Integrated analysis of mRNA-seq and miRNA-seq reveals the potential roles of Egr1, Rxra and Max in kidney stone disease

Huang, Lijie; Shi, Yu-Xuan; Hu, Junjie; Ding, Jiarong; Guo, Zhiyong; Yu, Bing

doi:10.1007/s00240-022-01384-5

Cited by 6 publications

(2 citation statements)

References 49 publications

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“…On the other hand, future research should analyze the prospective clinical applications of both miRNAs and lncRNAs to kidney diseases that have not yet been extensively studied in the arena of ncRNA drugs (Figure 7D). In this context, variations in the expression profile of miRNAs (e.g., miR-7b-3p, miR-22-3p, miR-127-3p, miR-181a-5p, miR-214-5p, and miR-223-3p) and lncRNAs (lnc-EVI5L-1, lnc-FAM72B-4, lnc-KIN-1, lnc-MB-6, lnc-SERPINI1-2, and lnc-TIGD1L2-3) have been found to be associated with kidney stones [154][155][156]. Moreover, some miRNAs and lncRNAs that have been reported to have prospective therapeutic relevance in kidney stone disease include miR-34a, miR-30c-5p, miR-204, LINC00339, and LINC01197 [157][158][159][160][161].…”

Section: Future Insightsmentioning

confidence: 99%

Exploring the Therapeutic Significance of microRNAs and lncRNAs in Kidney Diseases

Bravo-Vázquez,

Paul,

Colín-Jurado

et al. 2024

Genes

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MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are two crucial classes of transcripts that belong to the major group of non-coding RNAs (ncRNAs). These RNA molecules have significant influence over diverse molecular processes due to their crucial role as regulators of gene expression. However, the dysregulated expression of these ncRNAs constitutes a fundamental factor in the etiology and progression of a wide variety of multifaceted human diseases, including kidney diseases. In this context, over the past years, compelling evidence has shown that miRNAs and lncRNAs could be prospective targets for the development of next-generation drugs against kidney diseases as they participate in a number of disease-associated processes, such as podocyte and nephron death, renal fibrosis, inflammation, transition from acute kidney injury to chronic kidney disease, renal vascular changes, sepsis, pyroptosis, and apoptosis. Hence, in this current review, we critically analyze the recent findings concerning the therapeutic inferences of miRNAs and lncRNAs in the pathophysiological context of kidney diseases. Additionally, with the aim of driving advances in the formulation of ncRNA-based drugs tailored for the management of kidney diseases, we discuss some of the key challenges and future prospects that should be addressed in forthcoming investigations.

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Section: Future Insightsmentioning

confidence: 99%

Exploring the Therapeutic Significance of microRNAs and lncRNAs in Kidney Diseases

Bravo-Vázquez,

Paul,

Colín-Jurado

et al. 2024

Genes

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“…Meanwhile, explainable artificial intelligence (XAI) [34] is a pragmatic tool that accelerates the creation of predictive models with domain knowledge and increases the transparency of automatically generated prediction models in the medical sector [35,36], which has assisted in providing results that are understandable to humans [37]. We noted that most of the recent research defined the detection of kidney problems in terms of individual categories, including stones [38], tumors [39], and cysts [40]. However, the proposed study classified all three abnormalities using a single model, and it provided the following contributions.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Capabilities of a Lightweight CNN Model in Accurately Identifying Renal Abnormalities: Cysts, Stones, and Tumors, Using LIME and SHAP

et al. 2023

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Kidney abnormality is one of the major concerns in modern society, and it affects millions of people around the world. To diagnose different abnormalities in human kidneys, a narrow-beam x-ray imaging procedure, computed tomography, is used, which creates cross-sectional slices of the kidneys. Several deep-learning models have been successfully applied to computer tomography images for classification and segmentation purposes. However, it has been difficult for clinicians to interpret the model’s specific decisions and, thus, creating a “black box” system. Additionally, it has been difficult to integrate complex deep-learning models for internet-of-medical-things devices due to demanding training parameters and memory-resource cost. To overcome these issues, this study proposed (1) a lightweight customized convolutional neural network to detect kidney cysts, stones, and tumors and (2) understandable AI Shapely values based on the Shapley additive explanation and predictive results based on the local interpretable model-agnostic explanations to illustrate the deep-learning model. The proposed CNN model performed better than other state-of-the-art methods and obtained an accuracy of 99.52 ± 0.84% for K = 10-fold of stratified sampling. With improved results and better interpretive power, the proposed work provides clinicians with conclusive and understandable results.

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Bioinformatics Analysis of miR-181a and Its Role in Adipogenesis, Obesity, and Lipid Metabolism Through Review of Literature

Hongfang,

Khan,

El-Mansi

2023

Mol Biotechnol

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Integrated analysis of mRNA-seq and miRNA-seq reveals the potential roles of Egr1, Rxra and Max in kidney stone disease

Cited by 6 publications

References 49 publications

Exploring the Therapeutic Significance of microRNAs and lncRNAs in Kidney Diseases

Exploring the Therapeutic Significance of microRNAs and lncRNAs in Kidney Diseases

Exploring the Capabilities of a Lightweight CNN Model in Accurately Identifying Renal Abnormalities: Cysts, Stones, and Tumors, Using LIME and SHAP

Bioinformatics Analysis of miR-181a and Its Role in Adipogenesis, Obesity, and Lipid Metabolism Through Review of Literature

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