Machine learning on MRI radiomic features: identification of molecular subtype alteration in breast cancer after neoadjuvant therapy.

OBJECTIVES: Recent studies have revealed the change of molecular subtypes in breast cancer (BC) after neoadjuvant therapy (NAT). This study aims to construct a non-invasive model for predicting molecular subtype alteration in breast cancer after NAT. METHODS: Eighty-two estrogen receptor (ER)-negative/ human epidermal growth factor receptor 2 (HER2)-negative or ER-low-positive/HER2-negative breast cancer patients who underwent NAT and completed baseline MRI were retrospectively recruited between July 2010 and November 2020. Subtype alteration was observed in 21 cases after NAT. A 2D-DenseUNet machine-learning model was built to perform automatic segmentation of breast cancer. 851 radiomic features were extracted from each MRI sequence (T2-weighted imaging, ADC, DCE, and contrast-enhanced T1-weighted imaging), both in the manual and auto-segmentation masks. All samples were divided into a training set (n = 66) and a test set (n = 16). XGBoost model with 5-fold cross-validation was performed to predict molecular subtype alterations in breast cancer patients after NAT. The predictive ability of these models was subsequently evaluated by the AUC of the ROC curve, sensitivity, and specificity. RESULTS: A model consisting of three radiomics features from the manual segmentation of multi-sequence MRI achieved favorable predictive efficacy in identifying molecular subtype alteration in BC after NAT (cross-validation set: AUC = 0.908, independent test set: AUC = 0.864); whereas an automatic segmentation approach of BC lesions on the DCE sequence produced good segmentation results (Dice similarity coefficient = 0.720). CONCLUSIONS: A machine learning model based on baseline MRI is proven useful for predicting molecular subtype alterations in breast cancer after NAT. KEY POINTS: • Machine learning models using MRI-based radiomics signature have the ability to predict molecular subtype alterations in breast cancer after neoadjuvant therapy, which subsequently affect treatment protocols. • The application of deep learning in the automatic segmentation of breast cancer lesions from MRI images shows the potential to replace manual segmentation..

tsRFun: a comprehensive platform for decoding human tsRNA expression, functions and prognostic value by high-throughput small RNA-Seq and CLIP-Seq data.

tRNA-derived small RNA (tsRNA), a novel type of regulatory small noncoding RNA, plays an important role in physiological and pathological processes. However, the understanding of the functional mechanism of tsRNAs in cells and their role in the occurrence and development of diseases is limited. Here, we integrated multiomics data such as transcriptome, epitranscriptome, and targetome data, and developed novel computer tools to establish tsRFun, a comprehensive platform to facilitate tsRNA research (http://rna.sysu.edu.cn/tsRFun/ or http://biomed.nscc-gz.cn/DB/tsRFun/). tsRFun evaluated tsRNA expression profiles and the prognostic value of tsRNAs across 32 types of cancers, identified tsRNA target molecules utilizing high-throughput CLASH/CLEAR or CLIP sequencing data, and constructed the interaction networks among tsRNAs, microRNAs, and mRNAs. In addition to its data presentation capabilities, tsRFun offers multiple real-time online tools for tsRNA identification, target prediction, and functional enrichment analysis. In summary, tsRFun provides a valuable data resource and multiple analysis tools for tsRNA investigation.

Compositional characterization of Pyrus ussuriensis Maxim and their antioxidant activities and induction of apoptosis in Bel-7402 cell.

In this work, a comparison study on active sites (the total phenolic, total flavonoids, and total triterpenes contents) and antioxidant activities (DPPH, ABTS, and FRAP assays) of different fractions from Pyrus ussuriensis Maxim was evaluated. Moreover, the inhibition capability on human hepatocarcinoma cells Bel-7402 cells and the mechanism was also discussed. Results showed that the ethyl acetate fraction significantly scavenged DPPH and ABTS+ radicals, exhibited ferric ion reducing antioxidant, and inhibited Bel-7402 cells proliferation. In addition, oleanolic acid was the dominant compound act on the Bel-7402 cells in the extract and it induced apoptosis by the caspase pathway and induced cell cycle arrest at the G0/G1 phase by inhibiting the cyclin D1/CDK4 pathway. The extracts of P. ussuriensis Maxim were confirmed to have anti-oxidative and antiproliferative effects against Bel-7402 cell in vitro. PRACTICAL APPLICATIONS: Fruits and vegetables which contain high levels of antioxidants can help to reduce the risk of chronic diseases such as cancer. Pyrus ussuriensis Maxim is a kind of edible and medical fruit with multiple bioactivities, whereas the capability to anti-lung cancer activity has not been investigated. The extracts of P. ussuriensis Maxim were revealed to have anti-oxidative and antiproliferative effects against Bel-7402 cell in vitro. Accordingly, it is the first time to verify that oleanolic acid was the main chemical components of P. ussuriensis with antitumor potential.