Yong Liu1, Tao Wang2, Ziqi Fang3, Junjie Kong1,2, Jun Liu4,5. 1. Department of Liver Transplantation and Hepatobiliary Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250021, Shandong Province, China. 2. Department of Liver Transplantation and Hepatobiliary Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong Province, China. 3. Department of Clinical Laboratory, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250021, Shandong Province, China. 4. Department of Liver Transplantation and Hepatobiliary Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250021, Shandong Province, China. dr_liujun1967@126.com. 5. Department of Liver Transplantation and Hepatobiliary Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, 250021, Shandong Province, China. dr_liujun1967@126.com.
Abstract
BACKGROUND: Pancreatic cancer (PC) is a rare solid malignancy with a poor prognosis. N6-methyladenosine (m6A) and long noncoding RNAs (lncRNAs) play essential roles in tumorigenesis and progression. However, little is known about the role of m6A-related lncRNAs in PC. METHODS: m6A-related lncRNAs were extracted by Pearson analysis, and then prognosis-related lncRNAs were filtered from the m6A-related lncRNAs by univariate Cox regression analysis. Based on the expression patterns of the prognosis-related lncRNAs, samples were classified into distinct clusters. Least absolute shrinkage and selection operator (LASSO) Cox regression was used to construct a m6A-lncRNA-related prognostic signature for PC patients. Receiver operating characteristic (ROC) curves and the corresponding area under the curve (AUC) values were used to evaluate the prognostic ability of the model. RESULTS: A total of 178 tumor and 4 normal samples were extracted from The Cancer Genome Atlas (TCGA) database in our study. Based on the expression of 12 filtered prognosis-related lncRNAs, two distinct clusters were eventually identified; these clusters were characterized by differences in the tumor immune microenvironment (TIME) and prognosis. A risk model comprising ten m6A-related lncRNAs was identified as an independent predictor of prognosis. ROC analysis revealed that this model had an acceptable prognostic value for PC patients. The prognostic signature was related to the TIME and the expression of critical immune checkpoint molecules. CONCLUSION: This study comprehensively assessed the expression pattern and prognostic value of m6A-related lncRNAs in PC. The different clusters correlated with distinct TIMEs and prognoses. The study also constructed a ten-gene signature prognostic model based on m6A-related lncRNAs, which showed good accuracy in predicting overall survival.
BACKGROUND: Pancreatic cancer (PC) is a rare solid malignancy with a poor prognosis. N6-methyladenosine (m6A) and long noncoding RNAs (lncRNAs) play essential roles in tumorigenesis and progression. However, little is known about the role of m6A-related lncRNAs in PC. METHODS: m6A-related lncRNAs were extracted by Pearson analysis, and then prognosis-related lncRNAs were filtered from the m6A-related lncRNAs by univariate Cox regression analysis. Based on the expression patterns of the prognosis-related lncRNAs, samples were classified into distinct clusters. Least absolute shrinkage and selection operator (LASSO) Cox regression was used to construct a m6A-lncRNA-related prognostic signature for PC patients. Receiver operating characteristic (ROC) curves and the corresponding area under the curve (AUC) values were used to evaluate the prognostic ability of the model. RESULTS: A total of 178 tumor and 4 normal samples were extracted from The Cancer Genome Atlas (TCGA) database in our study. Based on the expression of 12 filtered prognosis-related lncRNAs, two distinct clusters were eventually identified; these clusters were characterized by differences in the tumor immune microenvironment (TIME) and prognosis. A risk model comprising ten m6A-related lncRNAs was identified as an independent predictor of prognosis. ROC analysis revealed that this model had an acceptable prognostic value for PC patients. The prognostic signature was related to the TIME and the expression of critical immune checkpoint molecules. CONCLUSION: This study comprehensively assessed the expression pattern and prognostic value of m6A-related lncRNAs in PC. The different clusters correlated with distinct TIMEs and prognoses. The study also constructed a ten-gene signature prognostic model based on m6A-related lncRNAs, which showed good accuracy in predicting overall survival.
Authors: Vincent P Groot; Lois A Daamen; Jeroen Hagendoorn; Inne H M Borel Rinkes; Hjalmar C van Santvoort; I Quintus Molenaar Journal: J Surg Res Date: 2017-09-21 Impact factor: 2.192
Authors: Sonja Gillen; Tibor Schuster; Christian Meyer Zum Büschenfelde; Helmut Friess; Jörg Kleeff Journal: PLoS Med Date: 2010-04-20 Impact factor: 11.069