ArticleFrontiers in pharmacology2022
Screening and Verification of Differentially Expressed Long Non-coding RNAs in the Peripheral Blood of Patients With asthma.
Article in Frontiers in pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Epigenetics in asthma: implications for treatable traits.Frontiers in allergy · 2026Review
- Identification and validation of differentially expressed genes in allergic asthma pathogenesis using whole-transcriptome sequencing.Frontiers in medicine · 2025Article
- Bioinformatic analysis and preliminary validation of potential therapeutic targets for COVID-19 infection in asthma patients.Cell communication and signaling : CCS · 2022Article
- Functional transcriptome analyses of Drosophila suzukii midgut reveal mating-dependent reproductive plasticity in females.BMC genomics · 2022Article
- Transcriptome analysis of sex-biased gene expression in the spotted-wing Drosophila, Drosophila suzukii (Matsumura).G3 (Bethesda, Md.) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Growing evidence suggests that long non-coding RNAs (lncRNAs) play a key role in the pathogenesis of asthma. Although some differentially expressed lncRNAs have been identified in asthmatic patients, many asthma-related lncRNAs have not been annotated. In the present study, six patients and three healthy subjects were randomly selected from 34 asthmatic patients and 17 healthy subjects. Second-generation high-throughput sequencing was performed on their peripheral blood samples. There were 1,137 differentially expressed lncRNAs in the asthma patients compared to in the healthy controls, of which 485 were upregulated and 652 were downregulated. The top 30 enriched GO and KEGG terms were identified, and the cytosolic ribosome (GO:0022626) and ribosome (hsa03010) were associated with the most differentially expressed lncRNAs. The top 10 differentially expressed lncRNAs associated with asthma were verified by an lncRNA-mRNA co-expression network and RT-qPCR. Seven of the these (NONHSAT015495.2, MSTRG.71212.2, NONHSAT163272.1, NONHSAT181891.1, NONHSAT190964.1, ENST00000564809, and NONHSAT076890.2) were down-regulated in the peripheral blood of asthmatic patients, which was consistent with the sequencing results. Three patients and three healthy subjects were randomly selected from the remaining subjects to verify these seven lncRNAs by RT-qPCR, which further confirmed the sequencing results. Public database GSE106230 was also in agreement with the FPKM (Fragments Per kilobase of exon model per Million mapped reads) trends of ENST00000564809, NONHSAT015495.2, NONHSAT181891.1, and NONHSAT190964.1. In conclusion, the present study identified seven lncRNAs that may serve as potential biological markers for asthma.
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.