# Investigating Associations Between Prognostic Factors in Gliomas: Unsupervised Multiple Correspondence Analysis

## TL;DR

This paper uses a data science method called MCA to explore how different factors in glioma tumors are related, especially focusing on their connection to a stem-like cell trait called stemness.

## Contribution

The novel application of unsupervised multiple correspondence analysis (MCA) to uncover associations between glioma prognostic factors and stemness phenotype.

## Key findings

- Higher DNA methylation stemness index is strongly associated with poor prognosis features like glioblastoma and IDH wild type.
- MCA successfully identifies significant associations between clinical and molecular variables in glioma datasets.
- MGMT promoter unmethylation and telomerase expression are linked to increased stemness in gliomas.

## Abstract

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 the analysis and interpretation of the associations between clinical and molecular features in gliomas.

This study aims to apply MCA to identify associations between glioma prognostic factors and also explore their associations with stemness phenotype.

Clinical and molecular data from 448 patients with brain tumors were obtained from the Cancer Genome Atlas. The DNA methylation stemness index, derived from DNA methylation patterns, was built using a one-class logistic regression. Associations between variables were evaluated using the χ² test with k degrees of freedom, followed by analysis of the adjusted standardized residuals (ASRs >1.96 indicate a significant association between variables). MCA was used to uncover associations between glioma prognostic factors and stemness.

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 DNA methylation stemness index and features related to poorer prognosis such as glioblastoma cancer type (ASR: 8.507), grade 4 (ASR: 8.507), isocitrate dehydrogenase wild type (ASR:15.904), unmethylated MGMT (methylguanine methyltransferase) Promoter (ASR: 9.983), and telomerase reverse transcriptase expression (ASR: 3.351), demonstrating the utility of MCA as an analytical tool for elucidating potential prognostic factors.

MCA is 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.