Environmental variables and machine learning models to predict cetacean abundance in the Central-eastern Mediterranean Sea.

Although the Mediterranean Sea is a crucial hotspot in marine biodiversity, it has been threatened by numerous anthropogenic pressures. As flagship species, Cetaceans are exposed to those anthropogenic impacts and global changes. Assessing their conservation status becomes strategic to set effective management plans. The aim of this paper is to understand the habitat requirements of cetaceans, exploiting the advantages of a machine-learning framework. To this end, 28 physical and biogeochemical variables were identified as environmental predictors related to the abundance of three odontocete species in the Northern Ionian Sea (Central-eastern Mediterranean Sea). In fact, habitat models were built using sighting data collected for striped dolphins Stenella coeruleoalba, common bottlenose dolphins Tursiops truncatus, and Risso's dolphins Grampus griseus between July 2009 and October 2021. Random Forest was a suitable machine learning algorithm for the cetacean abundance estimation. Nitrate, phytoplankton carbon biomass, temperature, and salinity were the most common influential predictors, followed by latitude, 3D-chlorophyll and density. The habitat models proposed here were validated using sighting data acquired during 2022 in the study area, confirming the good performance of the strategy. This study provides valuable information to support management decisions and conservation measures in the EU marine spatial planning context.

An intelligent non-invasive system for automated diagnosis of anemia exploiting a novel dataset.

Anemia is a condition in which the oxygen-carrying capacity of red blood cells is insufficient to meet the body's physiological needs. It affects billions of people worldwide. An early diagnosis of this disease could prevent the advancement of other disorders. Traditional methods used to detect anemia consist of venipuncture, which requires a patient to frequently undergo laboratory tests. Therefore, anemia diagnosis using noninvasive and cost-effective methods is an open challenge. The pallor of the fingertips, palms, nail beds, and eye conjunctiva can be observed to establish whether a patient suffers from anemia. This article addresses the above challenges by presenting a novel intelligent system, based on machine learning, that supports the automated diagnosis of anemia. This system is innovative from different points of view. Specifically, it has been trained on a dataset that contains eye conjunctiva photos of Indian and Italian patients. This dataset, which was created using a very strict experimental set, is now made available to the Scientific Community. Moreover, compared to previous systems in the literature, the proposed system uses a low-cost device, which makes it suitable for widespread use. The performance of the learning algorithms utilizing two different areas of the mucous membrane of the eye is discussed. In particular, the RUSBoost algorithm, when appropriately trained on palpebral conjunctiva images, shows good performance in classifying anemic and nonanemic patients. The results are very robust, even when considering different ethnicities.

Detection of iron deficiency anemia by medical images: a comparative study of machine learning algorithms.

BACKGROUND: Anemia is one of the global public health problems that affect children and pregnant women. Anemia occurs when the level of red blood cells within the body decreases or when the structure of the red blood cells is destroyed or when the Hb level in the red blood cell is below the normal threshold, which results from one or more increased red cell destructions, blood loss, defective cell production or a depleted sum of Red Blood Cells. METHODS: The method used in this study is divided into three phases: the datasets were gathered, which is the palm, pre-processed the image, which comprised; Extracted images, and augmented images, segmented the Region of Interest of the images and acquired their various components of the CIE L*a*b* colour space (also referred to as the CIELAB), and finally developed the proposed models for the detection of anemia using the various algorithms, which include CNN, k-NN, Nave Bayes, SVM, and Decision Tree. The experiment utilized 527 initial datasets, rotation, flipping and translation were utilized and augmented the dataset to 2635. We randomly divided the augmented dataset into 70%, 10%, and 20% and trained, validated and tested the models respectively. RESULTS: The results of the study justify that the models performed appropriately when the palm is used to detect anemia, with the Naïve Bayes achieving a 99.96% accuracy while the SVM achieved the lowest accuracy of 96.34%, as the CNN also performed better with an accuracy of 99.92% in detecting anemia. CONCLUSIONS: The invasive method of detecting anemia is expensive and time-consuming; however, anemia can be detected through the use of non-invasive methods such as machine learning algorithms which is efficient, cost-effective and takes less time. In this work, we compared machine learning models such as CNN, k-NN, Decision Tree, Naïve Bayes, and SVM to detect anemia using images of the palm. Finally, the study supports other similar studies on the potency of the Machine Learning Algorithm as a non-invasive method in detecting iron deficiency anemia.

