ArticleMolecular and cellular biochemistry2022
Transcriptome expression profiles associated with diabetic nephropathy development.
Article in Molecular and cellular biochemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 7 citations in OpenAlex.
- LncRNA RAD51-AS1 Alleviates Oxidative Stress and Inflammation in Diabetic Nephropathy by Adsorbing miR-154-5p.Kidney & blood pressure research · 2026Article
- Circular RNAs in diabetes mellitus and its complications: a systematic review and in silico analyses.Endocrine connections · 2025Article
- EIF4A3-induced circFAT1 promotes high glucose-induced podocyte damage via miR-30e-5p/SOX4 axis.RNA biology · 2025Article
- Targeting LINC02544/miR-497-5p/CAPRIN1 axis via exosome-based siRNA to overcome immunotherapy resistance in triple-negative breast cancer.Molecular medicine (Cambridge, Mass.) · 2025Article
- Upregulation of circGDI2 inhibits tumorigenesis by stabilizing the expression of RNA m6A demethylase FTO in oral squamous cell carcinoma.Non-coding RNA research · 2025Article
- Integrative analyses of biomarkers and pathways for diabetic nephropathy.Frontiers in genetics · 2023Article
- An integrated RNA sequencing and network pharmacology approach reveals the molecular mechanism of dapagliflozin in the treatment of diabetic nephropathy.Frontiers in endocrinology · 2022Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The objective of this study was to identify different transcriptome expression profiles involved in the pathogenesis of diabetic nephropathy (DN) and to illustrate the diagnostic and therapeutic potential of mRNAs, long noncoding RNAs (lncRNAs), and circular RNAs (circRNAs) in DN progression. The participants were divided into four groups: normoalbuminuria (group DM), microalbuminuria (group A2), macroalbuminuria (group A3) and healthy controls (group N). There were three individuals in each group for sequencing. Transcriptome sequencing analysis was performed on the peripheral blood of all the participants to identify the differential expression of mRNAs, lncRNAs, and circRNAs between intervention groups and controls. The functional enrichment analysis, the short time-series expression miner (STEM) program, and the miRNA-circRNA-mRNA network were further conducted. To verify the reproducibility of transcriptome sequencing, 10 and 30 blood samples were collected from the control and diseased groups, respectively. Four candidate biomarkers were selected from differentially expressed circRNAs (circ_0005379, circ_0002024, and circ_0000567, and circ_0001017) and their concentrations in the blood were measured using quantitative PCR (qPCR). In the comparison of A2 with N, 549 mRNAs, 1259 lncRNAs, and 12 circRNAs were screened. In the comparison of A3 with N, 1217 mRNAs, 1613 lncRNAs, and 24 circRNAs were screened. Moreover, in the comparison of diabetes mellitus (DM) with N, 948 mRNAs, 1495 lncRNAs, and 25 circRNAs were screened. Functional enrichment analysis showed that differentially expressed mRNAs were related to insulin secretion, insulin resistance, and inflammation, while differentially expressed lncRNAs were mainly associated with crossover junction endodeoxyribonuclease activity. In STEM analysis, a total of 481 mRNAs and 152 differential expression circRNAs showed a significant tendency. The key relationships in the miRNA-circRNA-mRNA network were identified, such as hsa-miR-103a-3p-circ_0005379-PTEN, hsa-miR-497-5p-circ_0002024-IGF1R and hsa-miR-1269a-circ_0000567-SOX6. In addition, qPCR showed consistent results with RNA sequencing. We found that differentially expressed mRNAs, lncRNAs, and circRNAs participated in DN development. Circ_0005379, circ_0002024, and circ_0000567 could be adopted as potential biomarkers for DN.
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