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. Total discectomy was performed in all the individuals and the samples were sent to the laboratory. The nucleus pulposus (NP) cells were isolated and the RNA was extracted. cDNA was synthesized by reverse transcriptase and the expression was measured using real-time polymerase chain reaction (RT-PCT).

RESULTS

We overall included 20 patients in two study groups. Both study groups were comparable regarding the baseline and clinical characteristics except for age (p=0.026). The fold change (p=0.007), and relative expression (p=0.012) of the miRNA-199a- 5p was found to be significantly higher in patients compared to controls. The fold change (p=0.001), and relative expression (p < 0.001) were also associated with the Pfirrmann grading. We found that the area under curve (AUC) was 0.880 (95%CI: 0.721- 0.938) indicative of moderate accuracy.

CONCLUSION

Expression of the miRNA-199a-5p is increased in the IDD. The expression of the miRNA-199a-5p was also associated with the grade of the degeneration based on the Pfirrmann grading.

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. The availability of The Cancer Genome Atlas (TCGA) allows the use of machine-learning methods for the molecular profiling of tumors. The present study employed empirical negative control microRNAs (miRs) in lung cancer to normalize lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) datasets from TCGA to model decision trees in order to classify lung cancer status and subtype. The two primary classification models consisted of four miRNAs for lung cancer diagnosis and subtyping. hsa-miR-183 and hsa-miR-135b were used to distinguish lung tumors from normal samples taken from tissues adjacent to the tumor site, and hsa-miR-944 and hsa-miR-205 to further classify the tumors into LUAD and LUSC major subtypes. Specific cancer status classification models were also presented for each subtype.

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. Here, we aimed to develop a panel of noncoding RNAs (ncRNAs) in sputum for NSCLC early detection. Expression of 11 ncRNAs were analyzed by real-time polymerase chain reaction in sputum samples of 30 NSCLC patients and 30 sex- and age-matched cancer-free controls. Stability of endogenous microRNAs (miRNAs) in sputum was evaluated after 3 and 6 days at 4°C, 6 months, and 1 year at -80°C. Nine ncRNAs showed significant differences of their expression in sputum between NSCLC patients and controls. A logistic regression model with the best prediction was built based on miR-145, miR-126, and miR-7. The composite of the three miRNAs produced 90% sensitivity and specificity in distinguishing NSCLC patients from the controls. Results indicate that miRNAs could be useful biomarkers based on their stability under various storage conditions and maintain differential changes between cancer and control groups. Moreover, measurement of miRNAs in sputum could be a noninvasive approach for detection of lung cancer.

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. Availability of diverse and vast amount of cancer datasets like The Cancer Genome Atlas facilitated the molecular profiling of patients' tumors and introduced new challenges like clinical grade interpretations from big data. In this study, miRNA expression dataset of breast cancer patients from TCGA database was used to develop prediction models from which miRNA biomarkers were identified for diagnosis and molecular subtyping of this cancer. I took the advantage of interpretability of tree-based classification models to extract their rules and identify minimal set of biomarkers in this cancer. Empirical negative control miRNAs in breast cancer obtained and used to normalize the dataset. Tree-based machine learning models trained in my analysis used hsa-miR-139 with hsa-miR-183 to classify breast tumors from normal samples, and hsa-miR4728 with hsa-miR190b to further classify these tumors into three major subtypes of breast cancer. In addition to the proposed biomarkers, the most important miRNAs in breast cancer classification were also presented.

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. We used available target databases of miRNAs and lncRNAs to find their protein coding targets in human. We were able to recognize evolutionary hallmarks of ncRNA targets by phylostratigraphic analysis. We found the conventional 3'-UTR and lesser known 5'-UTR targets of miRNAs to be enriched at three consecutive phylostrata. Firstly, in eukaryata phylostratum corresponding to the emergence of miRNAs, our study revealed that miRNA targets function primarily in cell cycle processes. Moreover, the same overrepresentation of the targets observed in the next two consecutive phylostrata, opisthokonta and eumetazoa, corresponded to the expansion periods of miRNAs in animals evolution. Coding sequence targets of miRNAs showed a delayed rise at opisthokonta phylostratum, compared to the 3' and 5' UTR targets of miRNAs. LncRNA regulatory network was the latest to evolve at eumetazoa.

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. Our goals were to examine miR-223, miR-212, miR-192, miR-3074, SNORD33 and SNORD37 expression levels in tissue and sputum of NSCLC patients and cancer free subjects for molecular diagnosis of NSCLC.

METHODS

Relative expressions of miR-223, miR-212, miR-192, miR-3074, SNORD33 and SNORD37 were examined with quantitative real-time RT-PCR assay in tissue and sputum obtained from 17 NSCLC patients and 17 controls.

RESULTS

miR-3074 was upregulated in tissue samples of NSCLC patients compared with control group. miR-223 was upregulated, miR-212 and SNORD37 were downergulated in sputum samples of patients compared with controls. miR-223 quantification produced 82% sensitivity and 95% specificity with areas under the ROC curve at 0.90 in detection of NSCLC.

CONCLUSION

miR-223 clearly discriminated cancer patients from cancer-free subjects and our results suggest that miR-223 could be a diagnostic useful biomarker. The measurement of altered miRNA expression in sputum samples manifested the potential noninvasive approach for detection of lung cancer.