Electronic Theses and Dissertations (Masters)

Permanent URI for this collection

https://hdl.handle.net/10539/38018

Browse

Browsing Electronic Theses and Dissertations (Masters) by Author "Kaur, Mandeep"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    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, Mandeep
    Pancreatic 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.
  • Thumbnail Image
    Item
    Establishing and characterizing organoid cultures from colon tissue of South African individuals
    (University of the Witwatersrand, Johannesburg, 2024) Du Plessis, Thea-Leonie; Kaur, Mandeep
    Colorectal cancer (CRC) has been poorly studied in South Africa, with limited studies on disease progression and development. Studies that have investigated CRC in South Africa have indicated that there is racial disparity between different racial groups that may be attributed to alternative developmental pathways, differences in genetic compositions or CRC initiators that result in these different clinical presentations. Furthermore, the lack of population-based studies substantiates the need for more intensive CRC research. A particular model used to study cancer in general is the use of two-dimensional (2D) cell cultures, which have provided novel insight into many cancers and their development processes. However, these models lack the complex biology observed in vivo. One such model that is gaining research interest is the use of three-dimensional (3D) organoid cultures. Organoids are derived from stem cells and are able to self-organize and mimic the corresponding organ from which they were derived. Research has indicated that organoids are able to maintain cell-type heterogeneity as well as gene expression levels that resemble the organ of origin. Therefore, this project aimed at standardizing a protocol to establish and characterise colorectal organoid cultures from South African patient-derived tissues. Patient samples were obtained from individual patients with informed consent and were processed to generate organoids. The morphology of the organoids was monitored across several days and across passages. Once the organoids had reached maturity and were at passage 2, characterization was performed using real-time quantitative polymerase chain reaction (RT-qPCR) and immunofluorescence which indicated that the genetic composition and spatial localization of cell types of interest in non-cancerous tissue was recapitulated in the organoids. Based on these observations, it is proposed that organoids could be a promising model to investigate CRC disease development and progression and potentially search for novel therapeutics. This project has established the protocols for growing and characterizing organoids from African samples and provides baseline data, and outlines the complexities and issues involved in growing organoid cultures for the future studies