MicroRNA-199a Upregulation Mediates Lumbar Intervertebral Disc Degeneration and is Associated with Clinical Grades of Degeneration.

Aim: To determine the expression profile of miRNA-199a-5p in intervertebral disc degeneration (IDD) and its correlation to the grade of IDD.

Material And Methods: This case-controlled study was conducted during a 6-month period from 2017 to 2018 in two university hospitals in Shiraz, Iran. We included 15 patients with grade 3 and 4 of Pfirrmann and 5 patients with traumatic lumbosacral fractures with grade I.

Decision tree-based classifiers for lung cancer diagnosis and subtyping using TCGA miRNA expression data.

Lung cancer has the world's highest cancer- associated mortality rate, making biomarker discovery for this cancer a pressing issue. Machine learning approaches to identify molecular biomarkers are not as prevalent as screening of potential biomarkers by differential expression analysis. However, several differentially expressed miRNAs involved in cancer have been identified using this approach.

MicroRNA Expression Profiles, Target Genes, and Pathways in Intervertebral Disk Degeneration: A Meta-Analysis of 3 Microarray Studies.

Background: Determining the expression profile and target genes of microRNA (miRNA) would assist in determining the pathophysiologic pathways in intervertebral disk degeneration (IDD). The aim of this study was to determine the expression profile of miRNA in degenerated intervertebral disks compared with normal healthy intervertebral disks.

Methods: We conducted a meta-analysis of 3 available miRNA expression datasets to identify a panel of co-deregulated miRNA genes and overlapping biological processes in IDD.

A panel of noncoding RNAs in non-small-cell lung cancer.

Non-small-lung cancer (NSCLC) is the leading cause of cancer death. Early detection of NSCLC could pave the way for effective therapies. Analysis of molecular genetic biomarkers in biological fluids has been proposed as a useful tool for cancer diagnosis.

Tree-based machine learning algorithms identified minimal set of miRNA biomarkers for breast cancer diagnosis and molecular subtyping.

Breast cancer is a complex disease and its effective treatment needs affordable diagnosis and subtyping signatures. While the use of machine learning approach in clinical computation biology is still in its infancy, the prevalent approach in identifying molecular biomarkers remains to be screening of all biomarkers by differential expression analysis. Many of these attempts used miRNAs expression data in breast cancer and amounted to the multitude of differentially expressed miRNAs in this cancer; hence, the minimal set of miRNA biomarkers to classify breast cancer is yet to be identified.

Altered miR-223 Expression in Sputum for Diagnosis of Non-Small Cell Lung Cancer.

Background: Diagnosis of Non-small Cell Lung Cancer (NSCLC) at an early stage is a daunting challenge due to the deficiency of specific noninvasive markers. MicroRNAs (miRNAs) play important roles in the initiation and progression of NSCLC. Measuring miRNA expression levels could provide a potential approach for the diagnosis of NSCLC.

The origins and evolutionary history of human non-coding RNA regulatory networks.

The evolutionary history and origin of the regulatory function of animal non-coding RNAs are not well understood. Lack of conservation of long non-coding RNAs and small sizes of microRNAs has been major obstacles in their phylogenetic analysis. In this study, we tried to shed more light on the evolution of ncRNA regulatory networks by changing our phylogenetic strategy to focus on the evolutionary pattern of their protein coding targets.