Exosomes are small membrane-bound vesicles secreted by most cell types. Exosomes contain various functional proteins, mRNAs and microRNAs (miRNAs) that could be used for diagnostic and therapeutic purposes. How we should store the samples before RNA isolation and whether those long term stored samples could be used for circulating RNA investigation because of RNase is unknown. The aim of the study was to determine the stability of circulating miRNA in exosomes and plasma. Exosomes were isolated from plasma samples by using ExoQuick Precipitation methods. RNA was extracted from exosomes and the corresponding plasma samples with a Qiagen miRNeasy Mini kit. The concentration of RNA was measured by a Qubit^® RNA HS Assay Kit, and quantitative PCR was used for individual miRNA expression level detection. Results showed that exosomal miRNA showed extra stability under different storage conditions and no significant influence on plasma miRNA, except for short term storage at 4 °C. It is thus indicated that exosome miRNAs can be good biomarkers based on their stability under various storage conditions.

Cell-free DNA molecules are released into the plasma via apoptotic or necrotic events and active release mechanisms, which carry the genetic and epigenetic information of its origin tissues. However, cfDNA is the mixture of various cell fragments, and the efficient enrichment of cfDNA fragments with diagnostic value remains a great challenge for application in the clinical setting. Evidence from recent years shows that cfDNA fragmentomics’ characteristics differ in normal and diseased individuals without the need to distinguish the source of the cfDNA fragments, which makes it a promising novel biomarker. Moreover, cfDNA fragmentomics can identify tissue origins by inferring epigenetic information. Thus, further insights into the fragmentomics of plasma cfDNA shed light on the origin and fragmentation mechanisms of cfDNA during physiological and pathological processes in diseases and enhance our ability to take the advantage of plasma cfDNA as a molecular diagnostic tool. In this review, we focus on the cfDNA fragment characteristics and its potential application, such as fragment length, end motifs, jagged ends, preferred end coordinates, as well as nucleosome footprints, open chromatin region, and gene expression inferred by the cfDNA fragmentation pattern across the genome. Furthermore, we summarize the methods for deducing the tissue of origin by cfDNA fragmentomics.

Little is known about circular RNAs (circRNAs) in specific brain cells and human neuropsychiatric disease. Here, we systematically identify over 11,039 circRNAs expressed in vulnerable dopamine and pyramidal neurons laser-captured from 190 human brains and non-neuronal cells using ultra-deep, total RNA sequencing. 1526 and 3308 circRNAs are custom-tailored to the cell identity of dopamine and pyramidal neurons and enriched in synapse pathways. 29% of Parkinson’s and 12% of Alzheimer’s disease-associated genes produced validated circRNAs. circDNAJC6, which is transcribed from a juvenile-onset Parkinson’s gene, is already dysregulated during prodromal, onset stages of common Parkinson’s disease neuropathology. Globally, addiction-associated genes preferentially produce circRNAs in dopamine neurons, autism-associated genes in pyramidal neurons, and cancers in non-neuronal cells. This study shows that circular RNAs in the human brain are tailored to neuron identity and implicate circRNA-regulated synaptic specialization in neuropsychiatric diseases.

Parkinson’s disease (PD) is the second most common neurodegenerative disease and although many studies have been done on this disease, the underlying mechanisms are still poorly understood and further studies are warranted. Therefore, this study identified circRNA expression profiles in the cerebral cortex (CC), hippocampus (HP), striatum (ST), and cerebellum (CB) regions of the 1-methyl-1,2,3,6-tetrahydropyridine (MPTP)-induced PD mouse model using RNA sequencing (RNA-seq), and differentially expressed circRNA were validated using reverse transcription quantitative real-time PCR (qRT-PCR). Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and competing endogenous RNA (ceRNA) network analyses were also performed to explore the potential function of circRNAs. The results show that, compared with the control group, 24, 66, 71, and 121 differentially expressed circRNAs (DE-circRNAs) were found in the CC, HP, ST, and CB, respectively. PDST vs. PDCB, PDST vs. PDHP, and PDCB vs. PDHP groups have 578, 110, and 749 DE-circRNAs, respectively. Then, seven DE-cirRNAs were selected for qRT-PCR verification, where the expressions were consistent with the sequencing analysis. The GO and KEGG pathway analyses revealed that these DE-circRNAs participate in several biological functions and signaling pathways, including glutamic synapse, neuron to neuron synapse, cell morphogenesis involved in neuron differentiation, Parkinson’s disease, axon guidance, cGMP-PKG signaling pathway, and PI3K-Akt signaling pathway. Furthermore, the KEGG analysis of the target genes predicted by DE-circRNAs indicated that the target genes predicted by mmu_circRNA_0003292, mmu_circRNA_0001320, mmu_circRNA_0005976, and mmu_circRNA_0005388 were involved in the PD-related pathway. Overall, this is the first study on the expression profile of circRNAs in the different brain regions of PD mouse model. These results might facilitate our understanding of the potential roles of circRNAs in the pathogenesis of PD. Moreover, the results also indicate that the mmu_circRNA_0003292-miRNA-132-Nr4a2 pathway might be involved in the regulation of the molecular mechanism of Parkinson’s disease.

Circular RNAs (circRNAs) are considered as functional biomolecules with tissue/development-specific expression patterns. Generally, a single gene may generate multiple circRNA variants by alternative splicing, which contain different combinations of exons and/or introns. Due to the low abundance of circRNAs as well as overlapped with their linear counterparts, circRNA enrichment protocol is needed prior to sequencing. Compared with numerous algorithms, which use back-splicing reads for detection and functional characterization of circRNAs, original bioinformatic analyzing tools have been developed to large-scale determination of full-length circRNAs and accurate quantification. This review provides insights into the complexity of circRNA biogenesis and surveys the recent progresses in the experimental and bioinformatic methodologies that focus on accurately full-length circRNAs identification.

Purpose: Gastric cancer (GC) is aggressive cancer with a high mortality rate worldwide. N6-methyladenosine (m6A) RNA methylation is related to tumorigenesis, which is dynamically regulated by m6A modulators (“writer,” “eraser,” and “reader”). We conducted a comprehensive analysis of the m6A genes of GC patients in TCGA datasets to identify the potential diagnostic biomarkers. Materials and Methods: We analyzed the expression profile of m6A genes in the TCGA cohort and constructed a diagnostic-m6A-score (DMS) by the LASSO-logistic model. In addition, by consensus cluster analysis, we identified two different subgroups of GC risk individuals by the expression profile of m6A modulators, revealing that YTHDF1’s expression variation profile in GC diagnosis. We also performed RT-qPCR and WB verification in 17 pairs of GC specimens and paired adjacent non-tumor tissues and GC cell lines, and verified the expression trend of YTHDF1 in five GEO GC datasets. YTHDF1 expression and clinical features of GC patients were assessed by the UALCAN. Results: The DMS with high specificity and sensitivity (AUC = 0.986) is proven to distinguish cancer from normal controls better. Moreover, we found that the expression profile variation of YTHDF1 was significantly associated with the high-risk subtype of GC patients. RT-qPCR and Western blot results are consistent with silicon analysis, revealing that YTHDF1’s potential oncogene role in GC tumor. Conclusion: In conclusion, we developed the m6A gene-based diagnostic signature for GC and found that YTHDF1 was significantly correlated with the high-risk subtype of GC patients, suggesting that YTHDF1 might be a potential target in GC early diagnosis.