Four Autophagy-Related Long Noncoding RNAs Provide Coexpression and ceRNA Mechanisms in Retinoblastoma through Bioinformatics and Experimental Evidence

Identification of hub genes and their correlation with immune infiltrating cells in membranous nephropathy: an integrated bioinformatics analysis

BACKGROUND

Membranous nephropathy (MN) is a chronic glomerular disease that leads to nephrotic syndrome in adults. The aim of this study was to identify novel biomarkers and immune-related mechanisms in the progression of MN through an integrated bioinformatics approach.

METHODS

The microarray data were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between MN and normal samples were identified and analyzed by the Gene Ontology analysis, the Kyoto Encyclopedia of Genes and Genomes analysis and the Gene Set Enrichment Analysis (GSEA) enrichment. Hub The hub genes were screened and identified by the weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) algorithm. The receiver operating characteristic (ROC) curves evaluated the diagnostic value of hub genes. The single-sample GSEA analyzed the infiltration degree of several immune cells and their correlation with the hub genes.

RESULTS

We identified a total of 574 DEGs. The enrichment analysis showed that metabolic and immune-related functions and pathways were significantly enriched. Four co-expression modules were obtained using WGCNA. The candidate signature genes were intersected with DEGs and then subjected to the LASSO analysis, obtaining a total of 6 hub genes. The ROC curves indicated that the hub genes were associated with a high diagnostic value. The CD4+ T cells, CD8+ T cells and B cells significantly infiltrated in MN samples and correlated with the hub genes.

CONCLUSIONS

We identified six hub genes (ZYX, CD151, N4BP2L2-IT2, TAPBP, FRAS1 and SCARNA9) as novel biomarkers for MN, providing potential targets for the diagnosis and treatment.

The Long Non-Coding RNA ANRIL in Cancers

ANRIL (Antisense Noncoding RNA in the INK4 Locus), a long non-coding RNA encoded in the human chromosome 9p21 region, is a critical factor for regulating gene expression by interacting with multiple proteins and miRNAs. It has been found to play important roles in various cellular processes, including cell cycle control and proliferation. Dysregulation of ANRIL has been associated with several diseases like cancers and cardiovascular diseases, for instance. Understanding the oncogenic role of ANRIL and its potential as a diagnostic and prognostic biomarker in cancer is crucial. This review provides insights into the regulatory mechanisms and oncogenic significance of the 9p21 locus and ANRIL in cancer.

A novel autophagy-related long non-coding RNAs prognostic risk score for clear cell renal cell carcinoma

BACKGROUND

As the main histological subtype of renal cell carcinoma, clear cell renal cell carcinoma (ccRCC) places a heavy burden on health worldwide. Autophagy-related long non-coding RNAs (ARlncRs) have shown tremendous potential as prognostic signatures in several studies, but the relationship between them and ccRCC still has to be demonstrated.

METHODS

The RNA-sequencing and clinical characteristics of 483 ccRCC patients were downloaded download from the Cancer Genome Atlas and International Cancer Genome Consortium. ARlncRs were determined by Pearson correlation analysis. Univariate and multivariate Cox regression analyses were applied to establish a risk score model. A nomogram was constructed considering independent prognostic factors. The Harrell concordance index calibration curve and the receiver operating characteristic analysis were utilized to evaluate the nomogram. Furthermore, functional enrichment analysis was used for differentially expressed genes between the two groups of high- and low-risk scores.

RESULTS

A total of 9 SARlncRs were established as a risk score model. The Kaplan-Meier survival curve, principal component analysis, and subgroup analysis showed that low overall survival of patients was associated with high-risk scores. Age, M stage, and risk score were identified as independent prognostic factors to establish a nomogram, whose concordance index in the training cohort, internal validation, and external ICGC cohort was 0.793, 0.671, and 0.668 respectively. The area under the curve for 5-year OS prediction in the training cohort, internal validation, and external ICGC cohort was 0.840, 0.706, and 0.708, respectively. GO analysis and KEGG analysis of DEGs demonstrated that immune- and inflammatory-related pathways are likely to be critically involved in the progress of ccRCC.

CONCLUSIONS

We established and validated a novel ARlncRs prognostic risk model which is valuable as a potential therapeutic target and prognosis indicator for ccRCC. A nomogram including the risk model is a promising clinical tool for outcomes prediction of ccRCC patients and further formulation of individualized strategy.

Comprehensive Analysis of Serum Small Extracellular Vesicles-Derived Coding and Non-Coding RNAs from Retinoblastoma Patients for Identifying Regulatory Interactions

The present study employed nanoparticle tracking analysis, transmission electron microscopy, immunoblotting, RNA sequencing, and quantitative real-time PCR validation to characterize serum-derived small extracellular vesicles (sEVs) from RB patients and age-matched controls. Bioinformatics methods were used to analyze functions, and regulatory interactions between coding and non-coding (nc) sEVs RNAs. The results revealed that the isolated sEVs are round-shaped with a size < 150 nm, 5.3 × 1011 ± 8.1 particles/mL, and zeta potential of 11.1 to −15.8 mV, and expressed exosome markers CD9, CD81, and TSG101. A total of 6514 differentially expressed (DE) mRNAs, 123 DE miRNAs, and 3634 DE lncRNAs were detected. Both miRNA-mRNA and lncRNA-miRNA-mRNA network analysis revealed that the cell cycle-specific genes including CDKNI1A, CCND1, c-MYC, and HIF1A are regulated by hub ncRNAs MALAT1, AFAP1-AS1, miR145, 101, and 16-5p. Protein-protein interaction network analysis showed that eye-related DE mRNAs are involved in rod cell differentiation, cone cell development, and retinol metabolism. In conclusion, our study provides a comprehensive overview of the RB sEV RNAs and regulatory interactions between them.

Identifying Autophagy-Related lncRNAs and Potential ceRNA Networks in NAFLD

Nonalcoholic fatty liver disease (NAFLD) is a common chronic disease with complex pathogenesis, which brings economic burden to the society, and there is still no effective therapy. Impaired autophagy has been implicated in the development of NAFLD. Long noncoding RNAs (lncRNAs) are also reported to play a role in the pathogenesis of NAFLD. However, the role of autophagy-related lncRNAs in NAFLD disease has not been elucidated. Here, we mined GSE135251, GSE160016, GSE130970 and GSE185062 datasets from the Gene Expression Omnibus database (GEO) and obtained the human autophagy-related gene list from the Human Autophagy Database (HADb) for in-depth bioinformatic analysis. Following differential expression analysis and intersection of the datasets, Pearson correlation analysis was performed on DElncRNAs and autophagy-related DEmRNAs to obtain autophagy-related lncRNAs, and then Starbase3.0 and TargetScan7.2 were used to construct competing endogenous RNAs (ceRNA) regulatory networks. We constructed four lncRNA-dominated ceRNA regulatory networks (PSMG3-AS1, MIRLET7BHG, RP11-136K7.2, LINC00925), and visualized with Cytoscape. Then we performed co-expression analysis of the ceRNA networks and autophagy-related genes, and functionally annotated them with Metascape. Finally, we performed receiver operating characteristic curve (ROC) analysis on lncRNAs and mRNAs within the ceRNA networks. Conclusively, our project is the first to study autophagy-related lncRNAs in NAFLD and finally mined four autophagy-related lncRNAs (PSMG3-AS1, MIRLET7BHG, RP11-136K7.2, LINC00925). We suggested that the four autophagy-related lncRNAs may be closely associated with the occurrence and development of NAFLD through the corresponding ceRNA regulatory networks. This research brings new horizons to the study of NAFLD.