A systematic mapping study on machine learning techniques for the prediction of CRISPR/Cas9 sgRNA target cleavage.

CRISPR/Cas9 technology has greatly accelerated genome engineering research. The CRISPR/Cas9 complex, a bacterial immune response system, is widely adopted for RNA-driven targeted genome editing. The systematic mapping study presented in this paper examines the literature on machine learning (ML) techniques employed in the prediction of CRISPR/Cas9 sgRNA on/off-target cleavage, focusing on improving support in sgRNA design activities and identifying areas currently being researched. This area of research has greatly expanded recently, and we found it appropriate to work on a Systematic Mapping Study (SMS), an investigation that has proven to be an effective secondary study method. Unlike a classic review, in an SMS, no comparison of methods or results is made, while this task can instead be the subject of a systematic literature review that chooses one theme among those highlighted in this SMS. The study is illustrated in this paper. To the best of the authors' knowledge, no other SMS studies have been published on this topic. Fifty-seven papers published in the period 2017-2022 (April, 30) were analyzed. This study reveals that the most widely used ML model is the convolutional neural network (CNN), followed by the feedforward neural network (FNN), while the use of other models is marginal. Other interesting information has emerged, such as the wide availability of both open code and platforms dedicated to supporting the activity of researchers or the fact that there is a clear prevalence of public funds that finance research on this topic.

Dolphins and sea turtles may host zoonotic parasites and pathogenic bacteria as indicators of anthropic pressure in the Gulf of Taranto (Northern Ionian Sea, Central-Eastern Mediterranean Sea).

The occurrence of protozoan parasites Giardia duodenalis and Cryptosporidium spp. such as the pathogenic bacteria Salmonella spp. and Escherichia coli was molecularly investigated in the following free ranging species of striped dolphins (Stenella coeruleoalba), Risso's dolphins (Grampus griseus) as well as loggerhead (Caretta caretta) and green (Chelonia mydas) sea turtles living in the Gulf of Taranto (Mediterranean Sea). Out of forty-one investigated individuals belonging to the 4 species, 13 (31.7%) were positive to one or more pathogens and zoonotic G. duodenalis assemblage A, Cryptosporidium parvum and S. enterica were identified in striped dolphins, loggerhead and green sea turtles. In this work, the presence of these opportunistic pathogens has been investigated in fecal samples of free ranging dolphin and sea turtle species for the first time. Moreover, this is the first record of C. parvum in loggerhead sea turtles. These results may provide baseline data for the potential role of cetaceans and sea turtles as potential sentinel species for zoonotic and terrestrial pathogens in the marine environment.

Managing multiple pressures for cetaceans' conservation with an Ecosystem-Based Marine Spatial Planning approach.

Despite the recognized important ecological role that cetaceans play in the marine environment, their protection is still scarcely enforced in the Mediterranean Sea even though this area is strongly threatened by local human pressures and climate change. The piecemeal of knowledge related to cetaceans' ecology and distribution in the basin undermines the capacity of addressing cetaceans' protection and identifying effective conservation strategies. In this study, an Ecosystem-Based Marine Spatial Planning (EB-MSP) approach is applied to assess human pressures on cetaceans and guide the designation of a conservation area in the Gulf of Taranto, Northern Ionian Sea (Central-eastern Mediterranean Sea). The Gulf of Taranto hosts different cetacean species that accomplish important phases of their life in the area. Despite this fact, the gulf does not fall within any area-based management tools (ABMTs) for cetacean conservation. We pin down the Gulf of Taranto being eligible for the designation of diverse ABMTs for conservation, both legally and non-legally binding. Through a risk-based approach, this study explores the cause-effect relationships that link any human activities and pressures exerted in the study area to potential effects on cetaceans, by identifying major drivers of potential impacts. These were found to be underwater noise, marine litter, ship collision, and competition and disturbance on preys. We draw some recommendations based on different sources of available knowledge produced so far in the area (i.e., empirical evidence, scientific and grey literature, and expert judgement) to boost cetaceans' conservation. Finally, we stress the need of sectoral coordination for the management of human activities by applying an EB-MSP approach and valuing the establishment of an ABMT in the Gulf of Taranto.

