Integrative functional genomic analysis of human brain development and neuropsychiatric risks

INTRODUCTION The brain is responsible for cognition, behavior, and much of what makes us uniquely human. The development of the brain is a highly complex process, and this process is reliant on precise regulation of molecular and cellular events grounded in the spatiotemporal regulation of the transcriptome. Disruption of this regulation can lead to neuropsychiatric disorders. RATIONALE The regulatory, epigenomic, and transcriptomic features of the human brain have not been comprehensively compiled across time, regions, or cell types. Understanding the etiology of neuropsychiatric disorders requires knowledge not just of endpoint differences between healthy and diseased brains but also of the developmental and cellular contexts in which these differences arise. Moreover, an emerging body of research indicates that many aspects of the development and physiology of the human brain are not well recapitulated in model organisms, and therefore it is necessary that neuropsychiatric disorders be understood in the broader context of the developing and adult human brain. RESULTS Here we describe the generation and analysis of a variety of genomic data modalities at the tissue and single-cell levels, including transcriptome, DNA methylation, and histone modifications across multiple brain regions ranging in age from embryonic development through adulthood. We observed a widespread transcriptomic transition beginning during late fetal development and consisting of sharply decreased regional differences. This reduction coincided with increases in the transcriptional signatures of mature neurons and the expression of genes associated with dendrite development, synapse development, and neuronal activity, all of which were temporally synchronous across neocortical areas, as well as myelination and oligodendrocytes, which were asynchronous. Moreover, genes including MEF2C, SATB2, and TCF4, with genetic associations to multiple brain-related traits and disorders, converged in a small number of modules exhibiting spatial or spatiotemporal specificity. CONCLUSION We generated and applied our dataset to document transcriptomic and epigenetic changes across human development and then related those changes to major neuropsychiatric disorders. These data allowed us to identify genes, cell types, gene coexpression modules, and spatiotemporal loci where disease risk might converge, demonstrating the utility of the dataset and providing new insights into human development and disease

Identification of Breast Cancer Metastasis Markers from Gene Expression Profiles Using Machine Learning Approaches

Cancer metastasis accounts for approximately 90% of cancer deaths, and elucidating markers in metastasis is the first step in its prevention. To characterize metastasis marker genes (MGs) of breast cancer, XGBoost models that classify metastasis status were trained with gene expression profiles from TCGA. Then, a metastasis score (MS) was assigned to each gene by calculating the inner product between the feature importance and the AUC performance of the models. As a result, 54, 202, and 357 genes with the highest MS were characterized as MGs by empirical p-value cutoffs of 0.001, 0.005, and 0.01, respectively. The three sets of MGs were compared with those from existing metastasis marker databases, which provided significant results in most comparisons (p-value p-value < 0.01) in the survival analysis. The MGs that could not be identified by statistical analysis (e.g., GOLM1, ELAVL1, UBP1, and AZGP1), as well as the MGs with the highest MS (e.g., ZNF676, FAM163B, LDOC2, IRF1, and STK40), were verified via the literature. Additionally, we checked how close the MGs were to each other in the protein–protein interaction networks. We expect that the characterized markers will help understand and prevent breast cancer metastasis

Exploring Predictive Factors for Heart Failure Progression in Hypertensive Patients Based on Medical Diagnosis Data from the MIMIC-IV Database

Heart failure is associated with a significant mortality rate, and an elevated prevalence of this condition has been noted among hypertensive patients. The identification of predictive factors for heart failure progression in hypertensive individuals is crucial for early intervention and improved patient outcomes. In this study, we aimed to identify these predictive factors by utilizing medical diagnosis records for hypertension patients from the MIMIC-IV database. In particular, we employed only diagnostic history prior to hypertension to enable patients to anticipate the onset of heart failure at the moment of hypertension diagnosis. In the methodology, chi-square tests and XGBoost modeling were applied to examine age-specific predictive factors across four groups: AL (all ages), G1 (0 to 65 years), G2 (65 to 80 years), and G3 (over 80 years). As a result, the chi-square tests identified 34, 28, 20, and 10 predictive factors for the AL, G1, G2, and G3 groups, respectively. Meanwhile, the XGBoost modeling uncovered 19, 21, 27, and 33 predictive factors for these respective groups. Ultimately, our findings reveal 21 overall predictive factors, encompassing conditions such as atrial fibrillation, the use of anticoagulants, kidney failure, obstructive pulmonary disease, and anemia. These factors were assessed through a comprehensive review of the existing literature. We anticipate that the results will offer valuable insights for the risk assessment of heart failure in hypertensive patients

