Improved Prediction of Smoking Status via Isoform-Aware RNA-seq Deep Learning Models
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ABSTRACT:
PROVIDER: PRJNA666220 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Predictive models based on gene expression are already a part of medical decision making for selected situations such as early breast cancer treatment. Most of these models are based on measures that do not capture critical aspects of gene splicing, butwith RNA sequencing it is possible to capture some of these aspects of alternative splicing and use them to improve clinical predictions. Building on previous models to predict cigarette smoking status, we show that measures of alternative splicing significantly improve the accuracy of these predictive models.
2020-12-31 | GSE158699 | GEO
Project description:Cigarette smoking accounts for approximately one in five deaths in the United States. Previous genomic studies have primarily focused on gene level differential expression to identify related molecular signatures and pathways, but the genome-wide effects of smoking on alternative isoform regulation and posttranscriptional modulation have not yet been described. We attempted to fill this void by identifying smoking-associated isoform switches and their responsible splicing events and consequences using RNA-seq.
2021-12-01 | GSE171730 | GEO
Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status. RNA was isolated from 199 primary breast cancer patients. A machine learning classifier was built to predict ER status using only three gene features.
2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress
Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status.
2013-01-01 | GSE29210 | GEO
Project description:Cigarette smoke is associated with the majority of lung cancers: however, 25% of lung cancer patients are non-smokers, and half of all newly diagnosed lung cancer patients are former smokers. Lung tumors exhibit distinct epidemiological, clinical, pathological, and molecular features depending on smoking status, suggesting divergent mechanisms underlie tumorigenesis in smokers and non-smokers. MicroRNAs (miRNAs) are integral contributors to tumorigenesis and mediate biological responses to smoking. Based on the hypothesis that smoking-specific miRNA differences in lung adenocarcinomas reflect distinct tumorigenic processes selected by different smoking and non-smoking environments, we investigated the contribution of miRNA disruption to lung tumor biology and patient outcome in the context of smoking status. Results: We discovered novel and distinct smoking-status-specific patterns of miRNA and miRNA-mediated gene networks, and identified miRNAs that were prognostically significant in a smoking-dependent manner. Conclusions: We conclude that miRNAs disrupted in a smoking-status-dependent manner affect distinct cellular pathways and differentially influence lung cancer patient prognosis in current, former and never smokers. Our findings may represent promising biologically relevant markers for lung cancer prognosis or therapeutic intervention.
2014-10-30 | GSE62182 | GEO
Project description:Cigarette Smoking-Associated Isoform Switching and 3’ UTR Lengthening Via Alternative Polyadenylation
| PRJNA720679 | ENA
Project description:Cigarette smoke is associated with the majority of lung cancers: however, 25% of lung cancer patients are non-smokers, and half of all newly diagnosed lung cancer patients are former smokers. Lung tumors exhibit distinct epidemiological, clinical, pathological, and molecular features depending on smoking status, suggesting divergent mechanisms underlie tumorigenesis in smokers and non-smokers. MicroRNAs (miRNAs) are integral contributors to tumorigenesis and mediate biological responses to smoking. Based on the hypothesis that smoking-specific miRNA differences in lung adenocarcinomas reflect distinct tumorigenic processes selected by different smoking and non-smoking environments, we investigated the contribution of miRNA disruption to lung tumor biology and patient outcome in the context of smoking status. Results: We discovered novel and distinct smoking-status-specific patterns of miRNA and miRNA-mediated gene networks, and identified miRNAs that were prognostically significant in a smoking-dependent manner. Conclusions: We conclude that miRNAs disrupted in a smoking-status-dependent manner affect distinct cellular pathways and differentially influence lung cancer patient prognosis in current, former and never smokers. Our findings may represent promising biologically relevant markers for lung cancer prognosis or therapeutic intervention. We applied a whole transcriptome sequencing based approach to interrogate miRNA levels in 94 patient-matched lung adenocarcinoma and non-malignant lung parenchymal tissue pairs from current [CS], former [FS] and never smokers [NS].
2014-10-30 | E-GEOD-62182 | biostudies-arrayexpress
Project description:Smoking is common in people who live with HIV infection and has significant adverse effects on HIV outcomes. The impacts of smoking on methylome has been well established in non-HIV populations. However, the smoking’s effects on host methylome in HIV-positive population has not been investigated and it is unknown if smoking-associated DNA methylation link to HIV outcomes. In this study, we applied machine learning methods selected smoking-associated DNA methylation features to predict HIV related frailty and mortality.
2019-07-23 | GSE117860 | GEO
Project description:Smoking is common in people who live with HIV infection and has significant adverse effects on HIV outcomes. The impacts of smoking on methylome has been well established in non-HIV populations. However, the smoking’s effects on host methylome in HIV-positive population has not been investigated and it is unknown if smoking-associated DNA methylation link to HIV outcomes. In this study, we applied machine learning methods selected smoking-associated DNA methylation features to predict HIV related frailty and mortality.
2019-07-23 | GSE117859 | GEO