Effects of High-Dose Ionizing Radiation in Human Gene Expression: A Meta-Analysis

Abstract

The use of high-dose Ionizing Radiation (IR) is currently one of the most common modalities in treatment of many types of cancer. The objective of this work was to investigate the effects of high-dose ionizing radiation on healthy human tissue, utilizing quantitative analysis of gene expression. To this end, publicly available transcriptomics datasets from human samples irradiated with a high dose of radiation and non-irradiated (control) ones were selected, and gene expression was determined using RNA-Seq data analysis. Raw data from these studies were subjected to quality control and trimming. Mapping of RNA-Seq reads was performed by the partial selective alignment method, and differential gene expression analysis was conducted. Subsequently, a meta-analysis was performed to select differentially expressed genes across datasets. Based on the differentially expressed genes discovered by meta-analysis, we constructed a protein-to-protein interaction network, and we identified biological pathways and processes related to high-dose IR effects. Our findings suggest that cell cycle arrest is activated, supported by our top down-regulated genes associated with cell cycle activation. DNA repair genes are down-regulated in their majority. However, several genes implicated in the nucleotide excision repair pathway are upregulated. Nevertheless, apoptotic mechanisms seem to be activated probably due to severe high-dose-induced complex DNA damage. The significant upregulation of CDKN1A, as a downstream gene of TP53, further validates programmed cell death. Finally, down-regulation of TIMELESS, signifies a correlation between IR response and circadian rhythm. Nonetheless, high-dose IR exposure effects regarding normal tissue (radiation toxicity) and its possible long-term outcomes should be studied to a greater extend.

Investigating Molecular Determinants of Cancer Cell Resistance to Ionizing Radiation Through an Integrative Bioinformatics Approach

“…Accumulated knowledge over years of research on the biological effects of radiation points toward the development of holistic approaches to “big data” analysis by employing systems biology methodologies (Unger, 2014 ; Beheshti et al, 2019 ; Spratt and Speers, 2019 ; Kanakoglou et al, 2020 ). Herein, we employed a rigorous systems biology approach to unravel the molecular determinants of resistance of cancer cells to IR, based solely on HTP data.…”

Benchmarking of next and third generation sequencing technologies and their associated algorithms for de novo genome assembly

“…The use of a reference genome in the study of medical genetics, with the help of novel tools and methods, can help the identification of novel drug-sequence variant interactions ( 46 ) and the identification of variants which may be related to mutations with a genetic base of a variety of genetic diseases, such as cancer ( 47 ) and produce further analysis ( 48 ). By studying these variants, we are able to analyse the differences and the heterogeneity of different populations in order to understand their differences ( 49 ).…”

A Meta-Analysis of the Effects of High-LET Ionizing Radiations in Human Gene Expression

“…The presence of the CDKN1A gene is identified in all three networks (Figures 4-6). This gene has also been found to be overexpressed in multiple IR-exposed human cell lines, such as a series of healthy cells after exposure to high doses of X-rays and gamma-rays [68], and healthy [95] and cancerous [96] breast epithelial cells exposed to high doses of electron radiation. The p21 protein encoded by this gene, which is known to be interdependent with tumor suppressor protein TP53 [13], is also responsible for inhibiting cellular proliferation in response to DNA damage and highly correlated with DNA repair and apoptosis [76].…”

“…In order to achieve optimal results, DEG lists were sorted into 3 groups (Table 1) depending on radiation type, radiation dose and time of collection post-irradiation before being subjected to a meta-analysis. Our meta-analysis method combined unadjusted p-values of each study for every gene, using a weighted version of Stouffer's meta-analysis [66], proposed by Mosteller and Bush [67], as previously described [68]. For each gene and study, its two-tailed unadjusted p-value was converted into a one-tailed p-value, based on the sign of the corresponding Log 2 FC.…”