Dataset Information

Delineating Molecular Subtypes through Gene Set Variation Analysis Confers Therapeutic and Prognostic Capability in Gastric Cancer.

ABSTRACT: To claim the features of nontumor tissue in gastric cancer patients, especially in those who have undergone gastrectomy, and to identify the molecular subtypes, we collected the immunogenic and hallmark gene sets from gene set enrichment analysis. The activity changes of these gene sets between tumor (375) and nontumor (32) tissues acquired from the Cancer Genome Atlas (TCGA-STAD) were calculated, and the novel molecular subtypes were delineated. Subsequently, prognostic gene sets were determined using least absolute shrinkage and selection operator (lasso) regression prognostic method. In addition, functional analysis was conducted. Totally, three subtypes were constructed in the present study, and there were differences in survival among three groups. Functional analysis showed genes from normal gene set were related to cell adhesion, and genes from tumor gene set were associated with focal adhesion, PI3K-Akt signaling pathway, regulation of actin cytoskeleton, and VEGF signaling pathway. Our study created lasting value beyond molecular subtypes and underscored the significance of normal tissues in gastric cancer development, which drawn a novel prognostic model for gastric treatment.

SUBMITTER: Zhu Y

PROVIDER: S-EPMC9307400 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Delineating Molecular Subtypes through Gene Set Variation Analysis Confers Therapeutic and Prognostic Capability in Gastric Cancer.

Zhu Yuzhang Y Sun Ting T Zhang Lei L Fei Faming F Bao Yi Y Gao Zhenzhen Z

Canadian journal of gastroenterology & hepatology 20220715

To claim the features of nontumor tissue in gastric cancer patients, especially in those who have undergone gastrectomy, and to identify the molecular subtypes, we collected the immunogenic and hallmark gene sets from gene set enrichment analysis. The activity changes of these gene sets between tumor (375) and nontumor (32) tissues acquired from the Cancer Genome Atlas (TCGA-STAD) were calculated, and the novel molecular subtypes were delineated. Subsequently, prognostic gene sets were determine ...[more]

PMID: 35875363

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Project description:Temporal variability in health-care processes or protocols is intrinsic to medicine. Such variability can potentially introduce dataset shifts, a data quality issue when reusing electronic health records (EHRs) for secondary purposes. Temporal data-set shifts can present as trends, as well as abrupt or seasonal changes in the statistical distributions of data over time. The latter are particularly complicated to address in multimodal and highly coded data. These changes, if not delineated, can harm population and data-driven research, such as machine learning. Given that biomedical research repositories are increasingly being populated with large sets of historical data from EHRs, there is a need for specific software methods to help delineate temporal data-set shifts to ensure reliable data reuse. EHRtemporalVariability is an open-source R package and Shiny app designed to explore and identify temporal data-set shifts. EHRtemporalVariability estimates the statistical distributions of coded and numerical data over time; projects their temporal evolution through non-parametric information geometric temporal plots; and enables the exploration of changes in variables through data temporal heat maps. We demonstrate the capability of EHRtemporalVariability to delineate data-set shifts in three impact case studies, one of which is available for reproducibility. EHRtemporalVariability enables the exploration and identification of data-set shifts, contributing to the broad examination and repurposing of large, longitudinal data sets. Our goal is to help ensure reliable data reuse for a wide range of biomedical data users. EHRtemporalVariability is designed for technical users who are programmatically utilizing the R package, as well as users who are not familiar with programming via the Shiny user interface.Availability: https://github.com/hms-dbmi/EHRtemporalVariability/Reproducible vignette: https://cran.r-project.org/web/packages/EHRtemporalVariability/vignettes/EHRtemporalVariability.htmlOnline demo: http://ehrtemporalvariability.upv.es/.

Dataset Information

Delineating Molecular Subtypes through Gene Set Variation Analysis Confers Therapeutic and Prognostic Capability in Gastric Cancer.

Publications

Delineating Molecular Subtypes through Gene Set Variation Analysis Confers Therapeutic and Prognostic Capability in Gastric Cancer.

Similar Datasets

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