Browsing by Subject "Noncoding RNA"

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    Discovering lncRNA mediated sponge interactions in breast cancer molecular subtypes
    (BioMed Central, 2018) Olgun, G.; Sahin, O.; Tastan, O.
    Background: Long non-coding RNAs (lncRNAs) can indirectly regulate mRNAs expression levels by sequestering microRNAs (miRNAs), and act as competing endogenous RNAs (ceRNAs) or as sponges. Previous studies identified lncRNA-mediated sponge interactions in various cancers including the breast cancer. However, breast cancer subtypes are quite distinct in terms of their molecular profiles; therefore, ceRNAs are expected to be subtype-specific as well. Results: To find lncRNA-mediated ceRNA interactions in breast cancer subtypes, we develop an integrative approach. We conduct partial correlation analysis and kernel independence tests on patient gene expression profiles and further refine the candidate interactions with miRNA target information. We find that although there are sponges common to multiple subtypes, there are also distinct subtype-specific interactions. Functional enrichment of mRNAs that participate in these interactions highlights distinct biological processes for different subtypes. Interestingly, some of the ceRNAs also reside in close proximity in the genome; for example, those involving HOX genes, HOTAIR, miR-196a-1 and miR-196a-2. We also discover subtype-specific sponge interactions with high prognostic potential. We found that patients differ significantly in their survival distributions if they are group based on the expression patterns of specific ceRNA interactions. However, it is not the case if the expression of individual RNAs participating in ceRNA is used. Conclusion: These results can help shed light on subtype-specific mechanisms of breast cancer, and the methodology developed herein can help uncover sponges in other diseases.
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    Discovering regulatory non-coding RNA interactions
    (2019-09) Olgun, Gülden
    The vast majority of eukaryotic transcriptomes comprise noncoding RNAs (ncRNAs) which are not translated into proteins. Despite the accumulating evidence on the functional roles of ncRNAs, we are still far from understanding the whole spectrum of molecular functions ncRNAs can undertake and how they accomplish them. In this thesis we develop computational methods for discovering interactions among ncRNAs and tools to analyze them functionally. In the first part of the thesis, we present an integrative approach to discover long non-coding RNA (lncRNA) mediated sponge interactions where lncRNAs can indirectly regulate mRNAs expression levels by sequestering microRNAs (miRNAs), and act as sponges. We conduct partial correlation analysis and kernel independence tests on patient gene expression profiles and further refine the candidate interactions with miRNA target information. We use this approach to find sponge interactions specific to breast-cancer subtypes. We find that although there are sponges common to multiple subtypes, there are also distinct subtype-specific interactions with high prognostic potential. Secondly, we develop a method to identify synergistically acting miRNA pairs. These pairs have weak or no repression on the target mRNA when they act individually, but when together they induce strong repression of their target gene expression. We test the combinations of RNA triplets using non-parametric kernel-based interaction tests. In forming the triplets to test, we consider target predictions between the miRNAs and mRNA. We apply our approach on kidney tumor samples. The discovered triplets have several lines of biological evidence on a functional association among them or their relevance to kidney tumors. In the third part of the thesis, we focus on functional enrichment analysis of noncoding RNAs while some non-coding RNAs (ncRNAs) have been found to play critical regulatory roles in biological processes, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs set needs to be analyzed in a functional context. We develop a method that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out by using the functional annotations of the coding genes located proximally to the input ncRNAs. To demonstrate how this method could be used to gain insight into the functional importance of a list of interesting ncRNAs, we tackle different biological questions on datasets of cancer and psychiatric disorders. Particularly, we also analyze 28 different types of cancers in terms of molecular process perturbed and linked to altered lncRNA expression. We hope that the methods developed herein will help elucidate functional roles of ncRNAs and aid the development of therapies based on ncRNAs.
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    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.