{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Lei Y"],"funding":["National Science Fund for Distinguished Young Scholars","Scientific Innovation Project of Shanghai Education Committee","Clinical and Scientific Innovation Project of Shanghai Hospital Development Center","National Natural Science Foundation of China","Shanghai Rising-Star Program","Shanghai Sailing Program","Fudan University Qing Feng Scholar Q5 Project of Shanghai Medical College","Shanghai Anticancer Association Young Eagle Program"],"pagination":["3593-3602"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8806465"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["12(1)"],"pubmed_abstract":["Immune-related long noncoding RNAs (irlncRNAs) are actively involved in regulating the immune status. This study aimed to establish a risk model of irlncRNAs and further investigate the roles of irlncRNAs in predicting prognosis and the immune landscape in pancreatic cancer. The transcriptome profiles and clinical information of 176 pancreatic cancer patients were retrieved from The Cancer Genome Atlas (TCGA). Immune-related genes (irgenes) downloaded from ImmPort were used to screen 1903 immune-related lncRNAs (irlncRNAs) using Pearson's correlation analysis (R > 0.5; p < 0.001). Random survival forest (RSF) and survival tree analysis showed that 9 irlncRNAs were highly correlated with overall survival (OS) according to the variable importance (VIMP) and minimal depth. Next, Cox regressio"],"journal":["Bioengineered"],"pubmed_title":["Construction of a novel risk model based on the random forest algorithm to distinguish pancreatic cancers with different prognoses and immune microenvironment features."],"pmcid":["PMC8806465"],"funding_grant_id":["20YF1409000","81772555","81802352","20QA1402100","SHDC12018109","81625016","QF2110","19YF1409400","SHDC12019109","81902428","SACA-CY19A06","2019-01-07- 00-07-E00057"],"pubmed_authors":["Liang C","Liu J","Hua J","Meng Q","Shi S","Lei Y","Tang R","Wang W","Zhang B","Xu J","Yu X"],"additional_accession":[]},"is_claimable":false,"name":"Construction of a novel risk model based on the random forest algorithm to distinguish pancreatic cancers with different prognoses and immune microenvironment features.","description":"Immune-related long noncoding RNAs (irlncRNAs) are actively involved in regulating the immune status. This study aimed to establish a risk model of irlncRNAs and further investigate the roles of irlncRNAs in predicting prognosis and the immune landscape in pancreatic cancer. The transcriptome profiles and clinical information of 176 pancreatic cancer patients were retrieved from The Cancer Genome Atlas (TCGA). Immune-related genes (irgenes) downloaded from ImmPort were used to screen 1903 immune-related lncRNAs (irlncRNAs) using Pearson's correlation analysis (R > 0.5; p < 0.001). Random survival forest (RSF) and survival tree analysis showed that 9 irlncRNAs were highly correlated with overall survival (OS) according to the variable importance (VIMP) and minimal depth. Next, Cox regressio","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Dec","modification":"2026-06-19T03:18:45.579Z","creation":"2025-04-06T14:56:58.348Z"},"accession":"S-EPMC8806465","cross_references":{"pubmed":["34238114"],"doi":["10.1080/21655979.2021.1951527"]}}