Jie Xu

SRSF9 Forms Phase-Separated Condensates to Promote Ovarian Cancer Progression by Inducing RNA Alternative Splicing That Is Inhibited by m6A Modification

Abstract Deregulation of RNA alternative splicing and modification can play an important role in tumor initiation and progression. Elucidation of the interplay between alternative splicing and modifications of RNA could provide important insights into cancer biology. In this study, we showed that serine/arginine-rich splicing factor 9 (SRSF9) recognized non-N6-methyladenosine (m6A)–modified NUMB mRNA and induced an oncogenic isoform switch in ovarian cancer. NUMB mRNA m6A modification antagonized SRSF9-mediated alternative splicing. Notably, SRSF9 formed phase-separated condensates within the nucleus, which was indispensable for its splicing function as well as its tumor-promoting effect in ovarian cancer. Furthermore, SRSF9 was aberrantly upregulated in ovarian cancer, correlating with poor patient prognosis. Loss of SRSF9 or antisense oligonucleotide–mediated isoform switch of NUMB mRNA inhibited ovarian cancer growth in vitro and in vivo. In conclusion, this study reveals that SRSF9 condensation promotes ovarian cancer progression through modulation of alternative splicing, in competition with m6A modification. Significance: Phase separation increases activity of the splicing factor SRSF9 to support progression of ovarian cancer by generating an oncogenic isoform of NUMB mRNA competitively with m6A modification, which provides promising therapeutic targets.

Machine Learning for Preoperative Assessment and Postoperative Prediction in Cervical Cancer: Multicenter Retrospective Model Integrating MRI and Clinicopathological Data

Abstract Background Machine learning (ML) has been increasingly applied to cervical cancer (CC) research. However, few studies have combined both clinical parameters and imaging data. At the same time, there remains an urgent need for more robust and accurate preoperative assessment of parametrial invasion and lymph node metastasis, as well as postoperative prognosis prediction. Objective The objective of this study is to develop an integrated ML model combining clinicopathological variables and magnetic resonance image features for (1) preoperative parametrial invasion and lymph node metastasis detection and (2) postoperative recurrence and survival prediction. Methods Retrospective data from 250 patients with CC (2014‐2022; 2 tertiary hospitals) were analyzed. Variables were assessed for their predictive value regarding parametrial invasion, lymph node metastasis, survival, and recurrence using 7 ML models: K-nearest neighbor (KNN), support vector machine, decision tree, random forest (RF), balanced RF, weighted DT, and weighted KNN. Performance was assessed via 5-fold cross-validation using accuracy, sensitivity, specificity, precision, F1-score, and area under the receiver operating characteristic curve (AUC). The optimal models were deployed in an artificial intelligence–assisted contouring and prognosis prediction system. Results Among 250 women, there were 11 deaths and 24 recurrences. (1) For preoperative evaluation, the integrated model using balanced RF achieved optimal performance (sensitivity 0.81, specificity 0.85) for parametrial invasion, while weighted KNN achieved the best performance for lymph node metastasis (sensitivity 0.98, AUC 0.72). (2) For postoperative prognosis, weighted KNN also demonstrated high accuracy for recurrence (accuracy 0.94, AUC 0.86) and mortality (accuracy 0.97, AUC 0.77), with relatively balanced sensitivity of 0.80 and 0.33, respectively. (3) An artificial intelligence–assisted contouring and prognosis prediction system was developed to support preoperative evaluation and postoperative prognosis prediction. Conclusions The integration of clinical data and magnetic resonance images provides enhanced diagnostic capability to preoperatively detect parametrial invasion and lymph node metastasis detection and prognostic capability to predict recurrence and mortality for CC, facilitating personalized, precise treatment strategies.