Multilayer network analysis of miRNA and protein expression profiles in breast cancer patients

Identifying miRNA as biomarker for breast cancer subtyping using association rule

- This paper presents a comprehensive study focused on breast cancer subtyping, utilizing a multifaceted approach that integrates feature selection, machine learning classifiers, and miRNA regulatory networks. The feature selection process begins with the CFS algorithm, followed by the Apriori algorithm for association rule generation, resulting in the identification of significant features tailored to Luminal A, Luminal B, HER-2 enriched, and Basal-like subtypes. The subsequent application of Random Forest (RF) and Support Vector Machine (SVM) classifiers yielded promising results, with the SVM model achieving an overall accuracy of 76.60 % and the RF model demonstrating robust performance at 80.85 %. Detailed accuracy metrics revealed strengths and areas for refinement, emphasizing the potential for optimizing subtype-specific recall. To explore the regulatory landscape in depth, an analysis of selected miRNAs was conducted using MIENTURNET, a tool for visualizing miRNA-target interactions. While FDR analysis raised concerns for HER-2 and Basal-like subtypes, Luminal A and Luminal B subtypes showcased significant miRNA-gene interactions. Functional enrichment analysis for Luminal A highlighted the role of Ovarian steroidogenesis, implicating specific miRNAs such as hsa-let-7c-5p and hsa-miR-125b-5p as potential diagnostic biomarkers and regulators of Luminal A breast cancer. Luminal B analysis uncovered associations with the MAPK signaling pathway, with miRNAs like hsa-miR-203a-3p and hsa-miR-19a-3p exhibiting potential diagnostic and therapeutic significance. In conclusion, this integrative approach combines machine learning techniques with miRNA analysis to provide a holistic understanding of breast cancer subtypes. The identified miRNAs and associated pathways offer insights into potential diagnostic biomarkers and therapeutic targets, contributing to the ongoing efforts to improve breast cancer diagnostics and personalized treatment strategies.

Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs

Network theory has attracted much attention from the biological community because of its high efficacy in identifying tumor-associated genes. However, most researchers have focused on single networks of single omics, which have less predictive power. With the available multiomics data, multilayer networks can now be used in molecular research. In this study, we achieved this with the construction of a bilayer network of DNA methylation sites and RNAs. We applied the network model to five types of tumor data to identify key genes associated with tumors. Compared with the single network, the proposed bilayer network resulted in more tumor-associated DNA methylation sites and genes, which we verified with prognostic and KEGG enrichment analyses.

MiR-519d-5p modulates the sensitivity of breast cancer to chemotherapy by forming a negative feedback loop with RELA

Background

The chemoresistance of breast cancer (BC) has become the main cause of treatment failure. MicroRNAs (miRNAs) play a critical role in tumorigenesis, development, and chemoresistance, but the underlying mechanism of miR-519d in BC development and chemotherapy sensitivity remains to be elucidated.

Methods

The levels of miR-519d-5p in BC samples and cell lines were measured by quantitative reverse transcription polymerase chain reaction (RT-qPCR). Cell viability was monitored by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay. The in vivo effect of miR-519d-5p on tumor formation and doxorubicin response were investigated in a xenograft study. Bioinformatic analysis, luciferase reporter assay, RT-qPCR, and western blotting were conducted to validate RELA as a target gene of miR-519d-5p. We performed RT-qPCR, western blotting, chromatin immunoprecipitation (ChIP), and DNA pull down to verify miR-519d-5p as a transcriptional target of RELA.

Results

This study found that miR-519d-5p was expressed at lower levels in BC cells and tissues, and overexpression of miR-519d-5p sensitized BC to chemotherapy both in vitro and in vivo. Meanwhile, the expression of RELA was negatively correlated with miR-519d-5p. We then showed that RELA is one of the targets of miR-519d-5p: miR-519d-5p inhibited RELA expression by directly binding to its 3'-unstranslated region (3'-UTR). Conversely, it was verified that miR-519d-5p is one of the targets of transcription factor RELA, and RELA repressed miR-519d-5p by binding to the promoter region of miR-519d-5p, which forms a feedback loop.

Conclusions

Overall, the results provide a novel therapeutic strategy for the combinational use of miR-519d-5p and chemotherapeutic agents to overcome chemo-resistance by forming a negative feedback loop with RELA.

A propagation-based seed-centric local community detection for multilayer environment: The case study of colon adenocarcinoma

Regardless of all efforts on community discovery algorithms, it is still an open and challenging subject in network science. Recognizing communities in a multilayer network, where there are several layers (types) of connections, is even more complicated. Here, we concentrated on a specific type of communities called seed-centric local communities in the multilayer environment and developed a novel method based on the information cascade concept, called PLCDM. Our simulations on three datasets (real and artificial) signify that the suggested method outstrips two known earlier seed-centric local methods. Additionally, we compared it with other global multilayer and single-layer methods. Eventually, we applied our method on a biological two-layer network of Colon Adenocarcinoma (COAD), reconstructed from transcriptomic and post-transcriptomic datasets, and assessed the output modules. The functional enrichment consequences infer that the modules of interest hold biomolecules involved in the pathways associated with the carcinogenesis.

SMALF: miRNA-disease associations prediction based on stacked autoencoder and XGBoost

BACKGROUND

Identifying miRNA and disease associations helps us understand disease mechanisms of action from the molecular level. However, it is usually blind, time-consuming, and small-scale based on biological experiments. Hence, developing computational methods to predict unknown miRNA and disease associations is becoming increasingly important.

RESULTS

In this work, we develop a computational framework called SMALF to predict unknown miRNA-disease associations. SMALF first utilizes a stacked autoencoder to learn miRNA latent feature and disease latent feature from the original miRNA-disease association matrix. Then, SMALF obtains the feature vector of representing miRNA-disease by integrating miRNA functional similarity, miRNA latent feature, disease semantic similarity, and disease latent feature. Finally, XGBoost is utilized to predict unknown miRNA-disease associations. We implement cross-validation experiments. Compared with other state-of-the-art methods, SAMLF achieved the best AUC value. We also construct three case studies, including hepatocellular carcinoma, colon cancer, and breast cancer. The results show that 10, 10, and 9 out of the top ten predicted miRNAs are verified in MNDR v3.0 or miRCancer, respectively.

CONCLUSION

The comprehensive experimental results demonstrate that SMALF is effective in identifying unknown miRNA-disease associations.