Biology subjects

Olgun, G.

Publications and source records attributed to Olgun, G..

6 recordsLinked to original sources

bioRxiv2024

Characterizing the role of exosomal miRNAs in metastasis

BackgroundExosomal microRNAs (exomiRs), transported via exosomes, play a pivotal role in intercellular communication. In cancer, exomiRs influence tumor progression by regulating key cellular processes such as proliferation, angiogenesis, and metastasis. Their role in mediating communication between cancer cells and the tumor microenvironment highlights their significance as potential diagnostic and therapeutic targets. MethodologyIn this study, we aimed to characterize the role of exomiRs in influencing the pre-metastatic niche (PMN). Across 7 tumor types, including 4 cell lines and three tumors, we extracted high confidence exomiRs (Log FC >= 2 in exosomes relative to control) and their targets (experimentally identified and targeted by at least 2 exomiRs). Subsequently, we identified enriched pathways and selected the top 100 high-confidence exomiR targets based on the frequency of their appearance in the enriched pathways. These top 100 targets were consistently used throughout the analysis. ResultsCancer cell line and tumor derived ExomiRs have significantly higher GC content relative to genomic background. Pathway enriched among the top exomiR targets included general cancer-associated processes such as "wound healing" and "regulation of epithelial cell proliferation", as well as cancer-specific processes, such as "regulation of angiogenesis in kidney" (KIRC), "ossification" in lung (LUAD), and "positive regulation of cytokine production" in pancreatic cancer (PAAD). Similarly, Pathways in cancer and MicroRNAs in cancer ranked among the top 10 enriched KEGG pathways in all cancer types. ExomiR targets were not only enriched for cancer-specific tumor suppressor genes (TSG) but are also downregulated in pre-metastatic niche formed in lungs compared to normal lung. Motif analysis shows high similarity among motifs identified from exomiRs across cancer types. Our analysis recapitulates exomiRs associated with M2 macrophage differentiation and chemoresistance such as miR-21 and miR-222-3p, regulating signaling pathways such as PTEN/PI3/Akt, NF-{kappa}B, etc. Cox regression indicated that exomiR targets are significantly associated with overall survival of patients in TCGA. Lastly, a Support Vector Machine (SVM) model using exomiR target gene expression classified responders and non-responders to neoadjuvant chemotherapy with an AUROC of 0.96 (in LUAD), higher than other previously reported gene signatures. ConclusionOur study characterizes the pivotal role of exomiRs in shaping the PMN in diverse cancers, underscoring their diagnostic and therapeutic potential.

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bioRxiv2021

miRSCAPE - Inferring miRNA expression in single-cell clusters

Micro-RNAs (miRNA) are critical in development, homeostasis, and diseases, including cancer. However, our understanding of miRNA function at cellular resolution is thwarted by the inability of the standard single cell RNA-seq protocols to capture miRNAs. Here we introduce a machine learning tool -- miRSCAPE -- to infer miRNA expression in a sample from its RNA-seq profile. We establish miRSCAPEs accuracy separately in 10 tissues comprising ~10,000 tumor and normal bulk samples and demonstrate that miRSCAPE accurately infers cell type-specific miRNA activities (predicted vs observed fold-difference correlation ~ 0.81) in two independent datasets where miRNA profiles of specific cell types are available (HEK-GBM, Kidney-Breast-Skin). When trained on human hematopoietic cancers, miRSCAPE can identify active miRNAs in 8 hematopoietic cell lines in mouse with a reasonable accuracy (auROC = 0.67). Finally, we apply miRSCAPE to infer miRNA activities in scRNA clusters in Pancreatic and Lung cancers, as well as in 56 cell types in the Human Cell Landscape (HCL). Across the board, miRSCAPE recapitulates and provides a refined view of known miRNA biology. miRSCAPE is freely available and promises to substantially expand our understanding of gene regulatory networks at cellular resolution.

