Automatic and Early Detection of Parkinson's Disease by Analyzing Acoustic Signals Using Classification Algorithms Based on Recursive Feature Elimination Method.

Parkinson's disease (PD) is a neurodegenerative condition generated by the dysfunction of brain cells and their 60-80% inability to produce dopamine, an organic chemical responsible for controlling a person's movement. This condition causes PD symptoms to appear. Diagnosis involves many physical and psychological tests and specialist examinations of the patient's nervous system, which causes several issues. The methodology method of early diagnosis of PD is based on analysing voice disorders. This method extracts a set of features from a recording of the person's voice. Then machine-learning (ML) methods are used to analyse and diagnose the recorded voice to distinguish Parkinson's cases from healthy ones. This paper proposes novel techniques to optimize the techniques for early diagnosis of PD by evaluating selected features and hyperparameter tuning of ML algorithms for diagnosing PD based on voice disorders. The dataset was balanced by the synthetic minority oversampling technique (SMOTE) and features were arranged according to their contribution to the target characteristic by the recursive feature elimination (RFE) algorithm. We applied two algorithms, t-distributed stochastic neighbour embedding (t-SNE) and principal component analysis (PCA), to reduce the dimensions of the dataset. Both t-SNE and PCA finally fed the resulting features into the classifiers support-vector machine (SVM), K-nearest neighbours (KNN), decision tree (DT), random forest (RF), and multilayer perception (MLP). Experimental results proved that the proposed techniques were superior to existing studies in which RF with the t-SNE algorithm yielded an accuracy of 97%, precision of 96.50%, recall of 94%, and F1-score of 95%. In addition, MLP with the PCA algorithm yielded an accuracy of 98%, precision of 97.66%, recall of 96%, and F1-score of 96.66%.

Effective Early Detection of Epileptic Seizures through EEG Signals Using Classification Algorithms Based on t-Distributed Stochastic Neighbor Embedding and K-Means.

Epilepsy is a neurological disorder in the activity of brain cells that leads to seizures. An electroencephalogram (EEG) can detect seizures as it contains physiological information of the neural activity of the brain. However, visual examination of EEG by experts is time consuming, and their diagnoses may even contradict each other. Thus, an automated computer-aided diagnosis for EEG diagnostics is necessary. Therefore, this paper proposes an effective approach for the early detection of epilepsy. The proposed approach involves the extraction of important features and classification. First, signal components are decomposed to extract the features via the discrete wavelet transform (DWT) method. Principal component analysis (PCA) and the t-distributed stochastic neighbor embedding (t-SNE) algorithm were applied to reduce the dimensions and focus on the most important features. Subsequently, K-means clustering + PCA and K-means clustering + t-SNE were used to divide the dataset into subgroups to reduce the dimensions and focus on the most important representative features of epilepsy. The features extracted from these steps were fed to extreme gradient boosting, K-nearest neighbors (K-NN), decision tree (DT), random forest (RF) and multilayer perceptron (MLP) classifiers. The experimental results demonstrated that the proposed approach provides superior results to those of existing studies. During the testing phase, the RF classifier with DWT and PCA achieved an accuracy of 97.96%, precision of 99.1%, recall of 94.41% and F1 score of 97.41%. Moreover, the RF classifier with DWT and t-SNE attained an accuracy of 98.09%, precision of 99.1%, recall of 93.9% and F1 score of 96.21%. In comparison, the MLP classifier with PCA + K-means reached an accuracy of 98.98%, precision of 99.16%, recall of 95.69% and F1 score of 97.4%.

Automatic Classification of Colour Fundus Images for Prediction Eye Disease Types Based on Hybrid Features.

