Recent advancements in sequencing technology have allowed scientists to study the function and expression of different non-coding RNAs (ncRNAs). Circular RNAs (circRNAs) are increasingly recognized as prognostic and diagnostic biomarkers. These classes of ncRNAs are closed loops that are made from alternative mRNA splicing and can be found in large quantities with their evolutionarily conserved features across different species. These circRNAs can be derived from both coding and non-coding transcript and regulate cellular proteins and the expression of linear mRNA transcripts via their ability to act as miRNA sponges at various levels. Different circRNAs can affect the neurodegenerative disease pathogenesis such as Alzheimer's disease (AD), via altering a variety of methods. However, there is still limited knowledge about circRNAs in brain cells. Therefore, in this review, we will focus on the role of circRNAs in the brain to reveal a new potential therapeutic target and open the door to novel therapies and improved management of brain and neurodegenerative diseases.

Small extracellular vesicles (sEVs) are nanoscale (∼50–150 nm in diameter) membrane particles secreted by nearly all cell types that can be recovered easily from biofluids. sEVs mediate specific intercellular communication due to their selective molecular cargo of proteins, lipids, and nucleic acids and are mostly composed of vesicles that have an endosomal origin. Cancer tumor cells, in principle, could secrete sEVs to either promote tumor progression via cell proliferation, invasion, immunosuppression, or even boost antitumor responses. Among various types of vesicles, sEVs have an ideal combination of characteristics enabling them to be promising for oncology applications: inherent physiological stability in biological fluid, non-immunogenicity, and the capacity to target the tumor microenvironments intrinsically. For this reason, sEVs have become potential candidates for tumor diagnosis, prognosis, and progression detection as well as an engineered vehicle for cancer treatment. In this review, we summarize the latest developments in engineered sEVs for targeted therapy by analyzing their design and applications, in contrast to well-known artificial carriers, such as liposomes and lipid nanoparticles (LNPs). Moreover, we compare the therapeutic potential and limitations of sEV-therapeutics and present perspectives on their efficient utilization in recent studies.

Background: Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related mortality worldwide and exhibits substantial molecular heterogeneity, highlighting the need for reliable prognostic biomarkers and therapeutic targets. Increasing evidence suggests that autophagy plays a critical role in HCC progression. This study aimed to identify autophagy-related candidate biomarkers associated with prognosis and immune infiltration in HCC using an integrative machine learning and bioinformatics approach. Methods: Gene expression profiles from the GEO datasets GSE121248 and GSE64041 were analyzed to identify differentially expressed genes (DEGs) between HCC and normal liver tissues. Autophagy-related DEGs (ARDEGs) were identified by intersecting DEGs with the Human Autophagy Database, Molecular Signatures Database and GeneCards. Functional enrichment analyses (GO and KEGG) were performed to characterize their biological functions. Clinical information, including overall survival (OS), was obtained from the TCGA-LIHC cohort. Three machine learning algorithms, LASSO Cox regression, Random Survival Forest (RSF), and Support Vector Machine Recursive Feature Elimination (SVM-RFE), were applied to prioritize prognostically relevant genes followed by external validation. Integrated analyses of gene expression, clinicopathological characteristics, immune cell infiltration, transcription factor–miRNA regulatory networks, and drug–gene interactions were subsequently performed. Results: Sixteen autophagy-related DEGs were identified and were significantly enriched in longevity regulation, FoxO signaling, and AMPK signaling pathways. Integrating the results from LASSO Cox regression, RSF, and SVM-RFE identified six candidate prognostic genes: ECM1, EZH2, KLF4, CDH1, FAS, and GABARAPL1. Among these, ECM1 emerged as the primary candidate for further investigation because of its distinctive association with autophagy-related gene signatures, epithelial–mesenchymal transition (EMT), immune infiltration, and overall survival. Notably, ECM1 exhibited reduced expression in HCC while displaying altered correlations with autophagy-related genes compared with normal liver tissue, suggesting a potential disruption of its regulatory network during hepatocarcinogenesis. Conclusion: This integrative computational study identified six autophagy-related candidate prognostic genes in HCC and prioritized ECM1 as a promising candidate for future mechanistic investigation. The observed associations between ECM1, autophagy, immune infiltration, and patient prognosis provide a rationale for further experimental studies to determine its biological role and evaluate its potential clinical relevance. The findings should be considered hypothesis-generating until validated in independent experimental and clinical studies.