Knockdown of circ_HIPK3 inhibits tumorigenesis of hepatocellular carcinoma via the miR-582-3p/DLX2 axis.

Hepatocellular carcinoma (HCC) is the most common type in the sub-classification of liver cancer. Circular RNAs (circRNAs) play a fundamental role in tumor occurrence and progression. This research aimed to investigate the role and molecular basis of circRNA homeodomain-interacting protein kinase 3 (circ_HIPK3) in HCC. Circ_HIPK3 and DLX2 levels were enhanced, and miR-582-3p level was reduced in HCC tissues and cells. Silencing of circ_HIPK3 impeded proliferation, migration and invasion and expedited apoptosis in HCC cells. Furthermore, circ_HIPK3 modulated HCC progression via sponging miR-582-3p, and miR-582-3p suppressed HCC progression via targeting DLX2. Moreover, circ_HIPK3 knockdown inhibited tumor growth in vivo. Circ_HIPK3 facilitated HCC progression by mediating miR-582-3p/DLX2 pathway, suggesting a new potential biomarker for HCC treatment.

Long non-coding RNA TUG1 promotes cell progression in hepatocellular carcinoma via regulating miR-216b-5p/DLX2 axis.

BACKGROUND: Accumulating evidence indicates that the long noncoding RNA taurine upregulated gene 1(TUG1) plays a critical role in cancer progression and metastasis. However, the overall biological role and clinical significance of TUG1 in hepatocellular carcinoma (HCC) remain largely unknown. METHODS: The expressions of TUG1, microRNA-216b-5p and distal-less homeobox 2 (DLX2) were detected by Quantitative real-time polymerase chain reaction (qRT-PCR). The target relationships were predicted by StarBase v.2.0 or TargetScan and confirmed by dual-luciferase reporter assay. The cell growth, apoptosis, migration and invasion were detected by 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), Flow cytometry and Transwell assays, respectively. All protein expression levels were detected by western blot. Tumor xenografts were implemented to explore the role of TUG1 in vivo. RESULTS: We found that there was a marked rise in TUG1 expression in HCC tissues and cells, and knockdown of TUG1 repressed the growth and metastasis and promoted apoptosis of HCC cells. In particular, TUG1 could act as a ceRNA, effectively becoming a sink for miR-216b-5p to fortify the expression of DLX2. Additionally, repression of TUG1 impared the progression of HCC cells by inhibiting DLX2 expression via sponging miR-216b-5p in vitro. More importantly, TUG1 knockdown inhibited HCC tumor growth in vivo through upregulating miR-216b-5p via inactivation of the DLX2. CONCLUSION: TUG1 interacting with miR-216b-5p contributed to proliferation, metastasis, tumorigenesis and retarded apoptosis by activation of DLX2 in HCC.

Circular RNAs are differentially expressed in liver ischemia/reperfusion injury model.

Liver ischemia/reperfusion (I/R) injury has high mortality due to the intense inflammatory process occurs in the liver. However, the pathological mechanism underlying I/R injury is still not clear. Recent works showed that circular RNAs play critical roles in many human diseases. In this study, the occurrence of liver I/R injury was validated by an analysis of the blood samples and hematoxylin-eosin (HE) staining of liver tissues. Total RNA was purified and followed by RNA-seq in the purpose of screening the circRNAs in significant differentially expression, which were validated by quantitative PCR. GO and KEGG analysis were performed to determine the function of these differentially expressed circular RNAs. The circular structure of the circRNA was validated with gel electrophoresis and RNase R treatment. We found that some circular RNAs were differentially expressed in Liver I/R mouse models through bioinformatics analysis. These circular RNAs play roles in biological process, cellular component, and molecular function through GO analysis. Meanwhile, Hippo signaling pathway was found to be correlated with circular RNAs function in I/R models by KEGG analysis. To further validate bioinformatics data, two up-regulated and three down-regulated circular RNAs were confirmed in I/R models. The circularity of these differentially expressed circular RNAs was validated through gel electrophoresis and RNase R treatment. In summary, this work provides new insights into the mechanism underlying pathogenesis of liver I/R injury, providing new and potentially efficient targets against I/R injury.

MRI-based brain age prediction model for children under 3 years old using deep residual network.

Early identification and intervention of abnormal brain development individual subjects are of great significance, especially during the earliest and most active stage of brain development in children aged under 3. Neuroimage-based brain's biological age has been associated with health, ability, and remaining life. However, the existing brain age prediction models based on neuroimage are predominantly adult-oriented. Here, we collected 658 T1-weighted MRI scans from 0 to 3 years old healthy controls and developed an accurate brain age prediction model for young children using deep learning techniques with high accuracy in capturing age-related changes. The performance of the deep learning-based model is comparable to that of the SVR-based model, showcasing remarkable precision and yielding a noteworthy correlation of 91% between the predicted brain age and the chronological age. Our results demonstrate the accuracy of convolutional neural network (CNN) brain-predicted age using raw T1-weighted MRI data with minimum preprocessing necessary. We also applied our model to children with low birth weight, premature delivery history, autism, and ADHD, and discovered that the brain age was delayed in children with extremely low birth weight (less than 1000 g) while ADHD may cause accelerated aging of the brain. Our child-specific brain age prediction model can be a valuable quantitative tool to detect abnormal brain development and can be helpful in the early identification and intervention of age-related brain disorders.

LibMI: An Open Source Library for Efficient Histopathological Image Processing.

BACKGROUND: Whole-slide images (WSIs) as a kind of image data are rapidly growing in the digital pathology domain. With unusual high resolution, these images make them hard to be supported by conventional tools or file formats. Thus, it obstructs data sharing and automated analysis. Here, we propose a library, LibMI, along with its open and standardized image file format. They can be used together to efficiently read, write, modify, and annotate large images. MATERIALS AND METHODS: LibMI utilizes the concept of pyramid image structure and lazy propagation from a segment tree algorithm to support reading and modifying and to guarantee that both operations have linear time complexity. Further, a cache mechanism was introduced to speed up the program. RESULTS: LibMI is an open and efficient library for histopathological image processing. To demonstrate its functions, we applied it to several tasks including image thresholding, microscopic color correction, and storing pixel-wise information on WSIs. The result shows that libMI is particularly suitable for modifying large images. Furthermore, compared with congeneric libraries and file formats, libMI and modifiable multiscale image (MMSI) run 18.237 times faster on read-only tasks. CONCLUSIONS: The combination of libMI library and MMSI file format enables developers to efficiently read and modify WSIs, thus can assist in pixel-wise image processing on extremely large images to promote building image processing pipeline. The library together with the data schema is freely available on GitLab: https://gitlab.com/BioAI/libMI.