Integration of methylation, long non-coding RNA and mRNA expression data in Lung Cancer

1. Integration of methylation, long non-coding RNA and mRNA expression data in Lung Cancer Travers Ching, Sijia Huang, Fangxiu Xu, Jinli Qu, Jingxin Li, Herbert Yu, Biyun Qian, Lana Garmire Dept. of Molecular Biosciences and Bioengineering, University of Hawaiʻi at Mānoa Epidemiology Program, University of Hawaii Cancer Center Introduction Methylation modules correlate well with gene expression • Background: • Lung cancer is the most commonly diagnosed cancer (12.7%) • Lung cancer accounts for the most cancer death (18.2%) • The samples: • 24 paired tissue samples collected from 12 lung cancer patients • The High-throughput Microarray Platforms: • SBC lncRNA chip (Shanghai Biotechnlogy Co., Ltd.) • Illumina HumanMethylation450 BeadChip array (450K) • Methylation network analysis using “Spin Glass” community detection algorithm • Map epigenetic hotspots onto a protein interaction network (HPRD reference) • Validate methylation results with mRNA data • Hierarchical clustering of hotspots • Clustering of seeds (fig. 2B) Fig. 2: A: example epigenetically modified hotspot (epimod) B: mRNA expression heatmap of the 23 epimod “seeds” genes show separation of cancer and normal tissue samples Significant differences between tumor and normal tissues • Methods: Use of a general linear model (GLM) to test differential expression (DE). • expression level patient pairing • Model: • tissue • Results: • Significant DE (after MHT correction) • mRNA: 10,000 DE transcripts • lncRNA: 9,000 DE transcripts • methylation: 100,000 DE CpG sites • Strong separation between tissue types (Fig. 1) Effective prediction of gene expression from methylation features • Question: “How well can DNA methylation predict gene expression?” • Split data into training (80%) and testing (20%) sets • build model on training set • evaluate performance on test set • Features set (287 total): • methylation features, transcript features. • Correlation Feature Selection (CFS) selects 32 features • Performance (AUC): • Random Forest:0.806 • Linear SVM: 0.770 • Gaussian SVM: 0.794 • Logistic Regression: 0.76 Fig. 3. ROC curves on holdout test set for the prediction of up-regulated genes LncRNAsplay important roles in cancer • Predictions of lncRNA targets • Cis targets: targets based on proximity • Trans targets: targets based on sequence homology • Enrichment analysis of BIOCARTA and KEGG pathways pathways Future work • Additional features for gene prediction classifier (e.g., histone modification, intron information) • apply other non-linear classification methods • Find better metrics for lncRNA target prediction • generalize spin glass algorithm to other types of datasets (originally intended for Illumina 27K) Acknowledgements • Fig 1. Top: PCA of expression data • Middle: volcano plot of 450K • Bottom: example correlation plot of lncRNA/target gene Funding: This work is supported by the University of Hawaii Cancer Center Faculty Startup Grant, and the Collaboration Enhancement Award  from NIMHD Grant 5 U54 MD008149-07