Quantitative analysis of serum cell-free DNA as a predictive and prognostic marker in breast cancer patients.

Introduction: According to the GLOBOCAN (Global Cancer Observatory) 2020 report, 13,028 new cases of breast cancer (19%) were diagnosed in the United States, and 6,783 of them succumbed to the disease, making it the most common cancer among women. The clinical stage at the time of diagnosis is one of the most significant survival predictors in breast cancer. With delayed illness detection comes a lower survival rate. The prognosis of breast cancer may be predicted using circulating cell-free DNA (cfDNA), a non-invasive diagnosis technique. Objective: This study aimed to determine the most sensitive and effective method for detecting changes in cfDNA levels and for using cfDNA as a diagnostic and prognostic marker of breast cancer. Methods: The potential function of serum cfDNA levels as a marker for early breast cancer diagnosis was investigated using UV spectrophotometric, fluorometric, and real-time qPCR assays. Results: This research suggests that the most successful way to measure the amount of cfDNA described decades ago could be used as a "liquid biopsy" to track cancer in real time. The RT-qPCR (ALU115) method produced the most statistically significant results (p=0.000). At the threshold concentration of 395.65 ng/ml of cfDNA, the ROC curve reflects the maximum AUC= 0.7607, with a sensitivity of 0.65 and specificity of 0.80. Conclusion: For a preliminary assessment of total circulating cfDNA, a combination of all of the above techniques will be most efficacious. Based on our results, we conclude that the RT-qPCR technique combined with fluorometric measurement can identify a statistically significant difference in cfDNA levels between cohorts of breast cancer patients and healthy controls.

Coding-Complete Genome Sequences of Three SARS-CoV-2 Strains from Bangladesh.

We report the sequencing of three severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from Bangladesh. We have identified a unique mutation (NSP2_V480I) in one of the sequenced genomes (isolate hCoV-19/Bangladesh/BCSIR-NILMRC-006/2020) compared to the sequences available in the Global Initiative on Sharing All Influenza Data (GISAID) database. The data from this analysis will contribute to advancing our understanding of the epidemiology of SARS-CoV-2 in Bangladesh as well as worldwide at the molecular level and will identify potential new targets for interventions.