Prediction of survival and recurrence in patients with pancreatic cancer by integrating multi-omics data

Abstract

Predicting the prognosis of pancreatic cancer is important because of the very low survival rates of patients with this particular cancer. Although several studies have used microRNA and gene expression profiles and clinical data, as well as images of tissues and cells, to predict cancer survival and recurrence, the accuracies of these approaches in the prediction of high-risk pancreatic adenocarcinoma (PAAD) still need to be improved. Accordingly, in this study, we proposed two biological features based on multi-omics datasets to predict survival and recurrence among patients with PAAD. First, the clonal expansion of cancer cells with somatic mutations was used to predict prognosis. Using whole-exome sequencing data from 134 patients with PAAD from The Cancer Genome Atlas (TCGA), we found five candidate genes that were mutated in the early stages of tumorigenesis with high cellular prevalence (CP). CDKN2A, TP53, TTN, KCNJ18, and KRAS had the highest CP values among the patients with PAAD, and survival and recurrence rates were significantly different between the patients harboring mutations in these candidate genes and those harboring mutations in other genes (p = 2.39E−03, p = 8.47E−04, respectively). Second, we generated an autoencoder to integrate the RNA sequencing, microRNA sequencing, and DNA methylation data from 134 patients with PAAD from TCGA. The autoencoder robustly reduced the dimensions of these multi-omics data, and the K-means clustering method was then used to cluster the patients into two subgroups. The subgroups of patients had significant differences in survival and recurrence (p = 1.41E−03, p = 4.43E−04, respectively). Finally, we developed a prediction model for prognosis using these two biological features and clinical data. When support vector machines, random forest, logistic regression, and L2 regularized logistic regression were used as prediction models, logistic regression analysis generally revealed the best performance for both disease-free survival (DFS) and overall survival (OS) (accuracy [ACC] = 0.762 and area under the curve [AUC] = 0.795 for DFS; ACC = 0.776 and AUC = 0.769 for OS). Thus, we could classify patients with a high probability of recurrence and at a high risk of poor outcomes. Our study provides insights into new personalized therapies on the basis of mutation status and multi-omics data.

Multi‐omics analysis of intra‐tumoural and inter‐tumoural heterogeneity in pancreatic ductal adenocarcinoma

“…over 100), the data were from the TCGA and GEO databases. [39][40][41] For example, Kong et al used TCGA and GEO databases to perform genomics, epigenomics and transcriptomics analysis in 161 pancratic cancer patients to identify molecular subgroups and explore novel biomarkers. 39 Mishra et al combined multi-omics data (including gene expression, DNA methylation and miRNA expression data) and survival data of 153 PDAC patients from the TCGA database to identify potential prognostic markers of PDAC.…”

The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors

“…To increase the accuracy of predictions in prognosis, data on mutations have been integrated with those of gene expression [ 91 , 92 , 93 ]. However, it is difficult to train an expert system to consider the mutation load of a sample since the effect of a mutation depends on the function of the gene and its position along the gene [ 94 ].…”

Super.FELT: supervised feature extraction learning using triplet loss for drug response prediction with multi-omics data

“…In this study, although Super.FELT was applied for the drug response prediction, it can be further applied for other biomedical tasks using multi-omics datasets. Recently, disease progress prediction, such as survival and recurrence, and cancer subtype classification, has been performed using multi-omics datasets [52][53][54][55]. In those studies, AE, a chi-squared test, and a feedforward network have been used to represent features in omics data.…”