Early detection of eye diseases is the only solution to receive timely treatment and prevent blindness. Colour fundus photography (CFP) is an effective fundus examination technique. Because of the similarity in the symptoms of eye diseases in the early stages and the difficulty in distinguishing between the type of disease, there is a need for computer-assisted automated diagnostic techniques. This study focuses on classifying an eye disease dataset using hybrid techniques based on feature extraction with fusion methods. Three strategies were designed to classify CFP images for the diagnosis of eye disease. The first method is to classify an eye disease dataset using an Artificial Neural Network (ANN) with features from the MobileNet and DenseNet121 models separately after reducing the high dimensionality and repetitive features using Principal Component Analysis (PCA). The second method is to classify the eye disease dataset using an ANN on the basis of fused features from the MobileNet and DenseNet121 models before and after reducing features. The third method is to classify the eye disease dataset using ANN based on the fused features from the MobileNet and DenseNet121 models separately with handcrafted features. Based on the fused MobileNet and handcrafted features, the ANN attained an AUC of 99.23%, an accuracy of 98.5%, a precision of 98.45%, a specificity of 99.4%, and a sensitivity of 98.75%.

Hybrid Models for Endoscopy Image Analysis for Early Detection of Gastrointestinal Diseases Based on Fused Features.

The gastrointestinal system contains the upper and lower gastrointestinal tracts. The main tasks of the gastrointestinal system are to break down food and convert it into essential elements that the body can benefit from and expel waste in the form of feces. If any organ is affected, it does not work well, which affects the body. Many gastrointestinal diseases, such as infections, ulcers, and benign and malignant tumors, threaten human life. Endoscopy techniques are the gold standard for detecting infected parts within the organs of the gastrointestinal tract. Endoscopy techniques produce videos that are converted into thousands of frames that show the disease's characteristics in only some frames. Therefore, this represents a challenge for doctors because it is a tedious task that requires time, effort, and experience. Computer-assisted automated diagnostic techniques help achieve effective diagnosis to help doctors identify the disease and give the patient the appropriate treatment. In this study, many efficient methodologies for analyzing endoscopy images for diagnosing gastrointestinal diseases were developed for the Kvasir dataset. The Kvasir dataset was classified by three pre-trained models: GoogLeNet, MobileNet, and DenseNet121. The images were optimized, and the gradient vector flow (GVF) algorithm was applied to segment the regions of interest (ROIs), isolating them from healthy regions and saving the endoscopy images as Kvasir-ROI. The Kvasir-ROI dataset was classified by the three pre-trained GoogLeNet, MobileNet, and DenseNet121 models. Hybrid methodologies (CNN-FFNN and CNN-XGBoost) were developed based on the GVF algorithm and achieved promising results for diagnosing disease based on endoscopy images of gastroenterology. The last methodology is based on fused CNN models and their classification by FFNN and XGBoost networks. The hybrid methodology based on the fused CNN features, called GoogLeNet-MobileNet-DenseNet121-XGBoost, achieved an AUC of 97.54%, accuracy of 97.25%, sensitivity of 96.86%, precision of 97.25%, and specificity of 99.48%.

Hybrid Techniques for the Diagnosis of Acute Lymphoblastic Leukemia Based on Fusion of CNN Features.

Acute lymphoblastic leukemia (ALL) is one of the deadliest forms of leukemia due to the bone marrow producing many white blood cells (WBC). ALL is one of the most common types of cancer in children and adults. Doctors determine the treatment of leukemia according to its stages and its spread in the body. Doctors rely on analyzing blood samples under a microscope. Pathologists face challenges, such as the similarity between infected and normal WBC in the early stages. Manual diagnosis is prone to errors, differences of opinion, and the lack of experienced pathologists compared to the number of patients. Thus, computer-assisted systems play an essential role in assisting pathologists in the early detection of ALL. In this study, systems with high efficiency and high accuracy were developed to analyze the images of C-NMC 2019 and ALL-IDB2 datasets. In all proposed systems, blood micrographs were improved and then fed to the active contour method to extract WBC-only regions for further analysis by three CNN models (DenseNet121, ResNet50, and MobileNet). The first strategy for analyzing ALL images of the two datasets is the hybrid technique of CNN-RF and CNN-XGBoost. DenseNet121, ResNet50, and MobileNet models extract deep feature maps. CNN models produce high features with redundant and non-significant features. So, CNN deep feature maps were fed to the Principal Component Analysis (PCA) method to select highly representative features and sent to RF and XGBoost classifiers for classification due to the high similarity between infected and normal WBC in early stages. Thus, the strategy for analyzing ALL images using serially fused features of CNN models. The deep feature maps of DenseNet121-ResNet50, ResNet50-MobileNet, DenseNet121-MobileNet, and DenseNet121-ResNet50-MobileNet were merged and then classified by RF classifiers and XGBoost. The RF classifier with fused features for DenseNet121-ResNet50-MobileNet reached an AUC of 99.1%, accuracy of 98.8%, sensitivity of 98.45%, precision of 98.7%, and specificity of 98.85% for the C-NMC 2019 dataset. With the ALL-IDB2 dataset, hybrid systems achieved 100% results for AUC, accuracy, sensitivity, precision, and specificity.

