Alternative polyadenylation drives oncogenic gene expression in pancreatic ductal adenocarcinoma

Venkat, Swati; Tisdale, Arwen A.; Schwarz, Johann R.; Alahmari, Abdulrahman A.; Maurer, H. Carlo; Olive, Kenneth P.; Eng, Kevin H.; Feigin, Michael E.

doi:10.1101/gr.257550.119

Cited by 55 publications

(70 citation statements)

References 107 publications

(143 reference statements)

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“…Nevertheless, when compared with all DEGs, the genes with shortened 3 UTRs did not tend to be more upregulated in LUAD (p-value = 0.07 and 1, hypergeometric test). This result is consistent with prior analyses in pancreatic ductal adenocarcinoma (Venkat et al, 2020) and other types of cancer (Xiang et al, 2018), suggesting the presence of other mechanisms in regulating gene expression. In addition, we found three genes, COL5A1, COL1A2, and CP with lengthened 3 UTR were upregulated in tumors in both the datasets.…”

Section: Utr Shortening-mediated Loss Of Mirna-binding Sitessupporting

confidence: 92%

Comprehensive Analysis of APA Events and Their Association With Tumor Microenvironment in Lung Adenocarcinoma

et al. 2021

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BackgroundAlternative polyadenylation (APA) is a pervasive posttranscriptional mechanism regulating gene expression. However, the specific dysregulation of APA events and its potential biological or clinical significance in lung adenocarcinoma (LUAD) remain unclear.MethodsHere, we collected RNA-Seq data from two independent datasets: GSE40419 (n = 146) and The Cancer Genome Atlas (TCGA) LUAD (n = 542). The DaPars algorithm was employed to characterize the APA profiles in tumor and normal samples. Spearman correlation was used to assess the effects of APA regulators on 3′ UTR changes in tumors. The Cox proportional hazard model was used to identify clinically relevant APA events and regulators. We stratified 512 patients with LUAD in the TCGA cohort through consensus clustering based on the expression of APA factors.FindingsWe identified remarkably consistent alternative 3′ UTR isoforms between the two cohorts, most of which were shortened in LUAD. Our analyses further suggested that aberrant usage of proximal polyA sites resulted in escape from miRNA binding, thus increasing gene expression. Notably, we found that the 3′ UTR lengths of the mRNA transcriptome were correlated with the expression levels of APA factors. We further identified that CPSF2 and CPEB3 may serve as key regulators in both datasets. Finally, four LUAD subtypes according to different APA factor expression patterns displayed distinct clinical results and oncogenic features related to tumor microenvironment including immune, metabolic, and hypoxic status.InterpretationOur analyses characterize the APA profiles among patients with LUAD and identify two key regulators for APA events in LUAD, CPSF2 and CPEB3, which could serve as the potential prognostic genes in LUAD.

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Section: Utr Shortening-mediated Loss Of Mirna-binding Sitessupporting

confidence: 92%

Comprehensive Analysis of APA Events and Their Association With Tumor Microenvironment in Lung Adenocarcinoma

et al. 2021

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“…In a large-scale study of AS events in 32 cancer types (including pancreatic adenocarcinoma), the TCGA group found that tumor tissue contains 30% more AS events than normal tissue from the same site 31 . Changes in the use of polyadenylation sites leading to transcripts with different 3′ ends have been observed in PDACs with enrichment for alternative polyadenylation events in genes known to play a role in PDAC development 32 . Analyzing exon-specific microarray data from 28 PDACs and 6 normal pancreas tissues, Wang et al 33 found that exon skipping (14.3%), alternative first exon use (14%), and intron retention (8.4%) represented the three most prevalent categories of differentially spliced events comparing PDAC and normal samples.…”