DolFin: an innovative digital platform for studying Risso's dolphins in the Northern Ionian Sea (North-eastern Central Mediterranean).

The Risso's dolphin is a widely distributed species, found in deep temperate and tropical waters. Estimates of its abundance are available in a few regions, details of its distribution are lacking, and its status in the Mediterranean Sea is ranked as Data Deficient by the IUCN Red List. In this paper, a synergy between bio-ecological analysis and innovative strategies has been applied to construct a digital platform, DolFin. It contains a collection of sighting data and geo-referred photos of Grampus griseus, acquired from 2013 to 2016 in the Gulf of Taranto (Northern Ionian Sea, North-eastern Central Mediterranean Sea), and the first automated tool for Smart Photo Identification of the Risso's dolphin (SPIR). This approach provides the capability to collect and analyse significant amounts of data acquired over wide areas and extended periods of time. This effort establishes the baseline for future large-scale studies, essential to providing further information on the distribution of G. griseus. Our data and analysis results corroborate the hypothesis of a resident Risso's dolphin population in the Gulf of Taranto, showing site fidelity in a relatively restricted area characterized by a steep slope to around 800 m in depth, north of the Taranto Valley canyon system.

The Immune Landscapes of Polypoid and Nonpolypoid Precancerous Colorectal Lesions.

Little is known about the immunoediting process in precancerous lesions. We explored this aspect of benign colorectal adenomas with a descriptive analysis of the immune pathways and immune cells whose regulation is linked to the morphology and size of these lesions. Two series of polypoid and nonpolypoid colorectal adenomas were used in this study: 1) 84 samples (42 lesions, each with matched samples of normal mucosa) whose gene expression data were used to quantify the tumor morphology- and size-related dysregulation of immune pathways collected in the Molecular Signature Database, using Gene Set Enrichment Analysis; 2) 40 other lesions examined with immunohistochemistry to quantify the presence of immune cells in the stromal compartment. In the analysis of transcriptomic data, 429 immune pathways displayed significant differential regulation in neoplasms of different morphology and size. Most pathways were significantly upregulated or downregulated in polypoid lesions versus nonpolypoid lesions (regardless of size). Differential pathway regulation associated with lesion size was observed only in polypoid neoplasms. These findings were mirrored by tissue immunostaining with CD4, CD8, FOXP3, MHC-I, CD68, and CD163 antibodies: stromal immune cell counts (mainly T lymphocytes and macrophages) were significantly higher in polypoid lesions. Certain markers displayed significant size-related differences regardless of lesion morphology. Multivariate analysis of variance showed that the marker panel clearly discriminated between precancerous lesions of different morphologies and sizes. Statistical analysis of immunostained cell counts fully support the results of the transcriptomic data analysis: the density of infiltration of most immune cells in the stroma of polypoid precancerous lesions was significantly higher than that observed in nonpolypoid lesions. Large neoplasms also have more immune cells in their stroma than small lesions. Immunoediting in precancerous colorectal tumors may vary with lesion morphology and stage of development, and this variability could influence a given lesion's trajectory to cancer.

Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm.

