A novel detection method of non-small cell lung cancer using multiplexed bead-based serum biomarker profiling.

OBJECTIVES: Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality. Development of an early diagnosis method may improve survivals. We aimed to develop a new diagnostic model for NSCLC using serum biomarkers. METHODS: We set up a patient group diagnosed with NSCLC (n = 122) and a healthy control group (n = 225). Thirty serum analytes were selected on the basis of previous studies and a literature search. An antibody-bead array of 30 markers was constructed using the Luminex bead array platform (Luminex Inc, Austin, Tex) and was analyzed. Each marker was ranked by importance using the random forest method and then selected. Using selected markers, multivariate classification algorithms were constructed and were validated by application to independent validation cohort of 21 NSCLC and 28 control subjects. RESULTS: There was no difference in demographics between patients and the control population except for age (64.8 ± 10.0 for patients vs 53.0 ± 7.6 years for the control group). Among the 30 serum proteins, 23 showed a difference between the 2 groups (12 increased and 11 decreased in the patient group). We found the highest accuracy of multivariate classification algorithms when using the 5 highest-ranked biomarkers (A1AT, CYFRA 21-1, IGF-1, RANTES, AFP). When we applied the algorithms on a validation cohort, each method recognized the patients from the controls with high accuracy (89.8% with random forest, 91.8% with support vector machine, 88.2% with linear discriminant analysis, and 90.5% with logistic regression). CONCLUSIONS: We confirmed that a new diagnostic method using 5 serum biomarkers profiling constructed by multivariate classification algorithms could distinguish NSCLC from healthy controls with high accuracy.

ESM-1 silencing decreased cell survival, migration, and invasion and modulated cell cycle progression in hepatocellular carcinoma.

Endothelial cell-specific molecule-1 (ESM-1) is a secretory proteoglycan comprising a mature polypeptide of 165 amino acids and a single dermatan sulfate. The aim of this study was to evaluate endothelial cell-specific molecule-1 (ESM-1) as a hepatocellular carcinoma (HCC) marker and to analyze the effect of ESM-1 gene silencing in hepatocellular carcinoma cells. RT-PCR and Western Blot analysis revealed overexpression of ESM-1 in human HCC liver tissue and in serum from patients with HCC. Sandwich ELISA assay was used for quantitative analysis of ESM-1 in serum. Levels of ESM-1 were significantly elevated in the serum of patients with HCC (n = 40) as compared to serum from patients with hepatitis (AH, n = 40; CH, n = 39) or liver cirrhosis (n = 40) or from healthy subjects (n = 40). The accuracy of ESM-1 for HCC was higher than that of α-fetoprotein (AFP) according to ROC curve analysis. Expression of ESM-1 siRNA decreased cell survival through the inhibition of NF-κB pathway and induced cell cycle arrest by PTEN induction resulting in the inhibition of cyclin D1 in SK-Hep1 cells. Furthermore, ESM-1 silencing inhibited cell migration and invasion of SK-Hep1 cells. This study demonstrates that ESM-1 as a potential tumor marker is overexpressed in most tissues and serum in the presence of HCC and is involved with cell survival, cell cycle progression, migration, and invasion of hepatocellular carcinoma cells. Based on our results, we suggest that ESM-1 or a combination of ESM-1 and AFP is useful markers for diagnosis of HCC and ESM-1 may be useful therapeutic target of hepatocellular carcinoma.

The multiplex bead array approach to identifying serum biomarkers associated with breast cancer.

INTRODUCTION: Breast cancer is the most common type of cancer seen in women in western countries. Thus, diagnostic modalities sensitive to early-stage breast cancer are needed. Antibody-based array platforms of a data-driven type, which are expected to facilitate more rapid and sensitive detection of novel biomarkers, have emerged as a direct, rapid means for profiling cancer-specific signatures using small samples. In line with this concept, our group constructed an antibody bead array panel for 35 analytes that were selected during the discovery step. This study was aimed at testing the performance of this 35-plex array panel in profiling signatures specific for primary non-metastatic breast cancer and validating its diagnostic utility in this independent population. METHODS: Thirty-five analytes were selected from more than 50 markers through screening steps using a serum bank consisting of 4,500 samples from various types of cancer. An antibody-bead array of 35 markers was constructed using the Luminex bead array platform. A study population consisting of 98 breast cancer patients and 96 normal subjects was analysed using this panel. Multivariate classification algorithms were used to find discriminating biomarkers and validated with another independent population of 90 breast cancer and 79 healthy controls. RESULTS: Serum concentrations of epidermal growth factor, soluble CD40-ligand and proapolipoprotein A1 were increased in breast cancer patients. High-molecular-weight-kininogen, apolipoprotein A1, soluble vascular cell adhesion molecule-1, plasminogen activator inhibitor-1, vitamin-D binding protein and vitronectin were decreased in the cancer group. Multivariate classification algorithms distinguished breast cancer patients from the normal population with high accuracy (91.8% with random forest, 91.5% with support vector machine, 87.6% with linear discriminant analysis). Combinatorial markers also detected breast cancer at an early stage with greater sensitivity. CONCLUSIONS: The current study demonstrated the usefulness of the antibody-bead array approach in finding signatures specific for primary non-metastatic breast cancer and illustrated the potential for early, high sensitivity detection of breast cancer. Further validation is required before array-based technology is used routinely for early detection of breast cancer.