Xiaoling Feng

Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis

Background Machine learning is a potentially effective method for predicting the response to platinum-based treatment for ovarian cancer. However, the predictive performance of various machine learning methods and variables is still a matter of controversy and debate. Objective This study aims to systematically review relevant literature on the predictive value of machine learning for platinum-based chemotherapy responses in patients with ovarian cancer. Methods Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we systematically searched the PubMed, Embase, Web of Science, and Cochrane databases for relevant studies on predictive models for platinum-based therapies for the treatment of ovarian cancer published before April 26, 2023. The Prediction Model Risk of Bias Assessment tool was used to evaluate the risk of bias in the included articles. Concordance index (C-index), sensitivity, and specificity were used to evaluate the performance of the prediction models to investigate the predictive value of machine learning for platinum chemotherapy responses in patients with ovarian cancer. Results A total of 1749 articles were examined, and 19 of them involving 39 models were eligible for this study. The most commonly used modeling methods were logistic regression (16/39, 41%), Extreme Gradient Boosting (4/39, 10%), and support vector machine (4/39, 10%). The training cohort reported C-index in 39 predictive models, with a pooled value of 0.806; the validation cohort reported C-index in 12 predictive models, with a pooled value of 0.831. Support vector machine performed well in both the training and validation cohorts, with a C-index of 0.942 and 0.879, respectively. The pooled sensitivity was 0.890, and the pooled specificity was 0.790 in the training cohort. Conclusions Machine learning can effectively predict how patients with ovarian cancer respond to platinum-based chemotherapy and may provide a reference for the development or updating of subsequent scoring systems.

SP1‐induced lncRNA FOXD3‐AS1 contributes to tumorigenesis of cervical cancer by modulating the miR‐296‐5p/HMGA1 pathway

AbstractLong noncoding RNAs (lncRNAs) have drawn growing attention due to their regulatory roles in various diseases, including tumors. Recently, lncRNA FOXD3 antisense RNA 1 (FOXD3‐AS1) was shown to be overexpressed in colon adenocarcinoma and glioma, exerting oncogenic functions. However, its expression and effects in cervical cancer (CC) remained unknown. In this research, our group first reported that the levels of FOXD3‐AS1 were distinctly elevated in CC samples and cell lines. The distinct upregulation of FOXD3‐AS1 was associated with lymphatic invasion, distant metastasis, and International Federation of Gynecology and Obstetrics stage, and also predicted poor clinical results of CC patients. Next, transcription factor SP1 was demonstrated to resulting in the upregulation of FOXD3‐AS1 in CC. Functional assays indicated that knockdown of FOXD3‐AS1 distinctly suppressed CC progression via affecting cell proliferation, cell apoptosis, and metastasis. Moreover, mechanistic studies suggested that FOXD3‐AS1 acted as an endogenous sponge by directly binding miR‐296‐5p, resulting in the suppression of miR‐296‐5p. In addition, we also reported that high mobility group A, a direct target of miR‐296‐5p, could mediate the tumor‐promotive effects that FOXD3‐AS1 displayed. Overall, our present study might help to lead a better understanding of the pathogenesis of CC, provide a novel possible tumor biomarker, and probe the feasibility of lncRNA‐directed treatments for CC.