Glioma is the most common brain tumor with a dismal prognosis. While temozolomide (TMZ) based chemotherapy significantly improves survival in glioma patients, resistance against this compound commonly leads to glioma treatment failure. Overexpression of long-noncoding RNA (LncRNA) FoxD2 adjacent opposite strand RNA 1 (FoxD2-AS1) was identified to promote glioma development, but the role in TMZ resistance remains unclear. In this paper, we found that FoxD2-AS1 was overexpressed in recurrent glioma, high FoxD2-AS1 expression was significantly correlated with poor patient outcome. Methylation of O⁶-methylguanine-DNA methyltransferase (MGMT) is significantly less frequent in high FoxD2-AS1 expression patients. Knockdown of FoxD2-AS1 decreased the proliferation, metastatic ability of glioma cells and promote the sensitivity to TMZ in glioma cells. Furthermore, knockdown of FoxD2-AS1 induced hypermethylation of the promoter region of MGMT. Our data suggested that FoxD2-AS1 is a clinical relevance LncRNA and mediates TMZ resistance by regulating the methylation status of the MGMT promoter region.
Brain Neoplasms ; Drug Resistance ; Drug Therapy ; Glioma ; Humans ; Methylation ; Prognosis ; Promoter Regions, Genetic ; RNA ; RNA, Long Noncoding ; Treatment Failure

OBJECTIVE: To develop a deep features-based model to classify benign and malignant breast lesions on full- filed digital mammography. METHODS: The data of full-filed digital mammography in both craniocaudal view and mediolateral oblique view from 106 patients with breast neoplasms were analyzed. Twenty-three handcrafted features (HCF) were extracted from the images of the breast tumors and a suitable feature set of HCF was selected using -test. The deep features (DF) were extracted from the 3 pre-trained deep learning models, namely AlexNet, VGG16 and GoogLeNet. With abundant breast tumor information from the craniocaudal view and mediolateral oblique view, we combined the two extracted features (DF and HCF) as the two-view features. A multi-classifier model was finally constructed based on the combined HCF and DF sets. The classification ability of different deep learning networks was evaluated. RESULTS: Quantitative evaluation results showed that the proposed HCF+DF model outperformed HCF model, and AlexNet produced the best performances among the 3 deep learning models. CONCLUSIONS The proposed model that combines DF and HCF sets of breast tumors can effectively distinguish benign and malignant breast lesions on full-filed digital mammography.
Breast Neoplasms ; classification ; diagnostic imaging ; Deep Learning ; Diagnosis, Computer-Assisted ; methods ; Female ; Humans ; Mammography ; methods