![]() ![]() ![]() En particular, para el análisis de expresión diferencial de sfd-RNAs, a diferencia de los miRNAs, existen problemas asociados a la detección, anotación y cuantificación de las lecturas asociadas al ncRNA, que en términos estadísticos, impactan el número de variables y el tamaño de muestra por ende, se requiere una estrategia de anotación propia para sfd-RNA capaz de afrontar dos complicaciones de orden bioinformático. A la fecha existen diferentes pipelines y herramientas computacionales dirigidas a anotar y evaluar expresión diferencial (DE) de miRNAs no obstante, su extensión para el estudio de sfd-RNAs no es del todo adecuada, debido a que la fuente de los sfd-RNAs es a su vez una secuencia de ncRNA con una función alternativa, lo que implica una biogénesis diferente y por ende requiere estrategias computacionales propias. Por ejemplo, ambos tipos de moléculas pueden cargarse en proteínas Argonauta, quienes han sido vinculadas al fenómeno de interferencia mediada por RNA. El interés en los sfd-RNA se debe a su parecido estructural y funcional con los micro RNAs (miRNAs). Varios estudios han identificado un amplio número de pequeños fragmentos derivados del procesamiento alternativo de ncRNAs (sfd-RNA) que varían entre 16 y 40nt, cuyas fuentes son principalmente tRNAs y snoRNAs. M ⁶ A methylation may affect circRNA-miRNA-mRNA co-expression in CRC and further affect the regulation of cancer-related target genes.Įn los últimos años, el uso cada vez mayor de las tecnologías de secuenciación de nueva generación para el estudio del transcriptoma ha llevado al descubrimiento de un nuevo fenómeno biológico llamado fragmentación postranscripcional funcional de ncRNAs. Our study found that there were significant differences in the m ⁶ A methylation patterns of circRNAs between CRC and NC tissues. The distance between the m ⁶ A peak and IRES was not significantly related to the protein coding potential of circRNAs. There was no significant correlation between the level of m ⁶ A and the expression of circRNAs. In the ceRNA network, the 10 hyper-up circRNAs were shown to be associated with 19 miRNAs and regulate 16 mRNAs, 14 hypo-down circRNAs were associated with 30 miRNAs and regulated 27 mRNAs. The conjoint analysis of MeRIP-seq and RNA-seq data discovered 30 circRNAs with differentially m ⁶ A methylated and synchronously differential expression. Pathway analysis detected that differentially methylated and expressed circRNAs were closely related to cancer. A total of 2561 m ⁶ A circRNAs in CRC tissues and 2129 m ⁶ A circRNAs in NC tissues were detected. ![]() Finally, We describe the relationship of distance between the m ⁶ A peak and internal ribosome entry site (IRES) and protein coding potential of circRNAs.Ī total of 4340 m ⁶ A peaks of circRNAs in CRC tissue and 3216 m ⁶ A peaks of circRNAs in NC tissues were detected. Next, the networks of circRNA-miRNA-mRNA were visualized using the Target Scan and miRanda software. The correlations between m ⁶ A status and expression level were calculated using a Pearson correlation analysis. Then, GO and KEGG analysis detected pathways involved in differentially methylated and differentially expressed circRNAs (DEGs). To characterize the entire profile of m ⁶ A modifications and differential expression patterns for circRNAs in colorectal cancer (CRC).įirst, High-throughput MeRIP-sequencing and RNA-sequencing was used to determine the difference in m ⁶ A methylome and expression of circRNA between CRC tissues and tumor-adjacent normal control (NC) tissues. ![]()
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