Frontiers in Oncology (Jun 2023)

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

  • Gazi Nurun Nahar Sultana,
  • Ferdowsi Akter,
  • S. M. Hasan Israfil,
  • Utpal Chandra Ray,
  • Rumana Akther Jahan,
  • Mohammad Shawkat Ali,
  • Salim Al Din,
  • Shafiqur Rahman,
  • Rezaul Halim,
  • Mohammad Sahajadul Alam

DOI
https://doi.org/10.3389/fonc.2023.1171412
Journal volume & issue
Vol. 13

Abstract

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IntroductionAccording 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.ObjectiveThis 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.MethodsThe 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.ResultsThis 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.ConclusionFor 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.

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