Improving health is a crucial social target that has a direct impact on better lives for billions and an indirect impact on the economy. Cancer is considered one of the most burdening diseases on better lives and hence the economy in all countries due to the high cost of health care and productivity deficiency. Therefore, the efforts to prevent, early detect, cost-effectively treat cancer become more important than ever. Chemotherapy is an essential step for cancer treatment in numerous cases. However, chemotherapy has severe side effects and a significant portion of patients do not respond to the treatment while suffering from these painful side effects. Doxorubicin, commercially known as Adriamycin, is a chemotherapy medication for several types of cancer. Our work aims for identifying the patients who have a resistance to Adriamycin chemotherapy.

Exosomes nanovesicles contain proteins, mRNA, and microRNAs (miRNAs) that exist in most body fluids and are expelled by multiple cell types including cancer cells¹. Exosomes' unique composition allows them to represent their parental cells, which makes them promising biomarkers for tumor prognosis². Conventional detection techniques necessitate enormous amounts of samples and extensive technical steps. Exosomal-RNA has been significantly used as a promising biomarker for tumor prognosis³. Herein, we describe the construction of a novel and cost-effective electrochemical system to sensitively differentiate between the exosomal-RNA of breast cancer MCF7 and MCF7/ADR-resistant cells, using four different electrochemical techniques; Cyclic Voltammetry (CV), Square Wave Voltammetry (SWV), Normal Pulse Voltammetry (NPV), and Differential Pulse Voltammetry (DPV). The high sensitivity of the proposed bio-electrochemical sensor opens the door for further investigation that address the other type of cancer cells.

References

1. R. Kalluri, Journal of Clinical Investigation, 126, 1208–1215 (2016).
2. H. Im, K. Lee, R. Weissleder, H. Lee, and C. M. Castro, Lab on a Chip, 17, 2892–2898 (2017).
3. D. Taller et al., Lab on a Chip, 15, 1656–1666 (2015).