The automated identification of brain structure in Magnetic Resonance Imaging is very important both in neuroscience research and as a possible clinical diagnostic tool. In this study, a novel strategy for fully automated hippocampal segmentation in MRI is presented. It is based on a supervised algorithm, called RUSBoost, which combines data random undersampling with a boosting algorithm. RUSBoost is an algorithm specifically designed for imbalanced classification, suitable for large data sets because it uses random undersampling of the majority class. The RUSBoost performances were compared with those of ADABoost, Random Forest and the publicly available brain segmentation package, FreeSurfer. This study was conducted on a data set of 50 T1-weighted structural brain images. The RUSBoost-based segmentation tool achieved the best results with a Dice's index of [Formula: see text] ([Formula: see text]) for the left (right) brain hemisphere. An independent data set of 50 T1-weighted structural brain scans was used for an independent validation of the fully trained strategies. Again the RUSBoost segmentations compared favorably with manual segmentations with the highest performances among the four tools. Moreover, the Pearson correlation coefficient between hippocampal volumes computed by manual and RUSBoost segmentations was 0.83 (0.82) for left (right) side, statistically significant, and higher than those computed by Adaboost, Random Forest and FreeSurfer. The proposed method may be suitable for accurate, robust and statistically significant segmentations of hippocampi.

Systematic analysis of circadian genes using genome-wide cDNA microarrays in the inflammatory bowel disease transcriptome.

Simultaneous analysis of the transcripts of thousands of genes by cDNA microarrays allows the identification of genetic regulatory mechanisms involved in disease pathophysiology. The circadian clock circuitry controls essential cell processes and the functioning of organ systems, which are characterized by rhythmic variations with 24-hour periodicity. The derangement of these processes is involved in the basic mechanisms of inflammatory, metabolic, degenerative and neoplastic diseases. We evaluated by genome-wide cDNA microarray analysis the transcriptome of endoscopic mucosal biopsies of patients with inflammatory bowel diseases (IBD) focusing on the expression of circadian genes in Crohn's disease (CD) and ulcerative colitis (UC). Twenty-nine IBD patients (15 with CD and 14 with UC) were enrolled and mucosal biopsies were sampled at either inflamed or adjacent non-inflamed areas of the colon. A total of 150 circadian genes involved in pathways controlling crucial cell processes and tissue functions were investigated. In CD specimens 50 genes were differentially expressed, and 21 genes resulted up-regulated when compared to healthy colonic mucosa. In UC specimens 50 genes were differentially expressed, and 27 genes resulted up-regulated when compared to healthy colonic mucosa. Among the core clock genes ARNTL2 and RORA were up-regulated, while CSNK2B, NPAS2, PER1 and PER3 were down-regulated in CD specimens. Conversely, ARNTL2, CRY1, CSNK1E, RORA and TIPIN were up-regulated, while NR1D2 and PER3 were down-regulated in UC. In conclusion, in CD and UC patients there are differences in the expression of circadian genes between normal and diseased intestinal mucosa. The deregulated genes evidenced by transcriptome analysis in the major IBDs may play a crucial role in the pathophysiological mechanisms and may suggest novel therapeutic approaches.

Feature Selection Based on Machine Learning in MRIs for Hippocampal Segmentation.

Neurodegenerative diseases are frequently associated with structural changes in the brain. Magnetic resonance imaging (MRI) scans can show these variations and therefore can be used as a supportive feature for a number of neurodegenerative diseases. The hippocampus has been known to be a biomarker for Alzheimer disease and other neurological and psychiatric diseases. However, it requires accurate, robust, and reproducible delineation of hippocampal structures. Fully automatic methods are usually the voxel based approach; for each voxel a number of local features were calculated. In this paper, we compared four different techniques for feature selection from a set of 315 features extracted for each voxel: (i) filter method based on the Kolmogorov-Smirnov test; two wrapper methods, respectively, (ii) sequential forward selection and (iii) sequential backward elimination; and (iv) embedded method based on the Random Forest Classifier on a set of 10 T1-weighted brain MRIs and tested on an independent set of 25 subjects. The resulting segmentations were compared with manual reference labelling. By using only 23 feature for each voxel (sequential backward elimination) we obtained comparable state-of-the-art performances with respect to the standard tool FreeSurfer.

Genome-wide Pathway Analysis Using Gene Expression Data of Colonic Mucosa in Patients with Inflammatory Bowel Disease.

