Despite the strikingly grave prognosis for older patients with glioblastomas, significant variability in patient outcome is experienced. To explore the potential for developing improved prognostic capabilities based on the elucidation of potential biological relationships, we performed analysis of DNA microarray gene expression data from tumors of glioblastoma patients of age greater than 50 for whom survival is known. Statistical analysis of the gene expression data in connection with survival involved exploration of regression models on small subsets of genes, based on computational search over many models with cross-validation to assess predictive validity. The analysis generated a set of regression models that, when weighted and combined according to posterior probabilities implied by the statistical analysis, identify patterns in expression of a small subset of genes that are associated with survival and have predictive value in assessing survival risks. The dominant genes across multiple such regression models involve three key genes - sparc (osteonectin), doublecortex and semaphorin - that play roles in cellular migration processes. Additional analysis, based on statistical graphical association models constructed using similar computational analysis methods, reveals others genes that support the view that multiple mediators of tumor invasion may be important prognostic factors in glioblastomas in older patients.

The manuscript is available at the Cancer Research web site