Section: Introductionmentioning

confidence: 99%

Intron retention is a robust marker of intertumoral heterogeneity in pancreatic ductal adenocarcinoma

et al. 2020

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Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with a 5-year survival rate of <8%. Unsupervised clustering of 76 PDAC patients based on intron retention (IR) events resulted in two clusters of tumors (IR-1 and IR-2). While gene expression-based clusters are not predictive of patient outcome in this cohort, the clusters we developed based on intron retention were associated with differences in progression-free interval. IR levels are lower and clinical outcome is worse in IR-1 compared with IR-2. Oncogenes were significantly enriched in the set of 262 differentially retained introns between the two IR clusters. Higher IR levels in IR-2 correlate with higher gene expression, consistent with detention of intron-containing transcripts in the nucleus in IR-2. Out of 258 genes encoding RNA-binding proteins (RBP) that were differentially expressed between IR-1 and IR-2, the motifs for seven RBPs were significantly enriched in the 262-intron set, and the expression of 25 RBPs were highly correlated with retention levels of 139 introns. Network analysis suggested that retention of introns in IR-2 could result from disruption of an RBP protein−protein interaction network previously linked to efficient intron removal. Finally, IR-based clusters developed for the majority of the 20 cancer types surveyed had two clusters with asymmetrical distributions of IR events like PDAC, with one cluster containing mostly intron loss events. Taken together, our findings suggest IR may be an important biomarker for subclassifying tumors.

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“…It should be noted that GTEx database collected more than 7,000 normal samples from 449 healthy humans, and gene expression data were treated by the same sequencing platform with TCGA database for minimizing potential batch effects. Previous studies have proved that the gene expression data of TCGA and GTEx can be analyzed and integrated successfully ( Kosti et al, 2016 ; Aran et al, 2017 ; Raphael et al, 2017 ; Zeng et al, 2019 ; Venkat et al, 2020 ). Based on this, we integrated gene expression data from TCGA-PAAD and GTEx including 178 tumor samples and 171 normal samples (4 from TCGA-PAAD and 167 from GTEx).…”

Section: Methodsmentioning

confidence: 99%

Construction of an RNA-Binding Protein-Related Prognostic Model for Pancreatic Adenocarcinoma Based on TCGA and GTEx Databases

et al. 2021

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Background: Recently, RNA-binding proteins (RBPs) were reported to interact with target mRNA to regulate gene posttranscriptional expression, and RBP-mediated RNA modification can regulate the expression and function of proto-oncogenes and tumor suppressor genes. We systematically analyzed the expression of RBPs in pancreatic adenocarcinoma (PAAD) and constructed an RBP-associated prognostic risk model.Methods: Gene expression data of normal pancreatic samples as well as PAAD samples were downloaded from TCGA-PAAD and GTEx databases. Wilcoxon test and univariate Cox analysis were, respectively, applied to screen differential expression RBPs (DE-RBPs) and prognostic-associated RBPs (pRBPs). Functional enrichment was analyzed by GO, KEGG, and GSEA. Protein–protein interaction (PPI) network was constructed by STRING online database. Modeling RBPs were selected by multivariate Cox analysis. Kaplan–Meier survival and Cox analysis were applied to evaluate the effects of risk score on the overall survival of PAAD patients. ROC curves and validation cohort were applied to verify the accuracy of the model. Nomogram was applied for predicting 1-, 3-, and 5-year overall survival (OS) of PAAD patients. At last, modeling RBPs were further analyzed to explore their differential expression, prognostic value, as well as enrichment pathways in PAAD.Results: RBPs (453) were differentially expressed in normal and tumor samples, besides, 28 of which were prognostic associated. DE-RBPs (453) are functionally associated with ribosome, ribonuclease, spliceosome, etc. Eight RBPs (PABPC1, PRPF6, OAS1, RBM5, LSM12, IPO7, FXR1, and RBM6) were identified to construct a prognostic risk model. Higher risk score not only predicted poor prognosis but also was an independent poor prognostic indicator, which was verified by ROC curves and validation cohort. Eight modeling RBPs were confirmed to be significantly differentially expressed between normal and tumor samples from RNA and protein level. Besides, all of eight RBPs were related with overall survival of PAAD patients.Conclusions: We successfully constructed an RBP-associated prognostic risk model in PAAD, which has a potential clinical application prospect.

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Alternative polyadenylation drives oncogenic gene expression in pancreatic ductal adenocarcinoma

Cited by 55 publications

References 107 publications

Comprehensive Analysis of APA Events and Their Association With Tumor Microenvironment in Lung Adenocarcinoma

Comprehensive Analysis of APA Events and Their Association With Tumor Microenvironment in Lung Adenocarcinoma

Intron retention is a robust marker of intertumoral heterogeneity in pancreatic ductal adenocarcinoma

Construction of an RNA-Binding Protein-Related Prognostic Model for Pancreatic Adenocarcinoma Based on TCGA and GTEx Databases

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