The landscape of LncRNAs in diabetic kidney disease: a meta-analysis of transcriptomics data

• Through a meta-analysis approach, a list of robust DKD-associated lncRNAs is provided for T1DM and T2DM. • The degree distribution in the bipartite network of DKD meta-lncRNAs and meta-mRNAs is highly heterogeneous as only a few lncRNAs have dense interactions with mRNAs. • Despite the general belief in the importance of non-coding parts of the genome in the pathogenesis of complex disorders, the functional role of the majority of the identified DKD-associated meta-lncRNAs is elusive. Non-coding regions of the genome are known to influence complex disorders, yet the role of long non-coding RNAs (lncRNAs) in Diabetic Kidney Disease (DKD) remains underexplored. This study conducts a meta-analysis of RNA-sequencing data from murine kidney samples of type 1 (T1DM) and type 2 diabetes mellitus (T2DM) to identify lncRNAs associated with DKD. DKD-associated datasets were harvested, and after data pre-processing and quality assessment, 6 T1DM and 4 T2DM datasets were included. Data integration, batch correction, and normalization were performed, followed by the identification of differentially expressed lncRNAs (meta-DELs) and mRNAs (meta-DEMs). A DKD mouse model was developed to validate the expression of selected meta-DELs using qRT-PCR. The meta-analysis identified 188 meta-DELs in T1DM and 68 in T2DM. Notably, a small set of lncRNAs have dense mRNA interactions, including Dancer , Gm7628 , C4a , and Gm17300 in T1DM and Malat1 , C4a , Gm17300 , and Eif4a2 in T2DM. Expression analysis confirmed the up-regulation of seven selected meta-DELs in the DKD model, with Trp53cor1, Gm15462, and Gm42664 reaching statistical significance. This systematic analysis of high-quality expression profiles identified meta-DELs consistently associated with DKD, distinguishing actual lncRNA changes from those influenced by experimental conditions or gene expression noise.