which displayed regionalized expression patterns in cells and/or tissues. Caput cell-enriched miRNAs included miR-573 and miR-155. Cauda cell-enriched miRNAs included miR-1204 and miR-770. Next, we determined the gene ontology pathways associated with in silico predicted target genes of the differentially expressed miRNAs. The effect of androgen receptor stimulation on miRNA expression was also investigated. These data show novel epithelial cell-derived miRNAs that may regulate the expression of important gene networks that are responsible for the regionalized gene expression and function of the epididymis.

Bom-miR-2805 upregulates the expression of Bombyx mori fibroin light chain gene in vivo

MicroRNAs (miRs) are inner regulatory RNAs mainly by regulating expression of genes at the posttranscriptional level. To investigate the regulatory function of Bombyx mori (B. mori) fibroin protein genes, the mRNA 3'-untranslated region (3'-UTR) of fibroin light chain gene (BmFib-L) was used as the target and one miRNA, miR-2805 was predicted by using the Software. miR-2805 expression plasmid pcDNA3.0[ie1-egfp-pre-miR-2805-SV40] and BmFib-L 3'-UTR plasmid pGL3.0[A3-luc-Fib-L-3'-UTR-SV40] were constructed, respectively. The mentioned plasmids were cotransfected in BmN cells, and the regulatory function of miR-2805 on BmFib-L was detected by assay of dual luciferase activities, as well as synthesized mimic and inhibitor of miR-2805. The results revealed that miR-2805 significantly downregulated the expression of BmFib-L in BmN cells. To validate the function of miR-2805 in vivo, cultured silk glands or larvae were injected with solution containing pcDNA3.0[ie1-egfp-SV40], pcDNA3.0[ie1-egfp-pre-miR-2805-SV40], mimic, inhibitor respectively. BmFib-L expression was analyzed by quantitative reverse transcription polymerase chain reaction using total RNAs extracted from silk glands. The results showed that miR-2805 significantly upregulated the expression of BmFib-L in both cultured tissues and individuals. To find out how miR-2805 differentially regulates BmFib-L expression in cells and tissues or individuals, we analyzed the expression level of transcription factors (TFs) involved in expression of silk protein genes. The results showed that miR-2805 upregulated the expression of TFs BmAwh and Bmdimm. These results suggest that miR-2805 may up-regulate the expression of BmFib-L interaction with BmAwh and/or Bmdimm in vivo. These findings are beneficial to clarify the molecular mechanism of miRNAs in regulating B. mori silk protein biosynthesis.

The miR-124-p63 feedback loop modulates colorectal cancer growth

Among the diverse co-regulatory relationships between transcription factors (TFs) and microRNAs (miRNAs), feedback loops have received the most extensive research attention. The co-regulation of TFs and miRNAs plays an important role in colorectal cancer (CRC) growth. Here, we show that miR-124 can regulate two isoforms of p63, TAp63 and ΔNp63, via iASPP, while p63 modulates signal transducers and activators of transcription 1 (STAT1) expression by targeting miR-155. Moreover, STAT1 acts as a regulator of CRC growth by targeting miR-124. Taken together, these results reveal a feedback loop between miRNAs and TFs. This feedback loop comprises miR-124, iASPP, STAT1, miR-155, TAp63 and ΔNp63, which are essential for CRC growth. Moreover, this feedback loop is perturbed in human colon carcinomas, which suggests that the manipulation of this microRNA-TF feedback loop has therapeutic potential for CRC.

miR-129-3p controls centrosome number in metastatic prostate cancer cells by repressing CP110

The centrosome plays a key role in cancer invasion and metastasis. However, it is unclear how abnormal centrosome numbers are regulated when prostate cancer (PCa) cells become metastatic. CP110 was previously described for its contribution of centrosome amplification (CA) and early development of aggressive cell behaviour. However its regulation in metastatic cells remains unclear. Here we identified miR-129-3p as a novel metastatic microRNA. CP110 was identified as its target protein. In PCa cells that have metastatic capacity, CP110 expression was repressed by miR-129-3p. High miR-129-3p expression levels increased cell invasion, while increasing CP110 levels decreased cell invasion. Overexpression of CP110 in metastatic PCa cells resulted in a decrease in the number of metastasis. In tissues of PCa patients, low CP110 and high miR-129-3p expression levels correlated with metastasis, but not with the expression of genes related to EMT. Furthermore, overexpression of CP110 in metastatic PCa cells resulted in excessive-CA (E-CA), and a change in F-actin distribution which is in agreement with their reduced metastatic capacity. Our data demonstrate that miR-129-3p functions as a CA gatekeeper in metastatic PCa cells by maintaining pro-metastatic centrosome amplification (CA) and preventing anti-metastatic E-CA.