Identification of novel membrane markers in circulating tumor cells of mesenchymal state in breast cancer

Cancer metastasis is a major cause of cancer-related deaths worldwide. The ability to detect and monitor circulating tumor cells (CTCs) offers a promising approach to early detection and management of metastasis. Although studies on epithelial markers for CTC detection are actively underway, the discovery of mesenchymal markers has not been studied sufficiently. In this study, we developed a new pipeline to identify membrane markers in CTCs of mesenchymal state in breast cancer based on expression profiles of the 310 CTC samples. From the total CTC samples, only CTC samples in the mesenchymal state were collected by employing hierarchical clustering. In samples belonging to the mesenchymal state, we calculated the correlation coefficients between 1995 membrane genes and ZEB2, which was determined as the key mesenchymal signature, allowing the 84 positively correlated genes. Furthermore, to ensure clinical significance, Kaplan-Meier analysis were performed on the 124 breast cancer patients, resulting in the 14 genes predicting prognosis. By exploring genes commonly identified in the both analyses, F11R and PTGIR were characterized as membrane markers in CTCs of mesenchymal state in breast cancer, which were evaluated by enriched terms, literature evidence, and relevant molecular pathways. We expect that the results will be helpful to more effective strategies for metastasis management

Precise Characterization of Genetic Interactions in Cancer via Molecular Network Refining Processes

Genetic interactions (GIs), such as the synthetic lethal interaction, are promising therapeutic targets in precision medicine. However, despite extensive efforts to characterize GIs by large-scale perturbation screening, considerable false positives have been reported in multiple studies. We propose a new computational approach for improved precision in GI identification by applying constraints that consider actual biological phenomena. In this study, GIs were characterized by assessing mutation, loss of function, and expression profiles in the DEPMAP database. The expression profiles were used to exclude loss-of-function data for nonexpressed genes in GI characterization. More importantly, the characterized GIs were refined based on Kyoto Encyclopedia of Genes and Genomes (KEGG) or protein–protein interaction (PPI) networks, under the assumption that genes genetically interacting with a certain mutated gene are adjacent in the networks. As a result, the initial GIs characterized with CRISPR and RNAi screenings were refined to 65 and 23 GIs based on KEGG networks and to 183 and 142 GIs based on PPI networks. The evaluation of refined GIs showed improved precision with respect to known synthetic lethal interactions. The refining process also yielded a synthetic partner network (SPN) for each mutated gene, which provides insight into therapeutic strategies for the mutated genes; specifically, exploring the SPN of mutated BRAF revealed ELAVL1 as a potential target for treating BRAF-mutated cancer, as validated by previous research. We expect that this work will advance cancer therapeutic research

Additional file 2: of Petri net-based prediction of therapeutic targets that recover abnormally phosphorylated proteins in muscle atrophy

Table S1. The 37 characterized therapeutic targets. Table S2. The enriched GO terms and KEGG pathways. Table S3. Hypergeometric test with gold standard from CTD. (XLSX 19 kb

Additional file 1: of Petri net-based prediction of therapeutic targets that recover abnormally phosphorylated proteins in muscle atrophy

Figure S1. An average of rank correlations among ten simulations in pair-wise manner for each of eleven thresholds. We selected the enabling threshold 35 that show the highest rank correlation in the reference and atrophic states. (DOCX 51 kb