Virtual Library
Start Your Search
J.R. Enterina
Author of
-
+
P2.01 - Poster Session with Presenters Present (ID 461)
- Event: WCLC 2016
- Type: Poster Presenters Present
- Track: Biology/Pathology
- Presentations: 1
- Moderators:
- Coordinates: 12/06/2016, 14:30 - 15:45, Hall B (Poster Area)
-
+
P2.01-023 - Deregulation of Small Non-Coding RNAs at the DLK1-DIO3 Imprinted Locus Predicts Lung Adenocarcinoma Patient Outcome (ID 6142)
14:30 - 14:30 | Author(s): J.R. Enterina
- Abstract
Background:
Deregulation of small RNAs at the imprinted DLK1-DIO3 locus has been linked to lung adenocarcinoma (LUAD) patient outcome. While the contribution of microRNAs (miRNAs) is established, the role of Piwi-interacting RNAs (piRNAs), small RNAs involved in epigenetic regulation of gene transcription, is unexplored. We quantified expression of piRNAs and miRNAs mapping to this locus in two independent cohorts of LUAD and assessed the ability of a combined miRNA/piRNA signature to improve patient outcome stratification.
Methods:
Expression levels (RPKM) for miRNA/piRNA were determined from small RNA sequencing experiments from two cohorts (TCGA, n=154, 5-year follow up; BCCA, n=77, 8-year follow up). Associations with patient overall survival (OS) and recurrence free survival (RFS) were calculated by inputting miRNA and piRNA expression combinations into a Cox proportional hazard model. Risk scores were calculated by multiplying the expression value for each gene by its hazard coefficient, and summed per sample. Risk scores were ranked and divided into tertiles for log-rank survival analysis. DNA-level piRNA targets were predicted using MiRanda based on sequence complementarity in the region 3.5kb upstream of the transcription-start site of all human transcripts from ENSEMBL. Transcript-level miRNA targets were predicted using the miRDIP algorithm, which integrates 13 miRNA target prediction algorithms and six miRNA prediction databases.
Results:
Only 7 out of 138 piRNAs mapping to the locus were expressed. A combined miRNA/piRNA signature improved both OS and RFS predictions compared to signatures of miRNAs or piRNAs alone. In TCGA, log-rank analysis of risk groups indicated only the miRNA/piRNA signature significantly stratified patients (OS p=0.0038, RFS p=0.0229) into low, intermediate, and high risk groups compared to separated miRNA or piRNA signatures. Similarly, in the BCCA dataset, only the combined miRNA/piRNA signature significantly stratified high, intermediate, and low risk groups (p=0.0019). Target prediction of piRNAs and miRNAs from the signature indicated that 34 genes may be regulated at both the DNA (piRNA) and mRNA (miRNA) level.
Conclusion:
We find the combination of miRNA and piRNA expression derived from the DLK1-DIO3 locus produces a superior stratification of patient outcome than either metric alone. While the contribution of miRNAs to patient risk stratification is established, the improved model performance derived from the addition of piRNAs adds another layer of gene regulation at the DNA-level. Model performance is optimal when these two small RNA species are considered simultaneously; suggesting their coordinated biological effects as a result of deregulation at this locus in LUAD.
