Recent findings on miR‑370 expression, regulation and functions in cancer (Review)

Profile of Rat Adrenal microRNAs Induced by Gonadectomy and Testosterone or Estradiol Replacement

Sex-related differences in the structure and function of the adrenal cortex in mature rats are well recognized, largely driven by the action of sex hormones on the hypothalamic-pituitary-adrenal axis (HPA). By replacing testosterone or estradiol in gonadectomized rats, we aimed to elucidate the regulation of micro RNA (miRNA) profiles by sex hormones and their role in physiological adrenal function, providing new insights into gene expression modulation in the adrenal gland. This paper focuses on the description of miRNA profiles using the microarray technique. In our study, we observed significant sex differences in miRNA and mRNA expression levels. These differences are as follows: miRNA expression profiles Male C vs. Female C-0 down, 25 up-regulated, while mRNA profiles were 43 down and 27 up-regulated. Moreover, we observed the most significant differences in miRNA profiles between orchiectomized male rats supplemented with testosterone (ORX + T) and ovariectomized female rats treated with estradiol (OVX + E). Furthermore, we described changes in target gene expression and biological processes regulated by miRNAs. The processes most differentially expressed between the ORX + T and OVX + E groups are those related to the metabolism and synthesis of sterol compounds, the positive and negative regulation of metabolic processes in cells, e.g., cholesterol metabolism, response to various external factors, e.g., hormones, regulation of processes related to cell motility. We also identified several miRNAs, such as miR-370, miR-377, and miR-503, that exhibited interesting changes in their expression after testosterone or estradiol replacement. These results contribute to a deeper understanding of adrenal physiology.

The Role of Circular RNA in the Pathogenesis of Chemotherapy-Induced Cardiotoxicity in Cancer Patients: Focus on the Pathogenesis and Future Perspective

Cardiotoxicity is a serious challenge cancer patients face today. Various factors are involved in cardiotoxicity. Circular RNAs (circRNAs) are one of the effective factors in the occurrence and prevention of cardiotoxicity. circRNAs can lead to increased proliferation, apoptosis, and regeneration of cardiomyocytes by regulating the molecular pathways, as well as increasing or decreasing gene expression; some circRNAs have a dual role in cardiomyocyte regeneration or death. Identifying each of the pathways related to these processes can be effective on managing patients and preventing cardiotoxicity. In this study, an overview of the molecular pathways involved in cardiotoxicity by circRNAs and their effects on the downstream factors have been discussed.

X-LDA: An interpretable and knowledge-informed heterogeneous graph learning framework for LncRNA-disease association prediction

The identification of disease-related long noncoding RNAs (lncRNAs) is beneficial to unravel the intricacies of gene expression regulation and epigenetic signatures. Computational methods provide a cost-effective means to explore lncRNA-disease associations (LDAs). However, these methods often lack interpretability, leaving their predictions less convincing to biological and medical researchers. We propose an interpretable and knowledge-informed heterogeneous graph learning framework based on graph patch convolution and integrated gradients to predict LDAs and provides intuitive explanations for its predictions, called X-LDA. The heterogeneous graph is the foundation of the predictions of LDAs, we construct the knowledge-informed heterogeneous graph including LDAs drawn from biological experiments, lncRNA similarities rooted in gene sequences, disease similarities constructed based on disease categorizations. To integrate diverse biological premises and facilitate interpretability, we define nine distinct graph patch types, which encapsulate essential topological relationships within lncRNA-disease node pairs. X-LDA is designed to employ parameter sharing and multi-convolution kernels to grasp common and multiple perspectives of the graph patches, respectively. This approach culminates in the fusion of various semantic information into context embeddings. These post-hoc explanations hinge on graph patch features and integrated gradients, shedding light on the underlying factors driving predictions. Cross validation experiment on the dataset curated from databases and literatures demonstrates that the superior performance of X-LDA in comparison to nine state-of-the-art methods of three categories. X-LDA achieves a larger average area under the receiver operating curve 0.9891 (by at least 6.68%), and a larger average area under the precision-recall curve 0.7907 (by at least 23.2%) than competitive methods. The results of our well-designed ablation and interpretability experiments and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis demonstrate X-LDA's robustness, learnability, predictability, and interpretability. The applicability of X-LDA is also demonstrated through a case study involving the investigation of associated lncRNAs in prostate cancer, colorectal cancer, and breast cancer.