ArticleDiabetes & vascular disease research2018
Correct microarray analysis approaches in 'Hsa-circRNA11783-2 in peripheral blood is correlated with coronary artery disease and type 2 diabetes mellitus'.
Article in Diabetes & vascular disease research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Serum exosomal hsa_circRNA_0001842 is the potential biomarker for diagnosing lower limb vascular disease in type 2 diabetes mellitus.Journal of genetics · 2023Article
- circEPS15 Overexpression in Hepatocellular Carcinoma Modulates Tumor Invasion and Migration.Frontiers in genetics · 2022Article
- Circ_0091579 enhances the malignancy of hepatocellular carcinoma via miR-1287/PDK2 axis.Open life sciences · 2021Article
- Circular RNAs Hsa_circ_0002715 and Hsa_circ_0035197 in Peripheral Blood Are Novel Potential Biomarkers for New-Onset Rheumatoid Arthritis.Disease markers · 2019Article
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5 authors.
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Abstract
We read with great interest the article by Li et al. The results are illuminating for understanding the relationship among Hsa-circRNA11783-2, coronary artery disease (CAD) and type 2 diabetes mellitus (T2DM). However, from our perspective, the bioinformatics analyses need further context as the statistics for differential fold changes in expression data are not explained fully. The authors seem to use unadjusted p values for detecting differentially expressed circular RNA (differentially expressed genes (DEGs)) between the control, CAD and T2DM group. Due to the high false positives caused by a large number of probes and multiple comparisons, it seems essential to analyse microarray data properly to reach a reliable result by a statistical method. Only selecting circular RNA with greater than two fold change with unadjusted p values < 0.05 in expression is not reliable and suitable for high-level microarray analysis.
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