Differentially expressed lncRNAs in liver tissues of TX mice with hepatolenticular degeneration

Identification of lncRNA-miRNA-mRNA Networks in the Lenticular Nucleus Region of the Brain Contributes to Hepatolenticular Degeneration Pathogenesis and Therapy

Long non-coding RNAs (lncRNAs) are a recently discovered group of non-coding RNAs that play a crucial role in the regulation of various human diseases, especially in the study of nervous system diseases which has garnered significant attention. However, there is limited knowledge on the identification and function of lncRNAs in hepatolenticular degeneration (HLD). The objective of this study was to identify novel lncRNAs and determine their involvement in the networks associated with HLD. We conducted a comprehensive analysis of RNA sequencing (RNA-seq) data, reverse transcription-quantitative polymerase chain reaction (RT-qPCR), and computational biology to identify novel lncRNAs and explore their potential mechanisms in HLD. We identified 212 differently expressed lncRNAs, with 98 upregulated and 114 downregulated. Additionally, 32 differently expressed mRNAs were found, with 15 upregulated and 17 downregulated. We obtained a total of 1131 pairs of co-expressed lncRNAs and mRNAs by Pearson correlation test and prediction and annotation of the lncRNA-targeted miRNA-mRNA network. The differential lncRNAs identified in this study were found to be involved in various biological functions and signaling pathways. These include translational initiation, motor learning, locomotors behavior, dioxygenase activity, integral component of postsynaptic membrane, neuroactive ligand-receptor interaction, nuclear factor-kappa B (NF-κB) signaling pathway, cholinergic synapse, sphingolipid signaling pathway, and Parkinson's disease signaling pathway, as revealed by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Six lncRNAs, including XR_001782921.1 (P < 0.01), XR_ 001780581.1 (P < 0.01), ENSMUST_00000207119 (P < 0.01), XR_865512.2 (P < 0.01), TCONS_00005916 (P < 0.01), and TCONS_00020683 (P < 0.01), showed significant differences in expression levels between the model group and normal group by RT-qPCR. Among these, four lncRNAs (TCONS_00020683, XR_865512.2, XR_001780581.1, and ENSMUST00000207119) displayed a high degree of conservation. This study provides a unique perspective for the pathogenesis and therapy of HLD by constructing the lncRNA-miRNA-mRNA network. This insight provides a foundation for future exploration in this field.

A grading method for Kayser Fleischer ring images based on ResNet

The corneal K-F ring is the most common ophthalmic manifestation of WD patients. Early diagnosis and treatment have an important impact on the patient's condition. K-F ring is one of the gold standards for the diagnosis of WD disease. Therefore, this paper mainly focused on the detection and grading of the K-F ring. The aim of this study is three-fold. Firstly, to create a meaningful database, the K-F ring images are collected which contains 1850 images with 399 different WD patients, and then this paper uses the chi-square test and Friedman test to analyze the statistical significance. Subsequently, the all collected images were graded and labeled with an appropriate treatment approach, as a result, these images could be used to detect the corneal through the YOLO. After the detection of corneal, image segmentation was realized in batches. Finally, in this paper, different deep convolutional neural networks (VGG, ResNet, and DenseNet) were used to realize the grading of the K-F ring images in the KFID. Experimental results reveal that the entire pre-trained models obtain excellent performance. The global accuracies achieved by the six models i.e., VGG-16, VGG-19, ResNet18, ResNet34, ResNet50, and DenseNet are 89.88%, 91.89%, 94.18%, 95.31%, 93.59%, and 94.58% respectively. ResNet34 displayed the highest recall, specificity, and F1-score of 95.23%, 96.99%, and 95.23%. DenseNet showed the best precision of 95.66%. As such, the findings are encouraging, demonstrating the effectiveness of ResNet in the automatic grading of the K-F ring. Moreover, it provides effective help for the clinical diagnosis of HLD.

Cuproptosis-related long non-coding RNAs model that effectively predicts prognosis in hepatocellular carcinoma

BACKGROUND

Cuproptosis has recently been considered a novel form of programmed cell death. To date, long-chain non-coding RNAs (lncRNAs) crucial to the regulation of this process remain unelucidated.

AIM

To identify lncRNAs linked to cuproptosis in order to estimate patients' prognoses for hepatocellular carcinoma (HCC).

METHODS

Using RNA sequence data from The Cancer Genome Atlas Live Hepatocellular Carcinoma (TCGA-LIHC), a co-expression network of cuproptosis-related genes and lncRNAs was constructed. For HCC prognosis, we developed a cuproptosis-related lncRNA signature (CupRLSig) using univariate Cox, lasso, and multivariate Cox regression analyses. Kaplan-Meier analysis was used to compare overall survival among high- and low-risk groups stratified by median CupRLSig risk score. Furthermore, comparisons of functional annotation, immune infiltration, somatic mutation, tumor mutation burden (TMB), and pharmacologic options were made between high- and low-risk groups.

