A Survey on Computational Methods for Investigation on ncRNA-Disease Association through the Mode of Action Perspective.

Molecular and sequencing technologies have been successfully used in decoding biological mechanisms of various diseases. As revealed by many novel discoveries, the role of non-coding RNAs (ncRNAs) in understanding disease mechanisms is becoming increasingly important. Since ncRNAs primarily act as regulators of transcription, associating ncRNAs with diseases involves multiple inference steps. Leveraging the fast-accumulating high-throughput screening results, a number of computational models predicting ncRNA-disease associations have been developed. These tools suggest novel disease-related biomarkers or therapeutic targetable ncRNAs, contributing to the realization of precision medicine. In this survey, we first introduce the biological roles of different ncRNAs and summarize the databases containing ncRNA-disease associations. Then, we suggest a new trend in recent computational prediction of ncRNA-disease association, which is the mode of action (MoA) network perspective. This perspective includes integrating ncRNAs with mRNA, pathway and phenotype information. In the next section, we describe computational methodologies widely used in this research domain. Existing computational studies are then summarized in terms of their coverage of the MoA network. Lastly, we discuss the potential applications and future roles of the MoA network in terms of integrating biological mechanisms for ncRNA-disease associations.

Pan-cancer methylation analysis reveals an inverse correlation of tumor immunogenicity with methylation aberrancy.

Tumor immunogenicity is driven by various genomic and transcriptomic factors but the association with the overall status of methylation aberrancy is not well established. We analyzed The Cancer Genome Atlas pan-cancer database to investigate whether the overall methylation aberrancy links to the immune evasion of tumor. We created the definitions of hypermethylation burden, hypomethylation burden and methylation burden to establish the values that represent the degree of methylation aberrancy from human methylation 450 K array data. Both hypermethylation burden and hypomethylation burden significantly correlated with global methylation level as well as methylation subtypes defined in previous literatures. Then we evaluated whether methylation burden correlates with tumor immunogenicity and found that methylation burden showed a significant negative correlation with cytolytic activity score, which represent cytotoxic T cell activity, in pan-cancer (Spearman rho = - 0.37, p < 0.001) and 30 of 33 individual cancer types. Furthermore, this correlation was independent of mutation burden and chromosomal instability in multivariate regression analysis. We validated the findings in the external cohorts and outcomes of patients who were treated with immune checkpoint inhibitors, which showed that high methylation burden group had significantly poor progression-free survival (Hazard ratio 1.74, p = 0.038). Overall, the degree of methylation aberrancy negatively correlated with tumor immunogenicity. These findings emphasize the importance of methylation aberrancy for tumors to evade immune surveillance and warrant further development of methylation biomarker.

Multi-layered knowledge graph neural network reveals pathway-level agreement of three breast cancer multi-gene assays.

Multi-gene assays have been widely used to predict the recurrence risk for hormone receptor (HR)-positive breast cancer patients. However, these assays lack explanatory power regarding the underlying mechanisms of the recurrence risk. To address this limitation, we proposed a novel multi-layered knowledge graph neural network for the multi-gene assays. Our model elucidated the regulatory pathways of assay genes and utilized an attention-based graph neural network to predict recurrence risk while interpreting transcriptional subpathways relevant to risk prediction. Evaluation on three multi-gene assays-Oncotype DX, Prosigna, and EndoPredict-using SCAN-B dataset demonstrated the efficacy of our method. Through interpretation of attention weights, we found that all three assays are mainly regulated by signaling pathways driving cancer proliferation especially RTK-ERK-ETS-mediated cell proliferation for breast cancer recurrence. In addition, our analysis highlighted that the important regulatory subpathways remain consistent across different knowledgebases used for constructing the multi-level knowledge graph. Furthermore, through attention analysis, we demonstrated the biological significance and clinical relevance of these subpathways in predicting patient outcomes. The source code is available at http://biohealth.snu.ac.kr/software/ExplainableMLKGNN.

tRNA-Derived Small RNAs: Novel Epigenetic Regulators.

An epigenetic change is a heritable genetic alteration that does not involve any nucleotide changes. While the methylation of specific DNA regions such as CpG islands or histone modifications, including acetylation or methylation, have been investigated in detail, the role of small RNAs in epigenetic regulation is largely unknown. Among the many types of small RNAs, tRNA-derived small RNAs (tsRNAs) represent a class of noncoding small RNAs with multiple roles in diverse physiological processes, including neovascularization, sperm maturation, immune modulation, and stress response. Regarding these roles, several pioneering studies have revealed that dysregulated tsRNAs are associated with human diseases, such as systemic lupus, neurological disorder, metabolic disorder, and cancer. Moreover, recent findings suggest that tsRNAs regulate the expression of critical genes linked with these diseases by a variety of mechanisms, including epigenetic regulation. In this review, we will describe different classes of tsRNAs based on their biogenesis and will focus on their role in epigenetic regulation.

Preparation of CuCrO2 Hollow Nanotubes from an Electrospun Al2O3 Template.

A hollow nanostructure is attractive and important in different fields of applications, for instance, solar cells, sensors, supercapacitors, electronics, and biomedical, due to their unique structure, large available interior space, low bulk density, and stable physicochemical properties. Hence, the need to prepare hollow nanotubes is more important. In this present study, we have prepared CuCrO2 hollow nanotubes by simple approach. The CuCrO2 hollow nanotubes were prepared by applying electrospun Al2O3 fibers as a template for the first time. Copper chromium ions were dip-coated on the surface of electrospun-derived Al2O3 fibers and annealed at 600 °C in vacuum to form Al2O3-CuCrO2 core-shell nanofibers. The CuCrO2 hollow nanotubes were obtained by removing Al2O3 cores by sulfuric acid wet etching while preserving the rest of original structures. The structures of the CuCrO2-coated Al2O3 core-shell nanofibers and CuCrO2 hollow nanotubes were identified side-by-side by X-ray diffraction, field emission scanning electron microscopy, and transmission electron microscopy. The CuCrO2 hollow nanotubes may find applications in electrochemistry, catalysis, and biomedical application. This hollow nanotube preparation method could be extended to the preparation of other hollow nanotubes, fibers, and spheres.