mRNA and methylation profiling of radioresistant esophageal cancer cells: the involvement of Sall2 in acquired aggressive phenotypes

Abstract

Esophageal squamous cell carcinoma (ESCC) is one of the deadliest malignancies worldwide. Radiotherapy plays a critical role in the curative management of inoperable ESCC patients. However, radioresistance restricts the efficacy of radiotherapy for ESCC patients. The molecules involved in radioresistance remain largely unknown, and new approaches to sensitize cells to irradiation are in demand. Technical advances in analysis of mRNA and methylation have enabled the exploration of the etiology of diseases and have the potential to broaden our understanding of the molecular pathways of ESCC radioresistance. In this study, we constructed radioresistant TE-1 and Eca-109 cell lines (TE-1/R and Eca-109/R, respectively). The radioresistant cells showed an increased migration ability but reduced apoptosis and cisplatin sensitivity compared with their parent cells. mRNA and methylation profiling by microarray revealed 1192 preferentially expressed mRNAs and 8841 aberrantly methylated regions between TE-1/R and TE-1 cells. By integrating the mRNA and methylation profiles, we related the decreased expression of transcription factor Sall2 with a corresponding increase in its methylation in TE-1/R cells, indicating its involvement in radioresistance. Upregulation of Sall2 decreased the growth and migration advantage of radioresistant ESCC cells. Taken together, our present findings illustrate the mRNA and DNA methylation changes during the radioresistance of ESCC and the important role of Sall2 in esophageal cancer malignancy.

Nogo-B receptor increases the resistance to tamoxifen in estrogen receptor-positive breast cancer cells

“…After 14 days, cells were washed with PBS, fixed with methanol for 15 min and stained with 0.1% crystal violet for 15 min. Colonies containing 50 or more cells were counted [ 20 ].…”

Epigenetic silencing of SALL 2 confers tamoxifen resistance in breast cancer

“…It has been reported that promoter methylation‐mediated SALL2 downregulation occurs in multiple cancers (Sung et al , ; Luo et al , ; Imai et al , ). We also found that treatment with 5‐Aza‐2′‐deoxycytidine (5‐Aza‐dC), a DNA methyltransferase inhibitor, robustly increased the transcript level of SALL2 in MCF7‐TMR cells, but had no effect on MCF7 cells or tamoxifen‐treated MCF7 cells (Fig A and B).…”

“…SALL2 also contributes to cellular apoptosis, growth arrest, and quiescence maintenance by regulation of multiple downstream genes, such as p21Cip1/Waf1, p16Ink4a, c-MYC, BAX, and Noxa (Dawei Li et al, 2004;Gu et al, 2011;Sung et al, 2012;Escobar et al, 2015;Wu et al, 2015). Recently, SALL2 was found downregulated in various types of cancer, including leukemia, ovarian, lung, and radioresistant esophageal cancers (Sung et al, 2013;Liu et al, 2014;Luo et al, 2017), suggesting that SALL2 may act as a tumor suppressor. However, Suva et al (2014) found that SALL2 may also function as an oncogene, as it contributed to converting differentiated glioblastoma cells into cancer stem-like cells, thus favoring glioblastoma propagation.…”

“…Spalt-like transcription factor 2 (SALL2), a member of the C2H2 zinc finger transcription factor family, plays vital roles in normal development and is implicated in disease progression (de Celis & Barrio, 2009;Kelberman et al, 2014). Recently, SALL2 was found to be downregulated in several human cancer types, including breast cancer, ovarian cancer, and esophageal squamous cell carcinoma (Liu et al, 2007;Sung et al, 2013;Luo et al, 2017). Loss of SALL2 could abrogate serum deprivation-induced cell cycle arrest, but SALL2 overexpression suppressed cell growth (Liu et al, 2007).…”

Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance

“…1a). 10 of the 50 genes with largest feature importance scores were previously implicated in radiation therapy response [13][14][15][16][17][18][19][20][21][22] . To determine whether the identified set of 782 genes has more predictive value than previously-identified gene sets in RadiationGeneSigDB, machine learning classifiers were subsequently trained using only the genes from each respective gene set, and the predictive accuracy of each classifier was compared ( Fig.…”