Browsing by Subject "TCGA-PANCAN"

Now showing 1 - 3 of 3

Open Access
A shiny application for pancan survival analysis with paralog/miRNA pairs and in vitro validation of miRNA synergism in liver cancer
(2022-09) Tombaz, Melike
Emerging cancer survival tools can predict risk of disease and identify prognostic biomarkers. Multivariable Cox proportional hazards models with mRNA and microRNAs (miRNAs) expression can differentiate survival outcomes. Previous studies showed that genes that belong to the same pathways/families may act independently, and in a cancer-specific manner. In this thesis, cancer-dependent hazard ratios of paralog genes and sense-antisense strands of miRNAs were tested for TCGA PANCAN. The results were presented in a R/Shiny web application that provides gene-by-survival networks. The gene-by-survival network approach also was applied to the plasma membrane-endoplasmic reticulum (PM-ER) calcium channel geneset. Among paralogs, cancer-specific prognostic signatures and functional compartmentalization were observed. Some cancers like UVM, MESO emerged as hub cancers for PM-ER signalling. Further the proposed gene-by-survival network approach has been extended for miRNA-mRNA triplets that may act in synergy in hepatocellular carcinoma (HCC). Next, the effects of synergistic miRNA pairs provided by miRCoop algorithm were tested on cell viability and target gene expression for selected triplets. The results have revealed that the combinatorial miRNA treatments show promising results as RNAi therapeutics yet future studies with different doses and triplets are needed.
Open Access
SOX2 in focus: association of SOX2 copy number variation with TP53 mutation in TCGA pancancer cohorts and codon optimized design for de novo SOX2 synthesis using novel shiny application
(2024-01) Çelik, Siber Güneş
Recombinant proteins are crucial for diverse research applications such as biosensors and cancer studies. Proteins are engineered through de novo gene synthesis methods. Numerous tools and databases have emerged to facilitate the design of recombinant proteins, starting from the design of the gene sequence. De novo DNA synthesis enables the synthesis of custom-designed sequences, allowing codon optimization to enhance expression yield in heterologous systems. In cancer research, recombinant expression of proteins involved in tumorigenesis-related signaling pathways is employed for functional studies, potentially revealing new therapeutic targets. A notable example is the pivotal role of SOX2 expression in the formation of cancer stem cells (CSCs) across various cancer types. Previous studies highlight SOX2 expression functionally overlaps with TP53 expression on the PI3K/AKT signaling pathway. This association may stem from the p53-MDM2 interaction. This thesis investigates the association between SOX2 copy number gain and TP53 mutations within TCGA PanCancer cohorts. Fisher’s exact test results reveal varying association, dependent on tissue type and specific driver mutations within each cancer type. The findings suggest the potential therapeutic relevance of SOX2 in cancer research. Furthermore, the thesis employs an in-silico approach to design de novo SOX2 synthesis, utilizing a novel shiny app that integrates codon optimization and primer design functionalities. The app enables simultaneous codon optimization for multiple expression systems and offers distance analysis through hierarchical clustering. Codon optimization feature provides control over the rate of replacement value for codon substitution which validated through a case study involving human insulin. Finally, app design set of overlapping primers with synchronized melting temperature to be used in PCR assembly for de novo SOX2 gene synthesis.
Open Access
Survival analysis and its applications in identifying genes, signatures, and pathways in human cancers
(2021-09) Özhan, Ayşe
Cancer literature makes use of survival analyses focused on gene expression based on univariable or multivariable regression. However, there is still a need to understand whether a) incorporating exon or isoform information on expression would improve estimation of survival in cancer patients; and b) applying multivariable regression to gene sets would allow to obtain cancer-specific independent gene signatures in cancer. Differential usage of individual exons, as well as transcripts, are phenomena common to cancerous tissue when compared to normal tissue. The glioblastoma, GBM; liver cancer LIHC; stomach adenocarcinoma, STAD; and breast carcinoma, BRCA datasets from The Cancer Genome Atlas (TCGA) were investigated to identify individual exons and transcripts with transcriptome-wide impact and significance on survival. Aggregation analyses of exons revealed the important genes for survival in each dataset, including GNA12 in STAD, AKAP13 in LIHC and RBMXL1 and CARS1 in BRCA. GSEA was applied on gene sets formed from the exon-based analysis, revealing distinct enrichment profiles for each dataset as well as overlaps for certain GO terms and KEGG pathways. In the second focus of this thesis, multivariable analyses on gene sets whose expressions were obtained from UCSC Xena were used to create two Shiny applications: one for dataset-specific analyses and one for analyses across TCGA-PANCAN. The dataset specific SmulTCan application incorporates Cox regression analyses with expressions of input genes of the user’s choice. The SmulTCan application contains additional model validation, best subset selection and prognostic analyses. The ClusterHR application performs clustering analyses with Cox regression results, while it can also be used for bicluster identification and comparison. The axon-guidance ligand-receptor gene sets Slit-Robo, netrins-receptors and Semas-receptors were used for demonstrating the apps. Several hazard ratio signatures and best subsets that can differentiate between prognostic outcomes have been identified from the input gene sets, as well as ligand-receptor pairs with prognostic significance.