Using a panel of multiple tumor‐associated antigens to enhance the autoantibody detection in the immunodiagnosis of ovarian cancer

Abstract

Background: Ovarian cancer (OC) is a major malignancy affecting a large population over the world, and a biomarker that holds diagnostic potential is of critical importance. Recently, autoantibodies have been indicated as biomarkers in multiple cancer research. The current study was designed to explore the practice of using autoantibodies in diagnostic settings by the enzyme-linked immunosorbent assay of sera with a panel of tumor-associated antigens (TAAs).Methods: A panel of 12 TAAs was selected to detect the corresponding autoantibodies in sera sampled from 132 OC patients as case group and 147 normal healthy individuals as the control group. The diagnostic potential of this panel was evaluated by conventional evaluation, receiver operating characteristic (ROC) curve analyses, and classification tree analysis.Results: When the cutoff values were set as mean ± 2 SD for normal healthy individuals, the positive rates of antibodies to any single TAA were less than 20% both in OC and in normal healthy individuals. In a parallel screening approach, a panel of nine TAAs (p53, C-myc, p90, p62, AHSG, 14-3-3zeta, RalA, Koc, and p16), obtained optimal diagnostic performance in OC with the sensitivity of 61.4% at the 85.0% specificity. In addition, when the nine TAAs were combined with CA125, the sensitivity and specificity were improved to 94.7% and 78.2%, respectively. The ROC curve analyses showed that only the area under the receiver operating characteristic curves (AUCs) of antibodies against C-myc, Koc, and RalA was beyond 0.6, which were 0.732, 0.668, and 0.665, respectively. The AUC of the combination was up to 0.914 (P < 0.05).Decision tree analysis showed that C-myc, HCC1.3, RalA, and CA125 held high potential in the detection of OC. The panel of nine TAAs also identified 78.8% of OC patients who had normal CA125 levels in their serum samples, indicating that elevated CA125 and anti-TAA antibodies appeared to be independent but supplementary biomarkers for diagnosing OC. Conclusions:In summary, the current study further supports that a customized TAA panel can serve as a promising and powerful tool for immunodiagnosis of OC and may be particularly useful in patients with normal CA125 levels.

Autoantibodies against tumor‐associated antigens combined with microRNAs in detecting esophageal squamous cell carcinoma

“…Three panels (four TAAbs, three miRNAs, and three TAAbs+ three miRNAs) were constructed based on the logistic regression analysis. The combination of multiple biomarkers could attain higher sensitivity compared with single biomarkers, which was consistent with the published data . Importantly, the further results indicated that the diagnostic value could be greatly improved via the combination of miRNAs and TAAbs, with AUC of 0.89 (0.85‐0.93), sensitivity of 69%, specificity of 90%, and the accuracy of 81%, which was hopeful to surmount the tricky problem of low sensitivity of single type molecular marker.…”

Establishment and validation of an immunodiagnostic model for prediction of breast cancer

“…These findings were further supported by published data that the application of a panel of TAAs could enhance the detection of autoantibodies. 29,31,36,37 Moreover, the ELISA results of the 10 BC patients and 10 controls showed similar results to those of Western Blot (Supplemental Table 1-2). To control systematic errors and selection bias, 11 autoantibodies were tested in two BC groups from different hospitals, and were also tested in two different control groups.…”

“…Two tumor-associated antigens, 14-3-3ξ and RalA were constructed and purified using the same approach as used in our previous study. 27,31 The rest of TAAs, including p53, cyclin B1, cyclin D1, p16, IMP1, p62, koc, c-myc, survivin, were derived from our previous studies. 31,34…”

“…The protocol for western blotting analysis was performed as described in more detail in our previous study. 14,31…”

“…The existence of the 11 autoantibodies had been demonstrated in the serum samples from patients with hepatocellular carcinoma, ovarian and gastric cancer, which were collected in the previous study. 29,[31][32][33][34] Then, an optimal prediction model of multiple autoantibodies was constructed to detect BC with a higher diagnostic value. Both of the Binary logistic regression and Fisher linear discriminant analysis models were established to predict BC based on the relative concentrations of serum autoantibodies.…”