Ophiobolin A Induces an Apoptotic Transcriptional Signature and Modulates Redox Homeostasis in T98G and U118MG Glioblastoma Cells: A Machine Learning Approach

Gliomas are the most common group of primary brain tumors, among which glioblastoma multiforme (GBM) is characterized by a particularly poor prognosis and the limited effectiveness of available treatments. Ophiobolin A (OP-A), a natural sesterterpenoid, exhibits promising anticancer properties, including the ability to cross the blood-brain barrier and induce paraptosis-like cell death. However, the molecular mechanisms underlying its action, especially in the early phase of the cellular response, remain not fully understood. The aim of this study was to analyze early changes in the expression of genes associated with apoptosis, ferroptosis, and antioxidant mechanisms in T98G and U118MG glioma cells exposed to OP-A. Gene expression was assessed by RT-qPCR, apoptosis was evaluated using Annexin V/PI staining and flow cytometry, and treatment-induced morphological changes were documented by brightfield microscopy. Statistical analysis was performed using the Mann–Whitney U test. Descriptive Annexin V/PI analysis showed a lower proportion of viable cells and a higher proportion of early apoptotic cells in the analyzed OP-A-treated T98G and U118MG samples compared with the corresponding vehicle-control samples. These preliminary observations were based on technical replicates from a single biological experiment and require confirmation in independent biological replicates. Transcriptional profiling revealed a shift toward a pro-apoptotic phenotype, characterized by increased BAX and FAS expression together with a trend toward reduced BCL2 expression, whereas ferroptosis-associated genes remained largely unchanged. Notably, SLC7A11 upregulation suggested activation of compensatory antioxidant mechanisms in response to OP-A treatment. In T98G cells, OP-A induced a distinct and reproducible transcriptional signature that enabled accurate discrimination from control conditions (AUC = 0.833). Feature importance and SHAP analyses identified BAX as the most informative predictor, followed by SLC7A11 and FAS, with bootstrap validation confirming BAX as a stable marker. Pathway analysis demonstrated selective activation of apoptosis- and cysteine metabolism-related pathways, while hierarchical clustering revealed that OP-A generated a transcriptional profile distinct from oxidative stress-inducing agents. The predictive performance of this molecular signature was cell-line dependent, showing weaker discrimination in U118MG cells.Short-term OP-A exposure in T98G and U118MG cells was associated with exploratory trends in apoptosis- and redox-related gene expression and a higher proportion of Annexin V-positive cells. The machine-learning analyses identified candidate discriminatory features within this limited dataset but should be regarded as hypothesis-generating. Larger studies with independent biological replication, additional GBM models, different exposure conditions, and functional validation are required to confirm these observations and clarify the mechanism of OP-A action.