PDAC-ANN: an artificial neural network to predict Pancreatic Ductal Adenocarcinoma based on gene expression

Abstract

Background: Although the pancreatic ductal adenocarcinoma (PDAC) presents high mortality and metastatic potential, there is a lack of effective therapies and a low survival rate for this disease. This PDAC scenario urges new strategies for diagnosis, drug targets, and treatment. Methods:We performed a gene expression microarray meta-analysis of the tumor against healthy tissues in order to identify differentially expressed genes shared among all datasets, named core-genes (CG). We confirmed the pancreatic expressed proteins of the CG through The Human Protein Atlas. The five most expressed proteins in the tumor group were selected to train an artificial neural network to classify samples. 2 Results: This microarray included 110 tumor and 77 healthy samples. We identified a CG composed of 60 genes, 58 upregulated and two downregulated. The upregulated CG included proteins and extracellular matrix receptors linked to actin cytoskeleton reorganization. With the Human Protein Atlas, we verified that thirteen genes of the CG are translated, with high or medium expression in most of the pancreatic tumor samples. To train our artificial neural network, we used the five most expressed genes (KRT19, LAMC2, MELK, MET, TOP2A). The artificial neural network model (PDAC-ANN) classified the train samples with sensitivity of 0.95, specificity of 0.9, and f1-score of 0.93. The PDAC-ANN could classify the test samples with a sensitivity of 0.97, specificity of 0.88, and f1-score 0.94. Conclusion:The gene expression meta-analysis and confirmation of the protein expression allow us to select five genes highly expressed PDAC samples. We could build a python script to classify the samples based on mRNA expression. This software can be useful in the PDAC diagnosis.1 5 migration, and metastasis. The PDAC-ANN trained using gene expression information could classify the samples in normal and PDAC with an f1-score of 0.94 and sensitivity = 0.97. The PDAC-ANN tool can only be used when the gene expression information from KRT19, LAMC2, MELK, MET, and TOP2A are available, in addition to min-max gene expression values rescaling. The PDAC-ANN is a free tool (Additional file 4) that can support in the pancreatic ductal adenocarcinoma diagnosis.

A novel epigenetic signature for predicting the prognosis of pancreatic ductal adenocarcinoma

“…Only AHNAK2 involved in this signature have been previously investigated in PDAC. Several studies in PDAC have con rmed AHNAK2 as an oncogene [23,24]. Lu et al [24] indicated that AHNAK2 is upregulated in 79 PDAC tissues compared with adjacent normal tissue, and patients with high AHNAK2 expression had worse OS.…”

An Integrative Data Mining for the Identification and Validation of Oncogenic Biomarkers in Pancreatic Carcinoma

“…Data collection and DEGs/DEMs/tDEGs/tDEMs analysis Gene expression pro les were downloaded from the GEO public database (http://www.ncbi.nlm.nih.gov/geo/) and the inclusion criteria were as follows: (1) pancreatic cancer, (2) January 1, 2009 to December 31, 2019, (3) homo sapiens, (4) tissue, (5) sample count exceeds 30, (6) mRNA or miRNA expression data available. As a result, a total of 5 datasets were selected, which included GSE62165, GSE15471, GSE24279, GSE32676 and GSE32678.…”

“…It has revolutionized RNA and DNA studies and also been extensively conducted as an e cient tool for exploration of genes and biological processes of human diseases, which contribute to uncover the underlying biological mechanisms. Several studies reported the differentially expressed genes (DEGs) and differentially expressed microRNA (DEMs) of PC separately during the past years [2][3][4] . However, there remain questions about the interaction between DEGs and DEMs during the progression of the PC.…”

Artificial neural networks as clinical decision support systems

“…Down syndrome is a chromosomal disorder caused due to the presence of all or some part of an extra 21st chromosome. Pancreatic ductal adenocarcinoma (PDAC) has a low survival rate mainly due to inefficient therapies 87 . For the identification of Core Genes (CG), a meta‐analysis tumor against normal tissue has been carried out and as result, 05 genes (KRT 19 , LAMC 2 , S 100 P, AHNAK 2, and LAMB 3 ) are identified.…”