Thymoquinone (TQ) is the active ingredient extracted from the essential oil of Nigella sativa. A number of studies implicated TQ as an antitumor agent. In this study, cytotoxic effects of the oil of N. sativa and TQ were evaluated on human cervical cancer cell line, HeLa cells. IC50 value was ~0.125 μl/ml for N. sativa oil preparations and 12.5 μM for TQ. TQ strongly inhibited wound healing at all concentrations ranging from 12.5 to 100 μM in a scratch wound healing assay. Additionally, induction of apoptosis by TQ was assessed by Giemsa staining and TQ was found to induce apoptosis in cancer cells especially at concentrations of 50 and 100 μM. TQ-mediated transcriptional regulation of 84 genes involved in apoptosis was studied using a PCR array. At low dose (12.5 μM), TQ was found to induce expression of four pro-apoptotic genes: BIK (~22.7-fold), FASL (~2.9-fold), BCL2L10 (~2.1-fold), and CASP1 (~2-fold). TQ was also found to reduce the expression of an anti-apoptotic gene implicated in NF-kappa-B signaling and cancer: RELA (~8-fold). At high dose (100 μM), TQ mediated the expression of 21 genes implicated directly in apoptosis (6 genes), TNF signaling (10 genes), and NF-kappa-B signaling (3 genes) such as BIK, BID, TNFRSF10A, TNFRSF10B, TNF, TRAF3, RELA, and RELB. In conclusion, this study implicates the role of TQ in the inhibition of cancer cell proliferation and migration. At the same time, our results strongly suggest that TQ intervenes with TNF and NF-kappa-B signaling during TQ-mediated induction of apoptosis in cancer cells.

Glioblastoma multiforme (GBM) is the most aggressive type of brain tumor. Molecular heterogeneity is a hallmark of GBM tumors that is a barrier in developing treatment strategies. In this study, we used the nonsynonymous mutations of GBM tumors deposited in The Cancer Genome Atlas (TCGA) and applied a systems level approach based on biophysical characteristics of mutations and their organization in patient-specific subnetworks to reduce inter-patient heterogeneity and to gain potential clinically relevant insights. Approximately 10% of the mutations are located in “patches” which are defined as the set of residues spatially in close proximity that are mutated across multiple patients. Grouping mutations as 3D patches reduces the heterogeneity across patients. There are multiple patches that are relatively small in oncogenes, whereas there are a small number of very large patches in tumor suppressors. Additionally, different patches in the same protein are often located at different domains that can mediate different functions. We stratified the patients into five groups based on their potentially affected pathways that are revealed from the patient-specific subnetworks. These subnetworks were constructed by integrating mutation profiles of the patients with the interactome data. Network-guided clustering showed significant association between the groups and patient survival (P-value = 0.0408). Also, each group carries a set of signature 3D mutation patches that affect predominant pathways. We integrated drug sensitivity data of GBM cell lines with the mutation patches and the patient groups to analyze the possible therapeutic outcome of these patches. We found that Pazopanib might be effective in Group 3 by targeting CSF1R. Additionally, inhibiting ATM that is a mediator of PTEN phosphorylation may be ineffective in Group 2. We believe that from mutations to networks and eventually to clinical and therapeutic data, this study provides a novel perspective in the network-guided precision medicine.

Mutation profiles of Glioblastoma (GBM) tumors are very heterogeneous which is the main challenge in the interpretation of the effects of mutations in disease. Additionally, the impact of the mutations is not uniform across the proteins and protein-protein interactions. The pathway level representation of the mutations is very limited. In this work, we approach these challenges through a systems level perspective in which we analyze how the mutations in GBM tumors are distributed in protein structures/interfaces and how they are organized at the network level. Our results show that out of 14644 mutations, 4392 have structural information and ~13% of them form spatial patches. Despite a small portion of all mutations, 3D patches partially decrease the heterogeneity across the patients. Hub proteins adapt multiple patches of mutations usually with a very large one and connects mutations in multiple binding sites through the core of the protein. We reconstructed patient specific networks for 290 GBM tumors. Network-guided analysis of mutations completes the interaction components that mutated proteins potentially affect, and groups the patients according to the reconstructed networks. As a result, we found 4 tumor clusters that overcome the heterogeneity in mutation profiles, and reveal predominant pathways in each group. Additionally, the network-based similarity analysis shows that each group of patients carries a set of signature 3D mutation patches. We believe that this study provides another perspective to the analysis of mutation effects and a good training towards the network-guided precision medicine.