Identification of functional lncRNAs through constructing a lncRNA-associated ceRNA network in myocardial infarction

A transcriptomics-based investigation of the mechanism of pulmonary fibrosis induced by nickel oxide nanoparticles

Nickel oxide nanoparticles (NiONPs) are an emerging nanomaterial, which poses a huge threat to the health of workplace population. Nanoparticles induce pulmonary fibrosis, and its mechanisms are associated with noncoding RNAs (ncRNAs). However, ncRNAs and competing endogenous RNA (ceRNA) networks which involved in NiONP-induced pulmonary fibrosis are still unclear. This study aimed to identify ncRNA-related ceRNA networks and investigate the role of the Wnt/β-catenin pathway in pulmonary fibrosis. Male Wistar rats were intratracheally instilled with 0.015, 0.06, and 0.24 mg/kg NiONPs twice a week for 9 weeks. First, we found there were 93 circularRNAs (circRNAs), 74 microRNAs (miRNAs), 124 long non-coding RNAs (lncRNAs), and 1675 messenger RNAs (mRNAs) differentially expressed through microarray analysis. Second, we constructed ceRNA networks among lncRNAs/circRNAs, miRNAs and mRNAs and identified two ceRNA networks (lncMelttl16/miR-382-5p/Hsd17b7 and circIqch/miR-181d-5p/Stat1) after real time-quantitative polymerase chain reaction (RT-qPCR) validation. Furthermore, based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, ncRNAs were found to be involved in biological processes and signaling pathways related to pulmonary fibrosis. KEGG analysis showed that NiONPs activated the Wnt/β-catenin pathway in rats. In vitro, HFL1 cells were treated with 0, 50, 100, and 200 μg/mL NiONPs for 24 h. We found that NiONPs induced collagen deposition and Wnt/β-catenin pathway activation. Moreover, a blockade of Wnt/β-catenin pathway alleviated NiONP-induced collagen deposition. In conclusion, these observations suggested that ncRNAs were crucial in pulmonary fibrosis development and that the Wnt/β-catenin pathway mediated the deposition of collagen.

Construction of LncRNA-mediated CeRNA network for investigating the immune pathogenesis of myocardial infarction

BACKGROUND

Myocardial infarction (MI) is a cardiovascular disease that seriously threatens human health. However, an immune-related competitive endogenous RNA (ceRNA) network has not been reported in MI.

METHODS

The GSE66360, GSE19339, GSE97320, GSE61741, and GSE168281 datasets were acquired from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRNAs) from MI patients and healthy controls were screened and an immune-related ceRNA network was constructed. Furthermore, the key long noncoding RNAs(lncRNAs) highly related to the immune mechanism of MI were identified utilizing the random walk with restart algorithm. Finally, the expression of the hub genes was further verified in the GSE66360, GSE19339, and GSE97320 datasets, and quantitativereal-time polymerase chain reaction (qRT-PCR) was performed for the MI patients and healthy controls.

RESULTS

A total of 184 differentially expressed immune-related genes(DE-IRGs) and 432 DE-miRNAs were obtained, and an immune-related ceRNA network comprising 1421 lncRNAs, 61 DE-miRNAs, and 139 DE-IRGs was constructed. According to the order of stress, betweenness, and closeness, NEAT1, KCNQ1OT1, and XIST were identified as key lncRNAs. Moreover, random walk with restart analysis also suggested that NEAT1, KCNQ1OT1, and XIST are key lncRNAs. Subsequently, a ceRNA network of 10 hub genes and 3 lncRNAs was constructed. Finally, we found that the expression of FCER1G and TYROBP significantly differed between MI patients and control individuals in the GSE66360, GSE19339, and GSE97320 datasets. qRT-PCR revealed that the expression of NEAT1, KCNQ1OT1, XIST, FCER1G, and TYROBP was significantly elevated in MI tissue samples compared to healthy control tissue samples.

CONCLUSION

NEAT1, KCNQ1OT1, XIST, FCER1G, and TYROBP are involved in MI and can be used as molecular biomarkers for the screening and diagnosis of MI. Furthermore, the immune system plays an essential role in the onset and progression of MI.

Special issue: Informatics & data-driven medicine

The current state of the development of Medicine today is changing dramatically. Previously, data of the patient's health were collected only during a visit to the clinic. These were small chunks of information obtained from observations or experimental studies by clinicians, and were recorded on paper or in small electronic files. The advances in computer power development, hardware and software tools and consequently design an emergence of miniature smart devices for various purposes (flexible electronic devices, medical tattoos, stick-on sensors, biochips etc.) can monitor various vital signs of patients in real time and collect such data comprehensively. There is a steady growth of such technologies in various fields of medicine for disease prevention, diagnosis, and therapy. Due to this, clinicians began to face similar problems as data scientists. They need to perform many different tasks, which are based on a huge amount of data, in some cases with incompleteness and uncertainty and in most others with complex, non-obvious connections between them and different for each individual patient (observation) as well as a lack of time to solve them effectively. These factors significantly decrease the quality of decision making, which usually affects the effectiveness of diagnosis or therapy. That is why the new concept in Medicine, widely known as Data-Driven Medicine, arises nowadays. This approach, which based on IoT and Artificial Intelligence, provide possibilities for efficiently process of the huge amounts of data of various types, stimulates new discoveries and provides the necessary integration and management of such information for enabling precision medical care. Such approach could create a new wave in health care. It will provide effective management of a huge amount of comprehensive information about the patient's condition; will increase the speed of clinician's expertise, and will maintain high accuracy analysis based on digital tools and machine learning. The combined use of different digital devices and artificial intelligence tools will provide an opportunity to deeply understand the disease, boost the accuracy and speed of its detection at early stages and improve the modes of diagnosis. Such invaluable information stimulates new ways to choose patient-oriented preventions and interventions for each individual case.