JMIR Publications

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.