Ovarian cancer is the second most common of the gynecological cancers in Taiwan. It is challenging to diagnose at an early stage when proper treatment is the most effective. It is well recognized that the detection of tumor cells (TCs) is critical for determining cancer growth stages and may provide important information for accurate diagnosis and even prognosis. In this study, a new microfluidic platform integrated with a moving-wall micro-incubator, a micro flow cytometer and a molecular diagnosis module performed automated identification of ovarian cancer cells. By efficiently mixing the cells and immunomagnetic beads coated with specific antibodies, the target TCs were successfully isolated from the clinical samples. Then counting of the target cells was achieved by a combination of the micro flow cytometer and an optical detection module and showed a counting accuracy as high as 92.5 %. Finally, cancer-associated genes were amplified and detected by the downstream molecular diagnosis module. The fluorescence intensity of specific genes (CD24 and HE4) associated with ovarian cancer was amplified by the molecular diagnosis module and the results were comparable to traditional slab-gel electrophoresis analysis, with a limit of detection around 10 TCs. This integrated microfluidic platform realized the concept of a "lab-on-a-chip" and had advantages which included automation, disposability, lower cost and rapid diagnosis and, therefore, may provide a promising approach for the fast and accurate detection of cancer cells.
All Science Journal Classification (ASJC) codes
- Biomedical Engineering
- Molecular Biology