FoxO1 promotes ovarian cancer by increasing transcription and METTL14-mediated m6A modification of SMC4.

The transcription factor forkhead box protein O1 (FoxO1) is closely related to the occurrence and development of ovarian cancer (OC), however its role and molecular mechanisms remain unclear. Herein, we found that FoxO1 was highly expressed in clinical samples of OC patients and was significantly correlated with poor prognosis. FoxO1 knockdown inhibited the proliferation of OC cells in vitro and in vivo. ChIP-seq combined with GEPIA2 and Kaplan-Meier database analysis showed that structural maintenance of chromosome 4 (SMC4) is a downstream target of FoxO1, and FoxO1 promotes SMC4 transcription by binding to its -1400/-1390 bp promoter. The high expression of SMC4 significantly blocked the tumor inhibition effect of FoxO1 knockdown. Furtherly, FoxO1 increased SMC4 mRNA abundance by transcriptionally activating methyltransferase-like 14 (METTL14) and increasing SMC4 m6A methylation on its coding sequence region. The Cancer Genome Atlas dataset analysis confirmed a significant positive correlation between FoxO1, SMC4, and METTL14 expression in OC. In summary, this study revealed the molecular mechanisms of FoxO1 regulating SMC4 and established a clinical link between the expression of FoxO1/METTL14/SMC4 in the occurrence of OC, thus providing a potential diagnostic target and therapeutic strategy.

A point mutation in the meiotic crossover formation gene HEI10/TFS2 leads to thermosensitive genic sterility in rice.

Thermosensitive genic female sterility (TGFS) is a promising property to be utilized for hybrid breeding. Here, we identified a rice TGFS line, tfs2, through an ethyl methyl sulfone (EMS) mutagenesis strategy. This line showed sterility under high temperature and became fertile under low temperature. Few seeds were produced when the tfs2 stigma was pollinated, indicating that tfs2 is female sterile. Gene cloning and genetic complementation showed that a point mutation from leucine to phenylalanine in HEI10 (HEI10tfs2), a crossover formation protein, caused the TGFS trait of tfs2. Under high temperature, abnormal univalents were formed, and the chromosomes were unequally segregated during meiosis, similar to the reported meiotic defects in oshei10. Under low temperature, the number of univalents was largely reduced, and the chromosomes segregated equally, suggesting that crossover formation was restored in tfs2. Yeast two-hybrid assays showed that HEI10 interacted with two putative protein degradation-related proteins, RPT4 and SRFP1. Through transient expression in tobacco leaves, HEI10 were found to spontaneously aggregate into dot-like foci in the nucleus under high temperature, but HEI10tfs2 failed to aggregate. In contrast, low temperature promoted HEI10tfs2 aggregation. This result suggests that protein aggregation at the crossover position contributes to the fertility restoration of tfs2 under low temperature. In addition, RPT4 and SRFP1 also aggregated into dot-like foci, and these aggregations depend on the presence of HEI10. These findings reveal a novel mechanism of fertility restoration and facilitate further understanding of HEI10 in meiotic crossover formation.

Machine learning-based radiomics nomograms to predict number of fields in postoperative IMRT for breast cancer.

BACKGROUND: Breast cancer is now the most commonly diagnosed cancer in women worldwide. Radiotherapy is an important part of the treatment for breast cancer, while setting proper number of fields dramatically affects the benefits one can receive. Machine learning and radiomics have been widely investigated in the management of breast cancer. This study aims to provide models to predict the best number of fields based on machine learning and improve the prediction performance by adding clinical factors. METHODS: Two-hundred forty-two breast cancer patients were retrospectively enrolled for this study, all of whom received postoperative intensity modulated radiation therapy. The patients were randomized into a training set and a validation set at a ratio of 7:3. Radiomics shape features were extracted for eight machine learning algorithms to predict the number of fields. Univariate and multivariable logistic regression were implemented to screen clinical factors. A combined model of rad-score and clinical factors were finally constructed. The area under receiver operating characteristic curve, precision, recall, F1 measure and accuracy were used to evaluate the model. RESULTS: Random Forest outperformed from eight machine learning algorithms while predicting the number of fields. Prediction performance of the radiomics model was better than the clinical model, while the predictive nomogram combining the rad-score and clinical factors performed the best. CONCLUSIONS: The model combining rad-score and clinical factors performed the best. Nomograms constructed from the combined models can be of reliable references for medical dosimetrists.