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

Abstract

Eradication of cancer cells through exposure to high doses of ionizing radiation (IR) is a widely used therapeutic strategy in the clinical setting. However, in many cases, cancer cells can develop remarkable resistance to radiation. Radioresistance represents a prominent obstacle in the effective treatment of cancer. Therefore, elucidation of the molecular mechanisms and pathways related to radioresistance in cancer cells is of paramount importance. In the present study, an integrative bioinformatics approach was applied to three publicly available RNA sequencing and microarray transcriptome datasets of human cancer cells of different tissue origins treated with ionizing radiation. These data were investigated in order to identify genes with a significantly altered expression between radioresistant and corresponding radiosensitive cancer cells. Through rigorous statistical and biological analyses, 36 genes were identified as potential biomarkers of radioresistance. These genes, which are primarily implicated in DNA damage repair, oxidative stress, cell pro-survival, and apoptotic pathways, could serve as potential diagnostic/prognostic markers cancer cell resistance to radiation treatment, as well as for therapy outcome and cancer patient survival. In addition, our findings could be potentially utilized in the laboratory and clinical setting for enhancing cancer cell susceptibility to radiation therapy protocols.

Can Systems Biology Advance Clinical Precision Oncology?

“…In many patients, cancer cells develop resistance to ionizing radiation (IR). Recently, an integrative bioinformatics analysis was applied to publicly available transcriptomic datasets of human cancer cells of different tissue origins treated with IR [ 87 ]. Supplemented with literature data, this analysis led to the identification of a panel of interconnected genes, belonging to pathways involved in neoplastic processes and implicated in the mechanisms of resistance to IR.…”

In Silico Methods for the Identification of Diagnostic and Favorable Prognostic Markers in Acute Myeloid Leukemia

“…For microarray data analysis, the two-sample t-test was employed to identify genes differentially expressed in two groups: (i) leukemic blast cells from AML (n = 26) versus BM (n = 10), and (ii) AML (n = 26) versus PB (n = 10) (Table S1), by applying a methodology described in detail in previous studies [97,98]. The Benjamini-Hochberg method [85] for controlling FDR was applied; an FDR-adjusted p value less than or equal to 0.01 was considered as statistically significant in this study.…”

In Silico Investigation of the Biological Implications of Complex DNA Damage with Emphasis in Cancer Radiotherapy through a Systems Biology Approach

“…x FOR PEER REVIEW 4 of 14 In the generated network, the antiapoptotic BCL2 [39] is linked to hypertension, diabetes mellitus and colon cancer (Figure 2). Of note, studies have shown that BCL2 regulates radioresistance in colon cancer cells [40] and is up-regulated in radioresistant breast cancer cells [41]. Higher incidence of cardiovascular problems has also been observed in breast cancer patients when irradiated on the left breast versus the right, which is related to the late effect of RT [42].…”

“…The pro-apoptotic BAX (BCL2-associated X protein) [50], was found to be downregulated in acute lymphoblastic leukemia (ALL) and breast cancer cells resistant to radiation [41]. BAX is also linked to non-neoplastic diseases of different tissue origin (Figure 2), suggesting also a potential role of this molecule in radiotoxicity.…”

“…This would be particularly useful in clinical decision-making before applying RT regarding the anticipation effect of this modality. For example, RT is expected to be more effective for patients with decreased levels of BCL2 and increased expression of BAX as compared to fellow patients with opposite gene expression patterns [37,41]. Of note, cancer-associated comorbidities are associated with poor clinical outcomes in cancer patients [35,65].…”