Dataset Information

Control, MGO-treated, and Paclitaxel-treated RNAseq data with matched ribosome profiling

ABSTRACT: Control, MGO-treated, and Paclitaxel-treated RNAseq data with matched ribosome profiling

PROVIDER: PRJNA961154 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	SRR24293524.fastq.gz	Fastqsanger.gz
	SRR24293525.fastq.gz	Fastqsanger.gz
	SRR24293526.fastq.gz	Fastqsanger.gz
	SRR24293427.fastq.gz	Fastqsanger.gz
	SRR24293527.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 24

Similar Datasets

Project description:Glioblastoma (GB) is the most common primary malignant brain tumor, representing approximately 57% of all gliomas and 48% of all primary malignant central nervous system (CNS) tumors. Despite the best standard therapies, glioblastoma survivors have a brief survival time, about 24 months on average. The treatment is troublesome because the cancer cells may not respond well to specific therapies as they grow within an extensive network of blood vessels. A multi-omics approach to provide information on biomolecules from multiple layers appears promising for systematically and holistically understanding complex biology. However, till now, only few studies have utilized omics analysis to investigate the impact of anticancer drugs on GBM. Our study aimed to evaluate the impacts of the anticancer medications (paclitaxel 5.3 μg/mL and topotecan 0.26 μM) solely and in pairwise combination on the metabolic and proteomic signatures of the U87 cell line while utilizing the accurate ultra-high-performance liquid chromatography-electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-ESI-QTOF-MS) analytical technology. The studied cancer cells wear treated with DMSO (control group), paclitaxel 5.3 µM, topotecan 0.26 µM, and a combination of paclitaxel 5.3 µM and topotecan 0.26 µM. Using One-way ANOVA, we observed 14 significantly altered metabolites compared to those cells treated with DMSO. For combination treatment (paclitaxel and topotecan), 10 metabolites were significantly dysregulated. The sparse partial least squares-discriminant analysis (sPLS-DA) showed minimal overlapping, indicating a difference between the four groups. While for proteomics, a total of 79 proteins were significantly dysregulated among the groups. These findings can aid in identifying new biomarkers associated with the utilized drugs and creating a map for targeted therapy. EIF3F, GNB2L1, HINT2, and RPA3 were shown to be significantly upregulated in the combination group when compared to the control. Moreover, ribosome, apoptosis, HIF-1 signaling, arginine and proline, glutathione, purine metabolism, apelin signaling pathway, and glycolysis were significantly altered in the combination group.

Dataset Information

Control, MGO-treated, and Paclitaxel-treated RNAseq data with matched ribosome profiling

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets