Survival prediction of stage I lung adenocarcinomas by expression of 10 genes

Abstract

Adenocarcinoma is the predominant histological subtype of lung cancer, the leading cause of cancer deaths in the world. At stage I, the tumor is cured by surgery alone in about 60% of cases. Markers are needed to stratify patients by prognostic outcomes and may help in devising more effective therapies for poor prognosis patients. To achieve this goal, we used an integrated strategy combining meta-analysis of published lung cancer microarray data with expression profiling from an experimental model. The resulting 80-gene model was tested on an independent cohort of patients using RT-PCR, resulting in a 10-gene predictive model that exhibited a prognostic accuracy of approximately 75% in stage I lung adenocarcinoma when tested on 2 additional independent cohorts. Thus, we have identified a predictive signature of limited size that can be analyzed by RT-PCR, a technology that is easy to implement in clinical laboratories.

Pin1 expression contributes to lung cancer prognosis and carcinogenesis

“…23 Recent years, usefulness of gene expression profiles has been reported to predict survival of NSCLC cancer patients. [24][25][26][27] Kaplan-Meier showed patients with Pin1 negative tumors had substantially longer cancer-related survival than did patients with Pin1 positive tumors. We have shown that lymph node metastase and Pin1 positive expression were significant prognostic factors for overall survival by the univariate analyses of the clinicopathological factors.…”

Correlation analysis between genome-wide expression profiles and cytoarchitectural abnormalities in the prefrontal cortex of psychiatric disorders

“…After median normalization, potential noninformative genes were screened by the default gene filters, as previously described. 18,19 Genes were filtered out if less than 20% of their expression values across the samples showed at least a 1.5-fold change in either direction from the gene's median value or if the variance of the log ratios for each gene was less variable than the median of all the variances (P > 0.01). A total of 3572 genes passed this filtering step.…”

Investigating gene expression profile of non-small cell lung cancer

“…Some examples would include genes that affect adhesion (APC, CDH8, DSP), cell's mobility (IL8RB, ENPP2, CCL2), immune response (CASP8, CASP10), and apoptosis (INHA, PSEN1, BCL2). The possibility of differentiating between patients with high or low survival, through different number of markers, has been demonstrated repeatedly [25][26][27]. The first set of NSCLC prognosis predicting markers consisted of around 50 to 60 genes.…”