Trial reportVirology journal2018
Pretreatment microRNA levels can predict HBsAg clearance in CHB patients treated with pegylated interferon α-2a.
Trial report in Virology journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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
6 citing papers in PubMed, 10 citations in OpenAlex.
- Hepatitis B Virus and microRNAs: A Bioinformatics Approach.International journal of molecular sciences · 2023Review
- Liver Damage and microRNAs: An Update.Current issues in molecular biology · 2022Review
- Management and Treatment of Patients with Chronic Hepatitis B: Towards Personalized Medicine.Viruses · 2022Review
- Identification of Liver and Plasma microRNAs in Chronic Hepatitis B Virus infection.Frontiers in cellular and infection microbiology · 2022Article
- Article
- Circulating microRNAs as non-invasive biomarkers for hepatitis B virus liver fibrosis.World journal of gastroenterology · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo investigate the predictive capability of microRNAs (miRNAs) prior treatment for HBsAg clearance in chronic hepatitis B (CHB) treated with pegylated interferon α-2a (PEG-IFNα-2a).
methodsThe treatment effect was determined by HBsAg clearance and subjects were classified into HBsAg clearance group and non HBsAg clearance group. Differential miRNAs expression in peripheral blood mononuclear cells (PBMC) was screened using microarrays in an identification cohort (n = 20) and validated by quantitative reverse-transcription polymerase chain reaction (qRT-PCR) in a confirmation cohort (n = 47). Receiver operating characteristic curve (ROC), logistic regression and gene ontology (GO)/Pathway analyses were used to evaluate the predictive capability of selected miRNAs for HBsAg clearance and determine their mechanistic roles.
resultsTwenty-seven subjects (40.3%) acquired HBsAg clearance, ten in the identification cohort and seventeen in the confirmation cohort. Four miRNAs out of twelve (miR-3960, miR-126-3p, miR-335-5p, miR-23a-3p) were verified to be differential expressed by qRT-PCR in the confirmation cohort. Their expression patterns were consistent with the microarray results. Their levels were lower in the response group compared with the nonresponse group (p < 0.05). The areas under curve (AUC) were 0.8333 (p = 0.001), 0.751 (p = 0.01), 0.7294 (p = 0.013), 0.6275 (p = 0.094) and positive predict values (PPV) were 84.62, 60.00, 70.00, 28.57% for miR-3960, miR-126-3p, miR-335-5p, and miR-23a-3p respectively. The AUC and PPV of the combination of miR-3960 and miR-126-3p were 0.8529 and 92.31%, which were better than using miR-3960 alone, but the differences were not statistically significance (p > 0.05).
conclusionsWe identified differential expressed miRNAs between response and nonresponse groups of PEG-IFNα-2a treatment and demonstrated that miR-3960 was the optimal predictor for HBsAg clearance compared with other miRNAs, but it requires to be further comfired in larger cohort studies.
trial registrationChiCTR ChiCTR-ROC-16008735, registered retrospectively on 28 June, 2016.
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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.