Multi-Techniques for Analyzing X-ray Images for Early Detection and Differentiation of Pneumonia and Tuberculosis Based on Hybrid Features.

An infectious disease called tuberculosis (TB) exhibits pneumonia-like symptoms and traits. One of the most important methods for identifying and diagnosing pneumonia and tuberculosis is X-ray imaging. However, early discrimination is difficult for radiologists and doctors because of the similarities between pneumonia and tuberculosis. As a result, patients do not receive the proper care, which in turn does not prevent the disease from spreading. The goal of this study is to extract hybrid features using a variety of techniques in order to achieve promising results in differentiating between pneumonia and tuberculosis. In this study, several approaches for early identification and distinguishing tuberculosis from pneumonia were suggested. The first proposed system for differentiating between pneumonia and tuberculosis uses hybrid techniques, VGG16 + support vector machine (SVM) and ResNet18 + SVM. The second proposed system for distinguishing between pneumonia and tuberculosis uses an artificial neural network (ANN) based on integrating features of VGG16 and ResNet18, before and after reducing the high dimensions using the principal component analysis (PCA) method. The third proposed system for distinguishing between pneumonia and tuberculosis uses ANN based on integrating features of VGG16 and ResNet18 separately with handcrafted features extracted by local binary pattern (LBP), discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM) algorithms. All the proposed systems have achieved superior results in the early differentiation between pneumonia and tuberculosis. An ANN based on the features of VGG16 with LBP, DWT and GLCM (LDG) reached an accuracy of 99.6%, sensitivity of 99.17%, specificity of 99.42%, precision of 99.63%, and an AUC of 99.58%.

An analysis of nursing and medical students' attitudes towards and knowledge of Alzheimer's Disease (AD).

OBJECTIVES: Little is known about how Jordanian undergraduate medical and nursing students perceive Alzheimer's disease (AD) care. This study aimed to investigate nursing and medical students' AD knowledge, attitudes, and associated factors with their knowledge to inform reforms to multidisciplinary AD education undergraduate programs in Jordan. METHODS: Cross-sectional research was carried out using a self-administered questionnaire. Students' knowledge was measured using the Alzheimer's Disease Knowledge Scale (ADKS) and attitudes were measured using the Dementia Care Attitude Scale (DCAS). The survey was completed by 423 nursing and medical students. RESULTS: The overall mean score on the ADKS for students' AD knowledge was 17.50 (SD=3.08) out of 30 and the DCAS for students' attitudes toward AD was 26.76 (SD=6.19) out of 40. CONCLUSIONS: Medical students had a higher level of AD knowledge and a lower level of positive attitude than nursing students (p<0.05).

Complementary and alternative medicine use in coronary heart disease patients: a cross-sectional study from Palestine.

