Expression Differentiation Is Not Helpful in Identifying Prognostic Genes Based on TCGA Datasets

Abstract

A routine pipeline seems very common in many cancer studies that expression differentiation might be helpful in identifying prognostic molecules. There also exists a striking unanimity that molecules upregulated in cancer usually shorten survival, while downregulated ones have the opposite effect. In this study, based on the transcriptional profiles of 18 malignancies, cancer and corresponding adjacent normal tissues were used to calculate differential scores. Cox correlation coefficients of global genes were also calculated to denote survival association. The relationship between expression differentiation and survival association has been extensively studied in 18 malignancy types. Contradictory to our stereotypic research pattern, expression differentiation between cancer and adjacent normal tissues was proven irrelevant to corresponding survival correlation. Surprisingly, the more stringent cutoff we used in differentially expressed gene identification, the less prognostic information we would obtain from the collected gene groups. Moreover, the direction of dysregulated genes in cancer was irrelevant to the direction of corresponding survival correlation. Cancer-normal expression differentiation is irrelevant to genes’ survival correlation in multiple cancers and, therefore, not helpful in identifying prognostic genes. For future studies, it is more sensible to look into another alternative rather than collect differentially expressed molecules in the initial step.