Identification of a novel lncRNA‐miRNA‐mRNA competing endogenous RNA network associated with prognosis of breast cancer

A Regulatory Circuits Analysis Tool, "miRCuit," Helps Reveal Breast Cancer Pathways: Toward Systems Medicine in Oncology

A systems medicine understanding of the regulatory molecular circuits that underpin breast cancer is essential for early cancer detection and precision/personalized medicine in clinical oncology. Transcription factors (TFs), microRNAs (miRNAs), and long non-coding RNAs (lncRNAs) control gene expression and cell biology, and by extension, serve as pillars of the regulatory circuits that determine human health and disease. We report here the development of a regulatory circuit analysis program, miRCuit, constructing 10 different types of regulatory elements involving messenger RNA, miRNA, lncRNA, and TFs. Using the miRCuit, we analyzed expression profiling data from 179 invasive ductal breast carcinoma and 51 normal tissue samples from the Gene Expression Omnibus database. We identified eight circuit types along with two special types of circuits, one of which highlighted the significant roles of lncRNA CASC15, miR-130b-3p, and TF KLF5 in breast cancer development and progression. These findings advance our understanding of the regulatory molecules associated with breast cancer. Moreover, miRCuit offers a new avenue for users to construct circuits from regulatory molecules for potential applications to decipher disease pathogenesis.

Long non-coding RNA MEG3 acts as a suppressor in breast cancer by regulating miR-330/CNN1

BACKGROUND

The current study aimed to investigate the molecular mechanism of long non-coding RNA (lncRNA) MEG3 in the development of breast cancer.

METHODS

The regulating relationships among lncRNA MEG3, miRNA-330 and CNN1 were predicted by bioinformatics analysis of breast cancer samples in the Cancer Genome Atlas database. The differential expression of lncRNA MEG3, miRNA-330 and CNN1 was first validated in breast cancer tissues and cells. The effects of lncRNA MEG3 on breast cancer malignant properties were evaluated by manipulating its expression in MCF-7 and BT-474 cells. Rescue experiments, dual-luciferase assays, and RNA immunoprecipitation (RIP) experiments were further used to validate the relationships among lncRNA MEG3, miRNA-330 and CNN1.

RESULTS

Bioinformatics analysis showed that lncRNA MEGs and CNN1 were significantly downregulated in breast cancer tissues, while miR-330 was upregulated. These differential expressions were further validated in our cohort of breast cancer samples. High expression levels of lncRNA MEG3 and CNN1 as well as low expression of miR-330 were significantly associated with favorable overall survival. Overexpression of lncRNA MEG3 significantly inhibited cell viability, migration and invasion, decreased cells in S stage and promoted cell apoptosis. Dual-luciferase reporter gene assay and RIP experiments showed that lncRNA MEG3 could directly bind to miR-330. Moreover, miR-330 mimics on the basis of lncRNA MEG3 overexpression ameliorated the tumor-suppressing effects of lncRNA MEG3 in breast cancer malignant properties by decreasing CNN1 expression.

CONCLUSION

Our study indicated lncRNA MEG3 is a breast cancer suppressor by regulating miR-330/CNN1 axis.

A Complex Heterogeneous Network Model of Disease Regulated by Noncoding RNAs: A Case Study of Unstable Angina Pectoris

MicroRNAs (miRNAs) are important types of noncoding RNAs, and there is a lack of holistic and systematic understanding of the functions they play in disease. We proposed a research strategy, including two parts network analysis and network modelling, to analyze, model, and predict the regulatory network of miRNAs from a network perspective, using unstable angina pectoris as an example. In the network analysis section, we proposed the WGCNA & SimCluster method using both correlation and similarity to find hub miRNAs, and validation on two datasets showed better results than the methods using correlation or similarity alone. In the network modelling section, we used six knowledge graph or graph neural network models for link prediction of three types of edges and multilabel classification of two types of nodes. Comparative experiments showed that the RotatE model was a good model for link prediction, while the RGCN model was the best model for multilabel classification. Potential target genes were predicted for hub miRNAs and validation of hub miRNA-target gene interactions, target genes as biomarkers and target gene functions were performed using a three-step validation approach. In conclusion, our study provides a new strategy to analyze and model miRNA regulatory networks.

Differential expression of the Tmem132 family genes in the developing mouse nervous system

The family of novel transmembrane proteins (TMEM) 132 have been associated with multiple neurological disorders and cancers in humans, but have hardly been studied in vivo. Here we report the expression patterns of the five Tmem132 genes (a, b, c, d and e) in developing mouse nervous system with RNA in situ hybridization in wholemount embryos and tissue sections. Our results reveal differential and partially overlapping expression of multiple Tmem132 family members in both the central and peripheral nervous system, suggesting potential partial redundancy among them.