4. Electronic Theses and Dissertations (ETDs) - Faculties submissions
Permanent URI for this communityhttps://hdl.handle.net/10539/37773
Browse
2 results
Search Results
Item The effects of indigenous South African plant extracts (cotyledon c. orbiculata and tulbaghia. violacea) on triple negative breast cancer cells(University of the Witwatersrand, Johannesburg, 2024) Alaouna, Mohammed; Dlamini, ZodwaThis dissertation explored the potential therapeutic applications of water and methanol extracts of C. orbiculata and Tulbaghia violacea, indigenous to Southern Africa, targeting triple-negative breast cancer (TNBC). TNBC, a significant subset of breast cancer cases, is notably challenging because of the absence of key hormone receptors, often leading to less favourable patient outcomes and a high relapse rate within five years. The research approach was both thorough and meticulous, utilising two cell lines: one representing normal breast tissue and the other representing TNBC. Extensive cytotoxicity assays were conducted to determine the IC50 values for TNBC cells, which is critical for understanding how plant extracts affect cellular activities such as migration, invasion, adhesion, cell cycle regulation, and apoptosis induction. Additionally, the antioxidant properties of these extracts were examined, which showed significant effects, especially in the aqueous extract of Tulbaghia violacea, on TNBC cellular dynamics. This study employed a comprehensive array of analytical techniques, including Fourier transform infrared spectroscopy (FTIR), gas chromatography-mass spectrometry (GC-MS), and nuclear magnetic resonance (NMR) spectroscopy, to identify the specific molecular constituents of these extracts. Computational docking studies have focused on the interactions between these molecules and the anti-apoptotic protein, COX2. Whole transcriptome sequencing of RNA from both TNBC and normal breast cells treated with T. violacea extract provided valuable insights into the affected signaling pathways. An antibody array assay further elucidated protein changes in the receptor tyrosine kinase (RTK) pathway. The half-maximal inhibitory concentration (IC50) values were determined for the aqueous and methanol extracts of T. violacea at 400 μg/mL and 820 μg/mL, respectively, and for C. orbiculata at 830 μg/mL and 700 μg/mL, respectively. Exposure to the water-soluble extract of T. violacea resulted in a marked increase in apoptosis in TNBC cells, with approximately 82% undergoing programmed cell death, compared to 32% in normal breast cells. Chemical profiling identified a range of compounds, including 36 distinct compounds identified through GC-MS and 61 identified through NMR, many of which bear structural similarities to known anti-cancer agents. Notably, five compounds demonstrated a high affinity forbinding to COX2, with d-glycero-d-galacto-heptose achieving an impressive docking score, surpassing several established COX2 inhibitors. This study highlights the therapeutic potential of T. violacea compounds and lays the groundwork for further exploration of their mechanisms of action and potential applications in cancer treatment. This emphasises the importance of investigating natural plant extracts as a source for the development of new and effective treatments for TNBC, which is an area of urgent need in oncologyItem Analysing RNA-sequence data for pancreatic ductal adenocarcinoma tissue samples to identify potential biomarkers(University of the Witwatersrand, Johannesburg, 2023-09) Jamal, Khadija Sanober; Kaur, MandeepPancreatic ductal adenocarcinoma (PDAC) accounts for approximately 90% of pancreatic cancer and is the fourth leading cause of death with a five-year survival rate of less than 10%. Patients are asymptomatic until detection is observed at a metastatic stage, hence contributing massively towards the high mortality rate. This study was conducted to explore PDAC and its two main subtypes, the classical and basal-like subtype, in an in-depth level via bioinformatic analysis. Bioinformatics is a computational approach to evaluate biological data by analysing omics data including genomic expression and proteomic sequences. A workflow consisting of programmes and web-tools was used to analyse PDAC RNA-sequence data. The sample sets were grouped according to tumour, stage, and subtype. The workflow began with quality control using FastQC and Trimmomatic. Alignment of sequencing files and counts were done through HISAT2 and HTSeq. The main component of this workflow was differential gene expression analysis to identify differentially expressed genes (DEGs), statistically significant genes, per compared conditions. WGCNA was used for co-expression analysis to identify the hub genes involved in regulating the biological network. Lastly, in-silico validation was done by using available web tools to support the findings of this workflow. The identified tumour genes included S100A11, PKM, GPRC5A, LAMC2 and ITGA2, which may represent as universal biomarkers as sample extraction was performed from data generated from individuals belonging to 8 different countries. KRT13 and IL6 were identified in the advanced stage and their role in cancer progression have been explored in this current study. The basal-like subtype had CAV1, DCVLD2 and TGFB2 genes that contribute to treatment resistance. The common dysregulated genes in the basal-like subtype and advanced stage were analysed to evaluate the link between subtype and stage which included WNT3A, TP63, KRT13 and IGF2BP. Coexpression analysis revealed hub genes for tumour (KIF4A, SPAG5, RRM2 and AURKA), basal-like subtype (BUB1, DEPDC1 and KIF14) and classical-subtype (PTPRN and CAMK2B). Through a machine learning model, recall, precision and accuracy scores per sample conditions for the DEGs were all above 94%. These potential biomarkers all have significant roles in promoting cancer progression, aggression and resistance. Hence, these may serve as a less invasive screening method for PDAC as DEGs were classified based on tissue or blood (extracellular vesicle) biomarkers. However, further wet laboratory validation is required for these biomarkers.