94 research outputs found
Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis
Comparison of logistic regression, SVM and random forest performance in the plasma training data set. Table S2. Pathway significance and relative log fold changes in our metabolomics data and TCGA breast cancer RNA-Seq data. Table S3. Detected metabolites and their differential test results among the two models. a All-stage diagnosis model. b Early-stage diagnosis model. Table S4. Single-variate logistic analysis of metabolites or pathways selected as features in the metabolite-based or pathway-based early-stage diagnosis model. Table S5. Comparison of pathway features in the full-size (101 input pathways) and half-size (51 input pathways) pathway-based early-stage diagnosis models. (DOCX 34 kb
DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data
Abstract
Single-cell RNA sequencing (scRNA-seq) offers new opportunities to study gene expression of tens of thousands of single cells simultaneously. We present DeepImpute, a deep neural network-based imputation algorithm that uses dropout layers and loss functions to learn patterns in the data, allowing for accurate imputation. Overall, DeepImpute yields better accuracy than other six publicly available scRNA-seq imputation methods on experimental data, as measured by the mean squared error or Pearson’s correlation coefficient. DeepImpute is an accurate, fast, and scalable imputation tool that is suited to handle the ever-increasing volume of scRNA-seq data, and is freely available at
https://github.com/lanagarmire/DeepImpute
.https://deepblue.lib.umich.edu/bitstream/2027.42/152237/1/13059_2019_Article_1837.pd
Genome-scale hypomethylation in the cord blood DNAs associated with early onset preeclampsia
Background: Preeclampsia is one of the leading causes of fetal and maternal morbidity and mortality worldwide.
Preterm babies of mothers with early onset preeclampsia (EOPE) are at higher risks for various diseases later on in life,
including cardiovascular diseases. We hypothesized that genome-wide epigenetic alterations occur in cord blood DNAs
in association with EOPE and conducted a case control study to compare the genome-scale methylome differences in
cord blood DNAs between 12 EOPE-associated and 8 normal births.
Results: Bioinformatics analysis of methylation data from the Infinium HumanMethylation450 BeadChip shows a
genome-scale hypomethylation pattern in EOPE, with 51,486 hypomethylated CpG sites and 12,563 hypermethylated sites
(adjusted P <0.05). A similar trend also exists in the proximal promoters (TSS200) associated with protein-coding genes.
Using summary statistics on the CpG sites in TSS200 regions, promoters of 643 and 389 genes are hypomethylated and
hypermethylated, respectively. Promoter-based differential methylation (DM) analysis reveals that genes in the farnesoid X
receptor and liver X receptor (FXR/LXR) pathway are enriched, indicating dysfunction of lipid metabolism in cord blood
cells. Additional biological functional alterations involve inflammation, cell growth, and hematological system
development. A two-way ANOVA analysis among coupled cord blood and amniotic membrane samples shows
that a group of genes involved in inflammation, lipid metabolism, and proliferation are persistently differentially
methylated in both tissues, including IL12B, FAS, PIK31, and IGF1.
Conclusions: These findings provide, for the first time, evidence of prominent genome-scale DNA methylation
modifications in cord blood DNAs associated with EOPE. They may suggest a connection between inflammation and
lipid dysregulation in EOPE-associated newborns and a higher risk of cardiovascular diseases later in adulthood
Challenges and perspectives in computational deconvolution in genomics data
Deciphering cell type heterogeneity is crucial for systematically
understanding tissue homeostasis and its dysregulation in diseases.
Computational deconvolution is an efficient approach to estimate cell type
abundances from a variety of omics data. Despite significant methodological
progress in computational deconvolution in recent years, challenges are still
outstanding. Here we enlist four significant challenges from availability of
the reference data, generation of simulation data, limitations of computational
methodologies, and benchmarking design and implementation. Finally, we make
recommendations on reference data generation, new directions of computational
methodologies and strategies to promote rigorous benchmarking
Lowered circulating aspartate is a metabolic feature of human breast cancer
Distinct metabolic transformation is essential for cancer cells to sustain a high rate of proliferation and resist cell death signals. Such a metabolic transformation results in unique cellular metabolic phenotypes that are often reflected by distinct metabolite signatures in tumor tissues as well as circulating blood. Using a metabolomics platform, we find that breast cancer is associated with significantly (p = 6.27E-13) lowered plasma aspartate levels in a training group comprising 35 breast cancer patients and 35 controls. The result was validated with 103 plasma samples and 183 serum samples of two groups of primary breast cancer patients. Such a lowered aspartate level is specific to breast cancer as it has shown 0% sensitivity in serum from gastric (n = 114) and colorectal (n = 101) cancer patients. There was a significantly higher level of aspartate in breast cancer tissues (n = 20) than in adjacent non-tumor tissues, and in MCF-7 breast cancer cell line than in MCF-10A cell lines, suggesting that the depleted level of aspartate in blood of breast cancer patients is due to increased tumor aspartate utilization. Together, these findings suggest that lowed circulating aspartate is a key metabolic feature of human breast cancer
Alternative Splicing Promotes Tumour Aggressiveness and Drug Resistance in African American Prostate Cancer.
linical challenges exist in reducing prostate cancer (PCa) disparities. The RNA splicing landscape of PCa across racial populations has not been fully explored as a potential molecular mechanism contributing to race-related tumour aggressiveness. Here, we identify novel genome-wide, race-specific RNA splicing events as critical drivers of PCa aggressiveness and therapeutic resistance in African American (AA) men. AA-enriched splice variants of PIK3CD, FGFR3, TSC2 and RASGRP2 contribute to greater oncogenic potential compared with corresponding European American (EA)-expressing variants. Ectopic overexpression of the newly cloned AA-enriched variant, PIK3CD-S, in EA PCa cell lines enhances AKT/mTOR signalling and increases proliferative and invasive capacity in vitro and confers resistance to selective PI3Kδ inhibitor, CAL-101 (idelalisib), in mouse xenograft models. High PIK3CD-S expression in PCa specimens associates with poor survival. These results highlight the potential of RNA splice variants to serve as novel biomarkers and molecular targets for developmental therapeutics in aggressive PCa
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