The TGF-β signalling negative regulator PICK1 represses prostate cancer metastasis to bone

Backgroud:

Constitutive activation of TGF-β signalling is a well-recognised mechanism in bone metastasis of prostate cancer (PCa). Protein Interacting with PRKCA 1 (PICK1) is a critical negative regulator of the TGF-β pathway. However, the clinical significance and biological role of PICK1 in PCa bone metastasis remain obscure.

Methods:

PICK1 expression is evaluated by immunohistochemistry (IHC) in 198 PCa patients. Statistical analysis is performed to explore correlation between PICK1 expression and clinicopathological features in PCa patients. The biological role of PICK1 is examined in PC-3 and C4-2B cells in vitro and a mouse intracardial model in vivo.

Results:

PICK1 expression is decreased in PCa tissues with bone metastasis and bone-derived cells and downregulation of PICK1 positively correlates with serum PSA level, Gleason grade and bone metastasis status in PCa patients. Overexpression of PICK1 suppresses PCa cell invasion and migration in vitro and bone metastasis in vivo. Our results further indicate downregulation of PICK1 is caused by miR-210-3p overexpression in PCa tissues with bone metastasis. Clinical negative correlation of PICK1 with miR-210-3p is confirmed in PCa tissues.

Conclusions:

Our findings uncover a novel functionally and clinically relevant epigenetic regulatory mechanism for constitutive activation of TGF-β signalling in bone metastasis of PCa.

miR-135b Stimulates Osteosarcoma Recurrence and Lung Metastasis via Notch and Wnt/β-Catenin Signaling

Cancer stem cells (CSCs) play an important role in osteosarcoma (OS) metastasis and recurrence, and both Wnt/β-catenin and Notch signaling are essential for the development of the biological traits of CSCs. However, the mechanism that underlies the simultaneous hyperactivation of both Wnt/β-catenin and Notch signaling in OS remains unclear. Here, we report that expression of miR-135b correlates with the overall and recurrence-free survival of OS patients, and that miR-135b has an activating effect on both Wnt/β-catenin and Notch signaling. The overexpression of miR-135b simultaneously targets multiple negative regulators of the Wnt/β-catenin and Notch signaling pathways, including glycogen synthase kinase-3 beta (GSK3β), casein kinase 1a (CK1α), and ten-eleven translocation 3 (TET3). Therefore, upregulated miR-135b promotes CSC traits, lung metastasis, and tumor recurrence in OS. Notably, antagonizing miR-135b potently inhibits OS lung metastasis, cancer cell stemness, CSC-induced tumor formation, and recurrence in xenograft animal models. These findings suggest that miR-135b mediates the constitutive activation of Wnt/β-catenin and Notch signaling, and that the inhibition of miR-135b is a novel strategy to inhibit tumor metastasis and prevent CSC-induced recurrence in OS.

Association of microRNA-21 expression with clinicopathological characteristics and the risk of progression in advanced prostate cancer patients receiving androgen deprivation therapy

BACKGROUND

Despite androgen deprivation therapy (ADT) remains the mainstay therapy for advanced prostate cancer (PCa), the patients have widely variable durations of response to ADT. Unfortunately, there is limited knowledge of pre-treatment prognostic factors for response to ADT. Recently, microRNA-21 (miR-21) has been reported to play an important role in development of castration resistance of CaP. However, little is known about the expression of miR-21 in advanced PCa biopsy tissues, and data on its potential predictive value in advanced PCa are completely lacking.

METHODS

In this study, paraffin-embedded prostate carcinoma tissues obtained by needle biopsy from 85 advanced PCa patients were evaluated for the expression levels of miR-21 by quantitative real-time PCR (qRT-PCR). In situ hybridization (ISH) analysis was performed to further confirm the qRT-PCR results. Kaplan-Meier analysis and Cox proportional hazards regression models were performed to investigate the correlation between miR-21 expression and time to progression of advanced PCa patients.

RESULTS

Compared with adjacent non-cancerous prostate tissues, the expression level of miR-21 was significantly increased in PCa tissues (PCa vs. non-cancerous prostate: 1.3273 ± 0.3207 vs. 0.9970 ± 0.2054, P < 0.001). By and large, in ISH analysis miR-21 was expressed at a higher level in tumor areas than in adjacent non-cancerous areas. Additionally, PCa patients with higher expression of miR-21 were significantly more likely to be of high Gleason score and high clinical stage (P < 0.05). There was no significant association between miR-21 expression and the initial prostate-specific antigen (PSA) level or age at diagnosis. Moreover, Kaplan-Meier survival analysis found that PCa patients with high miR-21 expression have shorter progression-free survival than those with low miR-21 expression. Furthermore, Multivariate Cox analysis revealed both miR-21 expression status (P = 0.040) and clinical stage (P = 0.042) were all independent predictive factor for progression-free survival for advanced PCa.

CONCLUSION

These findings suggest for the first time that the up-regulation of miR-21 may serve as an independent predictor of progress-free survival in patients with advanced PCa. Prostate 76:986-993, 2016. © 2016 Wiley Periodicals, Inc.