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Molecular Predictors of Long-Term Survival in Glioblastoma Multiforme Patients. | LitMetric

AI Article Synopsis

  • Glioblastoma multiforme (GBM) is a highly aggressive brain cancer with less than 10% of patients surviving over three years, prompting researchers to study factors linked to long-term survival (LTS).
  • This study analyzed molecular data and clinical variables from long-term survivors to discover biomarkers associated with prolonged survival, focusing on multiple genomic factors like gene expression and DNA methylation.
  • The findings revealed that while DNA methylation was the strongest predictor of LTS, combining different molecular datasets did not enhance predictive accuracy, and no distinct profiles were found for LTS, showing that they shared similar gene expressions with non-long-term survivors.

Article Abstract

Glioblastoma multiforme (GBM) is the most common and aggressive adult primary brain cancer, with <10% of patients surviving for more than 3 years. Demographic and clinical factors (e.g. age) and individual molecular biomarkers have been associated with prolonged survival in GBM patients. However, comprehensive systems-level analyses of molecular profiles associated with long-term survival (LTS) in GBM patients are still lacking. We present an integrative study of molecular data and clinical variables in these long-term survivors (LTSs, patients surviving >3 years) to identify biomarkers associated with prolonged survival, and to assess the possible similarity of molecular characteristics between LGG and LTS GBM. We analyzed the relationship between multivariable molecular data and LTS in GBM patients from the Cancer Genome Atlas (TCGA), including germline and somatic point mutation, gene expression, DNA methylation, copy number variation (CNV) and microRNA (miRNA) expression using logistic regression models. The molecular relationship between GBM LTS and LGG tumors was examined through cluster analysis. We identified 13, 94, 43, 29, and 1 significant predictors of LTS using Lasso logistic regression from the somatic point mutation, gene expression, DNA methylation, CNV, and miRNA expression data sets, respectively. Individually, DNA methylation provided the best prediction performance (AUC = 0.84). Combining multiple classes of molecular data into joint regression models did not improve prediction accuracy, but did identify additional genes that were not significantly predictive in individual models. PCA and clustering analyses showed that GBM LTS typically had gene expression profiles similar to non-LTS GBM. Furthermore, cluster analysis did not identify a close affinity between LTS GBM and LGG, nor did we find a significant association between LTS and secondary GBM. The absence of unique LTS profiles and the lack of similarity between LTS GBM and LGG, indicates that there are multiple genetic and epigenetic pathways to LTS in GBM patients.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4849730PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0154313PLOS

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