Evidence map›Paper›PMID 40594478›Full record

ArticleScientific reports2025

Mining, validating, and quantifying circular RNA transcriptome from total RNA as a biomarker or target.

Tan Ze Wang, Raj Kumar Thapa, Frank Yu, Ayesha Warsi, Kassie Rong, Pious Jose, Tahereh Kashkoulinejad-Kouhi, Phyo Wei Win, Christina A Castellani, Tina Mele and 1 more

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Tan Ze WangDepartment of Pathology and Laboratory Medicine, Western University, London, ON, Canada.
Raj Kumar ThapaDepartment of Surgery, Western University, London, Canada.
Frank YuDepartment of Physiology and Pharmacology, Western University, London, ON, Canada.
Ayesha WarsiDepartment of Pathology and Laboratory Medicine, Western University, London, ON, Canada.
Kassie RongDepartment of Pathology and Laboratory Medicine, Western University, London, ON, Canada.
Pious JoseDepartment of Pathology and Laboratory Medicine, Western University, London, ON, Canada.
Tahereh Kashkoulinejad-KouhiDepartment of Pathology and Laboratory Medicine, Western University, London, ON, Canada.
Phyo Wei WinDepartment of Pathology and Laboratory Medicine, Western University, London, ON, Canada. pwin@uwo.ca.
Christina A CastellaniDepartment of Pathology and Laboratory Medicine, Western University, London, ON, Canada. christina.castellani@schulich.uwo.ca.
Tina MeleDepartment of Surgery, Western University, London, Canada. tina.mele@LHSC.on.ca.
Xiufen ZhengDepartment of Pathology and Laboratory Medicine, Western University, London, ON, Canada. xzheng26@uwo.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Circular RNAs (circRNAs) participate in complex diseases and have emerged as a novel biomarker and therapeutic target. It is challenging to identify circRNAs from total RNA-seq data and to validate and quantify their levels by qRT-PCR. We sought to characterize the circRNA transcriptome from RNA-seq with total RNA from septic peripheral blood mononuclear cells (PBMCs), assess/compare commonly used aligners, and optimize validation methods, considering sepsis is the leading cause of death in intensive care units (ICU) and there is no effective treatment. PBMCs were isolated from sepsis patients before and after intensive care, and RNA sequencing was conducted with total RNA. RNA-seq data for circRNAs were analyzed using four pipelines. A circRNA transcriptome has been generated, and four alignment and annotation pipelines have been evaluated. TopHat was found to be the most sensitive aligner while MapSplice was found to be the most accurate. Overall, circRNA expression in septic PBMCs were found to be more abundant and less diverse at ICU admission (ICU-AD) compared with ICU discharge (ICU-DC). There were three differentially expressed circRNAs between ICU-AD and ICU-DC. The addition of reverse primers to a reverse transcription reaction improved reproducibility and accuracy of qRT-PCR. Among these altered circRNAs, two different isoforms of circular RNA from the ASPH gene (circASPH(2,4), and circASPH(2,3,4) with the same backsplicing junction (BSJ) were identified and specifically quantified by qRT-PCR. All the two isoforms were highly expressed at ICU-AD and circASPH(2,3,4) levels positively correlated with the length of stay in ICU. The circRNAs predictably bind to miR-670, miR-7975, miR-6818, and miR-384 and interact with proteins TLR2 and ZC3H12D. In conclusion, it is feasible to identify circRNA transcriptome from RNA-seq with total RNA. Moreover, RT-PCR followed by gel electrophoresis is important to identify/ distinguish a new isoform of a circRNA with the same BSJ. CircRNA transcriptome in septic PBMCs evolves as infectious severity changes. CircASPH(2,3,4) has a clinical potential as an indicator of disease severity.

Indexed as

RNA, CircularSepsisTranscriptomeAgedBiomarkersFemaleGene Expression ProfilingHumansIntensive Care UnitsLeukocytes, MononuclearMaleMiddle AgedSequence Analysis, RNABiomarkersRNA, CircularCircular RNACircular RNA ASPHmiRNAPBMCsRBPsSepsis

Identifiers

PMID40594478
PMCPMC12218994

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Registered trials

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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.