BACKGROUND: Ulcerative colitis (UC) and Crohn's disease (CD) share some pathogenetic features. To provide new steps on the role of altered gene expression, and the involvement of gene networks, in the pathogenesis of these diseases, we performed a genome-wide analysis in 15 patients with CD and 14 patients with UC by comparing the RNA from inflamed and noninflamed colonic mucosa. METHODS: Two hundred ninety-eight differentially expressed genes in CD and 520 genes in UC were identified. By bioinformatic analyses, 34 pathways for CD, 6 of them enriched in noninflamed and 28 in inflamed tissues, and 19 pathways for UC, 17 in noninflamed and 2 in inflamed tissues, were also highlighted. RESULTS: In CD, the pathways included genes associated with cytokines and cytokine receptors connection, response to external stimuli, activation of cell proliferation or differentiation, cell migration, apoptosis, and immune regulation. In UC, the pathways were associated with genes related to metabolic and catabolic processes, biosynthesis and interconversion processes, leukocyte migration, regulation of cell proliferation, and epithelial-to-mesenchymal transition. CONCLUSIONS: In UC, the pattern of inflammation of colonic mucosa is due to a complex interaction network between host, gut microbiome, and diet, suggesting that bacterial products or endogenous synthetic/catabolic molecules contribute to impairment of the immune response, to breakdown of epithelial barrier, and to enhance the inflammatory process. In patients with CD, genes encoding a large variety of proteins, growth factors, cytokines, chemokines, and adhesion molecules may lead to uncontrolled inflammation with ensuing destruction of epithelial cells, inappropriate stimulation of antimicrobial and T cells differentiation, and inflammasome events.

Molecular pathways undergoing dramatic transcriptomic changes during tumor development in the human colon.

BACKGROUND: The malignant transformation of precancerous colorectal lesions involves progressive alterations at both the molecular and morphologic levels, the latter consisting of increases in size and in the degree of cellular atypia. Analyzing preinvasive tumors of different sizes can therefore shed light on the sequence of these alterations. METHODS: We used a molecular pathway-based approach to analyze transcriptomic profiles of 59 colorectal tumors representing early and late preinvasive stages and the invasive stage of tumorigenesis. Random set analysis was used to identify biological pathways enriched for genes differentially regulated in tumors (compared with 59 samples of normal mucosa). RESULTS: Of the 880 canonical pathways we investigated, 112 displayed significant tumor-related upregulation or downregulation at one or more stages of tumorigenesis. This allowed us to distinguish between pathways whose dysregulation is probably necessary throughout tumorigenesis and those whose involvement specifically drives progression from one stage to the next. We were also able to pinpoint specific changes within each gene set that seem to play key roles at each transition. The early preinvasive stage was characterized by cell-cycle checkpoint activation triggered by DNA replication stress and dramatic downregulation of basic transmembrane signaling processes that maintain epithelial/stromal homeostasis in the normal mucosa. In late preinvasive lesions, there was also downregulation of signal transduction pathways (e.g., those mediated by G proteins and nuclear hormone receptors) involved in cell differentiation and upregulation of pathways governing nuclear envelope dynamics and the G2>M transition in the cell cycle. The main features of the invasive stage were activation of the G1>S transition in the cell cycle, upregulated expression of tumor-promoting microenvironmental factors, and profound dysregulation of metabolic pathways (e.g., increased aerobic glycolysis, downregulation of pathways that metabolize drugs and xenobiotics). CONCLUSIONS: Our analysis revealed specific pathways whose dysregulation might play a role in each transition of the transformation process. This is the first study in which such an approach has been used to gain further insights into colorectal tumorigenesis. Therefore, these data provide a launchpad for further exploration of the molecular characterization of colorectal tumorigenesis using systems biology approaches.

The expression of leucine-rich repeat gene family members in colorectal cancer.

