A concise review on the role of MIR100HG in human disorders

RNA-sequencing: A reliable tool to unveil transcriptional landscape of paediatric B-other acute lymphoblastic leukaemia

B-cell precursor acute lymphoblastic leukaemia (BCP-ALL) comprises multiple subtypes characterized by different genetic alterations. With the use of current standard-of-care tests used in clinical practice, 20%-30% of the cases may not be classified into the main genetic subtypes and additional approaches are needed. These patients are grouped in the heterogeneous category B-other ALL. Transcriptome sequencing (RNA-seq) has allowed the identification of novel fusion genes and gene expression profiles that define new molecular subtypes. We present RNA-seq results integrated, in a real-world scenario, with clinical routine diagnostic data to identify new biomarkers and reclassify a cohort of 60 B-other ALL patients in the newly described genetic subtypes. Overall, 49 rearrangements were identified, including 32 different fusion genes in 41 B-other patients (68%). Moreover, we reported six novel rearrangements (IGK::PAX5, PAX5::IL1RAPL1, ETV6::KRT78, IGH::HIC1, IGH::MIR100HG and NKAIN4::PNPLA7). The integration of RNA-seq results with standard-of-care data allowed us to classify 72% of the patients (43/60) in 11 different subtypes, being DUX4 rearranged and PAX5alt the most represented subtypes. In summary, RNA-seq is a reliable tool for the identification of new emerging genetic subtypes contributing to a better genetic risk stratification of BCP-ALL paediatric patients on the path towards a more personalized medicine.

Discovering the role of microRNAs and exosomal microRNAs in chest and pulmonary diseases: a spotlight on chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a progressive respiratory condition and ranks as the fourth leading cause of mortality worldwide. Despite extensive research efforts, a reliable diagnostic or prognostic tool for COPD remains elusive. The identification of novel biomarkers may facilitate improved therapeutic strategies for patients suffering from this debilitating disease. MicroRNAs (miRNAs), which are small non-coding RNA molecules, have emerged as promising candidates for the prediction and diagnosis of COPD. Studies have demonstrated that dysregulation of miRNAs influences critical cellular and molecular pathways, including Notch, Wnt, hypoxia-inducible factor-1α, transforming growth factor, Kras, and Smad, which may contribute to the pathogenesis of COPD. Extracellular vesicles, particularly exosomes, merit further investigation due to their capacity to transport various biomolecules such as mRNAs, miRNAs, and proteins between cells. This intercellular communication can significantly impact the progression and severity of COPD by modulating signaling pathways in recipient cells. A deeper exploration of circulating miRNAs and the content of extracellular vesicles may lead to the discovery of novel diagnostic and prognostic biomarkers, ultimately enhancing the management of COPD. The current review focus on the pathogenic role of miRNAs and their exosomal counterparts in chest and respiratory diseases, centering COPD.

Integrated multi-omics analysis identifies features that predict human pluripotent stem cell-derived progenitor differentiation to cardiomyocytes

Human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) are advancing cardiovascular development and disease modeling, drug testing, and regenerative therapies. However, hPSC-CM production is hindered by significant variability in the differentiation process. Establishment of early quality markers to monitor lineage progression and predict terminal differentiation outcomes would address this robustness and reproducibility roadblock in hPSC-CM production. An integrated transcriptomic and epigenomic analysis assesses how attributes of the cardiac progenitor cell (CPC) affect CM differentiation outcome. Resulting analysis identifies predictive markers of CPCs that give rise to high purity CM batches, including TTN, TRIM55, DGKI, MEF2C, MAB21L2, MYL7, LDB3, SLC7A11, and CALD1. Predictive models developed from these genes provide high accuracy in determining terminal CM purities at the CPC stage. Further, insights into mechanisms of batch failure and dominant non-CM cell types generated in failed batches are elucidated. Namely EMT, MAPK, and WNT signaling emerge as significant drivers of batch divergence, giving rise to off-target populations of fibroblasts/mural cells, skeletal myocytes, epicardial cells, and a non-CPC SLC7A11+ subpopulation. This study demonstrates how integrated multi-omic analysis of progenitor cells can identify quality attributes of that progenitor and predict differentiation outcomes, thereby improving differentiation protocols and increasing process robustness.

Transcriptomics analysis of long non-coding RNAs in smooth muscle cells from patients with peripheral artery disease and diabetes mellitus

Diabetes mellitus (DM) is a significant risk factor for peripheral arterial disease (PAD), and PAD is an independent predictor of cardiovascular disorders (CVDs). Growing evidence suggests that long non-coding RNAs (lncRNAs) significantly contribute to disease development and underlying complications, particularly affecting smooth muscle cells (SMCs). So far, no study has focused on transcriptome analysis of lncRNAs in PAD patients with and without DM. Tissue samples were obtained from our Vascular Biobank. Due to the sample's heterogeneity, expression analysis of lncRNAs in whole tissue detected only ACTA2-AS1 with a 4.9-fold increase in PAD patients with DM. In contrast, transcriptomics of SMCs revealed 28 lncRNAs significantly differentially expressed between PAD with and without DM (FDR < 0.1). Sixteen lncRNAs were of unknown function, six were described in cancer, one connected with macrophages polarisation, and four were associated with CVDs, mainly with SMC function and phenotypic switch (NEAT1, MIR100HG, HIF1A-AS3, and MRI29B2CHG). The enrichment analysis detected additional lncRNAs H19, CARMN, FTX, and MEG3 linked with DM. Our study revealed several lncRNAs in diabetic PAD patients associated with the physiological function of SMCs. These lncRNAs might serve as potential therapeutic targets to improve the function of SMCs within the diseased tissue and, thus, the clinical outcome.

Co-Expression Network Analysis Unveiled lncRNA-mRNA Links Correlated to Epidermal Growth Factor Receptor-Tyrosine Kinase Inhibitor Resistance and/or Intermediate Epithelial-to-Mesenchymal Transition Phenotypes in a Human Non-Small Cell Lung Cancer Cellular Model System

We investigated mRNA-lncRNA co-expression patterns in a cellular model system of non-small cell lung cancer (NSCLC) sensitive and resistant to the epithelial growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) erlotinib/gefitinib. The aim of this study was to unveil insights into the complex mechanisms of NSCLC targeted therapy resistance and epithelial-to-mesenchymal transition (EMT). Genome-wide RNA expression was quantified for weighted gene co-expression network analysis (WGCNA) to correlate the expression levels of mRNAs and lncRNAs. Functional enrichment analysis and identification of lncRNAs were conducted on modules associated with the EGFR-TKI response and/or intermediate EMT phenotypes. We constructed lncRNA-mRNA co-expression networks and identified key modules and their enriched biological functions. Processes enriched in the selected modules included RHO (A, B, C) GTPase and regulatory signaling pathways, apoptosis, inflammatory and interleukin signaling pathways, cell adhesion, cell migration, cell and extracellular matrix organization, metabolism, and lipid metabolism. Interestingly, several lncRNAs, already shown to be dysregulated in cancer, are connected to a small number of mRNAs, and several lncRNAs are interlinked with each other in the co-expression network.