MicroRNAs serve as prediction and treatment-response biomarkers of attention-deficit/hyperactivity disorder and promote the differentiation of neuronal cells by repressing the apoptosis pathway

Wang, Liang‐Jen; Kuo, Ho‐Chang; Lee, Sheng-Yu; Huang, Lien‐Hung; Lin, Yu-Yu; Lin, Pei‐Hsien; Li, Sung‐Chou

doi:10.1038/s41398-022-01832-1

Cited by 14 publications

(15 citation statements)

References 57 publications

(56 reference statements)

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“…In our cohort, these environmental influences caused by a parent with depression or criminal convictions could have occurred during embryonic development, early infancy, or adolescence altering the expression of genes associated with ADHD and, in turn, affecting brain function with subsequent changes in behavior. Consistent with this idea, previous research indicated epigenetic modifications linked to ADHD such as DNA methylation, histone modifications and expression of noncoding micro RNAs (miRNA) [44][45][46][47][48][49]. Alternatively, the importance given to both parental criminal convictions and depression could be driven by the co-occurrence of ADHD in the parents rather than these two conditions per se, and thus, indirectly predicting the offspring ADHD due to the high heritability of the disorder.…”

Section: Discussionmentioning

confidence: 87%

Predicting childhood and adolescent attention-deficit/hyperactivity disorder onset: a nationwide deep learning approach

et al. 2022

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Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous disorder with a high degree of psychiatric and physical comorbidity, which complicates its diagnosis in childhood and adolescence. We analyzed registry data from 238,696 persons born and living in Sweden between 1995 and 1999. Several machine learning techniques were used to assess the ability of registry data to inform the diagnosis of ADHD in childhood and adolescence: logistic regression, random Forest, gradient boosting, XGBoost, penalized logistic regression, deep neural network (DNN), and ensemble models. The best fitting model was the DNN, achieving an area under the receiver operating characteristic curve of 0.75, 95% CI (0.74–0.76) and balanced accuracy of 0.69. At the 0.45 probability threshold, sensitivity was 71.66% and specificity was 65.0%. There was an overall agreement in the feature importance among all models (τ > .5). The top 5 features contributing to classification were having a parent with criminal convictions, male sex, having a relative with ADHD, number of academic subjects failed, and speech/learning disabilities. A DNN model predicting childhood and adolescent ADHD trained exclusively on Swedish register data achieved good discrimination. If replicated and validated in an external sample, and proven to be cost-effective, this model could be used to alert clinicians to individuals who ought to be screened for ADHD and to aid clinicians’ decision-making with the goal of decreasing misdiagnoses. Further research is needed to validate results in different populations and to incorporate new predictors.

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Section: Discussionmentioning

confidence: 87%

Predicting childhood and adolescent attention-deficit/hyperactivity disorder onset: a nationwide deep learning approach

et al. 2022

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“…A number of miRNAs are known to regulate PTEN expression, 25 and prior studies have underscored the utility of miRNA profiles as discerning biomarkers for evaluating treatment efficacy. 26 , 27 , 28 To that end, we analyzed PTEN messenger RNA expression levels in PBMCs before treatment and at 3 and 9 months after treatment initiation. The analysis revealed PTEN upregulation after alpelisib treatment ( Figure 5 A).…”