This study was conducted to evaluate the association of the leucine-rich repeat (LRR) gene family with colorectal cancer (CRC). The expression of members of the LRR gene family were analyzed in 17 CRC specimens and in 59 healthy colorectal tissues by using Human Exon1.0ST microarray, and in 25 CRC specimens and 32 healthy colorectal tissues by U133Plus2.0 microarray. An association was found for 25 genes belonging to the plant-specific (PS) class of LRR genes (P = 0.05 for Exon1.0 ST and P = 0.04 for U133Plus2.0). In both data-sets, in CRC, we found down-regulation of SHOC2 (P < 0.00003) and LRRC28 (P < 0.01) and up-regulation of LRSAM1 (P < 0.000001), while up-regulation of MFHAS1 (P = 0.0005) and down-regulation of WDFY3 (P = 0.026) were found only in the Exon1.0 ST data-set. The PS LLR gene class encodes proteins that activate immune cells and might play a key role in programmed cell death and autophagy. SHOC2 and LRRC28 genes involved in RAS-mediated signaling, which hinders nutrient deprivation-induced autophagy, might be a possible link between the negative control of autophagy and tumorigenesis.

Discovering genetic variants in Crohn's disease by exploring genomic regions enriched of weak association signals.

BACKGROUND: A meta-analysis has re-analysed previous genome-wide association scanning definitively confirming eleven genes and further identifying 21 new loci. However, the identified genes/loci still explain only the minority of genetic predisposition of Crohn's disease. AIMS: To identify genes weakly involved in disease predisposition by analysing chromosomal regions enriched of single nucleotide polymorphisms with modest statistical association. METHODS: We utilized the WTCCC data set evaluating 1748 CD and 2938 controls. The identification of candidate genes/loci was performed by a two-step procedure: first of all chromosomal regions enriched of weak association signals were localized; subsequently, weak signals clustered in gene regions were identified. The statistical significance was assessed by non parametric permutation tests. RESULTS: The cytoband enrichment analysis highlighted 44 regions (P≤0.05) enriched with single nucleotide polymorphisms significantly associated with the trait including 23 out of 31 previously confirmed and replicated genes. Importantly, we highlight further 20 novel chromosomal regions carrying approximately one hundred genes/loci with modest association. Amongst these we find compelling functional candidate genes such as MAPT, GRB2 and CREM, LCT, and IL12RB2. CONCLUSION: Our study suggests a different statistical perspective to discover genes weakly associated with a given trait, although further confirmatory functional studies are needed.

On the reproducibility of results of pathway analysis in genome-wide expression studies of colorectal cancers.

One of the major problems in genomics and medicine is the identification of gene networks and pathways deregulated in complex and polygenic diseases, like cancer. In this paper, we address the problem of assessing the variability of results of pathways analysis identified in different and independent genome wide expression studies, in which the same phenotypic conditions are assayed. To this end, we assessed the deregulation of 1891 curated gene sets in four independent gene expression data sets of subjects affected by colorectal cancer (CRC). In this comparison we used two well-founded statistical models for evaluating deregulation of gene networks. We found that the results of pathway analysis in expression studies are highly reproducible. Our study revealed 53 pathways identified by the two methods in all the four data sets analyzed with high statistical significance and strong biological relevance with the pathology examined. This set of pathways associated to single markers as well as to whole biological processes altered constitutes a signature of the disease which sheds light on the genetics bases of CRC.

Comparative study of gene set enrichment methods.

