Long non-coding RNA LY86-AS1 and HCG27_201 expression in type 2 diabetes mellitus

Saeidi, Leyla; Ghaedi, Hamid; Sadatamini, Mirsaber; Vahabpour, Rouhollah; Rahimipour, Ali; Shanaki, Mehrnoosh; Mansoori, Zahra

doi:10.1007/s11033-018-4429-8

Cited by 32 publications

(32 citation statements)

References 31 publications

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“…Through the years non-coding RNAs have been related to many degenerative illnesses, including Alzheimer, cancer and diabetes [272,274,277]. As mentioned before, when cells encounter environmental modifications, such as hyperglycemia, they promote altered expression of miRNAs.…”

Section: Non-coding Rnamentioning

confidence: 99%

“…Non-Coding RNAs are RNAs that are not translated into proteins, and instead they play a role as gene expression modulators. There are two types of non-coding RNAs classified by their length, small RNAs formed by 20-30 nucleotides and LncRNAs (long non-coding RNAs) having up to 200 nucleotides [272]. Small non-coding RNAs (20-30 nucleotides) are categorized as microRNAs (miRNAs), short interfering RNAs (siRNAs), and piwi-interacting RNAs (piRNAs).…”

Section: Non-coding Rnamentioning

confidence: 99%

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Importance of the Use of Oxidative Stress Biomarkers and Inflammatory Profile in Aqueous and Vitreous Humor in Diabetic Retinopathy

et al. 2020

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Diabetic retinopathy is one of the leading causes of visual impairment and morbidity worldwide, being the number one cause of blindness in people between 27 and 75 years old. It is estimated that ~191 million people will be diagnosed with this microvascular complication by 2030. Its pathogenesis is due to alterations in the retinal microvasculature as a result of a high concentration of glucose in the blood for a long time which generates numerous molecular changes like oxidative stress. Therefore, this narrative review aims to approach various biomarkers associated with the development of diabetic retinopathy. Focusing on the molecules showing promise as detection tools, among them we consider markers of oxidative stress (TAC, LPO, MDA, 4-HNE, SOD, GPx, and catalase), inflammation (IL-6, IL-1ß, IL-8, IL-10, IL-17A, TNF-α, and MMPs), apoptosis (NF-kB, cyt-c, and caspases), and recently those that have to do with epigenetic modifications, their measurement in different biological matrices obtained from the eye, including importance, obtaining process, handling, and storage of these matrices in order to have the ability to detect the disease in its early stages.

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Section: Non-coding Rnamentioning

confidence: 99%

Section: Non-coding Rnamentioning

confidence: 99%

Importance of the Use of Oxidative Stress Biomarkers and Inflammatory Profile in Aqueous and Vitreous Humor in Diabetic Retinopathy

et al. 2020

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“…From there, we used the GWAS catalog access in June 2017 to create a list of SNPs associated with T2DM. In the current study, we selected six lncRNA for expression analysis according to the scan carried out in the study of Mansoori et al [2] and Saeedi et al [3] We selected variants that had associations with increased risk of T2DM. We applied a quantitative PCR analysis of lncRNA expression levels in the 200 samples.…”

Section: Lncrna Extraction and Selectionmentioning

confidence: 99%

“…Diabetes mellitus (DM) is one of the most prevalent chronic non-communicable diseases (NCD) around the world; about 90% of the patients who have diabetes suffer from Type 2 DM (T2DM) [1]. The risk of developing T2DM is strongly associated with many predispositions, behavioral, and environmental risk factors and also genetic factors [1][2][3][4]. Besides the genetic factors, strong evidence indicates that factors such as obesity and physical inactivity are the main nongenetic determinants of the disease [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…The decision tree model (C5.0) had the best classification performance with an accuracy of 77.87% with a sensitivity of 80.68% and specificity of 75.13%. Another study performed by Saeidi et al [3] used logistic regression to assess the diagnostic value of LY86-AS1 and HCG27_201 as biomarkers for T2DM. They obtained a sensitivity of 64.6%, and specificity of 79.8%.…”

Section: Introductionmentioning

confidence: 99%

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Type2 diabetes mellitus prediction using data mining algorithms based on the long-noncoding RNAs expression: a comparison of four data mining approaches

et al. 2020

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Background: About 90% of patients who have diabetes suffer from Type 2 DM (T2DM). Many studies suggest using the significant role of lncRNAs to improve the diagnosis of T2DM. Machine learning and Data Mining techniques are tools that can improve the analysis and interpretation or extraction of knowledge from the data. These techniques may enhance the prognosis and diagnosis associated with reducing diseases such as T2DM. We applied four classification models, including Knearest neighbor (KNN), support vector machine (SVM), logistic regression, and artificial neural networks (ANN) for diagnosing T2DM, and we compared the diagnostic power of these algorithms with each other. We performed the algorithms on six LncRNA variables (LINC00523, LINC00995, HCG27_201, TPT1-AS1, LY86-AS1, DKFZP) and demographic data. Results: To select the best performance, we considered the AUC, sensitivity, specificity, plotted the ROC curve, and showed the average curve and range. The mean AUC for the KNN algorithm was 91% with 0.09 standard deviation (SD); the mean sensitivity and specificity were 96 and 85%, respectively. After applying the SVM algorithm, the mean AUC obtained 95% after stratified 10-fold cross-validation, and the SD obtained 0.05. The mean sensitivity and specificity were 95 and 86%, respectively. The mean AUC for ANN and the SD were 93% and 0.03, also the mean sensitivity and specificity were 78 and 85%. At last, for the logistic regression algorithm, our results showed 95% of mean AUC, and the SD of 0.05, the mean sensitivity and specificity were 92 and 85%, respectively. According to the ROCs, the Logistic Regression and SVM had a better area under the curve compared to the others.

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Epigenetics and Chronic Inflammation: Role in Early Detection of Type 2 Diabetes

Ghai

2020

Obesity and Diabetes

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Long non-coding RNA LY86-AS1 and HCG27_201 expression in type 2 diabetes mellitus

Cited by 32 publications

References 31 publications

Importance of the Use of Oxidative Stress Biomarkers and Inflammatory Profile in Aqueous and Vitreous Humor in Diabetic Retinopathy

Importance of the Use of Oxidative Stress Biomarkers and Inflammatory Profile in Aqueous and Vitreous Humor in Diabetic Retinopathy

Type2 diabetes mellitus prediction using data mining algorithms based on the long-noncoding RNAs expression: a comparison of four data mining approaches

Epigenetics and Chronic Inflammation: Role in Early Detection of Type 2 Diabetes

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