Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Novel Statistical Approaches for Survival Analysis of RNA-Sequencing Data(Bentham Science Publ Ltd, 2026) Cephe, Ahu; Karabulut, Erdem; Zararsız, Gözde Ertürk; Sezgin, Ahmet; Koçhan, NeclaIntroduction/Objective Accurate patient survival predictions are vital for effective cancer treatments. Precision medicine uses gene expression data to improve prognosis by considering genetic variability. Predicting survival in cancer patients using high-dimensional gene expression data, such as RNA-sequencing (RNA-seq), attracted much attention in recent years. However, the literature contains limited algorithms for survival modeling that account for the high dimensionality, heterogeneity, and correlated genes of RNA-seq data. This study aims to develop novel approaches for predicting survival and identifying biomarkers using RNA-seq data.Introduction/Objective Accurate patient survival predictions are vital for effective cancer treatments. Precision medicine uses gene expression data to improve prognosis by considering genetic variability. Predicting survival in cancer patients using high-dimensional gene expression data, such as RNA-sequencing (RNA-seq), attracted much attention in recent years. However, the literature contains limited algorithms for survival modeling that account for the high dimensionality, heterogeneity, and correlated genes of RNA-seq data. This study aims to develop novel approaches for predicting survival and identifying biomarkers using RNA-seq data.Methods Survival data of RNA-seq is first transformed into binary classification data using a stacking algorithm. Then, block-based priority-Lasso and IPF-Lasso algorithms are applied to the dataset, which includes two distinct types of variables. Additionally, sample weights obtained from the voom transformation are incorporated. Our approaches, named voomStackLasso, are tested on 12 real datasets from the TCGA database. We used Harrell's concordance index and the integrated Brier score to evaluate model performance, and the number of selected features to assess model sparsity.Results The results indicated that the voomStackLasso algorithms demonstrated comparable or superior performance compared to other existing survival algorithms. Furthermore, we have introduced an R package called MLSeqSurv, which allows for the utilization of both established survival algorithms from the literature and voomStackLasso algorithms for RNA-seq data.Conclusion This study introduces two new algorithms for the survival analysis of RNA-seq data. Additionally, this study has led to new research directions for applying both existing and newly developed classification algorithms to the survival analysis of RNA-seq data.Article Bi-Allelic Variants in OLA1 Cause a Neurodevelopmental Disorder with Joint Hypermobility(Cell Press, 2026-04) Cevik, Sebiha; Alzahrani, Fatema; Sezer, Abdullah; AlAbdi, Lama; Demir, Zanyar; Abdullah, Nor Linda; Alkuraya, Fowzan S.Cytoskeletal organization, cell adhesion, and cell motility are key to neuronal development and functional synapses. Obg-like ATPase 1 (OLA1) regulates cell-matrix adhesion by modulating focal adhesion kinase (FAK) levels, therefore regulating cytoskeletal dynamics and cell motility. To date, however, no Mendelian phenotypes in humans have been linked to OLA1. We identified fourteen individuals from nine families in whom hypermobility-neurodevelopmental disorder with distinct facies is linked to bi-allelic deleterious variants in OLA1. The hypermobility phenotype evoked a diagnosis of Ehlers-Danlos syndrome (EDS) in some affected individuals. The loss-of-function nature of these variants is confirmed in proband-derived fibroblasts, recapitulating the impaired migration and proliferation phenotype previously described in OLA1-deficient cells. To explore the pathogenesis of abnormal neurodevelopment in our probands, we investigated neurons derived from proband fibroblasts and identified impaired adhesion and cytoskeletal control. Modeling ola-1 deficiency in C. elegans revealed reduced neurite numbers compared to the wild type. Additionally, transcriptomic analysis of the ola-1-deficient worms suggested that dysregulation of key signaling pathways results in suppression of microtubule dynamics and axon regrowth, ultimately crippling the regenerative competence of mutant animals compared to wild-type controls. Our results support an autosomal-recessive OLA1-related hypermobility-neurodevelopmental disorder and suggest that dysregulation of key signaling pathways results in the suppression of microtubule dynamics as a potential underlying mechanism.