RESULTS

Three hundred and forty-three patients with complete follow-up data were recruited in the analysis. Pearson correlation analysis identified 157 cuproptosis-related lncRNAs related to 14 cuproptosis genes. Next, we divided the TCGA-LIHC sample into a training set and a validation set. In univariate Cox regression analysis, 27 LncRNAs with prognostic value were identified in the training set. After lasso regression, the multivariate Cox regression model determined the identified risk equation as follows: Risk score = (0.2659 × PICSAR expression) + (0.4374 × FOXD2-AS1 expression) + (-0.3467 × AP001065.1 expression). The CupRLSig high-risk group was associated with poor overall survival (hazard ratio = 1.162, 95%CI = 1.063-1.270; P < 0.001) after the patients were divided into two groups depending upon their median risk score. Model accuracy was further supported by receiver operating characteristic and principal component analysis as well as the validation set. The area under the curve of 0.741 was found to be a better predictor of HCC prognosis as compared to other clinicopathological variables. Mutation analysis revealed that high-risk combinations with high TMB carried worse prognoses (median survival of 30 mo vs 102 mo of low-risk combinations with low TMB group). The low-risk group had more activated natural killer cells (NK cells, P = 0.032 by Wilcoxon rank sum test) and fewer regulatory T cells (Tregs, P = 0.021) infiltration than the high-risk group. This finding could explain why the low-risk group has a better prognosis. Interestingly, when checkpoint gene expression (CD276, CTLA-4, and PDCD-1) and tumor immune dysfunction and rejection (TIDE) scores are considered, high-risk patients may respond better to immunotherapy. Finally, most drugs commonly used in preclinical and clinical systemic therapy for HCC, such as 5-fluorouracil, gemcitabine, paclitaxel, imatinib, sunitinib, rapamycin, and XL-184 (cabozantinib), were found to be more efficacious in the low-risk group; erlotinib, an exception, was more efficacious in the high-risk group.

CONCLUSION

The lncRNA signature, CupRLSig, constructed in this study is valuable in prognostic estimation of HCC. Importantly, CupRLSig also predicts the level of immune infiltration and potential efficacy of tumor immunotherapy.

Construction of a Novel circRNA/miRNA/mRNA Regulatory Network to Explore the Potential Pathogenesis of Wilson’s Disease

Studies show that non-coding RNAs, especially microRNAs (miRNAs) and circular RNAs (circRNAs), and protein-coding genes are involved in the pathophysiology of multi-organ damage caused by Wilson’s disease (WD). However, circRNA expression profiles and their role in initiation and progression of WD kidney injury remain largely unclear at present. Here, we explored potential critical protein-coding genes, miRNAs, and circRNAs, as well as identify competitive endogenous RNAs (ceRNAs) in a WD mouse model by high-throughput sequencing. We investigated the expression profiles of circRNAs, miRNAs, and protein-coding genes, and identified 32 DEcircRs, 45 DEmiRs, and 1623 DEPs. Identified DEcircRs, DEmiRs, and DEPs were used to construct a ceRNA network, which consisted of 15 DEcircRNAs (four upregulated and 11 downregulated), 18 DEmiRNAs (14 upregulated and four downregulated), and 352 DEmRNAs (205 upregulated and 147 downregulated). Further experiments proved that mmu_circ_0001333 and mmu_circ_0000355 acted as sponges of miR-92b-5p, miR-107-3p, and miR-187-3p to regulate the expression of genes including Smad9, Mapk10, and Aldh3a2, which may participate in WD-related kidney injury. Taken together, this study identified the circRNA/miRNA/mRNA network involved in kidney failure in WD, which may serve as a potential biomarker for the pathogenesis of WD.

Wilson Disease: Update on Pathophysiology and Treatment

Wilson disease (WD) is a potentially fatal genetic disorder with a broad spectrum of phenotypic presentations. Inactivation of the copper (Cu) transporter ATP7B and Cu overload in tissues, especially in the liver, are established causes of WD. However, neither specific ATP7B mutations nor hepatic Cu levels, alone, explain the diverse clinical presentations of WD. Recently, the new molecular details of WD progression and metabolic signatures of WD phenotypes began to emerge. Studies in WD patients and animal models revealed the contributions of non-parenchymal liver cells and extrahepatic tissues to the liver phenotype, and pointed to dysregulation of nuclear receptors (NR), epigenetic modifications, and mitochondria dysfunction as important hallmarks of WD pathogenesis. This review summarizes recent advances in the characterization of WD pathophysiology and discusses emerging targets for improving WD diagnosis and treatment.