Data processing for comparison of gene expression levels from raw datasets using free software r in hepatocellular carcinoma patients

Abstract

Many datasets on mutations, changes in the number or expressionlevels of genes, as well as clinics are now available for differentcancers. Thus, organization in data banks, with comparison andvisualization tolos, is growing and useful for biomedical research.Moreover, the use of raw data has a great potential for specific biostatistical studies.Hepatocellular carcinoma (HCC) is the second most lethal cancerand it lacks effective therapy. Data analysis from patient databases can be useful to identify molecular markers, targets for potentialtreatments, and to investigate hypothetical mechanisms.The aim of this study was to process raw data from the ?The CancerGenome Atlas-Liver Hepatocellular Carcinoma? project (TCGA-LIHC) dataset to analyze them with the free software for statisticalcomputing R, and to compare the expression levels of genes involved in an antimigratory AMPK-p53 axis that we study at the cellular level: the different genes for the three AMPK subunits (PRKAA1,A2, B1, etc.); TP53; and EMT transcription factors SNAI1 and 2,which are p53 targets.First, bioinformatic analyses of TP53 mRNA expression with opensource online tools showed an increase in the overall survival inpatients with low versus high TP53 expression: 55.1 months versus61.7 months, respectively (P=0.03). Besides, TP53 showed mutations (61% missense and 31% truncating) in 30.5% of patients.To better analyze mRNA levels, the whole raw data was organizedwith R, leaving only those of the genes of interest, separating insamples of healthy and tumor tissue and matching those corresponding to the same patient. mRNA (RNA-seq Illumina) data were selected and compared. As an example, the median value of mRNAlevels for SNAI2 increased from 144.7 in healthy to 205.7 in tumortissue (P=0.01, Wilcoxon Signed Rank Test).This methodology can be systematized to compare gene expressionin HCC patients with respect to non-tumor tissue, and association intheir changes can also be analyzed.