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. 2019 Jul 18:10:624.
doi: 10.3389/fgene.2019.00624. eCollection 2019.

Survival Analysis of Multi-Omics Data Identifies Potential Prognostic Markers of Pancreatic Ductal Adenocarcinoma

Nitish Kumar Mishra  1 , Siddesh Southekal  1 , Chittibabu Guda  1
Affiliations

Survival Analysis of Multi-Omics Data Identifies Potential Prognostic Markers of Pancreatic Ductal Adenocarcinoma

Nitish Kumar Mishra et al. Front Genet. .

Abstract

Pancreatic ductal adenocarcinoma (PDAC) is the most common and among the deadliest of pancreatic cancers. Its 5-year survival is only ∼8%. Pancreatic cancers are a heterogeneous group of diseases, of which PDAC is particularly aggressive. Like many other cancers, PDAC also starts as a pre-invasive precursor lesion (known as pancreatic intraepithelial neoplasia, PanIN), which offers an opportunity for both early detection and early treatment. Even advanced PDAC can benefit from prognostic biomarkers. However, reliable biomarkers for early diagnosis or those for prognosis of therapy remain an unfulfilled goal for PDAC. In this study, we selected 153 PDAC patients from the TCGA database and used their clinical, DNA methylation, gene expression, and micro-RNA (miRNA) and long non-coding RNA (lncRNA) expression data for multi-omics analysis. Differential methylations at about 12,000 CpG sites were observed in PDAC tumor genomes, with about 61% of them hypermethylated, predominantly in the promoter regions and in CpG-islands. We correlated promoter methylation and gene expression for mRNAs and identified 17 genes that were previously recognized as PDAC biomarkers. Similarly, several genes (B3GNT3, DMBT1, DEPDC1B) and lncRNAs (PVT1, and GATA6-AS) are strongly correlated with survival, which have not been reported in PDAC before. Other genes such as EFR3B, whose biological roles are not well known in mammals are also found to strongly associated with survival. We further identified 406 promoter methylation target loci associated with patients survival, including known esophageal squamous cell carcinoma biomarkers, cg03234186 (ZNF154), and cg02587316, cg18630667, and cg05020604 (ZNF382). Overall, this is one of the first studies that identified survival associated genes using multi-omics data from PDAC patients.

Keywords: DEG: differentially expressed gene; DMR: differentially methylated region; Dm-CpG: Differentially methylated CpG; FDR: false discovery rate; GDC: The Genomic Data Commons; HR: hazard ratio; TCGA: The Cancer Genome Atlas.

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Figures

Figure 1
Figure 1
Differential DNA methylation distribution. (A) Circular plot of CpGs, chromosomes are shown in a clockwise direction from 1 to 22 in the outermost circle. Chromosomes X, Y, and M were excluded from analysis. The two innermost circles represent the differential hypermethylation and hypomethylation frequencies in a 10 Mb sliding window across the genome. (B) Pyramid (stacked) plot for differential hyper and hypomethylation frequencies for each chromosome. Chromosomes are sorted based on total differential methylation in per megabase pair length of the chromosomes. (C) Bubble plot of differentially methylated CpGs in genomic regions. Size of bubble represents a total number of dm-CpGs.
Figure 2
Figure 2
Volcano plot for the differentially expressed genes. Genes which are in red and blue colors are highly upregulated and downregulated, respectively in PDAC. Vertical and horizontal dot line represents a cutoff point for log fold-change p-value respectively.
Figure 3
Figure 3
Correlation plot for survival associated CpGs. We used CpGs which have survival p < = 0.01 and Spearman correlation > 0.5 (p-value < 0.005). This plot is for four promoter CpGs which are negatively correlated with genes expression and also strongly associated with patients’ survival. Distribution of DNA methylation and gene expression in PDAC patients on the right side and top respectively.
Figure 4
Figure 4
Correlation plot for the MUC5B promoter methylation sites. Boxplot for gene expression and DNA methylation on top and right side respectively, tumor samples are in red and normal samples in blue color. (A) Correlation plot and boxplot for cg20911165. (B) Correlation plot and boxplot for cg03609102.
Figure 5
Figure 5
Survival plots for zinc finger gene promoter DNA methylation sites which are associated with PDAC patients’ survival. (A, D) Boxplot for cg02587316 and cg03234186 DNA methylation distribution for tumor and normal samples with Welch t-test. (B, E) ROC plot for cg02587316 and cg03234186 for the generalized linear model classifier. (C, F) Survival plot for high vs low methylation group for cg02587316 and cg03234186 with a p-value for Kaplan–Meier plot (log-rank test) and Cox proportional hazards model.
Figure 6
Figure 6
Survival plots for the miRNA which are strongly associated with the PDAC patients’ survival. (A, D) Boxplot for miR-196b and miR-111 miRNA expression distribution for tumor and normal samples with Welch t-test. (B, E) ROC plot for miR-196b and miR-111 miRNA for the generalized linear model classifier. (C, F) Survival plot for high vs low methylation group for miR-196b and miR-111 miRNA with a p-value for Kaplan–Meier plot (log-rank test) and Cox proportional hazards model.
Figure 7
Figure 7
Survival plots for the lncRNA which are strongly associated with the PDAC patients’ survival. (A, D) Boxplot for PVT1 and RP11-54H7.4 lncRNA expression distribution for tumor and normal samples with Welch t-test. (B, E) ROC plot for PVT1 and RP11-54H7.4 lncRNA for the generalized linear model classifier. (C, F) Survival plot for high vs low methylation group for PVT1 and RP11-54H7.4 lncRNA with a p-value for Kaplan–Meier plot (log-rank test) and Cox proportional hazards model.
Figure 8
Figure 8
Survival plots for the genes which are strongly associated with the PDAC patients’ survival. (A, D) Boxplot for B3GNT3 and DMBT1 gene expression distribution for tumor and normal samples with Welch t-test. (B, E) ROC plot for B3GNT3 and DMBT1 for the generalized linear model classifier. (C, F) Survival plot for high vs low expression group for B3GNT3 and DMBT1 with a p-value for Kaplan–Meier plot (log-rank test) and Cox proportional hazards model.
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