An efficient framework to identify key miRNA-mRNA regulatory modules in cancer

HetFCM: functional co-module discovery by heterogeneous network co-clustering

Functional molecular module (i.e., gene-miRNA co-modules and gene-miRNA-lncRNA triple-layer modules) analysis can dissect complex regulations underlying etiology or phenotypes. However, current module detection methods lack an appropriate usage and effective model of multi-omics data and cross-layer regulations of heterogeneous molecules, causing the loss of critical genetic information and corrupting the detection performance. In this study, we propose a heterogeneous network co-clustering framework (HetFCM) to detect functional co-modules. HetFCM introduces an attributed heterogeneous network to jointly model interplays and multi-type attributes of different molecules, and applies multiple variational graph autoencoders on the network to generate cross-layer association matrices, then it performs adaptive weighted co-clustering on association matrices and attribute data to identify co-modules of heterogeneous molecules. Empirical study on Human and Maize datasets reveals that HetFCM can find out co-modules characterized with denser topology and more significant functions, which are associated with human breast cancer (subtypes) and maize phenotypes (i.e., lipid storage, drought tolerance and oil content). HetFCM is a useful tool to detect co-modules and can be applied to multi-layer functional modules, yielding novel insights for analyzing molecular mechanisms. We also developed a user-friendly module detection and analysis tool and shared it at http://www.sdu-idea.cn/FMDTool.

Hsa_circ_0005050 regulated the progression of oral squamous cell carcinoma via miR-487a-3p/CHSY1 axis

Background/purpose

Circular RNAs (circRNAs) have been identified as potential functional modulators of the cellular physiology processes. This study aims to learn the potential molecular mechanisms of hsa_circ_0005050 (circ_0005050) in oral squamous cell carcinoma (OSCC).

Materials and methods

Quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) was used to examine the expression of circ_0005050, miR-487a-3p, and chondroitin sulfate synthase 1 (CHSY1). Dual-luciferase reporter system, RNA pull-down, and RNA Immunoprecipitation (RIP) assays were used to determine the binding between miR-487a-3p and circ_0005050 or CHSY1. Colony formation experiment and EdU assay were used to investigate proliferation. Wound-healing and transwell assays were used to detect the migration of cells. The apoptosis rate of OSCC cells was tested by flow cytometry. Protein levels of related factors were determined by Western blot. Tumor xenograft was established to determine the regulatory role of circ_0005050 on tumor growth in vivo, and Ki-67 expression was detected in this xenograft using Immunohistochemical (IHC).

Results

We implicated that circ_0005050 was apparently upregulated in OSCC tissues cells. In function experiments, repressing of circ_0005050 remarkably retarded OSCC growth in vitro. Furthermore, we conducted dual-luciferase reporter assays and RNA pull-down assays to verify that circ_0005050 sponged miR-487a-3p. Suppression of miR-487a-3p rescued the inhibition of proliferation in SCC15 and SCC25 cells induced by circ_0005050 knockdown. In addition, we found that overexpression of CHSY1 also reversed the inhibitory effect of circ_0005050 silencing on cell proliferation. Moreover, circ_0005050 knockdown inhibited tumor growth in vivo.

Conclusion

Circ_0005050 acted as an oncogenic factor in OSCC progression through miR-487a-3p/CHSY1 axis.

Identifying Tissue- and Cohort-Specific RNA Regulatory Modules in Cancer Cells Using Multitask Learning

Simple Summary

Understanding the underlying biological mechanisms of primary tumors is crucial for predicting how tumors respond to therapies and exploring accurate treatment strategies. miRNA–mRNA interactions have a major effect on many biological processes that are important in the formation and progression of cancer. In this study, we introduced a computational pipeline to extract tissue- and cohort-specific miRNA–mRNA regulatory modules of multiple cancer types from the same origin using miRNA and mRNA expression profiles of primary tumors. Our model identified regulatory modules of underlying cancer types (i.e., cohort-specific) and shared regulatory modules between cohorts (i.e., tissue-specific).

Abstract

MicroRNA (miRNA) alterations significantly impact the formation and progression of human cancers. miRNAs interact with messenger RNAs (mRNAs) to facilitate degradation or translational repression. Thus, identifying miRNA–mRNA regulatory modules in cohorts of primary tumor tissues are fundamental for understanding the biology of tumor heterogeneity and precise diagnosis and treatment. We established a multitask learning sparse regularized factor regression (MSRFR) method to determine key tissue- and cohort-specific miRNA–mRNA regulatory modules from expression profiles of tumors. MSRFR simultaneously models the sparse relationship between miRNAs and mRNAs and extracts tissue- and cohort-specific miRNA–mRNA regulatory modules separately. We tested the model’s ability to determine cohort-specific regulatory modules of multiple cancer cohorts from the same tissue and their underlying tissue-specific regulatory modules by extracting similarities between cancer cohorts (i.e., blood, kidney, and lung). We also detected tissue-specific and cohort-specific signatures in the corresponding regulatory modules by comparing our findings from various other tissues. We show that MSRFR effectively determines cancer-related miRNAs in cohort-specific regulatory modules, distinguishes tissue- and cohort-specific regulatory modules from each other, and extracts tissue-specific information from different cohorts of disease-related tissue. Our findings indicate that the MSRFR model can support current efforts in precision medicine to define tumor-specific miRNA–mRNA signatures.

miR-199a-3p/5p regulate tumorgenesis via targeting Rheb in non-small cell lung cancer

Lung cancer is one of the deadliest cancers, in which non-small cell lung cancer (NSCLC) accounting for 85% and has a low survival rate of 5 years. Dysregulation of microRNAs (miRNAs) can participate in tumor regulation and many major diseases. In this study, we found that miR-199a-3p/5p were down-expressed in NSCLC tissue samples, cell lines, and the patient sample database. MiR-199a-3p/5p overexpression could significantly suppress cell proliferation, migration ability and promote apoptosis. Through software prediction, ras homolog enriched in brain (Rheb) was identified as a common target of miR-199a-3p and miR-199a-5p, which participated in regulating mTOR signaling pathway. The same effect of inhibiting NSCLC appeared after down-regulating the expression of Rheb. Furthermore, our findings revealed that miR-199a can significantly inhibit tumor growth and metastasis in vivo, which fully demonstrates that miR-199a plays a tumor suppressive role in NSCLC. In addition, miR-199a-3p/5p has been shown to enhance the sensitivity of gefitinib to EGFR-T790M in NSCLC. Collectively, these results prove that miR-199a-3p/5p can act as cancer suppressor genes to inhibit the mTOR signaling pathway by targeting Rheb, which in turn inhibits the regulatory process of NSCLC. Thus, to investigate the anti-cancer effect of pre-miR-199a/Rheb/mTOR axis in NSCLC, miR-199a-3p and miR-199a-5p have the potential to become an early diagnostic marker or therapeutic target for NSCLC.

Modular network inference between miRNA-mRNA expression profiles using weighted co-expression network analysis

Connecting transcriptional and post-transcriptional regulatory networks solves an important puzzle in the elucidation of gene regulatory mechanisms. To decipher the complexity of these connections, we build co-expression network modules for mRNA as well as miRNA expression profiles of breast cancer data. We construct gene and miRNA co-expression modules using the weighted gene co-expression network analysis (WGCNA) method and establish the significance of these modules (Genes/miRNAs) for cancer phenotype. This work also infers an interaction network between the genes of the turquoise module from mRNA expression data and hubs of the turquoise module from miRNA expression data. A pathway enrichment analysis using a miRsystem web tool for miRNA hubs and some of their targets, reveal their enrichment in several important pathways associated with the progression of cancer.