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bioRxiv2020

Functional Enrichment Analysis of Deregulated Long Non-Coding RNAs in Cancer Based on their Genomic Neighbors

The dysregulation of long non-coding RNAs (lncRNAs) expressions has been implicated in cancer. Since most of the lncRNAs are not functionally characterized well, investigating the set of perturbed lncRNAs are is challenging. Existing methods that inspect lncRNAs functionally rely on the co-expressed coding genes, which are far better characterized functionally. LncRNAs can be known to act as transcriptional regulators; they may activate or repress the neighborhoods coding genes on the genome. Based on this, in this work, we aim to analyze the deregulated lncRNAs in cancer by taking into account their ability to regulate nearby loci on the genome. We perform functional analysis on differentially expressed lncRNAs for 28 different cancers considering their adjacent coding genes. We identify that some deregulated lncRNAs are cancer-specific, but a substantial number of lncRNAs are shared across cancers. Next, we assess the similarities of the cancer types based on the functional enrichment of the deregulated lncRNA sets. We find some cancers are very similar in the functions and biological processes related to the deregulated lncRNAs. We observe that some of the cancers for which we find similarity can be linked through primary, metastatic site relations. We investigate the similarity of enriched functional terms for the deregulated lncRNAs and the mRNAs. We further assess the enriched functions similarity to the functions and processes that the known cancer driver genes take place. We believe that our methodology help to understand the impact of the lncRNAs in cancer functionally.

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bioRxiv2019

miRCoop: Identifying Cooperating miRNAs via Kernel Based Interaction Tests

Although miRNAs can cause widespread changes in expression programs, single miRNAs typically induce mild repression on their targets. Cooperativity is reported as one strategy to overcome this constraint. Expanding the catalog of synergistic miRNAs is critical for understanding gene regulation and for developing miRNA-based therapeutics. In this study, we develop miRCoop to identify synergistic miRNA pairs that have weak or no repression on the target mRNA, but when bound together, induce strong repression. miRCoop uses kernel-based interaction tests together with miRNA and mRNA target information. We apply our approach to kidney tumor patient data and identify 66 putative triplets. For 64 of these triplets, there is at least one common transcription factor that potentially regulates all participating RNAs of the triplet, supporting a functional association among them. Furthermore, we find that triplets are enriched for certain biological processes that are relevant to kidney cancer. Some of the synergistic miRNAs are very closely encoded in the genome, hinting a functional association among them. We believe miRCoop can aid our understanding of the complex regulatory interactions in different health and disease states of the cell and can help in designing miRNA-based therapies. Matlab code for the methodology is provided in https://github.com/guldenolgun/miRCoop.

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bioRxiv2019

NoRCE: Non-coding RNA Sets Cis Enrichment Tool

SummaryWhile some non-coding RNAs (ncRNAs) are assigned to critical regulatory roles, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together. This genomic spatial proximity can lead to a functional association. Based on this idea, we present a tool, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out using the functional annotations of the coding genes located proximal to the input ncRNAs. NoRCE allows incorporating other biologically relevant information such as topologically associating domain (TAD) boundaries, co-expression patterns, and miRNA target prediction information. NoRCE repository provides several data, such as cell-line specific TAD boundaries, functional gene sets, and expression data for coding and ncRNAs specific to cancer for the analysis. Additionally, users can utilize their custom data files in their investigation. Enrichment results can be retrieved in a tabular format or visualized in several different ways. NoRCE is currently available for the following species: human, mouse, rat, zebrafish, fruit fly, worm, and yeast. NoRCE is a platform-independent, user-friendly, comprehensive R package that could be used to gain insight into the functional importance of a list of any type of interesting ncRNAs. Users can run the pipeline in a single function; also, the tool offers flexibility to conduct the users preferred analysis in a single base and design their pipeline. It is available in Bioconductor and https://github.com/guldenolgun/NoRCE.

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bioRxiv2017

Discovering Competing Endogenous RNA Interactions in Breast Cancer Molecular Subtypes

MotivationLong 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.\n\nResultsTo 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. For instance, when grouping is based on the expression patterns of specific sponge interactions, patients differ significantly in their survival distributions. If on the other hand, patients are grouped based on the individual RNA expression profiles of the sponge participants, they do not exhibit a significant difference in survival. 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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