BACKGROUND: There is a lack of data on the use of complementary and alternative medicine (CAM) in patients with coronary heart disease (CHD). This study examined the use of CAM among patients with CHD, the reasons and factors influencing their use, the types of CAM used, and the relationship between patient's demographics and the use of CAM. METHODS: In order to determine the prevalence and usage of CAM among Palestinian patients with CHD, a cross-sectional descriptive study was performed from three different hospitals. Using a convenient sampling method, a questionnaire was completed in a face-to-face interview with the patients. Descriptive statistics were used for socio-demographic, and clinical variables. Siahpush scale was used to examine the attitude of CHD patients toward CAM use. RESULTS: Of the 150 patients that were interviewed, 128 (85.3%) of the patients completed the questionnaire. The majority of CAM users reported CAM use for health problems other than CHD, while a total of 59 (45.9%) patients have used CAM for their heart problems. On the other hand, it was found that the place of residency and pattern of CHD were significantly associated with CAM use (p = 0.039 and 0.044, respectively). In addition, religious practices were found to be the most common form of CAM used by patients, while body and traditional alternative methods were the least being used. A significant association between the attitudes of patients with CHD and their use of CAM was found (patients' attitudes towards alternative medicine and natural remedies were p = 0.011 and 0.044, respectively). CONCLUSIONS: CAM use among our respondents is common. Despite a lack of evidence-based research supporting its potential benefits and side effects. Understanding the factors that affect CAM use by CHD patients offers healthcare workers and policymakers an opportunity to better understand CAM use and ultimately improve patient-physician interactions.

Development of a VLP-Based Vaccine Displaying an xCT Extracellular Domain for the Treatment of Metastatic Breast Cancer.

Metastatic breast cancer (MBC) is the leading cause of cancer death in women due to recurrence and resistance to conventional therapies. Thus, MBC represents an important unmet clinical need for new treatments. In this paper we generated a virus-like particle (VLP)-based vaccine (AX09) to inhibit de novo metastasis formation and ultimately prolong the survival of patients with MBC. To this aim, we engineered the bacteriophage MS2 VLP to display an extracellular loop of xCT, a promising therapeutic target involved in tumor progression and metastasis formation. Elevated levels of this protein are observed in a high percentage of invasive mammary ductal tumors including triple negative breast cancer (TNBC) and correlate with poor overall survival. Moreover, xCT expression is restricted to only a few normal cell types. Here, we tested AX09 in several MBC mouse models and showed that it was well-tolerated and elicited a strong antibody response against xCT. This antibody-based response resulted in the inhibition of xCT's function in vitro and reduced metastasis formation in vivo. Thus, AX09 represents a promising novel approach for MBC, and it is currently advancing to clinical development.

Total and Specific Immunoglobulin E for Detection of Most Prevalent Aeroallergens in a Jordanian Cohort.

INTRODUCTION: Allergies are defined as an immune response to non-microbial environmental antigens (allergens) that involve TH2 cells, mast cells, eosinophils and immunoglobulin E (IgE). Atopic disorders such as urticaria, asthma, hay fever, and eczema exhibit a strong familial predisposition and specific IgE-mediated reaction after exposure to the allergens. Aeroallergens involved in the hypersensitivity reactions include pollens, animal dander, fungal spores and house dust mite. Frequency and type of aeroallergens vary in different countries based on climate, vegetation and geographic areas. AIM: Due to increased prevalence of allergic diseases, in vitro diagnostic tests are commonly utilized in our area. The aim of our study is to evaluate the association between total and specific IgE and to study frequency of different aeroallergens in the population. METHODS: The study was conducted in a time period between 1/12/2017 and 15/11/2018 at King Hussein Medical Center, Amman, Jordan. A total of 80 patients with symptoms of allergic disorders were included, ages of individual's ranged between 1 year and 77 years, 58.8 % (n=47) of which male and 41.2 % (n=33) female. Blood samples from all patients were collected into a 10 ml gel separator (with clot activator) tubes and tested for total IgE and specific IgE. RESULTS: A total of 80 patients aged 1-77 years were divided into 4 groups depending on the normal value of total IgE as follow: 1-5 years, 6-9 years, 10-15 years, and adult. A total of 43(53.75%) patients exhibited elevated total IgE level, and 37(46.25%) had normal level. 41(51.2%) patients had elevated total IgE and positive specific IgE. The sensitivity and specificity of total IgE when using specific IgE as standard test was 77.4% and 92.5% respectively. The accuracy rate of the total IgE test was 82.5%. The most common aeroallergens were dermatophagoides pteronyssinus (13.6%), followed by grass mix (12.8). CONCLUSION: Testing of specific IgE is an essential procedure that helps to detect the cause of allergy. Although negative specific IgE could not exclude allergen sensitization due to limitations of detection method and allergen selection, and positive total and specific IgE indicate probability of sensitization.

