Dataset Information

Mutation-based clustering and classification analysis reveals distinctive age groups and age-related biomarkers for glioma.

ABSTRACT:

Background

Malignant brain tumor diseases exhibit differences within molecular features depending on the patient's age.

Methods

In this work, we use gene mutation data from public resources to explore age specifics about glioma. We use both an explainable clustering as well as classification approach to find and interpret age-based differences in brain tumor diseases. We estimate age clusters and correlate age specific biomarkers.

Results

Age group classification shows known age specifics but also points out several genes which, so far, have not been associated with glioma classification.

Conclusions

We highlight mutated genes to be characteristic for certain age groups and suggest novel age-based biomarkers and targets.

SUBMITTER: Jean-Quartier C

PROVIDER: S-EPMC7913451 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

Mutation-based clustering and classification analysis reveals distinctive age groups and age-related biomarkers for glioma.

Jean-Quartier Claire C Jeanquartier Fleur F Ridvan Aydin A Kargl Matthias M Mirza Tica T Stangl Tobias T Markaĉ Robi R Jurada Mauro M Holzinger Andreas A

BMC medical informatics and decision making 20210227 1

<h4>Background</h4>Malignant brain tumor diseases exhibit differences within molecular features depending on the patient's age.<h4>Methods</h4>In this work, we use gene mutation data from public resources to explore age specifics about glioma. We use both an explainable clustering as well as classification approach to find and interpret age-based differences in brain tumor diseases. We estimate age clusters and correlate age specific biomarkers.<h4>Results</h4>Age group classification shows know ...[more]

PMID: 33639927

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Project description:BACKGROUND:Age is associated with the prognosis of glioma patients, but there is no uniform standard of age-group classification to evaluate the prognosis of glioma patients. In this study, we aimed to establish an age group classification for risk stratification in glioma patients. METHODS:1502 patients diagnosed with gliomas at Nanfang Hospital between 2000 and 2018 were enrolled. The WHO grade of glioma was used as a dependent variable to evaluate the effect of age on risk stratification. The evaluation model was established by logistic regression, and the Akaike information criterion (AIC) value of the model was used to determine the optimal cutoff points for age-classification. The differences in gender, WHO grade, pathological subtype, tumor cell differentiation, tumor size, tumor location, and molecular markers between different age groups were analyzed. The molecular markers included GFAP, EMA, MGMT, P53, NeuN, Oligo2, EGFR, VEGF, IDH1, Ki-67, PR, CD3, H3K27M, TS, and 1p/19q status. RESULTS:The proportion of men with glioma was higher than that of women with glioma (58.3% vs 41.7%). Analysis of age showed that appropriate classifications of age group were 0-14?years old (pediatric group), 15-47?years old (youth group), 48-63?years old (middle-aged group), and???64?years old (elderly group).The proportions of glioblastoma and large tumor size (4-6?cm) increased with age (p?=?0.000, p?=?0.018, respectively). Analysis of the pathological molecular markers across the four age groups showed that the proportion of patients with larger than 10% area of Ki-67 expression or positive PR expression increased with age (p?=?0.000, p?=?0.017, respectively). CONCLUSIONS:Appropriate classifications of the age group for risk stratification are 0-14?years old (pediatric group), 15-47?years old (young group), 48-63?years old (middle age group) and???64?years old (elderly group). This age group classification is effective in evaluating the risk of glioblastoma in glioma patients.

Project description:Please see related commentary: http://www.biomedcentral.com/1741-7015/10/21/abstractAge-related macular degeneration (AMD) is a leading cause of blindness that affects the central region of the retinal pigmented epithelium (RPE), choroid, and neural retina. Initially characterized by an accumulation of sub-RPE deposits, AMD leads to progressive retinal degeneration, and in advanced cases, irreversible vision loss. Although genetic analysis, animal models, and cell culture systems have yielded important insights into AMD, the molecular pathways underlying AMD's onset and progression remain poorly delineated. We sought to better understand the molecular underpinnings of this devastating disease by performing the first comparative transcriptome analysis of AMD and normal human donor eyes.RPE-choroid and retina tissue samples were obtained from a common cohort of 31 normal, 26 AMD, and 11 potential pre-AMD human donor eyes. Transcriptome profiles were generated for macular and extramacular regions, and statistical and bioinformatic methods were employed to identify disease-associated gene signatures and functionally enriched protein association networks. Selected genes of high significance were validated using an independent donor cohort.We identified over 50 annotated genes enriched in cell-mediated immune responses that are globally over-expressed in RPE-choroid AMD phenotypes. Using a machine learning model and a second donor cohort, we show that the top 20 global genes are predictive of AMD clinical diagnosis. We also discovered functionally enriched gene sets in the RPE-choroid that delineate the advanced AMD phenotypes, neovascular AMD and geographic atrophy. Moreover, we identified a graded increase of transcript levels in the retina related to wound response, complement cascade, and neurogenesis that strongly correlates with decreased levels of phototransduction transcripts and increased AMD severity. Based on our findings, we assembled protein-protein interactomes that highlight functional networks likely to be involved in AMD pathogenesis.We discovered new global biomarkers and gene expression signatures of AMD. These results are consistent with a model whereby cell-based inflammatory responses represent a central feature of AMD etiology, and depending on genetics, environment, or stochastic factors, may give rise to the advanced AMD phenotypes characterized by angiogenesis and/or cell death. Genes regulating these immunological activities, along with numerous other genes identified here, represent promising new targets for AMD-directed therapeutics and diagnostics.

Dataset Information

Mutation-based clustering and classification analysis reveals distinctive age groups and age-related biomarkers for glioma.

Background

Methods

Results

Conclusions

Publications

Mutation-based clustering and classification analysis reveals distinctive age groups and age-related biomarkers for glioma.

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