Key result
RNA-Seq meta-analysis identifies 14 novel gene signatures linked to diabetic vasculopathy.
Why the study?
Integrating individual RNA-Seq datasets via meta-analysis can overcome sample size limitations and low statistical power to reveal latent gene regulators in conditions like diabetic vasculopathy.
Meta-Analysis (n=14)
A custom meta-analysis pipeline of RNA-Seq data identified 14 novel transcriptomic biomarkers for diabetic vasculopathy, providing new insights into its molecular mechanisms.
These signatures should not yet guide diabetic vasculopathy management; leaves open targeted biomarker validation in prospective cohorts.
Enormous gene expression data generated through next-generation sequencing (NGS) technologies are accessible to the scientific community via public repositories. The data harboured in these repositories are foundational for data integrative studies enabling large-scale data analysis whose potential is yet to be fully realized. Prudent integration of individual gene expression data i.e. RNA-Seq datasets is remarkably challenging as it encompasses an assortment and series of data analysis steps that requires to be accomplished before arriving at meaningful insights on biological interrogations. These insights are at all times latent within the data and are not usually revealed from the modest individual data analysis owing to the limited number of biological samples in individual studies. Nevertheless, a sensibly designed meta-analysis of select individual studies would not only maximize the sample size of the analysis but also significantly improves the statistical power of analysis thereby revealing the latent insights. In the present study, a custom-built meta-analysis pipeline is presented for the integration of multiple datasets from different origins. As a case study, we have tested with the integration of two relevant datasets pertaining to diabetic vasculopathy retrieved from the open source domain. We report the meta-analysis ameliorated distinctive and latent gene regulators of diabetic vasculopathy and uncovered a total of 975 i.e. 930 up-regulated and 45 down-regulated gene signatures. Further investigation revealed a subset of 14 DEGs including CTLA4, CALR, G0S2, CALCR, OMA1, and DNAJC3 as latent i.e. novel as these signatures have not been reported earlier. Moreover, downstream investigations including enrichment analysis, and protein-protein interaction (PPI) network analysis of DEGs revealed durable disease association signifying their potential as novel transcriptomic biomarkers of diabetic vasculopathy. While the meta-analysis of individual whole transcriptomic datasets for diabetic vasculopathy is exclusive to our comprehension, however, the novel meta-analysis pipeline could very well be extended to study the mechanistic links of DEGs in other disease conditions.
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Pandey et al. (2023) conducted a meta-analysis in Diabetic vasculopathy (n=14). Custom-built meta-analysis pipeline vs. Individual RNA-Seq data analysis was evaluated on Differentially expressed genes (DEGs) between T2DM and healthy controls (p=<0.05). A custom-built meta-analysis pipeline of RNA-Seq datasets identified 975 differentially expressed genes associated with diabetic vasculopathy, including 14 novel gene signatures not found in individual studies.
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