Section: Resultsmentioning

confidence: 99%

Mutant PIK3CA is a targetable driver alteration in histiocytic neoplasms

Durham,

Hershkovitz-Rokah,

Abdel-Wahab

et al. 2023

Blood Advances

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Langerhans cell histiocytosis (LCH) is an inflammatory myeloid neoplasm characterized by the accumulation of clonal mononuclear phagocyte system cells expressing CD1a and CD207. In the past decade, molecular profiling of LCH, as well as other histiocytic neoplasms demonstrated that these diseases are driven by MAP kinase (MAPK) activating alterations, with somatic BRAFV600E mutations in >50% of LCH patients, and clinical inhibition of MAPK signaling has demonstrated remarkable clinical efficacy. At the same time, activating alterations in kinase-encoding genes such as PIK3CA, ALK, RET, and CSF1R which can activate mitogenic pathways independent from the MAPK pathway have been reported in a subset of histiocytic neoplasms with anecdotal evidence of successful targeted treatment of histiocytoses harboring driver alterations in RET, ALK, and CSF1R. However, evidence supporting the biological consequences of expression of PIK3CA mutations in hematopoietic cells has been lacking, and whether targeted inhibition of PI3K is clinically efficacious in histiocytic neoplasms is unknown. Here, we provide evidence that activating mutations in PIK3CA can drive histiocytic neoplasms in vivo using a conditional knock-in mouse expressing mutant PIK3CAH1047R in monocyte/dendritic cell progenitors. In parallel, we demonstrate successful treatment of PIK3CA-mutated, multisystemic LCH using alpelisib, an inhibitor of the alpha catalytic subunit of PI3K. Alpelisib demonstrated a tolerable safety profile at a dose of 750mg/week and clinical and metabolic complete remission in a PIK3CA-mutated LCH patient. These data demonstrate PIK3CA as a targetable non-canonical driver of LCH and underscore the importance of mutational analysis-based personalized treatment in histiocytic neoplasms.

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“…MicroRNAs are small non-coding RNAs that typically suppress gene expression. Using the next-generation sequencing (NGS) technique (Illumina) and support vector machine classification model, several microRNAs were identified by different groups with the potential to diagnose ADHD (68)(69)(70)(71)(72), which required to be tested in future clinical studies to identify the optimal combination. A large epigenome-wide association study (EWAS) discovered several novel epigenetic biomarkers for ADHD from saliva DNA samples, for example, cg17478313 annotated to SLC7A8 and cg21609804 annotated to Microtubule Affinity Regulating Kinase (MARK)2 that are regulated by single nucleotide polymorphisms (SNPs) (65).…”

Section: Molecular Biomarkersmentioning

confidence: 99%

Can biomarkers be used to diagnose attention deficit hyperactivity disorder?

et al. 2023

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Currently, the diagnosis of attention deficit hyperactivity disorder (ADHD) is solely based on behavioral tests prescribed by the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5). However, biomarkers can be more objective and accurate for diagnosis and evaluating treatment efficacy. Thus, this review aimed to identify potential biomarkers for ADHD. Search terms “ADHD,” and “biomarker” combined with one of “protein,” “blood/serum,” “gene,” and “neuro” were used to identify human and animal studies in PubMed, Ovid Medline, and Web of Science. Only papers in English were included. Potential biomarkers were categorized into radiographic, molecular, physiologic, or histologic markers. The radiographic analysis can identify specific activity changes in several brain regions in individuals with ADHD. Several molecular biomarkers in peripheral blood cells and some physiologic biomarkers were found in a small number of participants. There were no published histologic biomarkers for ADHD. Overall, most associations between ADHD and potential biomarkers were properly controlled. In conclusion, a series of biomarkers in the literature are promising as objective parameters to more accurately diagnose ADHD, especially in those with comorbidities that prevent the use of DSM-5. However, more research is needed to confirm the reliability of the biomarkers in larger cohort studies.

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MicroRNAs serve as prediction and treatment-response biomarkers of attention-deficit/hyperactivity disorder and promote the differentiation of neuronal cells by repressing the apoptosis pathway

Cited by 14 publications

References 57 publications

Predicting childhood and adolescent attention-deficit/hyperactivity disorder onset: a nationwide deep learning approach

Predicting childhood and adolescent attention-deficit/hyperactivity disorder onset: a nationwide deep learning approach

Mutant PIK3CA is a targetable driver alteration in histiocytic neoplasms

Can biomarkers be used to diagnose attention deficit hyperactivity disorder?

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