Gastric cancer, a leading cause of cancer-related deaths, is a heterogeneous disease. We aim to establish clinically relevant molecular subtypes that would encompass this heterogeneity and provide useful clinical information. We use gene expression data to describe four molecular subtypes linked to distinct patterns of molecular alterations, disease progression and prognosis. The mesenchymal-like type includes diffuse-subtype tumors with the worst prognosis, the tendency to occur at an earlier age and the highest recurrence frequency (63%) of the four subtypes. Microsatellite-unstable tumors are hyper-mutated intestinal-subtype tumors occurring in the antrum; these have the best overall prognosis and the lowest frequency of recurrence (22%) of the four subtypes. The tumor protein 53 (TP53)-active and TP53-inactive types include patients with intermediate prognosis and recurrence rates (with respect to the other two subtypes), with the TP53-active group showing better prognosis. We describe key molecular alterations in each of the four subtypes using targeted sequencing and genome-wide copy number microarrays. We validate these subtypes in independent cohorts in order to provide a consistent and unified framework for further clinical and preclinical translational research.

Molecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes

MicroRNAs (miRNAs) are an important class of small noncoding RNAs capable of regulating other genes’ expression. Much progress has been made in computational target prediction of miRNAs in recent years. More than 10 miRNA target prediction programs have been established, yet, the prediction of animal miRNA targets remains a challenging task. We have developed miRecords, an integrated resource for animal miRNA–target interactions. The Validated Targets component of this resource hosts a large, high-quality manually curated database of experimentally validated miRNA–target interactions with systematic documentation of experimental support for each interaction. The current release of this database includes 1135 records of validated miRNA–target interactions between 301 miRNAs and 902 target genes in seven animal species. The Predicted Targets component of miRecords stores predicted miRNA targets produced by 11 established miRNA target prediction programs. miRecords is expected to serve as a useful resource not only for experimental miRNA researchers, but also for informatics scientists developing the next-generation miRNA target prediction programs. The miRecords is available at http:// miRecords.umn.edu/miRecords.

/pdf/mirecords-an-integrated-resource-for-microrna-target-1d8tz4tcvw.pdf

miRecords: an integrated resource for microRNA–target interactions

The MicroRNA miR-124 Promotes Neuronal Differentiation by Triggering Brain-Specific Alternative Pre-mRNA Splicing

Our understanding of the biological functions of small non-coding RNAs has been fostered by the analysis of genetic deletions of individual microRNAs (miRNAs) in mammals. These studies show that miRNAs are key regulators of animal development and are potential human disease loci.

Small non-coding RNAs in animal development

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and mi...

/pdf/applications-of-machine-learning-in-cancer-prediction-and-2oti5pk54y.pdf

Applications of Machine Learning in Cancer Prediction and Prognosis

MIR-124 antagonizes the anti-neural rest/scp1 pathway during embryonic development

Neuronal gene expression is tightly regulated in developing CNS Here, we demonstrate the anti-neural function of phosphatase SCP1 (small C-terminal domain phosphatase 1) during development We further show that the neuron-enriched microRNA miR-124 directly targets SCP1-3' untranslated region (UTR) to suppress SCP1 expression In developing spinal cord, expression of miR-124 and SCP1 is complementary, and miR-124 antagonism phenocopies SCP1 overexpression and vice versa In P19 cells, miR-124 suppresses SCP1 expression and induces neurogenesis, and SCP1 counteracts this proneural activity of miR-124 Our results suggest that, during CNS development, timely down-regulation of SCP1 is critical for inducing neurogenesis, and miR-124 contributes to this process at least in part by down-regulating SCP1 expression

http://genesdev.cshlp.org/content/21/7/744.full.pdf

The microRNA miR-124 antagonizes the anti-neural REST/SCP1 pathway during embryonic CNS development.

Comparative genomic hybridization (CGH), microsatellite instability (MSI) assays, and expression microarrays were used to molecularly subclassify a common set of gastric tumor samples. We identified a number of novel genomic aberrations associated with gastric cancer and discovered that gastric tumors could be grouped by their expression profiles into three broad classes: “tumorigenic,” “reactive,” and “gastric-like.” Patients with gastric-like tumors exhibited a significantly better overall survival than patients belonging to the other two classes ( P < 0.05). A novel supervised learning methodology for multiclass prediction was used to identify optimal predictor gene sets that accurately predicted the class of an unknown tumor sample. These predictor sets may prove useful in the development of new diagnostic applications for gastric cancer staging and prognostication.

https://cancerres.aacrjournals.org/content/canres/63/12/3309.full.pdf

A Combined Comparative Genomic Hybridization and Expression Microarray Analysis of Gastric Cancer Reveals Novel Molecular Subtypes

Osteosarcoma is the most common malignant bone tumor in children. After initial diagnosis is made with a biopsy, treatment consists of preoperative chemotherapy followed by definitive surgery and postoperative chemotherapy. The degree of tumor necrosis in response to preoperative chemotherapy is a reliable prognostic factor and is used to guide the choice of postoperative chemotherapy. Patients with tumors, which reveal > or = 90% necrosis (good responders), have a much better prognosis than those with < 90% necrosis (poor responders). Despite previous attempts to improve the outcome of poor responders by modifying the postoperative chemotherapy, their prognosis remains poor. Therefore, there is a need to predict at the time of diagnosis patients' response to preoperative chemotherapy. This will provide the basis for developing potentially effective therapy that can be given at the outset for those who are likely to have a poor response. Here, we report the analysis of 34 pediatric osteosarcoma samples by expression profiling. Using parametric two-sample t test, we identified 45 genes that discriminate between good and poor responders (P < 0.005) in 20 definitive surgery samples. A support vector machine classifier was built using these predictor genes and was tested for its ability to classify initial biopsy samples. Five of six initial biopsy samples that had corresponding definitive surgery samples in the training set were classified correctly (83%; confidence interval, 36%, 100%). When this classifier was used to predict eight independent initial biopsy samples, there was 100% accuracy (confidence interval, 63%, 100%). Many of the predictor genes are implicated in bone development, drug resistance, and tumorigenesis.

https://cancerres.aacrjournals.org/content/canres/65/18/8142.full.pdf

Expression Profiles of Osteosarcoma That Can Predict Response to Chemotherapy

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MIR-124 antagonizes the anti-neural rest/scp1 pathway during embryonic development

The microRNA miR-124 antagonizes the anti-neural REST/SCP1 pathway during embryonic CNS development.

A Combined Comparative Genomic Hybridization and Expression Microarray Analysis of Gastric Cancer Reveals Novel Molecular Subtypes

Expression Profiles of Osteosarcoma That Can Predict Response to Chemotherapy