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Spinocerebellar ataxia type 3/Machado–Joseph disease (SCA3/MJD) is a neurodegenerative disorder caused by the ATXN3 CAG repeat expansion. Preimplantation genetic testing for monogenic disorders (PGT-M) of SCA3/MJD should include reliable repeat expansion detection coupled with high-risk allele determination using informative linked markers. One couple underwent SCA3/MJD PGT-M combining ATXN3 (CAG)n triplet-primed PCR (TP-PCR) with customized linkage-based risk allele genotyping on whole-genome-amplified trophectoderm cells. Microsatellites closely linked to ATXN3 were identified and 16 markers were genotyped on 187 anonymous DNAs to verify their polymorphic information content. In the SCA3/MJD PGT-M case, the ATXN3 (CAG)n TP-PCR and linked marker analysis results concurred completely. Among the three unaffected embryos, a single embryo was transferred and successfully resulted in an unaffected live birth. A total of 139 microsatellites within 1 Mb upstream and downstream of the ATXN3 CAG repeat were identified and 8 polymorphic markers from each side were successfully co-amplified in a single-tube reaction. A PGT-M assay involving ATXN3 (CAG)n TP-PCR and linkage-based risk allele identification has been developed for SCA3/MJD. A hexadecaplex panel of highly polymorphic microsatellites tightly linked to ATXN3 has been developed for the rapid identification of informative markers in at-risk couples for use in the PGT-M of SCA3/MJD. [ABSTRACT FROM AUTHOR]

Simple Summary: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. HCC is often diagnosed at a late stage when treatment effectiveness is limited and its prognosis remains dire. Exosomes are secreted by all living cells, including cancer cells, and contain biological material with potential to highlight disease conditions or dysregulated pathways involved in tumourigenesis. This study employs artificial intelligence methods to identify a signature of exosomal RNAs from 114,602 exosomal RNAs in 118 HCC patients and 112 healthy individuals that can predict HCC. A signature of nine exosomal RNAs, mainly in the immune, platelet/neutrophil and cytoskeletal pathways, was identified to predict HCC with an accuracy of ~85%. Hence, these nine exosomal RNAs have potential to be developed as clinically useful minimally invasive biomarkers for HCC. Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Although alpha fetoprotein (AFP) remains a commonly used serological marker of HCC, the sensitivity and specificity of AFP in detecting HCC is often limited. Exosomal RNA has emerged as a promising diagnostic tool for various cancers, but its use in HCC detection has yet to be fully explored. Here, we employed Machine Learning on 114,602 exosomal RNAs to identify a signature that can predict HCC. The exosomal expression data of 118 HCC patients and 112 healthy individuals were stratified split into Training, Validation and Unseen Test datasets. Feature selection was then performed on the initial training dataset using permutation importance, and the predictive performance of the selected features were tested on the validation dataset using Support Vector Machine (SVM) Classifier. A minimum of nine features were identified to be predictive of HCC and these nine features were then evaluated across six different models in an unseen test set. These features, mainly in the immune, platelet/neutrophil and cytoskeletal pathways, exhibited good predictive performance with ROC-AUC from 0.79–0.88 in the unseen test set. Hence, these nine exosomal RNAs have potential to be clinically useful minimally invasive biomarkers for HCC. [ABSTRACT FROM AUTHOR]

Objective To develop a hypothesis-free model that best predicts response to MTX drug in RA patients utilizing biologically meaningful genetic feature selection of potentially functional single nucleotide polymorphisms (pfSNPs) through robust machine learning (ML) feature selection methods. Methods MTX-treated RA patients with known response were divided in a 4:1 ratio into training and test sets. From the patients' exomes, potential features for classifier prediction were identified from pfSNPs and non-genetic factors through ML using recursive feature elimination with cross-validation incorporating the random forest classifier. Feature selection was repeated on random subsets of the training cohort, and consensus features were assembled into the final feature set. This feature set was evaluated for predictive potential using six ML classifiers, first by cross-validation within the training set, and finally by analysing its performance with the unseen test set. Results The final feature set contains 56 pfSNPs and five non-genetic factors. The majority of these pfSNPs are located in pathways related to RA pathogenesis or MTX action and are predicted to modulate gene expression. When used for training in six ML classifiers, performance was good in both the training set (area under the curve: 0.855–0.916; sensitivity: 0.715–0.892; and specificity: 0.733–0.862) and the unseen test set (area under the curve: 0.751–0.826; sensitivity: 0.581–0.839; and specificity: 0.641–0.923). Conclusion Sensitive and specific predictors of MTX response in RA patients were identified in this study through a novel strategy combining biologically meaningful and machine learning feature selection and training. These predictors may facilitate better treatment decision-making in RA management. [ABSTRACT FROM AUTHOR]

PubMed was text mined to glean insights into the role of Hepatitis B virus (HBV) in hepatocellular carcinoma (HCC) from the massive number of publications (9249) available to date. Reports from ∼70 countries identified >1300 human genes associated with either the Core, Surface or X gene in HBV‐associated HCC. One hundred and forty‐three of these host genes, which can potentially yield 1180 biomolecular interactions, each were reported in at least three different publications to be associated with the same HBV. These 143 genes function in 137 pathways, involved mainly in the cell cycle, apoptosis, inflammation and signalling. Fourteen of these molecules, primarily transcriptional regulators or kinases, play roles in several pathways pertinent to the hallmarks of cancers. 'Chronic' was the most frequent word used across the 9249 abstracts. A key event in chronic HBV infection is the integration of HBV into the host genome. The advent of cost‐effective, next‐generation sequencing technology facilitated the employment of big‐data analytics comprehensively to characterize HBV–host integration within HCC patients. A total of 5331 integration events were reported across seven publications, with most of these integrations observed between the Core/X gene and the introns of genes. Nearly one‐quarter of the intergenic integrations are within repeats, especially long interspersed nuclear elements (LINE) repeats. Integrations within 13 genes were each reported by at least three different studies. The human gene with the most HBV integrations observed is the TERT gene where a total of 224 integrations, primarily at its promoter and within the tumour tissue, were reported by six of seven publications. This unique review, which employs state‐of‐the‐art text‐mining and data‐analytics tools, represents the most complete, systematic and comprehensive review of nearly all the publications associated with HBV‐associated HCC research. It provides important resources to either focus future research or develop therapeutic strategies to target key molecules reported to play important roles in key pathways of HCC, through the systematic analyses of the commonly reported molecules associated with the various HBV genes in HCC, including information about the interactions amongst these commonly reported molecules, the pathways in which they reside as well as detailed information regarding the viral and host genes associated with HBV integration in HCC patients. Hence this review, which highlights pathways and key human genes associated with HBV in HCC, may facilitate the deeper elucidation of the role of HBV in hepato‐carcinogenesis, potentially leading to timely intervention against this deadly disease. [ABSTRACT FROM AUTHOR]