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. 2019 Aug;20(5):340-348.
doi: 10.2174/1389202920666190820142043.

Identification of Novel Molecular Network Expression in Acute Myocardial Infarction

Marwa Matboli  1 , Ayman E Shafei  2   3 , Sara H A Agwa  4 , Sherif Sammir Elzahy  5 , Ahmed K Anwar  6 , Amr R Mansour  6 , Ahmed I Gaber  6 , Ali E A Said  6 , Paula Lwis  6 , Marwa Hamdy  1
Affiliations

Identification of Novel Molecular Network Expression in Acute Myocardial Infarction

Marwa Matboli et al. Curr Genomics. 2019 Aug.

Abstract

Background: In the current study, we aimed to analyze the hypothesis that human myocardial-specific extracellular RNAs expression could be used for acute myocardial injury(AMI) diagnosis.

Methodology: We used bioinformatics' analysis to identify RNAs linked to ubiquitin system and specific to AMI, named, (lncRNA-RP11-175K6.1), (LOC101927740), microRNA-106b-5p (miR-106b-5p) and Anaphase, promoting complex 11 (ANapc11mRNA). We measured the serum expression of the chosen RNAs in 69 individuals with acute coronary syndromes, 31 individuals with angina pectoris without MI and non-cardiac chest pain and 31 healthy control individuals by real-time reverse-transcription PCR.

Results: Our study revealed a significant decrease in both lncRNA-RP11-175K6.1 and ANapc11mRNA expression of in the sera samples of AMI patients compared to that of the two control groups alongside with significant upregulation of miR-106b-5p.

Conclusion: Of note, the investigated serum RNAs decrease the false discovery rate of AMI to 3.2%.

Keywords: Myocardial infarction; diagnosis; extracellular RNAs; lncRNA; miRNA; serum.

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Figures

Fig. (1)
Fig. (1)
Box plot shows relative expression of the serum RNAs between AMI and control groups. The data is presented as median fold changes (P<0.05).
Fig. (2a)
Fig. (2a)
ROC curve analysis for lncRNA-RP11-175K6 used to calculate the best cut-off point to discriminate between the AMI and healthy groups. AUC [SE]=0.995 [0.024], 95% confidence interval=0.655-0.842, (P<0.01).
Fig. (2b)
Fig. (2b)
ROC curve analysis for RQ-ANPCII mRNA used to calculate the best cut-off point to discriminate between the AMI and control groups. AUC [SE]=0.930 [0.033], 95% confidence interval=0.722-0.811, (P<0.01).
Fig. (2c)
Fig. (2c)
ROC curve analysis for RQ-mIR-106b used to calculate the best cut-off point to discriminate between the AMI and control groups. AUC [SE]=0.995 [0.038], 95% confidence interval=0.845-0.931, (P<0.01).
Fig. (3)
Fig. (3)
A schematic diagram to map the suggested association between the chosen genes. Schematic diagram of the study hypothesis.
See this image and copyright information in PMC

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