Blood gene signature for early hepatocellular carcinoma detection in patients with chronic hepatitis B.

PURPOSE: Up to 25% of chronic hepatitis B (CHB) patients eventually develop hepatocellular carcinoma (HCC), a disease with poor prognosis unless detected early. This study identifies a blood-based RNA biomarker panel for early HCC detection in CHB. MATERIALS AND METHODS: A genome-wide RNA expression study was performed using RNA extracted from blood samples from Malaysian patients (matched HCC, CHB, controls). Genes differentiating HCC from controls were selected for further testing using quantitative real-time polymerase chain reaction. Finally, a 6-gene biomarker panel was identified and characterized using a training set (cohort I = 126), and tested against 2 test sets (cohort II = 222; cohort III = 174). The total number of samples used for each group is: HCC + CHB = 143, CHB = 211, control = 168. RESULTS: Our gene panel displays a consistent trend distinguishing HCC from controls in our test sets, with an area under receiver-operating characteristic curve of 0.9 in cohort III. Our independent test set (cohort III) showed that the gene panel had a sensitivity of 70% with a specificity of 92%. The biomarker profile for HCC was consistently detected in a small subgroup of CHB patients, thus potentially predicting early, preclinical cases of cancer that should be screened more intensively. CONCLUSION: The biomarkers identified in this study can be used as the basis of a blood-based test for the detection of early HCC in CHB.

Whole blood transcriptome correlates with treatment response in nasopharyngeal carcinoma.

BACKGROUND: Treatment protocols for nasopharyngeal carcinoma (NPC) developed in the past decade have significantly improved patient survival. In most NPC patients, however, the disease is diagnosed at late stages, and for some patients treatment response is less than optimal. This investigation has two aims: to identify a blood-based gene-expression signature that differentiates NPC from other medical conditions and from controls and to identify a biomarker signature that correlates with NPC treatment response. METHODS: RNA was isolated from peripheral whole blood samples (2 x 10 ml) collected from NPC patients/controls (EDTA vacutainer). Gene expression patterns from 99 samples (66 NPC; 33 controls) were assessed using the Affymetrix array. We also collected expression data from 447 patients with other cancers (201 patients) and non-cancer conditions (246 patients). Multivariate logistic regression analysis was used to obtain biomarker signatures differentiating NPC samples from controls and other diseases. Differences were also analysed within a subset (n=28) of a pre-intervention case cohort of patients whom we followed post-treatment. RESULTS: A blood-based gene expression signature composed of three genes - LDLRAP1, PHF20, and LUC7L3 - is able to differentiate NPC from various other diseases and from unaffected controls with significant accuracy (area under the receiver operating characteristic curve of over 0.90). By subdividing our NPC cohort according to the degree of patient response to treatment we have been able to identify a blood gene signature that may be able to guide the selection of treatment. CONCLUSION: We have identified a blood-based gene signature that accurately distinguished NPC patients from controls and from patients with other diseases. The genes in the signature, LDLRAP1, PHF20, and LUC7L3, are known to be involved in carcinoma of the head and neck, tumour-associated antigens, and/or cellular signalling. We have also identified blood-based biomarkers that are (potentially) able to predict those patients who are more likely to respond to treatment for NPC. These findings have significant clinical implications for optimizing NPC therapy.