Dataset Information

Metabolomics Analysis Revealed Significant Metabolic Changes in Brain Cancer Cells Treated with Paclitaxel and/or Etoposide.

ABSTRACT: Cancer of the central nervous system (CNS) is ranked as the 19th most prevalent form of the disease in 2020. This study aims to identify candidate biomarkers and metabolic pathways affected by paclitaxel and etoposide, which serve as potential treatments for glioblastoma, and are linked to the pathogenesis of glioblastoma. We utilized an untargeted metabolomics approach using the highly sensitive ultra-high-performance liquid chromatography-electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-ESI-QTOF-MS) for identification. In this study, 92 and 94 metabolites in U87 and U373 cell lines were profiled, respectively. The produced metabolites were then analyzed utilizing t-tests, volcano plots, and enrichment analysis modules. Our analysis revealed distinct metabolites to be significantly dysregulated (nutriacholic acid, L-phenylalanine, L-arginine, guanosine, ADP, hypoxanthine, and guanine), and to a lesser extent, mevalonic acid in paclitaxel and/or etoposide treated cells. Furthermore, both urea and citric acid cycles, and metabolism of polyamines and amino acids (aspartate, arginine, and proline) were significantly enriched. These findings can be used to create a map that can be utilized to assess the antitumor effect of paclitaxel and/or etoposide within the studied cancer cells.

SUBMITTER: Semreen AM

PROVIDER: S-EPMC9693830 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Metabolomics Analysis Revealed Significant Metabolic Changes in Brain Cancer Cells Treated with Paclitaxel and/or Etoposide.

Semreen Ahlam M AM Alsoud Leen Oyoun LO El-Huneidi Waseem W Ahmed Munazza M Bustanji Yasser Y Abu-Gharbieh Eman E El-Awady Raafat R Ramadan Wafaa S WS Alqudah Mohammad A Y MAY Shara Mohd M Abuhelwa Ahmad Y AY Soares Nelson C NC Semreen Mohammad H MH Alzoubi Karem H KH

International journal of molecular sciences 20221111 22

Cancer of the central nervous system (CNS) is ranked as the 19th most prevalent form of the disease in 2020. This study aims to identify candidate biomarkers and metabolic pathways affected by paclitaxel and etoposide, which serve as potential treatments for glioblastoma, and are linked to the pathogenesis of glioblastoma. We utilized an untargeted metabolomics approach using the highly sensitive ultra-high-performance liquid chromatography-electrospray ionization quadrupole time-of-flight mass ...[more]

PMID: 36430415

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Project description:Glioblastoma (GBM) stands as the most common primary malignant brain tumor. Despite the best standard therapies, GBM 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. Our study aims to evaluate the impact of paclitaxel 5.3 μg/mL and topotecan 0.26 μM solely and in pairwise combination on the resultant metabolic and proteomic signatures of the U87 cell line while using the precise ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF) analytical technology. The U87 cells wear treated with DMSO, paclitaxel 5.3 μM, topotecan 0.26 μM, and their combinations. Using One-way ANOVA, we observed 14 significantly altered metabolites compared to those cells treated with DMSO. For combination treatment (paclitaxel and topotecan), 11 metabolites were significantly dysregulated. Sparse partial least squares-discriminant analysis (sPLS-DA) revealed minimal overlap, highlighting distinctions among 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 relative 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. Overall, this study underscores the effectiveness of multi-omics approaches in revealing the molecular mechanisms driving chemotherapy responses in cancer cells. Additionally, this work generates a comprehensive list of molecular alterations that can serve as a foundation for further investigations and inform personalized healthcare strategies to enhance patient outcomes.

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

Metabolomics Analysis Revealed Significant Metabolic Changes in Brain Cancer Cells Treated with Paclitaxel and/or Etoposide.

Publications

Metabolomics Analysis Revealed Significant Metabolic Changes in Brain Cancer Cells Treated with Paclitaxel and/or Etoposide.

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