Differential expression profiles of mRNA in radioresistant non-small cell lung cancer cell line A549 model using gene expression microarray / 解剖学报

Objective To investigate the radioresistance factors in non-small cell lung cancer (NSCLC)cell line A549, and provide new targets for radiotherapy sensitization drugs development. Methods Establish the stable model of radioresistant NSCLC cell line A549 under irradiation; investigate the whole-transcriptome alteration of radioresistance cell line and radiosensitive cell line using gene expression microarray; perform bioinformatic approaches gene ontology (GO) analysis and Pathway analysis. Results The expression profile microarray showed that 1410 differentially expressed genes (733 up-regulated and 677 down-regulated) were detected in resistant and sensitive strains; GO analysis showed that it was mainly related to cell cycle and DNA replication; Pathway significant enrichment analysis showed that mitogen-activated protein kiase(MAPK) signaling pathway, phosphatidylinositol 3-kinase/protein kinase B(PI3K/Akt) signaling pathway, were mainly associated with radioresistance. Conclusion Multiple genes and signaling pathways are involved in radioresistance, further studies are needed to investigate the radioresistance factors, which could provide new targets for radiotherapy sensitization drugs development.

Prediction of Exposure to 1763MHz Radiofrequency Radiation Using Support Vector Machine Algorithm in Jurkat Cell Model System

We have investigated biological responses to radiofrequency (RF) radiation in in vitro and in vivo models. By measuring the levels of heat shock proteins as well as the activation of mitogen activated protein kinases (MAPKs), we could not detect any differences upon RF exposure. In this study, we used more sensitive method to find the molecular responses to RF radiation. Jurkat, human T-Iymphocyte cells were exposed to 1763 MHz RF radiation at an average specific absorption rate (SAR) of 10 W/kg for one hour and harvested immediately (R0) or after five hours (R5). From the profiles of 30,000 genes, we selected 68 differentially expressed genes among sham (S), R0 and R5 groups using a random-variance F-test. Especially 45 annotated genes were related to metabolism, apoptosis or transcription regulation. Based on support vector machine (SVM) algorithm, we designed prediction model using 68 genes to discriminate three groups. Our prediction model could predict the target class of 19 among 20 examples exactly (95% accuracy). From these data, we could select the 68 biomarkers to predict the RF radiation exposure with high accuracy, which might need to be validated in in vivo models.