Cystic Fibrosis Gene Mutation Frequency Among a Group of Suspected Children in King Hussein Medical Center.

INTRODUCTION: Cystic fibrosis (CF) is a genetic multisystem disorder that affects mostly the lungs, but other organs such as liver, pancreas and intestine also affected. CF is inherited in an autosomal recessive manner and occurs in males and females equally. Cystic fibrosis Transmembrane Conductance Regulator (CFTR) mutations are classified into five classes. Class 1 (non-functional protein), class 2 (near-absence of mature CFTR protein at the apical cell membrane), class 3 (full-length CFTR protein incorporated into the cell membrane), class 4 (reduced conductance CFTR mutation), and class 5 (reduced amount of CFTR protein with normal function). Globally F508 mutation is the most common. AIM: The aim of this study was to determine the frequency of CFTR gene mutation in Jordanian populations attending a major hospital (KHMC). MATERIAL AND METHODS: This is a retrospective study was conducted on 777 sera samples for patients clinically suspected to have cystic fibrosis over a six year period 1/1/2013-1/10/2018. The patient's age range between 1year and 33 years, of which 59.2% (460) were male and 40.8% (317) female. Blood samples were analyzed at Princess Iman Centre for Research and Laboratory Sciences at King Hussein Medical Centre. The samples were tested for 34 mutations of CFTR gene using CF Strip Assay VIENNA LAB Diagnostics GmbH, Austria by polymerase chain reaction (PCR). RESULTS: A total of 777 patients samples were analyzed for cystic gene mutations. Twelve (12) mutations were identified. In 49 patients (6.3%) were heterozygous genotype mutant and 28 (3.6%) were homozygous. The most frequent mutation F508del was found in 32/77 (41.5%). 20 (25.9%) of them were heterozygous genotype mutant and 12 (15.6%) were homozygous genotype mutant. The second frequent mutation was N1303K with frequency rate 15.6% (12/77), 9 (11.7%) of them were heterozygous and 3 (3.9%) were homozygous. Regarding frequency of cystic fibrosis gene mutation depending on sex, 55.8% (43/77) of mutations were found in male, whereas 44.2% (34/77) in female. CONCLUSION: Our findings suggest that cystic fibrosis in Jordan is not a rare disease, and found that the most frequent CFTR gene mutation was F508del, which is in keeping with results from other Mediterranean countries.

HER3 and LINC00052 interplay promotes tumor growth in breast cancer.

Here we report that the lncRNA LINC00052 expression correlates positively with HER3/ErbB3 levels in breast cancer cells. Gene silencing of LINC00052 diminished both LINC00052 and HER3 expression and reduced cancer cell growth in vitro and in vivo. LINC00052 overexpression promoted cancer cell growth in vitro and in vivo and increased HER3-mediated downstream signaling. Importantly, neutralization of HER3 signaling with HER3 targeting monoclonal antibodies blocked LINC00052 mediated cancer cell proliferation in vitro and tumor growth in vivo, suggesting LINC00052 promoting cancer growth through HER3 signaling. Taken together, our results indicate that high LINC00052 levels predict activation of HER3-mediated signaling, and LINC00052 expression level may serve as a potential biomarker for HER3 targeted antibody cancer therapies.

Novel association of DJ-1 with HER3 potentiates HER3 activation and signaling in cancer.

HER3/ErbB3 has emerged as a new therapeutic target for cancer. Currently, more than a dozen anti-HER3 antibodies are in clinical trials for treatment of various cancers. However, limited understanding of the complex HER3 signaling in cancer and lack of established biomarkers have made it challenging to stratify cancer patients who can benefit from HER3 targeted therapies. In this study, we identified DJ-1/PARK7 (Parkinson Protein 7) as a novel interaction partner of HER3 and demonstrated the potential of DJ-1 as a biomarker for anti-HER3 cancer therapy. DJ-1 association with HER3 protects HER3 from ubiquitination and degradation through the proteasomal pathway in breast cancer cells. However, neuregulin 1 (NRG-1) mediated HER3 activation results in a reduced association of DJ-1 with HER3. DJ-1 shRNA knockdown in cancer cells resulted in decreased levels of HER3 and its downstream signaling through the PI3K/AKT and Ras/Raf/ERK pathways. DJ-1 shRNA knockdown cancer cells significantly reduced cell proliferation and migration in vitro and tumor growth in vivo. Conversely, overexpression of DJ-1 increased HER3 levels and promoted cancer cell proliferation in vitro and tumor growth in vivo. Notably, cancer cells with high DJ-1 expression showed more sensitivity than DJ-1 knockdown cells to anti-HER3 antibody inhibition. In addition, there was a significant co-expression of HER3 and DJ-1 in tumor tissues of breast cancer patients. Taken together, these results suggest that high DJ-1 expression in breast cancer cells predicts elevated HER3 signaling and may therefore serve as a biomarker for HER3 targeted antibody cancer therapies.

