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The m(6)A-Related mRNA Signature Predicts the Prognosis of Pancreatic Cancer Patients

N(6)-methyladenosine (m(6)A) has an important epitranscriptomic modification that controls cancer self-renewal and cell fate. The addition of m(6)A to mRNA is a reversible modification. The deposition of m(6)A is encoded by a methyltransferase complex involving three homologous factors, jargonized a...

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Detalles Bibliográficos
Autores principales: Meng, Zibo, Yuan, Qingchen, Zhao, Jingyuan, Wang, Bo, Li, Shoukang, Offringa, Rienk, Jin, Xin, Wu, Heshui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society of Gene & Cell Therapy 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256444/
https://www.ncbi.nlm.nih.gov/pubmed/32490170
http://dx.doi.org/10.1016/j.omto.2020.04.011
Descripción
Sumario:N(6)-methyladenosine (m(6)A) has an important epitranscriptomic modification that controls cancer self-renewal and cell fate. The addition of m(6)A to mRNA is a reversible modification. The deposition of m(6)A is encoded by a methyltransferase complex involving three homologous factors, jargonized as “writers,” “erasers,” and “readers.” However, their roles in pancreatic adenocarcinoma (PAAD) are underexploited. With the use of The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases, we provided an mRNA signature that may improve the prognostic prediction of PAAD patients based on the genetic status of m(6)A regulators. PAAD patients with genetic alteration of m(6)A regulators had worse disease-free and overall survival. After comparing PAAD groups with/without genetic alteration of m(6)A regulators, we identified 196 differentially expressed genes (DEGs). Then, we generated a 16-mRNA signature score system through least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Multivariate cox regression analysis demonstrated that a high-risk score significantly correlates with poor prognosis. Moreover, time-dependent receiver operating characteristic (ROC) curves revealed it was effective in predicting the overall survival in both training and validation sets. PAH, ZPLD1, PPFIA3, and TNNT1 from our signature also exhibited an independent prognostic value. Collectively, these findings can improve the understanding of m(6)A modifications in PAAD and potentially guide therapies in PAAD patients.