Radiotherapy is essential to treat breast cancer and microRNA (miRNA) miR-200c is considered as a radiosensitizer of breast cancer. However, the molecular mechanisms by which miR-200c regulates radiosensitivity remain largely unknown. In the present study, we showed that induction of miR-200c led to widespread alteration in long noncoding RNA (lncRNA) expression in breast cancer cells. We identified lncRNA LINC02582 as a target of miR-200c. Inhibition of LINC02582 expression increased radiosensitvity, while overexpression of LINC02582 promoted radioresistance. Mechanistically, LINC02582 interacts with deubiquitinating enzyme ubiquitin specific peptidase 7 (USP7) to deubiquitinate and stabilize checkpoint kinase 1 (CHK1), a critical effector kinase in DNA damage response, thus promoting radioresistance. Furthermore, we detected an inverse correlation between the expression of miR-200c vs. LINC02582 and CHK1 in breast cancer samples. These findings identified LINC02582 as a downstream target of miR-200c linking miR-200c to CHK1, in which miR-200c increases radiosensitivity by downregulation of CHK1.

MicroRNAs (miRNAs) play an essential role in the self-renewal of breast cancer stem cells (BCCs). Our study aimed to clarify the role of proto-oncogene c-Jun-regulated miR-5188 in breast cancer progression and its association with Timeless-mediated cancer stemness. In the present study, we showed that miR-5188 exerted an oncogenic effect by inducing breast cancer stemness, proliferation, metastasis, and chemoresistance in vitro and in vivo. The mechanistic analysis demonstrated that miR-5188 directly targeted FOXO1, which interacted with b-catenin in the cytoplasm, facilitated b-catenin degradation, and impaired the nuclear accumulation of b-catenin, thus stimulating the activation of known Wnt targets, epithelialmesenchymal transition (EMT) markers, and key regulators of cancer stemness. Moreover, miR-5188 potentiated Wnt/ b-catenin/c-Jun signaling to promote breast cancer progression. Interestingly, c-Jun enhanced miR-5188 transcription to form a positive regulatory loop, and Timeless interacted with Sp1/c-Jun to induce miR-5188 expression by promoting c-Jun-mediated transcription, which further activated miR-5188-FOXO1/b-catenin-c-Jun loop and facilitated breast cancer progression. Importantly, miR-5188 was upregulated in breast cancer and was positively correlated with poor patient prognosis. This study identifies miR-5188 as a novel oncomiR and provides a new theoretical basis for the clinical use of miR-5188 antagonists in the treatment of breast cancer.

Purpose To classify radiation necrosis versus recurrence in glioma patients using a radiomics model based on combinational features and multimodality MRI images. Methods Fifty-one glioma patients who underwent radiation treatments after surgery were enrolled in this study. Sixteen patients revealed radiation necrosis while 35 patients showed tumor recurrence during the follow-up period. After treatment, all patients underwent T1-weighted, T1-weighted postcontrast, T2-weighted, and fluid-attenuated inversion recovery scans. A total of 41,284 handcrafted and 24,576 deep features were extracted for each patient. The 0.623 + bootstrap method and the area under the curve (denoted as 0.632 + bootstrap AUC) metric were used to select the features. The stepwise forward method was applied to construct 10 logistic regression models based on different combinations of image features. Results For handcrafted features on multimodality MRI, model 7 with seven features yielded the highest AUC of 0.9624, sensitivity of 0.8497, and specificity of 0.9083 in the validation set. These values were higher than the accuracy of using handcrafted features on single-modality MRI (paired t-test, p < 0.05, except sensitivity). For combined handcrafted and AlexNet features on multimodality MRI, model 6 with six features achieved the highest AUC of 0.9982, sensitivity of 0.9941, and specificity of 0.9755 in the validation set. These values were higher than the accuracy of using handcrafted features on multimodality MRI (paired t-test, p < 0.05). Conclusions Handcrafted and deep features extracted from multimodality MRI images reflecting the heterogeneity of gliomas can provide useful information for glioma necrosis/recurrence classification.