Prohibitin/annexin 2 interaction regulates fatty acid transport in adipose tissue.

We have previously identified prohibitin (PHB) and annexin A2 (ANX2) as proteins interacting on the surface of vascular endothelial cells in white adipose tissue (WAT) of humans and mice. Here, we demonstrate that ANX2 and PHB also interact in adipocytes. Mice lacking ANX2 have normal WAT vascularization, adipogenesis, and glucose metabolism but display WAT hypotrophy due to reduced fatty acid uptake by WAT endothelium and adipocytes. By using cell culture systems in which ANX2/PHB binding is disrupted either genetically or through treatment with a blocking peptide, we show that fatty acid transport efficiency relies on this protein complex. We also provide evidence that the interaction between ANX2 and PHB mediates fatty acid transport from the endothelium into adipocytes. Moreover, we demonstrate that ANX2 and PHB form a complex with the fatty acid transporter CD36. Finally, we show that the colocalization of PHB and CD36 on adipocyte surface is induced by extracellular fatty acids. Together, our results suggest that an unrecognized biochemical interaction between ANX2 and PHB regulates CD36-mediated fatty acid transport in WAT, thus revealing a new potential pathway for intervention in metabolic diseases.

Vitamin D deficiency: knowledge and practices among the adult population in Sharjah, United Arab Emirates.

UNLABELLED: Research demonstrate that vitamin D deficiency is significantly associated with bone diseases (e.g., osteoporosis), muscle cramps, back pain, heart diseases, diabetes mellitus, etc. The lack of knowledge and poor practice of study subjects regarding vitamin D deficiency became evident in this study. PURPOSE: Vitamin D is vital for the growth and development of the body throughout the life span. The aim of this study is to investigate the knowledge and the practices related to vitamin D deficiency among the adult population in Sharjah, UAE. METHODS: A cross-sectional study was conducted among adults aged 20-40 years in Sharjah, UAE. Participants were selected from public places using convenience sampling method. They were subjected to a self-administered questionnaire. Data was analyzed using SPSS 20 software. All knowledge-related questions were summed up, where a correct answer was given 1 point and incorrect one a 0, yielding a score out of 43. Subjects' scores were then transformed to a percentage. RESULTS: A total of 503 adults were included in the study. They had a mean age of 30 years (±5.47) with a relatively homogenous gender distribution (51 % females). The mean knowledge score on vitamin D deficiency was 16.7 out of 43 (39 %). Less than half of the respondents (43 %) knew that sunlight is the main source of vitamin D. The mean score for participants' practice was 2.34 out of 6 (39 %); 77 % of them reported that they tried to avoid sun exposure, and 97 % had not tested vitamin D levels in their blood before. CONCLUSION: The majority of the adults demonstrated significant lack of knowledge and poor practices towards vitamin D and its deficiency. Therefore, attempts to increase the awareness about this issue are required through establishing educational campaigns targeting the general public in Sharjah, UAE.

Fatal Granulomatous Amoebic Encephalitis Caused by Acanthamoeba in a Patient With Kidney Transplant: A Case Report.

Granulomatous amoebic encephalitis (GAE) due to Acanthamoeba is almost a uniformly fatal infection in immune-compromised hosts despite multidrug combination therapy. We report a case of GAE in a female who received a deceased donor kidney graft. She was treated with a combination of miltefosine, pentamidine, sulfadiazine, fluconazole, flucytosine, and azithromycin.

