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Effect of chronic PM exposure on gene expression in the mouse lung tissue

ABSTRACT: Effect of chronic PM exposure on gene expression in the mouse lung tissue

PROVIDER: PRJNA987717 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR25031530.fastq.gz	Fastqsanger.gz
	SRR25031531.fastq.gz	Fastqsanger.gz
	SRR25031532.fastq.gz	Fastqsanger.gz
	SRR25031533.fastq.gz	Fastqsanger.gz
	SRR25031534.fastq.gz	Fastqsanger.gz

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Project description:Objectives Our goal was to evaluate the diagnostic value of DNA methylation analysis in combination with machine learning to differentiate pleural mesothelioma (PM) from important histopathological mimics. Material and methods DNA methylation data of PM, lung adenocarcinomas, lung squamous cell carcinomas and chronic pleuritis was used to train a random forest as well as a support vector machine. These classifiers were validated using an independent validation cohort including pleural carcinosis and pleomorphic variants of lung adeno- and squamous cell carcinomas. Furthermore, we used a deconvolution method to estimate the composition of the tumor microenvironment. Results T-distributed stochastic neighbor embedding clearly separated PM from lung adenocarcinomas and squamous cell carcinomas, but there was a considerable overlap between chronic pleuritis specimens and PM with low tumor cell content. While both machine learning algorithms achieved comparable accuracies in a nested cross validation on the training cohort (random forest: 94.9%; support vector machine: 95.5%), the support vector machine outperformed the random forest in distinguishing PM from chronic pleuritis. Differential methylation analysis revealed promoter hypermethylation in PM specimens, including the tumor suppressor genes BCL11B, EBF1, FOXA1, and WNK2. Furthermore, we observed comparable accuracies for the support vector machine on the validation cohort (97.1%) while the random forest performed considerably worse (89.9%). Deconvolution of the stromal and immune cell composition revealed higher rates of regulatory T-cells and endothelial cells in tumor specimens and a heterogenous inflammation including macrophages, B-cells and natural killer cells in chronic pleuritis. Conclusion DNA methylation in combination with machine learning is a promising tool to reliably differentiate PM from chronic pleuritis and lung cancer, including pleomorphic carcinomas. Furthermore, our study highlights new candidate genes for PM carcinogenesis and shows that deconvolution of DNA methylation data can provide reasonable insights into the composition of the tumor microenvironment.

Project description:This study chronically exposed human lung epithelial BEAS-2B cells to low-dose arsenic trioxide in vitro to elucidate cancer promoting gene signaling networks (GSNs) associated with As-transformed (B-As) cells. Following a six month exposure, exposed cells were assessed for enhanced cell proliferation, colony formation, invasion ability and in vivo tumor formation compared to control cell lines. Collected mRNA was subjected to whole genome expression microarray profiling followed by in silico Ingenuity Pathway Analysis (IPA) to identify lung carcinogenesis modes of action. B-As cells displayed significant increases in proliferation, colony formation and invasion ability compared to BEAS-2B cells. B-As injections into nude mice resulted in development of primary and secondary metastatic tumors. As exposure resulted in widespread up-regulation of genes associated with mitochondrial metabolism and increased ROS protection suggesting mitochondrial dysfunction. Carcinogenic initiation via ROS and epigenetic mechanisms was further supported by altered DNA repair, histone, and ROS-sensitive signaling. NF-ÎºB, MAPK and NCOR1 signaling disrupted PPARÎ±/Î´-mediated lipid homeostasis. A âpro-cancerâ GSN identified increased survival, proliferation, inflammation, metabolism, anti-apoptosis and mobility signaling. IPA-ranked signaling networks identified altered p21, EF1Î±, Akt, MAPK, and NF-ÎºB signaling networks promoting genetic disorder, altered cell cycle, cancer and changes in nucleic acid and energy metabolism. In conclusion, transformed B-As cells with their whole genome GSN profile provide an in vitro As model for future lung cancer signaling research and data for chronic As exposure risk assessment. Whole genome expression profiling was conducted on arsenic (III) oxide-exposed human immortalized lung epithelial cells (BEAS-2B) following 6 month in vitro chronic exposure. As2O3 exposed cells (B-As) gene expression were compared to unexposed, passage control (B-Control) cell gene expression. Three B-As and four B-Control biological replicate cDNA samples were analyzed.

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Effect of chronic PM exposure on gene expression in the mouse lung tissue

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