Browsing by Subject "Transcriptomics"
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Item Open Access Identification and targeting of deregulated metabolic pathways in metastatic prostate cancer cells(Bilkent University, 2023-01) Kaysudu, IrmakProstate cancer is the most diagnosed cancer type and the second leading cause of death in men globally. The pathogenesis of prostate cancer mainly relies on the androgen signaling axis. Therefore, androgen deprivation therapy is the primary treatment for prostate cancer. Nevertheless, the disease progression proceeds, followed by castration resistance and androgen independence. Aberrant androgen signaling activity intertwined with the hyperactivated PI3K-Akt signaling pathway has important oncogenic consequences for castration resistance mechanisms. PTEN, a negative regulator of the PI3K/Akt pathway, is one of the most altered tumor suppressor genes in prostate cancer. PTEN loss occurs in the initial stages of prostate cancer and the frequency of its alteration increases in metastatic and castration-resistant prostate cancer. PTEN has both lipid and protein phosphatase activity, with the former antagonizing the PI3K-Akt pathway by converting membrane-associated PIP3 to PIP2. PTEN loss may cause metabolic rewiring in metastatic prostate cancer cells and the associated metabolic vulnerabilities may be tackled for the disease therapy. To understand the impact of PTEN loss in metastatic prostate cancer cells, we created a dox-inducible system in PTEN-null metastatic and castration-naïve LNCaP cells to re-express WT-PTEN and various PTEN functional mutants, and we employed targeted metastatic prostate cancer. Our multidirectional omics studies suggest that the acquisition of resistance to castration depends on the deregulation of the sphingolipid metabolism in metastatic prostate cancer cells. Furthermore, we showed that PTEN re-expression in metastatic and castration-naïve LNCaP cells attenuated sphingosine kinase levels, which might switch the sphingolipid metabolism towards increased sphingomyelin biosynthesis and ceramide phosphorylation. Moreover, we showed decreased PI3K/Akt pathway activity when we inhibited sphingosine kinase with opaganib in LNCaP cells. Our results also showed a significant upregulation in sphingolipid metabolism in castration-resistant C4-2 cells compared to castration-naïve LNCaP. We treated these cells with several sphingolipid metabolism inhibitors and discovered that castration-resistant prostate cancer cells were more sensitive to opaganib or ARN14988, but not to fingolimod, than castration-naïve prostate cancer cells. These findings suggest that sphingolipid metabolism might be a promising target for the treatment of metastatic and castration-resistant prostate cancer. Understanding changes in sphingolipid metabolism may be critical for developing rational combinatorial targeting strategies for prostate cancer in the long run.Item Open Access A stemness and EMT based gene expression signature identifies phenotypic plasticity and is a predictive but not prognostic biomarker for breast cancer(Ivyspring International Publisher, 2020) Akbar, Muhammad Waqas; Belder, Nevin; Demirkol-Canlı, Seçil; Küçükkaraduman, Barış; Türk, Can; Şahin, Özgür; Güre, Ali OsmayAims: Molecular heterogeneity of breast cancer results in variation in morphology, metastatic potential and response to therapy. We previously showed that breast cancer cell line sub-groups obtained by a clustering approach using highly variable genes overlapped almost completely with sub-groups generated by a drug cytotoxicity-profile based approach. Two distinct cell populations thus identified were CSC(cancer stem cell)-like and non-CSC-like. In this study we asked whether an mRNA based gene signature identifying these two cell types would explain variation in stemness, EMT, drug sensitivity, and prognosis in silico and in vitro. Main methods: In silico analyses were performed using publicly available cell line and patient tumor datasets. In vitro analyses of phenotypic plasticity and drug responsiveness were obtained using human breast cancer cell lines. Key findings: We find a novel gene list (CNCL) that can generate both categorical and continuous variables corresponding to the stemness/EMT (epithelial to mesenchymal transition) state of tumors. We are presenting a novel robust gene signature that unites previous observations related either to EMT or stemness in breast cancer. We show in silico, that this signature perfectly predicts behavior of tumor cells tested in vitro, and can reflect tumor plasticity. We thus demonstrate for the first time, that breast cancer subtypes are sensitive to either Lapatinib or Midostaurin. The same gene list is not capable of predicting prognosis in most cohorts, except for one that includes patients receiving neo-adjuvant taxene therapy. Significance: CNCL is a robust gene list that can identify both stemness and the EMT state of cell lines and tumors. It can be used to trace tumor cells during the course of phenotypic changes they undergo, that result in altered responses to therapeutic agents. The fact that such a list cannot be used to identify prognosis in most patient cohorts suggests that presence of factors other than stemness and EMT affect mortality.Item Open Access Survival analysis and its applications in identifying genes, signatures, and pathways in human cancers(Bilkent University, 2021-09) Özhan, AyşeCancer 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.