BACKGROUND: The analysis of high-throughput gene expression data with respect to sets of genes rather than individual genes has many advantages. A variety of methods have been developed for assessing the enrichment of sets of genes with respect to differential expression. In this paper we provide a comparative study of four of these methods: Fisher's exact test, Gene Set Enrichment Analysis (GSEA), Random-Sets (RS), and Gene List Analysis with Prediction Accuracy (GLAPA). The first three methods use associative statistics, while the fourth uses predictive statistics. We first compare all four methods on simulated data sets to verify that Fisher's exact test is markedly worse than the other three approaches. We then validate the other three methods on seven real data sets with known genetic perturbations and then compare the methods on two cancer data sets where our a priori knowledge is limited. RESULTS: The simulation study highlights that none of the three method outperforms all others consistently. GSEA and RS are able to detect weak signals of deregulation and they perform differently when genes in a gene set are both differentially up and down regulated. GLAPA is more conservative and large differences between the two phenotypes are required to allow the method to detect differential deregulation in gene sets. This is due to the fact that the enrichment statistic in GLAPA is prediction error which is a stronger criteria than classical two sample statistic as used in RS and GSEA. This was reflected in the analysis on real data sets as GSEA and RS were seen to be significant for particular gene sets while GLAPA was not, suggesting a small effect size. We find that the rank of gene set enrichment induced by GLAPA is more similar to RS than GSEA. More importantly, the rankings of the three methods share significant overlap. CONCLUSION: The three methods considered in our study recover relevant gene sets known to be deregulated in the experimental conditions and pathologies analyzed. There are differences between the three methods and GSEA seems to be more consistent in finding enriched gene sets, although no method uniformly dominates over all data sets. Our analysis highlights the deep difference existing between associative and predictive methods for detecting enrichment and the use of both to better interpret results of pathway analysis. We close with suggestions for users of gene set methods.

Statistical assessment of discriminative features for protein-coding and non coding cross-species conserved sequence elements.

BACKGROUND: The identification of protein coding elements in sets of mammalian conserved elements is one of the major challenges in the current molecular biology research. Many features have been proposed for automatically distinguishing coding and non coding conserved sequences, making so necessary a systematic statistical assessment of their differences. A comprehensive study should be composed of an association study, i.e. a comparison of the distributions of the features in the two classes, and a prediction study in which the prediction accuracies of classifiers trained on single and groups of features are analyzed, conditionally to the compared species and to the sequence lengths. RESULTS: In this paper we compared distributions of a set of comparative and non comparative features and evaluated the prediction accuracy of classifiers trained for discriminating sequence elements conserved among human, mouse and rat species. The association study showed that the analyzed features are statistically different in the two classes. In order to study the influence of the sequence lengths on the feature performances, a predictive study was performed on different data sets composed of coding and non coding alignments in equal number and equally long with an ascending average length. We found that the most discriminant feature was a comparative measure indicating the proportion of synonymous nucleotide substitutions per synonymous sites. Moreover, linear discriminant classifiers trained by using comparative features in general outperformed classifiers based on intrinsic ones. Finally, the prediction accuracy of classifiers trained on comparative features increased significantly by adding intrinsic features to the set of input variables, independently on sequence length (Kolmogorov-Smirnov P-value

Promoter methylation correlates with reduced NDRG2 expression in advanced colon tumour.

BACKGROUND: Aberrant DNA methylation of CpG islands of cancer-related genes is among the earliest and most frequent alterations in cancerogenesis and might be of value for either diagnosing cancer or evaluating recurrent disease. This mechanism usually leads to inactivation of tumour-suppressor genes. We have designed the current study to validate our previous microarray data and to identify novel hypermethylated gene promoters. METHODS: The validation assay was performed in a different set of 8 patients with colorectal cancer (CRC) by means quantitative reverse-transcriptase polymerase chain reaction analysis. The differential RNA expression profiles of three CRC cell lines before and after 5-aza-2'-deoxycytidine treatment were compared to identify the hypermethylated genes. The DNA methylation status of these genes was evaluated by means of bisulphite genomic sequencing and methylation-specific polymerase chain reaction (MSP) in the 3 cell lines and in tumour tissues from 30 patients with CRC. RESULTS: Data from our previous genome search have received confirmation in the new set of 8 patients with CRC. In this validation set six genes showed a high induction after drug treatment in at least two of three CRC cell lines. Among them, the N-myc downstream-regulated gene 2 (NDRG2) promoter was found methylated in all CRC cell lines. NDRG2 hypermethylation was also detected in 8 out of 30 (27%) primary CRC tissues and was significantly associated with advanced AJCC stage IV. Normal colon tissues were not methylated. CONCLUSION: The findings highlight the usefulness of combining gene expression patterns and epigenetic data to identify tumour biomarkers, and suggest that NDRG2 silencing might bear influence on tumour invasiveness, being associated with a more advanced stage.