Tao Tao

AI-Derived Blood Biomarkers for Ovarian Cancer Diagnosis: Systematic Review and Meta-Analysis

Background Emerging evidence underscores the potential application of artificial intelligence (AI) in discovering noninvasive blood biomarkers. However, the diagnostic value of AI-derived blood biomarkers for ovarian cancer (OC) remains inconsistent. Objective We aimed to evaluate the research quality and the validity of AI-based blood biomarkers in OC diagnosis. Methods A systematic search was performed in the MEDLINE, Embase, IEEE Xplore, PubMed, Web of Science, and the Cochrane Library databases. Studies examining the diagnostic accuracy of AI in discovering OC blood biomarkers were identified. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies–AI tool. Pooled sensitivity, specificity, and area under the curve (AUC) were estimated using a bivariate model for the diagnostic meta-analysis. Results A total of 40 studies were ultimately included. Most (n=31, 78%) included studies were evaluated as low risk of bias. Overall, the pooled sensitivity, specificity, and AUC were 85% (95% CI 83%-87%), 91% (95% CI 90%-92%), and 0.95 (95% CI 0.92-0.96), respectively. For contingency tables with the highest accuracy, the pooled sensitivity, specificity, and AUC were 95% (95% CI 90%-97%), 97% (95% CI 95%-98%), and 0.99 (95% CI 0.98-1.00), respectively. Stratification by AI algorithms revealed higher sensitivity and specificity in studies using machine learning (sensitivity=85% and specificity=92%) compared to those using deep learning (sensitivity=77% and specificity=85%). In addition, studies using serum reported substantially higher sensitivity (94%) and specificity (96%) than those using plasma (sensitivity=83% and specificity=91%). Stratification by external validation demonstrated significantly higher specificity in studies with external validation (specificity=94%) compared to those without external validation (specificity=89%), while the reverse was observed for sensitivity (74% vs 90%). No publication bias was detected in this meta-analysis. Conclusions AI algorithms demonstrate satisfactory performance in the diagnosis of OC using blood biomarkers and are anticipated to become an effective diagnostic modality in the future, potentially avoiding unnecessary surgeries. Future research is warranted to incorporate external validation into AI diagnostic models, as well as to prioritize the adoption of deep learning methodologies. Trial Registration PROSPERO CRD42023481232; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023481232

Ubiquitin Ligase TRIM22 Inhibits Ovarian Cancer Malignancy via TCF4 Degradation

Abstract Ovarian cancer is one of the most common malignancies in women. Tripartite motif-containing protein 22 (TRIM22) plays an important role in the initiation and progression of malignant tumors. Similarly, the transcription factor 4 (TCF4) is an essential factor involved in the initiation and progression of many tumors. However, it is still unclear whether TRIM22 can affect TCF4 in ovarian cancer. Therefore, this study aims to investigate the mechanism related to TRIM22 and TCF4 in ovarian cancer. TRIM22 protein and mRNA levels were analyzed in samples from clinical and cell lines. The effects of TRIM22 knockdown and overexpression on cell proliferation, colony formation, migration, invasion, and related biomarkers were evaluated. In addition, the role of ubiquitination-mediated degradation of TCF4 was investigated by qRT-PCR and Western blotting. The association between TRIM22 and TCF4 was evaluated by Western blotting, coimmunoprecipitation, proliferation, colony formation, invasion, migration, and related biomarkers. The results showed that the expression of TRIM22 was minimal in ovarian cancer tissues. Furthermore, upregulation of TRIM22 significantly inhibited ovarian cancer cell proliferation, colony formation, migration, and invasion. In addition, TRIM22 was observed to regulate the degradation of TCF4 through the ubiquitination pathway. TCF4 can reverse the effects of TRIM22 on proliferation, colony formation, migration, and invasion in ovarian cancer cells. TRIM22-mediated ubiquitination of TCF4 at K48 is facilitated by the RING domain. Implications: In conclusion, ubiquitination of TCF4 protein in ovarian cancer is regulated by TRIM22, which has the potential to limit the proliferation, migration, and invasion of ovarian cancer.