Transcription profiling of human NSCLC samples to predict recurrence-free survival in postoperative nsclc patients

ABSTRACT: Background:; One of the main fields of lung cancer research is identifying patients who are at high risk of post-resection recurrence. Individual recurrence risk evaluation by accurate but simple and reproducible method is needed for the clinical practice. Results:; The log-rank test and further selection by our criteria of assayability generated 87 genes from microarray data with significant level 5%. Of these, by PTQ-PCR, the expression of most significant 18 genes was obtained. Using these gene expression information and clinical parameters, by stepwise variable selection method, the recurrence prediction model, which composed of 6 genes (CALB1, MMP7, SLC1A7, GSTA1, CCL19, IFI44) and pStage and cell differentiation, were developed. Validation into the two independent cohorts showed good

ORGANISM(S): Homo sapiens

SUBMITTER: Jinkook Kim 

PROVIDER: E-GEOD-8894 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress