ArticleMolecular medicine reports2018
Expression of long non‑coding RNA and mRNA in the hippocampus of mice with type 2 diabetes.
Article in Molecular medicine reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 12 citations in OpenAlex.
- Distinct Hippocampal Expression Profiles of lncRNAs in Obese Type 2 Diabetes Mice Exhibiting Cognitive Impairment.Neuromolecular medicine · 2024Article
- Hippocampal proteins discovery of Panax quinquefolius and Acorus gramineus ameliorating cognitive impairment in diabetic rats.Psychopharmacology · 2023Article
- 2-Dodecyl-6-Methoxycyclohexa-2,5-Diene-1,4-Dione Ameliorates Diabetic Cognitive Impairment Through Inhibiting Hif3α and Apoptosis.Frontiers in pharmacology · 2021Article
- The Differential Expression of Long Noncoding RNAs in Type 2 Diabetes Mellitus and Latent Autoimmune Diabetes in Adults.International journal of endocrinology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Long non‑coding RNAs (lncRNAs) serve key roles in cell growth, development and various diseases associated with the central nervous system. However, differential expression profiles of lncRNAs in type 2 diabetes have not been reported. The present study aimed to analyze the expression pattern of lncRNA‑mRNA in a type 2 diabetic mouse model using microarray analysis. The mouse model of type 2 diabetes was established and the total RNAs were extracted from the hippocampus of the mice used in the present study. The total RNAs were then examined by the GeeDom human lncRNA + mRNA V4.0 expression profile and analyzed through comparing Gene Ontology (GO) enrichment analysis and signal pathway analysis with the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. There were statistically significant differences between the expression of IncRNAs and mRNA in the healthy mice and that of the diabetic mice. In the diabetic mice, 130 different lncRNAs were expressed with 126 significantly upregulated and 4 significantly downregulated and 49 different mRNAs were detected with 45 significantly upregulated and 4 downregulated. GO analysis indicated that the mRNAs that are affected are involved in transport, cell adhesion, ion transport and metabolic processes. KEGG and Reactome enrichment analysis indicated that mRNAs impact on cholinergic synapses, nuclear factor‑kB pathway, Toll like receptor 4 cascade and zinc transporter are correlated with cognitive dysfunction in type 2 diabetes. A dynamic lncRNA‑mRNA network was constructed containing 123 lncRNAs and 48 mRNAs, which can elucidate the interaction between lncRNA and mRNA. Overall, this is the first study to indicate that lncRNAs are differentially expressed in the type 2 diabetic mice.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.