About This Project

Breast cancer is a pressing global health concern, affecting millions of people every year. Despite advances in treatment, there is a lack of effective and easily accessible biomarkers for early-stage breast cancer detection, resulting in missed opportunities for timely intervention. My project aims to leverage the power of bioinformatical analysis to identify novel blood-secreted biomarkers that can serve as early indicators of breast cancer.

Ask the Scientists

What is the context of this research?

Breast cancer is a pressing global health concern, affecting millions of people every year. Despite advances in treatment, there is a lack of effective and easily accessible biomarkers, resulting in missed opportunities for timely intervention. Mammography, the primary screening tool currently employed, has limitations in sensitivity and specificity, leading to false negatives and false positives. Analyzing specific biomarkers present in blood samples has already improved the early-detection of leading diseases as lung cancer. Thus, the development of non-invasive, blood-based biomarkers holds immense potential to provide a more effective screening method for breast cancer.

What is the significance of this project?

Breast cancer is typically detected through a combination of methods, including mammograms. However, its effectiveness have been a concern for a long time as well as its inability in detecting small cancers. Early detection has improved outcomes in a variety of diseases. Regular screenings have reduced mortality rates in colorectal cancer, while low-doseCT scans have helped identify lung cancer at an early stage. Recent advances in blood biomarkers have the potential to reduce mortality. In breast cancer, early detection allows for a greater likelihood of higher survival rates. Blood biomarkers offer a non-invasive and accessible method for identifying specific signatures in breast cancer.

What are the goals of the project?

The project goals include conducting an extensive meta-analysis by integrating RNA-seq data from primary breast cancer tissues to identify key genetic signatures. Additionally, the project aims to uncover specific signatures for each type of breast cancer, enabling personalized approaches. Next, the genes will be filtered by blood secretion, confirming their potential as early-biomarkers. These biomarkers will be validated using data from blood samples. The project will employ a range of bioinformatic tools, including meta-analysis, differential expression analysis, and clustering methods. Collectively, these goals aim to leverage bioinformatics to enhance our understanding of breast cancer and pave the way for improved detection strategies.