A Transcriptomics-Based Meta-Analysis Combined With Machine Learning Identifies a Secretory Biomarker Panel for Diagnosis of Pancreatic Adenocarcinoma - Khatri, Indu; Bhasin, Manoj K. (Frontiers in Genetics, 2020)
Pancreatic ductal adenocarcinoma (PDAC) is largely incurable due to late diagnosis and absence of markers that are concordant with expression in several sample sources (i.e. tissue, blood, plasma) and platform (i.e. Microarray, sequencing). We optimized meta-analysis of 19 PDAC (tissue and blood) transcriptome studies from multiple platforms. The key biomarkers for PDAC diagnosis with secretory potential were identified and validated in different cohorts. Machine learning approach i.e. support vector machine supported by leave-one-out cross-validation was used to build and test the classifier. We identified a 9-gene panel (IFI27, ITGB5, CTSD, EFNA4, GGH, PLBD1, HTATIP2, IL1R2, CTSA) that achieved ~0.92 average sensitivity and ~0.90 specificity in discriminating PDAC from non-tumor samples in five training-sets on cross-validation. This classifier accurately discriminated PDAC from chronic-pancreatitis (AUC=0.95), early stages of progression (Stage I and II (AUC=0.82), IPMA and IPMN (AUC=1), IPMC (AUC=0.81)). The 9-gene marker outperformed the previously known markers in blood studies particularly (AUC=0.84). The discrimination of PDAC from early precursor lesions in non-malignant tissue (AUC>0.81) and peripheral blood (AUC>0.80) may facilitate early blood-diagnosis and risk stratification upon validation in prospective clinical-trials. Furthermore, the validation of these markers in proteomics and single-cell transcriptomics studies suggest their prognostic role in the diagnosis of PDAC.