bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2022.09.04.506551

In silico evaluation of the role of the long non-coding RNA LINC00092 in thyroid cancer progression ; regulation of the miR-34a-5p/RCAN1 axis

Morovat, S.·Morovat, P.·Kamali, M. J.·Teimourian, S.
View original source ↗Export metadata ↓

Abstract

BackgroundAs the most prevalent endocrine cancer, thyroid cancer (TC) accounts for 1.7% of all cancer cases. A significant increase in TC morbidity has been observed over the past three decades. TC diagnosis has been reported to be problematic based on the current approach. As a result, it is imperative to develop molecular biomarkers to improve the accuracy of the diagnosis. An analysis of bioinformatics data was conducted in this study to analyze lncRNAs and their roles as ceRNAs associated with the development and progression of TC. Materials and MethodThe first step in this study was to collect RNA-seq data from the GDC database. Then, DESeq2 was used to analyze differentially expressed lncRNAs (DElncRNAs), miRNAs (DEMIs), and mRNAs (DEGs) between TC patients and healthy subjects. Our study identified DElnc-related miRNAs and miRNA-related genes to develop a lncRNA/miRNA/mRNA axis using online tools and screening. A co-expression analysis was performed to investigate correlations between DElncs and their associated mRNAs. Next, a protein-protein interaction (PPI) network was constructed. Functional enrichment and pathway enrichment were conducted on genes in the PPI network to discover additional biological activities among these molecules. Lastly, a correlation between the expression levels and the infiltration abundance of immune cells was assessed through immune infiltration analysis. ResultsThere were 58 DElncs, 34 DEMIs, and 864 DEGs in thyroid tumor tissue and non-tumor tissue samples. Following validation of our lncRNA results with the intersection of differentially expressed lncRNAs in TCGA and GEPIA2, we selected two downregulated DElncs, including AC007743.1 and LINC00092, as the final research elements. We then performed an interaction analysis to predict lncRNAs-miRNAs and miRNAs-mRNAs interactions, which led to identifying the LINC00092/miR-34a-5p and miR-34a-5p/RCAN1 axis, respectively. There was a correlation between LINC00092 and RCAN1 according to Pearson correlation analysis. To improve our understanding of RCAN1, we developed a PPI network. According to the Immune Infiltration Analysis, RCAN1 expression was positively correlated with CD8+ T cells, macrophages, and neutrophils. ConclusionThe results of this study suggest that LINC00092/miR-34a-5p/RCAN1 axis may have a functional role in the progression of TC. LINC00092 may be used as a promising biomarker for TC prognosis and may be a better diagnostic and therapeutic target.

Explore related subjects

bioinformaticsPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Morovat, S., Morovat, P., Kamali, M. J., Teimourian, S.. 2022-09-06. In silico evaluation of the role of the long non-coding RNA LINC00092 in thyroid cancer progression ; regulation of the miR-34a-5p/RCAN1 axis. https://doi.org/10.1101/2022.09.04.506551

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

spatialMET: an open and scalable framework for spatial metabolomics analysis

Mass spectrometry imaging (MSI) enables spatially resolved metabolomics in intact tissue sections, but analysis remains challenging at scale. Existing MSI workflows often require users to combine multiple software tools, while others rely on proprietary vendor software that limits interoperability and reproducibility. To address these challenges, we developed spatialMET, an open-source framework that provides an end-to-end workflow for MSI analysis. spatialMET provides a unified platform for preprocessing, spatial domain detection, and visualization. Downstream analyses include differential abundance testing, spatial autocorrelation and gradient analysis, dimensionality reduction, and correlation network analysis. Spatial domain detection uses hcdist, a C-based hierarchical clustering implementation that substantially reduces runtime and memory use relative to existing R-based approaches. spatialMET can be run through an interactive R Shiny application or as a standalone command-line workflow for larger datasets or high-performance computing environments. Applied to mouse small cell lung cancer MALDI-MSI data containing 284,673 pixels, spatialMET identified tumor-associated, stromal, and adjacent lung spatial domains that aligned with matched histology. Differential abundance analysis identified 117 m/z features that differed between tumor and stromal regions, while spatial autocorrelation analyses revealed spatially structured abundance patterns. Applying spatialMET to mouse lung adenocarcinoma data from an entire lung lobe containing 338,477 pixels further demonstrated scalability and captured spatial heterogeneity across tumor and surrounding lung tissue. In summary, spatialMET provides a scalable, open-source framework for end-to-end spatial metabolomics analysis, and it is distributed as a Docker container for reproducible deployment. Source code and installation instructions are available at https://github.com/biodatalab/spatialMET.

bioinformatics↗
bioRxiv2026

Probing the transcriptome response to shivering in skeletal muscle using a multilayered bioinformatics approach

Cold acclimation holds therapeutic potential for improving metabolic health. We previously demonstrated that repeated cold-induced shivering enhances insulin sensitivity in humans. However, the molecular pathways that underlie the skeletal muscle shivering response, and how these relate to beneficial physiological effects, remain poorly understood. In this study, we combined complementary bioinformatics approaches to allow in-depth analysis of the transcriptomic response of human skeletal muscle to repeated shivering. We identified a robust transcriptional signature and show a sex-specific component in the shivering skeletal muscle response, which seemed to diminish following cold adaptation. Our findings provide mechanistic insights into cold-induced muscle adaptations, shed light on potential interesting molecular targets for further investigation, and emphasize the importance of including both sexes in future cold acclimation studies.

bioinformatics↗
bioRxiv2026

An Information Geometry approach to model topological trajectories and Gene Expression Radius from UMAP geometry.

Understanding the relationship between gene expression dynamics and cellular identity remains a central challenge in single cell biology. Here, we introduce a novel computational and mathematical framework that integrates information geometry, fuzzy topology, and UMAP analysis to model gene expression landscapes derived from single cell RNA sequencing data. We formalize gene expression data as a fuzzy topological space, where interactions between expression points are governed by probabilistic distributions inspired by manifold learning approaches such as UMAP. Within this framework, we define an information geometric structure through a Fisher metric induced by these distributions, enabling the computation of geodesic trajectories that capture cellular differentiation processes. A key contribution of this work is the derivation of analytical conditions, expressed as expression radius formulas, that characterize local neighborhoods in gene expression space. These conditions allow for the identification of genes associated with stem cell states and predictions in transitional cell types in future work. Application of the proposed framework to single cell datasets reveals biologically meaningful gene sets enriched in key regulatory pathways and transcription factors, demonstrating the capacity of our approach to uncover latent structure in complex gene expression data. Our results suggest that integrating differential geometry with statistical learning theory offers a powerful paradigm for modeling genotype and phenotype relationships and cellular state transitions, with potential implications for precision medicine and systems biology.

bioinformatics↗