AI Article Synopsis

  • The study aimed to identify new autoantibodies linked to tumor-associated antigens (TAAs) for diagnosing ovarian cancer (OC) and tested them using enzyme-linked immunosorbent assay on samples from both OC patients and normal controls.
  • Machine learning techniques were employed to create a diagnostic panel that included the autoantibodies anti-CFL1, anti-EZR, and anti-CYPA, which showed elevated levels in OC patients compared to controls.
  • This diagnostic panel demonstrated a promising performance with a sensitivity of 55.56% and specificity of 81.31%, identifying a significant portion of OC patients who were negative for existing biomarkers CA125 and HE4, suggesting its potential as an additional diagnostic tool.

Article Abstract

The purpose of this study was to identify novel autoantibodies against tumor-associated antigens (TAAs) and explore a diagnostic panel for Ovarian cancer (OC). Enzyme-linked immunosorbent assay was used to detect the expression of five anti-TAA autoantibodies in the discovery (70 OC and 70 normal controls) and validation cohorts (128 OC and 128 normal controls). Machine learning methods were used to construct a diagnostic panel. Serum samples from 81 patients with benign ovarian disease were used to identify the specificity of anti-TAA autoantibodies for OC. In both the discovery and validation cohorts, the expression of anti-CFL1, anti-EZR, anti-CYPA, and anti-PFN1 was higher in patients with OC than that in normal controls. The area under the receiver operating characteristic curve, sensitivity, and specificity of the panel containing anti-CFL1, anti-EZR, and anti-CYPA were 0.762, 55.56%, and 81.31%. The panel identified 53.06%, 53.33%, and 51.11% of CA125 negative, HE4 negative and the Risk of Ovarian Malignancy Algorithm negative OC patients, respectively. The combination of the three anti-TAA autoantibodies can serve as a favorable diagnostic tool for OC and has the potential to be a complementary biomarker for CA125 and HE4 in the diagnosis of ovarian cancer.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11058243PMC
http://dx.doi.org/10.1038/s41598-024-60544-2DOI Listing

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Article Synopsis
  • The study aimed to identify new autoantibodies linked to tumor-associated antigens (TAAs) for diagnosing ovarian cancer (OC) and tested them using enzyme-linked immunosorbent assay on samples from both OC patients and normal controls.
  • Machine learning techniques were employed to create a diagnostic panel that included the autoantibodies anti-CFL1, anti-EZR, and anti-CYPA, which showed elevated levels in OC patients compared to controls.
  • This diagnostic panel demonstrated a promising performance with a sensitivity of 55.56% and specificity of 81.31%, identifying a significant portion of OC patients who were negative for existing biomarkers CA125 and HE4, suggesting its potential as an additional diagnostic tool.
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