Accepted for/Published in: JMIR Bioinformatics and Biotechnology
Date Submitted: Aug 21, 2024
Open Peer Review Period: Jan 8, 2025 - Mar 5, 2025
Date Accepted: Feb 4, 2025
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Unsupervised multiple correspondence analysis is a relevant tool for investigating associations between prognostic factors in gliomas
ABSTRACT
Background:
Multiple Correspondence Analysis (MCA) is an unsupervised data science methodology that aims to identify and represent associations between categorical variables. Gliomas are an aggressive type of cancer characterized by diverse molecular and clinical features that serve as key prognostic factors. Thus, advanced computational approaches are essential to enhance analysis and interpretation of the associations between clinical and molecular features in gliomas.
Objective:
This study aims to apply MCA to identify associations between glioma prognostic factors and also explore their associations with stemness phenotype.
Methods:
Clinical and molecular data from 448 brain tumor patients were obtained from The Cancer Genome Atlas (TCGA). The mDNA stemness index, derived from DNA methylation patterns, was built using a one-class logistic regression (OCLR). Associations between variables were evaluated using the chi-square test with k degrees of freedom, followed by analysis of the adjusted standardized residuals. MCA was employed to uncover associations between glioma prognostic factors and stemness.
Results:
Our analysis revealed significant associations among molecular and clinical characteristics in gliomas. Additionally, we demonstrated the capability of MCA to identify associations between stemness and these prognostic factors. Our results exhibited a strong association between higher mDNA stemness index and features related to poorer prognosis, demonstrating the utility of MCA as an analytical tool for elucidating potential prognostic factors.
Conclusions:
MCA proves to be a valuable tool for understanding the complex interdependence of prognostic markers in gliomas. MCA facilitates the exploration of large-scale datasets and enhances the identification of significant associations.
Citation
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Copyright
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