Multi-omics analysis revealed significant metabolic changes in brain cancer cells treated with paclitaxel and/or topotecan - Ahlam M. Semreen; Leen Oyoun Alsoud; Mohammad H. Semreen; Munazza Ahmed; Hamza M. Al-Hroub; Raafat El-Awady; Wafaa S. Ramadan; Ahmad Abuhelwa; Yasser Bustanji; Nelson C. Soares; Karem H. Alzoubi (Heliyon)
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.