PRUNE2 is a human prostate cancer suppressor regulated by the intronic long noncoding RNA PCA3.

Prostate cancer antigen 3 (PCA3) is the most specific prostate cancer biomarker but its function remains unknown. Here we identify PRUNE2, a target protein-coding gene variant, which harbors the PCA3 locus, thereby classifying PCA3 as an antisense intronic long noncoding (lnc)RNA. We show that PCA3 controls PRUNE2 levels via a unique regulatory mechanism involving formation of a PRUNE2/PCA3 double-stranded RNA that undergoes adenosine deaminase acting on RNA (ADAR)-dependent adenosine-to-inosine RNA editing. PRUNE2 expression or silencing in prostate cancer cells decreased and increased cell proliferation, respectively. Moreover, PRUNE2 and PCA3 elicited opposite effects on tumor growth in immunodeficient tumor-bearing mice. Coregulation and RNA editing of PRUNE2 and PCA3 were confirmed in human prostate cancer specimens, supporting the medical relevance of our findings. These results establish PCA3 as a dominant-negative oncogene and PRUNE2 as an unrecognized tumor suppressor gene in human prostate cancer, and their regulatory axis represents a unique molecular target for diagnostic and therapeutic intervention.

Reduced adenosine-to-inosine miR-455-5p editing promotes melanoma growth and metastasis.

Although recent studies have shown that adenosine-to-inosine (A-to-I) RNA editing occurs in microRNAs (miRNAs), its effects on tumour growth and metastasis are not well understood. We present evidence of CREB-mediated low expression of ADAR1 in metastatic melanoma cell lines and tumour specimens. Re-expression of ADAR1 resulted in the suppression of melanoma growth and metastasis in vivo. Consequently, we identified three miRNAs undergoing A-to-I editing in the weakly metastatic melanoma but not in strongly metastatic cell lines. One of these miRNAs, miR-455-5p, has two A-to-I RNA-editing sites. The biological function of edited miR-455-5p is different from that of the unedited form, as it recognizes a different set of genes. Indeed, wild-type miR-455-5p promotes melanoma metastasis through inhibition of the tumour suppressor gene CPEB1. Moreover, wild-type miR-455 enhances melanoma growth and metastasis in vivo, whereas the edited form inhibits these features. These results demonstrate a previously unrecognized role for RNA editing in melanoma progression.

Ligand-directed targeting of lymphatic vessels uncovers mechanistic insights in melanoma metastasis.

Metastasis is the most lethal step of cancer progression in patients with invasive melanoma. In most human cancers, including melanoma, tumor dissemination through the lymphatic vasculature provides a major route for tumor metastasis. Unfortunately, molecular mechanisms that facilitate interactions between melanoma cells and lymphatic vessels are unknown. Here, we developed an unbiased approach based on molecular mimicry to identify specific receptors that mediate lymphatic endothelial-melanoma cell interactions and metastasis. By screening combinatorial peptide libraries directly on afferent lymphatic vessels resected from melanoma patients during sentinel lymphatic mapping and lymph node biopsies, we identified a significant cohort of melanoma and lymphatic surface binding peptide sequences. The screening approach was designed so that lymphatic endothelium binding peptides mimic cell surface proteins on tumor cells. Therefore, relevant metastasis and lymphatic markers were biochemically identified, and a comprehensive molecular profile of the lymphatic endothelium during melanoma metastasis was generated. Our results identified expression of the phosphatase 2 regulatory subunit A, α-isoform (PPP2R1A) on the cell surfaces of both melanoma cells and lymphatic endothelial cells. Validation experiments showed that PPP2R1A is expressed on the cell surfaces of both melanoma and lymphatic endothelial cells in vitro as well as independent melanoma patient samples. More importantly, PPP2R1A-PPP2R1A homodimers occur at the cellular level to mediate cell-cell interactions at the lymphatic-tumor interface. Our results revealed that PPP2R1A is a new biomarker for melanoma metastasis and show, for the first time to our knowledge, an active interaction between the lymphatic vasculature and melanoma cells during tumor progression.