BACKGROUND: Delayed cerebral ischemia (DCI) is a potentially devastating complication after intracranial aneurysm rupture and its mechanisms remain poorly elucidated. Early identification of the patients prone to developing DCI after rupture may represent a major breakthrough in its prevention and treatment. The single gene approach of DCI has demonstrated interest in humans. We hypothesized that whole genome expression profile of blood cells may be useful for better comprehension and prediction of aneurysmal DCI. METHODS: Over a 35-month period, 218 patients with aneurysm rupture were included in this study. DCI was defined as the occurrence of a new delayed neurological deficit occurring within 2 weeks after aneurysm rupture with evidence of ischemia either on perfusion-diffusion MRI, CT angiography or CT perfusion imaging, or with cerebral angiography. DCI patients were matched against controls based on 4 out of 5 criteria (age, sex, Fisher grade, aneurysm location and smoking status). Genome-wide expression analysis of blood cells obtained at admission was performed by microarrays. Transcriptomic analysis was performed using long oligonucleotide microarrays representing 25,000 genes. Quantitative PCR: 1 µg of total RNA extracted was reverse-transcribed, and the resulting cDNA was diluted 10-fold before performing quantitative PCR. Microarray data were first analyzed by 'Significance Analysis of Microarrays' software which includes the Benjamini correction for multiple testing. In a second step, microarray data fold change was compared using a two-tailed, paired t test. Analysis of receiver-operating characteristic (ROC) curves and the area under the ROC curves were used for prediction analysis. Logistic regression models were used to investigate the additive value of multiple biomarkers. RESULTS: A total of 16 patients demonstrated DCI. Significance Analysis of Microarrays software failed to retrieve significant genes, most probably because of the heterogeneity of the patients included in the microarray experiments and the small size of the DCI population sample. Standard two-tailed paired t test and C-statistic revealed significant associations between gene expression and the occurrence of DCI: in particular, the expression of neuroregulin 1 was 1.6-fold upregulated in patients with DCI (p = 0.01) and predicted DCI with an area under the ROC curve of 0.96. Logistic regression analyses revealed a significant association between neuroregulin 1 and DCI (odds ratio 1.46, 95% confidence interval 1.02-2.09, p = 0.02). CONCLUSIONS: This pilot study suggests that blood cells may be a reservoir of prognostic biomarkers of DCI in patients with intracranial aneurysm rupture. Despite an evident lack of power, this study elicited neuroregulin 1, a vasoreactivity-, inflammation- and angiogenesis-related gene, as a possible candidate predictor of DCI. Larger cohort studies are needed but genome-wide microarray-based studies are promising research tools for the understanding of DCI after intracranial aneurysm rupture.
BACKGROUND:Delayed cerebral ischemia (DCI) is a potentially devastating complication after intracranial aneurysm rupture and its mechanisms remain poorly elucidated. Early identification of the patients prone to developing DCI after rupture may represent a major breakthrough in its prevention and treatment. The single gene approach of DCI has demonstrated interest in humans. We hypothesized that whole genome expression profile of blood cells may be useful for better comprehension and prediction of aneurysmal DCI. METHODS: Over a 35-month period, 218 patients with aneurysm rupture were included in this study. DCI was defined as the occurrence of a new delayed neurological deficit occurring within 2 weeks after aneurysm rupture with evidence of ischemia either on perfusion-diffusion MRI, CT angiography or CT perfusion imaging, or with cerebral angiography. DCIpatients were matched against controls based on 4 out of 5 criteria (age, sex, Fisher grade, aneurysm location and smoking status). Genome-wide expression analysis of blood cells obtained at admission was performed by microarrays. Transcriptomic analysis was performed using long oligonucleotide microarrays representing 25,000 genes. Quantitative PCR: 1 µg of total RNA extracted was reverse-transcribed, and the resulting cDNA was diluted 10-fold before performing quantitative PCR. Microarray data were first analyzed by 'Significance Analysis of Microarrays' software which includes the Benjamini correction for multiple testing. In a second step, microarray data fold change was compared using a two-tailed, paired t test. Analysis of receiver-operating characteristic (ROC) curves and the area under the ROC curves were used for prediction analysis. Logistic regression models were used to investigate the additive value of multiple biomarkers. RESULTS: A total of 16 patients demonstrated DCI. Significance Analysis of Microarrays software failed to retrieve significant genes, most probably because of the heterogeneity of the patients included in the microarray experiments and the small size of the DCI population sample. Standard two-tailed paired t test and C-statistic revealed significant associations between gene expression and the occurrence of DCI: in particular, the expression of neuroregulin 1 was 1.6-fold upregulated in patients with DCI (p = 0.01) and predicted DCI with an area under the ROC curve of 0.96. Logistic regression analyses revealed a significant association between neuroregulin 1 and DCI (odds ratio 1.46, 95% confidence interval 1.02-2.09, p = 0.02). CONCLUSIONS: This pilot study suggests that blood cells may be a reservoir of prognostic biomarkers of DCI in patients with intracranial aneurysm rupture. Despite an evident lack of power, this study elicited neuroregulin 1, a vasoreactivity-, inflammation- and angiogenesis-related gene, as a possible candidate predictor of DCI. Larger cohort studies are needed but genome-wide microarray-based studies are promising research tools for the understanding of DCI after intracranial aneurysm rupture.