Heterogeneity of triple-negative breast cancer is well known at clinical, histopathological, and molecular levels. Genomic instability and greater mutation rates, which may result in the creation of neoantigens and enhanced immunogenicity, are additional characteristics of this breast cancer type. Clinical outcome is poor due to early age of onset, high metastatic potential, and increased likelihood of distant recurrence. Consequently, efforts to elucidate molecular mechanisms of breast cancer development, progression, and metastatic spread have been initiated to improve treatment options and improve outcomes for these patients. The extremely complex and heterogeneous tumor immune microenvironment is made up of several cell types and commonly possesses disorganized gene expression. Altered signaling pathways are mainly associated with mutated genes including p53, PIK3CA, and MAPK, and which are positively correlated with genes regulating immune response. Of note, particular immunity-associated genes could be used in prognostic indexes to assess the most effective management. Recent findings highlight the fact that long non-coding RNAs also play an important role in shaping tumor microenvironment formation, and can mediate tumor immune evasion. Identification of molecular signatures, through the use of multi-omics approaches, and effector pathways that drive early stages of the carcinogenic process are important steps in developing new strategies for targeted cancer treatment and prevention. Advances in immunotherapy by remodeling the host immune system to eradicate tumor cells have great promise to lead to novel therapeutic strategies. Current research is focused on combining immune checkpoint inhibition with chemotherapy, PARP inhibitors, cancer vaccines, or natural killer cell therapy. Targeted therapies may improve therapeutic response, eliminate therapeutic resistance, and improve overall patient survival. In the future, these evolving advancements should be implemented for personalized medicine and state-of-art management of cancer patients.
MotivationGenome-wide measurements of paired miRNA and gene expression data have enabled the prediction of competing endogenous RNAs (ceRNAs). It has been shown that the sponge effect mediated by protein-coding as well as non-coding ceRNAs can play an important regulatory role in the cell in health and disease. Therefore, many computational methods for the computational identification of ceRNAs have been suggested. In particular, methods based on Conditional Mutual Information (CMI) have shown promising results. However, the currently available implementation is slow and cannot be used to perform computations on a large scale.ResultsHere, we present JAMI, a Java tool that uses a non-parametric estimator for CMI values from gene and miRNA expression data. We show that JAMI speeds up the computation of ceRNA networks by a factor of ∼70 compared to currently available implementations. Further, JAMI supports multi-threading to make use of common multi-core architectures for further performance gain.RequirementsJava 8.Availability and implementationJAMI is available as open-source software from https://github.com/SchulzLab/JAMI.Supplementary information
Supplementary data are available at Bioinformatics online.
MicroRNAs (miRNAs) are one of the important regulators of cellular functions fundamental for healthy pregnancy processes, including angiogenesis and differentiation of trophoblast cells, and their deregulation could be implicated in the pathogenesis of pregnancy complications, including preeclampsia (PE). The aim of this study was to assess the association of miRNA expression in plasma samples with PE, its onset, and severity. Our study enrolled 59 pregnant women, 27 in the preeclamptic study group and 32 in the control group with physiological pregnancy. Preeclamptic pregnancies were divided into subgroups based on the severity and onset of disease. Relative expression of miR-21-5p, miR-155-5p, miR-210-5p, miR-16-5p, and miR-650 isolated from plasma samples was analysed by quantitative real-time PCR and normalised to experimentally established reference genes. Our results revealed upregulation of miR-21-5p (1.16-fold change, p = 0.0015), miR-155-5p (1.62-fold change, p = 0.0005) in preeclamptic pregnancies, compared to controls. Overexpression of these two miRNAs was observed, especially in subgroups of severe and late-onset PE compared to healthy pregnancies. Although we hypothesised that the expression level of studied miRNAs could vary between PE subtypes (mild vs. severe, early onset vs. late-onset), no obvious differences were detected. In conclusion, our study could contribute to the large-scale studies for the identification of non-invasive biomarkers for PE detection to improve outcomes for women and their new-borns.
Highlights Fetal fraction is a quality control parameter in NIPS There are several methods for determining fetal fraction, including snpFF snpFF is based on targeted sequencing of highly polymorphic SNPs Individual SNP-level variability of fetal fraction is rather high Uncertainty of snpFF increases with fetal fraction snpFF cannot reliably measure fetal fraction below 2.
Structured prediction problems are one of the fundamental tools in machine learning. In order to facilitate algorithm development for their numerical solution, we collect in one place a large number of datasets in easy to read formats for a diverse set of problem classes. We provide archival links to datasets, description of the considered problems and problem formats, and a short summary of problem characteristics including size, number of instances etc. For reference we also give a non-exhaustive selection of algorithms proposed in the literature for their solution. We hope that this central repository will make benchmarking and comparison to established works easier. We welcome submission of interesting new datasets and algorithms for inclusion in our archive 1 .
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