Integrated Analysis of Transcriptomic and Genomic Data Reveals Blood Biomarkers With Diagnostic and Prognostic Potential in Non-small Cell Lung Cancer

Abstract

Background: Lung cancer is the second most common cancer and the main leading cause of cancer-associated death worldwide. Non-small cell lung cancer (NSCLC) accounts for about 85% of lung cancer diagnoses and more than 50% of all lung cancer cases are diagnosed at an advanced stage; hence have poor prognosis. Therefore, it is important to diagnose NSCLC patients reliably and as early as possible in order to reduce the risk of mortality. Methods: We identified blood-based gene markers for early NSCLC by performing a multi-omics approach utilizing integrated analysis of global gene expression and copy number alterations of NSCLC patients using array-based techniques. We also validated the diagnostic and the prognostic potential of the gene signature using independent datasets with detailed clinical information. Results: We identified 12 genes that are significantly expressed in NSCLC patients’ blood, at the earliest stages of the disease, and associated with a poor disease outcome. We then validated 12-gene signature’s diagnostic and prognostic value using independent datasets of gene expression profiling of over 1000 NSCLC patients. Indeed, 12-gene signature predicted disease outcome independently of other clinical factors in multivariate regression analysis (HR = 2.64, 95% CI = 1.72–4.07; p = 1.3 × 10−8). Significantly altered functions, pathways, and gene networks revealed alterations in several key genes and cancer-related pathways that may have importance for NSCLC transformation, including FAM83A, ZNF696, UBE2C, RECK, TIMM50, GEMIN7, and XPO5. Conclusion: Our findings suggest that integrated genomic and network analyses may provide a reliable approach to identify genes that are associated with NSCLC, and lead to improved diagnosis detecting the disease in early stages in patients’ blood instead of using invasive techniques and also have prognostic potential for discriminating high-risk patients from the low-risk ones.