Coexpression network analysis linked H2AFJ to chemoradiation resistance in colorectal cancer.

Abstract

Neoadjuvant chemoradiotherapy (CRT) resistance is a complex phenomenon and it remains a major problem for patients with a priori resistant tumor. Therefore, there is a strong need to investigate molecular biomarkers which may guide for treatment decision-making. In our study, weighted gene coexpression network analysis was applied to identify CRT-resistance hub modules in 12 colorectal cancer (CRC) cell lines with different CRT sensitivities from GSE20298 data set. The green module and purple module had the highest correlations with CRT resistance. Gene ontology enrichment analysis indicated that the function of these two modules focused on interferon-mediated signaling pathway, immune response, chromatin modulation, Rho GTPases activities, and regulation of apoptotic process. Then, 15 hub genes in both the coexpression and protein-protein interaction networks were selected. Among these hub genes, higher H2A histone family member J (H2AFJ) expression was independently validated in patient cohorts from two testing data sets of GSE46862 and GSE68204 to be related to CRT resistance. The receiver operating characteristic curve showed that H2AFJ could efficiently distinguish CRT-resistance cases from CRT-sensitive cases in another two testing data sets. Furthermore, meta-analysis of 12 Gene Expression Omnibus-sourced data sets showed that H2AFJ messenger RNA levels were significantly higher in CRC tissues than in normal colon tissues. High H2AFJ expression was correlated with a significant worse event- and relapse-free survival by analyzing the data from the R2: Genomics Analysis and Visualization Platform. Gene set enrichment analysis determined that the mechanism of H2AFJ-mediated CRT resistance might involve the ERK5 (MAPK7), human immunodeficiency virus Nef (HIV Nef), and inflammatory pathways. This study is the first, to the best of our knowledge, to implicate and verify H2AFJ as an effective new marker for CRT response prediction.