Dataset Information

Identification and analysis of circulating long non-coding RNAs with high significance in diabetic cardiomyopathy.

ABSTRACT: Diabetic cardiomyopathy (DCM) lacks diagnostic biomarkers. Circulating long non-coding RNAs (lncRNAs) can serve as valuable diagnostic biomarkers in cardiovascular disease. To seek potential lncRNAs as a diagnostic biomarker for DCM, we investigated the genome-wide expression profiling of circulating lncRNAs and mRNAs in type 2 diabetic db/db mice with and without DCM and performed bioinformatic analyses of the deregulated lncRNA-mRNA co-expression network. Db/db mice had obesity and hyperglycemia with normal cardiac function at 6 weeks of age (diabetes without DCM) but with an impaired cardiac function at 20 weeks of age (DCM) on an isolated Langendorff apparatus. Compared with the age-matched controls, 152 circulating lncRNAs, 127 mRNAs and 3355 lncRNAs, 2580 mRNAs were deregulated in db/db mice without and with DCM, respectively. The lncRNA-mRNA co-expression network analysis showed that five deregulated lncRNAs, XLOC015617, AK035192, Gm10435, TCR-? chain, and MouselincRNA0135, have the maximum connections with differentially expressed mRNAs. Bioinformatic analysis revealed that these five lncRNAs were highly associated with the development and motion of myofilaments, regulation of inflammatory and immune responses, and apoptosis. This finding was validated by the ultrastructural examination of myocardial samples from the db/db mice with DCM using electron microscopy and changes in the expression of myocardial tumor necrosis factor-? and phosphorylated p38 mitogen-activated protein kinase in db/db mice with DCM. These results indicate that XLOC015617, AK035192, Gm10435, TCR-? chain, and MouselincRNA0135 are crucial circulating lncRNAs in the pathogenesis of DCM. These five circulating lncRNAs may have high potential as a diagnostic biomarker for DCM.

SUBMITTER: Pant T

PROVIDER: S-EPMC7843621 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification and analysis of circulating long non-coding RNAs with high significance in diabetic cardiomyopathy.

Pant Tarun T Dhanasekaran Anuradha A Zhao Ming M Thorp Edward B EB Forbess Joseph M JM Bosnjak Zeljko J ZJ Benjamin Ivor J IJ Ge Zhi-Dong ZD

Scientific reports 20210128 1

Diabetic cardiomyopathy (DCM) lacks diagnostic biomarkers. Circulating long non-coding RNAs (lncRNAs) can serve as valuable diagnostic biomarkers in cardiovascular disease. To seek potential lncRNAs as a diagnostic biomarker for DCM, we investigated the genome-wide expression profiling of circulating lncRNAs and mRNAs in type 2 diabetic db/db mice with and without DCM and performed bioinformatic analyses of the deregulated lncRNA-mRNA co-expression network. Db/db mice had obesity and hyperglycem ...[more]

PMID: 33510471

Similar Datasets

Project description:In the past decade, hundreds of long noncoding RNAs (lncRNAs) have been identified as significant players in diverse types of cancer; however, the functions and mechanisms of most lncRNAs in cancer remain unclear. Several computational methods have been developed to detect associations between cancer and lncRNAs, yet those approaches have limitations in both sensitivity and specificity. With the goal of improving the prediction accuracy for associations of lncRNA with cancer, we upgraded our previously developed cancer-related lncRNA classifier, CRlncRC, to generate CRlncRC2. CRlncRC2 is an eXtreme Gradient Boosting (XGBoost) machine learning framework, including Synthetic Minority Over-sampling Technique (SMOTE)-based over-sampling, along with Laplacian Score-based feature selection. Ten-fold cross-validation showed that the AUC value of CRlncRC2 for identification of cancer-related lncRNAs is much higher than previously reported by CRlncRC and others. Compared with CRlncRC, the number of features used by CRlncRC2 dropped from 85 to 51. Finally, we identified 439 cancer-related lncRNA candidates using CRlncRC2. To evaluate the accuracy of the predictions, we first consulted the cancer-related long non-coding RNA database Lnc2Cancer v2.0 and relevant literature for supporting information, then conducted statistical analysis of somatic mutations, distance from cancer genes, and differential expression in tumor tissues, using various data sets. The results showed that our approach was highly reliable for identifying cancer-related lncRNA candidates. Notably, the highest ranked candidate, lncRNA AC074117.1, has not been reported previously; however, integrated multi-omics analyses demonstrate that it is the target of multiple cancer-related miRNAs and interacts with adjacent protein-coding genes, suggesting that it may act as a cancer-related competing endogenous RNA, which warrants further investigation. In conclusion, CRlncRC2 is an effective and accurate method for identification of cancer-related lncRNAs, and has potential to contribute to the functional annotation of lncRNAs and guide cancer therapy.

Dataset Information

Identification and analysis of circulating long non-coding RNAs with high significance in diabetic cardiomyopathy.

Publications

Identification and analysis of circulating long non-coding RNAs with high significance in diabetic cardiomyopathy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets