Evidence map›Paper›PMID 39843617›Full record

ReviewMolecular biotechnology2026

Research Trends and Development Dynamics of qPCR-based Biomarkers: A Comprehensive Bibliometric Analysis.

Li Liu, Ben-Rong Mu, Ya Zhou, Qing-Lin Wu, Bin Li, Dong-Mei Wang, Mei-Hong Lu

Abstract readReview
PubMed Publisher
In one paragraph

Review in Molecular biotechnology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Cells · 2026
    Article
  2. Review
  3. Article
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

7 authors.

Li Liu *Chongqing Key Laboratory of Sichuan-Chongqing Co-Construction for Diagnosis and Treatment of Infectious Diseases Integrated Traditional Chinese and Western Medicine, College of Medical Technology, Chengdu University of Traditional Chinese Medicine, 1166 Liutai Avenue, Wenjiang District, Chengdu, 611137, Sichuan, China.
Ben-Rong Mu *Chongqing Key Laboratory of Sichuan-Chongqing Co-Construction for Diagnosis and Treatment of Infectious Diseases Integrated Traditional Chinese and Western Medicine, College of Medical Technology, Chengdu University of Traditional Chinese Medicine, 1166 Liutai Avenue, Wenjiang District, Chengdu, 611137, Sichuan, China.
Ya ZhouChongqing Key Laboratory of Sichuan-Chongqing Co-Construction for Diagnosis and Treatment of Infectious Diseases Integrated Traditional Chinese and Western Medicine, College of Medical Technology, Chengdu University of Traditional Chinese Medicine, 1166 Liutai Avenue, Wenjiang District, Chengdu, 611137, Sichuan, China.
Qing-Lin WuChongqing Key Laboratory of Sichuan-Chongqing Co-Construction for Diagnosis and Treatment of Infectious Diseases Integrated Traditional Chinese and Western Medicine, College of Medical Technology, Chengdu University of Traditional Chinese Medicine, 1166 Liutai Avenue, Wenjiang District, Chengdu, 611137, Sichuan, China.
Bin LiDepartment of Respiratory Medicine, Guangyuan Hospital of Traditional Chinese Medicine, No.133 Jianshe Road, Lizhou District, Guangyuan, 628099, Sichuan, China.
Dong-Mei WangSchool of Basic Medical Sciences, Chengdu University of Traditional Chinese Medicine, 1166 Liutai Avenue, Wenjiang District, Chengdu, 611137, Sichuan, China. dongmeiwang@cdutcm.edu.cn.
Mei-Hong LuChongqing Key Laboratory of Sichuan-Chongqing Co-Construction for Diagnosis and Treatment of Infectious Diseases Integrated Traditional Chinese and Western Medicine, College of Medical Technology, Chengdu University of Traditional Chinese Medicine, 1166 Liutai Avenue, Wenjiang District, Chengdu, 611137, Sichuan, China. lmh0929@cdutcm.edu.cn.ORCID http://orcid.org/0000-0001-7863-8870

Funding

Joint Innovation Fund Project of Chengdu University of TCM and Affiliated Hospitals LH202402056
6 · The paper itself

Abstract

Quantitative polymerase chain reaction (qPCR) is a vital molecular technique for biomarker detection; however, its clinical application is impeded by the scarcity of robust biomarkers and the inherent limitations of the technology. This study conducted a bibliometric analysis of 4063 qPCR-based biomarker studies sourced from the Web of Science (WOS) database, employing VOSviewer and CiteSpace to generate multi-dimensional structural insights into this field. The results reveal a growing trend in research within this domain, with gene expression analysis playing a central role in the identification of potential biomarkers. Among these, cancer-related biomarkers are the most prominent, while research on biomarkers for other diseases remains limited. Liquid biopsy biomarkers, including microRNA (miRNA), circulating free DNA (cfDNA), and circulating tumor DNA (ctDNA), are increasingly being explored. The integration of bioinformatics, omics analysis, and high-throughput technologies with qPCR is accelerating biomarker discovery. Furthermore, large-scale parallel sequencing is emerging as a potential alternative to relative quantification and microarray techniques. Nevertheless, qPCR remains essential for validating specific biomarkers, and further standardization of its protocols is necessary to enhance reliability. This study provides a systematic analysis of qPCR-based biomarker research and underscores the need for future technological integration and standardization to facilitate broader clinical applications.

Indexed as

BibliometricsBiomarkersBiomarkers, TumorReal-Time Polymerase Chain ReactionComputational BiologyHumansLiquid BiopsyMicroRNAsNeoplasmsBiomarkersBiomarkers, TumorMicroRNAsBibliometric analysisBiomarkersQuantitative polymerase chain reaction (qPCR)Research hotspotsResearch trends

Identifiers

PMID39843617

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.